Listed below are known citations to the NASA Socioeconomic Data and Applications Center (SEDAC) Gridded Population of the World (GPW) v4 data collection. The data collection, and specific data set (if known), being cited are beneath each citation. Citations to multiple collections/sets are listed on separate lines. If a publication cites remotely sensed earth observation data, whether from NASA or another source, those instruments and/or platforms are listed as well.
List last updated on 3 October 2023.

Abatzoglou, J. T., Smith, C., M., Swain. Daniel, L., Ptak, T., & Kolden, C. A. (2020). Population exposure to pre-emptive de-energization aimed at averting wildfires in Northern California. Environmental Research Letters, 15(9), 094046. doi:10.1088/1748-9326/aba135
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ
NASA REMOTE SENSING (MODIS)

Abbar, S., Zanouda, T., Al-Emadi, N., & Zegour, R. (2018). City of the people, for the people: Sensing urban dynamics via social media interactions. Paper presented at the Social Informatics, 10th International Conference, St. Petersburg, Russia.
Gridded Population of the World (GPW) v4 (population density)

Abdalla, L., Augusto, D. A., Chame, M., Dufek, A. S., Oliveira, L., & Krempser, E. (2022). Statistically enriched geospatial datasets of Brazilian municipalities for data-driven modeling. Scientific Data, 9(1), 489. doi:10.1038/s41597-022-01581-2
Gridded Population of the World (GPW) v4.11 (population count)
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (Landsat)

Abdrakhmanov, S. K., Mukhanbetkaliyev, Y. Y., Sultanov, A. A., Yessembekova, G. N., Borovikov, S. N., Namet, A., . . . Korennoy, F. I. (2022). Mapping the risks of the spread of Peste des Petits Ruminants in the Republic of Kazakhstan. Transboundary and Emerging Diseases, 69(4), 2296-2305. doi:10.1111/tbed.14237
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Abdul Shakor, A. S. a., Pahrol, M. A., & Mazeli, M. I. (2020). Effects of population weighting on PM10 concentration estimation. Journal of Environmental and Public Health, 2020, 1561823. doi:10.1155/2020/1561823
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MODIS)

Abel, C., Horion, S., Tagesson, T., De Keersmaecker, W., Seddon, A. W. R., Abdi, A. M., & Fensholt, R. (2021). The human–environment nexus and vegetation–rainfall sensitivity in tropical drylands. Nature Sustainability, 4, 25-32. doi:10.1038/s41893-020-00597-z
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
Last of the Wild v3 (Human Footprint, 2018 Release (2009)) - 10.7927/H46T0JQ4
NASA REMOTE SENSING (MODIS)

Abera, T. A., Heiskanen, J., Maeda, E. E., Hailu, B. T., & Pellikka, P. K. E. (2022). Improved detection of abrupt change in vegetation reveals dominant fractional woody cover decline in Eastern Africa. Remote Sensing of Environment, 271, 112897. doi:10.1016/j.rse.2022.112897
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)

Acevedo, S., Mrkaic, M., Novta, N., Poplawski-Ribeiro, M., Pugacheva, E., & Topalova, P. (2017). The effects of weather shocks on economic activity:  How can low-income countries cope? In World Economic Outlook, October 2017: Seeking Sustainable Growth - Short-Term Recovery, Long-Term Challenges (pp. 117-183): International Monetary Fund (IMF).
Gridded Population of the World (GPW) v3 (population count) - 10.​7927/​H4639MPP
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
Global Rural-Urban Mapping Project (GRUMP) v1 (settlement points)

Acevedo, S., Mrkaic, M., Novta, N., Pugacheva, E., & Topalova, P. (2018). The Effects of Weather Shocks on Economic Activity: What are the Channels of Impact? Retrieved from https://www.imf.org/en/Publications/WP/Issues/2018/06/22/The-Effects-of-Weather-Shocks-on-Economic-Activity-What-are-the-Channels-of-Impact-45970
Gridded Population of the World (GPW) v3 (population count) - 10.​7927/​H4639MPP
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Acevedo, S., Mrkaic, M., Novta, N., Pugacheva, E., & Topalova, P. (2020). The effects of weather shocks on economic activity: What are the channels of impact? Journal of Macroeconomics, 65, 103207. doi:10.1016/j.jmacro.2020.103207
Gridded Population of the World (GPW) v3 (population count) - 10.​7927/​H4639MPP
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Achakulwisut, P., Brauer, M., Hystad, P., & Anenberg, S. C. (2019). Global, national, and urban burdens of paediatric asthma incidence attributable to ambient NO2 pollution: estimates from global datasets. The Lancet Planetary Health, 3(4), e166-e178. doi:10.1016/S2542-5196(19)30046-4
Gridded Population of the World (GPW) v4.10 (basic demographic characteristics)
Gridded Population of the World (GPW) v4.10 (national identifier grid)
NASA REMOTE SENSING (OMI NO2)

Ackermann, K., Awaworyi Churchill, S., & Smyth, R. (2021). Mobile phone coverage and violent conflict. Journal of Economic Behavior & Organization, 188, 269-287. doi:10.1016/j.jebo.2021.04.038
Gridded Population of the World (GPW) v4.11 (population count)
REMOTE SENSING (VIIRS)

Acuña, V., Bregoli, F., Font, C., Barceló, D., Corominas, L. l., Ginebreda, A., . . . Marcé, R. (2020). Management actions to mitigate the occurrence of pharmaceuticals in river networks in a global change context. Environment International, 143, 105993. doi:10.1016/j.envint.2020.105993
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Adam, C., & Drakos, P. (2022). Climate change: north and south EU economies—an application of dynamic asymmetric panel data models. Environmental Science and Pollution Research, 29(46), 70573-70590. doi:10.1007/s11356-022-22907-y
Gridded Population of the World (GPW) v3 (population count) - 10.​7927/​H4639MPP
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
Population Dynamics (Global Population Count Grid Time Series Estimates, v1) - 10.7927/H4CC0XNV

Adame, M. F., Reef, R., Santini, N. S., Najera, E., Turschwell, M. P., Hayes, M. A., . . . Lovelock, C. E. (2021). Mangroves in arid regions: Ecology, threats, and opportunities. Estuarine, Coastal and Shelf Science, 248, 106796. doi:10.1016/j.ecss.2020.106796
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Addison, T., Boly, A., & Mvenyange, A. (2016). Mining and Economic Development:  Did China's WTO Accession Affect African Local Economic Development? Retrieved from Washington DC: http://documents.worldbank.org/curated/en/414931480967981511/Mining-and-economic-development-did-Chinas-WTO-accession-affect-African-local-economic-development
Gridded Population of the World (GPW) v3 (population count future estimates)
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
REMOTE SENSING (DMSP-OLS)

Adema, J. A. H., Aksoy, C. G., & Poutvaara, P. (2021). Mobile Internet Access and the Desire to Emigrate. Retrieved from Munich: https://www.ifo.de/publikationen/2021/working-paper/mobile-internet-access-and-desire-emigrate
Gridded Population of the World (GPW) v4.11 (population count)

Aghahosseini, A., & Breyer, C. (2020). From hot rock to useful energy: A global estimate of enhanced geothermal systems potential. Applied Energy, 279, 115769. doi:10.1016/j.apenergy.2020.115769
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Aguilera, E., Reyes-Palomo, C., Díaz-Gaona, C., Sanz-Cobena, A., Smith, P., García-Laureano, R., & Rodríguez-Estévez, V. (2021). Greenhouse gas emissions from Mediterranean agriculture: Evidence of unbalanced research efforts and knowledge gaps. Global Environmental Change, 69, 102319. doi:10.1016/j.gloenvcha.2021.102319
Gridded Population of the World (GPW) v4.11 (population count)

Ahadji-Dabla, K. M., Romero-Alvarez, D., Djègbè, I., Amoudji, A. D., Apétogbo, G. Y., Djouaka, R., . . . Ketoh, G. K. (2020). Potential roles of environmental and socio-economic factors in the distribution of insecticide resistance in Anopheles gambiae sensu lato (Culicidae: Diptera) across Togo, West Africa. Journal of Medical Entomology, 57(4), 1168-1175. doi:10.1093/jme/tjaa023
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
NASA REMOTE SENSING (MODIS - MOD44B)
REMOTE SENSING (DMSP-OLS)

Aili, A., Yin, X., & Yang, R. (2021). Global radiative sky cooling potential adjusted for population density and cooling demand. Atmosphere, 12(11), 1379. doi:10.3390/atmos12111379
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
NASA REMOTE SENSING (MODIS)

Akbari, M., Bahrami-Rad, D., & Kimbrough, E. O. (2019). Kinship, fractionalization and corruption. Journal of Economic Behavior & Organization, 166, 493-528. doi:10.1016/j.jebo.2019.07.015
Gridded Population of the World (GPW) v4 (population count)

Akinyemi, F. O., & Ifejika Speranza, C. (2022). Agricultural landscape change impact on the quality of land: An African continent-wide assessment in gained and displaced agricultural lands. International Journal of Applied Earth Observation and Geoinformation, 106, 102644. doi:10.1016/j.jag.2021.102644
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
Global Rural-Urban Mapping Project (GRUMP) v1 (settlement points) - 10.7927/H4M906KR
NASA REMOTE SENSING (MODIS)

Akpoti, K., Groen, T., Dossou-Yovo, E., Kabo-bah, A. T., & Zwart, S. J. (2022). Climate change-induced reduction in agricultural land suitability of West-Africa's inland valley landscapes. Agricultural Systems, 200, 103429. doi:10.1016/j.agsy.2022.103429
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)

Akpoti, K., Higginbottom, T. P., Foster, T., Adhikari, R., & Zwart, S. J. (2021). Mapping land suitability for informal, small-scale irrigation development using spatial modelling and machine learning in the Upper East Region, Ghana. Science of The Total Environment, 803, 149959. doi:10.1016/j.scitotenv.2021.149959
Gridded Population of the World (GPW) v4 (population density)

Akpoti, K., Kabo-bah, A. T., Dossou-Yovo, E. R., Groen, T. A., & Zwart, S. J. (2020). Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling. Science of The Total Environment, 709, 136165. doi:10.1016/j.scitotenv.2019.136165
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MODIS - MYD13A1)

Aksoy, C. G., & Ginn, T. C. (2022). Attitudes and Policies toward Refugees: Evidence from Low- and Middle-Income Countries. Retrieved from Washington DC: http://documents.worldbank.org/curated/en/099736203242266938/IDU018ed89c00b34c04bb00ad9f04fb78818b0e3
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Akyapi, B., Bellon, M., & Massetti, E. (2022). Estimating Macro-Fiscal Effects of Climate Shocks from Billions of Geospatial Weather Observations. Retrieved from https://doi.org/10.5089/9798400217203.001
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Akyuz, E., Samavati, M., & Kaynak, B. (2020). Spatial distribution of health risks associated with PM2.5 in Turkey and Iran using satellite and ground observations. Atmospheric Pollution Research, 11(12), 2350-2360. doi:10.1016/j.apr.2020.08.011
Gridded Population of the World (GPW) v4.10 (national identifier grid)

Alabi, T., & Muthoni, F. K. (2023). Recommendation Domains for Conservation Agriculture (CA) in Eastern and Southern Africa. Retrieved from Ibadan, Nigeria: https://hdl.handle.net/10568/129623
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (CHIRPS-2)

Alaniz, A. J., Carvajal, J. G., Carvajal, M. A., Cogliati, M., & Vergara, P. M. (2019). Spatial quantification of the population exposed to Cryptococcus neoformans and Cryptococcus gattii species complexes in Europe: Estimating the immunocompetent and HIV/AIDS patients under risk. Risk Analysis, 14(11), 114002. doi:10.1111/risa.13410
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Alaniz, A. J., Carvajal, M. A., Bacigalupo, A., & Cattan, P. E. (2019). Global spatial assessment of Aedes aegypti and Culex quinquefasciatus: a scenario of Zika virus exposure. Epidemiology and Infection, 147, e52. doi:10.1017/S0950268818003102
Gridded Population of the World (GPW) v4 (population count)
Gridded Population of the World (GPW) v4 (population density)

Alaniz, A. J., Smith-Ramírez, C., Rendón-Funes, A., Hidalgo-Corrotea, C., Carvajal, M. A., Vergara, P. M., & Fuentes, N. (2022). Multiscale spatial analysis of headwater vulnerability in South-Central Chile reveals a high threat due to deforestation and climate change. Science of The Total Environment, 849, 157930. doi:10.1016/j.scitotenv.2022.157930
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (ALOS PALSAR)
NASA REMOTE SENSING (MODIS Snow Cover Daily L3 Global 500m SIN Grid, v6)
REMOTE SENSING (Landsat)

Albin, E. K. (2020). Telescope: Earth. (Ph.D.). University of California, Irvine, Irvine, CA. Retrieved from https://escholarship.org/uc/item/5b751142 
Gridded Population of the World (GPW) v4.11 (population density)

Alder, S., Croke, K., Duhaut, A., Marty, R. A., & Vaisey, A. B. (2022). The Impact of Ethiopia’s Road Investment Program on Economic Development and Land Use : Evidence from Satellite Data. Retrieved from Washington DC: http://documents.worldbank.org/curated/en/099332404062230683/IDU073a7158605532046490b712098aed9008539
Gridded Population of the World (GPW) v4 (unspecified)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS NTL)

Alegana, V. A., Okiro, E. A., & Snow, R. W. (2020). Routine data for malaria morbidity estimation in Africa: challenges and prospects. BMC Medicine, 18(1), 121. doi:10.1186/s12916-020-01593-y
Gridded Population of the World (GPW) v4 (population density)

Alencar, M. V., Gimenez, B. G., Sasahara, C., Elliff, C. I., Velis, C. A., Rodrigues, L. S., . . . Turra, A. (2023). Advancing plastic pollution hotspotting at the subnational level: Brazil as a case study in the Global South. Marine Pollution Bulletin, 194, 115382. doi:10.1016/j.marpolbul.2023.115382
Gridded Population of the World (GPW) v4.11 (population density)

Algarra, I., Eiras-Barca, J., Nieto, R., & Gimeno, L. (2019). Global climatology of nocturnal low-level jets and associated moisture sources and sinks. Atmospheric Research, 229, 39-59. doi:10.1016/j.atmosres.2019.06.016
Gridded Population of the World (GPW) v4 (unspecified)

Alizadeh, M. R., Abatzoglou, J. T., Adamowski, J. F., Prestemon, J. P., Chittoori, B., Akbari Asanjan, A., & Sadegh, M. (2022). Increasing heat-stress inequality in a warming climate. Earth's Future, 10(2), e2021EF002488. doi:10.1029/2021EF002488
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Allison, M. A., Nittrouer, C. A., Ogston, A. S., Mullarney, J. C., & Nguyen, T. T. (2017). Sedimentation and survival of the Mekong Delta: A case study of decreased sediment supply and accelerating rates of relative sea level rise. Oceanography, 30(3), 98-109. doi:10.5670/oceanog.2017.318
Anthropogenic Biomes of the World v2 (1700) - 10.7927/H4SF2T3M
Anthropogenic Biomes of the World v2 (1900) - 10.7927/H4J1012K
Anthropogenic Biomes of the World v2 (2000) - 10.7927/H4D798B9
Land Use and Land Cover (LULC) (Global Grid of Probabilities of Urban Expansion to 2030, v1) - ​10.7927/H4Z899CG
Gridded Population of the World (GPW) v4 (collection) - 10.7927/H4D50JX4

Alston, J. M., & Pardey, P. G. (2017). Transforming Traditional Agriculture Redux. Retrieved from Abidjan, Côte d’Ivoire: https://www.afdb.org/en/documents/document/working-paper-260-transforming-traditional-agriculture-redux-95593/
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Alves, D. M. C. C., Eduardo, A. A., da Silva Oliveira, E. V., Villalobos, F., Dobrovolski, R., Pereira, T. C., . . . Gouveia, S. F. (2020). Unveiling geographical gradients of species richness from scant occurrence data. Global Ecology and Biogeography, 29(4), 748-759. doi:10.1111/geb.13055
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates) - 10.7927/H4F47M2C

Ameca, E. I., Dai, Q., Nie, Y., Gu, X., & Wei, F. (2019). Implications of flood disturbance for conservation and management of giant panda habitat in human-modified landscapes. Biological Conservation, 232, 35-42. doi:10.1016/j.biocon.2019.01.019
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Aminjonov, U., Bargain, O., & Bernard, T. (2023). Gimme shelter. Social distancing and income support in times of pandemic. European Economic Review, 157, 104507. doi:10.1016/j.euroecorev.2023.104507
Gridded Population of the World (GPW) v4.11 (population density)

An, L., Wang, J., Huang, J., Pokhrel, Y., Hugonnet, R., Wada, Y., . . . Zhang, G. (2021). Divergent causes of terrestrial water storage decline between drylands and humid regions globally. Geophysical Research Letters, 48(23), e2021GL095035. doi:10.1029/2021GL095035
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
NASA REMOTE SENSING (GRACE)

Andrade-Pacheco, R., Savory, D. J., Midekisa, A., Gething, P. W., Sturrock, H. J. W., & Bennett, A. (2019). Household electricity access in Africa (2000–2013): Closing information gaps with model-based geostatistics. PLoS ONE, 14(5), e0214635. doi:10.1371/journal.pone.0214635
Gridded Population of the World (GPW) v4 (population count)
REMOTE SENSING (DMSP-OLS)

Andrade-Rivas, F., Paul, N., Spiegel, J., Henderson, S. B., Parrott, L., Delgado-Ron, J. A., . . . van den Bosch, M. (2023). Mapping potential population-level pesticide exposures in Ecuador using a modular and scalable geospatial strategy. GeoHealth, 7(7), e2022GH000775. doi:10.1029/2022GH000775
Global Agricultural Inputs (PEST-CHEMGRIDS) - 10.7927/weq9-pv30
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
Gridded Species Distribution (Amphibians 2015) - 10.7927/H4RR1W66

Andrew, N. L., Bright, P., de la Rua, L., Teoh, S. J., & Vickers, M. (2019). Coastal proximity of populations in 22 Pacific Island Countries and Territories. PLoS ONE, 14(9), e0223249. doi:10.1371/journal.pone.0223249
Gridded Population of the World (GPW) v4 (collection)

Anenberg, S. C., Achakulwisut, P., Brauer, M., Moran, D., Apte, J. S., & Henze, D. K. (2019). Particulate matter-attributable mortality and relationships with carbon dioxide in 250 urban areas worldwide. Scientific Reports, 9(1), 11552. doi:10.1038/s41598-019-48057-9
Gridded Population of the World (GPW) v4 (population count)

Anenberg, S. C., Bindl, M., Brauer, M., Castillo, J. J., Cavalieri, S., Duncan, B. N., . . . West, J. J. (2020). Using satellites to track indicators of global air pollution and climate change impacts: Lessons learned from a NASA-supported science-stakeholder collaborative. GeoHealth, 4(7), e2020GH000270. doi:10.1029/2020gh000270
Gridded Population of the World (GPW) v4.10 (population count)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (OMI NO2)
REMOTE SENSING (TROPOMI)

Anenberg, S. C., Henze, D. K., Tinney, V., Kinney, P. L., Raich, W., Fann, N., . . . Kuylenstierna, J. C. I. (2018). Estimates of the global burden of ambient PM2.5, ozone, and NO2 on asthma incidence and emergency room visits. Environmental Health Perspectives, 126(10), 107004. doi:10.1289/EHP3766
Gridded Population of the World (GPW) v4 (population count)

Anenberg, S. C., Miller, J., Henze, D. K., & Minjares, R. (2019). A Global Snapshot of the Air Pollution-related Health Impacts of Transportation Sector Emissions in 2010 and 2015. Retrieved from Washington DC: https://www.theicct.org/publications/health-impacts-transport-emissions-2010-2015
Gridded Population of the World (GPW) v4.10 (population count)

Anenberg, S. C., Miller, J., Henze, D. K., Minjares, R., & Achakulwisut, P. (2019). The global burden of transportation tailpipe emissions on air pollution- related mortality in 2010 and 2015. Environmental Research Letters, 14(9), 094012. doi:10.1088/1748-9326/ab35fc
Gridded Population of the World (GPW) v4 (population count)

Angel, S., Lamson-Hall, P., Blei, A., Shingade, S., & Kumar, S. (2021). Densify and expand: A global analysis of recent urban growth. Sustainability, 13(7), 3835. doi:10.3390/su13073835
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics) - 10.7927/H46M34XX
REMOTE SENSING (Landsat)

Anonymous. (2018). Atlas of Environmental Risks Facing China Under Climate Change (Q. Tang & Q. Ge Eds.). Singapore: Springer Singapore.
Gridded Population of the World (GPW) v4 (population density)

Antonescu, B., Fairman, J. G., & Schultz, D. M. (2018). What is the worst that could happen? Re-examining the 24–25 June 1967 tornado outbreak over Western Europe. Weather, Climate, and Society, 10(2), 323-340. doi:10.1175/WCAS-D-17-0076.1
Gridded Population of the World (GPW) v4 (population count)

Ao, X., Grimmond, C. S. B., Ward, H. C., Gabey, A. M., Tan, J., Yang, X.-Q., . . . Zhang, N. (2018). Evaluation of the Surface Urban Energy and Water balance Scheme (SUEWS) at a dense urban site in Shanghai: Sensitivity to anthropogenic heat and irrigation. Journal of Hydrometeorology, 19(12), 1983-2005. doi:10.1175/JHM-D-18-0057.1
Gridded Population of the World (GPW) v4 (population density)

Appelt, J. L., Garcia Rojas, D. C., Verburg, P. H., & van Vliet, J. (2022). Socioeconomic outcomes of agricultural land use change in Southeast Asia. Ambio, 51, 1094-1109. doi:10.1007/s13280-022-01712-4
Gridded Population of the World (GPW) v4.10 (population density)

Arambepola, R., Keddie, S. H., Collins, E. L., Twohig, K. A., Amratia, P., Bertozzi-Villa, A., . . . Cameron, E. (2020). Spatiotemporal mapping of malaria prevalence in Madagascar using routine surveillance and health survey data. Scientific Reports, 10(1), 18129. doi:10.1038/s41598-020-75189-0
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates)

Arango, C. C. S., Calle, C. A., & García-Merchán, V. H. (2023). Human population density influences genetic diversity of two Rattus species worldwide: A macrogenetic approach. Genes, 14(7), 1442. doi:10.3390/genes14071442
Gridded Population of the World (GPW) v4.11 (population density)
Poverty Mapping (Global Gridded Relative Deprivation Index (GRDI), v1)

Araujo, J. C., & Dias, F. F. (2021). Multicriterial method of AHP analysis for the identification of coastal vulnerability regarding the rise of sea level: case study in Ilha Grande Bay, Rio de Janeiro, Brazil. Natural Hazards, 107, 53-72. doi:10.1007/s11069-021-04573-4
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Archer, E. J., Baker-Austin, C., Osborn, T. J., Jones, N. R., Martínez-Urtaza, J., Trinanes, J., . . . Lake, I. R. (2023). Climate warming and increasing Vibrio vulnificus infections in North America. Scientific Reports, 13(1), 3893. doi:10.1038/s41598-023-28247-2
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)
Population Dynamics (Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, v1)

Archila Bustos, M. F., Hall, O., Niedomysl, T., & Ernstson, U. (2020). A pixel level evaluation of five multitemporal global gridded population datasets: a case study in Sweden, 1990–2015. Population and Environment, 42, 255-277. doi:10.1007/s11111-020-00360-8
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4PN93PB
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
Global Rural-Urban Mapping Project (GRUMP) v1 (population count) - 10.7927/H4VT1Q1H
POPGRID

Ardelean, M., & Minnebo, P. (2023). The suitability of seas and shores for building submarine power interconnections. Renewable and Sustainable Energy Reviews, 176, 113210. doi:10.1016/j.rser.2023.113210
Gridded Population of the World (GPW) v4.11 (population count)
Global Rural-Urban Mapping Project (GRUMP) v1.01 (settlement points)

Armand, A., Gomes, J. F., & Taveras, I. K. (2019). Managing Agricultural Risk in Mozambique. Retrieved from London: https://www.theigc.org/project/managing-agricultural-risk-in-mozambique/
Gridded Population of the World (GPW) v4 (population count)
REMOTE SENSING (DMSP-OLS)

Arnold, T., He, J., Jiang, W., Calder, M., Cunha, I., Giotsas, V., & Katz-Bassett, E. (2020). Cloud provider connectivity in the flat Internet. Paper presented at the Proceedings of the ACM Internet Measurement Conference. https://doi.org/10.1145/3419394.3423613
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Arrillaga, J. A., Jiménez, P., Vilà-Guerau de Arellano, J., Jiménez, M. A., Román-Cascón, C., Sastre, M., & Yagüe, C. (2020). Analyzing the synoptic, meso and local scales involved in sea-breeze formation and frontal characteristics. Journal of Geophysical Research: Atmospheres, 125(4), e2019JD031302. doi:10.1029/2019jd031302
Gridded Population of the World (GPW) v4 (collection)

Artelle, K. A., Zurba, M., Bhattacharrya, J., Chan, D. E., Brown, K., Housty, J., & Moola, F. (2019). Supporting resurgent Indigenous-led governance: A nascent mechanism for just and effective conservation. Biological Conservation, 240, 108284. doi:10.1016/j.biocon.2019.108284
Gridded Population of the World (GPW) v4.11 (population count)

Asadieh, B., & Krakauer, N. Y. (2017). Global change in streamflow extremes under climate change over the 21st century. Hydrology and Earth System Sciences, 21(11), 5863-5874. doi:10.5194/hess-21-5863-2017
Gridded Population of the World (GPW) v4 (population count)

Asare, E., Mantyka-Pringle, C., Anderson, E., Kenneth, B., & Clark, R. (2022). Evaluating ecosystem services for agricultural wetlands: a systematic review and meta-analysis. Wetlands Ecology and Management, 30, 1129-1149. doi:10.1007/s11273-022-09857-5
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D
Gridded Species Distribution (Amphibians 2015) - 10.7927/H4RR1W66
Gridded Species Distribution (Mammals 2015) - 10.7927/H4N014G5

Ascencio-Vásquez, J., Brecl, K., & Topič, M. (2019). Methodology of Köppen-Geiger-Photovoltaic climate classification and implications to worldwide mapping of PV system performance. Solar Energy, 191, 672-685. doi:10.1016/j.solener.2019.08.072
Gridded Population of the World (GPW) v4.10 (population density)

Ashfaq, A., & Ianakiev, A. (2018). Features of fully integrated renewable energy atlas for Pakistan; wind, solar and cooling. Renewable and Sustainable Energy Reviews, 97, 14-27. doi:10.1016/j.rser.2018.08.011
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
NASa REMOTE SENSING (MODIS)

Ashton, R. A., Bennett, A., Yukich, J., Bhattarai, A., Keating, J., & Eisele, T. P. (2017). Methodological considerations for use of routine health information system data to evaluate malaria program impact in an era of declining malaria transmission. The American Journal of Tropical Medicine and Hygiene, 97(3 (Supplement)), 46-57. doi:10.4269/ajtmh.16-0734
Gridded Population of the World (GPW) v4 (collection)

Asmus, G., Eichenauer, V., Fuchs, A., & Parks, B. C. (2021). Does India Use Development Finance to Compete with China? A Subnational Analysis. Retrieved from Kiel: https://www.ifw-kiel.de/publications/kiel-working-papers/2021/does-india-use-development-finance-to-compete-with-china-a-subnational-analysis-16355/
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Asmus, G., Eichenauer, V. Z., Fuchs, A., & Parks, B. (2021). Does India Use Development Finance to Compete with China? A Subnational Analysis. Retrieved from Williamsburg VA: https://www.aiddata.org/publications/does-india-use-development-finance-to-compete-with-china-a-subnational-analysis
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Asner, G. P., Vaughn, N. R., Heckler, J., Knapp, D. E., Balzotti, C., Shafron, E., . . . Gove, J. M. (2020). Large-scale mapping of live corals to guide reef conservation. Proceedings of the National Academy of Sciences, 117(52), 33711-33718. doi:10.1073/pnas.2017628117
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (SeaWiFS Ocean Color)

Athingo, R., Tenzin, T., Coetzer, A., Hikufe, E. H., Peter, J., Hango, L., . . . Torres, G. (2021). Application of the GARC Data Logger—a custom-developed data collection device—to capture and monitor mass dog vaccination campaigns in Namibia. PLoS Neglected Tropical Diseases, 14(12), e0008948. doi:10.1371/journal.pntd.0008948
Gridded Population of the World (GPW) v4.11 (population density)

Atif, S. B., Saqib, Z., Ali, A., & Zaman, M. H. (2018). The impacts of socio-economic factors on the perception of residents about urban vegetation:  A comparative study of planned versus semi-planned cities of Islamabad and Rawalpindi, Pakistan. Applied Ecology and Environmental Research, 16(4), 4265-4287. doi:10.15666/aeer/1604_42654287
Gridded Population of the World (GPW) v4 (population density)

Atkinson, W., Eastham, S. D., Chen, Y.-H. H., Morris, J., Paltsev, S., Schlosser, C. A., & Selin, N. E. (2022). A Tool for Air Pollution Scenarios (TAPS v1.0) to enable global, long-term, and flexible study of climate and air quality policies. Geoscientific Model Development, 15, 7767-7789. doi:10.5194/gmd-15-7767-2022
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4PN93PB

Aubrecht, C., Gunasekera, R., Ungar, J., & Ishizawa, O. (2016). Consistent yet adaptive global geospatial identification of urban–rural patterns: The iURBAN model. Remote Sensing of Environment, 187, 230-240. doi:10.1016/j.rse.2016.10.031
Gridded Population of the World (GPW) v3 (population count)
Gridded Population of the World (GPW) v4 (population count)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (DMSP-OLS)

Augusto Hernandes Rocha, T., Grapiuna de Almeida, D., Shankar Kozhumam, A., Cristina da Silva, N., Bárbara Abreu Fonseca Thomaz, E., Christine de Sousa Queiroz, R., . . . Ricardo Nickenig Vissoci, J. (2021). Microplanning for designing vaccination campaigns in low-resource settings: A geospatial artificial intelligence-based framework. Vaccine, 39(42), 6276-6282. doi:10.1016/j.vaccine.2021.09.018
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)
POPGRID

Avogadro, N., & Redondi, R. (2023). Diverted and induced demand: Evidence from the London-Paris passenger market. Research in Transportation Economics, 100, 101304. doi:10.1016/j.retrec.2023.101304
Gridded Population of the World (GPW) v4.11 (unspecified)

Axelsson, C. R., & Hanan, N. P. (2018). Rates of woody encroachment in African savannas reflect water constraints and fire disturbance. Journal of Biogeography, 45(6), 1209-1218. doi:10.1111/jbi.13221
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
REMOTE SENSING (Quickbird 2)
REMOTE SENSING (WorldView-2)

Aximoff, I., Carvalho, W. D., Romero, D., Esbérard, C. E. L., Guerrero, J. C., & Rosalino, L. M. (2020). Unravelling the drivers of maned wolf activity along an elevational gradient in the Atlantic Forest, south-eastern Brazil. Mammalian Biology, 100(2), 187-201. doi:10.1007/s42991-020-00017-x
Gridded Population of the World (GPW) v4 (population density)

Azad, S., & Ghandehari, M. (2022). Emissions of nitrogen dioxide in the northeast U.S. during the 2020 COVID-19 lockdown. Journal of Environmental Management, 312, 114902. doi:10.1016/j.jenvman.2022.114902
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics) - 10.7927/H46M34XX
REMOTE SENSING (TROPOMI)

Azam, M. F., Kargel, J. S., Shea, J. M., Nepal, S., Haritashya, U. K., Srivastava, S., . . . Bahuguna, I. M. (2021). Glaciohydrology of the Himalaya-Karakoram. Science, 373(6557), eabf3668. doi:10.1126/science.abf3668
Gridded Population of the World (GPW) v4.11 (population density)

Baarsch, F., Granadillos, J. R., Hare, W., Knaus, M., Krapp, M., Schaeffer, M., & Lotze-Campen, H. (2020). The impact of climate change on incomes and convergence in Africa. World Development, 126, 104699. doi:10.1016/j.worlddev.2019.104699
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates) - 10.7927/H4F47M2C

Backer, D., & Billing, T. (2021). Validating Famine Early Warning Systems Network projections of food security in Africa, 2009–2020. Global Food Security, 29, 100510. doi:10.1016/j.gfs.2021.100510
Gridded Population of the World (GPW) v4 (population count)

Badr, H. S., Zaitchik, B. F., Kerr, G. H., Nguyen, N.-L. H., Chen, Y.-T., Hinson, P., . . . Gardner, L. M. (2023). Unified real-time environmental-epidemiological data for multiscale modeling of the COVID-19 pandemic. Scientific Data, 10(1), 367. doi:10.1038/s41597-023-02276-y
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)
NASA REMOTE SENSING (OMI NO2)

Badreldin, A. (2019). Redefining natural resources in economic research. Eurasian Journal of Social Sciences, 7(2), 48-55. doi:10.15604/ejss.2019.07.02.004
Gridded Population of the World (GPW) v4 (population density)

Bagaria, P., Thapa, A., Sharma, L. K., Joshi, B. D., Singh, H., Sharma, C. M., . . . Chandra, K. (2021). Distribution modelling and climate change risk assessment strategy for rare Himalayan Galliformes species using archetypal data abundant cohorts for adaptation planning. Climate Risk Management, 31, 100264. doi:10.1016/j.crm.2020.100264
Global Agricultural Lands (Cropland) - 10.7927/H4C8276G
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
Land Use and Land Cover (LULC) (Development Threat Index, v1) - 10.7927/61jv-th84
Global High Resolution Urban Data from Landsat (HBASE) - 10.7927/H4DN434S
NASA REMOTE SENSING (MODIS - MOD13Q1)
NASA REMOTE SENSING (SRTM)

Bai, H., Gao, W., Seong, M., Yan, R., Wei, J., & Liu, C. (2023). Evaluating and optimizing PM2.5 stations in Yangtze River Delta from a spatial representativeness perspective. Applied Geography, 154, 102949. doi:10.1016/j.apgeog.2023.102949
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MODIS)

Bai, H., Shi, Y., Seong, M., Gao, W., & Li, Y. (2022). Influence of spatial resolution on satellite-based PM2.5 estimation: Implications for health assessment. Remote Sensing, 14(12), 2933. doi:10.3390/rs14122933
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MODIS)

Bai, H., Yan, R., Gao, W., Wei, J., & Seong, M. (2022). Spatial representativeness of PM2.5 monitoring stations and its implication for health assessment. Air Quality, Atmosphere & Health, 15, 1571-1581. doi:10.1007/s11869-022-01202-2
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MODIS)

Bai, H., Zheng, Z., Zhang, Y., Huang, H., & Wang, L. (2021). Comparison of satellite-based PM2.5 estimation from aerosol optical depth and top-of-atmosphere reflectance. Aerosol and Air Quality Research, 21(2), 200257. doi:10.4209/aaqr.2020.05.0257
Gridded Population of the World (GPW) v4.11 (population count)
REMOTE SENSING (Advanced Himawari Imager)

Bai, Y., Arabadzhyan, A., & Li, Y. (2022). The legacy of the Great Wall. Journal of Economic Behavior & Organization, 196, 120-147. doi:10.1016/j.jebo.2022.01.010
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (DMSP-OLS)

Bai, Y., Li, Y., & Wang, Y. (2022). Chinese aid and local political attitudes. Economic Modelling, 113, 105893. doi:10.1016/j.econmod.2022.105893
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (DMSP-OLS)

Baiyinbaoligao, Liu, H., Chen, X., & Mu, X. (2020). Overview of the Mekong River Basin. In H. Liu (Ed.), Flood Prevention and Drought Relief in Mekong River Basin (pp. 1-25). Singapore: Springer Singapore.
Gridded Population of the World (GPW) v4.11 (documentation) - 10.7927/H45Q4T5F

Bajaj, S., & Geraldine Bessie Amali, D. (2019). Species environmental niche distribution modeling for Panthera Tigris Tigris ‘Royal Bengal Tiger’ using machine learning. Paper presented at the Emerging Research in Computing, Information, Communication and Applications, Singapore.
Gridded Population of the World (GPW) v4 (population density)
Last of the Wild v2 (Global Human Footprint (Geographic))
NASA REMOTE SENSING (MODIS - MCD12C1)

Bajpai, R., Shukla, V., Raju, A., Singh, C. P., & Upreti, D. K. (2022). A geostatistical approach to compare metal accumulation pattern by lichens in plain and mountainous regions of northern and central India. Environmental Earth Sciences, 81(7), 203. doi:10.1007/s12665-022-10336-6
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Baker, R. E., Yang, W., Vecchi, G. A., Metcalf, C. J. E., & Grenfell, B. T. (2020). Susceptible supply limits the role of climate in the early SARS-CoV-2 pandemic. Science, 369(6501), 315-319. doi:10.1126/science.abc2535
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MERRA-2)

Bakimchandra, O., Oinam, J., & Kajal, R. K. (2020). A geospatial approach to assess health coverage and scaling-up of healthcare facilities. Current Science, 118(5), 728-736. Retrieved from https://www.currentscience.ac.in/Volumes/118/05/0728.pdf
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (Landsat)

Balbi, S., Selomane, O., Sitas, N., Blanchard, R., Kotzee, I., O'Farrell, P., & Villa, F. (2019). Human dependence on natural resources in rapidly urbanising South African regions. Environmental Research Letters, 14(4), 044008. doi:10.1088/1748-9326/aafe43
Gridded Population of the World (GPW) v4 (population density)

Baldacchino, F., Bussola, F., Arnoldi, D., Marcantonio, M., Montarsi, F., Capelli, G., . . . Rizzoli, A. (2017). An integrated pest control strategy against the Asian tiger mosquito in northern Italy: a case study. Pest Management Science, 73(1), 87-93. doi:10.1002/ps.4417
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H46T0JKB

Ballesteros, C., & Esteves, L. S. (2021). Integrated assessment of coastal exposure and social vulnerability to coastal hazards in East Africa. Estuaries and Coasts, 44(8), 2056-2072. doi:10.1007/s12237-021-00930-5
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
NASA REMOTE SENSING (ASTER GDEM)

Ballesteros-González, K., Sullivan, A. P., & Morales-Betancourt, R. (2020). Estimating the air quality and health impacts of biomass burning in northern South America using a chemical transport model. Science of The Total Environment, 739, 139755. doi:10.1016/j.scitotenv.2020.139755
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MERRA-2)

Banerjee, O., Cicowiez, M., Malek, Ž., Verburg, P., Vargas, R., & Goodwin, S. (2020). The Value of Biodiversity in Economic Decision Making: Applying the IEEM ESM Approach to Conservation Strategies in Colombia. Retrieved from https://doi.org/10.18235/0002945
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D
Global Rural-Urban Mapping Project (GRUMP) v1 (population density) - 10.7927/H4R20Z93

Banerjee, P. (2021). Maximum entropy-based forest fire likelihood mapping: analysing the trends, distribution, and drivers of forest fires in Sikkim Himalaya. Scandinavian Journal of Forest Research, 36(4), 275-288. doi:10.1080/02827581.2021.1918239
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics) - 10.7927/H46M34XX
NASA REMOTE SENSING (MODIS - MOD13Q1)
REMOTE SENSING (Landsat)

Banzhaf, E., Bulley, H. N., Inkoom, J. N., & Elze, S. (2022). Mapping open data and big data to address climate resilience of urban informal settlements in Sub-Saharan Africa. Climate, 10(12), 186. doi:10.3390/cli10120186
Gridded Population of the World (GPW) v4.11 (population count)
Gridded Population of the World (GPW) v4.11 (population density)
Satellite-Derived Environmental Indicators (Global Urban Heat Island (UHI) Data Set, v1)

Bar, S., Parida, B. R., Mandal, S. P., Pandey, A. C., Kumar, N., & Mishra, B. (2021). Impacts of partial to complete COVID-19 lockdown on NO2 and PM2.5 levels in major urban cities of Europe and USA. Cities, 117, 103308. doi:10.1016/j.cities.2021.103308
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (TROPOMI)

Bar, S., Parida, B. R., Pandey, A. C., Shankar, B. U., Kumar, P., Panda, S. K., & Behera, M. D. (2023). Modeling and prediction of fire occurrences along an elevational gradient in Western Himalayas. Applied Geography, 151, 102867. doi:10.1016/j.apgeog.2022.102867
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (ALOS)

Barad, R., Fletcher, E. K., & Hillbruner, C. (2020). Leveraging existing household survey data to map livelihoods in Nigeria. World Development, 126, 104727. doi:10.1016/j.worlddev.2019.104727
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4HX19NJ
NASA REMOTE SENSING (SRTM)

Baragwanath, K., & Bayi, E. (2020). Collective property rights reduce deforestation in the Brazilian Amazon. Proceedings of the National Academy of Sciences, 117(34), 20495-20502. doi:10.1073/pnas.1917874117
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MODIS - MOD44W)
REMOTE SENSING (DMSP-OLS)

Barber, R. A., Ball, S. G., Morris, R. K. A., & Gilbert, F. (2022). Target-group backgrounds prove effective at correcting sampling bias in Maxent models. Diversity and Distributions, 28(1), 128-141. doi:10.1111/ddi.13442
Gridded Population of the World (GPW) v4 (population density)

Barbet-Massin, M., Salles, J.-M., & Courchamp, F. (2020). The economic cost of control of the invasive yellow-legged Asian hornet. NeoBiota, 55, 11-25. doi:10.3897/neobiota.55.38550
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Barbier, E. B., & Hochard, J. P. (2018). Land degradation and poverty. Nature Sustainability, 1(11), 623-631. doi:10.1038/s41893-018-0155-4
Global Rural-Urban Mapping Project (GRUMP) v1 (urban extent)
Gridded Population of the World (GPW) v4 (collection)
Poverty Mapping (Global Subnational Infant Mortality Rates, v1)

Barbour, A. J., Langbein, J. O., & Farghal, N. S. (2021). Earthquake magnitudes from dynamic strain. Bulletin of the Seismological Society of America, 111(3), 1325-1346. doi:10.1785/0120200360
Gridded Population of the World (GPW) v4 (population density) - 10.7927//H4NP22DQ

Barregard, L., Molnàr, P., Jonson, J. E., & Stockfelt, L. (2019). Impact on population health of Baltic shipping emissions. International Journal of Environmental Research and Public Health, 16(11), 1954. doi:10.3390/ijerph16111954
Gridded Population of the World (GPW) v4.11 (admin unit center points)

Basel, A. M., Simaika, J. P., Samways, M. J., Midgley, G. F., MacFadyen, S., & Hui, C. (2021). Assemblage reorganization of South African dragonflies due to climate change. Diversity and Distributions, 27(12), 2542-2558. doi:10.1111/ddi.13422
Gridded Population of the World (GPW) v4 (population count)

Batibeniz, F., Hauser, M., & Seneviratne, S. I. (2023). Countries most exposed to individual and concurrent extremes and near-permanent extreme conditions at different global warming levels. EGUsphere, 14(2), 485-505. doi:10.5194/esd-14-485-2023
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
Population Dynamics (Global One-Eighth Degree Population Base Year and Projection Grids Based on the SSPs, v1.01) - 10.7927/m30p-j498

Batista e Silva, F., Freire, S., Schiavina, M., Rosina, K., Marín-Herrera, M. A., Ziemba, L., . . . Lavalle, C. (2020). Uncovering temporal changes in Europe’s population density patterns using a data fusion approach. Nature Communications, 11(1), 4631. doi:10.1038/s41467-020-18344-5
Gridded Population of the World (GPW) v4.11 (population count)

Bauer, J., Brüggmann, D., Klingelhöfer, D., Maier, W., Schwettmann, L., Weiss, D. J., & Groneberg, D. A. (2020). Access to intensive care in 14 European countries: a spatial analysis of intensive care need and capacity in the light of COVID-19. Intensive Care Medicine, 46, 2026-2034. doi:10.1007/s00134-020-06229-6
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)

Bauer, V., Reese, M., & Ruby, K. (2022). Does insurgent selective punishment deter collaboration? Evidence from the drone war in Pakistan. Journal of Conflict Resolution, 66(2), 297-326. doi:10.1177/00220027211041158
Gridded Population of the World (GPW) v4 (population density) - 10.7927//H4NP22DQ

BBC. (2016). MWC 2016: Facebook uses AI to map people's homes. Retrieved from http://www.bbc.com/news/technology-35633915
Gridded Population of the World (GPW) v4 (collection)

Beck, J., López-Ballesteros, A., Omar, A., Johannessen, T., Skjelvan, I., Helmschrot, J., & Saunders, M. (2018). Identification of Key Variables for Climate Change Observation across Africa:  SEACRIFOG Deliverable 4.1: Ideal and Mandatory Sets of Variables and Criteria. Retrieved from https://www.seacrifog.eu/fileadmin/seacrifog/Deliverables/2018.08.18_SEACRIFOG_Deliverable_4.1.pdf
Gridded Population of the World (GPW) v4.10 (population density)

Beekman, M., Upla, P., & Refisch, J. (2020). Getting Climate-Smart with the Mountain Gorilla in the Greater Virunga Landscape: A Species and Climate Change Brief for the Vanishing Treasures Programme. Retrieved from Nairobi: https://vanishingtreasures.org/wp-content/uploads/2020/12/VTbrief_Gorilla.pdf
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Beesley, L. J., Patelli, P., Kaufeld, K., Schwenk, J., Martinez, K. M., Pitts, T., . . . Del Valle, S. Y. (2023). Multi-dimensional resilience: A quantitative exploration of disease outcomes and economic, political, and social resilience to the COVID-19 pandemic in six countries. PLoS ONE, 18(1), e0279894. doi:10.1371/journal.pone.0279894
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)

Beibei, E., Zhang, S., Driscoll, C. T., & Wen, T. (2023). Human and natural impacts on the U.S. freshwater salinization and alkalinization: A machine learning approach. Science of The Total Environment, 889, 164138. doi:10.1016/j.scitotenv.2023.164138
Gridded Population of the World (GPW) v4 (population density)

Bellucci, A., Tholey, N., Studer, M., Goester, J. F., & Fuentes, N. (2018). Extrapolation of population grids for risk analysis. Journal of Space Safety Engineering, 5(3-4), 192-196. doi:10.1016/j.jsse.2018.01.006
Gridded Population of the World (GPW) v4 (collection)

Beloconi, A., Chrysoulakis, N., Lyapustin, A., Utzinger, J., & Vounatsou, P. (2018). Bayesian geostatistical modelling of PM10 and PM2.5 surface level concentrations in Europe using high-resolution satellite-derived products. Environment International, 121, 57-70. doi:10.1016/j.envint.2018.08.041
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (DMSP-OLS)

Beloconi, A., & Vounatsou, P. (2021). Substantial reduction in particulate matter air pollution across Europe during 2006–2019: A spatiotemporal modeling analysis. Environmental Science & Technology, 55(22), 15505-15518. doi:10.1021/acs.est.1c03748
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Benítez-López, A., Santini, L., Schipper, A. M., Busana, M., & Huijbregts, M. A. J. (2019). Intact but empty forests? Patterns of hunting-induced mammal defaunation in the tropics. PLoS Biology, 17(5), e3000247. doi:10.1371/journal.pbio.3000247
Gridded Population of the World (GPW) v4 (population density)
Global Rural-Urban Mapping Project (GRUMP) v1 (population density)

Bennett, B. F., Hope, A. P., Salawitch, R. J., Tribett, W. R., & Canty, T. P. (2017). Implementation. In Paris Climate Agreement: Beacon of Hope (pp. 147-181): Springer International Publishing.
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (VIIRS)

BenYishay, A., Parks, B. C., Runfola, D., & Trichler, R. (2016). Forest Cover Impacts of Chinese Development Projects in Ecologically Sensitive Areas. Retrieved from Williamsburg VA: http://aiddata.org/working-papers
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (DMSP-OLS)

Benz, S. A., Menberg, K., Bayer, P., & Kurylyk, B. L. (2022). Shallow subsurface heat recycling is a sustainable global space heating alternative. Nature Communications, 13(1), 3962. doi:10.1038/s41467-022-31624-6
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
Population Dynamics (Global One-Eighth Degree Population Projection Grids for the SSPs, v1)
REMOTE SENSING (DMSP-OLS)

Berkhout, E., Malan, M., & Kram, T. (2018). Better soils for healthier lives? Assessing the link between soil nutrients and malnutrition in Sub-Saharan Africa. Retrieved from The Hague: http://www.pbl.nl/en/topics/energy-and-climate-change/publications/better-soils-for-healthier-lives-assessing-the-link-between-soil-nutrients-and-malnutrition-in-sub-saharan-africa
Gridded Population of the World (GPW) v4 (population density)

Berkhout, E. D., Malan, M., & Kram, T. (2019). Better soils for healthier lives? An econometric assessment of the link between soil nutrients and malnutrition in Sub-Saharan Africa. PLoS ONE, 14(1), e0210642. doi:10.1371/journal.pone.0210642
Gridded Population of the World (GPW) v4 (population density)
REMOTE SENSING (DMSP-OLS)

Bernhofen, M. V., Trigg, M. A., Sleigh, P. A., Sampson, C. C., & Smith, A. M. (2021). Global flood exposure from different sized rivers. Natural Hazards and Earth System Sciences, 21(9), 2829-2847. doi:10.5194/nhess-21-2829-2021
Gridded Population of the World (GPW) v4 (population count)

Bernos, T., Yates, M. C., Docker, M. F., Fitzgerald, A., Hanner, R., Heath, D. D., . . . Mandrak, N. E. (2023). Environmental DNA (eDNA) applications in freshwater fisheries management and conservation in Canada: overview of current challenges and opportunities. Canadian Journal of Fisheries and Aquatic Sciences, 80(7), 1170-1186. doi:10.1139/cjfas-2022-0162
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Bernstein, D., Hamilton, D. S., Krasnoff, R., Mahowald, N., M., Connelly, D. S., Tilmes, S., & Hess, P. G. M. (2021). Short-term impacts of 2017 western North American wildfires on meteorology, the atmosphere’s energy budget, and premature mortality. Environmental Research Letters, 16(6), 064065. doi:10.1088/1748-9326/ac02ee
Gridded Population of the World (GPW) v4.11 (population count)

Bertassoni, A., Gouvea, J. A., Bianchi, R. d. C., Costa, R. T., Ribeiro, J. W., Vancine, M. H., & Ribeiro, M. C. (2019). Land-use changes and the expansion of biofuel crops threaten the giant anteater in southeastern Brazil. Journal of Mammalogy, 100(2), 435-444. doi:10.1093/jmammal/gyz042
Gridded Population of the World (GPW) v4 (population density)

Bessagnet, B., Pisoni, E., de Meij, A., Létinois, L., & Thunis, P. (2023). A simple and fast method to downscale chemistry transport model output fields from the regional to the urban/district scale. Environmental Modelling & Software, 164, 105692. doi:10.1016/j.envsoft.2023.105692
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Bessenbacher, V., Gudmundsson, L., & Seneviratne, S. I. (2023). Optimizing soil moisture station networks for future climates. Geophysical Research Letters, 50(4), e2022GL101667. doi:10.1029/2022GL101667
Gridded Population of the World (GPW) v4.11 (population density)

Besset, M., Anthony, E. J., & Bouchette, F. (2019). Multi-decadal variations in delta shorelines and their relationship to river sediment supply: An assessment and review. Earth-Science Reviews, 193, 199-219. doi:10.1016/j.earscirev.2019.04.018
Gridded Population of the World (GPW) v4 (collection)

Bevis, L., Kim, K., & Guerena, D. (2023). Soil zinc deficiency and child stunting: Evidence from Nepal. Journal of Health Economics, 87, 102691. doi:10.1016/j.jhealeco.2022.102691
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
NASA REMOTE SENSING (Black Marble -VIIRS)
NASA REMOTE SENSING (SRTM)

Bhanja, S. N., & Mukherjee, A. (2021). Chapter 34 - Groundwater sustainability and security in South Asia. In A. Mukherjee, B. R. Scanlon, A. Aureli, S. Langan, H. Guo, & A. A. McKenzie (Eds.), Global Groundwater (pp. 469-476): Elsevier.
Gridded Population of the World (GPW) v4.11 (population density) map

Bhatt, C. M., Gupta, A., Roy, A., Dalal, P., & Chauhan, P. (2021). Geospatial analysis of September, 2019 floods in the lower gangetic plains of Bihar using multi-temporal satellites and river gauge data. Geomatics, Natural Hazards and Risk, 12(1), 84-102. doi:10.1080/19475705.2020.1861113
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (AMSR-2)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Sentinel-1 SAR)

Bhatt, C. M., & Karnatak, H. C. (2019). Geoweb services and open online data repositories for North West Himalayas studies including disaster monitoring and mitigation. In R. R. Navalgund, A. S. Kumar, & S. Nandy (Eds.), Remote Sensing of Northwest Himalayan Ecosystems (pp. 501-536). Singapore: Springer Singapore.
Global Agricultural Lands (collection)
Anthropogenic Biomes of the World (collection)
Gridded Population of the World (GPW) v4 (collection)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)
Human Appropriation of Net Primary Productivity (HANPP) (collection)
Natural Disaster Hotspots (collection)
Last of the Wild v2 (collection)
NASA EOSDIS (Earthdata website)
NASA REMOTE SENSING (ASTER GDEM)
NASA REMOTE SENSING (FIRMS)
NASA REMOTE SENSING (ISCCP)
NASA REMOTE SENSING (MODIS Land cover)

Bhattacharjee, S., Tencar, J., Danilchyk, T., Svitek, M., Horak, T., & Naghdi, Y. (2020, 25-25 June 2020). Urban Heat Vulnerability Assessment of Smart Border. Paper presented at the 2020 Smart City Symposium Prague (SCSP).
Gridded Population of the World (GPW) v4.10 (population count)

Bhunia, G. S., & Shit, P. K. (2019). Spatial Database for Public Health and Cartographic Visualization. In G. S. Bhunia & P. K. Shit (Eds.), Geospatial Analysis of Public Health (pp. 29-57). Cham: Springer International Publishing.
Global Agricultural Lands (collection)
Gridded Population of the World (GPW) v4 (collection)
Global Reservoir and Dam (GRanD) v1.01 (dams)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
NASA REMOTE SENSING (ASTER GDEM)

Biardeau, L. T., Davis, L. W., Gertler, P., & Wolfram, C. (2020). Heat exposure and global air conditioning. Nature Sustainability, 3, 25-28. doi:10.1038/s41893-019-0441-9
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Biass, S., Jenkins, S., Lallemant, D., Lim, T. N., Williams, G., & Yun, S.-H. (2021). Chapter 12 - Remote sensing of volcanic impacts. In P. Papale (Ed.), Forecasting and Planning for Volcanic Hazards, Risks, and Disasters (Vol. 2, pp. 473-491): Elsevier.
Gridded Population of the World (GPW) v4 (collection)
NASA REMOTE SENSING (MODIS - MCD12Q1)

Biazzo, I. (2022). CityChrone: an Interactive Platform for Transport Network Analysis and Planning in Urban Systems. Paper presented at the Complex Networks 2021.
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Biazzo, I., Monechi, B., & Loreto, V. (2019). General scores for accessibility and inequality measures in urban areas. Royal Society Open Science, 6(8), 190979. doi:10.1098/rsos.190979
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Biazzo, I., & Ramezanpour, A. (2020). Efficiency and irreversibility of movements in a city. Scientific Reports, 10(1), 4334. doi:10.1038/s41598-020-60744-6
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Bilsback, K. R., Kerry, D., Croft, B., Ford, B., Jathar, S. H., Carter, E., . . . Pierce, J. R. (2020). Beyond SOx reductions from shipping: Assessing the impact of NOx and carbonaceous-particle controls on human health and climate. Environmental Research Letters, 15(12), 124046. doi:10.1088/1748-9326/abc718
Gridded Population of the World (GPW) v4.11 (population density)

Bird, J., Lebrand, M., & Venables, A. J. (2019). The Belt and Road Initiative:  Reshaping Economic Geography in Central Asia. Retrieved from Washington DC: https://openknowledge.worldbank.org/handle/10986/31536
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42

Bird, J., Lebrand, M., & Venables, A. J. (2020). The Belt and Road Initiative: Reshaping economic geography in Central Asia? Journal of Development Economics, 144, 102441. doi:10.1016/j.jdeveco.2020.102441
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42

Birkman, L., Sweijs, T., & Malekovic, N. (2022). Not When But Why:  Embedding Causal Methods in Policy and Programming. Retrieved from The Hague: https://hcss.nl/report/not-when-but-why-causal-methods-policy-programming/
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (FLDAS Noah Land Surface Model L4 Global Monthly Climatology)
NASA REMOTE SENSING (IMERG)
NASA REMOTE SENSING (MODIS)

Birolini, S., Antunes, A. P., Cattaneo, M., Malighetti, P., & Paleari, S. (2021). Integrated flight scheduling and fleet assignment with improved supply-demand interactions. Transportation Research Part B: Methodological, 149, 162-180. doi:10.1016/j.trb.2021.05.001
Gridded Population of the World (GPW) v4.11 (admin unit center points)

Birolini, S., Cattaneo, M., Malighetti, P., & Morlotti, C. (2020). Integrated origin-based demand modeling for air transportation. Transportation Research Part E: Logistics and Transportation Review, 142, 102050. doi:10.1016/j.tre.2020.102050
Gridded Population of the World (GPW) v4.11 (population count)

Biswas, M. S., & Choudhury, A. D. (2021). Impact of COVID-19 control measures on trace gases (NO2, HCHO and SO2) and aerosols over India during pre-monsoon of 2020. Aerosol and Air Quality Research, 21(1), 200306. doi:10.4209/aaqr.2020.06.0306
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (OMI)

Boakye, K., Bovbjerg, M., Schuna, J., Branscum, A., Varma, R. P., Ismail, R., . . . Hystad, P. (2023). Urbanization and physical activity in the global Prospective Urban and Rural Epidemiology study. Scientific Reports, 13(1), 290. doi:10.1038/s41598-022-26406-5
Gridded Population of the World (GPW) v4.11 (population count)

Bockarie, A. S., Marais, E. A., & MacKenzie, A. R. (2020). Air pollution and climate forcing of the charcoal industry in Africa. Environmental Science & Technology, 54(21), 13429-13438. doi:10.1021/acs.est.0c03754
Gridded Population of the World (GPW) v4.10 (population density)

Boere, E., Valin, H., Bodirsky, B., Baier, F., Balkovic, J., Batka, M., . . . Lotze-Campen, H. (2019). D2.2 Impacts on Agriculture Including Forestry & Fishery. Retrieved from https://www.coacch.eu/wp-content/uploads/2019/11/D2.2-Impacts-on-agriculture-forestry-fishery-1.pdf
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)

Bogdanovich, E., Guenther, L., Reichstein, M., Frank, D., Ruhrmann, G., Brenning, A., . . . Orth, R. (2023). Societal attention to heat waves can indicate public health impacts. Weather, Climate, and Society, 15(3), 557-569. doi:10.1175/WCAS-D-22-0147.1
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Boke-Olén, N., Abdi, A. M., Hall, O., & Lehsten, V. (2017). High-resolution African population projections from radiative forcing and socio-economic models, 2000 to 2100. Scientific Data, 4(160130). doi:10.1038/sdata.2016.130
Gridded Population of the World (GPW) v1
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)
Global Roads (Global Roads Open Access Data Set (gROADS), v1) 
Global Rural-Urban Mapping Project (GRUMP) v1 (National Administrative Boundaries)

Bombieri, G., Naves, J., Penteriani, V., Selva, N., Fernández-Gil, A., López-Bao, J. V., . . . Delgado, M. M. (2019). Brown bear attacks on humans: a worldwide perspective. Scientific Reports, 9(1), 8573. doi:10.1038/s41598-019-44341-w
Gridded Population of the World (GPW) v4.10 (population density)

Bommer, C., Dreher, A., & Perez-Alvarez, M. (2022). Home bias in humanitarian aid: The role of regional favoritism in the allocation of international disaster relief. Journal of Public Economics, 208, 104604. doi:10.1016/j.jpubeco.2022.104604
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM
REMOTE SENSING (DMSP-OLS)

Bommera, C., Dreher, A., & Pérez-Alvarez, M. (2019). Home bias in humanitarian aid: The role of regional favoritism in the allocation of international disaster relief. Retrieved from Göttingen: http://hdl.handle.net/10419/203140
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM

Bonamente, E., Rinaldi, S., Nicolini, A., & Cotana, F. (2017). National water footprint: Toward a comprehensive approach for the evaluation of the sustainability of water use in Italy. Sustainability, 9(8), 12pp. doi:10.3390/su9081341
Gridded Population of the World (GPW) v4 (documentation)

Bondi-Kelly, E., Chen, H., Golden, C. D., Behari, N., & Tambe, M. (2023). Predicting micronutrient deficiency with publicly available satellite data. AI Magazine, 44(1), 30-40. doi:10.1002/aaai.12080
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (NASADEM)
REMOTE SENSING (VIIRS NTL)

Bora, K. (2021). Air pollution as a determinant of undernutrition prevalence among under-five children in India: An exploratory study. Journal of Tropical Pediatrics, 67(5), fmab089. doi:10.1093/tropej/fmab089
Gridded Population of the World (GPW) v4.10 (admin unit center points)

Borck, R., & Schrauth, P. (2022). Urban pollution. Retrieved from Potsdam: https://doi.org/10.25932/publishup-57204
Gridded Population of the World (GPW) v4.11 (population density)

Borrell, J. S., Goodwin, M., Blomme, G., Jacobsen, K., Wendawek, A. M., Gashu, D., . . . Wilkin, P. (2020). Enset-based agricultural systems in Ethiopia: A systematic review of production trends, agronomy, processing and the wider food security applications of a neglected banana relative. Plants, People, Planet, 2(3), 212-228. doi:10.1002/ppp3.10084
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted) - 10.7927/H4JQ0XZW

Botero-Mesa, S., Coelho, F. C., Nwosu, K., Wicht, B., Venkatasubramanian, A., Wagner, O., . . . Keiser, O. (2022). Leveraging human resources for outbreak analysis: lessons from an international collaboration to support the sub-Saharan African COVID-19 response. BMC Public Health, 22(1), 1073. doi:10.1186/s12889-022-13327-1
Gridded Population of the World (GPW) v4 (collection)

Boucher, J., & Billard, G. (2020). The Mediterranean: Mare Plasticum. Retrieved from Gland, Switzerland: https://portals.iucn.org/library/node/49124
Gridded Population of the World (GPW) v4 (population density)

Boudet, H. S., Zanocco, C. M., Howe, P. D., & Clarke, C. E. (2018). The effect of geographic proximity to unconventional oil and gas development on public support for hydraulic fracturing. Risk Analysis, 38(9), 1871-1890. doi:10.1111/risa.12989
Gridded Population of the World (GPW) v4 (population density)

Boulange, J., Hanasaki, N., Yamazaki, D., & Pokhrel, Y. (2021). Role of dams in reducing global flood exposure under climate change. Nature Communications, 12(1), 417. doi:10.1038/s41467-020-20704-0
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)

Boulange, J., Yoshida, T., Nishina, K., Okada, M., & Hanasaki, N. (2023). Delivering the latest global water resource simulation results to the public. Climate Services, 30, 100386. doi:10.1016/j.cliser.2023.100386
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)

Bowater, D., & Stefanakis, E. (2019). On the isolatitude property of the rHEALPix Discrete Global Grid System. Big Earth Data, 3(4), 362-377. doi:10.1080/20964471.2019.1658494
Gridded Population of the World (GPW) v4.11 (admin unit center points) - 10.7927/H4BC3WMT

Bowler, D. E., Bjorkman, A. D., Dornelas, M., Myers-Smith, I. H., Navarro, L. M., Niamir, A., . . . Bates, A. E. (2020). Mapping human pressures on biodiversity across the planet uncovers anthropogenic threat complexes. People and Nature, 2(2), 380-394. doi:10.1002/pan3.10071
Gridded Population of the World (GPW) v4.10 (population density)
NASA REMOTE SENSING (Global Maps of Atmospheric Nitrogen Deposition, 1860, 1993, and 2050) ORNL DAAC

Bowman, D. M. J. S., Williamson, G. J., Abatzoglou, J. T., Kolden, C. A., Cochrane, M. A., & Smith, A. M. S. (2017). Human exposure and sensitivity to globally extreme wildfire events. Nature Ecology & Evolution, 1(0058), 6 pp. doi:10.1038/s41559-016-0058
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
NASA REMOTE SENSING (MODIS Active Fires)
REMOTE SENSING (Landsat)

Boyle, E. H., King, M. L., Garcia, S., Culver, C., & Bourdeaux, J. (2020). Contextual data in IPUMS DHS: physical and social environment variables linked to the Demographic and Health Surveys. Population and Environment, 41, 529-549. doi:10.1007/s11111-020-00348-4
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)

Brahney, J., Hallerud, M., Heim, E., Hahnenberger, M., & Sukumaran, S. (2020). Plastic rain in protected areas of the United States. Science, 368(6496), 1257-1260. doi:10.1126/science.aaz5819
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Brahney, J., Mahowald, N., Prank, M., Cornwell, G., Klimont, Z., Matsui, H., & Prather, K. A. (2021). Constraining the atmospheric limb of the plastic cycle. Proceedings of the National Academy of Sciences, 118(16), e2020719118. doi:10.1073/pnas.2020719118
Gridded Population of the World (GPW) v4.11 (population density)

Brandell, E. E., Cross, P. C., Craft, M. E., Smith, D. W., Dubovi, E. J., Gilbertson, M. L. J., . . . Hudson, P. J. (2021). Patterns and processes of pathogen exposure in gray wolves across North America. Scientific Reports, 11(1), 3722. doi:10.1038/s41598-021-81192-w
Gridded Population of the World (GPW) v4.11 (population density)

Brandsch, J., & Python, A. (2021). Provoking ordinary people: The effects of terrorism on civilian violence. Journal of Conflict Resolution, 65(1), 135-165. doi:10.1177/0022002720937748
Gridded Population of the World (GPW) v4 (population count)
Global Rural-Urban Mapping Project (GRUMP) v1 (urban extent)
REMOTE SENSING (DMSP-OLS)

Brandt, J., Christensen, J. H., Jensen, S. S., & Im, U. (2021). Unit Costs of Air Emissions in Vietname for Energy System Modeling. Retrieved from https://dce2.au.dk/pub/SR441.pdf
Gridded Population of the World (GPW) v4.11 (population density)
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)

Branoff, B. L. (2017). Quantifying the influence of urban land use on mangrove biology and ecology: A meta-analysis. Global Ecology and Biogeography, 26(11), 1339-1356. doi:10.1111/geb.12638
Environmental Performance Index (EPI) (2008)
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
NASA REMOTE SENSING (MODIS)

Braun, V., Funke, Q., Lemmens, S., & Sanvido, S. (2019). DRAMA 3.0 - Upgrade of ESA's Debris Risk Assessment and Mitigation Analysis Tool Suite. Paper presented at the First International Orbital Debris Conference, Sugar Land, TX. https://www.hou.usra.edu/meetings/orbitaldebris2019/orbital2019paper/pdf/6136.pdf
Gridded Population of the World (GPW) v4 (unspecified)

Braun, V., Funke, Q., Lemmens, S., & Sanvido, S. (2020). DRAMA 3.0 - Upgrade of ESA’s debris risk assessment and mitigation analysis tool suite. Journal of Space Safety Engineering, 7(3), 206-212. doi:10.1016/j.jsse.2020.07.020
Gridded Population of the World (GPW) v4 (unspecified)

Breckner, M. (2019). Climatic and Geographic Determinants of Economic Development. (Ph.D. Dissertation, LMU Munich). Ludwig Maximilians Universität, Munich, Munich. Retrieved from https://edoc.ub.uni-muenchen.de/23734/ (ediss:23734)
Gridded Population of the World (GPW) v3 (population density)
Gridded Population of the World (GPW) v4 (population density)
Population Dynamics (Global Estimated Net Migration Grids By Decade, v1)
NASA REMOTE SENSING (MODIS - MOD13C1)

Breckner, M., & Sunde, U. (2019). Temperature extremes, global warming, and armed conflict: new insights from high resolution data. World Development, 123, 104624. doi:10.1016/j.worlddev.2019.104624
Gridded Population of the World (GPW) v3 (population density)
Gridded Population of the World (GPW) v4 (population density)
Population Dynamics (Global Estimated Net Migration Grids By Decade, v1)

Bressler, R. D., Moore, F. C., Rennert, K., & Anthoff, D. (2021). Estimates of country level temperature-related mortality damage functions. Scientific Reports, 11(1), 20282. doi:10.1038/s41598-021-99156-5
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - data set unspecified)

Bretzler, A., Lalanne, F., Nikiema, J., Podgorski, J., Pfenninger, N., Berg, M., & Schirmer, M. (2017). Groundwater arsenic contamination in Burkina Faso, West Africa: Predicting and verifying regions at risk. Science of The Total Environment, 584-585, 958-970. doi:10.1016/j.scitotenv.2017.01.147
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Brito, J. C., & Naia, M. (2020). Coping with sea-level rise in African protected areas: Priorities for action and adaptation measures. BioScience, 70(10), 924-932. doi:10.1093/biosci/biaa087
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics) - 10.7927/H46M34XX

Brlík, V., Procházka, P., Hansson, B., Stricker, C. A., Yohannes, E., Powell, R. L., & Wunder, M. B. (2023). Animal tracing with sulfur isotopes: Spatial segregation and climate variability in Africa likely contribute to population trends of a migratory songbird. Journal of Animal Ecology, 92(7), 1320-1331. doi:10.1111/1365-2656.13848
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Brooks, N., Biswas, D., Hossin, R., Yu, A., Saha, S., Saha, S., . . . Luby, S. P. (2023). Health consequences of small-scale industrial pollution: Evidence from the brick sector in Bangladesh. World Development, 170, 106318. doi:10.1016/j.worlddev.2023.106318
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Brottrager, M., Crespo Cuaresma, J., Kniveton, D., & Ali, S. H. (2023). Natural resources modulate the nexus between environmental shocks and human mobility. Nature Communications, 14(1), 1393. doi:10.1038/s41467-023-37074-y
Gridded Population of the World (GPW) v4.11 (population count)

Brown, G., & Hausner, V. H. (2017). An empirical analysis of cultural ecosystem values in coastal landscapes. Ocean & Coastal Management, 142, 49-60. doi:10.1016/j.ocecoaman.2017.03.019
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Bruederle, A., & Hodler, R. (2017). Nighttime Lights as a Proxy for Human Development at the Local Level. Retrieved from Munich: https://www.cesifo-group.de/de/ifoHome/publications/working-papers/CESifoWP/CESifoWPdetails?wp_id=19333511
Gridded Population of the World (GPW) v4 (population density)
REMOTE SENSING (DMSP-OLS)

Bruederle, A., & Hodler, R. (2018). Nighttime lights as a proxy for human development at the local level. PLoS ONE, 13(9), e0202231. doi:10.1371/journal.pone.0202231
Gridded Population of the World (GPW) v3 (population density)
Gridded Population of the World (GPW) v4 (population density)
REMOTE SENSING (DMSP-OLS)

Buchholz, R. R., Park, M., Worden, H. M., Tang, W., Edwards, D. P., Gaubert, B., . . . Magzamen, S. (2022). New seasonal pattern of pollution emerges from changing North American wildfires. Nature Communications, 13(1), 2043. doi:10.1038/s41467-022-29623-8
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (MOPITT CO)

Bunte, J. B., Desai, H., Gbala, K., Parks, B. C., & Runfola, D. M. (2017). Natural Resource Sector FDI and Growth in Post Conflict Settings: Subnational Evidence from Liberia. Retrieved from Williamsburg VA: http://aiddata.org/natural-resource-sector-fdi-and-growth-in-post-conflict-settings-evidence-from-liberia
Gridded Population of the World (GPW) v4 (collection)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (DMSP-OLS)

Bunte, J. B., Desai, H., Gbala, K. B., Parks, B. C., & Runfola, D. (2018). Natural Resource Sector FDI, Government Policy, and Economic Growth: Quasi-experimental Evidence from Liberia. Retrieved from London: https://www.theigc.org/wp-content/uploads/2018/04/Natural-Resource-Sector-FDI-cover.pdf
Gridded Population of the World (GPW) v4 (population density)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
REMOTE SENSING (DMSP-OLS)

Burger, J. R., & Fristoe, T. S. (2018). Hunter-gatherer populations inform modern ecology. Proceedings of the National Academy of Sciences, 115(6), 1137-1139. doi:10.1073/pnas.1721726115
Gridded Population of the World (GPW) v4 (population density)

Burgert-Brucker, C. R., Dontamsetti, T., Marshall, A. M. J., & Gething, P. W. (2016). Guidance for Use of the DHS Program Modeled Map Surfaces. Retrieved from Rockville, MD: https://dhsprogram.com/publications/publication-SAR14-Spatial-Analysis-Reports.cfm
Gridded Population of the World (GPW) v4 (collection)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (VIIRS NTL)

Burke, M., Zahid, M., Diffenbaugh, N., & Hsiang, S. M. (2023). Quantifying Climate Change Loss and Damage Consistent with a Social Cost of Greenhouse Gases. Retrieved from https://doi.org/10.3386/w31658
Gridded Population of the World (GPW) v4.11 (population count)

Burkhardt, J., Bayham, J., Wilson, A., Carter, E., Berman, J. D., O'Dell, K., . . . Pierce, J. R. (2019). The effect of pollution on crime: Evidence from data on particulate matter and ozone. Journal of Environmental Economics and Management, 98, 102267. doi:10.1016/j.jeem.2019.102267
Gridded Population of the World (GPW) v4.10 (population density)
REMOTE SENSING (NOAA Hazard Mapping System (HMS))

Burrell, A. L., Evans, J. P., & De Kauwe, M. G. (2020). Anthropogenic climate change has driven over 5 million km2 of drylands towards desertification. Nature Communications, 11(1), 3853. doi:10.1038/s41467-020-17710-7
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted)
NASA REMOTE SENSING (AVHRR GIMMS)

Busch, J., & Amarjargal, O. (2022). 100 global bright spots of green growth: Co-occurrence of nighttime light gain and forest gain, 1990–2015. Global Environmental Change, 75, 102556. doi:10.1016/j.gloenvcha.2022.102556
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS NTL)

Bussi, G., Whitehead, P. G., Jin, L., Taye, M. T., Dyer, E., Hirpa, F. A., . . . Charles, K. J. (2021). Impacts of climate change and population growth on river nutrient loads in a data scarce region: The Upper Awash River (Ethiopia). Sustainability, 13(3), 1254. doi:10.3390/su13031254
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (Global Maps of Atmospheric Nitrogen Deposition, 1860, 1993, and 2050) ORNL DAAC
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (MERIS GlobCover)

Bussi, G., Whitehead, P. G., Nelson, R., Bryden, J., Jackson, C. R., Hughes, A. G., . . . Russell, I. (2022). Green infrastructure and climate change impacts on the flows and water quality of urban catchments: Salmons Brook and Pymmes Brook in north-east London. Hydrology Research, 53(4), 638-656. doi:10.2166/nh.2022.013
Gridded Population of the World (GPW) v4 (population density)

Butt, E. W., Conibear, L., Smith, C., Baker, J. C. A., Rigby, R., Knote, C., & Spracklen, D. V. (2022). Achieving Brazil’s deforestation target will reduce fire and deliver air quality and public health benefits. Earth's Future, 10(12), e2022EF003048. doi:10.1029/2022EF003048
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (China Brazil Earth Resources Satellite (CBERS))
REMOTE SENSING (Landsat)
REMOTE SENSING (Sentinel-2)

Butt, E. W., Conibear, L. A., Reddington, C., L., Darbyshire, E., Morgan, W., T., Coe, H., . . . Spracklen, D., V. (2020). Large air quality and human health impacts due to Amazon forest and vegetation fires. Environmental Research Communications, 2(9), 095001. doi:10.1088/2515-7620/abb0db
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
NASA REMOTE SENSING (MODIS)

Byass, P. (2020). Eco-epidemiological assessment of the COVID-19 epidemic in China, January–February 2020. Global Health Action, 13(1), 1760490. doi:10.1080/16549716.2020.1760490
Gridded Population of the World (GPW) v4.11 (unspecified)

Byers, M., Wright, E., Boley, A., & Byers, C. (2022). Unnecessary risks created by uncontrolled rocket reentries. Nature Astronomy, 6, 1093-1097. doi:10.1038/s41550-022-01718-8
Gridded Population of the World (GPW) v4.11 (population density)

Cai, G., Zhang, J., Du, M., Li, C., & Peng, S. (2021). Identification of urban land use efficiency by indicator-SDG 11.3.1. PLoS ONE, 15(12), e0244318. doi:10.1371/journal.pone.0244318
Gridded Population of the World (GPW) v4 (population count)

Cai, Q., Zeng, N., Zhao, F., Han, P., Liu, D., Lin, X., & Chen, J. (2022). The impact of human and livestock respiration on CO2 emissions from 14 global cities. Carbon Balance and Management, 17(1), 17. doi:10.1186/s13021-022-00217-7
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4PN93PB
Global Rural-Urban Mapping Project (GRUMP) v1 (population density) - 10.7927/H4R20Z93
NASA REMOTE SENSING (MODIS)

Cai, W., Zhang, C., Zhang, S., Bai, Y., Callaghan, M., Chang, N., . . . Gong, P. (2022). The 2022 China report of the Lancet Countdown on health and climate change: leveraging climate actions for healthy ageing. The Lancet Public Health, 7(12), e1073-e1090. doi:10.1016/S2468-2667(22)00224-9
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
Global Rural-Urban Mapping Project (GRUMP) v1.02 (urban extent polygons)
NASA REMOTE SENSING (MODIS)

Cai, X., Yan, Y., Li, S., Kong, S., Liu, M., & Zhang, Z. (2022). Trend reversal from source region to remote tropospheric NO2 columns. Environmental Science and Pollution Research, 29, 15763-15776. doi:10.1007/s11356-021-16857-0
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (OMI NO2)

Cai, Z., Tian, L., & Bowen, G. J. (2017). ENSO variability reflected in precipitation oxygen isotopes across the Asian Summer Monsoon region. Earth and Planetary Science Letters, 475, 25-33. doi:10.1016/j.epsl.2017.06.035
Gridded Population of the World (GPW) v4 (population count)

Cairns, S., & Chen, T. (2019). Cartographic technique and artifice: The case of the Chengdu Plain. In S. Cairns & D. Tunas (Eds.), Future Cities Laboratory: Indicia 02 (pp. 191-199). Zurich: Lars Müller Publishers.
Gridded Population of the World (GPW) v4.10 (population density UN WPP-adjusted) - 10.7927/H49884ZR
Human Appropriation of Net Primary Productivity (HANPP) (collection)
Population Dynamics (Global Estimated Net Migration Grids By Decade, v1)
Poverty Mapping (Global Subnational Infant Mortality Rates, v1)
REMOTE SENSING (many)

Caldarola, B., Grazzi, M., Occelli, M., & Sanfilippo, M. (2023). Mobile internet, skills and structural transformation in Rwanda. Research Policy, 52(10), 104871. doi:10.1016/j.respol.2023.104871
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Calderon, A., & Silva, V. (2022). Forecasting seismic risk within the context of the Sendai framework: An application to the Dominican Republic. International Journal of Disaster Risk Reduction, 82, 103364. doi:10.1016/j.ijdrr.2022.103364
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates) - 10.7927/H4F47M2C
Global Rural-Urban Mapping Project (GRUMP) v1 (National Identifier Grid) - 10.7927/H40K26HS

Calderón, A., & Silva, V. (2021). Exposure forecasting for seismic risk estimation: Application to Costa Rica. Earthquake Spectra, 37(3), 1806-1826. doi:10.1177/8755293021989333
Gridded Population of the World (GPW) v4 (unspecified)
Global Rural-Urban Mapping Project (GRUMP) v1 (population count) - 10.7927/H4VT1Q1H

Caldwell, J. M., LaBeaud, A. D., Lambin, E. F., Stewart-Ibarra, A. M., Ndenga, B. A., Mutuku, F. M., . . . Mordecai, E. A. (2021). Climate predicts geographic and temporal variation in mosquito-borne disease dynamics on two continents. Nature Communications, 12(1), 1233. doi:10.1038/s41467-021-21496-7
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
NASA REMOTE SENSING (MODIS)

Calka, B., & Bielecka, E. (2020). GHS-POP accuracy assessment: Poland and Portugal case study. Remote Sensing, 12(7), 1105. doi:10.3390/rs12071105
Gridded Population of the World (GPW) v3 (collection)
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - data set unspecified)
Global Rural-Urban Mapping Project (GRUMP) v1 (Balk et al 2006)

Callahan, C. W., & Mankin, J. S. (2022). National attribution of historical climate damages. Climatic Change, 172(3), 40. doi:10.1007/s10584-022-03387-y
Gridded Population of the World (GPW) v4 (population count)

Campbell, S., Remenyi, T. A., White, C. J., & Johnston, F. H. (2018). Heatwave and health impact research: A global review. Health & Place, 53, 210-218. doi:10.1016/j.healthplace.2018.08.017
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Cao, G., & Zhao, N. (2022). Integrating remote sensing and social sensing to examine socioeconomic dynamics: A case study of Twitter and nighttime light imagery. In X. Yang (Ed.), Urban Remote Sensing: Monitoring, Synthesis, and Modeling in the Urban Environment (2nd ed., pp. 131-149): John Wiley & Sons Ltd.
Gridded Population of the World (GPW) v4.10 (population density UN WPP-adjusted)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS NTL)

Cao, S., Weng, Q., & Lu, L. (2022). Distinctive roles of two- and three-dimensional urban structures in surface urban heat islands over the conterminous United States. Urban Climate, 44, 101230. doi:10.1016/j.uclim.2022.101230
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
NASA REMOTE SENSING (MODIS)

Cao, Y., Carver, S., & Yang, R. (2019). Mapping wilderness in China: Comparing and integrating Boolean and WLC approaches. Landscape and Urban Planning, 192, 103636. doi:10.1016/j.landurbplan.2019.103636
Gridded Population of the World (GPW) v4 (population density)

Capinha, C., Seebens, H., Cassey, P., García-Díaz, P., Lenzner, B., Mang, T., . . . Essl, F. (2017). Diversity, biogeography and the global flows of alien amphibians and reptiles. Diversity and Distributions, 23(11), 1313-1322. doi:10.1111/ddi.12617
Gridded Population of the World (GPW) v4 (population count)

Carbajal, N., León-Cruz, J. F., Pineda-Martínez, L. F., Tuxpan-Vargas, J., & Gaviño-Rodríguez, J. H. (2019). Occurrence of anticyclonic tornadoes in a topographically complex region of Mexico. Advances in Meteorology, 2019, 11. doi:10.1155/2019/2763153
Gridded Population of the World (GPW) v4.10 (population density)

Cariolle, J., Le Goff, M., & Santoni, O. (2017). Fast Internet, Digital Vulnerabilities and Firm Performances in Developing and Transition Countries. Retrieved from http://www.ferdi.fr/fr/publication/p195-fast-internet-digital-vulnerabilities-and-firm-performances
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4BG2KXS

Cariolle, J., Le Goff, M., & Santoni, O. (2019). Digital Vulnerability and Performance of Firms in Developing Countries. Retrieved from Paris: https://publications.banque-france.fr/en/digital-vulnerability-and-performance-firms-developing-countries
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4BG2KXS

Carlson, C. J., Kracalik, I. T., Ross, N., Alexander, K. A., Hugh-Jones, M. E., Fegan, M., . . . Blackburn, J. K. (2019). The global distribution of Bacillus anthracis and associated anthrax risk to humans, livestock and wildlife. Nature Microbiology, 4, 1337-1343. doi:10.1038/s41564-019-0435-4
Gridded Population of the World (GPW) v4 (population density)
Global High Resolution Urban Data from Landsat (HBASE)
NASA REMOTE SENSING (AVHRR)

Carnicer, C., de Lima, L. B., Pelicice, F. M., & Junior, D. P. L. (2023). Global trends, biases and gaps in the scientific literature about freshwater fish eggs and larvae. Journal of Fish Biology, 102(1), 83-95. doi:10.1111/jfb.15242
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Caron, L. (2022). Empty digital wallets: new technologies and old inequalities in digital financial services among women. Oxford Open Economics, 1, odac001. doi:10.1093/ooec/odac001
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Carrão, H., Naumann, G., & Barbosa, P. (2016). Mapping global patterns of drought risk: An empirical framework based on sub-national estimates of hazard, exposure and vulnerability. Global Environmental Change, 39, 108-124. doi:10.1016/j.gloenvcha.2016.04.012
Global Agricultural Lands (Cropland)
Gridded Population of the World (GPW) v4 beta (population count)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)

Carratalà, A., & Joost, S. (2019). Population density and water balance influence the global occurrence of hepatitis E epidemics. Scientific Reports, 9(1), 10042. doi:10.1038/s41598-019-46475-3
Gridded Population of the World (GPW) v4 (population density)

Carter, D. B., Kaplan, M. L., & Schultz, K. A. (2022). The geography of separatist violence. International Studies Quarterly, 66(3), sqac030. doi:10.1093/isq/sqac030
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM

Carter, T. S., Heald, C. L., Jimenez, J. L., Campuzano-Jost, P., Kondo, Y., Moteki, N., . . . Kaiser, J. W. (2020). How emissions uncertainty influences the distribution and radiative impacts of smoke from fires in North America. Atmospheric Chemistry and Physics, 20(4), 2073-2097. doi:10.5194/acp-20-2073-2020
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4PN93PB
NASA REMOTE SENSING (MODIS)

Carter, T. S., Heald, C. L., & Selin, N. E. (2023). Large mitigation potential of smoke PM2.5 in the US from human-ignited fires. Environmental Research Letters, 18(1), 014002. doi:10.1088/1748-9326/aca91f
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4PN93PB

Cassará, P., Gotta, A., Marchese, M., & Patrone, F. (2022). Orbital edge offloading on mega-LEO satellite constellations for equal access to computing. IEEE Communications Magazine, 60(4), 32-36. doi:10.1109/MCOM.001.2100818
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Castells-Quintana, D., Krause, M., & McDermott, T. K. J. (2021). The urbanising force of global warming: the role of climate change in the spatial distribution of population. Journal of Economic Geography, 21(4), 531-556. doi:10.1093/jeg/lbaa030
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)
Global Rural-Urban Mapping Project (GRUMP) v1 (urban extent)
REMOTE SENSING (DMSP-OLS)

Castro-Llanos, F., Hyman, G., Rubiano, J., Ramirez-Villegas, J., & Achicanoy, H. (2019). Climate change favors rice production at higher elevations in Colombia. Mitigation and Adaptation Strategies for Global Change, 24, 1401-1430. doi:10.1007/s11027-019-09852-x
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ
NASA REMOTE SENSING (SRTM)

Cattaneo, C., & Foreman, T. (2021). Climate Change, International Migration, and Interstate Conflict. Retrieved from London: https://www.cream-migration.org/publ_uploads/CDP_09_21.pdf
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates)

Cattau, M. E., Mahood, A. L., Balch, J. K., & Wessman, C. A. (2022). Modern pyromes: Biogeographical patterns of fire characteristics across the contiguous United States. Fire, 5(4), 95. doi:10.3390/fire5040095
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MODIS)

Cazalis, V., Loreau, M., & Barragan-Jason, G. (2023). A global synthesis of trends in human experience of nature. Frontiers in Ecology and the Environment, 21(2), 85-93. doi:10.1002/fee.2540
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Čengić, M., Steinmann, Z. J. N., Defourny, P., Doelman, J. C., Lamarche, C., Stehfest, E., . . . Huijbregts, M. A. J. (2023). Global maps of agricultural expansion potential at a 300 m resolution. Land, 12(3), 579. doi:10.3390/land12030579
Gridded Population of the World (GPW) v4.11 (population density)

Center for International Earth Science Information Network. (2020). Global COVID-19 Map Viewer Shows Case Data, Age/Sex Features of At-Risk Populations.  Retrieved from https://blogs.ei.columbia.edu/2020/04/28/covid-19-mapping-tool/
COVID-19 Viewer
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)

Ceola, S., Laio, F., & Montanari, A. (2019). Global-scale human pressure evolution imprints on sustainability of river systems. Hydrology and Earth System Sciences, 23, 3933-3944. doi:10.5194/hess-23-3933-2019
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ\
REMOTE SENSING (DMSP-OLS)

Cesario, E., Lindia, P., & Vinci, A. (2023). Detecting multi-density urban hotspots in a smart city: Approaches, challenges and applications. Big Data and Cognitive Computing, 7(1), 29. doi:10.3390/bdcc7010029
Gridded Population of the World (GPW) v4.11 (population count)

Ch, R., Martin, D. A., & Vargas, J. F. (2018). Measuring the Size and Growth of Cities Using Nighttime Light. Retrieved from http://scioteca.caf.com/handle/123456789/1279
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)
REMOTE SENSING (DMSP-OLS)

Ch, R., Martin, D. A., & Vargas, J. F. (2021). Measuring the size and growth of cities using nighttime light. Journal of Urban Economics, 125, 103254. doi:10.1016/j.jue.2020.103254
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS NTL)

Chakrabarty, M., Panja, S., & Homechaudhuri, S. (2023). Predictive modeling of a fish-based index of biotic integrity in assessing environmental degradation in the Lower Teesta River Basin of Eastern Himalayan Foothills, India. Proceedings of the Zoological Society, 76, 22-35. doi:10.1007/s12595-023-00464-7
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (MODIS)

Chakraborty, R., Guha, B. K., Talukdar, S., Ratnam, M. V., & Maitra, A. (2019). Growth in mid-monsoon dry phases over the Indian region: prevailing influence of anthropogenic aerosols. Atmospheric Chemistry and Physics, 19(19), 12325-12341. doi:10.5194/acp-19-12325-2019
Gridded Population of the World (GPW) v4.11 (population density)

Chambers, J. (2020). Global and cross-country analysis of exposure of vulnerable populations to heatwaves from 1980 to 2018. Climatic Change, 163, 539-558. doi:10.1007/s10584-020-02884-2
Gridded Population of the World (GPW) v4.11 (population count)
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)

Chan, E. Y. Y., Huang, Z., Lam, H. C. Y., Wong, C. K. P., & Zou, Q. (2019). Health vulnerability index for disaster risk reduction: Application in Belt and Road Initiative (BRI) region. International Journal of Environmental Research and Public Health, 16(3), 380. doi:10.3390/ijerph16030380
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Chan, K. L., Khorsandi, E., Liu, S., Baier, F., & Valks, P. (2021). Estimation of surface NO2 concentrations over Germany from TROPOMI satellite observations using a machine learning method. Remote Sensing, 13(5), 969. doi:10.3390/rs13050969
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - data set unspecified)
REMOTE SENSING (TROPOMI)

Chanda, A., & Kabiraj, S. (2020). Shedding light on regional growth and convergence in India. World Development, 133, 104961. doi:10.1016/j.worlddev.2020.104961
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM
REMOTE SENSING (DMSP-OLS)

Chang, Y. S., You, B.-J., & Kim, H. E. (2020). Dynamic trends of fine particulate matter exposure across 190 countries: Analysis and key insights. Sustainability, 12(7), 2910. doi:10.3390/su12072910
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Chaplin-Kramer, R., Neugarten, R. A., Sharp, R. P., Collins, P. M., Polasky, S., Hole, D., . . . Watson, R. A. (2023). Mapping the planet’s critical natural assets. Nature Ecology & Evolution, 7, 51-61. doi:10.1038/s41559-022-01934-5
Gridded Population of the World (GPW) v4.11 (population count)

Chaplin-Kramer, R., Sharp, R. P., Weil, C., Bennett, E. M., Pascual, U., Arkema, K. K., . . . Daily, G. C. (2019). Global modeling of nature’s contributions to people. Science, 366(6462), 255-258. doi:10.1126/science.aaw3372
Gridded Population of the World (GPW) v4.10 (basic demographic characteristics) - 10.7927/H45H7D7F
NASA REMOTE SENSING (ASTER GDEM)

Chaudhari, S. (2021). Understanding Large-Scale Human-Water Interactions for Sustainable Hydropower Development. (Ph.D.). Michigan State University, East Lansing. Retrieved from https://doi.org/10.25335/a51w-6539 
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42

Chaudhari, S., Brown, E., Quispe-Abad, R., Moran, E., Müller, N., & Pokhrel, Y. (2021). In-stream turbines for rethinking hydropower development in the Amazon basin. Nature Sustainability, 4, 680-687. doi:10.1038/s41893-021-00712-8
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42

Chaudhary, P., Garg, S., George, T., Shabin, M., Saha, S., Subodh, S., & Sinha, B. (2021). Underreporting and open burning – the two largest challenges for sustainable waste management in India. Resources, Conservation and Recycling, 175, 105865. doi:10.1016/j.resconrec.2021.105865
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Chen, A. (2020). Tropical Cyclone Induced Extreme Wind, Rainfall, and Floods in the Mekong River Basin. (Ph.D.). University of Gothenburg, Gothenburg. Retrieved from http://hdl.handle.net/2077/64068 
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
Population Dynamics (Global Population Count Grid Time Series Estimates, v1) - 10.7927/H4CC0XNV
NASA REMOTE SENSING (TRMM)

Chen, A., Giese, M., & Chen, D. (2020). Flood impact on Mainland Southeast Asia between 1985 and 2018—The role of tropical cyclones. Journal of Flood Risk Management, 13(2), e12598. doi:10.1111/jfr3.12598
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
Population Dynamics (Global Population Count Grid Time Series Estimates, v1) - 10.7927/H4CC0XNV

Chen, A., Yang, J., He, Y., Yuan, Q., Li, Z., & Zhu, L. (2023). High spatiotemporal resolution estimation of AOD from Himawari-8 using an ensemble machine learning gap-filling method. Science of The Total Environment, 857(Part 3), 159673. doi:10.1016/j.scitotenv.2022.159673
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (Himawari-8)

Chen, B., Hu, J., Song, Z., Zhou, X., Zhao, L., Wang, Y., . . . Ren, Y. (2023). Exploring high-resolution near-surface CO concentrations based on Himawari-8 top-of-atmosphere radiation data: Assessing the distribution of city-level CO hotspots in China. Atmospheric Environment, 120021. doi:10.1016/j.atmosenv.2023.120021
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (Himawari-8)

Chen, B., & Jin, Y. (2022). Spatial patterns and drivers for wildfire ignitions in California. Environmental Research Letters, 17(5), 055004. doi:10.1088/1748-9326/ac60da
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
REMOTE SENSING (Landsat)

Chen, B., Jin, Y., Scaduto, E., Moritz, M. A., Goulden, M. L., & Randerson, J. T. (2021). Climate, fuel, and land use shaped the spatial pattern of wildfire in California’s Sierra Nevada. Journal of Geophysical Research: Biogeosciences, 126(2), e2020JG005786. doi:10.1029/2020JG005786
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS - MCD12)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (Landsat)

Chen, B., Song, Y., Kwan, M.-P., Huang, B., & Xu, B. (2018). How do people in different places experience different levels of air pollution? Using worldwide Chinese as a lens. Environmental Pollution, 238, 874-883. doi:10.1016/j.envpol.2018.03.093
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Chen, B., Song, Z., Shi, B., & Li, M. (2022). An interpretable deep forest model for estimating hourly PM10 concentration in China using Himawari-8 data. Atmospheric Environment, 268, 118827. doi:10.1016/j.atmosenv.2021.118827
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (Himawari-8)

Chen, B., Tu, Y., Song, Y., Theobald, D. M., Zhang, T., Ren, Z., . . . Xu, B. (2021). Mapping essential urban land use categories with open big data: Results for five metropolitan areas in the United States of America. ISPRS Journal of Photogrammetry and Remote Sensing, 178, 203-218. doi:10.1016/j.isprsjprs.2021.06.010
Gridded Population of the World (GPW) v4.11 (population count)
Global High Resolution Urban Data from Landsat (GMIS)
Global High Resolution Urban Data from Landsat (HBASE)
REMOTE SENSING (USDA National Agriculture Imagery Program (NAIP) very high resolution multispectral imagery)
REMOTE SENSING (Sentinel-1 SAR)
REMOTE SENSING (Sentinel-2 Multispectral Imager (MSI))
REMOTE SENSING (VIIRS NTL)

Chen, B., Tu, Y., Wu, S., Song, Y., Jin, Y., Webster, C., . . . Gong, P. (2022). Beyond green environments: Multi-scale difference in human exposure to greenspace in China. Environment International, 166, 107348. doi:10.1016/j.envint.2022.107348
Gridded Population of the World (GPW) v4.11 (population count)
REMOTE SENSING (Sentinel-2)

Chen, B., Wang, Y., Huang, J., Zhao, L., Chen, R., Song, Z., & Hu, J. (2023). Estimation of near-surface ozone concentration and analysis of main weather situation in China based on machine learning model and Himawari-8 TOAR data. Science of The Total Environment, 864, 160928. doi:10.1016/j.scitotenv.2022.160928
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
REMOTE SENSING (Himawari-8)

Chen, B., Wu, S., Song, Y., Webster, C., Xu, B., & Gong, P. (2022). Contrasting inequality in human exposure to greenspace between cities of Global North and Global South. Nature Communications, 13(1), 4636. doi:10.1038/s41467-022-32258-4
Gridded Population of the World (GPW) v4.11 (population count)
REMOTE SENSING (Landsat)
REMOTE SENSING (Sentinel-1)
REMOTE SENSING (Sentinel-2)

Chen, C., Grier, A., Malfa, M., Booen, E., Harding, H., Xia, C., . . . Quirk, K. (2017). High-speed optical links for UAV applications. Paper presented at the Free-Space Laser Communication and Atmospheric Propagation XXIX, San Francisco.
Gridded Population of the World (GPW) v4 beta (population count)

Chen, C., Holyoak, M., Xu, J., de Oliveira Caetano, G. H., & Wang, Y. (2023). Range restriction, climate variability and human-related risks imperil lizards world-wide. Global Ecology and Biogeography, 32(5), 780-792. doi:10.1111/geb.13655
Gridded Population of the World (GPW) v4.11 (population density)
Last of the Wild v3 (Human Footprint, 2018 Release (2009))

Chen, C., Li, D., & Keenan, T. F. (2021). Enhanced surface urban heat islands due to divergent urban-rural greening trends. Environmental Research Letters, 16(12), 124071. doi:10.1088/1748-9326/ac36f8
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
NASA REMOTE SENSING (MODIS)

Chen, C., Saikawa, E., Comer, B., Mao, X., & Rutherford, D. (2019). Ship emission impacts on air quality and human health in the Pearl River Delta (PRD) Region, China, in 2015, with projections to 2030. GeoHealth, 3(9), 284-306. doi:10.1029/2019gh000183
Gridded Population of the World (GPW) v4 (population count)

Chen, F., Chen, S., Jia, M., Jiang, M., Leng, Z., Ma, L., . . . Yang, W. (2023). Exploring meteorological impacts based on Köppen-Geiger climate classification after reviewing China's response to COVID-19. Applied Mathematical Modelling, 114, 133-146. doi:10.1016/j.apm.2022.09.008
Gridded Population of the World (GPW) v4.11 (population count)

Chen, J., Bu, J., Su, Y., Yuan, M., Cao, K., & Gao, Y. (2022). Urban evapotranspiration estimation based on anthropogenic activities and modified Penman-Monteith model. Journal of Hydrology, 610, 127879. doi:10.1016/j.jhydrol.2022.127879
Gridded Population of the World (GPW) v4.11 (unspecified)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)

Chen, J., Mueller, V., Durand, F., Lisco, E., Zhong, Q., Sherin, V. R., & Saiful Islam, A. K. M. (2022). Salinization of the Bangladesh Delta worsens economic precarity. Population and Environment, 44, 226-247. doi:10.1007/s11111-022-00411-2
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
NASA REMOTE SENSING (MODIS - NDVI)
REMOTE SENSING (DMSP-OLS)

Chen, K., Wang, P., Zhao, H., Wang, P., Gao, A., Myllyvirta, L., & Zhang, H. (2021). Summertime O3 and related health risks in the north China plain: A modeling study using two anthropogenic emission inventories. Atmospheric Environment, 246, 118087. doi:10.1016/j.atmosenv.2020.118087
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Chen, L., Liang, S., Li, X., Mao, J., Gao, S., Zhang, H., . . . Azzi, M. (2021). A hybrid approach to estimating long-term and short-term exposure levels of ozone at the national scale in China using land use regression and Bayesian maximum entropy. Science of The Total Environment, 752, 141780. doi:10.1016/j.scitotenv.2020.141780
Gridded Population of the World (GPW) v4 (unspecified)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (Landsat)

Chen, L., Wu, Z., Tu, W., & Cao, Z. (2020). Applying LUR model to estimate spatial variation of PM2.5 in the Greater Bay Area, China. In L. Li, X. Zhou, & W. Tong (Eds.), Spatiotemporal Analysis of Air Pollution and Its Application in Public Health (pp. 207-215): Elsevier.
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (SRTM)

Chen, M., Sui, Y., Liu, W., Liu, H., & Huang, Y. (2019). Urbanization patterns and poverty reduction: A new perspective to explore the countries along the Belt and Road. Habitat International, 84, 1-14. doi:10.1016/j.habitatint.2018.12.001
Gridded Population of the World (GPW) v4.10 (population density UN WPP-adjusted)

Chen, M., Xian, Y., Huang, Y., Zhang, X., Hu, M., Guo, S., . . . Liang, L. (2022). Fine-scale population spatialization data of China in 2018 based on real location-based big data. Scientific Data, 9(1), 624. doi:10.1038/s41597-022-01740-5
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)

Chen, Q., & Fei, X. (2023). Effective reduction of land-to-ocean plastic leakage in Thailand from 2000 to 2019 and implications for low- and middle-income countries. Resources, Conservation and Recycling, 198, 107204. doi:10.1016/j.resconrec.2023.107204
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Chen, Q.-X., Huang, C.-L., Yuan, Y., Mao, Q.-J., & Tan, H.-P. (2020). Spatiotemporal distribution of major aerosol types over China based on MODIS products between 2008 and 2017. Atmosphere, 11(7), 703. doi:10.3390/atmos11070703
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)

Chen, R., Yan, H., Liu, F., Du, W., & Yang, Y. (2020). Multiple global population datasets: Differences and spatial distribution characteristics. ISPRS International Journal of Geo-Information, 9(11), 637. doi:10.3390/ijgi9110637
Gridded Population of the World (GPW) v4.11 (population density)

Chen, R. S. (2016). Working with Facebook to Create Better Population Maps.  Retrieved from http://blogs.ei.columbia.edu/2016/02/22/working-with-facebook-to-create-better-population-maps/
Gridded Population of the World (GPW) v4 (collection)

Chen, S., Zhang, Y., & Zheng, J. (2021). Assessment on global urban photovoltaic carrying capacity and adjustment of photovoltaic spatial planning. Sustainability, 13(6), 3149. doi:10.3390/su13063149
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Chen, T., Deng, S., & Li, M. (2018). Spatial patterns of satellite-retrieved PM2.5 and long-term exposure assessment of China from 1998 to 2016. International Journal of Environmental Research and Public Health, 15(12), 2785. doi:10.3390/ijerph15122785
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (MISR)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SeaWiFS)

Chen, W., Wu, M., Zhang, L., & Gardoni, P. (2022). Multi-objective optimization for enhancing hospital network resilience under earthquakes. International Journal of Disaster Risk Reduction, 82, 103281. doi:10.1016/j.ijdrr.2022.103281
Gridded Population of the World (GPW) v4 (population density) - 10.7927//H4NP22DQ

Chen, W., & Zhang, L. (2022). An automated machine learning approach for earthquake casualty rate and economic loss prediction. Reliability Engineering & System Safety, 225, 108645. doi:10.1016/j.ress.2022.108645
Gridded Population of the World (GPW) v4.11 (population density)

Chen, X., Jeong, S., Park, H., Kim, J., & Park, C.-R. (2020). Urbanization has stronger impacts than regional climate change on wind stilling: a lesson from South Korea. Environmental Research Letters, 15(5), 054016. doi:10.1088/1748-9326/ab7e51
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ
REMOTE SENSING (DMSP-OLS)

Chen, X., Li, X., Li, X., Liang, J., Li, J., Chen, G., . . . Zeng, G. (2022). Estimating aerosol optical extinction across eastern China in winter during 2014–2019 using the random forest approach. Atmospheric Environment, 269, 118864. doi:10.1016/j.atmosenv.2021.118864
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (SRTM)

Chen, Y., Destouni, G., Goldenberg, R., & Prieto, C. (2021). Nutrient source attribution: Quantitative typology distinction of active and legacy source contributions to waterborne loads. Hydrological Processes, 35(7), e14284. doi:10.1002/hyp.14284
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Chen, Y., Han, H., Zhang, M., Zhao, Y., Huang, Y., Zhou, M., . . . Hu, Y. (2021). Trends and variability of ozone pollution over the mountain-basin areas in Sichuan Province during 2013-2020: Synoptic impacts and formation regimes. Atmosphere, 12(12), 1557. doi:10.3390/atmos12121557
Gridded Population of the World (GPW) v4.11 (population count)

Chen, Y., Li, X., Huang, K., Luo, M., & Gao, M. (2020). High-resolution gridded population projections for China under the Shared Socioeconomic Pathways. Earth's Future, 8(6), e2020EF001491. doi:10.1029/2020ef001491
Gridded Population of the World (GPW) v3 (collection)
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)

Chen, Y., Zhang, L., Henze, D. K., Zhao, Y., Lu, X., Winiwarter, W., . . . Song, Y. (2021). Interannual variation of reactive nitrogen emissions and their impacts on PM2.5 air pollution in China during 2005-2015. Environmental Research Letters, 16(12), 125004. doi:10.1088/1748-9326/ac3695
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
NASA REMOTE SENSING (MERRA-2)
NASA REMOTE SENSING (MODiS)
NASA REMOTE SENSING (OMI)

Chen, Y., Zhang, R., Ge, Y., Jin, Y., & Xia, Z. (2019). Downscaling census data for gridded population mapping with geographically weighted area-to-point regression kriging. IEEE Access, 7, 149132-149141. doi:10.1109/ACCESS.2019.2945000
Gridded Population of the World (GPW) v4 (population count)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)

Cheng, C., Zhang, T., Su, K., Gao, P., & Shen, S. (2019). Assessing the intensity of the population affected by a complex natural disaster using social media data. ISPRS International Journal of Geo-Information, 8(8), 358. doi:10.3390/ijgi8080358
Gridded Population of the World (GPW) v4 .11 (admin unit center points with population estimates)

Cheng, J., Tong, D., Liu, Y., Bo, Y., Zheng, B., Geng, G., . . . Zhang, Q. (2021). Air quality and health benefits from China's current and upcoming clean air policies. Faraday Discussions, 226, 584-606. doi:10.1039/D0FD00090F
Gridded Population of the World (GPW) v4.11 (population count)

Cheng, J., Tong, D., Liu, Y., Geng, G., Davis, S. J., He, K., & Zhang, Q. (2023). A synergistic approach to air pollution control and carbon neutrality in China can avoid millions of premature deaths annually by 2060. One Earth, 6(8), 978-989. doi:10.1016/j.oneear.2023.07.007
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)

Cheng, J., Tong, D., Liu, Y., Yu, S., Yan, L., Zheng, B., . . . Zhang, Q. (2021). Comparison of current and future PM2.5 air quality in China under CMIP6 and DPEC emission scenarios. Geophysical Research Letters, 48(11), e2021GL093197. doi:10.1029/2021GL093197
Gridded Population of the World (GPW) v4.11 (population count)

Cheng, J., Tong, D., Zhang, Q., Liu, Y., Lei, Y., Yan, G., . . . He, K. (2021). Pathways of China's PM2.5 air quality 2015–2060 in the context of carbon neutrality. National Science Review, 8(12), nwab078. doi:10.1093/nsr/nwab078
Gridded Population of the World (GPW) v4 (unspecified)

Cheng, M., Liu, X., Sheng, H., & Yuan, Z. (2023). MAPS: A new model using data fusion to enhance the accuracy of high-resolution mapping for livestock production systems. One Earth, 6(9), 1190-1201. doi:10.1016/j.oneear.2023.08.012
Global Human Settlement Layer (GHSL) (Population (POP), Built-Up Estimates (BUILT), and Degree of Urbanization Settlement Model Grid (SMOD), v1)
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Cheng, Y., Chen, X., Ding, X., & Zeng, L. (2019). Optimizing location of car-sharing stations based on potential travel demand and present operation characteristics: The case of Chengdu. Journal of Advanced Transportation, 2019(7546303), 13. doi:10.1155/2019/7546303
Gridded Population of the World (GPW) v4 (population density)

Cheng, Z., Wang, J., & Ge, Y. (2022). Mapping monthly population distribution and variation at 1-km resolution across China. International Journal of Geographical Information Science, 36(6), 1166-1184. doi:10.1080/13658816.2020.1854767
Gridded Population of the World (GPW) v4 (unspecified)

Cherlet, M., Reynolds, J., Hutchinson, C., Hill, J., von Maltitz, G., Sommer, S., . . . Smid, M. (2018). World Atlas of Desertification (3rd ed.). Luxembourg: Publication Office of the European Union.
Gridded Population of the World (GPW) v4 Preliminary Release 2 (population density)

Chernokulsky, A., Kurgansky, M., Mokhov, I., Shikhov, A., Azhigov, I., Selezneva, E., . . . Kühne, T. (2020). Tornadoes in Northern Eurasia: from the Middle Age to the Information Era. Monthly Weather Review, 148(8), 3081-3110. doi:10.1175/mwr-d-19-0251.1
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)
REMOTE SENSING (Landsat)

Cheung, P. K., & Fok, L. (2017). Characterisation of plastic microbeads in facial scrubs and their estimated emissions in Mainland China. Water Research, 122, 53-61. doi:10.1016/j.watres.2017.05.053
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Chipeta, M. G., Kumaran, E. P. A., Browne, A. J., Hamadani, B. H. K., Haines-Woodhouse, G., Sartorius, B., . . . Moore, C. E. (2022). Mapping local variation in household overcrowding across Africa from 2000 to 2018: a modelling study. The Lancet Planetary Health, 6(8), e670-e681. doi:10.1016/S2542-5196(22)00149-8
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Cho, J. Y. N., & Kurdzo, J. M. (2019). Monetized Weather Radar Network Benefits for Tornado Cost Reduction. Retrieved from Lexington MA: https://apps.dtic.mil/docs/citations/AD1079677
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Cho, J. Y. N., & Kurdzo, J. M. (2019). Weather radar network benefit model for tornadoes. Journal of Applied Meteorology and Climatology, 58(5), 971-987. doi:10.1175/jamc-d-18-0205.1
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Cho, J. Y. N., & Kurdzo, J. M. (2020). Weather radar network benefit model for flash flood casualty reduction. Journal of Applied Meteorology and Climatology, 59(4), 589-604. doi:10.1175/jamc-d-19-0176.1
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Cho, J. Y. N., & Kurdzo, J. M. (2020). Weather radar network benefit model for nontornadic thunderstorm wind casualty cost reduction. Weather, Climate, and Society, 12(4), 789-804. doi:10.1175/wcas-d-20-0063.1
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Choi, H., Park, S., Kang, Y., Im, J., & Song, S. (2023). Retrieval of hourly PM2.5 using top-of-atmosphere reflectance from geostationary ocean color imagers I and II. Environmental Pollution, 323, 121169. doi:10.1016/j.envpol.2023.121169
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (GOCI 1 and 2)

Choi, H. J., & Raleigh, C. (2021). The geography of regime support and political violence. Democratization, 28(6), 1095-1114. doi:10.1080/13510347.2021.1901688
Gridded Population of the World (GPW) v4 (population count)

Choi, Y.-W., Campbell, D. J., Aldridge, J. C., & Eltahir, E. A. B. (2021). Near-term regional climate change over Bangladesh. Climate Dynamics, 57, 3055-3073. doi:10.1007/s00382-021-05856-z
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Choi, Y.-W., Campbell, D. J., & Eltahir, E. A. B. (2023). Near-term regional climate change in East Africa. Climate Dynamics, 61, 961-978. doi:10.1007/s00382-022-06591-9
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (IMERG)
NASA REMOTE SENSING (TRMM)

Choi, Y.-W., & Eltahir, E. A. B. (2023). Near-term climate change impacts on food crops productivity in East Africa. Theoretical and Applied Climatology, 152, 843-860. doi:10.1007/s00704-023-04408-1
Global Agricultural Lands (collection)
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Choi, Y.-W., & Eltahir, E. A. B. (2023). Uncertainty in future projections of precipitation decline over Mesopotamia. Journal of Climate, 36(4), 1213-1228. doi:10.1175/jcli-d-22-0268.1
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D
NASA REMOTE SENSING (TRMM)

Chossière, G. P., Xu, H., Dixit, Y., Isaacs, S., Eastham, S. D., Allroggen, F., . . . Barrett, S. R. H. (2021). Air pollution impacts of COVID-19–related containment measures. Science Advances, 7(21), eabe1178. doi:10.1126/sciadv.abe1178
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
REMOTE SENSING (TROPOMI)

Chu, L., Oloo, F., Bergstedt, H., & Blaschke, T. (2020). Assessing the link between human modification and changes in land surface temperature in Hainan, China using image archives from Google Earth Engine. Remote Sensing, 12(5), 888. doi:10.3390/rs12050888
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
NASA REMOTE SENSING (MODIS - MOD11A2.006) -GEE

Chua, P. L. C., Ng, C. F. S., Madaniyazi, L., Seposo, X., Salazar, M. A., Huber, V., & Masahiro, H. (2022). Projecting temperature-attributable mortality and hospital admissions due to enteric infections in the Philippines. Environmental Health Perspectives, 130(2), 027011. doi:10.1289/EHP9324
Gridded Population of the World (GPW) v4.11 (population density)

Chuvieco, E., Pettinari, M. L., Koutsias, N., Forkel, M., Hantson, S., & Turco, M. (2021). Human and climate drivers of global biomass burning variability. Science of The Total Environment, 779, 146361. doi:10.1016/j.scitotenv.2021.146361
Gridded Population of the World (GPW) v4.10 (population density)
NASA REMOTE SENSING (AMSR-E)
NASA REMOTE SENSING (MODIS)

Cimatti, M., Ranc, N., Benítez-López, A., Maiorano, L., Boitani, L., Cagnacci, F., . . . Santini, L. (2021). Large carnivore expansion in Europe is associated with human population density and land cover changes. Diversity and Distributions, 27(4), 602-617. doi:10.1111/ddi.13219
Gridded Population of the World (GPW) v4.11 (population density)
Global Rural-Urban Mapping Project (GRUMP) v1 (urban extent)

Cinner, Joshua E., Zamborain-Mason, J., Gurney, G. G., Graham, N. A. J., MacNeil, M. A., Hoey, A. S., . . . Mouillot, D. (2020). Meeting fisheries, ecosystem function, and biodiversity goals in a human-dominated world. Science, 368(6488), 307-311. doi:10.1126/science.aax9412
Gridded Population of the World (GPW) v4.11 (population count)

Clark, N. J., Umulisa, I., Ruberanziza, E., Owada, K., Colley, D. G., Ortu, G., . . . Turate, I. (2019). Mapping Schistosoma mansoni endemicity in Rwanda: a critical assessment of geographical disparities arising from circulating cathodic antigen versus Kato-Katz diagnostics. PLoS Neglected Tropical Diseases, 13(9), e0007723. doi:10.1371/journal.pntd.0007723
Gridded Population of the World (GPW) v4 (population density)

Coelho, F., Câmara, D. C. P., Araújo, E. C., Bianchi, L. M., Ogasawara, I., Dalal, J., . . . Keiser, O. (2023). A platform for data-centric, continuous epidemiological analyses (EpiGraphHub): Descriptive analysis. Journal of Medical Internet Research, 25, e40554. doi:10.2196/40554
Gridded Population of the World (GPW) v4.11 (population count)

Coelho, L., Romero, D., Queirolo, D., & Carlos Guerrero, J. (2018). Understanding factors affecting the distribution of the maned wolf (Chrysocyon brachyurus) in South America: spatial dynamics and environmental drivers. Mammalian Biology, 92, 54-61. doi:10.1016/j.mambio.2018.04.006
Gridded Population of the World (GPW) v4 (population density)

Cohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep, K., . . . Forouzanfar, M. H. (2017). Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. The Lancet, 389(10082), 1907-1918. doi:10.1016/S0140-6736(17)30505-6
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Cohen, J. E. (2020). Book Review: Population, population, and population. [Our One and Only Spaceship: Denial, Delusion, and the Population Crisis, Eric R. Pianka, Laurie J. Vitt]. Bulletin of the Ecological Society of America, 101(3), 1-17. doi:10.2307/26920138
Gridded Population of the World (GPW) v4 (collection)
POPGRID

Cohen, J. L., Desai, R. M., & Kharas, H. (2019). Spatial targeting of poverty hotspots. In H. Kharas, J. W. McArthur, & I. Ohno (Eds.), Leave No One Behind: Time for Specifics on the Sustainable Development Goals (pp. 209-237). Washington: Brookings Institution Press.
Gridded Population of the World (GPW) v4 (population count)
REMOTE SENSING (DMSP-OLS)

Coker, E. S., Molitor, J., Liverani, S., Martin, J., Maranzano, P., Pontarollo, N., & Vergalli, S. (2023). Bayesian profile regression to study the ecologic associations of correlated environmental exposures with excess mortality risk during the first year of the Covid-19 epidemic in Lombardy, Italy. Environmental Research, 216(Part 1), 114484. doi:10.1016/j.envres.2022.114484
Gridded Population of the World (GPW) v4.11 (population density)

Cole, D. G. (2023). Review of The Letchworth State Park Atlas: Exploring its Nature, History, and Tourism through Maps. Cartographic Perspectives, 101, 65-67. doi:10.14714/CP101.1817
Gridded Population of the World (GPW) v4.11 (population density)

Cole, L. E. S., Willis, K. J., & Bhagwat, S. A. (2021). The future of Southeast Asia's tropical peatlands: local and global perspectives. Anthropocene, 34, 100292. doi:10.1016/j.ancene.2021.100292
Gridded Population of the World (GPW) v4.11 (population density)

Collin, A., Calle, C., James, D., Costa, S., Maquaire, O., Davidson, R., & Trigo-Teixeira, A. (2020). Modelling 2D coastal flooding at fine-scale over vulnerable lowlands using satellite-derived topobathymetry, hydrodynamic and overflow simulations. Journal of Coastal Research, 95(sp1), 1052-1056, 1055. doi:10.2112/SI95-205.1
Gridded Population of the World (GPW) v4 (unspecified)
REMOTE SENSING (Pleiades-1)

Colon, C., Hallegatte, S., & Rozenberg, J. (2019). Transportation and Supply Chain Resilience in the United Republic of Tanzania : Assessing the Supply-Chain Impacts of Disaster-Induced Transportation Disruptions Background paper for Lifelines : The Resilient Infrastructure Opportunity. Retrieved from Washington DC: http://hdl.handle.net/10986/31909
Gridded Population of the World (GPW) v4.11 (documentation) - 10.7927/H45Q4T5F 

Colon, C., Hallegatte, S., & Rozenberg, J. (2021). Criticality analysis of a country’s transport network via an agent-based supply chain model. Nature Sustainability, 4, 209-215. doi:10.1038/s41893-020-00649-4
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Colón-González, F. J., Soares Bastos, L., Hofmann, B., Hopkin, A., Harpham, Q., Crocker, T., . . . Lowe, R. (2021). Probabilistic seasonal dengue forecasting in Vietnam: A modelling study using superensembles. PLoS Medicine, 18(3), e1003542. doi:10.1371/journal.pmed.1003542
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted) - 10.7927/H4JQ0XZW
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (TRMM)

Colston, J. M., Hinson, P., Nguyen, N.-L. H., Chen, Y. T., Badr, H. S., Kerr, G. H., . . . Zaitchik, B. F. (2023). Effects of hydrometeorological and other factors on SARS-CoV-2 reproduction number in three contiguous countries of Tropical Andean South America: a spatiotemporally disaggregated time series analysis. IJID Regions, 6, 29-41. doi:10.1016/j.ijregi.2022.11.007
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates)

Commission on Population and Development, U. N. (2016). Supplementary References to the Report of the Secretary-General on Strengthening the Demographic Evidence Base for the Post-2015 Development Agenda (E/CN.9/2016/3). Retrieved from New York: http://www.un.org/en/development/desa/population/commission/pdf/49/E_CN_9_2016_3_Supplement.pdf
Gridded Population of the World (GPW) v3 (collection)
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (settlement points)

Conibear, L., Butt, E. W., Knote, C., Arnold, S. R., & Spracklen, D. V. (2018). Residential energy use emissions dominate health impacts from exposure to ambient particulate matter in India. Nature Communications, 9(1), 9pp. doi:10.1038/s41467-018-02986-7
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ
NASA REMOTE SENSING (MODIS)

Conibear, L., Butt, E. W., Knote, C., Arnold, S. R., & Spracklen, D. V. (2018). Stringent emission control policies can provide large improvements in air quality and public health in India. GeoHealth, 2(7), 196-211. doi:10.1029/2018GH000139
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Conibear, L., Butt, E. W., Knote, C., Spracklen, D. V., & Arnold, S. R. (2018). Current and future disease burden from ambient ozone exposure in India. GeoHealth, 2(11), 334-355. doi:10.1029/2018GH000168
Gridded Population of the World (GPW) v4 (national identifier grid) - 10.7927/H41V5BX1

Coniglio, N. D., Peragine, V., & Vurchio, D. (2023). The effects of refugees’ camps on hosting areas: Social conflicts and economic growth. World Development, 168, 106273. doi:10.1016/j.worlddev.2023.106273
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (MODIS)

Cooper, M., J., Martin, R. V., McLinden, C., A., & Brook, J., R. (2020). Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument. Environmental Research Letters, 15(10), 104013. doi:10.1088/1748-9326/aba3a5
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (OMI NO2)
REMOTE SENSING (TROPOMI)

Cooper, M. J., Martin, R. V., Hammer, M. S., Levelt, P. F., Veefkind, P., Lamsal, L. N., . . . McLinden, C. A. (2022). Global fine-scale changes in ambient NO2 during COVID-19 lockdowns. Nature, 601(7893), 380-387. doi:10.1038/s41586-021-04229-0
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
NASA REMOTE SENSING (OMI NO2)
REMOTE SENSING (TROPOMI)

Coristine, L. E., Jacob, A. L., Schuster, R., Otto, S. P., Baron, N. E., Bennett, N. J., . . . Woodley, S. (2018). Informing Canada’s commitment to biodiversity conservation: A science-based framework to help guide protected areas designation through Target 1 and beyond. FACETS, 3(1), 531-562. doi:10.1139/facets-2017-0102
Gridded Population of the World (GPW) v4 (population density)

Cornish, L. (2020). Gridded Population datasets: Which is fit for purpose?  Retrieved from https://www.devex.com/news/gridded-population-datasets-which-one-is-fit-for-purpose-97212
Gridded Population of the World (GPW) v4 (collection)

Coro, G., Bove, P., & Kesner-Reyes, K. (2023). Global-scale parameters for ecological models. Scientific Data, 10(1), 7. doi:10.1038/s41597-022-01904-3
Gridded Population of the World (GPW) v4 (population density) - 10.7927//H4NP22DQ

Corona-Núñez, R. O., & Campo, J. E. (2023). Climate and socioeconomic drivers of biomass burning and carbon emissions from fires in tropical dry forests: a Pantropical analysis. Global Change Biology, 29(4), 1062-1079. doi:10.1111/gcb.16516
Global Agricultural Lands (Cropland)
Global Agricultural Lands (Pasture)
Spatial Economic Data (Global Gridded Geographically Based Economic Data (G-Econ), v4)
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (SRTM)

Correia, R. A., Ruete, A., Stropp, J., Malhado, A. C. M., dos Santos, J. W., Lessa, T., . . . Ladle, R. J. (2019). Using ignorance scores to explore biodiversity recording effort for multiple taxa in the Caatinga. Ecological Indicators, 106, 105539. doi:10.1016/j.ecolind.2019.105539
Gridded Population of the World (GPW) v4.10 (population count)

Costa, M. C., Hagopián, D., Simo, M. R., Guerrero, J. C., & Laborda, A. (2021). First description of the male of Mastophora extraordinaria Holmberg, 1876 (Araneae: Araneidae), with notes on the natural history and potential distribution of the species. Boletín de la Sociedad Zoológica del Uruguay, 30(1), 14-24. doi:10.26462/30.1.2
Gridded Population of the World (GPW) v4.11 (data quality indicators)

Costall, A., Harris, B., & Pigois, J. P. (2018). Electrical resistivity imaging and the saline water interface in high-quality coastal aquifers. Surveys in Geophysics, 39, 753–816. doi:10.1007/s10712-018-9468-0
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Costantini, M., Colin, J., & Decharme, B. (2023). Projected climate-driven changes of water table depth in the world's major groundwater basins. Earth's Future, 11(3), e2022EF003068. doi:10.1029/2022EF003068
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Cottier, F., & Salehyan, I. (2021). Climate variability and irregular migration to the European Union. Global Environmental Change, 69, 102275. doi:10.1016/j.gloenvcha.2021.102275
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4F47M65

Couch, C. S., Oliver, T. A., Dettloff, K., Huntington, B., Tanaka, K. R., & Vargas-Ángel, B. (2023). Ecological and environmental predictors of juvenile coral density across the central and western Pacific. Frontiers in Marine Science, 10. doi:10.3389/fmars.2023.1192102
Gridded Population of the World (GPW) v3 (population count)
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Coughlan de Perez, E., van Aalst, M., Bischiniotis, K., Mason, S., Nissan, H., Pappenberger, F., . . . van den Hurk, B. (2018). Global predictability of temperature extremes. Environmental Research Letters, 13(5), 054017. doi:10.1088/1748-9326/aab94a
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Courtney Mustaphi, C. J., Capitani, C., Boles, O., Kariuki, R., Newman, R., Munishi, L., . . . Lane, P. (2019). Integrating evidence of land use and land cover change for land management policy formulation along the Kenya-Tanzania borderlands. Anthropocene, 28, 100228. doi:10.1016/j.ancene.2019.100228
Gridded Population of the World (GPW) v4 (collection)
POPGRID

Cresswell, A. K., Edgar, G. J., Stuart-Smith, R. D., Thomson, R. J., Barrett, N. S., & Johnson, C. R. (2017). Translating local benthic community structure to national biogenic reef habitat types. Global Ecology and Biogeography, 26(10), 1112-1125. doi:10.1111/geb.12620
Gridded Population of the World (GPW) v4 (population density)

Croitoru, L., Miranda, J. J., Khattabi, A., & Lee, J. J. (2020). The Cost of Coastal Zone Degradation in Nigeria : Cross River, Delta and Lagos States. Retrieved from Washington DC: https://openknowledge.worldbank.org/handle/10986/34758
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
REMOTE SENSING (Landsat)

Crooks, J. L., Licker, R., Hollis, A. L., & Ekwurzel, B. (2022). The ozone climate penalty, NAAQS attainment, and health equity along the Colorado Front Range. Journal of Exposure Science & Environmental Epidemiology, 32, 545-553. doi:10.1038/s41370-021-00375-9
Gridded Population of the World (GPW) v4.11 (population density)

Crossman, J., Bussi, G., Whitehead, P. G., Butterfield, D., Lannergård, E., & Futter, M. N. (2021). A new, catchment-scale integrated water quality model of phosphorus, dissolved oxygen, biochemical oxygen demand and phytoplankton: INCA-Phosphorus Ecology (PEco). Water, 13(5), 723. doi:10.3390/w13050723
Gridded Population of the World (GPW) v4 (documentation)

Cruzatti, J., Dreher, A., & Matzat, J. (2020). Chinese Aid and Health at the Country and Local Level. Retrieved from Williamsburg, VA: https://www.aiddata.org/publications/chinese-aid-and-health-at-the-country-and-local-level
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42

Cserbik, D., Chen, J.-C., McConnell, R., Berhane, K., Sowell, E. R., Schwartz, J., . . . Herting, M. M. (2020). Fine particulate matter exposure during childhood relates to hemispheric-specific differences in brain structure. Environment International, 143, 105933. doi:10.1016/j.envint.2020.105933
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4D50JX4

Cui, C., Liu, Y., Chen, L., Liang, S., Shan, M., Zhao, J., . . . Ma, Z. (2022). Assessing public health and economic loss associated with black carbon exposure using monitoring and MERRA-2 data. Environmental Pollution, 313, 120190. Retrieved from https://doi.org/10.1016/j.envpol.2022.120190
Gridded Population of the World (GPW) v4 (unspecified)

Cui, F., Hamdi, R., Yuan, X., He, H., Yang, T., Kuang, W., . . . De Maeyer, P. (2021). Quantifying the response of surface urban heat island to urban greening in global north megacities. Science of The Total Environment, 801, 149553. doi:10.1016/j.scitotenv.2021.149553
Gridded Population of the World (GPW) v4.11 (admin unit center points) - 10.7927/H4BC3WMT
NASA REMOTE SENSING (MERRA-2)
NASA REMOTE SENSING (MODIS - MYD11A2)

Cui, R., Hultman, N., Jiang, K., McJeon, H., Yu, S., Cui, D., . . . Zhang, W. (2020). A High Ambition Coal Phaseout in China: Feasible Strategies through a Comprehensive Plant-by-Plant Assessment. Retrieved from College Park, MD: https://cgs.umd.edu/research-impact/publications/high-ambition-coal-phaseout-china-feasible-strategies-through
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Cui, T., Li, Y., Yang, L., Nan, Y., Li, K., Tudaji, M., . . . Tian, F. (2023). Non-monotonic changes in Asian Water Towers’ streamflow at increasing warming levels. Nature Communications, 14(1), 1176. doi:10.1038/s41467-023-36804-6
Gridded Population of the World (GPW) v4.11 (population count)

Cunningham, C., & Beazley, K. (2018). Changes in human population density and protected areas in terrestrial global biodiversity hotspots, 1995–2015. Land, 7(4), 136. doi:10.3390/land7040136
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted)

Cunningham, E., & Lekic, V. (2020). Constraining properties of sedimentary strata using receiver functions: An example from the Atlantic Coastal Plain of the southeastern United States. Bulletin of the Seismological Society of America, 110(2), 519-533. doi:10.1785/0120190191
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Cuo, L., Zhang, Y., Wu, Y., & Hou, M. (2020). Desertification over the Tibetan Plateau during 1971–2015 from a climate perspective. Land Degradation & Development, 31(15), 1956-1968. doi:10.1002/ldr.3575
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A., & Hansen, M. C. (2018). Classifying drivers of global forest loss. Science, 361(6407), 1108-1111. doi:10.1126/science.aau3445
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM
NASA REMOTE SENSING (FIRMS)

Cusack, S. (2023). A long record of European windstorm losses and its comparison to standard climate indices. Natural Hazards and Earth System Sciences, 23(8), 2841-2856. doi:10.5194/nhess-23-2841-2023
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Custodio, E., Herrador, Z., Nkunzimana, T., Węziak-Białowolska, D., Perez-Hoyos, A., & Kayitakire, F. (2019). Children’s dietary diversity and related factors in Rwanda and Burundi: A multilevel analysis using 2010 Demographic and Health Surveys. PLoS ONE, 14(10), e0223237. doi:10.1371/journal.pone.0223237
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
NASA REMOTE SENSING (MODIS NDVI)

D’Addario, M., Monroy-Vilchis, O., Zarco-González, M. M., & Santos-Fita, D. (2019). Potential distribution of Aquila chrysaetos in Mexico: Implications for conservation. Avian Biology Research, 12(1), 33-41. doi:10.1177/1758155918823424
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H46T0JKB

Daberger, M., Kuemmerle, T., Khaleghi Hamidi, A., Khalatbari, L., Abolghasemi, H., Mirzadeh, H. R., & Ghoddousi, A. (2022). Prioritizing livestock grazing right buyouts to safeguard Asiatic cheetahs from extinction. Conservation Science and Practice, 4(12), e12832. doi:10.1111/csp2.12832
Gridded Population of the World (GPW) v4.11 (population density)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)

Dada, O. A., Almar, R., Morand, P., Bergsma, E. W. J., Angnuureng, D. B., & Minderhoud, P. S. J. (2023). Future socioeconomic development along the West African coast forms a larger hazard than sea level rise. Communications Earth & Environment, 4(1), 150. doi:10.1038/s43247-023-00807-4
Gridded Population of the World (GPW) v4.11 (population count)
Population Dynamics (Global 1-km Downscaled Population Base Year and Projection Grids Based on the SSPs, v1.01) - 10.7927/q7z9-9r69
REMOTE SENSING (MERIT DEM)

Dahiya, S. (2022). Health Impacts Assessment of the Integrated Steel Plant, JSW Utkal Steel Limited in Odisha, India. Retrieved from https://energyandcleanair.org/hia-of-jsw-isp_odisha
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Dahmm, H., & Rabiee, M. (2020). Leaving No One Off the Map: A Guide For Gridded Population Data for Sustainable Development. Retrieved from https://static1.squarespace.com/static/5b4f63e14eddec374f416232/t/5eb2b65ec575060f0adb1feb/1588770424043/Leaving+no+one+off+the+map-4.pdf
Gridded Population of the World (GPW) v4 (collection)
Population Dynamics (Global One-Eighth Degree Population Base Year and Projection Grids Based on the SSPs, v1.01) - 10.7927/m30p-j498

Dai, J., Li, S., Bi, J., & Ma, Z. (2019). The health risk-benefit feasibility of nuclear power development. Journal of Cleaner Production, 224, 198-206. doi:10.1016/j.jclepro.2019.03.206
Gridded Population of the World (GPW) v4 (population density)

Dai, Y., Hitchcock, P., Mahowald, N. M., Domeisen, D. I. V., Hamilton, D. S., Li, L., . . . Aboagye-Okyere, A. (2022). Stratospheric impacts on dust transport and air pollution in West Africa and the Eastern Mediterranean. Nature Communications, 13(1), 7744. doi:10.1038/s41467-022-35403-1
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MERRA-2)

Dalagnol, R., Gramcianinov, C. B., Crespo, N. M., Luiz, R., Chiquetto, J. B., Marques, M. T. A., . . . Sparrow, S. (2021). Extreme rainfall and its impacts in the Brazilian Minas Gerais state in January 2020: Can we blame climate change? Climate Resilience and Sustainability, 1(1), e15. doi:10.1002/cli2.15
Gridded Population of the World (GPW) v4 (population count)

Dalgaard, C.-J., Kaarsen, N., Olsson, O., & Selaya, P. (2018). Roman Roads to Prosperity: Persistence and Non-Persistence of Public Goods Provision (12745). Retrieved from London: www.cepr.org/active/publications/discussion_papers/dp.php?dpno=12745
Gridded Population of the World (GPW) v4 (population count)
REMOTE SENSING (VIIRS DNB)

Dalgaard, C.-J., Kaarsen, N., Olsson, O., & Selaya, P. (2022). Roman roads to prosperity: Persistence and non-persistence of public infrastructure. Journal of Comparative Economics, 50(4), 896-916. doi:10.1016/j.jce.2022.05.003
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
REMOTE SENSING (VIIRS)

Damania, R., Desbureaux, S., Hyland, M., Islam, A., Moore, S., Rodella, A.-S., . . . Zaveri, E. (2017). Uncharted Waters:  The New Economics of Water Scarcity and Variability - Volume 2 Technical Background Papers (Vol. 2): World Bank.
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted) - 10.7927/H4JQ0XZW
Global Reservoir and Dam (GRanD) v1.01 (dams) - 10.7927/H4N877QK
NASA REMOTE SENSING (MODIS)

d'Amore, F., Mocellin, P., Vianello, C., Maschio, G., & Bezzo, F. (2018). Economic optimisation of European supply chains for CO2 capture, transport and sequestration, including societal risk analysis and risk mitigation measures. Applied Energy, 223, 401-415. doi:10.1016/j.apenergy.2018.04.043
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM

Danesh Yazdi, M., Kuang, Z., Dimakopoulou, K., Barratt, B., Suel, E., Amini, H., . . . Schwartz, J. (2020). Predicting fine particulate matter (PM2.5) in the Greater London area: An ensemble approach using machine learning methods. Remote Sensing, 12(6), 914. doi:10.3390/rs12060914
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (VIIRS NTL)

Dang, R., & Liao, H. (2019). Radiative forcing and health impact of aerosols and ozone in China as the consequence of clean air actions over 2012-2017. Geophysical Research Letters, 46(21), 12511-12519. doi:10.1029/2019gl084605
Gridded Population of the World (GPW) v4 (unspecified)

Dangar, S., Asoka, A., & Mishra, V. (2021). Causes and implications of groundwater depletion in India: A review. Journal of Hydrology, 596, 126103. doi:10.1016/j.jhydrol.2021.126103
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (GRACE)
NASA REMOTE SENSING (MODIS)

Dangar, S., & Mishra, V. (2023). Excessive pumping limits the benefits of a strengthening summer monsoon for groundwater recovery in India. One Earth, 6(4), 419-427. doi:10.1016/j.oneear.2023.03.005
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (GRACE)

D'Aoust, O., Gunneman, J., Patel, K. V., & Tassot, C. (2022). Cash in the City: The Case of Port-au-Prince. Retrieved from Washington DC: http://hdl.handle.net/10986/37443
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Daru, B. H., Park, D. S., Primack, R. B., Willis, C. G., Barrington, D. S., Whitfeld, T. J. S., . . . Davis, C. C. (2018). Widespread sampling biases in herbaria revealed from large-scale digitization. New Phytologist, 217(2), 939-955. doi:10.1111/nph.14855
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Darwish, T., & Fadel, A. (2017). Mapping of soil organic carbon stock in the Arab countries to mitigate land degradation. Arabian Journal of Geosciences, 10(21), 11pp. doi:10.1007/s12517-017-3267-7
Gridded Population of the World (GPW) v4 (unspecified)

Das, P., Behera, M. D., & Murthy, M. S. R. (2017). Forest fragmentation and human population varies logarithmically along elevation gradient in Hindu Kush Himalaya - utility of geospatial tools and free data set. Journal of Mountain Science, 14(12), 2432-2447. doi:10.1007/s11629-016-4159-0
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (MODIS - MCD12Q1)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (Landsat)

Dasgupta, S., Lall, S., & Wheeler, D. (2023). Subways and CO2 emissions: A global analysis with satellite data. Science of The Total Environment, 883, 163691. doi:10.1016/j.scitotenv.2023.163691
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (OCO-2)

Dasgupta, S., Wheeler, D., Bandyopadhyay, S., Ghosh, S., & Roy, U. (2022). Coastal dilemma: Climate change, public assistance and population displacement. World Development, 150, 105707. doi:10.1016/j.worlddev.2021.105707
Gridded Population of the World (GPW) v4 (unspecified)

David, L. M., Ravishankara, A. R., Kodros, J. K., Pierce, J. R., Venkataraman, C., & Sadavarte, P. (2019). Premature mortality due to PM2.5 over India: Effect of atmospheric transport and anthropogenic emissions. GeoHealth, 3(1), 2-10. doi:10.1029/2018GH000169
Gridded Population of the World (GPW) v4 (collection)

Davis, K. F., Bhattachan, A., D’Odorico, P., & Suweis, S. (2018). A universal model for predicting human migration under climate change: examining future sea level rise in Bangladesh. Environmental Research Letters, 13(6), 064030. doi:10.1088/1748-9326/aac4d4
Gridded Population of the World (GPW) v4 (population count)

Davis, K. F., Müller, M. F., Rulli, M. C., Tatlhego, M., Ali, S., Baggio, J. A., . . . Eckert, S. (2023). Transnational agricultural land acquisitions threaten biodiversity in the Global South. Environmental Research Letters, 18(2), 024014. doi:10.1088/1748-9326/acb2de
Gridded Population of the World (GPW) v4 (population count)
REMOTE SENSING (Landsat)

Day, J., Ashfield, S., Brown, D., Gale, P., Heeley, L., Snary, E., . . . Jones, G. (2021). Copernicus User Uptake (CUU): Applying Earth Observation (EO) to horizon scanning for Emerging Infectious Diseases (EIDs). Retrieved from Peterborough, UK: https://hub.jncc.gov.uk/assets/9efd4ce0-b7a9-4ad2-b7ed-f0e7646927b3
Global Agricultural Lands (collection)
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
Human Appropriation of Net Primary Productivity (HANPP) (collection)
Last of the Wild v3 (Human Footprint, 2018 Release (1993)) - 10.7927/H4H9938Z
Last of the Wild v3 (Human Footprint, 2018 Release (2009)) - 10.7927/H46T0JQ4
NASA REMOTE SENSING (ASTER)
NASA REMOTE SENSING (GRACE)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SMAP)
NASA REMOTE SENSING (VIIRS DNB)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (Landsat)

De Lotto, R., Pietra, C., & Venco, E. M. (2019). Risk analysis: A focus on urban exposure estimation. Paper presented at the Computational Science and Its Applications – ICCSA 2019, St. Petersburg, Russia.
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)

de Schrijver, E., Folly, C. L., Schneider, R., Royé, D., Franco, O. H., Gasparrini, A., & Vicedo-Cabrera, A. M. (2021). A comparative analysis of the temperature-mortality risks using different weather datasets across heterogeneous regions. GeoHealth, 5(5), e2020GH000363. doi:10.1029/2020GH000363
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4F47M65

de Sherbinin, A. M. (2017). Remote sensing and socioeconomic data integration:  Lessons from the NASA Socioeconomic Data and Applications Center. In D. A. Quattrochi, E. Wentz, N. S.-N. Lam, & C. W. Emerson (Eds.), Integrating Scale in Remote Sensing and GIS (pp. 371-388): CRC Press.
Gridded Population of the World (GPW) v3 (collection)
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)
Poverty Mapping (collection)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (DMSP-OLS)

De Vos, C. J., Taylor, R. A., Simons, R. R. L., Roberts, H., Hultén, C., A., D. K. A., . . . Snary, E. L. (2019). Generic approaches for Risk Assessment of Infectious animal Disease introduction (G-RAID). EFSA Supporting Publications, 16(11), 1743E. doi:10.2903/sp.efsa.2019.EN-1743
Gridded Population of the World (GPW) v4 (unspecified)

Degefu, D. M., Liao, Z., He, W., Yuan, L., An, M., Zhang, Z., & Xia, W. (2019). The impact of upstream sub-basins’ water use on middle stream and downstream sub-basins’ water security at country-basin unit spatial scale and monthly temporal resolution. International Journal of Environmental Research and Public Health, 16(3), 450. doi:10.3390/ijerph16030450
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Degefu, D. M., Weijun, H., Zaiyi, L., Liang, Y., Zhengwei, H., & Min, A. (2018). Mapping monthly water scarcity in global transboundary basins at country-basin mesh based spatial resolution. Scientific Reports, 8(1), 2144. doi:10.1038/s41598-018-20032-w
Gridded Population of the World (GPW) v3 (population density)
Gridded Population of the World (GPW) v4 beta (population count UN WPP-adjusted)

del Portillo, I., Cameron, B. G., & Crawley, E. F. (2019). A technical comparison of three low earth orbit satellite constellation systems to provide global broadband. Acta Astronautica, 159, 123-135. doi:10.1016/j.actaastro.2019.03.040
Gridded Population of the World (GPW) v4 (population count)

Demaya, G. S., Behangana, M., Petrozzi, F., Fa, J. E., & Luiselli, L. (2023). Human density impacts Nubian Flapshell turtle survival in Sub-Saharan Africa: Future conservation strategies. Journal of Arid Environments, 217, 105027. doi:10.1016/j.jaridenv.2023.105027
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Demir, F., & Duan, Y. (2020). Target at the Right Level: Aid, Spillovers and Growth in Sub-Saharan Africa. Retrieved from Williamsburg, VA: https://www.aiddata.org/publications/target-at-the-right-level-aid-spillovers-and-growth-in-sub-saharan-africa
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates)

Demuzere, M., Hankey, S., Mills, G., Zhang, W., Lu, T., & Bechtel, B. (2020). Combining expert and crowd-sourced training data to map urban form and functions for the continental US. Scientific Data, 7(1), 264. doi:10.1038/s41597-020-00605-z
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - data set unspecified)

Deng, X., Ma, W., Ren, Z., Zhang, M., Grieneisen, M. L., Chen, X., . . . Lv, X. (2020). Spatial and temporal trends of soil total nitrogen and C/N ratio for croplands of East China. Geoderma, 361, 114035. doi:10.1016/j.geoderma.2019.114035
Gridded Population of the World (GPW) v4.10 (population density)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)

Deng, Y., Wang, S., Bai, X., Luo, G., Wu, L., Cao, Y., . . . Tian, S. (2020). Variation trend of global soil moisture and its cause analysis. Ecological Indicators, 110, 105939. doi:10.1016/j.ecolind.2019.105939
Gridded Population of the World (GPW) v4.10 (population density)
NASA REMOTE SENSING (AVHRR GIMMS)

Depsky, N., Bolliger, I., Allen, D., Choi, J. H., Delgado, M., Greenstone, M., . . . Hsiang, S. M. (2023). DSCIM-Coastal v1.1: an open-source modeling platform for global impacts of sea level rise. Geoscientific Model Development, 16(14), 4331-4366. doi:10.5194/gmd-16-4331-2023
Gridded Population of the World (GPW) v4 (population density)

Depsky, N. J., Cushing, L., & Morello-Frosch, R. (2022). High-resolution gridded estimates of population sociodemographics from the 2020 census in California. PLoS ONE, 17(7), e0270746. doi:10.1371/journal.pone.0270746
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)
Gridded Population of the World (GPW) v4.11 (population count)
Gridded Population of the World (GPW) v4.11 (population density)

Desai, R. M., & Shambaugh, G. E. (2021). Measuring the global impact of destructive and illegal fishing on maritime piracy: A spatial analysis. PLoS ONE, 16(2), e0246835. doi:10.1371/journal.pone.0246835
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (DMSP-OLS)

Desbureaux, S., Damania, R., Rodella, A.-S., Russ, J., & Zaveri, E. (2019). The Impact of Water Quality on GDP Growth: Evidence from Around the World. Retrieved from Washington DC: http://hdl.handle.net/10986/33071
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42

Desforges, J.-P., Hall, A., McConnell, B., Rosing-Asvid, A., Barber, J. L., Brownlow, A., . . . Dietz, R. (2018). Predicting global killer whale population collapse from PCB pollution. Science, 361(6409), 1373-1376. doi:10.1126/science.aat1953
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4D50JX4

Dey, P. K., Mustafa, S., & Yuksel, M. (2021). Meta-peering: towards automated ISP peer selection. Paper presented at the Proceedings of the Applied Networking Research Workshop. https://doi.org/10.1145/3472305.3472325
Gridded Population of the World (GPW) v4 (population count)

Di Minin, E., Slotow, R., Fink, C., Bauer, H., & Packer, C. (2021). A pan-African spatial assessment of human conflicts with lions and elephants. Nature Communications, 12(1), 2978. doi:10.1038/s41467-021-23283-w
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper -population density)

Di Napoli, C., Romanello, M., Minor, K., Chambers, J., Dasgupta, S., Escobar, L. E., . . . Robinson, E. J. Z. (2023). The role of global reanalyses in climate services for health: Insights from the Lancet Countdown. Meteorological Applications, 30(2), e2122. doi:10.1002/met.2122
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)

Diamantini, E., Lutz, S. R., Mallucci, S., Majone, B., Merz, R., & Bellin, A. (2018). Driver detection of water quality trends in three large European river basins. Science of The Total Environment, 612, 49-62. doi:10.1016/j.scitotenv.2017.08.172
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Dickens, B. L., Sun, H., Jit, M., Cook, A. R., & Carrasco, L. R. (2018). Determining environmental and anthropogenic factors which explain the global distribution of Aedes aegypti and Ae. albopictus. BMJ Global Health, 3(4), e000801. doi:10.1136/bmjgh-2018-000801
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (SRTM)

Dieudonné, E., Chazette, P., Marnas, F., Totems, J., & Shang, X. (2017). Raman lidar observations of aerosol optical properties in 11 cities from France to Siberia. Remote Sensing, 9(10), 29pp. doi:10.3390/rs9100978
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (CALIOP Lidar)
NASA REMOTE SENSING (MODIS)

Dijkstra, L., Poelman, H., & Veneri, P. (2019). The EU-OECD definition of a functional urban area. Retrieved from https://doi.org/10.1787/d58cb34d-en
Gridded Population of the World (GPW) v4 (unspecified)

DiLorenzo, M. (2021). Trade layoffs and hate in the United States. Social Science Quarterly, 102(2), 771-785. doi:10.1111/ssqu.12930
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42

DiLorenzo, M. (2023). International politics and the subnational allocation of World Bank development projects. Political Studies Review, 21(2), 400-411. doi:10.1177/14789299231153821
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
Poverty Mapping (Global Subnational Infant Mortality Rates, v1)

Ding, F., Fu, J., Jiang, D., Hao, M., & Lin, G. (2018). Mapping the spatial distribution of Aedes aegypti and Aedes albopictus. Acta Tropica, 178, 155-162. doi:10.1016/j.actatropica.2017.11.020
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
Satellite-Derived Environmental Indicators (Global Summer Land Surface Temperature (LST) Grids, v1)
REMOTE SENSING (DMSP-OLS)

Ding, F., Wang, Q., Fu, J., Chen, S., Hao, M., Ma, T., . . . Jiang, D. (2019). Risk factors and predicted distribution of visceral leishmaniasis in the Xinjiang Uygur Autonomous Region, China, 2005–2015. Parasites & Vectors, 12(1), 528. doi:10.1186/s13071-019-3778-z
Gridded Population of the World (GPW) v4.10 (population density)
REMOTE SENSING (DMSP-OLS)

Ding, S., He, J., Liu, D., Zhang, R., & Yu, S. (2020). The spatially heterogeneous response of aerosol properties to anthropogenic activities and meteorology changes in China during 1980–2018 based on the singular value decomposition method. Science of The Total Environment, 724, 138135. doi:10.1016/j.scitotenv.2020.138135
Gridded Population of the World (GPW) v3 (population count) - 10.​7927/​H4639MPP
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM
NASA REMOTE SENSING (MERRA-2)

Djagba, J. F., Kouyaté, A. M., Baggie, I., & Zwart, S. J. (2019). A geospatial dataset of inland valleys in four zones in Benin, Sierra Leone and Mali. Data in Brief, 23, 103699. doi:10.1016/j.dib.2019.103699
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (SRTM)

Djagba, J. F., Sintondji, L. O., Kouyaté, A. M., Baggie, I., Agbahungba, G., Hamadoun, A., & Zwart, S. J. (2018). Predictors determining the potential of inland valleys for rice production development in West Africa. Applied Geography, 96, 86-97. doi:10.1016/j.apgeog.2018.05.003
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (SRTM)

Djemai, E. (2018). Roads and the spread of AIDS in Africa. Journal of Health Economics, 60, 118-141. doi:10.1016/j.jhealeco.2018.05.004
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Dmowska, A. (2019). Dasymetric modelling of population distribution – large data approach. Quaestiones Geographicae, 38(1), 15. doi:10.2478/quageo-2019-0008
Gridded Population of the World (GPW) v4 (collection)

Do, H. X., Gudmundsson, L., Leonard, M., & Westra, S. (2018). The Global Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of a daily streamflow archive and metadata. Earth System Science Data, 10, 765-785. doi:10.5194/essd-10-765-2018
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
Global Reservoir and Dam (GRanD) v1.01 (dams)

Doan, T. T. T. (2022). Essays on Impacts of Climate, Energy Conservation Policy, and Optimal Energy Transition. (Ph.D.). University of Hawai'i at Manoa, Manoa, HI. Retrieved from https://hdl.handle.net/10125/102221 
Gridded Population of the World (GPW) v4.11 (admin unit center points)

Dobbins, J., & Jones, S. G. (2017). The end of a caliphate. Survival, 59(3), 55-72. doi:10.1080/00396338.2017.1325596
Gridded Population of the World (GPW) v4 (population count)

Dobler, A. H., Geist, J., Stoeckl, K., & Inoue, K. (2019). A spatially explicit approach to prioritize protection areas for endangered freshwater mussels. Aquatic Conservation: Marine and Freshwater Ecosystems, 29(1), 12-23. doi:10.1002/aqc.2993
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
Last of the Wild v2 (Global Human Footprint (Geographic))

Dodlova, M., Kis-Katos, K., Kochanova, A., & Wirth, O. (2023). Mobile Technologies and Firm Formalization: Evidence from Uganda. Retrieved from Helsinki, Finland: https://doi.org/10.35188/UNU-WIDER/2023/407-6
Gridded Population of the World (GPW) v4.11 (population density)

Dolan, C. B., & McDade, K. K. (2020). Pulling the purse strings: Are there sectoral differences in political preferencing of Chinese aid to Africa? PLoS ONE, 15(4), e0232126. doi:10.1371/journal.pone.0232126
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
REMOTE SENSING (DMSP-OLS)

Dolan, H., & Rastelli, R. (2022). A model-based approach to assess epidemic risk. Statistics in Biosciences, 14(3), 452-484. doi:10.1007/s12561-021-09329-z
Gridded Population of the World (GPW) v4.11 (admin unit center points)

Donalisio, M. R., Souza, C. E., Angerami, R. N., & Samy, A. M. (2020). Mapping Brazilian Spotted Fever: linking etiological agent, vectors, and hosts. Acta Tropica, 207, 105496. doi:10.1016/j.actatropica.2020.105496
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)

Dong, Y., Zhou, Y., Zhang, L., Gu, Y., & Sutrisno, D. (2023). Intensive land-use is associated with development status in port cities of Southeast Asia. Environmental Research Letters, 18(4), 044006. doi:10.1088/1748-9326/acc2d2
Gridded Population of the World (GPW) v4.11 (population count)

Doni, A. R., & Sasipraba, T. (2020). LSTM-RNN based approach for prediction of dengue cases in India. Ingénierie des Systèmes d’Information, 25(3), 327-335. doi:10.18280/isi.250306
Gridded Population of the World (GPW) v4.11 (admin unit center points) - 10.7927/H4BC3WMT

Dorward, N., & Fox, S. (2022). Population pressure, political institutions, and protests: A multilevel analysis of protest events in African cities. Political Geography, 99, 102762. Retrieved from https://doi.org/10.1016/j.polgeo.2022.102762
Gridded Population of the World (GPW) v4.11 (collection)

Doss-Gollin, J., Farnham, D. J., Lall, U., & Modi, V. (2021). How unprecedented was the February 2021 Texas cold snap? Environmental Research Letters, 16(6), 064056. doi:10.1088/1748-9326/ac0278
Gridded Population of the World (GPW) v4 (population density)

Dossou-Yovo, E. R., Baggie, I., Djagba, J. F., & Zwart, S. J. (2017). Diversity of inland valleys and opportunities for agricultural development in Sierra Leone. PLoS ONE, 12(6), e0180059. doi:10.1371/journal.pone.0180059
Gridded Population of the World (GPW) v4 (population density)

Dottori, F., Alfieri, L., Rossi, L., Rudari, R., Ward, P. J., & Zhao, F. (2021). Global River Flood Risk Under Climate Change. In H. Wu, D. P. Lettenmaier, Q. Tang, & P. J. Ward (Eds.), Global Drought and Flood: Observation, Modeling, and Prediction (pp. 251-270): American Geophysical Union.
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Dou, J., Grimmond, C. S. B., Cheng, Z., Miao, S., Feng, D., & Liao, M. (2019). Summertime surface energy balance fluxes at two Beijing sites. International Journal of Climatology, 39(5), 2793-2810. doi:10.1002/joc.5989
Gridded Population of the World (GPW) v4 (population density)

Dowhaniuk, N. (2021). Exploring country-wide equitable government health care facility access in Uganda. International Journal for Equity in Health, 20(1), 38. doi:10.1186/s12939-020-01371-5
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4F47M65

Downs, R. R. (2018). Enabling the reuse of geospatial information. In J. B. Kruse, J. Crompvoets, & F. Pearlman (Eds.), GEOValue: The Socioeconomic Value of Geospatial Information. Boca Raton: CRC Press.
Gridded Population of the World (GPW) v4 (collection)

Doxsey-Whitfield, E., MacManus, K., Adamo, S. B., Pistolesi, L., Squires, J., Borkovska, O., & Baptista, S. R. (2015). Taking advantage of the improved availability of census data: A first look at the Gridded Population of the World, Version 4. Papers in Applied Geography, 1(3), 226-234. doi:10.1080/23754931.2015.1014272
Gridded Population of the World (GPW) v3 (collection)
Gridded Population of the World (GPW) v4 (collection)

Drouet, L., Bosetti, V., Padoan, S. A., Aleluia Reis, L., Bertram, C., Dalla Longa, F., . . . Tavoni, M. (2021). Net zero-emission pathways reduce the physical and economic risks of climate change. Nature Climate Change, 11, 1070-1076. doi:10.1038/s41558-021-01218-z
Gridded Population of the World (GPW) v4.11 (population density)

Du, D., Lu, Y., Zhou, Y., Zhang, M., Wang, C., Yu, M., . . . Chen, C. (2022). Perfluoroalkyl acids (PFAAs) in water along the entire coastal line of China: spatial distribution, mass loadings, and worldwide comparisons. Environment International, 169, 107506. doi:10.1016/j.envint.2022.107506
Gridded Population of the World (GPW) v4.11 (unspecified)

Du, H., Zhan, W., Liu, Z., Li, J., Li, L., Lai, J., . . . Jiang, S. (2021). Simultaneous investigation of surface and canopy urban heat islands over global cities. ISPRS Journal of Photogrammetry and Remote Sensing, 181, 67-83. doi:10.1016/j.isprsjprs.2021.09.003
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MODIS)

Du, H., Zhan, W., Voogt, J., Bechtel, B., Chakraborty, T. C., Liu, Z., . . . Miao, S. (2023). Contrasting trends and drivers of global surface and canopy urban heat islands. Geophysical Research Letters, 50(15), e2023GL104661. doi:10.1029/2023gl104661
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MODIS)

Du, P., Li, J., Bai, W., Sheng, M., & Zhou, D. (2023). Dual location area based distributed location management for hybrid LEO/MEO mega satellite networks. IEEE Transactions on Vehicular Technology, 72(2), 2307-2321. doi:10.1109/TVT.2022.3208940
Gridded Population of the World (GPW) v4 (population density)

Du, X., Jin, X., Zucker, N., Kennedy, R., & Urpelainen, J. (2020). Transboundary air pollution from coal-fired power generation. Journal of Environmental Management, 270, 110862. doi:10.1016/j.jenvman.2020.110862
Gridded Population of the World (GPW) v4.10 (admin unit center points)

Du, X., Tang, W., Zhang, Z., Li, Y., Yu, Y., Xiao, Z., & Meng, F. (2022). Sensitivity modeling of ozone and its precursors over the Chengdu metropolitan area. Atmospheric Environment, 277, 119071. doi:10.1016/j.atmosenv.2022.119071
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
NASA REMOTE SENSING (OMI)

Du, Z., Yang, B., Jalaludin, B., Knibbs, L., Yu, S., Dong, G., & Hao, Y. (2022). Association of neighborhood greenness with severity of hand, foot, and mouth disease. BMC Public Health, 22(1), 38. doi:10.1186/s12889-021-12444-7
Gridded Population of the World (GPW) v4.11 (population density)

Duan, W., Zou, S., Chen, Y., Nover, D., Fang, G., & Wang, Y. (2020). Sustainable water management for cross-border resources: The Balkhash Lake Basin of Central Asia, 1931–2015. Journal of Cleaner Production, 263, 121614. doi:10.1016/j.jclepro.2020.121614
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)

Dullaart, J. C. M., Muis, S., Bloemendaal, N., Chertova, M. V., Couasnon, A., & Aerts, J. C. J. H. (2021). Accounting for tropical cyclones more than doubles the global population exposed to low-probability coastal flooding. Communications Earth & Environment, 2(1), 135. doi:10.1038/s43247-021-00204-9
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4F47M65

Dunnett, S., Holland, R. A., Taylor, G., & Eigenbrod, F. (2022). Predicted wind and solar energy expansion has minimal overlap with multiple conservation priorities across global regions. Proceedings of the National Academy of Sciences, 119(6), e2104764119. doi:10.1073/pnas.2104764119
Gridded Population of the World (GPW) v4.11 (population density) 

Duprey, N. N., Wang, X. T., Kim, T., Cybulski, J. D., Vonhof, H. B., Crutzen, P. J., . . . Baker, D. M. (2020). Megacity development and the demise of coastal coral communities: evidence from coral skeleton δ15N records in the Pearl River estuary. Global Change Biology, 26(3), 1338-1353. doi:10.1111/gcb.14923
Gridded Population of the World (GPW) v4.10 (population density UN WPP-adjusted)

Durso, A. M., Ruiz de Castañeda, R., Montalcini, C., Mondardini, M. R., Fernandez-Marques, J. L., Grey, F., . . . Bolon, I. (2021). Citizen science and online data: Opportunities and challenges for snake ecology and action against snakebite. Toxicon: X, 9-10, 100071. doi:10.1016/j.toxcx.2021.100071
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Dutra, J., Ramos Pereira, M. J., Horn, P., Graves, V., & Tirelli, F. P. (2023). Sympatric procyonids in the Atlantic Forest: revealing differences in detection, occupancy, and activity of the coati and the crab-eating raccoon in a gradient of anthropogenic alteration. Mammalian Biology, 103, 289-301. doi:10.1007/s42991-023-00349-4
Gridded Population of the World (GPW) v4.11 (population density)

Duttagupta, S., Bhanja, S. N., Dutta, A., Sarkar, S., Chakraborty, M., Ghosh, A., . . . Mukherjee, A. (2021). Impact of Covid-19 lockdown on availability of drinking water in the arsenic-affected Ganges River Basin. International Journal of Environmental Research and Public Health, 18(6), 2832. doi:10.3390/ijerph18062832
Gridded Population of the World (GPW) v4.11 (documentation) - 10.7927/H45Q4T5F

Dwiyahreni, A. A., Fuad, H. A. H., Muhtar, S., Soesilo, T. E. B., Margules, C., & Supriatna, J. (2021). Changes in the human footprint in and around Indonesia’s terrestrial national parks between 2012 and 2017. Scientific Reports, 11(1), 4510. doi:10.1038/s41598-021-83586-2
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Dwomoh, F., & Wimberly, M. (2017). Fire regimes and their drivers in the Upper Guinean region of West Africa. Remote Sensing, 9(11), 19pp. doi:10.3390/rs9111117
Gridded Population of the World (GPW) v4 (population density)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (TRMM)

Dziba, L., Ramoelo, A., Ryan, C., Harrison, S., Pritchard, R., Tripathi, H., . . . Grundy, I. M. (2020). Scenarios for Just and Sustainable Futures in the Miombo Woodlands. In N. S. Ribeiro, Y. Katerere, P. W. Chirwa, & I. M. Grundy (Eds.), Miombo Woodlands in a Changing Environment: Securing the Resilience and Sustainability of People and Woodlands (pp. 191-234). Cham: Springer International Publishing.
Gridded Population of the World (GPW) v4.11 (population density)

Eales, A., Alsop, A., Frame, D., Strachan, S., & Galloway, S. (2020). Assessing the market for solar photovoltaic (PV) microgrids in Malawi. Journal of Sustainability Research, 2(1), e200008. doi:10.20900/jsr20200008
Gridded Population of the World (GPW) v4.10 (population density UN WPP-adjusted)

East, J. D., Henderson, B. H., Napelenok, S. L., Koplitz, S. N., Sarwar, G., Gilliam, R., . . . Garcia-Menendez, F. (2022). Inferring and evaluating satellite-based constraints on NOx emissions estimates in air quality simulations. Atmospheric Chemistry and Physics, 22, 15981-16001. doi:10.5194/acp-22-15981-2022
Gridded Population of the World (GPW) v4.10 (population density)
NASA REMOTE SENSING (OMI)
NASA REMOTE SENSING (TROPOMI)

Ebeling, P., Kumar, R., Lutz, S. R., Nguyen, T., Sarrazin, F., Weber, M., . . . Musolff, A. (2022). QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany. Earth System Science Data, 14(8), 3715-3741. doi:10.5194/essd-14-3715-2022
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Eberenz, S., Stocker, D., Röösli, T., & Bresch, D. N. (2020). Exposure data for global physical risk assessment. Earth System Science Data, 12, 817-833. doi:10.5194/essd-12-817-2020
Gridded Population of the World (GPW) v4.10 (collection)
REMOTE SENSING (VIIRS DNB)

Echevarria, D., Trimmer, J. T., Cusick, R. D., & Guest, J. S. (2021). Defining nutrient colocation typologies for human-derived supply and crop demand to advance resource recovery. Environmental Science & Technology, 55(15), 10704-10713. doi:10.1021/acs.est.1c01389
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Edgar, G. J., Bates, A. E., Bird, T. J., Jones, A. H., Kininmonth, S., Stuart-Smith, R. D., & Webb, T. J. (2016). New approaches to marine conservation through the scaling up of ecological data. Annual Review of Marine Science, 8(1), 435-461. doi:10.1146/annurev-marine-122414-033921
Gridded Population of the World (GPW) v4 (collection)

Edmonds, C., Wiegand, M., Koomen, E., Pradhan, M., & Andrée, B. P. J. (2018). The impact of road development on household welfare in rural Papua New Guinea. In N. Yoshino, M. Helble, & U. Abidhadjaev (Eds.), Financing Infrastructure in Asia: Capturing Impacts and New Sources (pp. 189-235). Tokyo: Asian Development Bank Institute.
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)
Gridded Population of the World (GPW) v4 (collection)
Poverty Mapping (Poverty and Food Security Case Studies, v1)

Eger, A. M., Marzinelli, E. M., Beas-Luna, R., Blain, C. O., Blamey, L. K., Byrnes, J. E. K., . . . Vergés, A. (2023). The value of ecosystem services in global marine kelp forests. Nature Communications, 14(1), 1894. doi:10.1038/s41467-023-37385-0
Gridded Population of the World (GPW) v4.11 (population density)

Ehrlich, D., Kemper, T., Pesaresi, M., & Corbane, C. (2018). Built-up area and population density: Two Essential Societal Variables to address climate hazard impact. Environmental Science & Policy, 90, 73-82. doi:10.1016/j.envsci.2018.10.001
Gridded Population of the World (GPW) v4.10 (population count)
REMOTE SENSING (Landsat)

Ehrlich, D., Melchiorri, M., Florczyk, A., Pesaresi, M., Kemper, T., Corbane, C., . . . Siragusa, A. (2018). Remote sensing derived built-up area and population density to quantify global exposure to five natural hazards over time. Remote Sensing, 10(9), 1378. doi:10.3390/rs10091378
Gridded Population of the World (GPW) v4.10 (population density)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (Landsat)

Eikenberry, S. E., & Gumel, A. B. (2019). Mathematics of Malaria and Climate Change. In H. G. Kaper & F. S. Roberts (Eds.), Mathematics of Planet Earth: Protecting Our Planet, Learning from the Past, Safeguarding for the Future (pp. 77-108). Cham: Springer International Publishing.
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM

Ekanayake, K. B., Gnanapragasam, J. J., Ranawana, K., Vidanapathirana, D. R., Abeyawardhana, U. T., Fernando, C., . . . Symonds, M. R. E. (2022). Ecological and environmental predictors of escape among birds on a large tropical island. Behavioral Ecology and Sociobiology, 76(2), 31. doi:10.1007/s00265-022-03138-0
Gridded Population of the World (GPW) v4.11 (population density)

Ekim, B., Dong, Z., Rashkovetsky, D., & Schmitt, M. (2021). The naturalness index for the identification of natural areas on regional scale. International Journal of Applied Earth Observation and Geoinformation, 105, 102622. doi:10.1016/j.jag.2021.102622
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (VIIRS NTL)

El Afandi, G., Ismael, H., Fall, S., & Ankumah, R. (2023). Effectiveness of utilizing remote sensing and GIS techniques to estimate the exposure to organophosphate pesticides drift over Macon, Alabama. Agronomy, 13(7), 1759. doi:10.3390/agronomy13071759
Gridded Population of the World (GPW) v4.11 (population count)
REMOTE SENSING (Landsat)
REMOTE SENSING (Sentinel-2)

El Hidan, M. A., Touloun, O., Bouazza, A., Laaradia, M. A., & Boumezzough, A. (2018). Androctonus genus species in arid regions: Ecological niche models, geographical distributions, and envenomation risk. Veterinary World, 11(3), 286-292. doi:10.14202/vetworld.2018.286-292
Gridded Population of the World (GPW) v4 (population count)

Elimbi Moudio, P., Pais, C., & Shen, Z.-J. M. (2021). Quantifying the impact of ecosystem services for landscape management under wildfire hazard. Natural Hazards, 106, 531-560. doi:10.1007/s11069-020-04474-y
Gridded Population of the World (GPW) v4.11 (population density)

Elise, S., Urbina-Barreto, I., Boadas-Gil, H., Galindo-Vivas, M., Arias-González, J. E., Floeter, S. R., . . . Kulbicki, M. (2017). Archipelago Los Roques: A potential baseline for reef fish assemblages in the southern Caribbean. Aquatic Conservation: Marine and Freshwater Ecosystems, 27(6), 1116-1132. doi:10.1002/aqc.2770
Gridded Population of the World (GPW) v4 (population density)

Ellison, G., Jones, M., Cain, B., & Bettridge, C. M. (2021). Taxonomic and geographic bias in 50 years of research on the behaviour and ecology of galagids. PLoS ONE, 16(12), e0261379. doi:10.1371/journal.pone.0261379
Gridded Population of the World (GPW) v4.11 (population density)

Elnashar, A., Zeng, H., Wu, B., Gebremicael, T. G., & Marie, K. (2022). Assessment of environmentally sensitive areas to desertification in the Blue Nile Basin driven by the MEDALUS-GEE framework. Science of The Total Environment, 815, 152925. doi:10.1016/j.scitotenv.2022.152925
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS - MOD13Q1)
NASA REMOTE SENSING (SRTM)

Elshorbagy, A., Bharath, R., Lakhanpal, A., Ceola, S., Montanari, A., & Lindenschmidt, K.-E. (2017). Topography- and nightlight-based national flood risk assessment in Canada. Hydrology and Earth System Sciences, 21(4), 2219-2232. doi:10.5194/hess-21-2219-2017
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
REMOTE SENSING (DMSP-OLS)

Emberson, R., Kirschbaum, D., & Stanley, T. (2021). Global connections between El Nino and landslide impacts. Nature Communications, 12(1), 2262. doi:10.1038/s41467-021-22398-4
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
NASA REMOTE SENSING (IMERG)
NASA (Landslide Hazard Assessment for Situational Awareness (LHASA))

Emberson, R., Kirschbaum, D. B., & Stanley, T. (2020). New global characterisation of landslide exposure. Natural Hazards and Earth System Sciences, 20(12), 3413-3424. doi:10.5194/nhess-20-3413-2020
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
NASA REMOTE SENSING (IMERG)
NASA REMOTE SENSING (TRMM)

Emediegwu, L. E., & Nnadozie, O. O. (2023). On the effects of COVID-19 on food prices in India: a time-varying approach. European Review of Agricultural Economics, 50(2), 232-249. doi:10.1093/erae/jbac015
Gridded Population of the World (GPW) v4.11 (population count)

Encalada, A. C., Flecker, A. S., Poff, N. L., Suárez, E., Herrera-R, G. A., Ríos-Touma, B., . . . Anderson, E. P. (2019). A global perspective on tropical montane rivers. Science, 365(6458), 1124-1129. doi:10.1126/science.aax1682
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Enenkel, M., Shrestha, R. M., Stokes, E., Roman, M., Wang, Z., Espinosa, M. T. M., . . . Vinck, P. (2020). Emergencies do not stop at night: Advanced analysis of displacement based on satellite-derived nighttime light observations. IBM Journal of Research and Development, 64(1/2), 8:1-8:12. doi:10.1147/JRD.2019.2954404
Gridded Population of the World (GPW) v4 (unspecified)
NASA REMOTE SENSING (Black Marble)

ENETWILD Consortium, Acevedo, P., Croft, S., Smith, G., & Vicente, J. (2019). ENETwild modelling of wild boar distribution and abundance: initial model output based on hunting data and update of occurrence-based models. EFSA Supporting Publications, 16(5), 1629E. doi:10.2903/sp.efsa.2019.EN-1629
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

ENETWILD Consortium, Croft, S., Smith, G., Acevedo, P., & Vicente, J. (2019). Wild boar in focus: initial model outputs of wild boar distribution based on occurrence data and identification of priority areas for data collection. EFSA Supporting Publications, 16(1), 1533E. doi:10.2903/sp.efsa.2019.EN-1533
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D
NASA REMOTE SENSING (SRTM)

Engel, F., Attermeyer, K., & Weyhenmeyer, G. A. (2020). A simplified approach to detect a significant carbon dioxide reduction by phytoplankton in lakes and rivers on a regional and global scale. The Science of Nature, 107(4), 29. doi:10.1007/s00114-020-01685-y
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H46T0JKB

Engstrom, R., Newhouse, D., & Soundararajan, V. (2019). Estimating Small Area Population Density Using Survey Data and Satellite Imagery : An Application to Sri Lanka. Retrieved from Washington DC: http://documents.worldbank.org/curated/en/920771552394454183/Estimating-Small-Area-Population-Density-Using-Survey-Data-and-Satellite-Imagery-An-Application-to-Sri-Lanka
Gridded Population of the World (GPW) v4 (collection)
NASA REMOTE SENSING (ASTER GDEM)
REMOTE SENSING (DigitalGlobe)
REMOTE SENSING (VIIRS NTL)

Engstrom, R., Newhouse, D., & Soundararajan, V. (2020). Estimating small-area population density in Sri Lanka using surveys and Geo-spatial data. PLoS ONE, 15(8), e0237063. doi:10.1371/journal.pone.0237063
Gridded Population of the World (GPW) v3 (collection)
Gridded Population of the World (GPW) v4 (collection)
NASA REMOTE SENSING (ASTER GDEM)
REMOTE SENSING (VIIRS)
REMOTE SENSING (DigitalGlobe)

Erbaugh, J. T. (2022). Impermanence and failure: The legacy of conservation-based payments in Sumatra, Indonesia. Environmental Research Letters, 17(5), 054015. doi:10.1088/1748-9326/ac6437
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)

Erbaugh, J. T., Pradhan, N., Adams, J., Oldekop, J. A., Agrawal, A., Brockington, D., . . . Chhatre, A. (2020). Global forest restoration and the importance of prioritizing local communities. Nature Ecology & Evolution, 4, 1472-1476. doi:10.1038/s41559-020-01282-2
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)
REMOTE SENSING (VIIRS DNB)

Erda, F. G. (2019). Where to Survey?  Prioritization of Survey Areas for the Ethiopian Large Carnivores Survey Project. Retrieved from https://www.wildcru.org/wp-content/uploads/2020/10/Where-to-survey_LCSE.pdf
Gridded Population of the World (GPW) v4 (population density)

Escurra Aguirre, J. J., & Jones, C. A. (2019). Water use efficiency and storage capacity in South Asia by 2050. JAWRA Journal of the American Water Resources Association, 55(6), 1519-1539. doi:10.1111/1752-1688.12804
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted) - 10.7927/H4JQ0XZW
Global Reservoir and Dam (GRanD) v1.01 (dams)
Global Reservoir and Dam (GRanD) v1.01 (reservoirs)
NASA REMOTE SENSING (SRTM)

Eser, P., Chokani, N., & Abhari, R. S. (2018). Impacts of battery electric vehicles on renewable integration within the 2030 European power system. International Journal of Energy Research, 42(13), 4142-4156. doi:10.1002/er.4161
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Etchie, A. T., Etchie, T. O., Elemile, O. O., Boladale, O., Oni, T., Akanno, I., . . . Swaminathan, N. (2020). Burn to kill: Wood ash a silent killer in Africa. Science of The Total Environment, 748, 141316. doi:10.1016/j.scitotenv.2020.141316
Gridded Population of the World (GPW) v4.11 (population density)

Etchie, T. O., Etchie, A. T., Adewuyi, G. O., Pillarisetti, A., Sivanesan, S., Krishnamurthi, K., & Arora, N. K. (2018). The gains in life expectancy by ambient PM2.5 pollution reductions in localities in Nigeria. Environmental Pollution, 236, 146-157. doi:10.1016/j.envpol.2018.01.034
Gridded Population of the World (GPW) v4 (population count)

Etchie, T. O., Etchie, A. T., Jauro, A., Pinker, R. T., & Swaminathan, N. (2021). Season, not lockdown, improved air quality during COVID-19 State of Emergency in Nigeria. Science of The Total Environment, 768, 145187. doi:10.1016/j.scitotenv.2021.145187
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS - MOD13)

Etefa, G., Frankl, A., Lanckriet, S., Biadgilgn, D., Gebreyohannes, Z., Amanuel, Z., . . . Nyssen, J. (2018). Changes in land use/cover mapped over 80 years in the Highlands of Northern Ethiopia. Journal of Geographical Sciences, 28(10), 1538-1563. doi:10.1007/s11442-018-1560-3
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4HX19NJ
REMOTE SENSING (Google Earth)

European Commission, E. (2021). Constructing a population grid. In Applying the Degree of Urbanisation: A Methodological Manual to Define Cities, Towns and Rural Areas for International Comparisons: 2021 Edition (pp. 25-32). Luxembourg: Publications Office of the European Union.
Gridded Population of the World (GPW) v4.11 (documentation)
POPGRID

Evangelopoulos, D., Perez-Velasco, R., Walton, H., Gumy, S., Williams, M., Kelly, F. J., & Künzli, N. (2020). The role of burden of disease assessment in tracking progress towards achieving WHO global air quality guidelines. International Journal of Public Health, 65(8), 1455-1465. doi:10.1007/s00038-020-01479-z
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates)

Everingham, J.-A., Svobodova, K., Lèbre, É., Owen, J. R., & Worden, S. (2022). Comparative capacity of global mining regions to transition to a post-mining future. The Extractive Industries and Society, 11, 101136. doi:10.1016/j.exis.2022.101136
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Eythorsson, D., Gardarsson, S. M., & Nijssen, B. (2023). Projected changes to Northern Hemisphere snow conditions over the period 1950–2100, given two emission scenarios. Remote Sensing Applications: Society and Environment, 30, 100954. doi:10.1016/j.rsase.2023.100954
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
NASA REMOTE SENSING (MODIS)

Fälth, H. E., Atsmon, D., Reichenberg, L., & Verendel, V. (2021). MENA compared to Europe: The influence of land use, nuclear power, and transmission expansion on renewable electricity system costs. Energy Strategy Reviews, 33, 100590. doi:10.1016/j.esr.2020.100590
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)

Fan, C. C., Marshall, A., Smolker, H., Gonzalez, M. R., Tapert, S. F., Barch, D. M., . . . Herting, M. M. (2021). Adolescent Brain Cognitive Development (ABCD) study Linked External Data (LED): Protocol and practices for geocoding and assignment of environmental data. Developmental Cognitive Neuroscience, 52, 101030. doi:10.1016/j.dcn.2021.101030
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Fang, B., & Lu, M. (2023). Asia faces a growing threat from intraseasonal compound weather whiplash. Earth's Future, 11(2), e2022EF003111. doi:10.1029/2022EF003111
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
Population Dynamics (Global One-Eighth Degree Population Base Year and Projection Grids Based on the SSPs, v1.01) - 10.7927/m30p-j498

Fang, Y., Du, S., Scussolini, P., Wen, J., He, C., Huang, Q., & Gao, J. (2018). Rapid population growth in Chinese floodplains from 1990 to 2015. International Journal of Environmental Research and Public Health, 15(8), 1602. doi:10.3390/ijerph15081602
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Fang, Z., Zhou, S., Zhang, S., Xing, W., Feng, X., Yang, Q., . . . Wang, J. (2023). Spatial distribution and influencing factors of urban soil organic carbon stocks in Xi'an City, China. Urban Ecosystems, 26, 677-388. doi:10.1007/s11252-022-01316-6
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (Landsat)

Faragò, M., Benini, L., Sala, S., Secchi, M., & Laurent, A. (2019). National inventories of land occupation and transformation flows in the world for land use impact assessment. The International Journal of Life Cycle Assessment, 24(8), 1333-1347. doi:10.1007/s11367-018-01581-8
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Farinosi, F., Giupponi, C., Reynaud, A., Ceccherini, G., Carmona-Moreno, C., De Roo, A., . . . Bidoglio, G. (2018). An innovative approach to the assessment of hydro-political risk: A spatially explicit, data driven indicator of hydro-political issues. Global Environmental Change, 52, 286-313. doi:10.1016/j.gloenvcha.2018.07.001
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42

Farthing, A., Rosenlieb, E., Steward, D., Reber, T., Njobvu, C., & Moyo, C. (2023). Quantifying agricultural productive use of energy load in Sub-Saharan Africa and its impact on microgrid configurations and costs. Applied Energy, 343, 121131. doi:10.1016/j.apenergy.2023.121131
Gridded Population of the World (GPW) v4 (population count)
REMOTE SENSING (Meteosat)

Fauzi, A., Sakti, A., Yayusman, L., Harto, A., Prasetyo, L., Irawan, B., . . . Wikantika, K. (2019). Contextualizing mangrove forest deforestation in Southeast Asia using environmental and socio-economic data products. Forests, 10(11), 952. doi:10.3390/f10110952
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS - MCD12Q1)
NASA REMOTE SENSING (MODIS - MOD44W)
REMOTE SENSING (DMSP-OLS)

Fedele, G., Donatti, C. I., Bornacelly, I., & Hole, D. G. (2021). Nature-dependent people: Mapping human direct use of nature for basic needs across the tropics. Global Environmental Change, 71, 102368. doi:10.1016/j.gloenvcha.2021.102368
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4F47M65

Fei, X., Fang, M., & Wang, Y. (2021). Climate change affects land-disposed waste. Nature Climate Change, 11, 1004-1005. doi:10.1038/s41558-021-01220-5
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Feinberg, A., Stenke, A., Peter, T., & Winkel, L. H. E. (2020). Constraining atmospheric selenium emissions using observations, global modelling, and Bayesian inference. Environmental Science & Technology, 54(12), 7146-7155. doi:10.1021/acs.est.0c01408
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Felbermayr, G., Gröschl, J., Sanders, M., Schippers, V., & Steinwachs, T. (2022). The economic impact of weather anomalies. World Development, 151, 105745. doi:10.1016/j.worlddev.2021.105745
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
REMOTE SENSING (DMSP-OLS)

Feliciani, C., Corbetta, A., Haghani, M., & Nishinari, K. (2023). Trends in crowd accidents based on an analysis of press reports. Safety Science, 164, 106174. doi:10.1016/j.ssci.2023.106174
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Feng, J., Li, Y., Qiu, Y., & Zhu, F. (2023). Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data. Atmospheric Chemistry and Physics, 23, 375-388. doi:10.5194/acp-23-375-2023
Gridded Population of the World (GPW) v4.11 (population density)

Feng, Q., An, C., Chen, Z., Lee, K., & Wang, Z. (2023). Identification of the driving factors of microplastic load and morphology in estuaries for improving monitoring and management strategies: A global meta-analysis. Environmental Pollution, 333, 122014. doi:10.1016/j.envpol.2023.122014
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Feng, Y., Jones, M. R., Chu, N. M., Segev, D. L., & McAdams-DeMarco, M. (2021). Ambient air pollution and mortality among older patients initiating maintenance dialysis. American Journal of Nephrology, 52, 217-227. doi:10.1159/000514233
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
Satellite-Derived Environmental Indicators (Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, v1) - 10.7927/H4ZK5DQS

Feng, Y., Wei, Y., Coull, B. A., & Schwartz, J. D. (2023). Measurement error correction for ambient PM2.5 exposure using stratified regression calibration: Effects on all-cause mortality. Environmental Research, 216(Part 4), 114792. doi:10.1016/j.envres.2022.114792
Gridded Population of the World (GPW) v4.11 (population density)

Fennessy, M. S., Moon, J. B., & Finlayson, C. M. (2023). 6 - Linking wetland ecological processes with the delivery of ecosystem services. In P. A. Gell, N. C. Davidson, & C. M. Finlayson (Eds.), Ramsar Wetlands (pp. 153-178): Elsevier.
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Fenske, J., & Wang, S. (2023). Tradition and mortality: Evidence from twin infanticide in Africa. Journal of Development Economics, 163, 103094. doi:10.1016/j.jdeveco.2023.103094
Gridded Population of the World (GPW) v4.11 (population density)

Fernández-García, V., & Kull, C. A. (2023). Refining historical burned area data from satellite observations. International Journal of Applied Earth Observation and Geoinformation, 120, 103350. doi:10.1016/j.jag.2023.103350
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Sentinel-2 ALOS)

Ferrara, A., Kosmas, C., Salvati, L., Padula, A., Mancino, G., & Nolè, A. (2020). Environmentally sensitive areas to land degradation and desertification (LDD) at the global level: Updating the MEDALUS-ESA framework for worldwide LDD assessment. Land Degradation & Development, 31(12), 1593-1607. doi:10.1002/ldr.3559
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
NASA REMOTE SENSING (MODIS - MOD13A3)

Ferraz, K. M. P. M. B. (2020). Modeling Report: Species Distribution Modeling (SDM). Retrieved from Apple Valley, MN: http://www.cpsg.org/content/conservation-planning-workshops-javan-leopard-panthera-pardus-melas-provisional-report
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
NASA REMOTE SENSING (SRTM)

Ferry, M., de Talancé, M., & Niño-Zarazúa, M. (2022). Less debt, more schooling? Evidence from cross-country micro data. Journal of Comparative Economics, 50(3), 153-173. doi:10.1016/j.jce.2021.07.002
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Ficetola, G. F., & Rubolini, D. (2021). Containment measures limit environmental effects on COVID-19 early outbreak dynamics. Science of The Total Environment, 761, 144432. doi:10.1016/j.scitotenv.2020.144432
Gridded Population of the World (GPW) v4.11 (population density)

Ficklin, D. L., Null, S. E., Abatzoglou, J. T., Novick, K. A., & Myers, D. T. (2022). Hydrological intensification will increase the complexity of water resource management. Earth's Future, 10(3), e2021EF002487. doi:10.1029/2021EF002487
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Fist, T., Adesanya, A. A., Denkenberger, D., & Pearce, J. M. (2021). Global distribution of forest classes and leaf biomass for use as alternative foods to minimize malnutrition. World Food Policy, 7(2), 128-146. doi:10.1002/wfp2.12030
Gridded Population of the World (GPW) v4.11 (population count)
Poverty Mapping (Global Subnational Prevalence of Child Malnutrition, v1) - 10.7927/H4K64G12

Fitrianto, G., Tanaka, S., & Nishii, R. (2018). Formulation of huge lattice spatial adjacency matrices with non-rectangular shape of socio-economic grid-cell data for the analysis of sustainable economy with high computational efficiency. Research in World Economy, 9(2). doi:10.5430/rwe.v9n2p1
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Fleischer, C. E. (2020). Minimising the effects of spatial scale reduction on power system models. Energy Strategy Reviews, 32, 100563. doi:10.1016/j.esr.2020.100563
Gridded Population of the World (GPW) v4.11 (unspecified)

Flockhart, D. T. T., Larrivée, M., Prudic, K. L., & Ryan Norris, D. (2019). Estimating the annual distribution of monarch butterflies in Canada over 16 years using citizen science data. FACETS, 4(1), 238-253. doi:10.1139/facets-2018-0011
Gridded Population of the World (GPW) v4.10 (population density UN WPP-adjusted)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (Daymet)

Florido Ngu, F., Kelman, I., Chambers, J., & Ayeb-Karlsson, S. (2021). Correlating heatwaves and relative humidity with suicide (fatal intentional self-harm). Scientific Reports, 11(1), 22175. doi:10.1038/s41598-021-01448-3
Gridded Population of the World (GPW) v4.11 (national identifier grid) - 10.7927/H4TD9VDP

Flückiger, M., & Ludwig, M. (2023). Mobile phone coverage and infant mortality in sub-Saharan Africa. Journal of Economic Behavior & Organization, 211, 462-485. doi:10.1016/j.jebo.2023.05.013
Gridded Population of the World (GPW) v4 (population count)
REMOTE SENSING (DMSP-OLS)

Flückiger, M., Ludwig, M., & Sina Önder, A. (2019). Ebola and state legitimacy. The Economic Journal, 129(621), 2064-2089. doi:10.1111/ecoj.12638
Gridded Population of the World (GPW) v4 (population count)

Font, C., Bregoli, F., Acuña, V., Sabater, S., & Marcé, R. (2019). GLOBAL-FATE (version 1.0.0): A geographical information system (GIS)-based model for assessing contaminants fate in the global river network. Geoscientific Model Development, 12(12), 5213-5228. doi:10.5194/gmd-12-5213-2019
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - data set unspecified)

Fontal, A., Bouma, M. J., San-José, A., López, L., Pascual, M., & Rodó, X. (2021). Climatic signatures in the different COVID-19 pandemic waves across both hemispheres. Nature Computational Science, 1(10), 655-665. doi:10.1038/s43588-021-00136-6
Gridded Population of the World (GPW) v4.11 (population count)

Formayer, H., Nadeem, I., Leidinger, D., Maier, P., Schöniger, F., Suna, D., . . . Lehner, F. (2023). SECURES-Met: A European meteorological data set suitable for electricity modelling applications. Scientific Data, 10(1), 590. doi:10.1038/s41597-023-02494-4
Gridded Population of the World (GPW) v4.11 (population density)

Frair, J. L., & Bastille-Rousseau, G. (2021). Data collection and quantitative considerations for studying pattern-process relationships on landscapes. In W. F. Porter, C. J. Parent, R. A. Stewart, & D. M. Williams (Eds.), Wildlife Management and Landscapes: Principles and Applications (pp. 114-136). Baltimore: Johns Hopkins University Press.
Gridded Population of the World (GPW) v4 (collection)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
NASA REMOTE SENSING (many)

Franco, A. C. S., Petry, A. C., Tavares, M. R., de Fátima Ramos Guimarães, T., & dos Santos, L. N. (2022). Global distribution of the South American peacock basses Cichla spp. follows human interference. Fish and Fisheries, 23(2), 407-421. doi:10.1111/faf.12624
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Frappier-Brinton, T., & Lehman, S. M. (2022). The burning island: Spatiotemporal patterns of fire occurrence in Madagascar. PLoS ONE, 17(3), e0263313. doi:10.1371/journal.pone.0263313
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (VIIRS)

Freese, L. M., Chossière, G. P., Eastham, S. D., Jenn, A., & Selin, N. E. (2023). Nuclear power generation phase-outs redistribute US air quality and climate-related mortality risk. Nature Energy, 8, 492-503. doi:10.1038/s41560-023-01241-8
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Freire, S., Florczyk, A. J., Pesaresi, M., & Sliuzas, R. (2019). An improved global analysis of population distribution in proximity to active volcanoes, 1975–2015. ISPRS International Journal of Geo-Information, 8(8), 341. doi:10.3390/ijgi8080341
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted)

Freire, S., Schiavina, M., Florczyk, A. J., MacManus, K., Pesaresi, M., Corbane, C., . . . Sliuzas, R. (2020). Enhanced data and methods for improving open and free global population grids: putting ‘leaving no one behind’ into practice. International Journal of Digital Earth, 13(1), 61-77. doi:10.1080/17538947.2018.1548656
Gridded Population of the World (GPW) v4 (collection)
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM

Fricker, T., Elsner, J. B., & Jagger, T. H. (2017). Population and energy elasticity of tornado casualties. Geophysical Research Letters, 44(8), 3941-3949. doi:10.1002/2017GL073093
Gridded Population of the World (GPW) v4 (population density)

Fries, B., Guerra, C. A., García, G. A., Wu, S. L., Smith, J. M., Oyono, J. N. M., . . . Dolgert, A. J. (2021). Measuring the accuracy of gridded human population density surfaces: A case study in Bioko Island, Equatorial Guinea. PLoS ONE, 16(9), e0248646. doi:10.1371/journal.pone.0248646
Gridded Population of the World (GPW) v4 (population density)

Fu, J., Gao, Q., Jiang, D., Li, X., & Lin, G. (2023). Spatial–temporal distribution of global production–living–ecological space during the period 2000–2020. Scientific Data, 10(1), 589. doi:10.1038/s41597-023-02497-1
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Fu, J., Tang, D., Grieneisen, M. L., Yang, F., Yang, J., Wu, G., . . . Zhan, Y. (2023). A machine learning-based approach for fusing measurements from standard sites, low-cost sensors, and satellite retrievals: Application to NO2 pollution hotspot identification. Atmospheric Environment, 302, 119756. doi:10.1016/j.atmosenv.2023.119756
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (MODIS - MOD13Q1)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (TROPOMI)

Fu, Y., Pang, Q., Suo Lang Zhuo, G., Wu, P., Wang, Y., Mao, M., . . . Zhang, Y. (2023). Modeling atmospheric microplastic cycle by GEOS-Chem: An optimized estimation by a global dataset suggests likely 50 times lower ocean emissions. One Earth, 6(6), 705-714. doi:10.1016/j.oneear.2023.05.012
Gridded Population of the World (GPW) v4.11 (population density)

Gabey, A. M., Grimmond, C. S. B., & Capel-Timms, I. (2019). Anthropogenic heat flux: advisable spatial resolutions when input data are scarce. Theoretical and Applied Climatology, 135(1-2), 791-807. doi:10.1007/s00704-018-2367-y
Gridded Population of the World (GPW) v4 (population density)

Gädeke, A., Langer, M., Boike, J., Burke, E., J., Chang, J., Head, M., . . . Thonicke, K. (2021). Climate change reduces winter overland travel across the Pan-Arctic even under low-end global warming scenarios. Environmental Research Letters, 16(2), 024049. doi:10.1088/1748-9326/abdcf2
Gridded Population of the World (GPW) v4 (documentation) - 10.7927/H4B56GPT

Gajdzik, L., DeCarlo, T. M., Aylagas, E., Coker, D. J., Green, A. L., Majoris, J. E., . . . Berumen, M. L. (2021). A portfolio of climate-tailored approaches to advance the design of marine protected areas in the Red Sea. Global Change Biology, 27(17), 3956-3968. doi:10.1111/gcb.15719
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Galimberti, J. K., Pichler, S., & Pleninger, R. (2020). Measuring Inequality using Geospatial Data. Retrieved from Auckland: https://www.aut.ac.nz/__data/assets/pdf_file/0010/399394/working-paper-20_07.pdf
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - data set unspecified)
REMOTE SENSING (DMSP-OLS)

Gallego-Zamorano, J., Benítez-López, A., Santini, L., Hilbers, J. P., Huijbregts, M. A. J., & Schipper, A. M. (2020). Combined effects of land use and hunting on distributions of tropical mammals. Conservation Biology, 34(5), 1271-1280. doi:10.1111/cobi.13459
Gridded Population of the World (GPW) v3 (population density)
Gridded Population of the World (GPW) v4.10 (population density)

Gan, R. W., Liu, J., Ford, B., O’Dell, K., Vaidyanathan, A., Wilson, A., . . . Magzamen, S. (2020). The association between wildfire smoke exposure and asthma-specific medical care utilization in Oregon during the 2013 wildfire season. Journal of Exposure Science & Environmental Epidemiology, 30, 618-628. doi:10.1038/s41370-020-0210-x
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D
NASA REMOTE SENSING (MODIS)

Ganglo, J. C. (2023). Ecological niche model transferability of the white star apple (Chrysophyllum albidum G. Don) in the context of climate and global changes. Scientific Reports, 13(1), 2430. doi:10.1038/s41598-023-29048-3
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Gao, H., Luo, Y., Jiang, X., Zhang, D.-L., Chen, Y., Wang, Y., & Shen, X. (2021). A statistical analysis of extreme hot characteristics and their relationships with urbanization in Southern China during 1971-2020. Journal of Applied Meteorology and Climatology, 60(9), 1301-1317. doi:10.1175/JAMC-D-21-0012.1
Gridded Population of the World (GPW) v4 (population density) - 10.7927//H4NP22DQ

Gao, J., & O’Neill, B. C. (2020). Mapping global urban land for the 21st century with data-driven simulations and Shared Socioeconomic Pathways. Nature Communications, 11(1), 2302. doi:10.1038/s41467-020-15788-7
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)

Gao, J., & O'Neill, B. (2019). Data-driven spatial modeling of long-term urban land development potential for global environmental change impact assessment: The SELECT model. Environmental Modelling & Software, 119, 458-471. doi:10.1016/j.envsoft.2019.06.015
Gridded Population of the World (GPW) v4 (population count)
Global Rural-Urban Mapping Project (GRUMP) v1 (land and geographic area grids)
Population Dynamics (Global One-Eighth Degree Population Projection Grids for the SSPs, v1)

Gao, M., Beig, G., Song, S., Zhang, H., Hu, J., Ying, Q., . . . McElroy, M. B. (2018). The impact of power generation emissions on ambient PM2.5 pollution and human health in China and India. Environment International, 121, 250-259. doi:10.1016/j.envint.2018.09.015
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Gao, M., Piao, S., Chen, A., Yang, H., Liu, Q., Fu, Y. H., & Janssens, I. A. (2019). Divergent changes in the elevational gradient of vegetation activities over the last 30 years. Nature Communications, 10(1), 2970. doi:10.1038/s41467-019-11035-w
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ
NASA REMOTE SENSING (AVHRR GIMMS)
NASA REMOTE SENSING (MODIS - MCD12C1)
REMOTE SENSING (DMSP-OLS)

Gao, M., Zhang, X., Yue, Y., Qiu, T., Wang, J., & Wang, X. (2022). Air path of antimicrobial resistance related genes from layer farms: emission inventory, atmospheric transport, and human exposure. Journal of Hazardous Materials, 430, 128417. doi:10.1016/j.jhazmat.2022.128417
Gridded Population of the World (GPW) v4.11 (admin unit center points)

Gao, P., Gao, Y., Zhang, X., Ye, S., & Song, C. (2023). CLUMondo-BNU for simulating land system changes based on many-to-many demand–supply relationships with adaptive conversion orders. Scientific Reports, 13(1), 5559. doi:10.1038/s41598-023-31001-3
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - data set unspecified)

Gao, P., Wu, T., Ge, Y., & Li, Z. (2022). Improving the accuracy of extant gridded population maps using multisource map fusion. GIScience & Remote Sensing, 59(1), 54-70. doi:10.1080/15481603.2021.2012371
Gridded Population of the World (GPW) v4 (collection)

Gao, X., Liu, J., & Huang, Z. (2022). The impact of climate change on the distribution of rare and endangered tree Firmiana kwangsiensis using the Maxent modeling. Ecology and Evolution, 12(8), e9165. doi:10.1002/ece3.9165
Gridded Population of the World (GPW) v4.11 (population density)

García de Jalón, S., Burgess, P. J., Curiel Yuste, J., Moreno, G., Graves, A., Palma, J. H. N., . . . Chiabai, A. (2019). Dry deposition of air pollutants on trees at regional scale: A case study in the Basque Country. Agricultural and Forest Meteorology, 278, 107648. doi:10.1016/j.agrformet.2019.107648
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D
NASA REMOTE SENSING (MODIS)

García-León, D., Casanueva, A., Standardi, G., Burgstall, A., Flouris, A. D., & Nybo, L. (2021). Current and projected regional economic impacts of heatwaves in Europe. Nature Communications, 12(1), 5807. doi:10.1038/s41467-021-26050-z
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted)

Garg, T., McCord, G. C., & Montfort, A. (2020). Can Social Protection Reduce Environmental Damages? Retrieved from Bonn: https://www.iza.org/en/publications/dp/13247/can-social-protection-reduce-environmental-damages
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates)

Garrett, J. K., Donald, P. F., & Gaston, K. J. (2020). Skyglow extends into the world's Key Biodiversity Areas. Animal Conservation, 23(2), 153-159. doi:10.1111/acv.12480
Gridded Population of the World (GPW) v4 (population density)
REMOTE SENSING (VIIRS)

Gassebner, M., Schaudt, P., & Wong, M. H. L. (2020). Armed Groups in Conflict: Competition and Political Violence in Pakistan. Retrieved from Munich: https://www.cesifo.org/en/publikationen/2020/working-paper/armed-groups-conflict-competition-and-political-violence-pakistan
Gridded Population of the World (GPW) v4 (population density)

Gassebner, M., Schaudt, P., & Wong, M. H. L. (2023). Armed groups: Competition and political violence. Journal of Development Economics, 162, 103052. doi:10.1016/j.jdeveco.2023.103052
Gridded Population of the World (GPW) v4 (population density)

Gaughan, A. E., Oda, T., Sorichetta, A., Stevens, F. R., Bondarenko, M., Bun, R., . . . Nghiem, S., V. (2019). Evaluating nighttime lights and population distribution as proxies for mapping anthropogenic CO2 emission in Vietnam, Cambodia and Laos. Environmental Research Communications, 1(9), 091006. doi:10.1088/2515-7620/ab3d91
Gridded Population of the World (GPW) v3 (collection)
Gridded Population of the World (GPW) v4.10 (documentation)
REMOTE SENSING (DMSP-OLS)

Gautam, R., & Singh, M. K. (2018). Urban heat island over Delhi punches holes in widespread fog in the Indo-Gangetic Plains. Geophysical Research Letters, 45(2), 1114-1121. doi:10.1002/2017GL076794
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (MODIS)

Gautier, A. (2017). Zika zone. Sensing Our Planet: NASA Earth Science Research Features, 8-11. Retrieved from https://earthdata.nasa.gov/user-resources/sensing-our-planet/zika-zone
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
NASA REMOTE SENSING (MODIS - MCD43B4)

Ge, E., Su, M., Zhao, R., Huang, Z., Shan, Y., & Wei, X. (2021). Geographical disparities in access to hospital care in Ontario, Canada: a spatial coverage modelling approach. BMJ Open, 11(1), e041474. doi:10.1136/bmjopen-2020-041474
Gridded Population of the World (GPW) v4.11 (population count)

Gearman, M., & Blinnikov, M. S. (2019). Mapping the potential distribution of Oak Wilt (Bretziella fagacearum) in east central and southeast Minnesota using Maxent. Journal of Forestry, 117(6), 579-591. doi:10.1093/jofore/fvz053
Gridded Population of the World (GPW) v4.10 (population density)

Gebremichael, E. (2018). Assessing Land Deformation and Sea Encroachment in the Nile Delta: A Radar Interferometric and Modeling Approach. (Ph.D.). Western Michigan University, Kalamazoo. Retrieved from https://scholarworks.wmich.edu/dissertations/3371 (3371)
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (ENVISAT Advanced Synthetic Aperture Radar)
REMOTE SENSING (TanDEM-X (TDX))

Gebremichael, E., Seyoum, W. M., Ishimwe, B., & Sataer, G. (2022). Lake surface area expansion: Insights into the role of volcano-tectonic processes, Lake Beseka, East Africa. Journal of Hydrology: Regional Studies, 41, 101093. doi:10.1016/j.ejrh.2022.101093
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
REMOTE SENSING (ALOS PALSAR)
REMOTE SENSING (Sentinel-1 SAR)

Gebremichael, E., Sultan, M., Becker, R., El Bastawesy, M., Cherif, O., & Emil, M. (2018). Assessing land deformation and sea encroachment in the Nile Delta: A radar interferometric and inundation modeling approach. Journal of Geophysical Research: Solid Earth, 123(4), 3208-3224. doi:10.1002/2017JB015084
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (ENVISAT Advanced Synthetic Aperture Radar)
REMOTE SENSING (TanDEM-X (TDX))

Gehring, K., Kaplan, L. C., & Wong, M. H. L. (2022). China and the World Bank — How contrasting development approaches affect the stability of African states. Journal of Development Economics, 158, 102902. doi:10.1016/j.jdeveco.2022.102902
Gridded Population of the World (GPW) v4 (documentation) - 10.7927/H45Q4T5F

Gehring, K., Langlotz, S., & Kienberger, S. (2018). Stimulant or Depressant?  Resource-related Income Shocks and Conflict. Retrieved from Heidelberg, Germany: https://www.uni-heidelberg.de/md/awi/institut/awlecture/dp652.pdf
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates) - 10.7927/H4F47M2C
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (DMSP-OLS)

Gehring, K., Wong, M. H. L., & Kaplan, L. C. (2018). Aid and conflict at the subnational level - Evidence from World Bank and Chinese development projects in Africa. Retrieved from Heidelberg, Germany: https://www.uni-heidelberg.de/md/awi/forschung/deseminar/dp657.pdf
Gridded Population of the World (GPW) v4 (population count)

Geijzendorffer, I. R., Galewski, T., Guelmami, A., Perennou, C., Popoff, N., & Grillas, P. (2019). Mediterranean Wetlands: A Gradient from Natural Resilience to a Fragile Social-Ecosystem. In M. Schröter, A. Bonn, S. Klotz, R. Seppelt, & C. Baessler (Eds.), Atlas of Ecosystem Services: Drivers, Risks, and Societal Responses (pp. 83-89). Cham: Springer International Publishing.
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Geng, G., Xiao, Q., Liu, S., Liu, X., Cheng, J., Zheng, Y., . . . Zhang, Q. (2021). Tracking air pollution in China: Near real-time PM2.5 retrievals from multisource data fusion. Environmental Science & Technology, 55(17), 12106-12115. doi:10.1021/acs.est.1c01863
Gridded Population of the World (GPW) v4 (unspecified)
NASA REMOTE SENSING (MERRA-2)
NASA REMOTE SENSING (MODIS)

Georg, I., Blaschke, T., & Taubenböck, H. (2018). Are we in Boswash yet? A multi-source geodata approach to spatially delimit urban corridors. ISPRS International Journal of Geo-Information, 7(1), 15pp. doi:10.3390/ijgi7010015
Gridded Population of the World (GPW) v4 (population count)
Gridded Population of the World (GPW) v4 (population density)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (Terra SAR-X (TSX))

Georg, I., Blaschke, T., & Taubenböck, H. (2023). Spatial delineation of urban corridors in North America: An approach incorporating fuzziness based on multi-source geospatial data. Cities, 133, 104129. doi:10.1016/j.cities.2022.104129
Gridded Population of the World (GPW) v4.10 (population density)
REMOTE SENSING (DMSP-OLS)

Georganos, S., Hafner, S., Kuffer, M., Linard, C., & Ban, Y. (2022). A census from heaven: Unraveling the potential of deep learning and Earth Observation for intra-urban population mapping in data scarce environments. International Journal of Applied Earth Observation and Geoinformation, 114, 103013. doi:10.1016/j.jag.2022.103013
Gridded Population of the World (GPW) v4 (collection)

Georgewill, I., Akani, G. C., Luiselli, L., Petrozzi, F., Dendi, D., Ugbomeh, A. P., . . . Onuegbu, G. C. (2022). Determining the composition and structure of antelope communities in three study sites within the Niger Delta (Nigeria) based on bushmeat market data. Tropical Ecology, 63, 145-150. doi:10.1007/s42965-021-00180-3
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Geraldi, N. R., Anton, A., Santana-Garcon, J., Bennett, S., Marbà, N., Lovelock, C. E., . . . Duarte, C. M. (2020). Ecological effects of non-native species in marine ecosystems relate to co-occurring anthropogenic pressures. Global Change Biology, 26(3), 1248-1258. doi:10.1111/gcb.14930
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted) - 10.7927/H4JQ0XZW

Gervasoni, L., Bosch, M., Fenet, S., & Sturm, P. (2017). Calculating spatial urban sprawl indices using open data. Paper presented at the 15th International Conference on Computers in Urban Planning and Urban Management, Adelaide, Australia.
Gridded Population of the World (GPW) v4 (collection)

Gervasoni, L., Fenet, S., Perrier, R., & Sturm, P. (2018). Convolutional neural networks for disaggregated population mapping using open data. Paper presented at the IEEE International Conference on Data Science and Advanced Analytics (DSAA), Turin, Italy.
Gridded Population of the World (GPW) v4 (collection)

Getirana, A., Jung, H. C., Arsenault, K., Shukla, S., Kumar, S., Peters-Lidard, C., . . . Mamane, B. (2020). Satellite gravimetry improves seasonal streamflow forecast initialization in Africa. Water Resources Research, 56(2), e2019WR026259. doi:10.1029/2019wr026259
Gridded Population of the World (GPW) v4.11 (admin unit center points) - 10.7927/H4BC3WMT
NASA REMOTE SENSING (GRACE)

Ghahremanloo, M., Choi, Y., & Lops, Y. (2023). Deep learning mapping of surface MDA8 ozone: The impact of predictor variables on ozone levels over the contiguous United States. Environmental Pollution, 326, 121508. doi:10.1016/j.envpol.2023.121508
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (OMI NO2)
REMOTE SENSING (TROPOMI)

Ghahremanloo, M., Choi, Y., Sayeed, A., Salman, A. K., Pan, S., & Amani, M. (2021). Estimating daily high-resolution PM2.5 concentrations over Texas: Machine Learning approach. Atmospheric Environment, 247, 118209. doi:10.1016/j.atmosenv.2021.118209
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MODIS - MOD04)

Ghahremanloo, M., Lops, Y., Choi, Y., Jung, J., Mousavinezhad, S., & Hammond, D. (2022). A comprehensive study of the COVID-19 impact on PM2.5 levels over the contiguous United States: A deep learning approach. Atmospheric Environment, 272, 118944. doi:10.1016/j.atmosenv.2022.118944
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MODIS - MOD13C1)

Ghahremanloo, M., Lops, Y., Choi, Y., Mousavinezhad, S., & Jung, J. (2023). A coupled deep learning model for estimating surface NO2 levels from remote sensing data: 15-Year study over the contiguous United States. Journal of Geophysical Research: Atmospheres, 128(2), e2022JD037010. doi:10.1029/2022JD037010
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MODIS -MOD13C1)
NASA REMOTE SENSING (OMI NO2)
NASA REMOTE SENSING (TROPOMI)

Ghahremanloo, M., Lops, Y., Choi, Y., & Yeganeh, B. (2021). Deep learning estimation of daily ground-level NO2 concentrations from remote sensing data. Journal of Geophysical Research: Atmospheres, 126(21), e2021JD034925. doi:10.1029/2021JD034925
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MODIS - MOD13A2)
NASA REMOTE SENSING (TROPOMI)

Ghosh-Harihar, M., An, R., Athreya, R., Borthakur, U., Chanchani, P., Chetry, D., . . . Price, T. D. (2019). Protected areas and biodiversity conservation in India. Biological Conservation, 237, 114-124. doi:10.1016/j.biocon.2019.06.024
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM

Gilkes, R. J., & Prakongkep, N. (2016). How the unique properties of soil kaolin affect the fertility of tropical soils. Applied Clay Science, 131, 100-106. doi:10.1016/j.clay.2016.01.007
Gridded Population of the World (GPW) v4 Preliminary release 2

Giller, K. E., Andersson, J., Delaune, T., Silva, J. V., Descheemaeker, K., van de Ven, G., . . . van Ittersum, M. (2022). The Future of Farming: Who Will Produce Our Food? Retrieved from Rome: https://www.ifad.org/en/web/knowledge/-/research-series-83-the-future-of-farming-who-will-produce-our-food-?
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Giller, K. E., Delaune, T., Silva, J. V., van Wijk, M., Hammond, J., Descheemaeker, K., . . . Andersson, J. A. (2021). Small farms and development in sub-Saharan Africa: Farming for food, for income or for lack of better options? Food Security, 13, 1431-1454. doi:10.1007/s12571-021-01209-0
Gridded Population of the World (GPW) v4.11 (population density)

Ginath Yuh, Y., N'Goran, P. K., Dongmo, Z. N., Tracz, W., Tangwa, E., Agunbiade, M., . . . Fotang, C. (2020). Mapping suitable great ape habitat in and around the Lobéké National Park, South-East Cameroon. Ecology and Evolution, 10(24), 14282-14299. doi:10.1002/ece3.7027
Gridded Population of the World (GPW) v4.11 (population density)

Giorgi, E., Kreppel, K., Diggle, P. J., Caminade, C., Ratsitorahina, M., Rajerison, M., & Baylis, M. (2016). Modelling of spatio-temporal variation in plague incidence in Madagascar from 1980 to 2007. Spatial and Spatio-temporal Epidemiology, 19, 125-135. doi:10.1016/j.sste.2016.10.001
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)

Gleditsch, J. M., Behm, J. E., Ellers, J., Jesse, W. A. M., & Helmus, M. R. (2023). Contemporizing island biogeography theory with anthropogenic drivers of species richness. Global Ecology and Biogeography, 32(2), 233-249. doi:10.1111/geb.13623
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
NASA REMOTE SENSING (MODIS - MCD12Q1)

Gleditsch, K. S., & Weidmann, N. B. (2020). From Hand-Counting to GIS: Richardson in the Information Age. In N. P. Gleditsch (Ed.), Lewis Fry Richardson: His Intellectual Legacy and Influence in the Social Sciences (pp. 73-85). Cham: Springer International Publishing.
Gridded Population of the World (GPW) v4 (collection)

Glison, N., Romero, D., Rosso, V., Guerrero, J. C., & Speranza, P. R. (2023). Understanding the geographic patterns of closely-related species of Paspalum (Poaceae) using distribution modelling and seed germination traits. Plants, 12(6), 1342. doi:10.3390/plants12061342
Gridded Population of the World (GPW) v4.11 (data quality indicators)

Gnanapragasam, J. J., Ekanayake, K. B., Ranawana, K., Symondsl, M. R. E., & Weston, M. (2021). Civil war is associated with longer escape distances among Sri Lankan birds. The American Naturalist, 198(5), 653-659. doi:10.1086/716660
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Goldenberg, R., Kalantari, Z., & Destouni, G. (2021). Comparative quantification of local climate regulation by green and blue urban areas in cities across Europe. Scientific Reports, 11(1), 23872. doi:10.1038/s41598-021-03140-y
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Gollin, D., Kirchberger, M., & Lagakos, D. (2021). Do urban wage premia reflect lower amenities? Evidence from Africa. Journal of Urban Economics, 121, 103301. doi:10.1016/j.jue.2020.103301
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - population density)

Gomez-Zapata, J. C., Brinckmann, N., Harig, S., Zafrir, R., Pittore, M., Cotton, F., & Babeyko, A. (2021). Variable-resolution building exposure modelling for earthquake and tsunami scenario-based risk assessment. An application case in Lima, Peru. Natural Hazards and Earth System Sciences, 21(11), 3599-3628. doi:10.5194/nhess-21-3599-2021
Gridded Population of the World (GPW) v4 (documentation)

Gonçalves, A. S., Costa, G. C., Bond-Buckup, G., Bartholomei-Santos, M. L., & Santos, S. (2018). Priority areas for conservation within four freshwater ecoregions in South America: A scale perspective based on freshwater crabs (Anomura, Aeglidae). Aquatic Conservation: Marine and Freshwater Ecosystems, 28(5), 1077-1088. doi:10.1002/aqc.2965
Gridded Population of the World (GPW) v4.10 (population density)

Gong, S., & Shi, Y. (2021). Evaluation of comprehensive monthly-gridded methane emissions from natural and anthropogenic sources in China. Science of The Total Environment, 784, 147116. doi:10.1016/j.scitotenv.2021.147116
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (GOSAT)

González-Saucedo, Z. Y., González-Bernal, A., & Martínez-Meyer, E. (2021). Identifying priority areas for landscape connectivity for three large carnivores in northwestern Mexico and southwestern United States. Landscape Ecology, 36, 877-896. doi:10.1007/s10980-020-01185-4
Gridded Population of the World (GPW) v4 (population count)

Goodman, S., BenYishay, A., Lv, Z., & Runfola, D. (2019). GeoQuery: Integrating HPC systems and public web-based geospatial data tools. Computers & Geosciences, 122, 103-112. doi:10.1016/j.cageo.2018.10.009
Gridded Population of the World (GPW) v3 (population count) - 10.​7927/​H4639MPP
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM
Global Roads (Global Roads Open Access Data Set (gROADS), v1) - 10.7927/H4VD6WCT
NASA REMOTE SENSING (MODIS land cover)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (DMSP-OLS)

Gorczynski, D., Hsieh, C., Ahumada, J., Akampurira, E., Andrianarisoa, M. H., Espinosa, S., . . . Beaudrot, L. (2022). Human density modulates spatial associations among tropical forest terrestrial mammal species. Global Change Biology, 28(24), 7205-7216. doi:10.1111/gcb.16434
Gridded Population of the World (GPW) v4 (population density) - 10.7927//H4NP22DQ
NASA REMOTE SENSING (MODIS)

Gorton, N., & Ianchovichina, E. (2021). Trade Networks in Latin America : Spatial Inefficiencies and Optimal Expansions. Retrieved from Washington: http://documents.worldbank.org/curated/en/936651636137366844/Trade-Networks-in-Latin-America-Spatial-Inefficiencies-and-Optimal-Expansions
Gridded Population of the World (GPW) v4 (unspecified)

Gourevitch, J. D., Koliba, C., Rizzo, D. M., Zia, A., & Ricketts, T. H. (2021). Quantifying the social benefits and costs of reducing phosphorus pollution under climate change. Journal of Environmental Management, 293, 112838. doi:10.1016/j.jenvman.2021.112838
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Gove, J. M., Lecky, J., Walsh, W. J., Ingram, R. J., Leong, K., Williams, I. D., . . . Williams, G. J. (2019). West Hawai'i Integrated Ecosystem Assessment Ecosystem Status Report. Retrieved from https://doi.org/10.25923/t3cc-2361
Gridded Population of the World (GPW) v4 (population density)

Grace, K., Nagle, N. N., Burgert-Brucker, C. R., Rutzick, S., Van Riper, D. C., Dontamsetti, T., & Croft, T. (2019). Integrating environmental context into DHS analysis while protecting participant confidentiality: A new remote sensing method. Population and Development Review, 45(1), 197-218. doi:10.1111/padr.12222
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM

Graco-Roza, C., Santos, J. B. O., Huszar, V. L. M., Domingos, P., Soininen, J., & Marinho, M. M. (2020). Downstream transport processes modulate the effects of environmental heterogeneity on riverine phytoplankton. Science of The Total Environment, 703, 135519. doi:10.1016/j.scitotenv.2019.135519
Gridded Population of the World (GPW) v4.10 (population density)

Graff, T. (2018). Spatial Ineﬃciencies in Africa’s Trade Network. Retrieved from https://www.csae.ox.ac.uk/papers/wps-2018-17
Gridded Population of the World (GPW) v4 (population count)
REMOTE SENSING (DMSP-OLS)

Graff, T. (2019). Spatial Inefficiencies in Africa's Trade Network. Retrieved from Cambridge, MA: https://doi.org/10.3386/w25951
Gridded Population of the World (GPW) v4 (population count)
REMOTE SENSING (DMSP-OLS)

Graham, A. M., Pope, R. J., McQuaid, J. B., Pringle, K., Arnold, S., Bruno, A. G., . . . Latter, B. G. (2020). Impact of the June 2018 Saddleworth Moor wildfires on air quality in northern England. Environmental Research Communications, 2(3), 031001. doi:10.1088/2515-7620/ab7b92
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (TROPOMI)

Graham, S. E., Loveless, J. P., & Meade, B. J. (2018). Global plate motions and earthquake cycle effects. Geochemistry, Geophysics, Geosystems, 19(7), 2032-2048. doi:10.1029/2017GC007391
Gridded Population of the World (GPW) v4 (population count)

Grange, Z. L., Goldstein, T., Johnson, C. K., Anthony, S., Gilardi, K., Daszak, P., . . . Mazet, J. A. K. (2021). Ranking the risk of animal-to-human spillover for newly discovered viruses. Proceedings of the National Academy of Sciences, 118(15), e2002324118. doi:10.1073/pnas.2002324118
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
Land Use and Land Cover (LULC) (Global Grid of Probabilities of Urban Expansion to 2030, v1)

Greenhill, R., & Desai, H. (2017). Aid Allocation Within Countries:  Does it go to Areas Left Behind? Retrieved from London: https://www.odi.org/publications/10860-aid-allocation-within-countries-does-it-go-areas-left-behind
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42

Grider, J. F., Russell, R. E., Ballmann, A. E., & Hefley, T. J. (2021). Long-term Pseudogymnoascus destructans surveillance data reveal factors contributing to pathogen presence. Ecosphere, 12(11), e03808. doi:10.1002/ecs2.3808
Gridded Population of the World (GPW) v4 (documentation)

Grilli, M. G., Bildstein, K. L., & Lambertucci, S. A. (2019). Nature’s clean-up crew: Quantifying ecosystem services offered by a migratory avian scavenger on a continental scale. Ecosystem Services, 39, 100990. doi:10.1016/j.ecoser.2019.100990
Gridded Population of the World (GPW) v4.10 (population density UN WPP-adjusted) - 10.7927/H49884ZR

Groeskamp, S., & Kjellsson, J. (2020). NEED The Northern European Enclosure Dam for if climate change mitigation fails. Bulletin of the American Meteorological Society, 101(7), E1174-E1189. doi:10.1175/bams-d-19-0145.1
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Gu, Y., Henze, D. K., Nawaz, M. O., Cao, H., & Wagner, U. J. (2023). Sources of PM2.5-associated health risks in Europe and corresponding emission-induced changes during 2005–2015. GeoHealth, 7(3), e2022GH000767. doi:10.1029/2022GH000767
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Gu, Z., Fan, H., & Wang, Y. (2020). Dynamic characteristics of sandbar evolution in the lower Lancang-Mekong River between 1993 and 2012 in the context of hydropower development. Estuarine, Coastal and Shelf Science, 237, 106678. doi:10.1016/j.ecss.2020.106678
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (Landsat)

Guegan, J.-F., Ayouba, A., Capelle, J., & de Thoisy, B. (2020). Forest and emerging infectious diseases: Unleashing the beast within. Environmental Research Letters, 15(8), 083007. doi:10.1088/1748-9326/ab8dd7
Gridded Population of the World (GPW) v4 (population density)

Guerisoli, M. d. l. M., & Schiaffini, M. I. (2022). “I Did, I Did Taw a Puddy Tat!” pumas in urban ecosystems of Latin America: A review of the mediatic information. Frontiers in Conservation Science, 3, 739026. doi:10.3389/fcosc.2022.739026
Gridded Population of the World (GPW) v4.11 (population density)

Guerrini, F. (2023). Data-informed models for the coupled dispersal of microplastics and related pollutants applied to the Mediterranean Sea. In C. G. Riva (Ed.), Special Topics in Information Technology (pp. 3-14). Cham: Springer International Publishing.
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Guerrini, F., Mari, L., & Casagrandi, R. (2021). The dynamics of microplastics and associated contaminants: Data-driven Lagrangian and Eulerian modelling approaches in the Mediterranean Sea. Science of The Total Environment, 777, 145944. doi:10.1016/j.scitotenv.2021.145944
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Guerrini, F., Mari, L., & Casagrandi, R. (2022). A coupled Lagrangian-Eulerian model for microplastics as vectors of contaminants applied to the Mediterranean Sea. Environmental Research Letters, 17(2), 024038. doi:10.1088/1748-9326/ac4fd9
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (FLDAS Noah Land Surface Model L4 Global Monthly Climatology)

Guevara, M., Jorba, O., Soret, A., Petetin, H., Bowdalo, D., Serradell, K., . . . Pérez García-Pando, C. (2021). Time-resolved emission reductions for atmospheric chemistry modelling in Europe during the COVID-19 lockdowns. Atmospheric Chemistry and Physics, 21, 773-797. doi:10.5194/acp-21-773-2021
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Guevara, M., Petetin, H., Jorba, O., Denier van der Gon, H. A. C., Kuenen, J., Super, I., . . . Pérez García-Pando, C. (2022). European primary emissions of criteria pollutants and greenhouse gases in 2020 modulated by the COVID-19 pandemic disruptions. Earth System Science Data, 14, 2521-2552. doi:10.5194/essd-14-2521-2022
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Gui, K., Che, H., Wang, Y., Wang, H., Zhang, L., Zhao, H., . . . Zhang, X. (2019). Satellite-derived PM2.5 concentration trends over Eastern China from 1998 to 2016: Relationships to emissions and meteorological parameters. Environmental Pollution, 247, 1125-1133. doi:10.1016/j.envpol.2019.01.056
Gridded Population of the World (GPW) v3 (population count)
Gridded Population of the World (GPW) v4 (population count)

Gulshad, K., Wang, Y., Li, N., Wang, J., & Yu, Q. (2022). Likelihood of transformation to green infrastructure using ensemble machine learning techniques in Jinan, China. Land, 11(3), 317. doi:10.3390/land11030317
Gridded Population of the World (GPW) v4.11 (admin unit center points) - 10.7927/H4BC3WMT
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (TRMM)
REMOTE SENSING (Landsat)

Günther, I., Harttgen, K., Seiler, J., & Utzinger, J. (2022). An index of access to essential infrastructure to identify where physical distancing is impossible. Nature Communications, 13(1), 3355. doi:10.1038/s41467-022-30812-8
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Guo, J., Rincon, D., Sallent, S., Yang, L., Chen, X., & Chen, X. (2021). Gateway placement optimization in LEO satellite networks based on traffic estimation. IEEE Transactions on Vehicular Technology, 70(4), 3860-3876. doi:10.1109/TVT.2021.3065994
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Guo, J., Xu, Q., Zeng, Y., Liu, Z., & Zhu, X. X. (2023). Nationwide urban tree canopy mapping and coverage assessment in Brazil from high-resolution remote sensing images using deep learning. ISPRS Journal of Photogrammetry and Remote Sensing, 198, 1-15. doi:10.1016/j.isprsjprs.2023.02.007
Gridded Population of the World (GPW) v4.11 (population count)
REMOTE SENSING (GeoEye-1)
REMOTE SENSING (Pleiades-1)
REMOTE SENSING (SkySat)
REMOTE SENSING (WorldView-2)
REMOTE SENSING (WorldView-3)

Guo, J., Yang, L., Rincon, D., Sallent, S., Chen, Q., & Liu, X. (2022). Static placement and dynamic assignment of SDN controllers in LEO satellite networks. IEEE Transactions on Network and Service Management, 19(4), 4975-4988. doi:10.1109/TNSM.2022.3184989
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Guo, L., He, P., He, Y., Gao, Y., Zhang, X., Huo, T., . . . Meng, F. (2023). Predicting the comprehensive geospatial pattern of two ephedrine-type alkaloids for Ephedra sinica in Inner Mongolia. PLoS ONE, 18(4), e0283967. doi:10.1371/journal.pone.0283967
Gridded Population of the World (GPW) v4.11 (population density)
Last of the Wild v3 (Human Footprint, 2018 Release (2009))
Land Use and Land Cover (LULC) (Global Human Modification of Terrestrial Systems, v1)

Guo, L., Shi, Y., & Zhao, Y. (2023). Future projections of extreme integrated water vapor transport and population exposure over the Asian monsoon region. Earth's Future, 11(8), e2023EF003583. doi:10.1029/2023EF003583
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Guo, Q., He, Z., & Wang, Z. (2023). Simulating daily PM2.5 concentrations using wavelet analysis and artificial neural network with remote sensing and surface observation data. Chemosphere, 340, 139886. doi:10.1016/j.chemosphere.2023.139886
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MODIS)

Gupta, L., Dev, R., Zaidi, K., Sunder Raman, R., Habib, G., & Ghosh, B. (2021). Assessment of PM10 and PM2.5 over Ghaziabad, an industrial city in the Indo-Gangetic Plain: spatio-temporal variability and associated health effects. Environmental Monitoring and Assessment, 193(11), 735. doi:10.1007/s10661-021-09411-5
Gridded Population of the World (GPW) v4.11 (population count)

Gupta, P. P., Dewi, M. B. K., Avi, S., Shrivastav, A., & Min, J. (2022). Innovative Solutions for Managing Tropical Cyclone Risk in India–Bangladesh Coastal Region of Bay of Bengal. Paper presented at the International Symposium of ISCAR on Coastal Agriculture.
Gridded Population of the World (GPW) v4.11 (population density)

Gurusamy, B. T., & Vasudeo, A. D. (2023). Socio-economic and ecological adaptability across South Asian Floodplains. Journal of Environmental Engineering and Landscape Management, 31(2), 121-131. doi:10.3846/jeelm.2023.19014
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)

Gutiérrez-Avila, I., Arfer, K. B., Carrión, D., Rush, J., Kloog, I., Naeger, A. R., . . . Just, A. C. (2022). Prediction of daily mean and one-hour maximum PM2.5 concentrations and applications in Central Mexico using satellite-based machine-learning models. Journal of Exposure Science & Environmental Epidemiology, 32, 917-925. doi:10.1038/s41370-022-00471-4
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS MAIAC)

Gutmann, M. P., Merchant, E. K., & Roberts, E. (2018). “Big Data” in economic history. The Journal of Economic History, 78(1), 268-299. doi:10.1017/S0022050718000177
Gridded Population of the World (GPW) v4 (collection)

Guzmán, P., Tarín-Carrasco, P., Morales-Suárez-Varela, M., & Jiménez-Guerrero, P. (2022). Effects of air pollution on dementia over Europe for present and future climate change scenarios. Environmental Research, 204, 112012. doi:10.1016/j.envres.2021.112012
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)

Guzy, A., & Malinowska, A. A. (2020). State of the art and recent advancements in the modelling of land subsidence induced by groundwater withdrawal. Water, 12(7), 2051. doi:10.3390/w12072051
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Ha, T. V., Kim, W., Nguyen-Tien, T., Lindahl, J., Nguyen-Viet, H., Thi, N. Q., . . . Lee, H. S. (2021). Spatial distribution of Culex mosquito abundance and associated risk factors in Hanoi, Vietnam. PLoS Neglected Tropical Diseases, 15(6), e0009497. doi:10.1371/journal.pntd.0009497
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS LST)
REMOTE SENSING (Landsat)

Haberman, D., & Bennett, E. (2019). Ecosystem service bundles in global hinterlands. Environmental Research Letters, 14(8), 084005. doi:10.1088/1748-9326/ab26f7
Gridded Population of the World (GPW) v4 (population count)

Hadley, L. (2019). Borders and the feasibility of rebel conflict. Borders in Globalization Review, 1(1), 66-82. doi:10.18357/bigr11201919259
Gridded Population of the World (GPW) v4.11 (population count)

Hakkim, H., Kumar, A., Annadate, S., Sinha, B., & Sinha, V. (2021). RTEII: A new high-resolution (0.1° × 0.1°) road transport emission inventory for India of 74 speciated NMVOCs, CO, NOx, NH3, CH4, CO2, PM2.5 reveals massive overestimation of NOx and CO and missing nitromethane emissions by existing inventories. Atmospheric Environment: X, 11, 100118. doi:10.1016/j.aeaoa.2021.100118
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Hamilton, S. E., Gallo, S. M., Krach, N., Nyamweya, C. S., Okechi, J. K., Aura, C., . . . Kaufman, L. (2020). The use of unmanned aircraft systems and high-resolution satellite imagery to monitor tilapia fish-cage aquaculture expansion in Lake Victoria, Kenya. Bulletin of Marine Science, 96(1), 71-93. doi:10.5343/bms.2019.0063
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)
REMOTE SENSING (Sentinel-2)

Hammer, M. S., van Donkelaar, A., Bindle, L., Sayer, A. M., Lee, J., Hsu, N. C., . . . Martin, R. V. (2023). Assessment of the impact of discontinuity in satellite instruments and retrievals on global PM2.5 estimates. Remote Sensing of Environment, 294, 113624. doi:10.1016/j.rse.2023.113624
Gridded Population of the World (GPW) v4.10 (population count)
NASA REMOTE SENSING (MISR)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (VIIRS)

Hammer, M. S., van Donkelaar, A., Li, C., Lyapustin, A., Sayer, A. M., Hsu, N. C., . . . Martin, R. V. (2020). Global estimates and long-term trends of fine particulate matter concentrations (1998-2018). Environmental Science & Technology, 54(13), 7879-7890. doi:10.1021/acs.est.0c01764
Gridded Population of the World (GPW) v4 (unspecified)
NASA REMOTE SENSING (MISR)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SeaWiFS)

Hammer, M. S., van Donkelaar, A., Martin, R. V., McDuffie, E. E., Lyapustin, A., Sayer, A. M., . . . Kahn, R. A. (2021). Effects of COVID-19 lockdowns on fine particulate matter concentrations. Science Advances, 7(26), eabg7670. doi:10.1126/sciadv.abg7670
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (MISR)
NASA REMOTE SENSING (MODIS)

Han, C., Xu, R., Gao, C. X., Yu, W., Zhang, Y., Han, K., . . . Li, S. (2021). Socioeconomic disparity in the association between long-term exposure to PM2.5 and mortality in 2640 Chinese counties. Environment International, 146, 106241. doi:10.1016/j.envint.2020.106241
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Han, C., Xu, R., Ye, T., Xie, Y., Zhao, Y., Liu, H., . . . Guo, Y. (2022). Mortality burden due to long-term exposure to ambient PM2.5 above the new WHO air quality guideline based on 296 cities in China. Environment International, 166, 107331. doi:10.1016/j.envint.2022.107331
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Han, C., Xu, R., Zhang, Y., Yu, W., Zhang, Z., Morawska, L., . . . Guo, Y. (2021). Air pollution control efficacy and health impacts: A global observational study from 2000 to 2016. Environmental Pollution, 287, 117211. doi:10.1016/j.envpol.2021.117211
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Han, H., Zhang, L., Liu, Z., Yue, X., Shu, L., Wang, X., & Zhang, Y. (2023). Narrowing differences in urban and nonurban surface ozone in the Northern Hemisphere over 1990–2020. Environmental Science & Technology Letters, 10(5), 410-417. doi:10.1021/acs.estlett.3c00105
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (OMI)
REMOTE SENSING (DMSP-OLS)

Han, W., & Tong, L. (2019). Satellite-based estimation of daily ground-level PM2.5 concentrations over urban agglomeration of Chengdu Plain. Atmosphere, 10(5), 245. doi:10.3390/atmos10050245
Gridded Population of the World (GPW) v4 (unspecified)
NASA REMOTE SENSING (MODIS)

Han, W., Xu, Z., Hu, X., Cao, R., Wang, Y., Jin, J., . . . Li, G. (2023). Air pollution, greenness and risk of overweight among middle-aged and older adults: A cohort study in China. Environmental Research, 216(Part 1), 114372. doi:10.1016/j.envres.2022.114372
Gridded Population of the World (GPW) v4.11 (population count)

Han, Y., Jiang, Y., Xiong, X., Sui, X., Zhu, R., Feng, X., . . . Chen, Y. (2023). Mercury biomagnification at higher rates than the global average in aquatic ecosystems of the Qinghai-Tibet Plateau. Journal of Hazardous Materials, 453, 131408. doi:10.1016/j.jhazmat.2023.131408
Gridded Population of the World (GPW) v4.11 (population density)

Han, Y., Zhao, W., & Pereira, P. (2022). Global COVID-19 pandemic trends and their relationship with meteorological variables, air pollutants and socioeconomic aspects. Environmental Research, 204(Part C), 112249. doi:10.1016/j.envres.2021.112249
Gridded Population of the World (GPW) v4 (population density)
REMOTE SENSING (Sentinel-5P)

Han, Z., & Song, W. (2022). Interannual trends of vegetation and responses to climate change and human activities in the Great Mekong Subregion. Global Ecology and Conservation, 38, e02215. doi:10.1016/j.gecco.2022.e02215
Gridded Population of the World (GPW) v3 (population density) - 10.7927/H4XK8CG2
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (GIMMS NDVI)

Hanafi-Bojda, A. A., Motazakker, M., Vatandoost, H., Dabiri, F., & Chavshin, A. R. (2021). Sindbis virus infection of mosquito species in the wetlands of northwestern Iran and modeling the probable ecological niches of SINV vectors in the country. Acta Tropica, 220, 105952. doi:10.1016/j.actatropica.2021.105952
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MODIS)

Hanberry, B. B. (2022). Imposing consistent global definitions of urban populations with gridded population density models: Irreconcilable differences at the national scale. Landscape and Urban Planning, 226, 104493. doi:10.1016/j.landurbplan.2022.104493
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42

Hancher, M. (2017). The Gridded Population of the World in the Google Earth Engine.  Retrieved from https://medium.com/google-earth/the-global-population-of-the-world-ae8b8b362c99
Gridded Population of the World (GPW) v4 (population count)
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
Gridded Population of the World (GPW) v4 (population density)
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Hansen, C. H., Pilla, R. M., Matson, P. G., Skinner, B., Griffiths, N. A., & Jager, H. I. (2023). Variability in modelled reservoir greenhouse gas emissions: comparison of select US hydropower reservoirs against global estimates. Environmental Research Communications, 4(12), 121008. doi:10.1088/2515-7620/acae24
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Hao, M., Jiang, D., Ding, F., Fu, J., & Chen, S. (2019). Simulating spatio-temporal patterns of terrorism incidents on the Indochina Peninsula with GIS and the random forest method. ISPRS International Journal of Geo-Information, 8(3), 133. doi:10.3390/ijgi8030133
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted)
Natural Disaster Hotspots (multihazard frequency and distribution)
NASA REMOTE SENSING (ASTER GDEM)
NASA REMOTE SENSING (MODIS LST)
REMOTE SENSING (DMSP-OLS)

Hao, X., & Nabe-Nielsen, J. (2023). Distribution and speed of recreational boats in Danish waters based on coastal observations and satellite images: Predicting where boats may affect harbour porpoises. Ocean & Coastal Management, 242, 106721. doi:10.1016/j.ocecoaman.2023.106721
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (GeoEye-1)
REMOTE SENSING (Pleiades-1)
REMOTE SENSING (WorldView-1)

Haqiqi, I., Grogan, D. S., Bahalou, M., Liu, J., Baldos, U. L. C., Lammers, R. B., & Hertel, T. W. (2023). Local, regional, and global adaptations to a compound pandemic-weather stress event. Environmental Research Letters, 18(3), 035005. doi:10.1088/1748-9326/acbbe3
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Haritha, V. S., Sreenath, K. R., Anakha, M., Joshi, K. K., & Shelton, P. (2019). Vulnerability of south east coastal villages of India on sea level rise. Journal of the Marine Biological Association of India, 61(1), 31-37. doi:10.6024/jmbai.2019.61.1.2085-04
Gridded Population of the World (GPW) v4 (population density)

Harmanny, K. S., & Malek, Ž. (2019). Adaptations in irrigated agriculture in the Mediterranean region: an overview and spatial analysis of implemented strategies. Regional Environmental Change, 19(5), 1401-1416. doi:10.1007/s10113-019-01494-8
Gridded Population of the World (GPW) v4 (population density)
Global Rural-Urban Mapping Project (GRUMP) v1 (unspecified)

Harrington, L. J. (2021). Temperature emergence at decision-relevant scales. Environmental Research Letters, 16(9), 094018. doi:10.1088/1748-9326/ac19dc
Gridded Population of the World (GPW) v4 (population density) - 10.7927//H4NP22DQ

Harrington, L. J., Frame, D., King, A. D., & Otto, F. E. L. (2018). How uneven are changes to impact-relevant climate hazards in a 1.5 °C world and beyond? Geophysical Research Letters, 45(13), 6672-6680. doi:10.1029/2018GL078888
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM

Harrington, L. J., & Otto, F. E. L. (2018). Changing population dynamics and uneven temperature emergence combine to exacerbate regional exposure to heat extremes under 1.5°C and 2°C of warming. Environmental Research Letters, 13(3), 034011. doi:10.1088/1748-9326/aaaa99
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Harrington, L. J., Schleussner, C.-F., & Otto, F. E. L. (2021). Quantifying uncertainty in aggregated climate change risk assessments. Nature Communications, 12(1), 7140. doi:10.1038/s41467-021-27491-2
Gridded Population of the World (GPW) v4 (population density)

Harrison-Atlas, D., Theobald, D. M., Dickson, B. G., Landau, V., & Leinwand, I. (2017). Description of the Approach, Data, and Analytical Methods Used to Evaluate River Systems in the Western U.S. Retrieved from Truckee CA: https://disappearingwest.org/rivers/methodology.pdf
Gridded Population of the World (GPW) v4 (population count)

Hart, O. E., & Halden, R. U. (2020). Modeling wastewater temperature and attenuation of sewage-borne biomarkers globally. Water Research, 172, 115473. doi:10.1016/j.watres.2020.115473
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Hartinger, S. M., Yglesias-González, M., Blanco-Villafuerte, L., Palmeiro-Silva, Y. K., Lescano, A. G., Stewart-Ibarra, A., . . . Romanello, M. (2023). The 2022 South America report of The Lancet Countdown on health and climate change: trust the science. Now that we know, we must act. The Lancet Regional Health – Americas, 20, 100470. doi:10.1016/j.lana.2023.100470
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Hartmann, A., Gleeson, T., Wada, Y., & Wagener, T. (2017). Enhanced groundwater recharge rates and altered recharge sensitivity to climate variability through subsurface heterogeneity. Proceedings of the National Academy of Sciences, 114(11), 2842-2847. doi:10.1073/pnas.1614941114
Gridded Population of the World (GPW) v4 (population density)

Hasan, E., & Tarhule, A. (2020). GRACE: Gravity Recovery and Climate Experiment long-term trend investigation over the Nile River Basin: Spatial variability drivers. Journal of Hydrology, 586, 124870. doi:10.1016/j.jhydrol.2020.124870
Gridded Population of the World (GPW) v4.10 (basic demographic characteristics)
NASA REMOTE SENSING (GRACE)
NASA REMOTE SENSING (MODIS - MOD14C2)

Haughan, A. E., Pettorelli, N., Potts, S. G., & Senapathi, D. (2022). Determining the role of climate change in India’s past forest loss. Global Change Biology, 28(12), 3883-3901. doi:10.1111/gcb.16161
Gridded Population of the World (GPW) v4.11 (population density)

Haughan, A. E., Pettorelli, N., Potts, S. G., & Senapathi, D. (2022). The role of climate in past forest loss in an ecologically important region of South Asia. Global Change Biology, 28(12), 3883-3901. doi:10.1111/gcb.16161
Gridded Population of the World (GPW) v4.11 (population density)

Haughan, A. E., & Senapathi, D. (2022). Changes in Climate Trends and Velocities, Forest Loss and Population Density Between 2001-2018 in India's Districts. Retrieved from: https://doi.org/10.17864/1947.000364
Gridded Population of the World (GPW) v4.11 (population density)

Hausfather, Z. (2018). Analysis: 'Global' warming varies greatly depending where you live.  Retrieved from https://www.carbonbrief.org/analysis-global-warming-varies-greatly-depending-where-you-live
Gridded Population of the World (GPW) v4.10 (population count)

He, C., Ji, M., Li, T., Liu, X., Tang, D., Zhang, S., . . . Zhan, Y. (2022). Deriving full-coverage and fine-scale XCO2 across China based on OCO-2 satellite retrievals and CarbonTracker Output. Geophysical Research Letters, 49(12), e2022GL098435. doi:10.1029/2022GL098435
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (OCO)

He, C., Yin, P., Liu, Z., Huang, J., Chen, Y., Gao, X., . . . Zhou, M. (2023). Projections of excess deaths related to cold spells under climate and population change scenarios: A nationwide time series modeling study. Environment International, 178, 108034. doi:10.1016/j.envint.2023.108034
Gridded Population of the World (GPW) v4 (population count)

He, Q., & Silliman, B. R. (2019). Climate change, human impacts, and coastal ecosystems in the Anthropocene. Current Biology, 29(19), R1021-R1035. doi:10.1016/j.cub.2019.08.042
Gridded Population of the World (GPW) v4 (population density)

Hedelius, J. K., Toon, G. C., Buchholz, R. R., Iraci, L. T., Podolske, J. R., Roehl, C. M., . . . Wunch, D. (2021). Regional and urban column CO trends and anomalies as observed by MOPITT over 16 years. Journal of Geophysical Research: Atmospheres, 126(5), e2020JD033967. doi:10.1029/2020JD033967
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MOPITT CO)

Heft-Neal, S., Gould, C. F., Childs, M. L., Kiang, M. V., Nadeau, K. C., Duggan, M., . . . Burke, M. (2023). Emergency department visits respond nonlinearly to wildfire smoke. Proceedings of the National Academy of Sciences, 120(39), e2302409120. doi:10.1073/pnas.2302409120
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
NASA REMOTE SENSING (MODIS)

Heger, M. P., Zens, G. F., & Bangalore, M. (2018). Does the environment matter for poverty reduction ? the role of soil fertility and vegetation vigor in poverty reduction. Retrieved from Washington DC: http://documents.worldbank.org/curated/en/732471533299978428/Does-the-environment-matter-for-poverty-reduction-the-role-of-soil-fertility-and-vegetation-vigor-in-poverty-reduction
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates) - 10.7927/H4F47M2C
NASA REMOTE SENSING (MODIS)

Heger, M. P., Zens, G. F., & Bangalore, M. (2020). Land and poverty: the role of soil fertility and vegetation quality in poverty reduction. Environment and Development Economics, 25(4), 315-333. doi:10.1017/S1355770X20000066
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates)
NASA REMOTE SENSING (MODIS NPP)

Hein, L., Spadaro, J. V., Ostro, B., Hammer, M., Sumarga, E., Salmayenti, R., . . . Castañeda, J.-P. (2022). The health impacts of Indonesian peatland fires. Environmental Health, 21(1), 62. doi:10.1186/s12940-022-00872-w
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MISR)
NASA REMOTE SENSING (MODIS)

Helmi, A. M., & Zohny, O. (2020). Flash Flood Risk Assessment in Egypt. In A. M. Negm (Ed.), Flash Floods in Egypt (pp. 253-312). Cham: Springer International Publishing.
Gridded Population of the World (GPW) v4.11 (population count)

Helminen, V., Tiitu, M., Kosonen, L., & Ristimäki, M. (2020). Identifying the areas of walking, transit and automobile urban fabrics in Finnish intermediate cities. Transportation Research Interdisciplinary Perspectives, 8, 100257. doi:10.1016/j.trip.2020.100257
Gridded Population of the World (GPW) v4 (collection)

Henderson, J. V., Storeygard, A., & Weil, D. N. (2020). Quality-adjusted Population Density. Retrieved from Cambridge, MA: https://www.nber.org/papers/w28070
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM

Hendrawan, V. S. A., Komori, D., & Kim, W. (2023). Possible factors determining global-scale patterns of crop yield sensitivity to drought. PLoS ONE, 18(2), e0281287. doi:10.1371/journal.pone.0281287
Gridded Population of the World (GPW) v4.11 (population density)
NASA (Global Soil Texture and Derived Water-Holding Capacities - ORNL)
NASA (Regridded Harmonized World Soil Database v1.2 - ORNL)

Hermoso, V., Morán-Ordóñez, A., & Brotons, L. (2018). Assessing the role of Natura 2000 at maintaining dynamic landscapes in Europe over the last two decades: implications for conservation. Landscape Ecology, 33(8), 1447-1460. doi:10.1007/s10980-018-0683-3
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Hernández-Pacheco, R., Sutherland, C., Thompson, L. M., & Grayson, K. L. (2019). Unexpected spatial population ecology of a widespread terrestrial salamander near its southern range edge. Royal Society Open Science, 6(6), 182192. doi:10.1098/rsos.182192
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Herold, N., Alexander, L., Green, D., & Donat, M. (2017). Greater increases in temperature extremes in low versus high income countries. Environmental Research Letters, 12(3), 4pp. doi:10.1088/1748-9326/aa5c43
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Herold, N., & Santoso, A. (2018). Indian Ocean warming during peak El Niño cools surrounding land masses. Climate Dynamics, 51(5-6), 2097-2112. doi:10.1007/s00382-017-4001-6
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4HX19NJ

Herrmann, S. M., Brandt, M., Rasmussen, K., & Fensholt, R. (2020). Accelerating land cover change in West Africa over four decades as population pressure increased. Communications Earth & Environment, 1(1), 53. doi:10.1038/s43247-020-00053-y
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
NASA REMOTE SENSING (ASTER)
REMOTE SENSING (Landsat)

Herzog, A., Hector, B., Cohard, J.-M., Vouillamoz, J.-M., Lawson, F. M. A., Peugeot, C., & de Graaf, I. (2021). A parametric sensitivity analysis for prioritizing regolith knowledge needs for modeling water transfers in the West African critical zone. Vadose Zone Journal, 20(6), e20163. doi:10.1002/vzj2.20163
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Hewson, J., Crema, S. C., González-Roglich, M., Tabor, K., & Harvey, C. A. (2019). New 1 km resolution datasets of global and regional risks of tree cover loss. Land, 8(1), 14. doi:10.3390/land8010014
Gridded Population of the World (GPW) v4 (population count)
Last of the Wild v2 Global Human Influence Index (Geographic)

Hicks, C. C., Cohen, P. J., Graham, N. A. J., Nash, K. L., Allison, E. H., D’Lima, C., . . . MacNeil, M. A. (2019). Harnessing global fisheries to tackle micronutrient deficiencies. Nature, 574, 95-98. doi:10.1038/s41586-019-1592-6
Gridded Population of the World (GPW) v4 (collection)

Higgins, S. A., Overeem, I., Rogers, K. G., & Kalina, E. A. (2018). River linking in India: Downstream impacts on water discharge and suspended sediment transport to deltas. Elementa: Science of the Anthropocene, 6(1), 24pp. doi:10.1525/elementa.269
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H46T0JKB

Hilario, M. R. A., Crosbie, E., Shook, M., Reid, J. S., Cambaliza, M. O. L., Simpas, J. B. B., . . . Sorooshian, A. (2021). Measurement report: Long-range transport patterns into the tropical northwest Pacific during the CAMP2Ex aircraft campaign: chemical composition, size distributions, and the impact of convection. Atmospheric Chemistry and Physics, 21(5), 3777-3802. doi:10.5194/acp-21-3777-2021
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (IMERG)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (OMI SO2)
NASA REMOTE SENSING (TRMM)

Hinkel, J., Aerts, J. C. J. H., Brown, S., Jiménez, J. A., Lincke, D., Nicholls, R. J., . . . Addo, K. A. (2018). The ability of societies to adapt to twenty-first-century sea-level rise. Nature Climate Change, 8(7), 570-578. doi:10.1038/s41558-018-0176-z
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4HX19NJ

Hirabayashi, Y., Tanoue, M., Sasaki, O., Zhou, X., & Yamazaki, D. (2021). Global exposure to flooding from the new CMIP6 climate model projections. Scientific Reports, 11(1), 3740. doi:10.1038/s41598-021-83279-w
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates) - 10.7927/H4F47M2C

Hirvonen, K., Machado, E. A., Simons, A. M., & Taraz, V. (2022). More than a safety net: Ethiopia’s flagship public works program increases tree cover. Global Environmental Change, 75, 102549. doi:10.1016/j.gloenvcha.2022.102549
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)

Hjort, J., Karjalainen, O., Aalto, J., Westermann, S., Romanovsky, V. E., Nelson, F. E., . . . Luoto, M. (2018). Degrading permafrost puts Arctic infrastructure at risk by mid-century. Nature Communications, 9(1), 5147. doi:10.1038/s41467-018-07557-4
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Ho, H.-A. (2021). Land tenure and economic development: Evidence from Vietnam. World Development, 140, 105275. doi:10.1016/j.worlddev.2020.105275
Gridded Population of the World (GPW) v4.10 (population density)
REMOTE SENSING (DMSP-OLS)

Ho, J. C., Michalak, A. M., & Pahlevan, N. (2019). Widespread global increase in intense lake phytoplankton blooms since the 1980s. Nature, 574(7780), 667-670. doi:10.1038/s41586-019-1648-7
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ
NASA REMOTE SENSING (NACP MsTMIP -ORNL)
REMOTE SENSING (Landsat)

Hobeichi, S., Abramowitz, G., Ukkola, A. M., De Kauwe, M., Pitman, A., Evans, J. P., & Beck, H. (2022). Reconciling historical changes in the hydrological cycle over land. npj Climate and Atmospheric Science, 5(1), 17. doi:10.1038/s41612-022-00240-y
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (GIMMS NDVI)
NASA REMOTE SENSING (GRACE)

Hodler, R., Valsecchi, M., & Vesperoni, A. (2021). Ethnic geography: Measurement and evidence. Journal of Public Economics, 200, 104446. doi:10.1016/j.jpubeco.2021.104446
Gridded Population of the World (GPW) v4 (population density)

Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., . . . Zhang, Q. (2018). Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS). Geoscientific Model Development, 11(1), 369-408. doi:10.5194/gmd-11-369-2018
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)

Hoffman-Hall, A., Loboda, T. V., Hall, J. V., Carroll, M. L., & Chen, D. (2019). Mapping remote rural settlements at 30 m spatial resolution using geospatial data-fusion. Remote Sensing of Environment, 233, 111386. doi:10.1016/j.rse.2019.111386
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)

Hoffmann, R., Šedová, B., & Vinke, K. (2021). Improving the evidence base: A methodological review of the quantitative climate migration literature. Global Environmental Change, 71, 102367. doi:10.1016/j.gloenvcha.2021.102367
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
Population Dynamics (Global Estimated Net Migration Grids By Decade, v1)

Hoffrichter, A., Barrios, H., Massmann, J., Venkataramanachar, B., & Schnettler, A. (2018). Impact of considering 110 kV grid structures on the congestion management in the German transmission grid. Journal of Physics: Conference Series, 977(1), 012004. doi:10.1088/1742-6596/977/1/012004
Gridded Population of the World (GPW) v4 (population count)

Höltinger, S., Mikovits, C., Schmidt, J., Baumgartner, J., Arheimer, B., Lindström, G., & Wetterlund, E. (2019). The impact of climatic extreme events on the feasibility of fully renewable power systems: a case study for Sweden. Energy, 178, 695-713. doi:10.1016/j.energy.2019.04.128
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42

Hong, H., Wu, S., Wang, Q., Dai, M., Qian, L., Zhu, H., . . . Yan, C. (2021). Fluorescent dissolved organic matter facilitates the phytoavailability of copper in the coastal wetlands influenced by artificial topography. Science of The Total Environment, 790, 147855. doi:10.1016/j.scitotenv.2021.147855
Gridded Population of the World (GPW) v4.11 (population density)

Hong, H., Wu, S., Wang, Q., Qian, L., Lu, H., Liu, J., . . . Yan, C. (2021). Trace metal pollution risk assessment in urban mangrove patches: Potential linkage with the spectral characteristics of chromophoric dissolved organic matter. Environmental Pollution, 272, 115996. doi:10.1016/j.envpol.2020.115996
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics) - 10.7927/H46M34XX

Hooijer, A., & Vernimmen, R. (2021). Global LiDAR land elevation data reveal greatest sea-level rise vulnerability in the tropics. Nature Communications, 12(1), 3592. doi:10.1038/s41467-021-23810-9
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (ICESat-2)

Hordijk, I., Maynard, D. S., Hart, S. P., Lidong, M., ter Steege, H., Liang, J., . . . Crowther, T. W. (2023). Evenness mediates the global relationship between forest productivity and richness. Journal of Ecology, 111(6), 1308-1326. doi:10.1111/1365-2745.14098
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
NASA REMOTE SENSING (MODIS)

Horn, P. E., Pereira, M. J. R., Trigo, T. C., Eizirik, E., & Tirelli, F. P. (2020). Margay (Leopardus wiedii) in the southernmost Atlantic Forest: Density and activity patterns under different levels of anthropogenic disturbance. PLoS ONE, 15(5), e0232013. doi:10.1371/journal.pone.0232013
Gridded Population of the World (GPW) v4.11 (population density)

Hou, X., & Feng, L. (2023). High-resolution satellite observations reveal extensive algal blooms in both small and large lakes in China. Sustainable Horizons, 6, 100054. doi:10.1016/j.horiz.2023.100054
Gridded Population of the World (GPW) v4.11 (admin unit center points)
REMOTE SENSING (Sentinel-2 Multispectral Imager (MSI))

Hou, X., Feng, L., Dai, Y., Hu, C., Gibson, L., Tang, J., . . . Zheng, C. (2022). Global mapping reveals increase in lacustrine algal blooms over the past decade. Nature Geoscience, 15(2), 130-134. doi:10.1038/s41561-021-00887-x
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (Landsat)

Howes, R. E., Hawa, K., Andriamamonjy, V. F., Franchard, T., Miarimbola, R., Mioramalala, S. A., . . . Taylor, C. A. (2019). A stakeholder workshop about modelled maps of key malaria indicator survey indicators in Madagascar. Malaria Journal, 18(1), 90. doi:10.1186/s12936-019-2729-7
Gridded Population of the World (GPW) v4 (collection)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)

Hsu, A., Wang, X., Tan, J., Toh, W., & Goyal, N. (2022). Predicting European cities’ climate mitigation performance using machine learning. Nature Communications, 13(1), 7487. doi:10.1038/s41467-022-35108-5
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates) - 10.7927/H4F47M2C
NASA REMOTE SENSING (MERRA-2)

Hsu, J. (2020). Here’s How Computer Models Simulate the Future Spread of New Coronavirus. Scientific American. Retrieved from https://www.scientificamerican.com//article/heres-how-computer-models-simulate-the-future-spread-of-new-coronavirus/
Gridded Population of the World (GPW) v4 (collection)

Hu, L., Montzka, S. A., Moore, F., Hintsa, E., Dutton, G., Siso, M. C., . . . Wofsy, S. (2022). Continental-scale contributions to the global CFC-11 emission increase between 2012 and 2017. Atmospheric Chemistry and Physics, 22, 2891-2907. doi:10.5194/acp-22-2891-2022
Gridded Population of the World (GPW) v4 (unspecified)

Hu, M., Chen, Y., Yuan, D., Yu, R., Lu, X., Fung, J. C. H., . . . Lau, A. K. H. (2022). Estimation and spatiotemporal analysis of NO2 pollution in East Asia during 2001–2016. Journal of Geophysical Research: Atmospheres, 127(2), e2021JD035129. doi:10.1029/2021JD035129
Gridded Population of the World (GPW) v4.11 (admin unit center points)
REMOTE SENSING (GOME)

Hu, T., & Huang, X. (2019). A novel locally adaptive method for modeling the spatiotemporal dynamics of global electric power consumption based on DMSP-OLS nighttime stable light data. Applied Energy, 240, 778-792. doi:10.1016/j.apenergy.2019.02.062
Gridded Population of the World (GPW) v4 (population density)
REMOTE SENSING (DMSP-OLS)

Hu, X., Hou, Y., Li, D., Hua, T., Marchi, M., Paola Forero Urrego, J., . . . Cherubini, F. (2023). Changes in multiple ecosystem services and their influencing factors in Nordic countries. Ecological Indicators, 146, 109847. doi:10.1016/j.ecolind.2022.109847
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS - MOD17A3H)

Hu, Y., Fan, H., Chen, Y., Chang, J., Zhan, X., Wu, H., . . . Wei, F. (2021). Spatial patterns and conservation of genetic and phylogenetic diversity of wildlife in China. Science Advances, 7(4), eabd5725. doi:10.1126/sciadv.abd5725
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (SRTM)

Hua, L., Li, S., Gao, D., & Li, W. (2022). Uncertainties of global historical land use datasets in pasture reconstruction for the Tibetan Plateau. Remote Sensing, 14(15), 3777. doi:10.3390/rs14153777
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
NASA REMOTE SENSING (MODIS)

Hua, T., Zhao, W., Cherubini, F., Hu, X., & Pereira, P. (2022). Continuous growth of human footprint risks compromising the benefits of protected areas on the Qinghai-Tibet Plateau. Global Ecology and Conservation, 34, e02053. doi:10.1016/j.gecco.2022.e02053
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS)

Hua, T., Zhao, W., Cherubini, F., Hu, X., & Pereira, P. (2022). Effectiveness of protected areas edges on vegetation greenness, cover and productivity on the Tibetan Plateau, China. Landscape and Urban Planning, 224, 104421. doi:10.1016/j.landurbplan.2022.104421
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)

Hua, X., Greenhill, S. J., Cardillo, M., Schneemann, H., & Bromham, L. (2019). The ecological drivers of variation in global language diversity. Nature Communications, 10(1), 2047. doi:10.1038/s41467-019-09842-2
Gridded Population of the World (GPW) v4.10 (population density UN WPP-adjusted)
Human Appropriation of Net Primary Productivity (HANPP) (Global Patterns in Net Primary Productivity, v1)

Hua, Z., & Chavas, D. R. (2019). The empirical dependence of tornadogenesis on elevation roughness: historical record analysis using Bayes’ Law in Arkansas. Journal of Applied Meteorology and Climatology, 58(2), 401-411. doi:10.1175/jamc-d-18-0224.1
Gridded Population of the World (GPW) v4 (population density)
Population Dynamics (Global Population Density Grid Time Series Estimates, v1)

Huang, J., Huang, J., Liu, X., Li, C., Ding, L., & Yu, H. (2018). The global oxygen budget and its future projection. Science Bulletin, 63(18), 1180-1186. doi:10.1016/j.scib.2018.07.023
Gridded Population of the World (GPW) v4 (population density)

Huang, J., Liu, Y., Liu, X., Ye, X., Li, X., Xiao, W., . . . Zuo, Y. (2021). Optimal Design of LEO Constellation for Communication and Navigation Fusion Based on Genetic Algorithm. Paper presented at the China Satellite Navigation Conference (CSNC 2021), Singapore.
Gridded Population of the World (GPW) v4.11 (population count)

Huang, L., Mao, F., Zang, L., Zhang, Y., Zhang, Y., & Zhang, T. (2021). Estimation of hourly PM1 concentration in China and its application in population exposure analysis. Environmental Pollution, 273, 115720. doi:10.1016/j.envpol.2020.115720
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (MODIS)

Huang, M., Crawford, J. H., Carmichael, G. R., Bowman, K. W., Kumar, S. V., & Sweeney, C. (2022). Satellite soil moisture data assimilation impacts on modeling weather variables and ozone in the southeastern US – Part 2: Sensitivity to dry-deposition parameterizations. Atmospheric Chemistry and Physics, 22, 7461-7487. doi:10.5194/acp-22-7461-2022
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (SMAP)

Huang, S.-L., Wang, X., Wu, H., Peng, C., & Jefferson, T. A. (2022). Habitat protection planning for Indo-Pacific humpback dolphins (Sousa chinensis) in deteriorating environments: Knowledge gaps and recommendations for action. Aquatic Conservation: Marine and Freshwater Ecosystems, 32(1), 171-185. doi:10.1002/aqc.3740
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
REMOTE SENSING (Landsat)

Huang, W., Duan, W., & Chen, Y. (2021). Rapidly declining surface and terrestrial water resources in Central Asia driven by socio-economic and climatic changes. Science of The Total Environment, 784, 147193. doi:10.1016/j.scitotenv.2021.147193
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4F47M65
NASA REMOTE SENSING (GRACE)

Huang, W., Duan, W., & Chen, Y. (2022). Unravelling lake water storage change in Central Asia: Rapid decrease in tail-end lakes and increasing risks to water supply. Journal of Hydrology, 614, 128546. doi:10.1016/j.jhydrol.2022.128546
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (Landsat)

Huang, W.-R., Liu, P.-Y., Lee, S.-Y., & Wu, C.-H. (2022). Changes in early summer precipitation characteristics over South China and Taiwan: CESM2-LE and CMIP6 multi-model simulations and projections. Journal of Geophysical Research: Atmospheres, 127(17), e2022JD037181. doi:10.1029/2022JD037181
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Huang, X., Ding, K., Liu, J., Wang, Z., Tang, R., Xue, L., . . . Ding, A. (2023). Smoke-weather interaction affects extreme wildfires in diverse coastal regions. Science, 379(6631), 457-461. doi:10.1126/science.add9843
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (TRMM)

Huang, X., Hayashi, K., Fujii, M., Villa, F., Yamazaki, Y., & Okazawa, H. (2023). Identification of potential locations for small hydropower plant based on resources time footprint: A case study in Dan River Basin, China. Renewable Energy, 205, 293-304. doi:10.1016/j.renene.2023.01.079
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Huang, X., Wang, C., & Li, Z. (2019). High-resolution population grid in the CONUs using Microsoft building footprints: A feasibility study. Paper presented at the GeoHumanities '19, Chicago. https://www.researchgate.net/profile/Xiao_Huang35/publication/336218849_High-Resolution_Population_Grid_in_the_CONUS_Using_Microsoft_Building_Footprints_A_Feasibility_Study/links/5d966015458515c1d391b4df/High-Resolution-Population-Grid-in-the-CONUS-Using-Microsoft-Building-Footprints-A-Feasibility-Study.pdf
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)

Huang, X., Wang, C., Li, Z., & Ning, H. (2021). A 100 m population grid in the CONUS by disaggregating census data with open-source microsoft building footprints. Big Earth Data, 5(1), 112-133. doi:10.1080/20964471.2020.1776200
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)
POPGRID

Huang, Y., Partha, D. B., Harper, K., & Heyes, C. (2021). Impacts of global solid biofuel stove emissions on ambient air quality and human health. GeoHealth, 5(3), e2020GH000362. doi:10.1029/2020GH000362
Gridded Population of the World (GPW) v4.11 (population density)

Huang, Y., Unger, N., Harper, K., & Heyes, C. (2020). Global climate and human health effects of the gasoline and diesel vehicle fleets. GeoHealth, 4(3), e2019GH000240. doi:10.1029/2019gh000240
Gridded Population of the World (GPW) v4 (population density)

Huang, Y., Yan, Q., & Zhang, C. (2018). Spatial–temporal distribution characteristics of PM2.5 in China in 2016. Journal of Geovisualization and Spatial Analysis, 2(2), 12. doi:10.1007/s41651-018-0019-5
Gridded Population of the World (GPW) v4 (collection)

Huang, Z., Yuan, X., & Liu, X. (2021). The key drivers for the changes in global water scarcity: Water withdrawal versus water availability. Journal of Hydrology, 601, 126658. doi:10.1016/j.jhydrol.2021.126658
Gridded Population of the World (GPW) v4.11 (population density)

Hüls, A., Van Cor, S., Christensen, G., Li, Z., Liu, Y., Shi, L., . . . Switchenko, J. M. (2023). Environmental, social and behavioral risk factors in association with spatial clustering of childhood cancer incidence. Spatial and Spatio-temporal Epidemiology, 45, 100582. doi:10.1016/j.sste.2023.100582
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Humphreys, J. M., Murrow, J. L., Sullivan, J. D., & Prosser, D. J. (2019). Seasonal occurrence and abundance of dabbling ducks across the continental United States: Joint spatio-temporal modelling for the Genus Anas. Diversity and Distributions, 25(9), 1497-1508. doi:10.1111/ddi.12960
Gridded Population of the World (GPW) v4 (population density)

Humphreys, J. M., Ramey, A. M., Douglas, D. C., Mullinax, J. M., Soos, C., Link, P., . . . Prosser, D. J. (2020). Waterfowl occurrence and residence time as indicators of H5 and H7 avian influenza in North American Poultry. Scientific Reports, 10(1), 2592. doi:10.1038/s41598-020-59077-1
Gridded Population of the World (GPW) v4 (population density)

Hunguana, H., Salam Fall, A., Titamben, G., Goases, M., & Gwarinda, S. (2020). Women's Financial Inclusion in the Democratic Republic of Congo: Final Report. Retrieved from http://hdl.handle.net/10625/59153
Gridded Population of the World (GPW) v4 (population density)

Huo, D., Huang, X., Dou, X., Ciais, P., Li, Y., Deng, Z., . . . Liu, Z. (2022). Carbon Monitor Cities near-real-time daily estimates of CO2 emissions from 1500 cities worldwide. Scientific Data, 9(1), 533. doi:10.1038/s41597-022-01657-z
Gridded Population of the World (GPW) v4.11 (population count)
REMOTE SENSING (TROPOMI)

Huo, M., Yamashita, K., Chen, F., & Sato, K. (2022). Spatial-temporal variation in health impact attributable to PM2.5 and ozone pollution in the Beijing Metropolitan Region of China. Atmosphere, 13(11), 1813. doi:10.3390/atmos13111813
Gridded Population of the World (GPW) v4.11 (admin unit center points)

Hyman, G., Barona, E., Biradar, C. M., Guevara, E., Dixon, J., Beebe, S., . . . Cardona, J. (2016). Priority regions for research on dryland cereals and legumes. F1000Research, 5(885), 20pp. doi:10.12688/f1000research.8657.2
Gridded Population of the World (GPW) v3 (population count)
Gridded Population of the World (GPW) v4 Preliminary release 2 (population count)
Poverty Mapping (Global Subnational Prevalence of Child Malnutrition, v1)

Hzami, A., Heggy, E., Amrouni, O., Mahé, G., Maanan, M., & Abdeljaouad, S. (2021). Alarming coastal vulnerability of the deltaic and sandy beaches of North Africa. Scientific Reports, 11(1), 2320. doi:10.1038/s41598-020-77926-x
Gridded Population of the World (GPW) v4.11 (admin unit center points)

I, N., Srivastava, S., Yarragunta, Y., Kumar, R., & Mitra, D. (2020). Distribution of surface carbon monoxide over the Indian subcontinent: Investigation of source contributions using WRF-Chem. Atmospheric Environment, 243, 117838. doi:10.1016/j.atmosenv.2020.117838
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (MOPITT CO)

Iacoella, F., Martorano, B., Metzger, L., & Sanfilippo, M. (2021). Chinese official finance and political participation in Africa. European Economic Review, 136, 103741. doi:10.1016/j.euroecorev.2021.103741
Gridded Population of the World (GPW) v4.11 (population count)

Iannucci, P. A., & Humphreys, T. E. (2020). Economical Fused LEO GNSS. Paper presented at the 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS).
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Ibáñez, C., Alcaraz, C., Caiola, N., Prado, P., Trobajo, R., Benito, X., . . . Syvitski, J. P. M. (2019). Basin-scale land use impacts on world deltas: Human vs natural forcings. Global and Planetary Change, 173, 24-32. doi:10.1016/j.gloplacha.2018.12.003
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Ichikawa, M. (2019). Immigration and adaptation of the Iban from rural to urban outskirts in Sarawak, Malaysia. Tropics, 28(2), 39-48. doi:10.3759/tropics.MS18-14
Gridded Population of the World (GPW) v4 (unspecified)

IIiyama, M., Kanamori, N., Kobayashi, S., & Funaki, Y. (2023). Driving Sustainable Food Systems Transformation in the Asia-Monsoon Region with Science, Technology, and Innovation: Background and Key Issues for "Green Asia" Project. Retrieved from https://www.jircas.go.jp/en/publication/gars-e/1
Gridded Population of the World (GPW) v4.11 (population density)

Im, U., Bauer, S. E., Frohn, L. M., Geels, C., Tsigaridis, K., & Brandt, J. (2023). Present-day and future PM2.5 and O3-related global and regional premature mortality in the EVAv6.0 health impact assessment model. Environmental Research, 216(Part 4), 114702. doi:10.1016/j.envres.2022.114702
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)
Gridded Population of the World (GPW) v4.11 (population density)

Imran, M., Sumra, K., Abbas, N., & Majeed, I. (2019). Spatial distribution and opportunity mapping: Applicability of evidence-based policy implications in Punjab using remote sensing and global products. Sustainable Cities and Society, 50, 101652. doi:10.1016/j.scs.2019.101652
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates) - 10.7927/H4F47M2C
REMOTE SENSING (VIIRS NTL)

Injaian, A. S., Francis, C. D., Ouyang, J. Q., Dominoni, D. M., Donald, J. W., Fuxjager, M. J., . . . Vitousek, M. N. (2020). Baseline and stress-induced corticosterone levels across birds and reptiles do not reflect urbanization levels. Conservation Physiology, 8(1), coz110. doi:10.1093/conphys/coz110
Gridded Population of the World (GPW) v4 (population density)

Irvine, P. J., Emanuel, K., He, J., Horowitz, L. W., Vecchi, G., & Keith, D. (2019). Halving warming with idealized solar geoengineering moderates key climate hazards. Nature Climate Change, 9, 295-299. doi:10.1038/s41558-019-0398-8
Gridded Population of the World (GPW) v4 (population count)

Irvine, P. J., & Keith, D. W. (2020). Halving warming with stratospheric aerosol geoengineering moderates policy-relevant climate hazards. Environmental Research Letters, 15(4), 044011. doi:10.1088/1748-9326/ab76de
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Islam, A., Sayeed, M. A., Rahman, M. K., Ferdous, J., Shano, S., Choudhury, S. D., & Hassan, M. M. (2021). Spatiotemporal patterns and trends of community transmission of the pandemic COVID-19 in South Asia: Bangladesh as a case study. Biosafety and Health, 3(1), 39-49. doi:10.1016/j.bsheal.2020.09.006
Gridded Population of the World (GPW) v4 (population density)

Islam, Z., Ranganathan, M., Bagyaraj, M., Singh, S. K., & Gautam, S. K. (2022). Multi-decadal groundwater variability analysis using geostatistical method for groundwater sustainability. Environment, Development and Sustainability, 24, 3146-3164. doi:10.1007/s10668-021-01563-1
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Islam, Z., Tewelemedhin, A. A., & Berhe, A. T. (2020). Spatial statistical analysis of the relation in between population density and human modification of terrestrial lands at Tabia level in the Tigray region of Ethiopia. Landscape and Environment, 14(1), 1-9. doi:10.21120/LE/14/1/1
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Jackson, B. (2018). The GREAT-ER Model as a Tool for Chemical Risk Assessment and Management for Chinese River Catchments. (Ph.D.). Lancaster University, Lancaster UK. Retrieved from http://eprints.lancs.ac.uk/126264/ (126264)
Gridded Population of the World (GPW) v4 (population count)
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (SRTM)

Jain, M. (2022). Increasing Atmospheric Extreme Events and Role of Disaster Risk Management: Dimensions and Approaches. In P. Saxena, A. Shukla, & A. K. Gupta (Eds.), Extremes in Atmospheric Processes and Phenomenon: Assessment, Impacts and Mitigation (pp. 303-328). Singapore: Springer Nature Singapore.
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
Satellite-Derived Environmental Indicators (Global Fire Emissions Indicators, Grids, v1) - 10.7927/H400002V
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (TRMM)

Jardeleza, J. M., Gotangco, C. K., & Guzman, M. A. L. (2019). Simulating national-scale deforestation in the Philippines using land cover change models. Philippine Journal of Science, 148(4), 597-608. Retrieved from http://philjournalsci.dost.gov.ph/images/pdf/pjs_pdf/vol148no4/simulating_national_scale_deforestation_in_the_philippines_.pdf
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
NASA REMOTE SENSING (MODIS Vegetation Continuous Fields)
REMOTE SENSING (Landsat)

Järvi, L., Grimmond, C. S. B., McFadden, J. P., Christen, A., Strachan, I. B., Taka, M., . . . Heimann, M. (2017). Warming effects on the urban hydrology in cold climate regions. Scientific Reports, 7(1), 8pp. doi:10.1038/s41598-017-05733-y
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (MODIS - MOD10CM)

Jat, R., & Gurjar, B. R. (2021). Contribution of different source sectors and source regions of Indo-Gangetic Plain in India to PM2.5 pollution and its short-term health impacts during peak polluted winter. Atmospheric Pollution Research, 12(4), 89-100. doi:10.1016/j.apr.2021.02.016
Gridded Population of the World (GPW) v4.11 (unspecified)

Jato-Espino, D., Lobo, A., & Ascorbe-Salcedo, A. (2019). Urban flood risk mapping using an optimised additive weighting methodology based on open data. Journal of Flood Risk Management, 12(S1), e12533. doi:10.1111/jfr3.12533
Gridded Population of the World (GPW) v4 (collection)

Jenkins, D. G., Haberl, H., Erb, K.-H., & Nevai, A. L. (2020). Global human “predation” on plant growth and biomass. Global Ecology and Biogeography, 29(6), 1052-1064. doi:10.1111/geb.13087
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Jenkins, S. F., Magill, C. R., & Blong, R. J. (2018). Evaluating relative tephra fall hazard and risk in the Asia-Pacific region. Geosphere, 14(2), 492-502. doi:10.1130/GES01549.1
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Jenny, J.-P., Anneville, O., Arnaud, F., Baulaz, Y., Bouffard, D., Domaizon, I., . . . Weyhenmeyer, G. A. (2020). Scientists’ warning to humanity: Rapid degradation of the world’s large lakes. Journal of Great Lakes Research, 46(4), 686-702. doi:10.1016/j.jglr.2020.05.006
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Jha, A., Banerjee, P., & Jha, A. (2022). Ecological and anthropogenic factors influencing presence of tiger: A GIS-based study in Sikkim. In H. K. D. Sarma, V. E. Balas, B. Bhuyan, & N. Dutta (Eds.), Contemporary Issues in Communication, Cloud and Big Data Analytics (pp. 399-414). Singapore: Springer Singapore.
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics) - 10.7927/H46M34XX

Jha, R. R. S., Thakuri, J. J., Rahmani, A. R., Dhakal, M., Khongsai, N., Pradhan, N. M. B., . . . Donald, P. F. (2018). Distribution, movements, and survival of the critically endangered Bengal Florican Houbaropsis bengalensis in India and Nepal. Journal of Ornithology, 159(3), 851-866. doi:10.1007/s10336-018-1552-1
Gridded Population of the World (GPW) v4 (population density)

Jha, S., Goyal, M. K., Gupta, B. B., Hsu, C.-H., Gilleland, E., & Das, J. (2022). A methodological framework for extreme climate risk assessment integrating satellite and location based data sets in intelligent systems. International Journal of Intelligent Systems, 37(12), 10268-10288. doi:10.1002/int.22475
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)
NASA REMOTE SENSING (VIIRS DNB)

Ji, P., Yuan, X., Liang, X.-Z., Jiao, Y., Zhou, Y., & Liu, Z. (2021). High-resolution land surface modeling of the effect of long-term urbanization on hydrothermal changes over Beijing metropolitan area. Journal of Geophysical Research: Atmospheres, 126(18), e2021JD034787. doi:10.1029/2021JD034787
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)

Jia, B., Gao, M., Zhang, X., Xiao, X., Zhang, S., & Lam Yung, K. K. (2021). Rapid increase in mortality attributable to PM2.5 exposure in India over 1998–2015. Chemosphere, 269, 128715. doi:10.1016/j.chemosphere.2020.128715
Gridded Population of the World (GPW) v3 (population count)
Gridded Population of the World (GPW) v4.11 (population count)

Jia, G., Alvioli, M., Gariano, S. L., Marchesini, I., Guzzetti, F., & Tang, Q. (2021). A global landslide non-susceptibility map. Geomorphology, 389, 107804. doi:10.1016/j.geomorph.2021.107804
Gridded Population of the World (GPW) v4.11 (population count)
REMOTE SENSING (Landsat)

Jia, Y., Jiang, Y., Liu, Y., Sui, X., Feng, X., Zhu, R., . . . Chen, Y. (2022). Unravelling fish community assembly in shallow lakes: Insights from functional and phylogenetic diversity. Reviews in Fish Biology and Fisheries, 32, 623-644. doi:10.1007/s11160-021-09688-2
Gridded Population of the World (GPW) v4.11 (population count)
Population Dynamics (Global Population Count Grid Time Series Estimates, v1)

Jiang, L., Zhang, J., Liu, Q., Meng, X., Shi, L., Zhang, D., & Xing, M. (2023). Spatiotemporal variations of the global compound heat wave and the drivers of its spatial heterogeneity. Journal of Cleaner Production, 408, 137201. doi:10.1016/j.jclepro.2023.137201
Spatial Economic Data (Global Gridded Geographically Based Economic Data (G-Econ), v4) - 10.7927/H42V2D1C
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MERRA-2)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (Landsat)

Jiang, Q., & Christakos, G. (2018). Space-time mapping of ground-level PM2.5 and NO2 concentrations in heavily polluted northern China during winter using the Bayesian maximum entropy technique with satellite data. Air Quality, Atmosphere & Health, 11(1), 23-33. doi:10.1007/s11869-017-0514-8
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (OMI NO2)

Jiang, X., Eum, Y., & Yoo, E.-H. (2023). The impact of fire-specific PM2.5 calibration on health effect analyses. Science of The Total Environment, 857, 159548. doi:10.1016/j.scitotenv.2022.159548
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)

Jian–Ting, Z., Bu–Da, S., Kumar Mondal, S., Yan–Jun, W., Hui, T., & Tong, J. (2021). Population exposure to precipitation extremes in the Indus River Basin at 1.5°C, 2 °C and 3°C warming levels. Advances in Climate Change Research, 12(2), 199-209. doi:10.1016/j.accre.2021.03.005
Gridded Population of the World (GPW) v4 (unspecified)

Jianying, X., Jixing, C., & Yanxu, L. (2020). Partitioned responses of ecosystem services and their tradeoffs to human activities in the Belt and Road region. Journal of Cleaner Production, 276, 123205. doi:10.1016/j.jclepro.2020.123205
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (VIIRS NTL)

Jiao, W., Huang, W., & Fan, H. (2022). Evaluating spatial accessibility to healthcare services from the lens of emergency hospital visits based on floating car data. International Journal of Digital Earth, 15(1), 108-133. doi:10.1080/17538947.2021.2014578
Gridded Population of the World (GPW) v4 (population density) - 10.7927//H4NP22DQ

Jimenez, R. B., Bozigar, M., Janulewicz, P., Lane, K. J., Hutyra, L. R., & Fabian, M. P. (2023). School greenness and student-level academic performance: Evidence from the Global South. GeoHealth, 7(8), e2023GH000830. doi:10.1029/2023GH000830
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
REMOTE SENSING (Landsat)

Jiménez-Parra, B., Alonso-Martínez, D., & Godos-Díez, J.-L. (2018). The influence of corporate social responsibility on air pollution: Analysis of environmental regulation and eco-innovation effects. Corporate Social Responsibility and Environmental Management, 25(6), 1363-1375. doi:10.1002/csr.1645
Gridded Population of the World (GPW) v4 (unspecified)

Jiménez-Ruano, A., Mimbrero, M. R., Urdíroz, F. A., Sjöström, J., Marrs, C., Ribeiro, L. M., . . . Vega-García, C. (2023, 20-23 June 2023). Assessing human-caused wildfire ignition likelihood across Europe. Paper presented at the 8th International Conference on Smart and Sustainable Technologies (SpliTech).
Gridded Population of the World (GPW) v4.11 (population density)

Jin, K., Wang, F., Chen, D., Liu, H., Ding, W., & Shi, S. (2019). A new global gridded anthropogenic heat flux dataset with high spatial resolution and long-term time series. Scientific Data, 6(1), 139. doi:10.1038/s41597-019-0143-1
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D
Population Dynamics (Global Population Count Grid Time Series Estimates, v1) - 10.7927/H4CC0XNV
Global High Resolution Urban Data from Landsat (GMIS)
REMOTE SENSING (DMSP-OLS)

Jin, K., Wang, F., & Wang, S. (2020). Assessing the spatiotemporal variation in anthropogenic heat and its impact on the surface thermal environment over global land areas. Sustainable Cities and Society, 63, 102488. doi:10.1016/j.scs.2020.102488
Gridded Population of the World (GPW) v4 (population density)
Global High Resolution Urban Data from Landsat (GMIS) - 10.7927/H4P55KKF
NASA REMOTE SENSING (MODIS)

Jin, M., Sun, R., Yang, X., Yan, M., & Chen, L. (2022). Remote sensing-based morphological analysis of core city growth across the globe. Cities, 131, 103982. doi:10.1016/j.cities.2022.103982
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS NTL)

Jin, N., Li, J., Jin, M., & Zhang, X. (2020). Spatiotemporal variation and determinants of population’s PM2.5 exposure risk in China, 1998–2017: a case study of the Beijing-Tianjin-Hebei region. Environmental Science and Pollution Research, 27, 31767-31777. doi:10.1007/s11356-020-09484-8
Gridded Population of the World (GPW) v4.10 (population count)

Jin, T., Di, Q., Réquia, W. J., Danesh Yazdi, M., Castro, E., Ma, T., . . . Schwartz, J. (2022). Associations between long-term air pollution exposure and the incidence of cardiovascular diseases among American older adults. Environment International, 170, 107594. doi:10.1016/j.envint.2022.107594
Air Quality Data for Health-Related Applications (Daily and Annual NO2 Concentrations for the Contiguous United States, 1-km Grids, v1) - 10.7927/f8eh-5864
Air Quality Data for Health-Related Applications (Daily 8-Hour Maximum and Annual O3 Concentrations for the Contiguous United States, 1-km Grids, v1) - 10.7927/a4mb-4t86
Air Quality Data for Health-Related Applications (Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, v1) - 10.7927/0rvr-4538
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Jin, W., Li, H., Wang, J., Zhao, L., Li, X., Fan, W., & Chen, J. (2023). Continuous remote sensing ecological index (CRSEI): A novel approach for multitemporal monitoring of eco-environmental changes on large scale. Ecological Indicators, 154, 110739. doi:10.1016/j.ecolind.2023.110739
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
NASA REMOTE SENSING (MODIS)

Jin, X., Fiore, A., M., Civerolo, K., Bi, J., Liu, Y., van Donkelaar, A., . . . Kinney, P., L. (2019). Comparison of multiple PM2.5 exposure products for estimating health benefits of emission controls over New York State, USA. Environmental Research Letters, 14(8), 084023. doi:10.1088/1748-9326/ab2dcb
Gridded Population of the World (GPW) v4 (unspecified)

Jin, Z., Ma, Y., Chu, L., Liu, Y., Dubrow, R., & Chen, K. (2022). Predicting spatiotemporally-resolved mean air temperature over Sweden from satellite data using an ensemble model. Environmental Research, 204, 111960. doi:10.1016/j.envres.2021.111960
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (ASTER GDEM)
NASA REMOTE SENSING (MODIS)

Johnston, L. M., Wang, X., Erni, S., Taylor, S. W., McFayden, C. B., Oliver, J. A., . . . Flannigan, M. D. (2020). Wildland fire risk research in Canada. Environmental Reviews, 28(2), 164-186. doi:10.1139/er-2019-0046
Gridded Population of the World (GPW) v4 (population density)

Joly, K., Gurarie, E., Sorum, M. S., Kaczensky, P., Cameron, M. D., Jakes, A. F., . . . Hebblewhite, M. (2019). Longest terrestrial migrations and movements around the world. Scientific Reports, 9(1), 15333. doi:10.1038/s41598-019-51884-5
Gridded Population of the World (GPW) v4 (population density)

Jones, B. (2020). Modeling Climate Change-Induced Migration in Central America & Mexico Methodological Report. Retrieved from New York: https://assets-c3.propublica.org/Climate-Migration-Modeling-Methodology.pdf
Digital Elevation Data Collection (DEDC) (Altimeter Corrected Elevations (ACE2), v2)
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
Gridded Population of the World (GPW) v4.10 (basic demographic characteristics) - 10.7927/H45H7D7F
Satellite-Derived Environmental Indicators (Trends in Global Freshwater Availability from the Gravity Recovery and Climate Experiment (GRACE), v1)

Jones, B., & O’Neill, B. C. (2016). Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways. Environmental Research Letters, 11(8), 10 pp. doi:10.1088/1748-9326/11/8/084003
Gridded Population of the World (GPW) v3 (population count)
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)
Global Rural-Urban Mapping Project (GRUMP) v1 (urban extent)

Jones, G. D., Droz, B., Greve, P., Gottschalk, P., Poffet, D., McGrath, S. P., . . . Winkel, L. H. E. (2017). Selenium deficiency risk predicted to increase under future climate change. Proceedings of the National Academy of Sciences, 114(11), 2848-2853. doi:10.1073/pnas.1611576114
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)

Jones, S. G., Dobbins, J., Byman, D., Chivvis, C. S., Connable, B., Martini, J., . . . Chandler, N. (2017). Rolling Back the Islamic State. Retrieved from Santa Monica: https://doi.org/10.7249/RR1912
Gridded Population of the World (GPW) v4 (collection)

Jordan, L. (2017). Applying Thiessen polygon catchment areas and gridded population weights to estimate conflict-driven population changes in South Sudan. ISPRS Annals of the Photogrammetry, Remote Sensing, and Spatial Information Sciences, IV-4/W2, 23-30. doi:10.5194/isprs-annals-IV-4-W2-23-2017
Gridded Population of the World (GPW) v4 (collection)

Jørgensen, P. S., Folke, C., & Carroll, S. P. (2019). Evolution in the Anthropocene: Informing governance and policy. Annual Review of Ecology, Evolution, and Systematics, 50(1), 527-546. doi:10.1146/annurev-ecolsys-110218-024621
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4VH5KS4

Jorquera, A., Castillo, C., Murillo, V., Araya, J., Pinochet, J., Narváez, D., . . . Urbina, M. A. (2022). Physical and anthropogenic drivers shaping the spatial distribution of microplastics in the marine sediments of Chilean fjords. Science of The Total Environment, 814, 152506. doi:10.1016/j.scitotenv.2021.152506
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Joseph, I.-L. (2022). The effect of natural disaster on economic growth: Evidence from a major earthquake in Haiti. World Development, 159, 106053. doi:10.1016/j.worlddev.2022.106053
Gridded Population of the World (GPW) v4.11 (land and water area)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS NTL)

Joshi, D., Saini, S., & Joshi, V. (2023). Exploring Aspects of Sustainable Rail Infrastructure Development Process and Alternative TOD Financing. Paper presented at the IW-HSR 2022: Socioeconomic Impacts of High-Speed Rail Systems.
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (Landsat)
REMOTE SENSING (VIIRS NTL)

Jusup, M., Holme, P., Kanazawa, K., Takayasu, M., Romić, I., Wang, Z., . . . Perc, M. (2022). Social physics. Physics Reports, 948, 1-148. doi:10.1016/j.physrep.2021.10.005
Gridded Population of the World (GPW) v4.11 (admin unit center points)

Juturu, P., Conlon, T., Zhang, Y., Avraam, C., Siddiqui, S., Simane, B., & Zaitchik, B. (2023). Optimal grid expansion under future electricity demand for groundwater irrigation in Ethiopia. Energy for Sustainable Development, 72, 351-377. doi:10.1016/j.esd.2022.12.006
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Kabundi, A., Mlachila, M., & Yao, J. (2022). How Persistent are Climate-Related Price Shocks? Retrieved from https://doi.org/10.5089/9798400223556.001
Gridded Population of the World (GPW) v4.11 (population count)

Kageyama, Y., & Sawada, Y. (2022). Global assessment of sub-national drought impact based on the Geocoded Disasters dataset and land reanalysis. Hydrology and Earth System Sciences, 26, 4707-4720. doi:10.5194/hess-26-4707-2022
Gridded Population of the World (GPW) v4.11 (population density)
Natural Disasters (Geocoded Disasters (GDIS) Dataset, v1)
NASA REMOTE SENSING (MODIS)

Kahn, M. E., Mohaddes, K., Ng, R. N. C., Pesaran, M. H., Raissi, M., & Yang, J.-C. (2021). Long-term macroeconomic effects of climate change: A cross-country analysis. Energy Economics, 104, 105624. doi:10.1016/j.eneco.2021.105624
Gridded Population of the World (GPW) v4 (population density)

Kalashnikov, D. A., Schnell, J. L., Abatzoglou, J. T., Swain, D. L., & Singh, D. (2022). Increasing co-occurrence of fine particulate matter and ground-level ozone extremes in the western United States. Science Advances, 8(1), eabi9386. doi:10.1126/sciadv.abi9386
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
NASA REMOTE SENSING (MODIS)

Kalura, P., Pandey, A., Chowdary, V. M., & Raju, P. V. (2021). Assessment of hydrological drought vulnerability using geospatial techniques in the Tons River Basin, India. Journal of the Indian Society of Remote Sensing, 49, 2623-2637. doi:10.1007/s12524-021-01413-7
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (SRTM)

Kamarajugedda, S. A., Johnson, J. A., McDonald, R., & Hamel, P. (2023). Carbon storage and sequestration in Southeast Asian urban clusters under future land cover change scenarios (2015–2050). Frontiers in Environmental Science, 11. doi:10.3389/fenvs.2023.1105759
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - data set unspecified)

Kamarajugedda, S. A., & Lo, E. Y. M. (2019). Modelling urban growth for Bangkok and assessing linkages with road density and socio-economic indicators. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4/W19, 255-262. doi:10.5194/isprs-archives-XLII-4-W19-255-2019
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
REMOTE SENSING (Landsat)

Kammerlander, A., & Unfried, K. (2022). Sending Peace Home:  The Effect of Political Favoritism on Conflict. Retrieved from https://hicn.org/working-paper/378/
Gridded Population of the World (GPW) v4.11 (population density)

Kandeil, A., Hicks, J. T., Young, S. G., El Taweel, A. N., Kayed, A. S., Moatasim, Y., . . . Ali, M. A. (2019). Active surveillance and genetic evolution of avian influenza viruses in Egypt, 2016–2018. Emerging Microbes & Infections, 8(1), 1370-1382. doi:10.1080/22221751.2019.1663712
Gridded Population of the World (GPW) v4.10 (population density)

Kang, J., Shim, G., Kim, H., & Yee, K. (2019). Development and flight test of affordable scientific balloon system suitable for sea recovery. International Journal of Aeronautical and Space Sciences, 20(4), 1024-1037. doi:10.1007/s42405-019-00181-6
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4HX19NJ

Kang, Y., Jang, E., Im, J., Kwon, C., & Kim, S. (2020). Developing a new hourly forest fire risk index based on Catboost in South Korea. Applied Sciences, 10(22), 8213. doi:10.3390/app10228213
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (ALOS Global Digital Surface Model - ALOS World 3D - 30m (AW3D30))

Kanzler, R., Lips, T., Fritsche, B., Fuentes, I. P., Bonetti, D., Letterio, F., . . . Lemmens, S. (2017). Upgrade of DRAMA's spacecraft entry survival analysis codes. Paper presented at the 7th European Conference on Space Debris, Darmstadt, Germany. https://conference.sdo.esoc.esa.int/proceedings/sdc7/paper/1063/SDC7-paper1063.pdf
Gridded Population of the World (GPW) v4 (documentation) - 10.7927/H4D50JX4

Kapur, D., Sircar, N., & Vaishnav, M. (2021). Gender, social change and urbanisation in four North Indian clusters. Urbanisation, 6(Issue1 (supplement)), S7-S19. doi:10.1177/24557471211040896
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)

Karcher, N. R., Shiffman, J. E., & Barch, D. M. (2021). Environmental risk factors and psychotic-like symptoms in children aged 9-11. Journal of the American Academy of Child & Adolescent Psychiatry, 60(4), 490-5000. doi:10.1016/j.jaac.2020.07.003
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
Satellite-Derived Environmental Indicators (Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, v1)

Karkarey, R., Arthur, R., Nash, K. L., Pratchett, M. S., Sankaran, M., & Graham, N. A. J. (2022). Spatial decoupling of α and β diversity suggest different management needs for coral reef fish along an extensive mid-oceanic ridge. Global Ecology and Conservation, 36, e02110. doi:10.1016/j.gecco.2022.e02110
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4F47M65

Karra, M., Canning, D., & Sato, R. (2020). Adding measurement error to location data to protect subject confidentiality while allowing for consistent estimation of exposure effects. Journal of the Royal Statistical Society: Series C (Applied Statistics), 69(5), 1251-1268. doi:10.1111/rssc.12439
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Kautz, T. M., Fowler, N. L., Petroelje, T. R., Beyer, D. E., Duquette, J. F., & Belant, J. L. (2022). White-tailed deer exploit temporal refuge from multi-predator and human risks on roads. Ecology and Evolution, 12(7), e9125. doi:10.1002/ece3.9125
Gridded Population of the World (GPW) v4.11 (population density)

Kautz, T. M., Fowler, N. L., Petroelje, T. R., Beyer, D. E., Svoboda, N. J., & Belant, J. L. (2021). Large carnivore response to human road use suggests a landscape of coexistence. Global Ecology and Conservation, 30, e01772. doi:10.1016/j.gecco.2021.e01772
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4F47M65

Kautz, T. M., Fowler, N. L., Petroelje, T. R., Duquette, J. F., Beyer, D. E., & Belant, J. L. (2022). Compensatory human and predator risk trade-offs in neonatal white-tailed deer. Global Ecology and Conservation, 36, e02089. doi:10.1016/j.gecco.2022.e02089
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Kavhu, B., Eric Mashimbye, Z., & Luvuno, L. (2022). Characterising social-ecological drivers of landuse/cover change in a complex transboundary basin using singular or ensemble machine learning. Remote Sensing Applications: Society and Environment, 27, 100773. doi:10.1016/j.rsase.2022.100773
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (Landsat)

Kavvada, A., Ishida, C., Juarez, J., Ramage, S., Merodio, P., & Friedl, L. (2022). EO4SDG: A GEO Initiative on Earth Observations for Sustainable Development Goals. In A. Kavvada, D. Cripe, & L. Friedl (Eds.), Earth Observation Applications and Global Policy Frameworks (pp. 147-157). Washington DC: American Geophysical Union.
Gridded Population of the World (GPW) v4 (collection)

Keles, D., Delacote, P., Pfaff, A., Qin, S., & Mascia, M. B. (2020). What drives the erasure of protected areas? Evidence from across the Brazilian Amazon. Ecological Economics, 176, 106733. doi:10.1016/j.ecolecon.2020.106733
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates)
NASA REMOTE SENSING (SRTM)

Keles, D., Pfaff, A., & Mascia, M. (2023). Does the selective erasure of protected areas raise deforestation in the Brazilian Amazon? Journal of the Association of Environmental and Resource Economists, 10(4), 1121-1147. doi:10.1086/723543
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
Satellite-Derived Environmental Indicators (Global Fire Emissions Indicators, Grids, v1)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (DMSP-OLS)

Keller, A. A., & Parker, N. (2019). Chapter 7 - Innovation in procedures for human and ecological health risk assessment of engineered nanomaterials. In N. Marmiroli, J. C. White, & J. Song (Eds.), Exposure to Engineered Nanomaterials in the Environment (pp. 185-208): Elsevier.
Gridded Population of the World (GPW) v4 (population density)

Kennard, H., Oreszczyn, T., Mistry, M., & Hamilton, I. (2022). Population-weighted degree-days: The global shift between heating and cooling. Energy and Buildings, 271, 112315. doi:10.1016/j.enbuild.2022.112315
Gridded Population of the World (GPW) v4.11 (population count)
Population Dynamics (Global Population Count Grid Time Series Estimates, v1)

Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch-Mordo, S., & Kiesecker, J. (2019). Managing the middle: A shift in conservation priorities based on the global human modification gradient. Global Change Biology, 25(3), 811-826. doi:10.1111/gcb.14549
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
REMOTE SENSING (DMSP-OLS)

Kerényi, A., & McIntosh, R. W. (2020). Changes on Earth as a result of interaction between the society and nature. In A. Kerényi & R. W. McIntosh (Eds.), Sustainable Development in Changing Complex Earth Systems (pp. 75-202). Cham: Springer International Publishing.
Gridded Population of the World (GPW) v4 (population density)

Keune, J., & Miralles, D. G. (2019). A precipitation recycling network to assess freshwater vulnerability: Challenging the watershed convention. Water Resources Research, 55(11), 9947-9961. doi:10.1029/2019wr025310
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM

Khan, A. S., MacManus, K., Mills, J., Madajewicz, M., & Ramasubramanian, L. (2018). Building Resilience of Urban Ecosystems and Communities to Sea-Level Rise: Jamaica Bay, New York City. In W. Leal Filho (Ed.), Handbook of Climate Change Resilience (pp. 1-21). Cham: Springer International Publishing.
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)

Khan, M. J. U., Durand, F., Emanuel, K., Krien, Y., Testut, L., & Islam, A. K. M. S. (2022). Storm surge hazard over Bengal delta: A probabilistic-deterministic modelling approach. Natural Hazards and Earth System Sciences, 22(7), 2359-2379. doi:10.5194/nhess-22-2359-2022
Gridded Population of the World (GPW) v4.11 (population density)

Khan, R., Anwar, R., Akanda, S., McDonald, M. D., Huq, A., Jutla, A., & Colwell, R. (2017). Assessment of risk of cholera in Haiti following Hurricane Matthew. The American Journal of Tropical Medicine and Hygiene, 97(3), 896-903. doi:10.4269/ajtmh.17-0048
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (GPM)
NASA REMOTE SENSING (TRMM)

Khan, Z., Thompson, I., Vernon, C. R., Graham, N. T., Wild, T. B., & Chen, M. (2023). Global monthly sectoral water use for 2010–2100 at 0.5° resolution across alternative futures. Scientific Data, 10(1), 201. doi:10.1038/s41597-023-02086-2
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Kharlan, A. A., Biktimirov, S. N., & Ivanov, A. B. (2020). Prospects for the development of global satellite communication constellations in the context of new services in the telecommunications market. Cosmic Research, 58(5), 402-410. doi:10.1134/S0010952520050044
Gridded Population of the World (GPW) v4.11 (unspecified)

Khavari, B., Korkovelos, A., Sahlberg, A., Howells, M., & Fuso Nerini, F. (2021). Population cluster data to assess the urban-rural split and electrification in Sub-Saharan Africa. Scientific Data, 8(1), 117. doi:10.1038/s41597-021-00897-9
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D
REMOTE SENSING (VIIRS NTL)

Khoshnazar, A., Corzo Perez, G., & Sajjad, M. (2023). Characterizing spatial–temporal drought risk heterogeneities: A hazard, vulnerability and resilience-based modeling. Journal of Hydrology, 619, 129321. doi:10.1016/j.jhydrol.2023.129321
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Kiacz, S., Wang, H.-H., & Brightsmith, D. J. (2023). Presence of endangered red-crowned parrots (Amazona viridigenalis) depends on urban landscapes. Diversity, 15(7), 878. doi:10.3390/d15070878
Gridded Population of the World (GPW) v4.11 (population density)
Urban Spatial Data (Urban Extents from VIIRS and MODIS for the Continental U.S. Using Machine Learning Methods, v1)

Kiala, Z., Jewitt, G., Senzanje, A., Mutanga, O., Dube, T., & Mabhaudhi, T. (2022). Chapter 3 - EO-WEF: a Earth Observations for Water, Energy, and Food nexus geotool for spatial data visualization and generation. In T. Mabhaudhi, A. Senzanje, A. Modi, G. Jewitt, & F. Massawe (Eds.), Water - Energy - Food Nexus Narratives and Resource Securities (pp. 33-48): Elsevier.
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)
NASA REMOTE SENSING (SMAP)
NASA REMOTE SENSING (GLDAS-2)
REMOTE SENSING (Sentinel-3)

Kiely, L., Spracklen, D., V., Wiedinmyer, C., Conibear, L., A., Reddington, C., L., Arnold, S., R., . . . Adrianto, H., A. (2020). Air quality and health impacts of vegetation and peat fires in Equatorial Asia during 2004 – 2015. Environmental Research Letters, 15(9), 094054. doi:10.1088/1748-9326/ab9a6c
Gridded Population of the World (GPW) v4 (population count)

Kiely, L., Spracklen, D. V., Arnold, S. R., Papargyropoulou, E., Conibear, L., Wiedinmyer, C., . . . Adrianto, H. A. (2021). Assessing costs of Indonesian fires and the benefits of restoring peatland. Nature Communications, 12(1), 7044. doi:10.1038/s41467-021-27353-x
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (MODIS)

Kii, M. (2021). Projecting future populations of urban agglomerations around the world and through the 21st century. npj Urban Sustainability, 1(1), 10. doi:10.1038/s42949-020-00007-5
Gridded Population of the World (GPW) v4.11 (population density)

Kim, D. (2022). Assessing regional economy in North Korea using nighttime light. Asia and the Global Economy, 2(3), 100046. doi:10.1016/j.aglobe.2022.100046
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
REMOTE SENSING (VIIRS NTL)

Kim, D., & Liem, R. P. (2022). Population-aware sequential flight path optimization for low-noise and low-fuel consumption departure trajectory. AIAA Journal, 60(11), 6116-6132. doi:10.2514/1.j061603
Gridded Population of the World (GPW) v4.11 (admin unit center points)

Kim, H. (2021). Technologies for Adapting to Climate Change: A Case Study of Korean Cities and Implications for Latin American Cities. Retrieved from Santiago: https://repositorio.cepal.org/handle/11362/46992
Gridded Population of the World (GPW) v4.11 (population count)

Kimura, Y., Hirabayashi, Y., Kita, Y., Zhou, X., & Yamazaki, D. (2023). Methodology for constructing a flood-hazard map for a future climate. EGUsphere, 27(8), 1627-1644. doi:10.5194/hess-27-1627-2023
Gridded Population of the World (GPW) v4.11 (population density)

Kipkulei, H. K., Bellingrath-Kimura, S. D., Lana, M., Ghazaryan, G., Boitt, M., & Sieber, S. (2022). Modelling cropland expansion and its drivers in Trans Nzoia County, Kenya. Modeling Earth Systems and Environment, 8, 5761-5778. doi:10.1007/s40808-022-01475-7
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (Landsat)

Kirchberger, M. (2021). Measuring internal migration. Regional Science and Urban Economics, 91, 103714. doi:10.1016/j.regsciurbeco.2021.103714
Gridded Population of the World (GPW) v4 (population density)

Kirezci, E., Young, I. a. R., Ranasinghe, R., Lincke, D., & Hinkel, J. (2023). Global-scale analysis of socioeconomic impacts of coastal flooding over the 21st century. Frontiers in Marine Science, 9. doi:10.3389/fmars.2022.1024111
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (EGM96)

Kirezci, E., Young, I. R., Ranasinghe, R., Muis, S., Nicholls, R. J., Lincke, D., & Hinkel, J. (2020). Projections of global-scale extreme sea levels and resulting episodic coastal flooding over the 21st Century. Scientific Reports, 10(1), 11629. doi:10.1038/s41598-020-67736-6
Gridded Population of the World (GPW) v4.11 (population count)

Kirkland, M., Atkinson, P. W., Pearce-Higgins, J. W., de Jong, M. C., Dowling, T. P. F., Grummo, D., . . . Ashton-Butt, A. (2023). Landscape fires disproportionally affect high conservation value temperate peatlands, meadows, and deciduous forests, but only under low moisture conditions. Science of The Total Environment, 884, 163849. doi:10.1016/j.scitotenv.2023.163849
Gridded Population of the World (GPW) v4.11 (population density) GEE
NASA REMOTE SENSING (MODIS) GEE

Kirschbaum, D., Watson, C. S., Rounce, D. R., Shugar, D. H., Kargel, J. S., Haritashya, U. K., . . . Jo, M. (2019). The state of remote sensing capabilities of cascading hazards over high mountain Asia. Frontiers in Earth Science, 7(197). doi:10.3389/feart.2019.00197
Gridded Population of the World (GPW) v4 (collection)
Global Roads (Global Roads Open Access Data Set (gROADS), v1) - 10.7927/H4VD6WCT
NASA REMOTE SENSING (ASTER)
NASA REMOTE SENSING (GRACE-FO)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)
NASA REMOTE SENSING (TRMM)

Kjær, L. J., Hjulsager, C. K., Larsen, L. E., Boklund, A. E., Halasa, T., Ward, M. P., & Kirkeby, C. T. (2022). Landscape effects and spatial patterns of avian influenza virus in Danish wild birds, 2006-2020. Transboundary and Emerging Diseases, 69(2), 706-719. doi:10.1111/tbed.14040
Gridded Population of the World (GPW) v4.11 (population density)

Kjær, L. J., Soleng, A., Edgar, K. S., Lindstedt, H. E. H., Paulsen, K. M., Andreassen, Å. K., . . . Bødker, R. (2019). Predicting and mapping human risk of exposure to Ixodes ricinus nymphs using climatic and environmental data, Denmark, Norway and Sweden, 2016. Eurosurveillance, 24(9), 1800101. doi:10.2807/1560-7917.ES.2019.24.9.1800101
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)

Kleemann, J., Schröter, M., Bagstad, K. J., Kuhlicke, C., Kastner, T., Fridman, D., . . . Bonn, A. (2020). Quantifying interregional flows of multiple ecosystem services – A case study for Germany. Global Environmental Change, 61, 102051. doi:10.1016/j.gloenvcha.2020.102051
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Klees van Bommel, J., Badry, M., Ford, A. T., Golumbia, T., & Burton, A. C. (2020). Predicting human-carnivore conflict at the urban-wildland interface. Global Ecology and Conservation, 24, e01322. doi:10.1016/j.gecco.2020.e01322
Gridded Population of the World (GPW) v4.10 (population density)
NASA REMOTE SENSING (ASTER GDEM)

Klinger, B. A., & Ryan, S. J. (2022). Population distribution within the human climate niche. PLOS Climate, 1(11), e0000086. doi:10.1371/journal.pclm.0000086
Gridded Population of the World (GPW) v4.11 (population density)

Kodros, J. K., Wiedinmyer, C., Ford, B., Cucinotta, R., Gan, R., Magzamen, S., & Pierce, J. R. (2016). Global burden of mortalities due to chronic exposure to ambient PM2.5 from open combustion of domestic waste. Environmental Research Letters, 11(12), 124022. doi:10.1088/1748-9326/11/12/124022
Gridded Population of the World (GPW) v4 (population count)

Kok, D.-J. D., Pande, S., Ortigara, A. R. C., Savenije, H., & Uhlenbrook, S. (2018). Socio-hydrological approach to the evaluation of global fertilizer substitution by sustainable struvite precipitants from wastewater. Proceedings of the International Association of Hydrological Sciences, 376, 83-86. doi:10.5194/piahs-376-83-2018
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Kok, D.-J. D., Pande, S., van Lier, J. B., Ortigara, A. R. C., Savenije, H., & Uhlenbrook, S. (2018). Global phosphorus recovery for agricultural reuse. Hydrology and Earth System Sciences, 22, 5781-5799. doi:10.5194/hess-22-5781-2018
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Kolios, S., Mitrakos, S., & Stylios, C. (2018). Detection of areas susceptible to land degradation in Cyprus using remote sensed data and environmental quality indices. Land Degradation & Development, 29(8), 2338-2350. doi:10.1002/ldr.3024
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (ASTER)
NASA REMOTE SENSING (TRMM)

Kölzsch, A., Lameris, T. K., Müskens, G. J. D. M., Schreven, K. H. T., Buitendijk, N. H., Kruckenberg, H., . . . Nolet, B. A. (2023). Wild goose chase: Geese flee high and far, and with aftereffects from New Year's fireworks. Conservation Letters, 16(1), e12927. doi:10.1111/conl.12927
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Komolafe, A. A., Awe, B. S., Olorunfemi, I. E., & Oguntunde, P. G. (2020). Modelling flood-prone area and vulnerability using integration of multi-criteria analysis and HAND model in the Ogun River Basin, Nigeria. Hydrological Sciences Journal, 65(10), 1766-1783. doi:10.1080/02626667.2020.1764960
Gridded Population of the World (GPW) v4 (documentation) - 10.7927/H4D50JX4
Gridded Population of the World (GPW) v4 (population count)
Gridded Population of the World (GPW) v4 (population density)
REMOTE SENSING (Landsat)

Kondash, A. J., Herrera, I., Castellanos, E., Baker, J., Leiva, B., Van Houtven, G., . . . Hoponick Redmon, J. (2021). Food, energy, and water nexus research in Guatemala – A systematic literature review. Environmental Science & Policy, 124, 175-185. doi:10.1016/j.envsci.2021.06.009
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Kong, H., Lin, J., Chen, L., Zhang, Y., Yan, Y., Liu, M., . . . Weng, H. (2022). Considerable unaccounted local sources of NOx emissions in China revealed from satellite. Environmental Science & Technology, 56(11), 7131-7142. doi:10.1021/acs.est.1c07723
Gridded Population of the World (GPW) v4 (population count)
REMOTE SENSING (TROPOMI)

Kong, H., Lin, J., Zhang, R., Liu, M., Weng, H., Ni, R., . . . Zhang, Q. (2019). High-resolution (0.05° × 0.05°) NOx emissions in the Yangtze River Delta inferred from OMI. Atmospheric Chemistry and Physics, 19(20), 12835-12856. doi:10.5194/acp-19-12835-2019
Gridded Population of the World (GPW) v4.10 (population density)
NASA REMOTE SENSING (OMI NO2)

Kong, H., Lin, J., Zhang, Y., Li, C., Xu, C., Shen, L., . . . Xu, W. (2023). High natural nitric oxide emissions from lakes on Tibetan Plateau under rapid warming. Nature Geoscience, 16(6), 474-477. doi:10.1038/s41561-023-01200-8
Gridded Population of the World (GPW) v4 (population count)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (TROPOMI)

Kong, Q., Martin‐Short, R., & Allen, R. M. (2020). Toward global earthquake early warning with the MyShake Smartphone Seismic Network, Part 1: Simulation platform and detection algorithm. Seismological Research Letters, 91(4), 2206-2217. doi:10.1785/0220190177
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42

Koplitz, S. N., Jacob, D. J., Sulprizio, M. P., Myllyvirta, L., & Reid, C. (2017). Burden of disease from rising coal-fired power plant emissions in Southeast Asia. Environmental Science & Technology, 51(3), 1467-1476. doi:10.1021/acs.est.6b03731
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Korobkov, R., Mukhachev, P., & Pritykin, D. (2020). Traffic prediction model for broadband microsatellites constellations. Paper presented at the 5th IAA Conference on University Satellite Missions and Cubesat Workshop, 2020.
Gridded Population of the World (GPW) v4.11 (national identifier grid) - 10.7927/H4TD9VDP
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Kouassi, J.-L., Wandan, N., & Mbow, C. (2022). Exploring spatio-temporal trends and environmental drivers of wildfire occurrence and impacts in Côte d'Ivoire, West Africa. African Journal of Ecology, 60(4), 1218-1236. doi:10.1111/aje.13066
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)

Kovács, K. D., & Haidu, I. (2022). Tracing out the effect of transportation infrastructure on NO2 concentration levels with Kernel Density Estimation by investigating successive COVID-19-induced lockdowns. Environmental Pollution, 309, 119719. doi:10.1016/j.envpol.2022.119719
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (Sentinel-5P)

Kozarcanin, S., & Andresen, G. B. (2021). The effect of increased coupling strength between electricity and heating systems in different climate scenarios for Europe. Energy and Climate Change, 2, 100039. doi:10.1016/j.egycc.2021.100039
Gridded Population of the World (GPW) v4 (population count)

Kozarcanin, S., Hanna, R., Staffell, I., Gross, R., & Andresen, G. B. (2020). Impact of climate change on the cost-optimal mix of decentralised heat pump and gas boiler technologies in Europe. Energy Policy, 140, 111386. doi:10.1016/j.enpol.2020.111386
Gridded Population of the World (GPW) v4 (population density)

Kraemer, M. U. G., Sadilek, A., Zhang, Q., Marchal, N. A., Tuli, G., Cohn, E. L., . . . Brownstein, J. S. (2020). Mapping global variation in human mobility. Nature Human Behaviour, 4, 800-810. doi:10.1038/s41562-020-0875-0
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted)

Krakauer, N. Y. (2023). Amplification of extreme hot temperatures over recent decades. Climate, 11(2), 42. doi:10.3390/cli11020042
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Krasovskii, A., Khabarov, N., Pirker, J., Kraxner, F., Yowargana, P., Schepaschenko, D., & Obersteiner, M. (2018). Modeling burned areas in Indonesia: The FLAM approach. Forests, 9(7), 437. doi:10.3390/f9070437
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (LIS)

Krengel, F., Bernhofer, C., Chalov, S., Efimov, V., Efimova, L., Gorbachova, L., . . . Karthe, D. (2018). Challenges for transboundary river management in Eastern Europe – three case studies. Die Erde, 149(2-3), 157-172. doi:10.12854/erde-2018-389 
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Krüger, O. O., Holanda, B. A., Chowdhury, S., Pozzer, A., Walter, D., Pöhlker, C., . . . Pöhlker, M. L. (2022). Black carbon aerosol reductions during COVID-19 confinement quantified by aircraft measurements over Europe. Atmospheric Chemistry and Physics, 22, 8683-8699. doi:10.5194/acp-22-8683-2022
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Kruger, S. E., Lorah, P. A., & Okamoto, K. W. (2022). Mapping climate change’s impact on cholera infection risk in Bangladesh. PLOS Global Public Health, 2(10), e0000711. doi:10.1371/journal.pgph.0000711
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
Population Dynamics (Global 1-km Downscaled Population Base Year and Projection Grids Based on the SSPs, v1.01) - 10.7927/q7z9-9r69

Kuffer, M., Owusu, M., Oliveira, L., Sliuzas, R., & Rijn, F. v. (2022). The missing millions in maps: Exploring causes of uncertainties in global gridded population datasets. ISPRS International Journal of Geo-Information, 11(7), 403. doi:10.3390/ijgi11070403
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)

Kugler, T. A., Grace, K., Wrathall, D. J., de Sherbinin, A., Van Riper, D., Aubrecht, C., . . . Van Den Hoek, J. (2019). People and Pixels 20 years later: the current data landscape and research trends blending population and environmental data. Population and Environment, 41, 209-234. doi:10.1007/s11111-019-00326-5
Anthropogenic Biomes of the World v2 (2000) - 10.7927/H4D798B9
Gridded Population of the World (GPW) v4.10 (basic demographic characteristics) - 10.7927/H45H7D7F
Satellite-Derived Environmental Indicators (Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, v1) - 10.7927/H4ZK5DQS
Satellite-Derived Environmental Indicators (Global 3-Year Running Mean Ground-Level NO2 Grids from GOME, SCIAMACHY and GOME-2, v1) - 10.7927/H4JW8BTT
POPGRID
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)

Kulp, S. A., & Strauss, B. H. (2019). New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nature Communications, 10(1), 4844. doi:10.1038/s41467-019-12808-z
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)
NASA REMOTE SENSING (SRTM)

Kumar, P., Gupta, K., Karnatak, H. C., Siddiqui, A., & Senthil Kumar, A. (2017). Geo-enabled e-Democracy Tools and Services for Smart Cities. In T. M. Vinod Kumar (Ed.), E-Democracy for Smart Cities (pp. 391-440). Singapore: Springer Singapore.
Gridded Population of the World (GPW) v4 (collection)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)
Last of the Wild v2 (collection)
Natural Disaster Hotspots (collection)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (ASTER GDEM)

Kumar, R., Rachunok, B., Maia-Silva, D., & Nateghi, R. (2020). Asymmetrical response of California electricity demand to summer-time temperature variation. Scientific Reports, 10(1), 10904. doi:10.1038/s41598-020-67695-y
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Kummu, M., Taka, M., & Guillaume, J. H. A. (2018). Gridded global datasets for Gross Domestic Product and Human Development Index over 1990–2015. Scientific Data, 5(180004), 15pp. doi:10.1038/sdata.2018.4
Gridded Population of the World (GPW) v4.10 (population count)
Spatial Economic Data (Global Gridded Geographically Based Economic Data (G-Econ), v4)

Kutzner, J. A., & Lung, D. (2016). Predicting ATSC 3.0 broadcast coverage. IEEE Transactions on Broadcasting, 62(1), 281-288. doi:10.1109/TBC.2015.2505413
Gridded Population of the World (GPW) v4 Preliminary release 2

Kuyper, B., Say, D., Labuschagne, C., Lesch, T., Joubert, W. R., Martin, D., . . . Shallcross, D. E. (2019). Atmospheric HCFC-22, HFC-125, and HFC-152a at Cape Point, South Africa. Environmental Science & Technology, 53(15), 8967-8975. doi:10.1021/acs.est.9b01612
Gridded Population of the World (GPW) v4 (unspecified)
REMOTE SENSING (DMSP-OLS)

Kvasnicka, J., Stylianou, K. S., Nguyen, V. K., Huang, L., Chiu, W. A., Burton, G. A., . . . Jolliet, O. (2019). Human health benefits from fish consumption vs. risks from inhalation exposures associated with contaminated sediment remediation: Dredging of the Hudson River. Environmental Health Perspectives, 127(12), 127004. doi:10.1289/EHP5034
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - population count)
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - population density)

Lafage, D., Bergman, E., Eckstein, R. L., Österling, E. M., Sadler, J. P., & Piccolo, J. J. (2019). Local and landscape drivers of aquatic-to-terrestrial subsidies in riparian ecosystems: a worldwide meta-analysis. Ecosphere, 10(4), e02697. doi:10.1002/ecs2.2697
Gridded Population of the World (GPW) v4 (population density)

Lai, Z., Li, H., & Li, J. (2020). STARPERF: Characterizing network performance for emerging mega-constellations. Paper presented at the 28th IEEE International Conference on Network Protocols (ICNP 2020), Madrid. https://icnp20.cs.ucr.edu/proceedings/main/StarPerf.pdf
Gridded Population of the World (GPW) v4.11 (population count)

Lamsal, P., Kumar, L., Aryal, A., & Atreya, K. (2018). Invasive alien plant species dynamics in the Himalayan region under climate change. Ambio, 46(6), 697-710. doi:10.1007/s13280-018-1017-z
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Lan, R., Eastham, S. D., Liu, T., Norford, L. K., & Barrett, S. R. H. (2022). Air quality impacts of crop residue burning in India and mitigation alternatives. Nature Communications, 13(1), 6537. doi:10.1038/s41467-022-34093-z
Gridded Population of the World (GPW) v4.11 (population count)
Satellite-Derived Environmental Indicators (Global (GL) Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD), v4.03) - 10.7927/fx80-4n39
NASA REMOTE SENSING (MODIS)

Landmann, T., Schmitt, M., Ekim, B., Villinger, J., Ashiono, F., Habel, J. C., & Tonnang, H. E. Z. (2023). Insect diversity is a good indicator of biodiversity status in Africa. Communications Earth & Environment, 4(1), 234. doi:10.1038/s43247-023-00896-1
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (VIIRS DNB)

Lang, P. A., & Gregory, K. B. (2018). Economic impact of energy consumption change caused by global warming. Retrieved from Canberra: https://cama.crawford.anu.edu.au/publication/cama-working-paper-series/13309/economic-impact-energy-consumption-change-caused-global
Gridded Population of the World (GPW) v4.10 (population count)

Larkin, A., Gu, X., Chen, L., & Hystad, P. (2021). Predicting perceptions of the built environment using GIS, satellite and street view image approaches. Landscape and Urban Planning, 216, 104257. doi:10.1016/j.landurbplan.2021.104257
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (Landsat)

Lattuada, M., Albrecht, C., & Wilke, T. (2019). Differential impact of anthropogenic pressures on Caspian Sea ecoregions. Marine Pollution Bulletin, 142, 274-281. doi:10.1016/j.marpolbul.2019.03.046
Global Agricultural Inputs (nitrogen fertilizer application)
Gridded Population of the World (GPW) v4.10 (population density UN WPP-adjusted)
NASA REMOTE SENSING (MODIS Ocean Color)

Lau, W. W. Y., Shiran, Y., Bailey, R. M., Cook, E., Stuchtey, M. R., Koskella, J., . . . Palardy, J. E. (2020). Evaluating scenarios toward zero plastic pollution. Science, 369(6510), 1455-1461. doi:10.1126/science.aba9475
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Lauer, D. A., Shipley, B. R., & McGuire, J. L. (2023). Habitat and not topographic heterogeneity constrains the range sizes of African mammals. Journal of Biogeography, 50(5), 846-857. doi:10.1111/jbi.14576
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4HX19NJ

Laurentius Peterson, E. (2022). Global and local bioclimatic predilections for rebalancing the heating and cooling of buildings. Energy and Buildings, 266, 112088. doi:10.1016/j.enbuild.2022.112088
Gridded Population of the World (GPW) v4.11 (population density)

Lausier, A. M., & Jain, S. (2018). Overlooked trends in observed global annual precipitation reveal underestimated risks. Scientific Reports, 8(1), 16746. doi:10.1038/s41598-018-34993-5
Anthropogenic Biomes of the World v1 - 10.7927/H4H12ZXD
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted) - 10.7927/H4JQ0XZW

Le Guenedal, T., Drobinski, P., & Tankov, P. (2022). Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA 1.0) compatible with CMIP climate data. Geoscientific Model Development, 15(21), 8001-8039. doi:10.5194/gmd-15-8001-2022
Gridded Population of the World (GPW) v4.10 (population count)
Population Dynamics (Global One-Eighth Degree Population Base Year and Projection Grids Based on the SSPs, v1.01)

Lea, A. J., Martins, D., Kamau, J., Gurven, M., & Ayroles, J. F. (2020). Urbanization and market integration have strong, nonlinear effects on cardiometabolic health in the Turkana. Science Advances, 6(43), eabb1430. doi:10.1126/sciadv.abb1430
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Leandro, C., Jay-Robert, P., Mériguet, B., Houard, X., & Renner, I. W. (2020). Is my SDM good enough? Insights from a citizen science dataset in a point process modeling framework. Ecological Modelling, 438, 109283. doi:10.1016/j.ecolmodel.2020.109283
Gridded Population of the World (GPW) v4 (population density)

Leasure, D. R., Jochem, W. C., Weber, E. M., Seaman, V., & Tatem, A. J. (2020). National population mapping from sparse survey data: A hierarchical Bayesian modeling framework to account for uncertainty. Proceedings of the National Academy of Sciences, 117(39), 24173-24179. doi:10.1073/pnas.1913050117
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - data set unspecified)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)

Leberger, R., Rosa, I. M. D., Guerra, C. A., Wolf, F., & Pereira, H. M. (2020). Global patterns of forest loss across IUCN categories of protected areas. Biological Conservation, 241, 108299. doi:10.1016/j.biocon.2019.108299
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Lecocq, T., Hicks, S. P., Van Noten, K., van Wijk, K., Koelemeijer, P., De Plaen, R. S. M., . . . Xiao, H. (2020). Global quieting of high-frequency seismic noise due to COVID-19 pandemic lockdown measures. Science, 369(6509), 1338-1343. doi:10.1126/science.abd2438
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Lee, H. W., Jakob, P. C., Ho, K., Shimizu, S., & Yoshikawa, S. (2018). Optimization of satellite constellation deployment strategy considering uncertain areas of interest. Acta Astronautica, 153, 213-228. doi:10.1016/j.actaastro.2018.03.054
Spatial Economic Data (Global Gridded Geographically Based Economic Data (G-Econ), v4) - 10.7927/H42V2D1C
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4HX19NJ

Lee, J., Mast, J. C., & Dessler, A. E. (2021). The effect of forced change and unforced variability in heat waves, temperature extremes, and associated population risk in a CO2-warmed world. Atmospheric Chemistry and Physics, 21(15), 11889-11904. doi:10.5194/acp-21-11889-2021
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Lee, J., Shigetomi, Y., & Kanemoto, K. (2023). Drivers of household carbon footprints across EU regions, from 2010 to 2015. Environmental Research Letters, 18(4), 044043. doi:10.1088/1748-9326/acc95e
Gridded Population of the World (GPW) v4.11 (population count)
Gridded Population of the World (GPW) v4.11 (population density)

Leguijt, G., Maasakkers, J. D., Denier van der Gon, H. A. C., Segers, A. J., Borsdorff, T., & Aben, I. (2023). Quantification of carbon monoxide emissions from African cities using TROPOMI. Atmospheric Chemistry and Physics, 23(15), 8899-8919. doi:10.5194/acp-23-8899-2023
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (TROPOMI)

Lehner, B., Messager, M. L., Korver, M. C., & Linke, S. (2022). Global hydro-environmental lake characteristics at high spatial resolution. Scientific Data, 9(1), 351. doi:10.1038/s41597-022-01425-z
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
NASA REMOTE SENSING (MODIS)

Lehtveer, M., Mattsson, N., & Hedenus, F. (2017). Using resource based slicing to capture the intermittency of variable renewables in energy system models. Energy Strategy Reviews, 18, 73-84. doi:10.1016/j.esr.2017.09.008
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
NASA REMOTE SENSING (MODIS)

Lei, Z., Xie, Y., Cheng, P., & Yang, H. (2023). From auxiliary data to research prospects, a review of gridded population mapping. Transactions in GIS, 27(1), 3-39. doi:10.1111/tgis.13020
Gridded Population of the World (GPW) v4 (collection)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)
POPGRID

Leijten, F., Lantz C Baldos, U., Johnson, J. A., Sim, S., & Verburg, P. H. (2023). Projecting global oil palm expansion under zero-deforestation commitments: direct and indirect land use change impacts. iScience, 26(6), 106971. doi:10.1016/j.isci.2023.106971
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)

Leng, X., Feng, X., Fu, B., Shi, Q., Ye, H., & Zhang, Y. (2023). ‘Asian water towers’ are not a sustainable solution to the downstream water crisis. Science of The Total Environment, 856, 159237. doi:10.1016/j.scitotenv.2022.159237
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)
Population Dynamics (Global One-Eighth Degree Population Base Year and Projection Grids Based on the SSPs, v1.01)

Leonard, A., Wheeler, S., & McCulloch, M. (2022). Power to the people: Applying citizen science and computer vision to home mapping for rural energy access. International Journal of Applied Earth Observation and Geoinformation, 108, 102748. doi:10.1016/j.jag.2022.102748
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
REMOTE SENSING (DMC-3)
REMOTE SENSING (KOMPSAT-3A)
REMOTE SENSING (Superview-1)

León-Cruz, J. F., Pineda-Martínez, L. F., & Carbajal, N. (2022). Tornado climatology and potentially severe convective environments in Mexico. Climate Research, 87, 147-165. doi:10.3354/cr01692
Gridded Population of the World (GPW) v4.11 (population density)

Lessa, T., dos Santos, J. W., Correia, R. A., Ladle, R. J., & Malhado, A. C. M. (2019). Known unknowns: Filling the gaps in scientific knowledge production in the Caatinga. PLoS ONE, 14(7), e0219359. doi:10.1371/journal.pone.0219359
Gridded Population of the World (GPW) v4 (population density)

Lever, J., & Arcucci, R. (2022). Sentimental wildfire: a social-physics machine learning model for wildfire nowcasting. Journal of Computational Social Science, 5, 1427-1465. doi:10.1007/s42001-022-00174-8
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
NASA REMOTE SENSING (MODIS) (via Global Fire Atlas)

Lewis, J., Hoover, J., & MacKenzie, D. (2017). Mining and environmental health disparities in Native American communities. Current Environmental Health Reports, 4(2), 130-141. doi:10.1007/s40572-017-0140-5
Gridded Population of the World (GPW) v4 (population count)

Leyk, S., Gaughan, A. E., Adamo, S. B., de Sherbinin, A. M., Balk, D., Freire, S., . . . Tatem, A. J. (2019). The spatial allocation of population: A review of large-scale gridded population data products and their fitness for use. Earth System Science Data, 11(3), 1385-1409. doi:10.5194/essd-11-1385-2019
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4PN93PB
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
Global Rural-Urban Mapping Project (GRUMP) v1 (population count) - 10.7927/H4VT1Q1H
Global Rural-Urban Mapping Project (GRUMP) v1 (population density) - 10.7927/H4R20Z93
POPGRID

Leyk, S., & Uhl, J. H. (2018). HISDAC-US, historical settlement data compilation for the conterminous United States over 200 years. Scientific Data, 5, 180175. doi:10.1038/sdata.2018.175
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42
Global Rural-Urban Mapping Project (GRUMP) v1 (urban extent) - 10.7927/H4GH9FVG

Li, B. V., Jenkins, C. N., & Xu, W. (2022). Strategic protection of landslide vulnerable mountains for biodiversity conservation under land-cover and climate change impacts. Proceedings of the National Academy of Sciences, 119(2), e2113416118. doi:10.1073/pnas.2113416118
Gridded Population of the World (GPW) v4.11 (population density)

Li, C., McLinden, C., Fioletov, V., Krotkov, N., Carn, S., Joiner, J., . . . Dickerson, R. R. (2017). India is overtaking China as the world’s largest emitter of anthropogenic sulfur dioxide. Scientific Reports, 7(1), 14304. doi:10.1038/s41598-017-14639-8
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
NASA REMOTE SENSING (OMI)

Li, C., van Donkelaar, A., Hammer, M. S., McDuffie, E. E., Burnett, R. T., Spadaro, J. V., . . . Martin, R. V. (2023). Reversal of trends in global fine particulate matter air pollution. Nature Communications, 14(1), 5349. doi:10.1038/s41467-023-41086-z
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (MISR)
NASA REMOTE SENSING (SeaWiFS)

Li, D., Yuan, J., & Kopp, R. (2020). Escalating global exposure to compound heat-humidity extremes with warming. Environmental Research Letters, 15(6), 064003. doi:10.1088/1748-9326/ab7d04
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
Population Dynamics (Global One-Eighth Degree Population Projection Grids for the SSPs, v1)

Li, H., Yang, X., & Zhang, K. (2021). Understanding global land degradation processes interacted with complex biophysics and socioeconomics from the perspective of the Normalized Difference Vegetation Index (1982–2015). Global and Planetary Change, 198, 103431. doi:10.1016/j.gloplacha.2021.103431
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (GIMMS NDVI)

Li, J., Chen, X., Kurban, A., Van de Voorde, T., De Maeyer, P., & Zhang, C. (2021). Coupled SSPs-RCPs scenarios to project the future dynamic variations of water-soil-carbon-biodiversity services in Central Asia. Ecological Indicators, 129, 107936. doi:10.1016/j.ecolind.2021.107936
Gridded Population of the World (GPW) v4.11 (population density)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)

Li, J., Chen, X., Kurban, A., Van de Voorde, T., De Maeyer, P., & Zhang, C. (2021). Identification of conservation priorities in the major basins of Central Asia: Using an integrated GIS-based ordered weighted averaging approach. Journal of Environmental Management, 298, 113442. doi:10.1016/j.jenvman.2021.113442
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS - MOD13A2)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (VIIRS NTL)

Li, J., Han, X., Jin, M., Zhang, X., & Wang, S. (2019). Globally analysing spatiotemporal trends of anthropogenic PM2.5 concentration and population's PM2.5 exposure from 1998 to 2016. Environment International, 128, 46-62. doi:10.1016/j.envint.2019.04.026
Gridded Population of the World (GPW) v4.10 (population count)

Li, J., Jin, M., & Li, H. (2019). Exploring spatial influence of remotely sensed PM2.5 concentration using a developed deep convolutional neural network model. International Journal of Environmental Research and Public Health, 16(3), 454. doi:10.3390/ijerph16030454
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Li, J., Pan, Y., Li, Q., Lenschow, D. H., Zhou, M., Xiao, X., . . . Cheng, Z. (2023). Observational analyses of a penetrating sea-breeze front in the Beijing-Tianjin-Hebei urban agglomeration. Urban Climate, 47, 101353. doi:10.1016/j.uclim.2022.101353
Gridded Population of the World (GPW) v4 (collection)

Li, J., Wang, N., Wang, J., & Li, H. (2018). Spatiotemporal evolution of the remotely sensed global continental PM2.5 concentration from 2000-2014 based on Bayesian statistics. Environmental Pollution, 238, 471-481. doi:10.1016/j.envpol.2018.03.050
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Li, J., & Zhang, C. (2021). Exploring the relationship between key ecosystem services and socioecological drivers in alpine basins: A case of Issyk-Kul Basin in Central Asia. Global Ecology and Conservation, 29, e01729. doi:10.1016/j.gecco.2021.e01729
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS - MOD13A2)
NASA REMOTE SENSING (SRTM)

Li, J., Zhang, C., & Chen, X. (2021). Exploring the relative importance of socio-ecological factors to ecosystem services clusters: a support to spatially targeted management. Environmental Research Letters, 16(8), 084053. doi:10.1088/1748-9326/ac12ef
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)

Li, K., Mehta, K., Wright, A., Lee, J., Yadav, M., Pham, T. N., . . . Stewart, B. (2021). Identifying hospitals in Nepal for acute burn care and stabilization capacity development: Location-allocation modeling for strategic service delivery. Journal of Burn Care & Research, 42(4), 621-626. doi:10.1093/jbcr/irab064
Gridded Population of the World (GPW) v4.11 (population count)

Li, L., Dominici, F., Blomberg, A. J., Bargagli-Stoffi, F. J., Schwartz, J. D., Coull, B. A., . . . Koutrakis, P. (2022). Exposure to unconventional oil and gas development and all-cause mortality in Medicare beneficiaries. Nature Energy, 7, 177-185. doi:10.1038/s41560-021-00970-y
Gridded Population of the World (GPW) v4.11 (population density)

Li, L., & Lu, D. (2016). Mapping population density distribution at multiple scales in Zhejiang Province using Landsat Thematic Mapper and census data. International Journal of Remote Sensing, 37(18), 4243-4260. doi:10.1080/01431161.2016.1212422
Gridded Population of the World (GPW) v3 (collection)
Gridded Population of the World (GPW) v4 (collection)
REMOTE SENSING (Landsat)

Li, L., Zhang, Y., Liu, L., Wang, Z., Zhang, H., Li, S., & Ding, M. (2020). Mapping changing population distribution on the Qinghai–Tibet Plateau since 2000 with multi-temporal remote sensing and point-of-interest data. Remote Sensing, 12(24), 4059. doi:10.3390/rs12244059
Gridded Population of the World (GPW) v4 (collection)
NASA REMOTE SENSING (MODIS - MOD13A2)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS NTL)

Li, N., Zhang, H., Zhu, S., Liao, H., Hu, J., Tang, K., . . . Li, J. (2023). Secondary PM2.5 dominates aerosol pollution in the Yangtze River Delta region: Environmental and health effects of the Clean air Plan. Environment International, 171, 107725. doi:10.1016/j.envint.2022.107725
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MODIS)

Li, P., & Wang, Z.-H. (2020). Modeling carbon dioxide exchange in a single-layer urban canopy model. Building and Environment, 184, 107243. doi:10.1016/j.buildenv.2020.107243
Gridded Population of the World (GPW) v4 (population density)
REMOTE SENSING (Quickbird)

Li, R., Ma, T., Xu, Q., & Song, X. (2018). Using MAIAC AOD to verify the PM2.5 spatial patterns of a land use regression model. Environmental Pollution, 243, 501-509. doi:10.1016/j.envpol.2018.09.026
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D
NASA REMOTE SENSING (ASTER GDEM)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)

Li, S., Cui, Y., Liu, M., He, H., & Ravan, S. (2017). Integrating global open geo-information for major disaster assessment: A case study of the Myanmar flood. ISPRS International Journal of Geo-Information, 6(7), 19pp. doi:10.3390/ijgi6070201
Gridded Population of the World (GPW) v4 (population density)
REMOTE SENSING (Landsat)
REMOTE SENSING (TanDEM-X (TDX))
REMOTE SENSING (Terra SAR-X (TSX))

Li, S., He, F., Liu, X., & Hua, L. (2023). Historical land use reconstruction for South Asia: Current understanding, challenges, and solutions. Earth-Science Reviews, 238, 104350. doi:10.1016/j.earscirev.2023.104350
Gridded Population of the World (GPW) v4 (population density) map

Li, S., Zhang, H., Zhou, X., Yu, H., & Li, W. (2020). Enhancing protected areas for biodiversity and ecosystem services in the Qinghai–Tibet Plateau. Ecosystem Services, 43, 101090. doi:10.1016/j.ecoser.2020.101090
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
REMOTE SENSING (DMSP-OLS)

Li, T., Shen, H., Yuan, Q., Zhang, X., & Zhang, L. (2017). Estimating ground-level PM2.5 by fusing satellite and station observations: A geo-intelligent deep learning approach. Geophysical Research Letters, 44(23), 11985-11993. doi:10.1002/2017GL075710
Gridded Population of the World (GPW) v4 (population count)

Li, W., Reichstein, M., O, S., May, C., Destouni, G., Migliavacca, M., . . . Orth, R. (2023). Contrasting drought propagation Into the terrestrial water cycle between dry and wet regions. Earth's Future, 11(7), e2022EF003441. doi:10.1029/2022EF003441
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (GRACE)
NASA REMOTE SENSING (MODIS)

Li, X., Cheng, S., Wang, Y., Zhang, G., Zhang, L., & Wu, C. (2023). Future land use spatial conflicts and habitat quality impacts based on SSPs-RCPs Scenarios—Qin-Ba Mountain City. Land, 12(9), 1708. doi:10.3390/land12091708
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (VIIRS NTL)

Li, X., Li, Y., Jia, T., Zhou, L., & Hijazi, I. H. (2022). The six dimensions of built environment on urban vitality: Fusion evidence from multi-source data. Cities, 121, 103482. doi:10.1016/j.cities.2021.103482
Gridded Population of the World (GPW) v4 (unspecified)
REMOTE SENSING (Landsat)

Li, X., Vernon, C. R., Hejazi, M. I., Link, R. P., Huang, Z., Liu, L., & Feng, L. (2018). Tethys – a Python package for spatial and temporal downscaling of global water withdrawals. Journal of Open Research Software, 6(1), 10pp. doi:10.5334/jors.197
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Li, X., Wang, H., McCauley, D. J., Altieri, A. H., Silliman, B. R., Lefcheck, J. S., . . . He, Q. (2023). A wide megafauna gap undermines China’s expanding coastal ecosystem conservation. Science Advances, 9(32), eadg3800. doi:10.1126/sciadv.adg3800
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Li, X., Zhou, L., Liu, X., Dun, Q., Ma, L., & Zou, Y. (2022). Community built environment and the associated ischemic heart disease risk: Evidence from multi-source data in Wuhan, China. Journal of Transport & Health, 25, 101371. doi:10.1016/j.jth.2022.101371
Gridded Population of the World (GPW) v4 (unspecified)

Li, X., & Zhou, W. (2018). Dasymetric mapping of urban population in China based on radiance corrected DMSP-OLS nighttime light and land cover data. Science of The Total Environment, 643, 1248-1256. doi:10.1016/j.scitotenv.2018.06.244
Gridded Population of the World (GPW) v4.10 (population count)
REMOTE SENSING (DMSP-OLS)

Li, X., & Zhou, W. (2019). Optimizing urban greenspace spatial pattern to mitigate urban heat island effects: Extending understanding from local to the city scale. Urban Forestry & Urban Greening, 41, 255-263. doi:10.1016/j.ufug.2019.04.008
Gridded Population of the World (GPW) v4.10 (population density UN WPP-adjusted)
NASA REMOTE SENSING (MODIS - MYD13A3)
REMOTE SENSING (DMSP-OLS)

Li, X., & Zhou, W. (2019). Spatial patterns and driving factors of surface urban heat island intensity: A comparative study for two farmland-dominated regions in China and the USA. Sustainable Cities and Society, 48, 101518. doi:10.1016/j.scs.2019.101518
Gridded Population of the World (GPW) v4.10 (population density)
NASA REMOTE SENSING (MODIS - MYD13A3)

Li, Y., Li, H., Liu, L., Liu, W., Liu, J., Wu, J., . . . Lai, Z. (2021). "Internet in Space" for terrestrial users via cyber-physical convergence. Paper presented at the Proceedings of the Twentieth ACM Workshop on Hot Topics in Networks, Virtual Event, United Kingdom. https://doi.org/10.1145/3484266.3487375
Gridded Population of the World (GPW) v4.11 (population count)

Li, Y., Li, Z., Wang, J., & Zeng, H. (2022). Analyses of driving factors on the spatial variations in regional eco-environmental quality using two types of species distribution models: A case study of Minjiang River Basin, China. Ecological Indicators, 139, 108980. doi:10.1016/j.ecolind.2022.108980
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (Landsat)
REMOTE SENSING (VIIRS)

Li, Y., Liao, Q., Zhao, X., Tao, Y., Bai, Y., & Peng, L. (2021). Premature mortality attributable to PM2.5 pollution in China during 2008–2016: Underlying causes and responses to emission reductions. Chemosphere, 263, 127925. doi:10.1016/j.chemosphere.2020.127925
Gridded Population of the World (GPW) v4 (unspecified)

Li, Y., Liu, M., Li, R., Sun, P., Xia, H., & He, T. (2020). Polycyclic aromatic hydrocarbons in the soils of the Yangtze River Delta Urban Agglomeration, China: Influence of land cover types and urbanization. Science of The Total Environment, 715, 137011. doi:10.1016/j.scitotenv.2020.137011
Gridded Population of the World (GPW) v4 (population count)

Li, Y., Osei, F. B., Hu, T., & Stein, A. (2023). Urban flood susceptibility mapping based on social media data in Chengdu city, China. Sustainable Cities and Society, 88, 104307. doi:10.1016/j.scs.2022.104307
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (Sentinel-2)

Li, Y., Wang, Y., Zhang, Y., Zhou, X., & Sun, H. (2021). Impact of economic development levels on the mortality rates of Asian earthquakes. International Journal of Disaster Risk Reduction, 62, 102409. doi:10.1016/j.ijdrr.2021.102409
Gridded Population of the World (GPW) v3 (population count)
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Li, Y., Xue, L., Tao, Y., Li, Y., Wu, Y., Liao, Q., . . . Bai, Y. (2023). Exploring the contributions of major emission sources to PM2.5 and attributable health burdens in China. Environmental Pollution, 322, 121177. doi:10.1016/j.envpol.2023.121177
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4PN93PB

Li, Y., Zhao, G., Allen, G. H., & Gao, H. (2023). Diminishing storage returns of reservoir construction. Nature Communications, 14(1), 3203. doi:10.1038/s41467-023-38843-5
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
REMOTE SENSING (Landsat)

Li, Y., Zhao, X., Liao, Q., Tao, Y., & Bai, Y. (2020). Specific differences and responses to reductions for premature mortality attributable to ambient PM2.5 in China. Science of The Total Environment, 742, 140643. doi:10.1016/j.scitotenv.2020.140643
Gridded Population of the World (GPW) v4 (population density)

Liang, S., Wang, D., Ziegler, A. D., Li, L. Z. X., & Zeng, Z. (2022). Madden–Julian Oscillation-induced extreme rainfalls constrained by global warming mitigation. npj Climate and Atmospheric Science, 5(1), 67. doi:10.1038/s41612-022-00291-1
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Liang, X., Lam, S. K., Gu, B., Galloway, J. N., Leach, A. M., & Chen, D. (2018). Reactive nitrogen spatial intensity (NrSI): A new indicator for environmental sustainability. Global Environmental Change, 52, 101-107. doi:10.1016/j.gloenvcha.2018.06.001
Gridded Population of the World (GPW) v4 (population density)

Liao, S., Yao, W., Han, X., Wen, J., & Cheng, S. (2017). Chronological operation simulation framework for regional power system under high penetration of renewable energy using meteorological data. Applied Energy, 203, 816-828. doi:10.1016/j.apenergy.2017.06.086
Gridded Population of the World (GPW) v4 (population density)

Liao, Y., Gil, J., Pereira, R. H. M., Yeh, S., & Verendel, V. (2020). Disparities in travel times between car and transit: Spatiotemporal patterns in cities. Scientific Reports, 10(1), 4056. doi:10.1038/s41598-020-61077-0
Gridded Population of the World (GPW) v4.11 (population count)

Lickley, M., & Solomon, S. (2018). Drivers, timing and some impacts of global aridity change. Environmental Research Letters, 13(10), 104010. doi:10.1088/1748-9326/aae013
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates) - 10.7927/H4F47M2C

Lieber, R., King, A., Brown, J., Ashcroft, L., Freund, M., & McMichael, C. (2022). ENSO teleconnections more uncertain in regions of lower socioeconomic development. Geophysical Research Letters, 49(21), e2022GL100553. doi:10.1029/2022GL100553
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Liersch, S., Fournet, S., Koch, H., Djibo, A. G., Reinhardt, J., Kortlandt, J., . . . Hattermann, F. F. (2019). Water resources planning in the Upper Niger River basin: Are there gaps between water demand and supply? Journal of Hydrology: Regional Studies, 21, 176-194. doi:10.1016/j.ejrh.2018.12.006
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42

Lieske, D. J., & Lloyd, V. K. (2018). Combining public participatory surveillance and occupancy modelling to predict the distributional response of Ixodes scapularis to climate change. Ticks and Tick-borne Diseases, 9(3), 695-706. doi:10.1016/j.ttbdis.2018.01.018
Gridded Population of the World (GPW) v4 Preliminary release 2 (population density)

Lilleskov, E., McCullough, K., Hergoualc’h, K., del Castillo Torres, D., Chimner, R., Murdiyarso, D., . . . Wayson, C. (2019). Is Indonesian peatland loss a cautionary tale for Peru? A two-country comparison of the magnitude and causes of tropical peatland degradation. Mitigation and Adaptation Strategies for Global Change, 24(4), 591-623. doi:10.1007/s11027-018-9790-3
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Lim, J., Kang, M., & Jung, C. (2019). Effect of national-level spatial distribution of cities on national transport CO2 emissions. Environmental Impact Assessment Review, 77, 162-173. doi:10.1016/j.eiar.2019.04.006
Gridded Population of the World (GPW) v3 (unspecified)
Gridded Population of the World (GPW) v4 (unspecified)

Lim, J.-S., Soares Magalhães, R. J., Chakma, S., You, D.-S., Lee, K.-N., Pak, S.-I., & Kim, E. (2022). Spatial epidemiology of highly pathogenic avian influenza subtype H5N6 in Gyeonggi Province, South Korea, 2016–2017. Transboundary and Emerging Diseases, 69(5), e2431-e2442. doi:10.1111/tbed.14587
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MODIS - MOD13Q1)

Lim, J.-S., Vergne, T., Pak, S.-I., & Kim, E. (2021). Modelling the spatial distribution of ASF-positive wild boar carcasses in South Korea using 2019–2020 national surveillance data. Animals, 11(5), 1208. doi:10.3390/ani11051208
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)

Lim, J. T., Maung, K., Tan, S. T., Ong, S. E., Lim, J. M., Koo, J. R., . . . Dickens, B. S. L. (2021). Estimating direct and spill-over impacts of political elections on COVID-19 transmission using synthetic control methods. PLOS Computational Biology, 17(5), e1008959. doi:10.1371/journal.pcbi.1008959
Gridded Population of the World (GPW) v4.11 (population density)

Lin, J., Huang, B., Kwan, M.-P., Chen, M., & Wang, Q. (2023). COVID-19 infection rate but not severity is associated with availability of greenness in the United States. Landscape and Urban Planning, 233, 104704. doi:10.1016/j.landurbplan.2023.104704
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS - MOD13A1)

Lin, M., Biswas, A., & Bennett, E. M. (2020). Socio-ecological determinants on spatio-temporal changes of groundwater in the Yellow River Basin, China. Science of The Total Environment, 731, 138725. doi:10.1016/j.scitotenv.2020.138725
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
Global Reservoir and Dam (GRanD) v1 (collection)
NASA REMOTE SENSING (GRACE)

Lin, X.-L., Jiang, K., Liu, W.-B., Liu, W., Bu, W.-J., Wang, X.-H., & Mo, L. (2021). Toward a global DNA barcode reference library of the intolerant nonbiting midge genus Rheocricotopus Brundin, 1956. Ecology and Evolution, 11(17), 12161-12172. doi:10.1002/ece3.7979
Gridded Population of the World (GPW) v4.11 (admin unit center points) - 10.7927/H4BC3WMT

Lin, Z., Li, H., Li, Y., Liu, J., Liu, L., Zhang, Q., . . . Lai, Z. (2022). Systematic Utilization Analysis of Mega-Constellation Networks. Paper presented at the 2022 International Wireless Communications and Mobile Computing (IWCMC).
Gridded Population of the World (GPW) v4.11 (population count)

Lin, Z., Li, H., Liu, J., Lai, Z., & Fan, G. (2022). Inter-networking and function optimization for mega-constellations. Paper presented at the 2022 IFIP Networking Conference (IFIP Networking).
Gridded Population of the World (GPW) v4.11 (population count)

Linard, C., Kabaria, C. W., Gilbert, M., Tatem, A. J., Gaughan, A. E., Stevens, F. R., . . . Snow, R. W. (2017). Modelling changing population distributions: an example of the Kenyan Coast, 1979–2009. International Journal of Digital Earth, 10(10), 1017-1029. doi:10.1080/17538947.2016.1275829
Gridded Population of the World (GPW) v3 (collection)
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)
REMOTE SENSING (Landsat)

Lincke, D., Hinkel, J., Mengel, M., & Nicholls, R. J. (2022). Understanding the drivers of coastal flood exposure and risk from 1860 to 2100. Earth's Future, 10(12), e2021EF002584. doi:10.1029/2021EF002584
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Lindersson, S., Brandimarte, L., Mård, J., & Di Baldassarre, G. (2020). A review of freely accessible global datasets for the study of floods, droughts and their interactions with human societies. WIREs Water, 7(3), e1424. doi:10.1002/wat2.1424
Gridded Population of the World (GPW) v4.10 (collection)
Population Dynamics (Global Estimated Net Migration Grids By Decade, v1)
Population Dynamics (Global Population Count Grid Time Series Estimates, v1) - 10.7927/H4CC0XNV
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)

Lindersson, S., Brandimarte, L., Mård, J., & Di Baldassarre, G. (2021). Global riverine flood risk – how do hydrogeomorphic floodplain maps compare to flood hazard maps? Natural Hazards and Earth System Sciences, 21(10), 2921-2948. doi:10.5194/nhess-21-2921-2021
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
Gridded Population of the World (GPW) v4 (population density) - 10.7927//H4NP22DQ
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)

Linh Nguyen, T. N., Pimonsree, S., Prueksakorn, K., Bich Thao, P. T., & Vongruang, P. (2022). Public health and economic impact assessment of PM2.5 from open biomass burning over countries in mainland Southeast Asia during the smog episode. Atmospheric Pollution Research, 13(6), 101418. doi:10.1016/j.apr.2022.101418
Gridded Population of the World (GPW) v4.11 (admin unit center points)
NASA REMOTE SENSING (MODIS)

Linke, S., Lehner, B., Ouellet Dallaire, C., Ariwi, J., Grill, G., Anand, M., . . . Thieme, M. (2019). Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution. Scientific Data, 6(1), 283. doi:10.1038/s41597-019-0300-6
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Lipner, E. M., O’Dell, K., Brey, S. J., Ford, B., Pierce, J. R., Fischer, E. V., & Crooks, J. L. (2019). The associations between clinical respiratory outcomes and ambient wildfire smoke exposure among pediatric asthma patients at National Jewish Health, 2012-2015. GeoHealth, 3(6), 146-159. doi:10.1029/2018gh000142
Gridded Population of the World (GPW) v4.10 (population count)

Lippi, C. A., Stewart-Ibarra, A. M., Loor, M. E. F. B., Zambrano, J. E. D., Lopez, N. A. E., Blackburn, J. K., & Ryan, S. J. (2019). Geographic shifts in Aedes aegypti habitat suitability in Ecuador using larval surveillance data and ecological niche modeling: Implications of climate change for public health vector control. PLoS Neglected Tropical Diseases, 13(4), e0007322. doi:10.1371/journal.pntd.0007322
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4HX19NJ

Liu, B., Zou, X., Yi, S., Sneeuw, N., Cai, J., & Li, J. (2021). Identifying and separating climate- and human-driven water storage anomalies using GRACE satellite data. Remote Sensing of Environment, 263, 112559. doi:10.1016/j.rse.2021.112559
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (GRACE)

Liu, H., Hu, Z., Zhou, M., Zhang, H., Zhang, X., Yue, Y., . . . Hu, B. (2022). PM2.5 drives bacterial functions for carbon, nitrogen, and sulfur cycles in the atmosphere. Environmental Pollution, 295, 118715. doi:10.1016/j.envpol.2021.118715
Gridded Population of the World (GPW) v4 (population density) - 10.7927//H4NP22DQ
NASA REMOTE SENSING (MODIS)

Liu, J. (2021). Mapping high resolution national daily NO2 exposure across mainland China using an ensemble algorithm. Environmental Pollution, 279, 116932. doi:10.1016/j.envpol.2021.116932
Gridded Population of the World (GPW) v4 (population count)
REMOTE SENSING (TROPOMI)

Liu, J. (2023). Generating 250 m-resolution regional NO2 concentration products first from MODIS retrievals using extreme gradient boosting. Air Quality, Atmosphere & Health, 16, 445-458. doi:10.1007/s11869-022-01285-x
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)

Liu, J., & Chen, W. (2022). First satellite-based regional hourly NO2 estimations using a space-time ensemble learning model: A case study for Beijing-Tianjin-Hebei Region, China. Science of The Total Environment, 820, 153289. doi:10.1016/j.scitotenv.2022.153289
Gridded Population of the World (GPW) v4 (unspecified)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)

Liu, J., Fang, P., Que, Y., Zhu, L.-J., Duan, Z., Tang, G., . . . Liu, Y. (2022). A dataset of lake-catchment characteristics for the Tibetan Plateau. Earth System Science Data, 14, 3791-3805. doi:10.5194/essd-14-3791-2022
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
REMOTE SENSING (DMSP-OLS)

Liu, J., Peng, L., Yu, L., Liu, X., Yao, Z., & Zhang, Q. (2023). Reduced rural residential emissions in the Northern China Plain from 2015 to 2021. Science of The Total Environment, 865, 161236. doi:10.1016/j.scitotenv.2022.161236
Gridded Population of the World (GPW) v4.11 (population count)
REMOTE SENSING (Landsat)

Liu, J., Weng, F., & Li, Z. (2022). Ultrahigh-resolution (250 m) regional surface PM2.5 concentrations derived first from MODIS measurements. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-12. doi:10.1109/TGRS.2021.3064191
Gridded Population of the World (GPW) v4 (unspecified)

Liu, J., Yin, H., Tang, X., Zhu, T., Zhang, Q., Liu, Z., . . . Yi, H. (2021). Transition in air pollution, disease burden and health cost in China: A comparative study of long-term and short-term exposure. Environmental Pollution, 277, 116770. doi:10.1016/j.envpol.2021.116770
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - data set unspecified)

Liu, L., Cheng, G., Yang, J., & Cheng, Y. (2023). Population spatialization in Zhengzhou city based on multi-source data and random forest model. Frontiers in Earth Science, 11. doi:10.3389/feart.2023.1092664
Gridded Population of the World (GPW) v4.11 (unspecified)
REMOTE SENSING (LuoJia1-01)

Liu, S., Geng, G., Xiao, Q., Zheng, Y., Liu, X., Cheng, J., & Zhang, Q. (2022). Tracking daily concentrations of PM2.5 chemical composition in China since 2000. Environmental Science & Technology, 56(22), 16517–16527. doi:10.1021/acs.est.2c06510
Gridded Population of the World (GPW) v4.11 (population count)

Liu, S., Tian, H., Bai, X., Zhu, C., Wu, B., Luo, L., . . . Hao, J. (2021). Significant but spatiotemporal-heterogeneous health risks caused by airborne exposure to multiple toxic trace elements in China. Environmental Science & Technology, 55(19), 12818-12830. doi:10.1021/acs.est.1c01775
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)

Liu, S., Tian, H., Luo, L., Bai, X., Zhu, C., Lin, S., . . . Lv, Y. (2022). Health impacts and spatiotemporal variations of fine particulate and its typical toxic constituents in five urban agglomerations of China. Science of The Total Environment, 806(Part 4), 151459. doi:10.1016/j.scitotenv.2021.151459
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)

Liu, S., Wang, Y., Zhang, G. J., Wei, L., Wang, B., & Yu, L. (2022). Contrasting influences of biogeophysical and biogeochemical impacts of historical land use on global economic inequality. Nature Communications, 13(1), 2479. doi:10.1038/s41467-022-30145-6
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)

Liu, S., Xing, J., Wang, S., Ding, D., Chen, L., & Hao, J. (2020). Revealing the impacts of transboundary pollution on PM2.5-related deaths in China. Environment International, 134, 105323. doi:10.1016/j.envint.2019.105323
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM
Satellite-Derived Environmental Indicators (Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, v1) - 10.7927/H4ZK5DQS

Liu, S., Xing, J., Wang, S., Ding, D., Cui, Y., & Hao, J. (2021). Health benefits of emission reduction under 1.5 °C pathways far outweigh climate-related variations in China. Environmental Science & Technology, 55(16), 10957-10966. doi:10.1021/acs.est.1c01583
Gridded Population of the World (GPW) v4.10 (population density)

Liu, T., Marlier, M. E., DeFries, R. S., Westervelt, D. M., Xia, K. R., Fiore, A. M., . . . Milly, G. (2018). Seasonal impact of regional outdoor biomass burning on air pollution in three Indian cities: Delhi, Bengaluru, and Pune. Atmospheric Environment, 172, 83-92. doi:10.1016/j.atmosenv.2017.10.024
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
NASA REMOTE SENSING (MODIS)

Liu, W., Lim, W. H., Sun, F., Mitchell, D., Wang, H., Chen, D., . . . Fischer, E. (2018). Global freshwater availability below normal conditions and population impact under 1.5°C and 2°C stabilization scenarios. Geophysical Research Letters, 45(18), 9803-9813. doi:10.1029/2018GL078789
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted) - 10.7927/H4JQ0XZW

Liu, X., Blackburn, T. M., Song, T., Li, X., Huang, C., & Li, Y. (2019). Risks of biological invasion on the Belt and Road. Current Biology, 29(3), 499-505.e494. doi:10.1016/j.cub.2018.12.036
Gridded Population of the World (GPW) v4 (population density)

Liu, X., Feng, S., Liu, H., & Ji, J. (2021). Patterns and determinants of woody encroachment in the eastern Eurasian steppe. Land Degradation & Development, 32(13), 3536-3549. doi:10.1002/ldr.3938
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)

Liu, X., Huang, Y., Xu, X., Li, X., Li, X., Ciais, P., . . . Zeng, Z. (2020). High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. Nature Sustainability, 3, 564-570. doi:10.1038/s41893-020-0521-x
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - data set unspecified)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (Landsat)

Liu, X., Li, C., Liu, D., Grieneisen, M. L., Yang, F., Chen, C., & Zhan, Y. (2022). Hybrid deep learning models for mapping surface NO2 across China: One complicated model, many simple models, or many complicated models? Atmospheric Research, 278, 106339. doi:10.1016/j.atmosres.2022.106339
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (OMI NO2)

Liu, X., Yang, S., Ye, T., An, R., & Chen, C. (2021). A new approach to estimating flood-affected populations by combining mobility patterns with multi-source data: A case study of Wuhan, China. International Journal of Disaster Risk Reduction, 55, 102106. doi:10.1016/j.ijdrr.2021.102106
Gridded Population of the World (GPW) v4 (collection)

Liu, Y., Cai, W., Lin, X., Li, Z., & Zhang, Y. (2023). Nonlinear El Niño impacts on the global economy under climate change. Nature Communications, 14(1), 5887. doi:10.1038/s41467-023-41551-9
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Liu, Y., Goudreau, S., Oiamo, T., Rainham, D., Hatzopoulou, M., Chen, H., . . . Smargiassi, A. (2020). Comparison of land use regression and random forests models on estimating noise levels in five Canadian cities. Environmental Pollution, 256, 113367. doi:10.1016/j.envpol.2019.113367
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (Landsat)

Liu, Y., Oiamo, T., Rainham, D., Chen, H., Hatzopoulou, M., Brook, J. R., . . . Smargiassi, A. (2021). Integrating random forests and propagation models for high-resolution noise mapping. Environmental Research, 195, 110905. doi:10.1016/j.envres.2021.110905
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (Landsat)

Liu, Y., Zhao, N., Vanos, J. K., & Cao, G. (2019). Revisiting the estimations of PM2.5-attributable mortality with advancements in PM2.5 mapping and mortality statistics. Science of The Total Environment, 666, 499-507. doi:10.1016/j.scitotenv.2019.02.269
Gridded Population of the World (GPW) v4.10 (population count)
Gridded Population of the World (GPW) v4.10 (basic demographic characteristics)

Liu, Z., Ma, T., Du, Y., Pei, T., Yi, J., & Peng, H. (2018). Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records. Transactions in GIS, 22(2), 494-513. doi:10.1111/tgis.12323
Gridded Population of the World (GPW) v2 (Deichmann, Balk, & Yetman 2001)
Gridded Population of the World (GPW) v3 (Balk and Yetman 2004)
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)

Liu, Z., Rieder, H. E., Schmidt, C., Mayer, M., Guo, Y., Winiwarter, W., & Zhang, L. (2023). Optimal reactive nitrogen control pathways identified for cost-effective PM2.5 mitigation in Europe. Nature Communications, 14(1), 4246. doi:10.1038/s41467-023-39900-9
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics) - 10.7927/H46M34XX
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4PN93PB

Liu, Z., Zhou, M., Chen, Y., Chen, D., Pan, Y., Song, T., . . . Lin, Z. (2021). The nonlinear response of fine particulate matter pollution to ammonia emission reductions in North China. Environmental Research Letters, 16(3), 034014. doi:10.1088/1748-9326/abdf86
Gridded Population of the World (GPW) v4.11 (population count)

Lloyd, C. T., Chamberlain, H., Kerr, D., Yetman, G., Pistolesi, L., Stevens, F. R., . . . Tatem, A. J. (2019). Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets. Big Earth Data, 3(2), 108-139. doi:10.1080/20964471.2019.1625151
Gridded Population of the World (GPW) v3 (population density) - 10.7927/H4XK8CG2
Gridded Population of the World (GPW) v4 (data quality indicators) - 10.7927/H49C6VBN
Gridded Population of the World (GPW) v4.11 (national identifier grid)
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4HX19NJ
Global Rural-Urban Mapping Project (GRUMP) v1 (population density) - 10.7927/H4R20Z93
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS Cloud Mask)

Lloyd, C. T., Sorichetta, A., & Tatem, A. J. (2017). High resolution global gridded data for use in population studies. Scientific Data, 4(170001). doi:10.1038/sdata.2017.1
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS NTL)

Loiselle, S. A., Gasparini Fernandes Cunha, D., Shupe, S., Valiente, E., Rocha, L., Heasley, E., . . . Baruch, A. (2016). Micro and macroscale drivers of nutrient concentrations in urban streams in South, Central and North America. PLoS ONE, 11(9), e0162684. doi:10.1371/journal.pone.0162684
Global Agricultural Inputs (phosphorous fertilizer application)
Gridded Population of the World (GPW) v4 Preliminary release 2 (population density)

Lokonon, B. E., Gbemavo, C. D. S. J., Agounde, G., Simbo, D., Samson, R., & Glèlè Kakaï, R. (2023). Modelling the current and future distribution of Caesalpinia bonduc (L.) Roxb: Its implication for future conservation of the species in the Southern Benin. African Journal of Ecology, 61(2), 389-398. doi:10.1111/aje.13122
Gridded Population of the World (GPW) v4.11 (population density)

Long, H., Ge, D., Zhang, Y., Tu, S., Qu, Y., & Ma, L. (2018). Changing man-land interrelations in China's farming area under urbanization and its implications for food security. Journal of Environmental Management, 209, 440-451. doi:10.1016/j.jenvman.2017.12.047
Gridded Population of the World (GPW) v4 (unspecified)

Louca, S. (2022). The rates of global bacterial and archaeal dispersal. The ISME Journal, 16, 159-167. doi:10.1038/s41396-021-01069-8
Gridded Population of the World (GPW) v4.11 (population density)

Loveridge, A. J., Sousa, L. L., Seymour-Smith, J., Hunt, J., Coals, P., O'Donnell, H., . . . Macdonald, D. W. (2020). Evaluating the spatial intensity and demographic impacts of wire-snare bush-meat poaching on large carnivores. Biological Conservation, 244, 108504. doi:10.1016/j.biocon.2020.108504
Gridded Population of the World (GPW) v4.10 (population density)

Low, B. W., Liew, J. H., Tan, H. H., Ahmad, A., Zeng, Y., & Yeo, D. C. J. (2022). The invasion and impacts of the African sharptooth catfish (Clariidae: Clarias gariepinus) in the Malay Peninsula. Freshwater Biology, 67(11), 1925-1937. doi:10.1111/fwb.13984
Gridded Population of the World (GPW) v4 (population density) - 10.7927//H4NP22DQ

LP DAAC. (2018). Using Population and Remote Sensing Data to Study Reforestation. Retrieved from https://lpdaac.usgs.gov/user_resources/data_in_action/using_population_and_remote_sensing_data_study_reforestation
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)

Lu, L., Weng, Q., Xie, Y., Guo, H., & Li, Q. (2019). An assessment of global electric power consumption using the Defense Meteorological Satellite Program-Operational Linescan System nighttime light imagery. Energy, 189, 116351. doi:10.1016/j.energy.2019.116351
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (Landsat)

Lu, M., Zou, Y., Xun, Q., Yu, Z., Jiang, M., Sheng, L., . . . Wang, D. (2021). Anthropogenic disturbances caused declines in the wetland area and carbon pool in China during the last four decades. Global Change Biology, 27(16), 3837-3845. doi:10.1111/gcb.15671
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
Last of the Wild v2 Global Human Influence Index (Geographic) - 10.7927/H4BP00QC
NASA REMOTE SENSING (VIIRS NPP)
REMOTE SENSING (Landsat)

Lu, X., Yuan, D., Chen, Y., & Fung, J. C. H. (2021). Impacts of urbanization and long-term meteorological variations on global PM2.5 and its associated health burden. Environmental Pollution, 270, 116003. doi:10.1016/j.envpol.2020.116003
Gridded Population of the World (GPW) v4.10 (basic demographic characteristics) - 10.7927/H45H7D7F
NASA REMOTE SENSING (AVHRR)
NASA REMOTE SENSING (MERRA-2)

Lucas, T. C. D., Nandi, A. K., Keddie, S. H., Chestnutt, E. G., Howes, R. E., Rumisha, S. F., . . . Weiss, D. J. (2022). Improving disaggregation models of malaria incidence by ensembling non-linear models of prevalence. Spatial and Spatio-temporal Epidemiology, 41, 100357. doi:10.1016/j.sste.2020.100357
Gridded Population of the World (GPW) v4 (unspecified)

Ludolph, L., Šedová, B., & Talevi, M. (2022). Inequality and Security in the Aftermath of Internal Population Displacement Shocks : Evidence from Nigeria. Retrieved from Washington DC: http://documents.worldbank.org/curated/en/099956405182219719/IDU07814c0ce004af042470b7070b7d58954fcdd
Gridded Population of the World (GPW) v4.11 (population count)

Lunt, M. F., Park, S., Li, S., Henne, S., Manning, A. J., Ganesan, A. L., . . . Rigby, M. (2018). Continued emissions of the ozone depleting substance carbon tetrachloride from eastern Asia. Geophysical Research Letters, 45(20), 11423-11430. doi:10.1029/2018GL079500
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Luo, J., Wang, W., Wu, Y., Peng, Y., & Zhang, L. (2021). Analysis of an urban development boundary policy in China based on the IAD framework. Land, 10(8), 855. doi:10.3390/land10080855
Gridded Population of the World (GPW) v4 (unspecified)

Luo, J., Xing, X., Wu, Y., Zhang, W., & Chen, R. S. (2018). Spatio-temporal analysis on built-up land expansion and population growth in the Yangtze River Delta Region, China: From a coordination perspective. Applied Geography, 96, 98-108. doi:10.1016/j.apgeog.2018.05.012
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM

Luo, Z., Wang, X., Yang, S., Cheng, X., Liu, Y., & Hu, J. (2021). Combining the responses of habitat suitability and connectivity to climate change for an East Asian endemic frog. Frontiers in Zoology, 18(1), 14. doi:10.1186/s12983-021-00398-w
Gridded Population of the World (GPW) v4.11 (land and water area) - 10.7927/H4Z60M4Z

Lyon, B., Barnston, A., G., Coffel, E. D., & Horton, R. (2019). Projected increase in the spatial extent of contiguous U.S. summer heat waves and associated attributes. Environmental Research Letters, 14(11), 114029. doi:10.1088/1748-9326/ab4b41
Gridded Population of the World (GPW) v4 (documentation) - 10.7927/H4D50JX4

Lyon, B., Dinku, T., Raman, A., & Thomson, M. C. (2017). Temperature suitability for malaria climbing the Ethiopian Highlands. Environmental Research Letters, 12(6), 064015. doi:10.1088/1748-9326/aa64e6
Gridded Population of the World (GPW) v4 (population density)

Lyon, S. L., & Payne, K. V. (2020). Understanding the Disease Vector Operational Environment by Predicting Presence of Anopheles Mosquito Breeding Sites Using Maximum Entropy Modeling and the Maxent Software Platform. Retrieved from https://erdc-library.erdc.dren.mil/jspui/handle/11681/38125
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H46T0JKB

Ma, J., Sun, Y., Meng, D., Huang, S., Li, N., & Zhu, H. (2021). Accuracy assessment of two global gridded population dataset: A case study in China. Paper presented at the 2021 The 4th International Conference on Information Science and Systems, Edinburgh, United Kingdom. https://doi.org/10.1145/3459955.3460610
Gridded Population of the World (GPW) v4.11 (documentation)

Ma, W., Huang, Z., Cui, J., Boré, A., Chen, G., Qiao, Z., . . . Fellner, J. (2023). Inhalation health risk assessment of incineration and landfill in the Bohai Rim, China. Chemosphere, 314, 137588. doi:10.1016/j.chemosphere.2022.137588
Gridded Population of the World (GPW) v4.11 (population density)

Maamoun, N., Chitkara, P., Yang, J., Shrimali, G., Busby, J., Shidore, S., . . . Urpelainen, J. (2022). Identifying coal plants for early retirement in India: A multidimensional analysis of technical, economic, and environmental factors. Applied Energy, 312, 118644. doi:10.1016/j.apenergy.2022.118644
Gridded Population of the World (GPW) v4.10 (population count)

Maamoun, N., Kennedy, R., Jin, X., & Urpelainen, J. (2020). Identifying coal-fired power plants for early retirement. Renewable and Sustainable Energy Reviews, 126, 109833. doi:10.1016/j.rser.2020.109833
Gridded Population of the World (GPW) v4.10 (population count)

Maamoun, N., Kennedy, R., Peng, W., D'souza, D., Gray, M., Lavelle, S., . . . Urpelainen, J. (2023). Multi-dimensional and region-specific planning for coal retirements. iScience, 26(6), 106739. doi:10.1016/j.isci.2023.106739
Gridded Population of the World (GPW) v4.11 (population count)

Määttä, I., Ferreira, T., & Leßmann, C. (2022). Nighttime lights and wealth in very small areas. Review of Regional Research, 42, 161-190. doi:10.1007/s10037-021-00159-6
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
REMOTE SENSING (DMSP-OLS)

MacDonald, A. J., Hyon, D. W., McDaniels, A., O'Connor, K. E., Swei, A., & Briggs, C. J. (2018). Risk of vector tick exposure initially increases, then declines through time in response to wildfire in California. Ecosphere, 9(5), e02227. doi:10.1002/ecs2.2227
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Macdonald, B. N. J. (2020). Room to Move: Political Accountability of “Lawmakers” in the Kenya National Assembly, 1998-2019. (Ph.D.). Stanford University, Retrieved from https://searchworks.stanford.edu/view/13680879 
Gridded Population of the World (GPW) v3 (population density)
Gridded Population of the World (GPW) v4 (population density)
REMOTE SENSING (DMSP-OLS)

Macdonald, D. W., Bothwell, H. M., Hearn, A. J., Cheyne, S. M., Haidir, I., Hunter, L. T. B., . . . Cushman, S. A. (2018). Multi-scale habitat selection modeling identifies threats and conservation opportunities for the Sunda clouded leopard (Neofelis diardi). Biological Conservation, 227, 92-103. doi:10.1016/j.biocon.2018.08.027
Gridded Population of the World (GPW) v4 (population density)
Last of the Wild v2 (Global Human Footprint (Geographic))
NASA REMOTE SENSING (SRTM)

Macdonald, D. W., Chiaverini, L., Bothwell, H. M., Kaszta, Ż., Ash, E., Bolongon, G., . . . Cushman, S. A. (2020). Predicting biodiversity richness in rapidly changing landscapes: climate, low human pressure or protection as salvation? Biodiversity and Conservation, 29, 4035-4057. doi:10.1007/s10531-020-02062-x
Gridded Population of the World (GPW) v4 (population density)
Last of the Wild v2 (Global Human Footprint (Geographic))

Macklin, M. G., Thomas, C. J., Mudbhatkal, A., Brewer, P. A., Hudson-Edwards, K. A., Lewin, J., . . . Mangalaa, K. R. (2023). Impacts of metal mining on river systems: a global assessment. Science, 381(6664), 1345-1350. doi:10.1126/science.adg6704
Gridded Population of the World (GPW) v4.10 (population count)

MacManus, K., Balk, D., Engin, H., McGranahan, G., & Inman, R. (2021). Estimating population and urban areas at risk of coastal hazards, 1990–2015: How data choices matter. Earth System Science Data, 13(12), 5747-5801. doi:10.5194/essd-13-5747-2021
Gridded Population of the World (GPW) v4.11 (national identifier grid) - 10.7927/H4TD9VDP
Global Rural-Urban Mapping Project (GRUMP) v1.02 (urban extent polygons) - 10.7927/np6p-qe61
Low Elevation Coastal Zone (LECZ) (Urban-Rural Population Estimates, v1) - 10.7927/H4TM782G
Low Elevation Coastal Zone (LECZ) (Urban-Rural Population and Land Area Estimates, v2) - 10.7927/H4MW2F2J
Low Elevation Coastal Zone (LECZ) (Urban-Rural Population and Land Area Estimates, v3) - 10.7927/d1x1-d702
Satellite-Derived Environmental Indicators (VIIRS Plus DMSP Change in Lights (VIIRS+DMSP dLIGHT), v1) - 10.7927/9ryj-6467
REMOTE SENSING (MERIT)

MacNeil, M. A., Chapman, D. D., Heupel, M., Simpfendorfer, C. A., Heithaus, M., Meekan, M., . . . Cinner, J. E. (2020). Global status and conservation potential of reef sharks. Nature, 583(7818), 801-806. doi:10.1038/s41586-020-2519-y
Gridded Population of the World (GPW) v4.11 (population count)

Maffenini, L., Schiavina, M., Freire, S., Melchiorri, M., Pesaresi, M., & Kemper, T. (2020). GHS-POP2 User Guide: Population to Grid Tool User Guide Version 2. Retrieved from Luxembourg: https://doi.org/10.2760/500887
Gridded Population of the World (GPW) v4.10 (population count)

Maggi, F., Tang, F. H. M., Black, A. J., Marks, G. B., & McBratney, A. (2021). The pesticide health risk index - An application to the world's countries. Science of The Total Environment, 801, 149731. doi:10.1016/j.scitotenv.2021.149731
Gridded Population of the World (GPW) v4.11 (population count)

Maggi, F., Tang, F. H. M., la Cecilia, D., & McBratney, A. (2019). PEST-CHEMGRIDS, global gridded maps of the top 20 crop-specific pesticide application rates from 2015 to 2025. Scientific Data, 6(1), 170. doi:10.1038/s41597-019-0169-4
Global Agricultural Lands (Pasture)
Global Agricultural Inputs (nitrogen fertilizer application) - 10.7927/H4Q81B0R
Global Agricultural Inputs (phosphorous fertilizer application) - 10.7927/H4FQ9TJR
Global Agricultural Inputs (PEST-CHEMGRIDS)
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - population density)
NASA (MEaSUREs Global Food Security Support Analysis Data (GFSAD) Crop Mask Global 1 kilometer (km))

Magliocca, N. R., Ellicott, E. A., Ingalls, M. L., Epprecht, M., Hett, C., Nanhthavong, V., & de Bremond, A. C. (2022). Spatio-temporal unevenness in local land system regime shifts caused by land deals in Lao PDR. Ecology and Society, 27(4). doi:10.5751/ES-13405-270407
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (TRMM)

Maino, J. L., Schouten, R., & Umina, P. (2021). Predicting the global invasion of Drosophila suzukii to improve Australian biosecurity preparedness. Journal of Applied Ecology, 58(4), 789-800. doi:10.1111/1365-2664.13812
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates) - 10.7927/H4F47M2C

Maji, K. J. (2020). Substantial changes in PM2.5 pollution and corresponding premature deaths across China during 2015–2019: A model prospective. Science of The Total Environment, 729, 138838. doi:10.1016/j.scitotenv.2020.138838
Gridded Population of the World (GPW) v4 (population count)

Malakar, P., Bhanja, S. N., Dash, A. A., Saha, D., Ray, R. K., Sarkar, S., . . . Mukherjee, A. (2023). Delineating variabilities of groundwater level prediction across the agriculturally intensive transboundary aquifers of South Asia. ACS ES&T Water, 3(6), 1547-1560. doi:10.1021/acsestwater.2c00220
Gridded Population of the World (GPW) v4.11 (population count)

Malek, Ž., & Verburg, P. H. (2017). Mediterranean land systems: Representing diversity and intensity of complex land systems in a dynamic region. Landscape and Urban Planning, 165, 102-116. doi:10.1016/j.landurbplan.2017.05.012
Global Rural-Urban Mapping Project (GRUMP) v1 (urban extent)
Gridded Population of the World (GPW) v4 (population density)

Malek, Ž., & Verburg, P. H. (2018). Adaptation of land management in the Mediterranean under scenarios of irrigation water use and availability. Mitigation and Adaptation Strategies for Global Change, 23(6), 821-837. doi:10.1007/s11027-017-9761-0
Gridded Population of the World (GPW) v4 (unspecified)

Malek, Ž., & Verburg, P. H. (2020). Mapping global patterns of land use decision-making. Global Environmental Change, 65, 102170. doi:10.1016/j.gloenvcha.2020.102170
Global Rural-Urban Mapping Project (GRUMP) v1 (population density) - 10.7927/H4R20Z93
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D
Last of the Wild v2 Global Human Influence Index (Geographic) - 10.7927/H4BP00QC
Poverty Mapping (Global Subnational Prevalence of Child Malnutrition, v1) - 10.7927/H4K64G12

Malek, Ž., Verburg, P. H., Geijzendorffer, I. R., Bondeau, A., & Cramer, W. (2018). Global change effects on land management in the Mediterranean region. Global Environmental Change, 50, 238-254. doi:10.1016/j.gloenvcha.2018.04.007
Gridded Population of the World (GPW) v3 (population density)
Gridded Population of the World (GPW) v4 (population density)

Malhado, A. C. M., Santos, J., Correia, R. A., Campos-Silva, J. V., Teles, D., Costa, M. H., . . . Ladle, R. J. (2020). Monitoring and mapping non-governmental conservation action in Amazonia. Land Use Policy, 94, 104556. doi:10.1016/j.landusepol.2020.104556
Gridded Population of the World (GPW) v4 (population count)

Mammides, C. (2020). A global assessment of the human pressure on the world's lakes. Global Environmental Change, 63, 102084. doi:10.1016/j.gloenvcha.2020.102084
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Mamun, M., & An, K.-G. (2022). Key factors determining water quality, fish community dynamics, and ecological health in an Asian temperate lotic system. Ecological Informatics, 72, 101890. doi:10.1016/j.ecoinf.2022.101890
Gridded Population of the World (GPW) v4 (population density)

Manalo, J. A., Matsumoto, J., Takahashi, H. G., Villafuerte II, M. Q., Olaguera, L. M. P., Ren, G., & Cinco, T. A. (2022). The effect of urbanization on temperature indices in the Philippines. International Journal of Climatology, 42(2), 850-867. doi:10.1002/joc.7276
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
REMOTE SENSING (VIIRS NTL)

Mancini, F., Coghill, G. M., & Lusseau, D. (2018). Using social media to quantify spatial and temporal dynamics of nature-based recreational activities. PLoS ONE, 13(7), e0200565. doi:10.1371/journal.pone.0200565
Gridded Population of the World (GPW) v4 (population count)

Mancino, C., Canestrelli, D., & Maiorano, L. (2022). Going west: Range expansion for loggerhead sea turtles in the Mediterranean Sea under climate change. Global Ecology and Conservation, 38, e02264. doi:10.1016/j.gecco.2022.e02264
Gridded Population of the World (GPW) v4.11 (population count)

Mandal, S., Madhipatla, K. K., Guttikunda, S. K., Kloog, I., Prabhakaran, D., & Schwartz, J. D. (2020). Ensemble averaging based assessment of spatiotemporal variations in ambient PM2.5 concentrations over Delhi, India, during 2010–2016. Atmospheric Environment, 224, 117309. doi:10.1016/j.atmosenv.2020.117309
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS DNB)

Manes, S., Gama-Maia, D., Vaz, S., Pires, A. P. F., Tardin, R. H., Maricato, G., . . . Vale, M. M. (2023). Nature as a solution for shoreline protection against coastal risks associated with ongoing sea-level rise. Ocean & Coastal Management, 235, 106487. doi:10.1016/j.ocecoaman.2023.106487
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Maney, C., Sassen, M., & Hill, S. L. L. (2022). Modelling biodiversity responses to land use in areas of cocoa cultivation. Agriculture, Ecosystems & Environment, 324, 107712. doi:10.1016/j.agee.2021.107712
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Manning, A. J., Redington, A. L., Say, D., O'Doherty, S., Young, D., Simmonds, P. G., . . . Arnold, T. (2021). Evidence of a recent decline in UK emissions of hydrofluorocarbons determined by the InTEM inverse model and atmospheric measurements. Atmospheric Chemistry and Physics, 21, 12739-12755. doi:10.5194/acp-21-12739-2021
Gridded Population of the World (GPW) v4 (population count)

Manning, M. I., Martin, R. V., Hasenkopf, C., Flasher, J., & Li, C. (2018). Diurnal patterns in global fine particulate matter concentration. Environmental Science & Technology Letters, 5(11), 687-691. doi:10.1021/acs.estlett.8b00573
Gridded Population of the World (GPW) v4 Preliminary release 2 (population density)

Mansuy, N., Miller, C., Parisien, M.-A., Parks, S. A., Batllori, E., & Moritz, M. A. (2019). Contrasting human influences and macro-environmental factors on fire activity inside and outside protected areas of North America. Environmental Research Letters, 14(6), 064007. doi:10.1088/1748-9326/ab1bc5
Gridded Population of the World (GPW) v4.10 (population density)
Last of the Wild v2 (Global Human Footprint (Geographic))
NASA REMOTE SENSING (MODIS)

Mao, L., Wu, X., Huang, Z., & Tatem, A. J. (2015). Modeling monthly flows of global air travel passengers: An open-access data resource. Journal of Transport Geography, 48, 52-60. doi:10.1016/j.jtrangeo.2015.08.017
Gridded Population of the World (GPW) v4 (population count)

Mao, X., Chen, C., Comer, B., & Rutherford, D. (2019). Costs and Benefits of a Pearl River Delta Emission Control Area. Retrieved from https://theicct.org/publications/pearl-river-delta-eca-201907
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates) - 10.7927/H4F47M2C

Mapulanga, A. M., & Naito, H. (2019). Effect of deforestation on access to clean drinking water. Proceedings of the National Academy of Sciences, 116(17), 8249-8254. doi:10.1073/pnas.1814970116
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
REMOTE SENSING (unspecified)

Marcantonio, M., Metz, M., Baldacchino, F., Arnoldi, D., Montarsi, F., Capelli, G., . . . Rizzoli, A. (2016). First assessment of potential distribution and dispersal capacity of the emerging invasive mosquito Aedes koreicus in Northeast Italy. Parasites & Vectors, 9(1), 1-19. doi:10.1186/s13071-016-1340-9
Gridded Population of the World (GPW) v4 (collection)
NASA REMOTE SENSING (MODIS - MOD13Q1)
NASA REMOTE SENSING (MODIS - MOD09A1)

Margolis, E. Q., Guiterman, C. H., Chavardès, R. D., Coop, J. D., Copes-Gerbitz, K., Dawe, D. A., . . . Weisberg, P. J. (2022). The North American tree-ring fire-scar network. Ecosphere, 13(7), e4159. doi:10.1002/ecs2.4159
Gridded Population of the World (GPW) v4.11 (population density)

Marjani, M., Ahmadi, S. A., & Mahdianpari, M. (2023). FirePred: A hybrid multi-temporal convolutional neural network model for wildfire spread prediction. Ecological Informatics, 78, 102282. doi:10.1016/j.ecoinf.2023.102282
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)

Marlier, M. E., Liu, T., Yu, K., Buonocore, J. J., Koplitz, S. N., DeFries, R. S., . . . Myers, S. S. (2019). Fires, smoke exposure, and public health: An integrative framework to maximize health benefits from peatland restoration. GeoHealth, 3(7), 178-189. doi:10.1029/2019GH000191
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4D50JX4
Global Roads (Global Roads Open Access Data Set (gROADS), v1) - 10.7927/H4VD6WCT
NASA REMOTE SENSING (MODIS - MCD64A1)

Marquez, N., Garimella, K., Toomet, O., Weber, I. G., & Zagheni, E. (2019). Segregation and sentiment: Estimating refugee segregation and its effects using digital trace data. In A. Salah, A. Pentland, B. Lepri, & E. Letouzé (Eds.), Guide to Mobile Data Analytics in Refugee Scenarios (pp. 265-282). Cham: Springer.
Gridded Population of the World (GPW) v4 (population density)

Martin, C., Young, C. A., Valluzzi, L., & Duarte, C. M. (2022). Ocean sediments as the global sink for marine micro- and mesoplastics. Limnology and Oceanography Letters, 7(3), 235-243. doi:10.1002/lol2.10257
Gridded Population of the World (GPW) v4 (population density)

Martin, C., Zhang, Q., Zhai, D., Zhang, X., & Duarte, C. M. (2021). Anthropogenic litter density and composition data acquired flying commercial drones on sandy beaches along the Saudi Arabian Red Sea. Data in Brief, 36, 107056. doi:10.1016/j.dib.2021.107056
Gridded Population of the World (GPW) v4.11 (population density)

Martin, C., Zhang, Q., Zhai, D., Zhang, X., & Duarte, C. M. (2021). Enabling a large-scale assessment of litter along Saudi Arabian red sea shores by combining drones and machine learning. Environmental Pollution, 277, 116730. doi:10.1016/j.envpol.2021.116730
Gridded Population of the World (GPW) v4.11 (population density)

Martin, R. V., Brauer, M., van Donkelaar, A., Shaddick, G., Narain, U., & Dey, S. (2019). No one knows which city has the highest concentration of fine particulate matter. Atmospheric Environment: X, 3, 100040. doi:10.1016/j.aeaoa.2019.100040
Gridded Population of the World (GPW) v4 (unspecified)

Martínez Arranz, A., Thomson, R., Zech, S., Hegde, G., Arunachalam, D., & Rao, A. B. (2021). The uneven expansion of electricity supply in India: The logics of clientelism, incrementalism and maximin. Energy Research & Social Science, 78, 102126. doi:10.1016/j.erss.2021.102126
Gridded Population of the World (GPW) v4.10 (admin unit center points) - 10.7927/h46h4fct
Global Rural-Urban Mapping Project (GRUMP) v1.01 (urban extent) - 10.7927/H4Z31WKF

Martinez, J. F., MacManus, K., Stokes, E. C., Wang, Z., & de Sherbinin, A. (2023). Suitability of NASA's Black Marble Daily Nighttime Lights for population studies at varying spatial and temporal scales. Remote Sensing, 15(10), 2611. doi:10.3390/rs15102611
Gridded Population of the World (GPW) v4.11 (national identifier grid) - 10.7927/H4TD9VDP
Global Roads (Global Roads Open Access Data Set (gROADS), v1) - 10.7927/H4VD6WCT
NASA REMOTE SENSING (Black Marble -VIIRS)

Martínez-Meyer, E., González-Bernal, A., Velasco, J. A., Swetnam, T. L., González-Saucedo, Z. Y., Servín, J., . . . Heffelfinger, J. R. (2021). Rangewide habitat suitability analysis for the Mexican wolf (Canis lupus baileyi) to identify recovery areas in its historical distribution. Diversity and Distributions, 27(4), 642-654. doi:10.1111/ddi.13222
Gridded Population of the World (GPW) v4 (population density)
Last of the Wild v2 Global Human Influence Index (Geographic)

Martini, G., Bracci, A., Riches, L., Jaiswal, S., Corea, M., Rivers, J., . . . Omodei, E. (2022). Machine learning can guide food security efforts when primary data are not available. Nature Food, 3, 716-728. doi:10.1038/s43016-022-00587-8
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Martini, J., Walther, F., Schenekar, T., Birnstiel, E., Wüthrich, R., Oester, R., . . . Vitecek, S. (2023). The last hideout: Abundance patterns of the not-quite-yet extinct mayfly Prosopistoma pennigerum in the Albanian Vjosa River network. Insect Conservation and Diversity, 16(2), 285-297. doi:10.1111/icad.12620
Gridded Population of the World (GPW) v4.11 (population density)

Masroor, M., Razavi-Termeh, S. V., Rahaman, M. H., Choudhari, P., Kulimushi, L. C., & Sajjad, H. (2023). Adaptive neuro fuzzy inference system (ANFIS) machine learning algorithm for assessing environmental and socio-economic vulnerability to drought: a study in Godavari middle sub-basin, India. Stochastic Environmental Research and Risk Assessment, 37, 233–259. doi:10.1007/s00477-022-02292-1
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Masson, V., Heldens, W., Bocher, E., Bonhomme, M., Bucher, B., Burmeister, C., . . . Zeidler, J. (2020). City-descriptive input data for urban climate models: Model requirements, data sources and challenges. Urban Climate, 31, 100536. doi:10.1016/j.uclim.2019.100536
Gridded Population of the World (GPW) v4.10 (population density)

Matsiuk, N. (2022). Thrive, survive or perish: the impact of regional autonomy on the demographic dynamics of Italian Alpine territories. Journal of Regional Science, 62(5), 1512-1558. doi:10.1111/jors.12613
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
REMOTE SENSING (DMSP-OLS)

Matsumoto, K. i., Tachiiri, K., & Su, X. (2021). Heat stress, labor productivity, and economic impacts: analysis of climate change impacts using two-way coupled modeling. Environmental Research Communications, 3(12), 125001. doi:10.1088/2515-7620/ac3e14
Gridded Population of the World (GPW) v4.11 (population count)

Matsumoto, T. (2019). Devolution and Local Development in Emerging States: The Case of Kenya. In Y. Takagi, V. Kanchoochat, & T. Sonobe (Eds.), Developmental State Building: The Politics of Emerging Economies (pp. 157-175). Singapore: Springer Singapore.
Gridded Population of the World (GPW) v3 (population density future estimates) - 10.7927/H4ST7MRB
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS)

Matthews, T., Wilby, R. L., & Murphy, C. (2019). An emerging tropical cyclone–deadly heat compound hazard. Nature Climate Change, 9(8), 602-606. doi:10.1038/s41558-019-0525-6
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted)

Mattsson, N., Verendel, V., Hedenus, F., & Reichenberg, L. (2019). An Autopilot for Energy Models - Automatic Generation of Renewable Supply Curves, Hourly Capacity Factors and Hourly Synthetic Electricity Demand for Arbitrary World Regions. Retrieved from Gothenburg, Sweden: https://research.chalmers.se/publication/514510
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Matyushok, V. M., Baranova, N. M., & Sorokin, L. V. (2020). Human capital impact for sustainable economic growth. In S. Sahdev, R. B. Singh, & M. Kumar (Eds.), Geoecology of Landscape Dynamics (pp. 21-36). Singapore: Springer Singapore.
Gridded Population of the World (GPW) v4 (population density)

Mayer, M., Olsen, K., Schulz, B., Matzen, J., Nowak, C., Thomsen, P. F., . . . Sunde, P. (2022). Occurrence and livestock depredation patterns by wolves in highly cultivated landscapes. Frontiers in Ecology and Evolution, 10. doi:10.3389/fevo.2022.783027
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Mayer, T., Poortinga, A., Bhandari, B., Nicolau, A. P., Markert, K., Thwal, N. S., . . . Saah, D. (2021). Deep learning approach for Sentinel-1 surface water mapping leveraging Google Earth Engine. ISPRS Open Journal of Photogrammetry and Remote Sensing, 2, 100005. doi:10.1016/j.ophoto.2021.100005
Gridded Population of the World (GPW) v4 (population density)
REMOTE SENSING (Landsat)
REMOTE SENSING (Sentinel-1)

Maystadt, J.-F., Mueller, V., Van Den Hoek, J., & van Weezel, S. (2020). Vegetation changes attributable to refugees in Africa coincide with agricultural deforestation. Environmental Research Letters, 15(4), 044008. doi:10.1088/1748-9326/ab6d7c
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (MODIS - MCD12Q1)
NASA REMOTE SENSING (MODIS - MCD43A4)
NASA REMOTE SENSING (MODIS - MOD13C2)
NASA REMOTE SENSING (MODIS - MOD17A3H)

Mazzoleni, M., Mård, J., Rusca, M., Odongo, V., Lindersson, S., & Di Baldassarre, G. (2021). Floodplains in the Anthropocene: A global analysis of the interplay between human population, built environment and flood severity. Water Resources Research, 57(2), e2020WR027744. doi:10.1029/2020WR027744
Gridded Population of the World (GPW) v4 (population density)

McClanahan, T. R., D’Agata, S., Graham, N. A. J., Kodia, M. A., & Maina, J. M. (2023). Multivariate environment-fish biomass model informs sustainability and lost income in Indian Ocean coral reefs. Marine Policy, 152, 105590. doi:10.1016/j.marpol.2023.105590
Gridded Population of the World (GPW) v4 (population count)
REMOTE SENSING (GlobColour)

McDonald, R. I., Beatley, T., & Elmqvist, T. (2018). The green soul of the concrete jungle: the urban century, the urban psychological penalty, and the role of nature. Sustainable Earth, 1(1), 3. doi:10.1186/s42055-018-0002-5
Gridded Population of the World (GPW) v4 (population density)

McDonald, R. I., Mansur, A. V., Ascensão, F., Colbert, M. l., Crossman, K., Elmqvist, T., . . . Ziter, C. (2020). Research gaps in knowledge of the impact of urban growth on biodiversity. Nature Sustainability, 3, 16-24. doi:10.1038/s41893-019-0436-6
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)

McDonnell, T. G., Mehr, J. A., & Ning, A. (2018). Multidisciplinary design optimization analysis of flexible solar-regenerative high-altitude long-endurance aircraft. Paper presented at the 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, AIAA SciTech Forum, Kissimmee. https://doi.org/10.2514/6.2018-0107
Gridded Population of the World (GPW) v4 (collection)

McDowell, G., & Guo, J. (2021). A nationally coherent characterization and quantification of mountain systems in Canada. Mountain Research and Development, 41(2), R21-R31. doi:10.1659/MRD-JOURNAL-D-20-00071.1
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

McDuffie, E. E., Smith, S. J., O'Rourke, P., Tibrewal, K., Venkataraman, C., Marais, E. A., . . . Martin, R. V. (2020). A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel-specific sources (1970-2017): An application of the Community Emissions Data System (CEDS). Earth System Science Data, 12, 3413-3442. doi:10.5194/essd-12-3413-2020
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - data set unspecified)

McKeen, T., Bondarenko, M., Kerr, D., Esch, T., Marconcini, M., Palacios-Lopez, D., . . . Sorichetta, A. (2023). High-resolution gridded population datasets for Latin America and the Caribbean using official statistics. Scientific Data, 10(1), 436. doi:10.1038/s41597-023-02305-w
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4PN93PB
Global Rural-Urban Mapping Project (GRUMP) v1 (population density) - 10.7927/H4R20Z93
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (VIIRS NTL)

McLachlan, M. S., Undeman, E., Zhao, F., & MacLeod, M. (2018). Predicting global scale exposure of humans to PCB 153 from historical emissions. Environmental Science: Processes & Impacts, 20(5), 747-756. doi:10.1039/C8EM00023A
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4HX19NJ

McMahon, K., & Gray, C. (2021). Climate change, social vulnerability and child nutrition in South Asia. Global Environmental Change, 71, 102414. doi:10.1016/j.gloenvcha.2021.102414
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

McManamay, R. A., Brinkley, C., Vernon, C. R., Raj, S., & Rice, J. S. (2022). Urban land teleconnections in the United States: A graphical network approach. Computers, Environment and Urban Systems, 95, 101822. doi:10.1016/j.compenvurbsys.2022.101822
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
Population Dynamics (Global Population Count Grid Time Series Estimates, v1) - 10.7927/H4CC0XNV

McNeill, J., Snider, G., Weagle, C. L., Walsh, B., Bissonnette, P., Stone, E., . . . Martin, R. V. (2020). Large global variations in measured airborne metal concentrations driven by anthropogenic sources. Scientific Reports, 10(1), 21817. doi:10.1038/s41598-020-78789-y
Gridded Population of the World (GPW) v4 (population density)

Mechenich, M. F., & Žliobaitė, I. (2023). Eco-ISEA3H, a machine learning ready spatial database for ecometric and species distribution modeling. Scientific Data, 10(1), 77. doi:10.1038/s41597-023-01966-x
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MODIS)

Mechiche-Alami, A., Yagoubi, J., & Nicholas, K. A. (2021). Agricultural land acquisitions unlikely to address the food security needs of African countries. World Development, 141, 105384. doi:10.1016/j.worlddev.2020.105384
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Meehan, T. D., Michel, N. L., & Rue, H. (2019). Spatial modeling of Audubon Christmas Bird Counts reveals fine-scale patterns and drivers of relative abundance trends. Ecosphere, 10(4), e02707. doi:10.1002/ecs2.2707
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4BG2KXS
Population Dynamics (Global Population Count Grid Time Series Estimates, v1) - 10.7927/H4CC0XNV

Mehring, M., Mehlhaus, N., Ott, E., & Hummel, D. (2020). A systematic review of biodiversity and demographic change: A misinterpreted relationship? Ambio, 49, 1297-1312. doi:10.1007/s13280-019-01276-w
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4HX19NJ

Meinecke, L., Soofi, M., Riechers, M., Khorozyan, I., Hosseini, H., Schwarze, S., & Waltert, M. (2018). Crop variety and prey richness affect spatial patterns of human-wildlife conflicts in Iran’s Hyrcanian forests. Journal for Nature Conservation, 43, 165-172. doi:10.1016/j.jnc.2018.04.005
Gridded Population of the World (GPW) v4 (population density)

Melchiorri, M., Florczyk, A., Freire, S., Schiavina, M., Pesaresi, M., & Kemper, T. (2018). Unveiling 25 years of planetary urbanization with remote sensing: Perspectives from the Global Human Settlement Layer. Remote Sensing, 10(5), 768. doi:10.3390/rs10050768
Gridded Population of the World (GPW) v4 (population density)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)
REMOTE SENSING (Landsat)

Melchiorri, M., & Siragusa, A. (2018). Analyzing Cities with the Global Human Settlement Layer: A Methodology to Compare Urban Growth Using Remote Sensing Data. Paper presented at the Smart and Sustainable Planning for Cities and Regions, Cham.
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Mellor, K. C., Meyer, A., Elkholly, D. A., Fournié, G., Long, P. T., Inui, K., . . . Stevens, K. B. (2018). Comparative epidemiology of highly pathogenic avian influenza virus H5N1 and H5N6 in Vietnamese live bird markets: Spatiotemporal patterns of distribution and risk factors. Frontiers in Veterinary Science, 5(51). doi:10.3389/fvets.2018.00051
Gridded Population of the World (GPW) v4 (population density)

Melo Araújo, S. C., Ceron, K., & Guedes, T. B. (2022). Use of geospatial analyses to address snakebite hotspots in mid-northern Brazil – A direction to health planning in shortfall biodiversity knowledge areas. Toxicon, 213, 43-51. doi:10.1016/j.toxicon.2022.03.012
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Menéndez, P., Losada, I. J., Torres-Ortega, S., Narayan, S., & Beck, M. W. (2020). The global flood protection benefits of mangroves. Scientific Reports, 10(1), 4404. doi:10.1038/s41598-020-61136-6
Gridded Population of the World (GPW) v4 (unspecified)
NASA REMOTE SENSING (SRTM)

Menéndez, P., Losada, I. J., Torres-Ortega, S., Toimil, A., & Beck, M. W. (2019). Assessing the effects of using high-quality data and high-resolution models in valuing flood protection services of mangroves. PLoS ONE, 14(8), e0220941. doi:10.1371/journal.pone.0220941
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Meng, J., Li, C., Martin, R. V., van Donkelaar, A., Hystad, P., & Brauer, M. (2019). Estimated long-term (1981-2016) concentrations of ambient fine particulate matter across North America from chemical transport modeling, satellite remote sensing and ground-based measurements. Environmental Science & Technology, 53(9), 5071-5079. doi:10.1021/acs.est.8b06875
Gridded Population of the World (GPW) v4 (collection)
Population Dynamics (Global Population Count Grid Time Series Estimates, v1)
NASA REMOTE SENSING (MODIS)

Meng, J., Martin, R. V., Li, C., van Donkelaar, A., Tzompa-Sosa, Z. A., Yue, X., . . . Burnett, R. T. (2019). Source Contributions to Ambient Fine Particulate Matter for Canada. Environmental Science & Technology, 53(17), 10269-10278. doi:10.1021/acs.est.9b02461
Gridded Population of the World (GPW) v4 (unspecified)

Meng, X., Hang, Y., Lin, X., Li, T., Wang, T., Cao, J., . . . Liu, Y. (2023). A satellite-driven model to estimate long-term particulate sulfate levels and attributable mortality burden in China. Environment International, 171, 107740. doi:10.1016/j.envint.2023.107740
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)
NASA REMOTE SENSING (MISR)

Merkens, J.-L., & Vafeidis, A. (2018). Using information on settlement patterns to improve the spatial distribution of population in coastal impact assessments. Sustainability, 10(9), 3170. doi:10.3390/su10093170
Gridded Population of the World (GPW) v4.10 (population density)
Global Rural-Urban Mapping Project (GRUMP) v1 (population count)

Merz, L., Yang, D., & Hull, V. (2020). A metacoupling framework for exploring transboundary watershed management. Sustainability, 12(5), 1879. doi:10.3390/su12051879
Gridded Population of the World (GPW) v4 (population count)

Messina, J. P., Brady, O. J., Golding, N., Kraemer, M. U. G., Wint, G. R. W., Ray, S. E., . . . Hay, S. I. (2019). The current and future global distribution and population at risk of dengue. Nature Microbiology, 4, 1508-1515. doi:10.1038/s41564-019-0476-8
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)

Messina, J. P., Kraemer, M. U. G., Brady, O. J., Pigott, D. M., Shearer, F. M., Weiss, D. J., . . . Hay, S. I. (2016). Mapping global environmental suitability for Zika virus. eLife, 5, e15272. doi:10.7554/eLife.15272
Gridded Population of the World (GPW) v4 (collection)
NASA REMOTE SENSING (MODIS EVI)

Mester, B., Frieler, K., & Schewe, J. (2023). Human displacements, fatalities, and economic damages linked to remotely observed floods. Scientific Data, 10(1), 482. doi:10.1038/s41597-023-02376-9
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics) - 10.7927/H46M34XX
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
Natural Disasters (Geocoded Disasters (GDIS) Dataset, v1)
NASA REMOTE SENSING (MODIS)

Metcalf, C. J. E., Andriamandimby, S. F., Baker, R. E., Glennon, E. E., Hampson, K., Hollingsworth, T. D., . . . Wesolowski, A. (2021). Challenges in evaluating risks and policy options around endemic establishment or elimination of novel pathogens. Epidemics, 37, 100507. doi:10.1016/j.epidem.2021.100507
Gridded Population of the World (GPW) v4.11 (unspecified)

Metelmann, S., Caminade, C., Jones, A. E., Medlock, J. M., Baylis, M., & Morse, A. P. (2019). The UK's suitability for Aedes albopictus in current and future climates. Journal of the Royal Society Interface, 16(152), 20180761. doi:10.1098/rsif.2018.0761
Gridded Population of the World (GPW) v4 (population density)

Metelmann, S., Liu, X., Lu, L., Caminade, C., Liu, K., Cao, L., . . . Liu, Q. (2021). Assessing the suitability for Aedes albopictus and dengue transmission risk in China with a delay differential equation model. PLoS Neglected Tropical Diseases, 15(3), e0009153. doi:10.1371/journal.pntd.0009153
Gridded Population of the World (GPW) v4.11 (population density)

Metya, A., Dagupta, P., Halder, S., Chakraborty, S., & Tiwari, Y. K. (2020). COVID-19 lockdowns improve air quality in the South-East Asian regions, as seen by the remote sensing satellites. Aerosol and Air Quality Research, 20(8), 1772-1782. doi:10.4209/aaqr.2020.05.0240
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (AIRS)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (OMI NO2)

Meunier, S., Kitanidis, P. K., Cordier, A., & MacDonald, A. M. (2023). Aquifer conditions, not irradiance determine the potential of photovoltaic energy for groundwater pumping across Africa. Communications Earth & Environment, 4(1), 52. doi:10.1038/s43247-023-00695-8
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Meyer, G., Bell, M. J., Doolette, C. L., Brunetti, G., Zhang, Y., Lombi, E., & Kopittke, P. M. (2020). Plant-available phosphorus in highly concentrated fertilizer bands: Effects of soil type, phosphorus form, and coapplied potassium. Journal of Agricultural and Food Chemistry, 68(29), 7571-7580. doi:10.1021/acs.jafc.0c01287
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4BG2KXS

Meyer, M. F., Brousil, M. R., Cramer, A. N., Lanouette, B. P., Padowski, J. C., & Hampton, S. E. (2020). The global lake area, climate, and population dataset: A new tool for addressing critical limnological questions. Limnology and Oceanography Bulletin, 29(4), 110-116. doi:10.1002/lob.10406
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - data set unspecified)

Meyer, M. F., Labou, S. G., Cramer, A. N., Brousil, M. R., & Luff, B. T. (2020). The global lake area, climate, and population dataset. Scientific Data, 7(1), 174. doi:10.1038/s41597-020-0517-4
Gridded Population of the World (GPW) v3 (population count) - 10.​7927/​H4639MPP
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Meyer, R. (2016, 2016/02/23). Facebook Is Making a Map of Everyone in the World. The Atlantic. Retrieved from http://www.theatlantic.com/technology/archive/2016/02/facebook-makes-a-new-map-of-everyone-in-the-world/470487/
Gridded Population of the World (GPW) v4 (collection)

Middleton, T. A., Parsons, B., & Walker, R. T. (2018). Comparison of seismic and geodetic strain rates at the margins of the Ordos Plateau, northern China. Geophysical Journal International, 212(2), 988-1009. doi:10.1093/gji/ggx446
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Midzi, N., Kavhu, B., Manangazira, P., Phiri, I., Mutambu, S. L., Tshuma, C., . . . Masocha, M. (2018). Inclusion of edaphic predictors for enhancement of models to determine distribution of soil-transmitted helminths: the case of Zimbabwe. Parasites & Vectors, 11(1), 13pp. doi:10.1186/s13071-017-2586-6
Gridded Population of the World (GPW) v4 (population density)

Mihaylova, I. (2023). Perpetuating the malign legacy of colonialism? Traditional chiefs’ power and deforestation in Sierra Leone. World Development, 164, 106176. doi:10.1016/j.worlddev.2022.106176
Gridded Population of the World (GPW) v4.11 (population count)
REMOTE SENSING (DMSP-OLS)

Milanesi, P., Breiner, F. T., Puopolo, F., & Holderegger, R. (2017). European human-dominated landscapes provide ample space for the recolonization of large carnivore populations under future land change scenarios. Ecography, 40(12), 1359-1368. doi:10.1111/ecog.02223
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
NASA REMOTE SENSING (ASTER GDEM)

Milanesi, P., Herrando, S., Pla, M., Villero, D., & Keller, V. (2017). Towards continental bird distribution models:  Environmental variables for the second European breeding bird atlas and identification of priorities for further surveys. Vogelwelt, 137(1), 53-60. Retrieved from https://www.researchgate.net/publication/314186852
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (GLAS LIDAR)

Milda, D., Ramesh, T., Kalle, R., Gayathri, V., & Thanikodi, M. (2020). Ranger survey reveals conservation issues across Protected and outside Protected Areas in southern India. Global Ecology and Conservation, 24, e01256. doi:10.1016/j.gecco.2020.e01256
Gridded Population of the World (GPW) v4 (population count)

Milda, D., Ramesh, T., Kalle, R., Gayathri, V., Thanikodi, M., & Ashish, K. (2023). Factors driving human–wild pig interactions: implications for wildlife conflict management in southern parts of India. Biological Invasions, 25, 221-235. doi:10.1007/s10530-022-02911-6
Gridded Population of the World (GPW) v4 (population count)

Miller, P. W., & Mote, T. L. (2018). The algorithmic detection of pulse thunderstorms within a large, mostly nonsevere sample. Meteorological Applications, 25(4), 629-641. doi:10.1002/met.1728
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4D50JX4

Min, K.-D., Kim, S., Cho, Y. Y., & Kim, S.-Y. (2022). Sensitivity of internet-based surveillance for unexplained death tend to be poor in low-income countries. Journal of Biomedical Translational Research, 23(4), 191-201. doi:10.12729/jbtr.2022.23.4.191
Gridded Population of the World (GPW) v4.10 (population count)
REMOTE SENSING (DMSP-OLS)

Minor, K., Bjerre-Nielsen, A., Jonasdottir, S. S., Lehmann, S., & Obradovich, N. (2022). Rising temperatures erode human sleep globally. One Earth, 5(5), 534-549. doi:10.1016/j.oneear.2022.04.008
Gridded Population of the World (GPW) v4.11 (population count)

Minson, S. E., Saunders, J. K., Bunn, J. J., Cochran, E. S., Baltay, A. S., Kilb, D. L., . . . Kodera, Y. (2020). Real‐time performance of the PLUM earthquake early warning method during the 2019 M 6.4 and 7.1 Ridgecrest, California, earthquakes. Bulletin of the Seismological Society of America, 110(4), 1887-1903. doi:10.1785/0120200021
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4BG2KXS

Mir, R. R., & Parvez, I. A. (2020). Ground motion modelling in northwestern Himalaya using stochastic finite-fault method. Natural Hazards, 103, 1987-2007. doi:10.1007/s11069-020-04068-8
Gridded Population of the World (GPW) v4.11 (population count)

Miranda, E. B. P., Menezes, J. F. S., Farias, C. C. L., Munn, C., & Peres, C. A. (2019). Species distribution modeling reveals strongholds and potential reintroduction areas for the world’s largest eagle. PLoS ONE, 14(5), e0216323. doi:10.1371/journal.pone.0216323
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ
NASA REMOTE SENSING (MODIS)

Mirski, P., Cenian, Z., Dagys, M., Daroczi, S., Dementavičius, D., Maciorowski, G., . . . Väli, Ü. (2021). Sex-, landscape- and climate-dependent patterns of home-range size – a macroscale study on an avian generalist predator. Ibis, 163(2), 641-657. doi:10.1111/ibi.12894
Gridded Population of the World (GPW) v4.10 (population density)
NASA REMOTE SENSING (MODIS)

Mirza, M. U., Xu, C., Bavel, B. v., van Nes, E. H., & Scheffer, M. (2021). Global inequality remotely sensed. Proceedings of the National Academy of Sciences, 118(18), e1919913118. doi:10.1073/pnas.1919913118
Gridded Population of the World (GPW) v3 (population count)
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS NTL)

Mishra, B., Panthi, S., Poudel, S., & Ghimire, B. R. (2023). Forest fire pattern and vulnerability mapping using deep learning in Nepal. Fire Ecology, 19(1), 3. doi:10.1186/s42408-022-00162-3
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
NASA REMOTE SENSING (MODIS)

Mitoma, H., Nagashima, F., Kagawa, S., & Nansai, K. (2021). Critical supply chains for mitigating PM2.5 emission-related mortalities in India. Scientific Reports, 11(1), 11914. doi:10.1038/s41598-021-91438-2
Gridded Population of the World (GPW) v4.10 (basic demographic characteristics)

Miyazaki, K., Bowman, K., Sekiya, T., Jiang, Z., Chen, X., Eskes, H., . . . Shindell, D. (2020). Air quality response in China linked to the 2019 novel coronavirus (COVID-19) lockdown. Geophysical Research Letters, 47(19), e2020GL089252. doi:10.1029/2020GL089252
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
NASA REMOTE SENSING (Aura Microwave Limb Sounder)
NASA REMOTE SENSING (MOPITT)
NASA REMOTE SENSING (OMI NO2)
REMOTE SENSING (TROPOMI)

Mo, H., You, Y., Wu, L., Yan, F., Chang, M., Wang, W., . . . Wang, X. (2023). Potential impact of industrial transfer on PM2.5 and economic development under scenarios oriented by different objectives in Guangdong, China. Environmental Pollution, 316(Part 2), 120562. doi:10.1016/j.envpol.2022.120562
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Moch, J. M., Mickley, L. J., Eastham, S. D., Lundgren, E. W., Shah, V., Buonocore, J. J., . . . Tai, A. P. K. (2023). Overlooked long-term atmospheric chemical feedbacks alter the impact of solar geoengineering: Implications for tropospheric oxidative capacity. AGU Advances, 4(5), e2023AV000911. doi:10.1029/2023AV000911
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Modi, P., Hanasaki, N., Yamazaki, D., Boulange, J. E. S., & Oki, T. (2022). Sensitivity of subregional distribution of socioeconomic conditions to the global assessment of water scarcity. Communications Earth & Environment, 3(1), 144. doi:10.1038/s43247-022-00475-w
Gridded Population of the World (GPW) v4.11 (admin unit center points)
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Mohanty, M. P., & Simonovic, S. P. (2021). Understanding dynamics of population flood exposure in Canada with multiple high-resolution population datasets. Science of The Total Environment, 759, 143559. doi:10.1016/j.scitotenv.2020.143559
Gridded Population of the World (GPW) v3 (population density) - 10.7927/H4XK8CG2
Gridded Population of the World (GPW) v4 (documentation) - 10.7927/H4B56GPT

Molino, G. D., Carr, J. A., Ganju, N. K., & Kirwan, M. L. (2022). Variability in marsh migration potential determined by topographic rather than anthropogenic constraints in the Chesapeake Bay region. Limnology and Oceanography Letters, 7(4), 321-331. doi:10.1002/lol2.10262
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted)

Molla, A., Di, L., Guo, L., Zhang, C., & Chen, F. (2022). Spatio-temporal responses of precipitation to urbanization with Google Earth Engine: A case study for Lagos, Nigeria. Urban Science, 6(2), 40. doi:10.3390/urbansci6020040
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS DNB)

Mollenhauer, H., Kasner, M., Haase, P., Peterseil, J., Wohner, C., Frenzel, M., . . . Zacharias, S. (2018). Long-term environmental monitoring infrastructures in Europe: observations, measurements, scales, and socio-ecological representativeness. Science of The Total Environment, 624, 968-978. doi:10.1016/j.scitotenv.2017.12.095
Gridded Population of the World (GPW) v4 (population density)

Molnár, G., Kovács, A., & Gál, T. (2020). How does anthropogenic heating affect the thermal environment in a medium-sized Central European city? A case study in Szeged, Hungary. Urban Climate, 34, 100673. doi:10.1016/j.uclim.2020.100673
Gridded Population of the World (GPW) v4.10 (documentation) - 10.7927/H4B56GPT

Molnár, G., Ürge-Vorsatz, D., & Chatterjee, S. (2022). Estimating the global technical potential of building-integrated solar energy production using a high-resolution geospatial model. Journal of Cleaner Production, 375, 134133. doi:10.1016/j.jclepro.2022.134133
Gridded Population of the World (GPW) v4.11 (national identifier grid)

Momeni-Dehaghi, I., Bennett, J. R., Mitchell, G. W., Rytwinski, T., & Fahrig, L. (2021). Mapping the premigration distribution of eastern Monarch butterflies using community science data. Ecology and Evolution, 11(16), 11275-11281. doi:10.1002/ece3.7912
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Mondal, P., McDermid, S. S., & Qadir, A. (2020). A reporting framework for Sustainable Development Goal 15: Multi-scale monitoring of forest degradation using MODIS, Landsat and Sentinel data. Remote Sensing of Environment, 237, 111592. doi:10.1016/j.rse.2019.111592
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
NASA REMOTE SEnSING (MODIS)
REMOTE SENSING (Landsat)
REMOTE SENSING (Sentinel-2 NDVI)

Mondal, S. K., Huang, J., Wang, Y., Su, B., Zhai, J., Tao, H., . . . Jiang, T. (2021). Doubling of the population exposed to drought over South Asia: CMIP6 multi-model-based analysis. Science of The Total Environment, 771, 145186. doi:10.1016/j.scitotenv.2021.145186
Gridded Population of the World (GPW) v4 (unspecified)

Monsieurs, E., Jacobs, L., Michellier, C., Basimike Tchangaboba, J., Ganza, G. B., Kervyn, F., . . . Dewitte, O. (2018). Landslide inventory for hazard assessment in a data-poor context: a regional-scale approach in a tropical African environment. Landslides, 15(11), 2195-2209. doi:10.1007/s10346-018-1008-y
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4HX19NJ

Monteiro, J., Martins, B., Costa, M., & Pires, J. M. (2021). Geospatial data disaggregation through self-trained encoder–decoder convolutional models. ISPRS International Journal of Geo-Information, 10(9), 619. doi:10.3390/ijgi10090619
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - data set unspecified)
Global Rural-Urban Mapping Project (GRUMP) v1 (Balk et al 2006)
REMOTE SENSING (VIIRS DNB)

Monteiro, J., Martins, B., & Pires, J. M. (2018). A hybrid approach for the spatial disaggregation of socio-economic indicators. International Journal of Data Science and Analytics, 5(2-3), 189-211. doi:10.1007/s41060-017-0080-z
Gridded Population of the World (GPW) v4 (collection)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS NTL)

Mooney, A. (2021). The Value of ex situ Collections for Global Biodiversity Conservation in the Wild. (Ph.D.). Trinity College Dublin, Dublin. Retrieved from https://hdl.handle.net/2262/95533 
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM

Mooney, A., Conde, D. A., Healy, K., & Buckley, Y. M. (2020). A system wide approach to managing zoo collections for visitor attendance and in situ conservation. Nature Communications, 11(1), 584. doi:10.1038/s41467-020-14303-2
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM

Moore, F. C., & Obradovich, N. (2020). Using remarkability to define coastal flooding thresholds. Nature Communications, 11(1), 530. doi:10.1038/s41467-019-13935-3
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Moran, M. D., Monroe, A., & Stallcup, L. (2019). A proposal for practical and effective biological corridors to connect protected areas in northwest Costa Rica. Nature Conservation, 36, 113-137. doi:10.3897/natureconservation.36.27430
Gridded Population of the World (GPW) v4 (data quality indicators) - 10.7927/H49C6VBN
REMOTE SENSING (Landsat)

Morant Echevarria, J. (2022). Behavioural Ecology and Conservation of the Egyptian Vulture in Human-Dominated Landscapes: Insights from Long-Term Monitoring and Movement Ecology. (Ph.D.). Universidad del Pais Vasco, Retrieved from http://hdl.handle.net/10810/56144 
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Moreno-Monroy, A. I., Schiavina, M., & Veneri, P. (2021). Metropolitan areas in the world. Delineation and population trends. Journal of Urban Economics, 125, 103242. doi:10.1016/j.jue.2020.103242
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM

Mori, E., Menchetti, M., Zozzoli, R., & Milanesi, P. (2019). The importance of taxonomy in species distribution models at a global scale: the case of an overlooked alien squirrel facing taxonomic revision. Journal of Zoology, 307(1), 43-52. doi:10.1111/jzo.12616
Gridded Population of the World (GPW) v4 beta (population density)
NASA REMOTE SENSING (ASTER GDEM)

Morley, J., Buchanan, G., Mitchard, E. T. A., & Keane, A. (2021). Potentially harmful World Bank projects are proximate to areas of biodiversity conservation importance. Global Environmental Change, 70, 102364. doi:10.1016/j.gloenvcha.2021.102364
Gridded Population of the World (GPW) v4.10 (population density)

Moslenko, L., Blagrave, K., Filazzola, A., Shuvo, A., & Sharma, S. (2020). Identifying the influence of land cover and human population on chlorophyll a concentrations using a pseudo-watershed analytical framework. Water, 12(11), 3215. doi:10.3390/w12113215
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MODIS - MCD12Q1)

Mouillot, D., Velez, L., Maire, E., Masson, A., Hicks, C. C., Moloney, J., & Troussellier, M. (2020). Global correlates of terrestrial and marine coverage by protected areas on islands. Nature Communications, 11(1), 4438. doi:10.1038/s41467-020-18293-z
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates) - 10.7927/H4F47M2C

Mounir, A. (2022). Quantifying the Synergies in the Water-Energy Nexus Generated by Renewable Energy in a Water-Limited Metropolitan Region Through Integrated Modeling. (Ph.D.). Arizona State University, Tempe, AZ. Retrieved from https://hdl.handle.net/2286/R.2.N.171625 
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
Gridded Population of the World (GPW) v3 (population count) - 10.​7927/​H4639MPP

Moya, D., Arroba, C., Castro, C., Pérez, C., Giarola, S., Kaparaju, P., . . . Hawkes, A. (2023). A methodology to estimate high-resolution gridded datasets on energy consumption drivers in Ecuador's residential sector during the 2010-2020 period. Energies, 16(10), 3973. doi:10.3390/en16103973
Gridded Population of the World (GPW) v4.11 (admin unit center points)
NASA REMOTE SENSING (MERRA-2)

Moya, D., Copara, D., Borja, A., Pérez, C., Kaparaju, P., Pérez-Navarro, Á., . . . Hawkes, A. (2022). Geospatial and temporal estimation of climatic, end-use demands, and socioeconomic drivers of energy consumption in the residential sector in Ecuador. Energy Conversion and Management, 261, 115629. doi:10.1016/j.enconman.2022.115629
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates) - 10.7927/H4F47M2C

Moya, D., Giarola, S., & Hawkes, A. (2021). Geospatial Big Data analytics to model the long-term sustainable transition of residential heating worldwide. Paper presented at the 2021 IEEE International Conference on Big Data, Orlando.
Gridded Population of the World (GPW) v4 (unspecified)

Mu, X., Wang, S., Jiang, P., & Wu, Y. (2023). Estimation of surface ozone concentration over Jiangsu province using a high-performance deep learning model. Journal of Environmental Sciences, 132, 122-133. doi:10.1016/j.jes.2022.09.032
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
NASA REMOTE SENSING (TROPOMI)

Mudau, N., & Mhangara, P. (2022). Towards understanding informal settlement growth patterns: Contribution to SDG reporting and spatial planning. Remote Sensing Applications: Society and Environment, 27, 100801. doi:10.1016/j.rsase.2022.100801
Gridded Population of the World (GPW) v4.11 (collection)

Mueller, V., Sheriff, G., Dou, X., & Gray, C. (2020). Temporary migration and climate variation in eastern Africa. World Development, 126, 104704. doi:10.1016/j.worlddev.2019.104704
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ
NASA REMOTE SENSING (MERRA)

Mueller, W., Steinle, S., Pärkkä, J., Parmes, E., Liedes, H., Kuijpers, E., . . . Loh, M. (2020). Urban greenspace and the indoor environment: Pathways to health via indoor particulate matter, noise, and road noise annoyance. Environmental Research, 180, 108850. doi:10.1016/j.envres.2019.108850
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (Sentinel-2 NDVI)

Mueller, W., Wilkinson, P., Milner, J., Loh, M., Vardoulakis, S., Petard, Z., . . . Arvind, D. K. (2022). The relationship between greenspace and personal exposure to PM2.5 during walking trips in Delhi, India. Environmental Pollution, 305, 119294. doi:10.1016/j.envpol.2022.119294
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (Sentinel-2)

Mueller, W., Wilkinson, P., Milner, J., Vardoulakis, S., Steinle, S., Pärkkä, J., . . . Loh, M. (2021). Neighbourhood and path-based greenspace in three European countries: associations with objective physical activity. BMC Public Health, 21(1), 282. doi:10.1186/s12889-021-10259-0
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
REMOTE SENSING (Landsat)
REMOTE SENSING (Sentinel-2 NDVI)

Muis, S., Haigh, I. D., Nobre, G. G., Aerts, J. C. J. H., & Ward, P. J. (2018). Influence of El Niño Southern Oscillation on global coastal flooding. Earth's Future, 6(9), 1311-1322. doi:10.1029/2018EF000909
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
NASA REMOTE SENSING (SRTM)

Mukherjee, A., McCarthy, M. C., Brown, S. G., Huang, S., Landsberg, K., & Eisinger, D. S. (2020). Influence of roadway emissions on near-road PM2.5: Monitoring data analysis and implications. Transportation Research Part D: Transport and Environment, 86, 102442. doi:10.1016/j.trd.2020.102442
Gridded Population of the World (GPW) v4.10 (population density)

Mukherjee, A., Sarkar, S., Chakraborty, M., Duttagupta, S., Bhattacharya, A., Saha, D., . . . Gupta, S. (2021). Occurrence, predictors and hazards of elevated groundwater arsenic across India through field observations and regional-scale modeling. Science of The Total Environment, 759, 143511. doi:10.1016/j.scitotenv.2020.143511
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Mukherjee, S., Mishra, A. K., Mann, M. E., & Raymond, C. (2021). Anthropogenic warming and population growth may double US heat stress by the late 21st century. Earth's Future, 9(5), e2020EF001886. doi:10.1029/2020EF001886
Gridded Population of the World (GPW) v4.11 (admin unit center points) - 10.7927/H4BC3WMT
Population Dynamics (Global One-Eighth Degree Population Projection Grids for the SSPs, v1) - 10.7927/H4RF5S0P

Mulanda Aura, C., Musa, S., Nyamweya, C. S., Ogari, Z., Njiru, J. M., Hamilton, S. E., & May, L. (2021). A GIS-based approach for delineating suitable areas for cage fish culture in a lake. Lakes & Reservoirs: Science, Policy and Management for Sustainable Use, 26(2), e12357. doi:10.1111/lre.12357
Gridded Population of the World (GPW) v4.10 (population density UN WPP-adjusted)

Mungai, L. M., Messina, J. P., Zulu, L. C., Qi, J., & Snapp, S. (2022). Modeling spatiotemporal patterns of land use/land cover change in Central Malawi using a neural network model. Remote Sensing, 14(14), 3477. doi:10.3390/rs14143477
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (Landsat)

Muñoz, Á. G., Chourio, X., Rivière-Cinnamond, A., Diuk-Wasser, M. A., Kache, P. A., Mordecai, E. A., . . . Thomson, M. C. (2020). AeDES: a next-generation monitoring and forecasting system for environmental suitability of Aedes-borne disease transmission. Scientific Reports, 10(1), 12640. doi:10.1038/s41598-020-69625-4
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates) - 10.7927/H4F47M2C

Munsey, A., Mwiine, F. N., Ochwo, S., Velazquez-Salinas, L., Ahmed, Z., Maree, F., . . . VanderWaal, K. (2021). Phylogeographic analysis of foot-and-mouth disease virus serotype O dispersal and associated drivers in East Africa. Molecular Ecology, 30(15), 3815-3825. doi:10.1111/mec.15991
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Murillo, E. M., & Homeyer, C. R. (2022). What determines above-anvil cirrus plume infrared temperature? Journal of the Atmospheric Sciences, 79(12), 3181-3194. doi:10.1175/jas-d-22-0080.1
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
REMOTE SENSING (GOES)

Murray, A. M., Jørgensen, G. H., Godiksen, P. N., Anthonj, J., & Madsen, H. (2023). DHI-GHM: Real-time and forecasted hydrology for the entire planet. Journal of Hydrology, 620, 129431. doi:10.1016/j.jhydrol.2023.129431
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)
NASA REMOTE SENSING (GPM IMERG)
NASA REMOTE SENSING (MODIS)

Muyobela, J., Pirk, C. W. W., Yusuf, A. A., & Sole, C. L. (2023). Spatial distribution of Glossina morsitans (Diptera: Glossinidae) in Zambia: A vehicle-mounted sticky trap survey and Maxent species distribution model. PLoS Neglected Tropical Diseases, 17(7), e0011512. doi:10.1371/journal.pntd.0011512
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)

Mwendwa, D. M., Tchouambe, J., Hu, E., Lanza, M. F., Brener, A. B., Hwang, G., . . . McCulloch, M. (2022). Spatial data starter kit for OnSSET energy planning in Kitui County, Kenya. Data in Brief, 45, 108691. doi:10.1016/j.dib.2022.108691
Gridded Population of the World (GPW) v4 (population count)

Myllyvirta, L. (2021). Air Quality, Health and Toxics Impacts of Coal Power and Coal Mining in Coahuila, Mexico. Retrieved from https://energyandcleanair.org/publication/air-quality-health-and-toxics-impacts-of-coal-power-and-coal-mining-in-coahuila-mexico/
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Myllyvirta, L. (2021). Briefing: Health Impacts of Coal-Fired Power Projects with HSBC Equity Stakes. Retrieved from https://energyandcleanair.org/wp/wp-content/uploads/2021/05/HSBC-coal-stakes_-health-impacts.pdf
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Myllyvirta, L., & Thieriot, H. (2021). 38,000 Air Pollution-related Deaths Avoided in Europe in 2020, as Fossil Fuel Burning Dropped. Retrieved from Helsinki: https://energyandcleanair.org/air-pollution-related-deaths-avoided-in-europe-in-2020
Gridded Population of the World (GPW) v4.11 (population density)

Næss, J. S., Cavalett, O., & Cherubini, F. (2021). The land–energy–water nexus of global bioenergy potentials from abandoned cropland. Nature Sustainability, 4, 525-536. doi:10.1038/s41893-020-00680-5
Gridded Population of the World (GPW) v4.11 (national identifier grid) - 10.7927/H4TD9VDP

Næss, J. S., Hu, X., Gvein, M. H., Iordan, C.-M., Cavalett, O., Dorber, M., . . . Cherubini, F. (2023). Climate change mitigation potentials of biofuels produced from perennial crops and natural regrowth on abandoned and degraded cropland in Nordic countries. Journal of Environmental Management, 325, 116474. doi:10.1016/j.jenvman.2022.116474
Gridded Population of the World (GPW) v4.11 (national identifier grid) - 10.7927/H4TD9VDP

Næss, J. S., Iordan, C. M., Muri, H., & Cherubini, F. (2022). Energy potentials and water requirements from perennial grasses on abandoned land in the former Soviet Union. Environmental Research Letters, 17(4), 045017. doi:10.1088/1748-9326/ac5e67
Gridded Population of the World (GPW) v4.11 (national identifier grid) - 10.7927/H4TD9VDP

Nagelkirk, R. L., & Dahlin, K. M. (2020). Woody cover fractions in African savannas from Landsat and high-resolution imagery. Remote Sensing, 12(5), 813. doi:10.3390/rs12050813
Gridded Population of the World (GPW) v4 (population count)
REMOTE SENSING (Landsat)

Naito, H., Ismailov, A., & Kimaro, A. B. (2021). The effect of mobile money on borrowing and saving: Evidence from Tanzania. World Development Perspectives, 23, 100342. doi:10.1016/j.wdp.2021.100342
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
Global High Resolution Urban Data from Landsat (HBASE) - 10.7927/H4DN434S
REMOTE SENSING (DMSP-OLS)

Naito, H., & Yamamoto, S. (2022). Is better access to mobile networks associated with increased mobile money adoption? Evidence from the micro-data of six developing countries. Telecommunications Policy, 46(6), 102314. doi:10.1016/j.telpol.2022.102314
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
REMOTE SENSING (DMSP-OLS)

Nakayama, T., & Osako, M. (2023). The flux and fate of plastic in the world's major rivers: Modelling spatial and temporal variability. Global and Planetary Change, 221, 104037. doi:10.1016/j.gloplacha.2023.104037
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Nakayama, T., Wang, Q., & Okadera, T. (2021). Evaluation of spatio-temporal variations in water availability using a process-based eco-hydrology model in arid and semi-arid regions of Mongolia. Ecological Modelling, 440, 109404. doi:10.1016/j.ecolmodel.2020.109404
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Nakayama, T., Wang, Q., & Okadera, T. (2021). Sensitivity analysis and parameter estimation of anthropogenic water uses for quantifying relation between groundwater overuse and water stress in Mongolia. Ecohydrology & Hydrobiology, 21(3), 490-500. doi:10.1016/j.ecohyd.2021.07.006
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Nansai, K., Tohno, S., Chatani, S., Kanemoto, K., Kagawa, S., Kondo, Y., . . . Lenzen, M. (2021). Consumption in the G20 nations causes particulate air pollution resulting in two million premature deaths annually. Nature Communications, 12(1), 6286. doi:10.1038/s41467-021-26348-y
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)
NASA REMOTE SENSING (MODIS)

Nansai, K., Tohno, S., Chatani, S., Kanemoto, K., Kurogi, M., Fujii, Y., . . . Lenzen, M. (2020). Affluent countries inflict inequitable mortality and economic loss on Asia via PM2.5 emissions. Environment International, 134, 105238. doi:10.1016/j.envint.2019.105238
Gridded Population of the World (GPW) v4.10 (basic demographic characteristics)

Napp, S., Chevalier, V., Busquets, N., Calistri, P., Casal, J., Attia, M., . . . Bayomy, S. (2018). Understanding the legal trade of cattle and camels and the derived risk of Rift Valley Fever introduction into and transmission within Egypt. PLoS Neglected Tropical Diseases, 12(1), e0006143. doi:10.1371/journal.pntd.0006143
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ
NASA REMOTE SENSING (MODIS)

Narra, P., Coelho, C., & Sancho, F. (2019). Multicriteria GIS-based estimation of coastal erosion risk: Implementation to Aveiro sandy coast, Portugal. Ocean & Coastal Management, 178, 104845. doi:10.1016/j.ocecoaman.2019.104845
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Nauditt, A., Stahl, K., Rodríguez, E., Birkel, C., Formiga-Johnsson, R. M., Marko, K., . . . Hann, H. (2022). Evaluating tropical drought risk by combining open access gridded vulnerability and hazard data products. Science of The Total Environment, 822, 153493. doi:10.1016/j.scitotenv.2022.153493
Gridded Population of the World (GPW) v4 (population density) - 10.7927//H4NP22DQ
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
NASA REMOTE SENSING (MODIS - MOD13Q13)

Nault, B. A., Jo, D. S., McDonald, B. C., Campuzano-Jost, P., Day, D. A., Hu, W., . . . Jimenez, J. L. (2021). Secondary organic aerosols from anthropogenic volatile organic compounds contribute substantially to air pollution mortality. Atmospheric Chemistry and Physics, 21(14), 11201-11224. doi:10.5194/acp-21-11201-2021
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)

Nauman, C., Anderson, E., Coughlan de Perez, E., Kruczkiewicz, A., McClain, S., Markert, A., . . . Suarez, P. (2021). Perspectives on flood forecast-based early action and opportunities for Earth observations. Journal of Applied Remote Sensing, 15(3), 032002. doi:10.1117/1.JRS.15.032002
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (SWOT)

Naus, S., Montzka, S. A., Patra, P. K., & Krol, M. C. (2021). A three-dimensional-model inversion of methyl chloroform to constrain the atmospheric oxidative capacity. Atmospheric Chemistry and Physics, 21(6), 4809-4824. doi:10.5194/acp-21-4809-2021
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Nava, V., Chandra, S., Aherne, J., Alfonso, M. B., Antão-Geraldes, A. M., Attermeyer, K., . . . Leoni, B. (2023). Plastic debris in lakes and reservoirs. Nature, 619(7969), 317-322. doi:10.1038/s41586-023-06168-4
Gridded Population of the World (GPW) v4.11 (admin unit center points) - 10.7927/H4BC3WMT
Last of the Wild v2 (Global Human Footprint (Geographic)) - 10.7927/H4M61H5F
NASA REMOTE SENSING (ASTER GDEM)
REMOTE SENSING (Proba-V)

Navarro, A., Moreno, R., Jiménez-Alcázar, A., & Tapiador, F. J. (2019). Coupling population dynamics with earth system models: the POPEM model. Environmental Science and Pollution Research, 26(4), 3184-3195. doi:10.1007/s11356-017-0127-7
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)

Nawaz, M. O., & Henze, D. K. (2020). Premature deaths in Brazil associated with long-term exposure to PM2.5 from Amazon fires between 2016-2019. GeoHealth, 4(8), e2020GH000268. doi:10.1029/2020gh000268
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates) - 10.7927/H4F47M2C

Nawaz, M. O., Henze, D. K., Anenberg, S. C., Braun, C., Miller, J., & Pronk, E. (2023). A source apportionment and emission scenario assessment of PM2.5- and O3-related health impacts in G20 countries. GeoHealth, 7(1), e2022GH000713. doi:10.1029/2022GH000713
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Neate-Clegg, M. H. C., Tonelli, B. A., Youngflesh, C., Wu, J. X., Montgomery, G. A., Şekercioğlu, Ç. H., & Tingley, M. W. (2023). Traits shaping urban tolerance in birds differ around the world. Current Biology, 33(9), 1677-1688.e1676. doi:10.1016/j.cub.2023.03.024
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (VIIRS NTL)

Neher, I., Crewell, S., Meilinger, S., Pfeifroth, U., & Trentmann, J. (2020). Photovoltaic power potential in West Africa using long-term satellite data. Atmospheric Chemistry and Physics, 20(21), 12871-12888. doi:10.5194/acp-20-12871-2020
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (Meteosat)

Nelms, S. E., Duncan, E. M., Patel, S., Badola, R., Bhola, S., Chakma, S., . . . Koldewey, H. (2021). Riverine plastic pollution from fisheries: Insights from the Ganges River system. Science of The Total Environment, 756, 143305. doi:10.1016/j.scitotenv.2020.143305
Gridded Population of the World (GPW) v4.11 (population count)

Nelson, E. J., Rogers, M., Wood, S., Chung, J., & Keeler, B. (2020). Using 'big data' to explain visits to lakes in 17 US states. Retrieved from https://digitalcommons.bowdoin.edu/econpapers/17
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Nelson, G. D. (2019). What micro-mapping a city's density reveals.  Retrieved from https://www.citylab.com/perspective/2019/07/urban-density-map-city-population-data-geography/591760/
Gridded Population of the World (GPW) v4 (population density)

Neroda, A. S., Goncharova, A. A., & Mishukov, V. F. (2020). PAHs in the atmospheric aerosols and seawater in the North–West Pacific ocean and sea of Japan. Atmospheric Environment, 222, 117117. doi:10.1016/j.atmosenv.2019.117117
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (FIRMS)

Ng'uni, D., Munkombwe, G., Mwila, G., Gaisberger, H., Brehm, J. M., Maxted, N., . . . Thormann, I. (2019). Spatial analyses of occurrence data of crop wild relatives (CWR) taxa as tools for selection of sites for conservation of priority CWR in Zambia. Plant Genetic Resources: Characterization and Utilization, 17(Special Issue 2 (Crop Wild Relatives)), 103-114. doi:10.1017/S1479262118000497
Gridded Population of the World (GPW) v4 (population density)

Nguyen, G. T. H., Nguyen, T. T. T., Shimadera, H., Uranishi, K., Matsuo, T., & Kondo, A. (2022). Estimating mortality related to O3 and PM2.5 under changing climate and emission in continental Southeast Asia. Aerosol and Air Quality Research, 22, 220105. doi:10.4209/aaqr.220105
Gridded Population of the World (GPW) v4.11 (population count)

Nguyen, L. D., Dang, P. D. N., & Nguyen, L. K. (2021). Estimating surface water and vadose water resources for an ungauged inland catchment in Vietnam. Journal of Water and Climate Change, 12(6), 2716-2733. doi:10.2166/wcc.2021.343
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Niassy, S., Murithii, B., Omuse, E. R., Kimathi, E., Tonnang, H., Ndlela, S., . . . Ekesi, S. (2022). Insight on fruit fly IPM technology uptake and barriers to scaling in Africa. Sustainability, 14(5), 2954. doi:10.3390/su14052954
Gridded Population of the World (GPW) v4.11 (population density)

Nicholson, S. K., Dickman, A., Hinks, A., Riggio, J., Bauer, H., Loveridge, A., . . . Jacobson, A. P. (2023). Socio-political and ecological fragility of threatened, free-ranging African lion populations. Communications Earth & Environment, 4(1), 302. doi:10.1038/s43247-023-00959-3
Gridded Population of the World (GPW) v4.11 (population density)

Nickel, S., & Schröder, W. (2019). Correlating elements content in mosses collected in 2015 across Germany with spatially associated characteristics of sampling sites and their surroundings. Environmental Sciences Europe, 31(1), 80. doi:10.1186/s12302-019-0260-7
Gridded Population of the World (GPW) v4 (population density)

Nievas, C. I., Bommer, J. J., Crowley, H., & van Elk, J. (2020). Global occurrence and impact of small-to-medium magnitude earthquakes: a statistical analysis. Bulletin of Earthquake Engineering, 18, 1-35. doi:10.1007/s10518-019-00718-w
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Nieves, J. J., Bondarenko, M., Kerr, D., Ves, N., Yetman, G., Sinha, P., . . . Tatem, A. J. (2021). Measuring the contribution of built-settlement data to global population mapping. Social Sciences & Humanities Open, 3(1), 100102. doi:10.2139/ssrn.3599775
Gridded Population of the World (GPW) v4.11 (population count)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS NTL)

Nieves, J. J., Bondarenko, M., Sorichetta, A., Steele, J. E., Kerr, D., Carioli, A., . . . Tatem, A. J. (2020). Predicting near-future built-settlement expansion using relative changes in small area populations. Remote Sensing, 12(10), 1545. doi:10.3390/rs12101545
Gridded Population of the World (GPW) v4 (collection)
POPGRID

Nieves, J. J., Sorichetta, A., Linard, C., Bondarenko, M., Steele, J., Stevens, F. R., . . . Tatem, A. J. (2020). Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night. Computers, Environment and Urban Systems, 80, 101444. doi:10.1016/j.compenvurbsys.2019.101444
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS NTL)

Nikolaou, P., & Dimitriou, L. (2020). Identification of critical airports for controlling global infectious disease outbreaks: Stress-tests focusing in Europe. Journal of Air Transport Management, 85, 101819. doi:10.1016/j.jairtraman.2020.101819
Gridded Population of the World (GPW) v4.11 (population density) map

Niles, M. T., Emery, B. F., Wiltshire, S., Brown, M. E., Fisher, B., & Ricketts, T. H. (2021). Climate impacts associated with reduced diet diversity in children across nineteen countries. Environmental Research Letters, 16(1), 015010. doi:10.1088/1748-9326/abd0ab
Gridded Population of the World (GPW) v4 (population density)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)

Nilsen, K., Tejedor-Garavito, N., Leasure, D. R., Utazi, C. E., Ruktanonchai, C. W., Wigley, A. S., . . . Tatem, A. J. (2021). A review of geospatial methods for population estimation and their use in constructing reproductive, maternal, newborn, child and adolescent health service indicators. BMC Health Services Research, 21(1), 370. doi:10.1186/s12913-021-06370-y
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates)

Nixdorf, E., Sun, Y., Su, J., Wang, Q., Wang, T., Kolditz, O., & Xi, B. (2018). Groundwater Risk Sources Identification and Risk Reduction Management in the Song-Liao-River-Basin. In Y. Song, B. Xi, Y. Zhang, K. Lei, R. Williams, M. Zhang, W. Kong, & O. Kolditz (Eds.), Chinese Water Systems: Volume 1: Liaohe and Songhuajiang River Basins (pp. 349-398). Cham: Springer International Publishing.
Gridded Population of the World (GPW) v4 (population density)

Nizar, S., & Dodamani, B. M. (2018). Spatio-temporal distribution of rainfall and aerosols over urban areas of Karnataka. Paper presented at the SPIE Remote Sensing. https://doi.org/10.1117/12.2325639
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)

Nock, D., Levin, T., & Baker, E. (2020). Changing the policy paradigm: A benefit maximization approach to electricity planning in developing countries. Applied Energy, 264, 114583. doi:10.1016/j.apenergy.2020.114583
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Norambuena, H. V., Rivera, R., Barros, R., Silva, R., Peredo, R., & Hernández, C. E. (2021). Living on the edge: genetic structure and geographic distribution in the threatened Markham’s Storm-Petrel (Hydrobates markhami). PeerJ, 9, e12669. doi:10.7717/peerj.12669
Gridded Population of the World (GPW) v4 (documentation) - 10.7927/H45Q4T5F

Nori-Sarma, A., Spangler, K. R., Wang, B., Cesare, N., Dukes, K. A., & Lane, K. J. (2023). Impacts of the choice of distance measurement method on estimates of access to point-based resources. Journal of Exposure Science & Environmental Epidemiology, 33, 237-243. doi:10.1038/s41370-022-00414-z
Gridded Population of the World (GPW) v4.10 (population count)

Noy, I., Doan, N., Ferrarini, B., & Park, D. (2020). The Economic Risk of COVID-19 in Developing Countries: Where is it Highest? Retrieved from London: http://www.lse.ac.uk/fmg/assets/documents/papers/special-papers/SP257.pdf
Spatial Economic Data (Global Gridded Geographically Based Economic Data (G-Econ), v4)
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
Population Dynamics (Global Estimated Net Migration Grids By Decade, v1) - 10.7927/H4319SVC
Poverty Mapping (Global Subnational Infant Mortality Rates, v2) - 10.7927/H4PN93JJ

Noy, I., Doan, N., Ferrarini, B., & Park, D. (2020). Measuring the economic risk of COVID-19. Global Policy, 11(4), 413-423. doi:10.1111/1758-5899.12851
Spatial Economic Data (Global Gridded Geographically Based Economic Data (G-Econ), v4)
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
Population Dynamics (Global Estimated Net Migration Grids By Decade, v1) - 10.7927/H4319SVC
Poverty Mapping (Global Subnational Infant Mortality Rates, v2) - 10.7927/H4PN93JJ

Nüchel, J., Bøcher, P. K., & Svenning, J.-C. (2019). Topographic slope steepness and anthropogenic pressure interact to shape the distribution of tree cover in China. Applied Geography, 103, 40-55. doi:10.1016/j.apgeog.2018.12.008
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4D50JX4
Last of the Wild v2 Global Human Influence Index (IGHP) - 10.7927/H46W980H
NASA REMOTE SENSING (MODIS - MOD44B)

Nüchel, J., Bøcher, P. K., Xiao, W., Zhu, A. X., & Svenning, J.-C. (2018). Snub-nosed monkeys (Rhinopithecus): potential distribution and its implication for conservation. Biodiversity and Conservation, 27(6), 1517-1538. doi:10.1007/s10531-018-1507-0
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
Last of the Wild v2 Global Human Influence Index (Geographic)
NASA REMOTE SENSING (MODIS Vegetation Continuous Fields)

Nüchel, J., & Svenning, J.-C. (2017). Recent tree cover increases in eastern China linked to low, declining human pressure, steep topography, and climatic conditions favoring tree growth. PLoS ONE, 12(6), e0177552. doi:10.1371/journal.pone.0177552
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
Last of the Wild v2 Global Human Influence Index (IGHP)
NASA REMOTE SENSING (MODIS Vegetation Continuous Fields)
NASA REMOTE SENSING (SRTM)

Nusrat, F., Haque, M., Rollend, D., Christie, G., & Akanda, A. S. (2022). A high-resolution earth observations and machine learning-based approach to forecast waterborne disease risk in post-disaster settings. Climate, 10(4), 48. doi:10.3390/cli10040048
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (IMERG)
NASA REMOTE SENSING (TRMM)

Nyadanu, S. D., Tessema, G. A., Mullins, B., Kumi-Boateng, B., Ofosu, A. A., & Pereira, G. (2022). Ambient particulate matter air pollution and stillbirth in Ghana: A difference-in-differences approach. Atmospheric Pollution Research, 13(7), 101471. doi:10.1016/j.apr.2022.101471
Gridded Population of the World (GPW) v4.11 (population density)

O, S., Hou, X., & Orth, R. (2020). Observational evidence of wildfire-promoting soil moisture anomalies. Scientific Reports, 10(1), 11008. doi:10.1038/s41598-020-67530-4
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
Gridded Population of the World (GPW) v4.11 (land and water area) - 10.7927/H4Z60M4Z
NASA REMOTE SENSING (MODIS)

O’Dell, K., Bilsback, K., Ford, B., Martenies, S. E., Magzamen, S., Fischer, E. V., & Pierce, J. R. (2021). Estimated mortality and morbidity attributable to smoke plumes in the US: Not just a Western US problem. GeoHealth, 5(9), e2021GH000457. doi:10.1029/2021GH000457
Gridded Population of the World (GPW) v4.11 (unspecified)

Oakleaf, J. R., Kennedy, C. M., Baruch-Mordo, S., Gerber, J. S., West, P. C., Johnson, J. A., & Kiesecker, J. (2019). Mapping global development potential for renewable energy, fossil fuels, mining and agriculture sectors. Scientific Data, 6(1), 101. doi:10.1038/s41597-019-0084-8
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Obahoundje, S., Youan Ta, M., Diedhiou, A., Amoussou, E., & Kouadio, K. (2021). Sensitivity of hydropower generation to changes in climate and land use in the Mono Basin (West Africa) using CORDEX dataset and WEAP model. Environmental Processes, 8, 1073-1097. doi:10.1007/s40710-021-00516-0
Gridded Population of the World (GPW) v4.11 (documentation)

Oberschelp, C., Pfister, S., & Hellweg, S. (2020). Globally regionalized monthly life cycle impact assessment of particulate matter. Environmental Science & Technology, 54(24), 16028–16038. doi:10.1021/acs.est.0c05691
Gridded Population of the World (GPW) v4.10 (population density)
NASA REMOTE SENSING (MODIS)

Obida, C. B., Blackburn, G. A., Whyatt, J. D., & Semple, K. T. (2018). Quantifying the exposure of humans and the environment to oil pollution in the Niger Delta using advanced geostatistical techniques. Environment International, 111(Supplement C), 32-42. doi:10.1016/j.envint.2017.11.009
Gridded Population of the World (GPW) v4 (population count)

Obura, D. O., Bandeira, S. O., Bodin, N., Burgener, V., Braulik, G., Chassot, E., . . . Ternon, J. F. (2019). Chapter 4 - The Northern Mozambique Channel. In C. Sheppard (Ed.), World Seas: an Environmental Evaluation (Second Edition) (pp. 75-99): Academic Press.
Gridded Population of the World (GPW) v4 (population density)

Occhiali, G., & Falchetta, G. (2018). The Changing Role of Natural Gas in Nigeria. Retrieved from Milano: https://www.feem.it/en/publications/feem-working-papers-note-di-lavoro-series/the-changing-role-of-natural-gas-in-nigeria/
Gridded Population of the World (GPW) v4 (population density) map

Oddo, P. C., Ahamed, A., & Bolten, J. D. (2018). Socioeconomic impact evaluation for near real-time flood detection in the Lower Mekong River Basin. Hydrology, 5(2), 23. doi:10.3390/hydrology5020023
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ
Global Roads (Global Roads Open Access Data Set (gROADS), v1) - 10.7927/H4VD6WCT
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (ENVISAT Advanced Synthetic Aperture Radar Wide Swath Mode (ASAR-WSM))
REMOTE SENSING (Landsat 5 TM)

Oddo, P. C., & Bolten, J. D. (2019). The value of near real-time Earth observations for improved flood disaster response. Frontiers in Environmental Science, 7(127). doi:10.3389/fenvs.2019.00127
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)

Ojwang, L., Rosendo, S., Celliers, L., Obura, D., Muiti, A., Kamula, J., & Mwangi, M. (2017). Assessment of coastal governance for climate change adaptation in Kenya. Earth's Future, 5(11), 1119-1132. doi:10.1002/2017EF000595
Gridded Population of the World (GPW) v4 Preliminary release 2 - 10.7927/H4D50JX4

Olin, M., Patoulias, D., Kuuluvainen, H., Niemi, J. V., Rönkkö, T., Pandis, S. N., . . . Dal Maso, M. (2021). Contribution of traffic-originated nanoparticle emissions to regional and local aerosol levels. Atmospheric Chemistry and Physics, 22, 1131-1148. doi:10.5194/acp-22-1131-2022
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Olokotum, M., Mitroi, V., Troussellier, M., Semyalo, R., Bernard, C., Montuelle, B., . . . Humbert, J.-F. (2020). A review of the socioecological causes and consequences of cyanobacterial blooms in Lake Victoria. Harmful Algae, 96, 101829. doi:10.1016/j.hal.2020.101829
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

O'Meara, L., Cohen, P. J., Simmance, F., Marinda, P., Nagoli, J., Teoh, S. J., . . . Byrd, K. A. (2021). Inland fisheries critical for the diet quality of young children in sub-Saharan Africa. Global Food Security, 28, 100483. doi:10.1016/j.gfs.2020.100483
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Onink, V., Jongedijk, C., Hoffman, M., van Sebille, E., & Laufkötter, C. (2021). Global simulations of marine plastic transport show plastic trapping in coastal zones. Environmental Research Letters, 16(6), 064053. doi:10.1088/1748-9326/abecbd
Gridded Population of the World (GPW) v4 (population density)

Operti, F. G., Oliveira, E. A., Carmona, H. A., Machado, J. C., & Andrade, J. S. (2018). The light pollution as a surrogate for urban population of the US cities. Physica A: Statistical Mechanics and its Applications, 492, 1088-1096. doi:10.1016/j.physa.2017.11.039
Gridded Population of the World (GPW) v4 (population count)
REMOTE SENSING (VIIRS NTL)

Orlando, L., Ortega, L., & Defeo, O. (2020). Urbanization effects on sandy beach macrofauna along an estuarine gradient. Ecological Indicators, 111, 106036. doi:10.1016/j.ecolind.2019.106036
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (Landsat)

Orlando, L., Ortega, L., & Defeo, O. (2021). Perspectives for sandy beach management in the Anthropocene: Satellite information, tourism seasonality, and expert recommendations. Estuarine, Coastal and Shelf Science, 262, 107597. doi:10.1016/j.ecss.2021.107597
Gridded Population of the World (GPW) v4 (population density)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (Landsat)

Orlov, A., Sillmann, J., Aaheim, A., Aunan, K., & de Bruin, K. (2019). Economic losses of heat-induced reductions in outdoor worker productivity: A case study of Europe. Economics of Disasters and Climate Change, 3(3), 191-211. doi:10.1007/s41885-019-00044-0
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted)

Orlov, A., Sillmann, J., Aunan, K., Kjellstrom, T., & Aaheim, A. (2020). Economic costs of heat-induced reductions in worker productivity due to global warming. Global Environmental Change, 63, 102087. doi:10.1016/j.gloenvcha.2020.102087
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted)

Orr, A., Cox, C. M., Ru, Y., & Ashby, J. (2018). Gender and Social Targeting in Plant Breeding. Retrieved from Lima, Peru: http://hdl.handle.net/10568/91276
Gridded Population of the World (GPW) v4 (collection)

Orth, R., O, S., Zscheischler, J., Mahecha, M. D., & Reichstein, M. (2022). Contrasting biophysical and social impacts of hydro-meteorological extremes. Environmental Research Letters, 17(1), 014044. doi:10.1088/1748-9326/ac4139
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted) - 10.7927/H4JQ0XZW

Orton, P., Talke, S., Jay, D., Yin, L., Blumberg, A., Georgas, N., . . . MacManus, K. (2015). Channel shallowing as mitigation of coastal flooding. Journal of Marine Science and Engineering, 3(3), 654-673. doi:10.3390/jmse3030654
Gridded Population of the World (GPW) v4 (collection)

Oskorouchi, H. R., Nie, P., & Sousa-Poza, A. (2018). The effect of floods on anemia among reproductive age women in Afghanistan. PLoS ONE, 13(2), e0191726. doi:10.1371/journal.pone.0191726
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (TRMM)

Ossola, A., Hoeppner, M. J., Burley, H. M., Gallagher, R. V., Beaumont, L. J., & Leishman, M. R. (2020). The Global Urban Tree Inventory: A database of the diverse tree flora that inhabits the world’s cities. Global Ecology and Biogeography, 29(11), 1907-1914. doi:10.1111/geb.13169
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Østby, G., Rustad, S. A., & Arasmith, A. (2022). Children Affected by Armed Conflict, 1990-2021. Retrieved from Oslo: https://www.prio.org/publications/13256
Gridded Population of the World (GPW) v4 (population count)

Østby, G., Rustad, S. A., Haer, R., & Arasmith, A. (2023). Children at risk of being recruited for armed conflict, 1990–2020. Children & Society, 37(2), 524-543. doi:10.1111/chso.12609
Gridded Population of the World (GPW) v4.11 (population density)

Østby, G., Shemyakina, O., Tollefsen, A. F., Urdal, H., & Verpoorten, M. (2021). Public health and armed conflict: Immunization in times of systemic disruptions. Population and Development Review, 47(4), 1143-1177. doi:10.1111/padr.12450
Gridded Population of the World (GPW) v4 (unspecified)

Østby, G., Urdal, H., Tollefsen, A. F., Kotsadam, A., Belbo, R., & Ormhaug, C. (2018). Organized violence and institutional child delivery: Micro-level evidence From Sub-Saharan Africa, 1989–2014. Demography, 55(4), 1295-1316. doi:10.1007/s13524-018-0685-4
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Ostro, B., Spadaro, J. V., Gumy, S., Mudu, P., Awe, Y., Forastiere, F., & Peters, A. (2018). Assessing the recent estimates of the global burden of disease for ambient air pollution: Methodological changes and implications for low- and middle-income countries. Environmental Research, 166, 713-725. doi:10.1016/j.envres.2018.03.001
Gridded Population of the World (GPW) v3 (unspecified)
Gridded Population of the World (GPW) v4 (unspecified)

Ostrom, C. L. (2017). Improving estimation of ground casualty risk from reentering space objects. Paper presented at the Ninth International Association for the Advancement of Space Safety (IAASS) Conference, Toulouse, France. https://ntrs.nasa.gov/search.jsp?R=20170008876
Gridded Population of the World (GPW) v2
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Ostrom, C. L. (2018). Update to the Ground Population Model in DAS 3.0. Orbital Debris Quarterly News, 22(4), 5-6. Retrieved from https://orbitaldebris.jsc.nasa.gov/quarterly-news/pdfs/odqnv22i4.pdf
Gridded Population of the World (GPW) v2 (population density)
Gridded Population of the World (GPW) v4 (population count)

Ostrom, C. L., & Smith, A. N. (2019). Effect of latitude bias in entry angle on ground casualty risk from naturally decaying space objects. Paper presented at the 10th International Association for the Advancement of Space Safety  (IAASS) Conference, El Segundo, CA. https://ntrs.nasa.gov/search.jsp?R=20190025209
Gridded Population of the World (GPW) v4 (population density)

Ouedraogo, R., & Stenzel, D. (2021). The Heavy Economic Toll of Gender-based Violence: Evidence from Sub-Saharan Africa. Retrieved from https://www.imf.org/en/Publications/WP/Issues/2021/11/19/The-Heavy-Economic-Toll-of-Gender-based-Violence-Evidence-from-Sub-Saharan-Africa-509667
Gridded Population of the World (GPW) v4.11 (unspecified)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
REMOTE SENSING (DMSP-OLS)

Ouyang, X., Duarte, C. M., Cheung, S.-G., Tam, N. F.-Y., Cannicci, S., Martin, C., . . . Lee, S. Y. (2022). Fate and effects of macro- and microplastics in coastal wetlands. Environmental Science & Technology, 56(4), 2386-2397. doi:10.1021/acs.est.1c06732
Gridded Population of the World (GPW) v4.11 (population count)

Pacifici, M., Visconti, P., & Rondinini, C. (2018). A framework for the identification of hotspots of climate change risk for mammals. Global Change Biology, 24(4), 1626-1636. doi:10.1111/gcb.13942
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)

Pagaduan, J. A. (2023). Spatial income inequality, convergence, and regional development in a lower middle-income country: Satellite evidence from the Philippines. The Developing Economies, 61(2), 117-154. doi:10.1111/deve.12354
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4PN93PB
REMOTE SENSING (VIIRS NTL)

Pai, S. J., Heald, C. L., Coe, H., Brooks, J., Shephard, M. W., Dammers, E., . . . Tibrewal, K. (2022). Compositional Constraints are Vital for Atmospheric PM2.5 Source Attribution over India. ACS Earth and Space Chemistry, 6(10), 2432-2445. doi:10.1021/acsearthspacechem.2c00150
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (CALIOP Lidar)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (OMI NO2)
REMOTE SENSING (Cross-track Infrared Sounder (CrIS)
REMOTE SENSING (TROPOMI)

Pais, C., Gonzalez-Olabarria, J. R., Elimbi Moudio, P., Garcia-Gonzalo, J., González, M. C., & Shen, Z.-J. M. (2023). Global scale coupling of pyromes and fire regimes. Communications Earth & Environment, 4(1), 267. doi:10.1038/s43247-023-00881-8
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)

Pakhtigian, E. L., Aziz, S., Boyle, K., Akanda, A. S., & Hanifi, M. A. (2022). Early Warning Systems, Mobile Technology, and Cholera Aversion: Evidence from Rural Bangladesh. Retrieved from https://www.rff.org/publications/working-papers/early-warning-systems-mobile-technology-and-cholera-aversion-evidence-from-rural-bangladesh/
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)
NASA REMOTE SENSING (TRMM)

Palacios-Lopez, D., Bachofer, F., Esch, T., Heldens, W., Hirner, A., Marconcini, M., . . . Reinartz, P. (2019). New perspectives for mapping global population distribution using world settlement footprint products. Sustainability, 11(21), 6056. doi:10.3390/su11216056
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)
Global Rural-Urban Mapping Project (GRUMP) v1 (population density)
REMOTE SENSING (Sentinel-1 SAR)
REMOTE SENSING (Landsat 8)

Palacios-Lopez, D., Bachofer, F., Esch, T., Marconcini, M., MacManus, K., Sorichetta, A., . . . Reinartz, P. (2021). High-resolution gridded population datasets: Exploring the capabilities of the World Settlement Footprint 2019 imperviousness layer for the African continent. Remote Sensing, 13(6), 1142. doi:10.3390/rs13061142
Gridded Population of the World (GPW) v4 (documentation)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)
REMOTE SENSING (Sentinel-1)
REMOTE SENSING (Sentinel-2)

Palacios-Lopez, D., Esch, T., MacManus, K., Marconcini, M., Sorichetta, A., Yetman, G., . . . Reinartz, P. (2022). Towards an improved large-scale gridded population dataset: A pan-European study on the integration of 3D settlement data into population modelling. Remote Sensing, 14(2), 325. doi:10.3390/rs14020325
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Paltan, H., Waliser, D., Lim, W. H., Guan, B., Yamazaki, D., Pant, R., & Dadson, S. (2017). Global floods and water availability driven by atmospheric rivers. Geophysical Research Letters, 44(20), 10387-10395. doi:10.1002/2017GL074882
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Paltsev, A., & Creed, I. F. (2022). Are northern lakes in relatively intact temperate forests showing signs of increasing phytoplankton biomass? Ecosystems, 25, 727-755. doi:10.1007/s10021-021-00684-y
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (Landsat)

Pandey, A. C., Kaushik, K., & Parida, B. R. (2022). Google Earth Engine for large-scale flood mapping using SAR data and impact assessment on agriculture and population of Ganga-Brahmaputra Basin. Sustainability, 14(7), 4210. doi:10.3390/su14074210
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
REMOTE SENSING (Sentinel-1 SAR)

Panthi, S., Pariyar, S., & Low, M. (2021). Factors influencing the global distribution of the endangered Egyptian vulture. Scientific Reports, 11(1), 21901. doi:10.1038/s41598-021-01504-y
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (PALSAR-2)

Panthi, S., Wang, T., Sun, Y., & Thapa, A. (2019). An assessment of human impacts on endangered red pandas (Ailurus fulgens) living in the Himalaya. Ecology and Evolution, 9(23), 13413-13425. doi:10.1002/ece3.5797
Gridded Population of the World (GPW) v4 (population density)
REMOTE SENSING (ALOS)

Paprotny, D., Morales-Nápoles, O., & Jonkman, S. N. (2018). HANZE: a pan-European database of exposure to natural hazards and damaging historical floods since 1870. Earth System Science Data, 10(1), 565-581. doi:10.5194/essd-10-565-2018
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Pardini, C., & Anselmo, L. (2022). The Kinetic casualty risk of uncontrolled re-entries before and after the transition to small satellites and mega-constellations. Journal of Space Safety Engineering, 9(3), 414-426. doi:10.1016/j.jsse.2022.04.003
Gridded Population of the World (GPW) v4 (population density)

Parida, B. R., Bar, S., Roberts, G., Mandal, S. P., Chandra Pandey, A., Kumar, M., & Dash, J. (2021). Improvement in air quality and its impact on land surface temperature in major urban areas across India during the first lockdown of the pandemic. Environmental Research, 199, 111280. doi:10.1016/j.envres.2021.111280
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (CERES)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (TROPOMI)

Parihar, S. M., Pandey, V. K., Anshu, Shree, K., Moin, K., Ali, M. B., . . . Kamil, A. (2022). Land use dynamics and impact on regional climate post-Tehri Dam in the Bhilangana Basin, Garhwal Himalaya. Sustainability, 14(16), 10221. doi:10.3390/su141610221
Gridded Population of the World (GPW) v4.11 (population count)
REMOTE SENSING (Landsat)

Park, H., Jeong, S., Park, H., Labzovskii, L. D., & Bowman, K. W. (2021). An assessment of emission characteristics of Northern Hemisphere cities using spaceborne observations of CO2, CO, and NO2. Remote Sensing of Environment, 254, 112246. doi:10.1016/j.rse.2020.112246
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)
NASA REMOTE SENSING (OCO-2)
REMOTE SENSING (TROPOMI)

Park, S., Im, J., Kim, J., & Kim, S. (2022). Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia. Environmental Pollution, 306, 119425. doi:10.1016/j.envpol.2022.119425
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (IMERG)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (GOCI)

Park, S., Lee, J., Im, J., Song, C.-K., Choi, M., Kim, J., . . . Quackenbush, L. J. (2020). Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models. Science of The Total Environment, 713, 136516. doi:10.1016/j.scitotenv.2020.136516
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS - MCD12Q1)
NASA REMOTE SENSING (MODIS - MYD13A2)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (GOCI)

Parsons, A. W., Dawrs, S. N., Nelson, S. T., Norton, G. J., Virdi, R., Hasan, N. A., . . . D., S. J. (2022). Soil properties and moisture synergistically influence nontuberculous mycobacterial prevalence in natural environments of Hawai’i. Applied and Environmental Microbiology, 88(9), e00018-00022. doi:10.1128/aem.00018-22
Gridded Population of the World (GPW) v4.11 (admin unit center points)

Parsons, D., J., Pelletier, T., A., Wieringa, J., G., Duckett, D., J., & Carstens, B., C. (2022). Analysis of biodiversity data suggests that mammal species are hidden in predictable places. Proceedings of the National Academy of Sciences, 119(14), e2103400119. doi:10.1073/pnas.2103400119
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (ASTER GDEM)
REMOTE SENSING (DMSP-OLS)

Parsons, L. A., Jung, J., Masuda, Y. J., Vargas Zeppetello, L. R., Wolff, N. H., Kroeger, T., . . . Spector, J. T. (2021). Tropical deforestation accelerates local warming and loss of safe outdoor working hours. One Earth, 4(12), 1730-1740. doi:10.1016/j.oneear.2021.11.016
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates)
NASA REMOTE SENSING (MODIS)

Parsons, L. A., Lo, F., Ward, A., Shindell, D., & Raman, S. R. (2023). Higher temperatures in socially vulnerable US communities increasingly limit safe use of electric fans for cooling. GeoHealth, 7(8), e2023GH000809. doi:10.1029/2023gh000809
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4PN93PB
U.S. Census Grids (U.S. Social Vulnerability Index Grids, v1) - 10.7927/6s2a-9r49

Parsons, L. A., Masuda, Y. J., Kroeger, T., Shindell, D., Wolff, N. H., & Spector, J. T. (2022). Global labor loss due to humid heat exposure underestimated for outdoor workers. Environmental Research Letters, 17(1), 014050. doi:10.1088/1748-9326/ac3dae
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)

Parsons, L. A., Shindell, D., Tigchelaar, M., Zhang, Y., & Spector, J. T. (2021). Increased labor losses and decreased adaptation potential in a warmer world. Nature Communications, 12(1), 7286. doi:10.1038/s41467-021-27328-y
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Parsons, L. A., Shindell, D. T., Faluvegi, G., & Nagamoto, E. (2023). Geophysical uncertainties in air pollution exposure and benefits of emissions reductions for global health. Earth's Future, 11(9), e2023EF003839. doi:10.1029/2023EF003839
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4PN93PB

Parsons, T., Ji, C., & Kirby, E. (2023). Evaluating a prospective fault‐based stress‐transfer forecast for the M 7.9 Wenchuan Earthquake Region, 15 years later. The Seismic Record, 3(3), 218-227. doi:10.1785/0320230021
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Partnership for Resilience and Preparedness (PREP). (2018). PREPdata. Retrieved from https://www.prepdata.org/
Energy Infrastructure (Population Exposure Estimates in Proximity to Nuclear Power Plants, Locations)
Spatial Economic Data (Global Gridded Geographically Based Economic Data (G-Econ), v4)
Land Use and Land Cover (LULC) (Global Grid of Probabilities of Urban Expansion to 2030, v1)
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
Global Reservoir and Dam (GRanD) v1 (collection)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
Satellite-Derived Environmental Indicators (Global Urban Heat Island (UHI) Data Set, v1)

Pasanen, T. P., White, M. P., Elliott, L. R., van den Bosch, M., Bratman, G. N., Ojala, A., . . . Fleming, L. E. (2023). Urban green space and mental health among people living alone: The mediating roles of relational and collective restoration in an 18-country sample. Environmental Research, 232, 116324. doi:10.1016/j.envres.2023.116324
Gridded Population of the World (GPW) v4.10 (population density UN WPP-adjusted)
NASA REMOTE SENSING (MODIS - MOD13A3)

Paschalis, A., Chakraborty, T., Fatichi, S., Meili, N., & Manoli, G. (2021). Urban forests as main regulator of the evaporative cooling effect in cities. AGU Advances, 2(2), e2020AV000303. doi:10.1029/2020AV000303
Gridded Population of the World (GPW) v4.11 (population count)
Global High Resolution Urban Data from Landsat (GMIS)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (SPOT VGT)
REMOTE SENSING (TROPOMI)

Pastore y Piontti, A., Perra, N., Rossi, L., Samay, N., & Vespignani, A. (2019). DATA, DATA, AND MORE DATA. In A. Pastore y Piontti, N. Perra, L. Rossi, N. Samay, & A. Vespignani (Eds.), Charting the Next Pandemic: Modeling Infectious Disease Spreading in the Data Science Age (pp. 11-28). Cham: Springer International Publishing.
Gridded Population of the World (GPW) v4 (collection)

Paul, N., Yao, J., McLean, K. E., Stieb, D. M., & Henderson, S. B. (2022). The Canadian Optimized Statistical Smoke Exposure Model (CanOSSEM): A machine learning approach to estimate national daily fine particulate matter (PM2.5) exposure. Science of The Total Environment, 850, 157956. doi:10.1016/j.scitotenv.2022.157956
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
NASA REMOTE SENSING (MODIS)

Pease, B. S., Pacifici, K., Kays, R., & Reich, B. (2022). What drives spatially varying ecological relationships in a wide-ranging species? Diversity and Distributions, 28(9), 1752-1768. doi:10.1111/ddi.13594
Gridded Population of the World (GPW) v4.11 (population density)

Peng, J., Qiao, R., Liu, Y., Blaschke, T., Li, S., Wu, J., . . . Liu, Q. (2020). A wavelet coherence approach to prioritizing influencing factors of land surface temperature and associated research scales. Remote Sensing of Environment, 246, 111866. doi:10.1016/j.rse.2020.111866
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
REMOTE SENSING (Luojia 1-01)

Peng, L., Liu, F., Zhou, M., Li, M., Zhang, Q., & Mauzerall, D. L. (2021). Alternative-energy-vehicles deployment delivers climate, air quality, and health co-benefits when coupled with decarbonizing power generation in China. One Earth, 4(8), 1127-1140. doi:10.1016/j.oneear.2021.07.007
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Peng, Q., Yang, R., Cao, Y., Wang, F., Hou, S., Tseng, T.-H., . . . Locke, H. (2021). One-third of lands face high conflict risk between biodiversity conservation and human activities in China. Journal of Environmental Management, 299, 113449. doi:10.1016/j.jenvman.2021.113449
Gridded Population of the World (GPW) v4 (population density)

Pereto, C., Lerat-Hardy, A., Baudrimont, M., & Coynel, A. (2023). European fluxes of medical gadolinium to the ocean: a model based on healthcare databases. Environment International, 173, 107868. doi:10.1016/j.envint.2023.107868
Gridded Population of the World (GPW) v4.11 (admin unit center points)

Peri, G., & Sasahara, A. (2019). The Impact of Global Warming on Rural-Urban Migrations: Evidence from Global Big Data. Retrieved from Cambridge, MA: https://doi.org/10.3386/w25728
Gridded Population of the World (GPW) v4 (national identifier grid)
Population Dynamics (Global Estimated Net Migration Grids By Decade, v1)

Perkins, O., Matej, S., Erb, K., & Millington, J. (2022). Towards a global behavioural model of anthropogenic fire: The spatiotemporal distribution of land-fire systems. Socio-Environmental Systems Modelling, 4, 18130. doi:10.18174/sesmo.18130
Gridded Population of the World (GPW) v4 (population density) - 10.7927//H4NP22DQ

Perosa, F., Fanger, S., Zingraff-Hamed, A., & Disse, M. (2021). A meta-analysis of the value of ecosystem services of floodplains for the Danube River Basin. Science of The Total Environment, 777, 146062. doi:10.1016/j.scitotenv.2021.146062
Gridded Population of the World (GPW) v4.11 (population density)

Perović, V., Čakmak, D., Srbinović, O. S., Mrvić, V., Simić, S. B., Matić, M., . . . Pavlović, P. (2023). A conceptual modelling framework for assessment multiple soil degradation: A case study in the region of Šumadija and Western Serbia. Ecological Indicators, 148, 110096. doi:10.1016/j.ecolind.2023.110096
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Perović, V., Kadović, R., Đurđević, V., Pavlović, D., Pavlović, M., Čakmak, D., . . . Pavlović, P. (2021). Major drivers of land degradation risk in Western Serbia: Current trends and future scenarios. Ecological Indicators, 123, 107377. doi:10.1016/j.ecolind.2021.107377
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Petracca, L. S., Frair, J. L., Cohen, J. B., Calderón, A. P., Carazo-Salazar, J., Castañeda, F., . . . Quigley, H. (2018). Robust inference on large-scale species habitat use with interview data: The status of jaguars outside protected areas in Central America. Journal of Applied Ecology, 55(2), 723-734. doi:10.1111/1365-2664.12972
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates) - 10.7927/H4F47M2C

Petrie, H. E., Eide, C. H., Haflidason, H., & Watton, T. (2022). A conceptual geological model for offshore wind sites in former ice stream settings: The Utsira Nord site, North Sea. Journal of the Geological Society, 179(5), 1-23. doi:10.1144/jgs2021-163
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Petrova, K. (2021). Natural hazards, internal migration and protests in Bangladesh. Journal of Peace Research, 58(1), 33-49. doi:10.1177/0022343320973741
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates)

Petrova, K. (2022). Floods, communal conflict and the role of local state institutions in Sub-Saharan Africa. Political Geography, 92, 102511. doi:10.1016/j.polgeo.2021.102511
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates)

Pettinari, M. L., & Chuvieco, E. (2020). Fire danger observed from space. Surveys in Geophysics, 41, 1437-1459. doi:10.1007/s10712-020-09610-8
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)

Petutschnig, A., Resch, B., Lang, S., & Havas, C. (2021). Evaluating the representativeness of socio-demographic variables over time for geo-social media data. ISPRS International Journal of Geo-Information, 10(5), 323. doi:10.3390/ijgi10050323
Gridded Population of the World (GPW) v4.11 (population count)

Pfeifer, S., Rechid, D., Reuter, M., Viktor, E., & Jacob, D. (2019). 1.5°, 2°, and 3° global warming: visualizing European regions affected by multiple changes. Regional Environmental Change, 19(6), 1777-1786. doi:10.1007/s10113-019-01496-6
Gridded Population of the World (GPW) v4 (population density)

Pfeiffer, M. B., Blackwell, B. F., & DeVault, T. L. (2020). Collective effect of landfills and landscape composition on bird-aircraft collisions. Human-Wildlife Interactions, 14(1), 9. doi:10.26077/rcfe-z054
Gridded Population of the World (GPW) v4 (population density)

Pfennig, M., Böttger, D., Häckner, B., Geiger, D., Zink, C., Bisevic, A., & Jansen, L. (2023). Global GIS-based potential analysis and cost assessment of Power-to-X fuels in 2050. Applied Energy, 347, 121289. doi:10.1016/j.apenergy.2023.121289
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (SRTM)

Pham, T. D., Pham, V. H., Luu, Q. T., Ngo, X. T., Nguyen, T. N. T., & Bui, Q. H. (2019). Analyzing the impacts of urban expansion on air pollution in Vietnam using the SEAP platform. IOP Conference Series: Earth and Environmental Science, 266, 012008. doi:10.1088/1755-1315/266/1/012008
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS DNB)

Phelps, L. N., Andela, N., Gravey, M., Davis, D. S., Kull, C. A., Douglass, K., & Lehmann, C. E. R. (2022). Madagascar's fire regimes challenge global assumptions about landscape degradation. Global Change Biology, 28(23), 6944-6960. doi:10.1111/gcb.16206
Gridded Population of the World (GPW) v4.11 (documentation) - 10.7927/H45Q4T5F
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Sentinel-2)

Phuong, N. H., Nam, N. T., & Truyen, P. T. (2018). Development of a web-GIS based Decision Support System for earthquake warning service in Vietnam. Vietnam Journal of Earth Sciences, 40(3), 193-206. doi:10.15625/0866-7187/40/3/12638
Gridded Population of the World (GPW) v4 (documentation) - 10.7927/H4D50JX4

Pi, X., Luo, Q., Feng, L., Xu, Y., Tang, J., Liang, X., . . . Bryan, B. A. (2022). Mapping global lake dynamics reveals the emerging roles of small lakes. Nature Communications, 13(1), 5777. doi:10.1038/s41467-022-33239-3
Gridded Population of the World (GPW) v3 (population density)
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (Circum-Arctic map of permafrost and ground-ice conditions)
REMOTE SENSING (Landsat)

Piemontese, L., Castelli, G., Fetzer, I., Barron, J., Liniger, H., Harari, N., . . . Jaramillo, F. (2020). Estimating the global potential of water harvesting from successful case studies. Global Environmental Change, 63, 102121. doi:10.1016/j.gloenvcha.2020.102121
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - population density)

Pilko, A., Sóbester, A., Scanlan, J. P., & Ferraro, M. (2021). Spatiotemporal ground risk mapping for uncrewed aerial systems operations. Paper presented at the AIAA SCITECH 2022 Forum. https://doi.org/10.2514/6.2022-1915
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Pinichka, C., Issarapan, P., Siriwong, W., Kongtip, P., Chotpantarat, S., & Bundhamcharoen, K. (2019). Application of Geographic Information Systems and remote sensing for pesticide exposure and health risk assessment in Thailand. Outbreak, Surveillance, Investigation and Response, 12(3), 75-83. Retrieved from http://osirjournal.net/index.php/osir/article/view/147
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)

Pittore, M., Haas, M., & Silva, V. (2020). Variable resolution probabilistic modeling of residential exposure and vulnerability for risk applications. Earthquake Spectra, 36(1_suppl.), 321-344. doi:10.1177/8755293020951582
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - data set unspecified)

Pittore, M., Wieland, M., & Fleming, K. (2017). Perspectives on global dynamic exposure modelling for geo-risk assessment. Natural Hazards, 86(Supplement 1), 7-30. doi:10.1007/s11069-016-2437-3
Gridded Population of the World (GPW) v3 (collection)
Gridded Population of the World (GPW) v4 (collection)

Plassin, S., Koch, J., Paladino, S., Friedman, J. R., Spencer, K., & Vaché, K. B. (2020). A socio-environmental geodatabase for integrative research in the transboundary Rio Grande/Río Bravo basin. Scientific Data, 7(1), 80. doi:10.1038/s41597-020-0410-1
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
Global Roads (Global Roads Open Access Data Set (gROADS), v1) - 10.7927/H4VD6WCT
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (Landsat)

Podgorski, J., Labhasetwar, P., Saha, D., & Berg, M. (2018). Prediction modeling and mapping of groundwater fluoride contamination throughout India. Environmental Science & Technology, 52(17), 9889-9898. doi:10.1021/acs.est.8b01679
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Pöhlker, C., Walter, D., Paulsen, H., Könemann, T., Rodríguez-Caballero, E., Moran-Zuloaga, D., . . . Andreae, M. O. (2019). Land cover and its transformation in the backward trajectory footprint region of the Amazon Tall Tower Observatory. Atmospheric Chemistry and Physics, 19(13), 8425-8470. doi:10.5194/acp-19-8425-2019
Gridded Population of the World (GPW) v4.10 (population density)
Global Reservoir and Dam (GRanD) v1.01 (dams)
Global Reservoir and Dam (GRanD) v1.01 (reservoirs)
NASA REMOTE SENSING (ISLSCP-II)
NASA REMOTE SENSING (MODIS - MOD13Q1)
NASA REMOTE SENSING (SRTM)

Poon, E. K. W., Kitsios, V., Pilcher, D., Bellomo, R., & Raman, J. (2023). Projecting future climate impact on national Australian respiratory-related intensive care unit demand. Heart, Lung and Circulation, 32(1), 95-104. doi:10.1016/j.hlc.2022.12.001
Gridded Population of the World (GPW) v4.11 (population density)

Poor, E. E., Scheick, B. K., & Mullinax, J. M. (2020). Multiscale consensus habitat modeling for landscape level conservation prioritization. Scientific Reports, 10(1), 17783. doi:10.1038/s41598-020-74716-3
Gridded Population of the World (GPW) v4 (population density)

Pope, R. J., Arnold, S. R., Chipperfield, M. P., Latter, B. G., Siddans, R., & Kerridge, B. J. (2018). Widespread changes in UK air quality observed from space. Atmospheric Science Letters, 19(5), e817. doi:10.1002/asl.817
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (MODIS)

Porfiri, M., & Marín, M. R. (2020). An information-theoretic approach to study spatial dependencies in small datasets. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 476(2242), 20200113. doi:10.1098/rspa.2020.0113
Gridded Population of the World (GPW) v4 (population count)

Porter, W. C., Jimenez, J. L., & Barsanti, K. C. (2021). Quantifying atmospheric parameter ranges for ambient secondary organic aerosol formation. ACS Earth and Space Chemistry, 5(9), 2380-2397. doi:10.1021/acsearthspacechem.1c00090
Gridded Population of the World (GPW) v4.11 (population density)

Powers, S. M., Chowdhury, R. B., MacDonald, G. K., Metson, G. S., Beusen, A. H. W., Bouwman, A. F., . . . Vaccari, D. A. (2019). Global opportunities to increase agricultural independence through phosphorus recycling. Earth's Future, 7(4), 370-383. doi:10.1029/2018ef001097
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4HX19NJ

Pozzer, A., Anenberg, S. C., Dey, S., Haines, A., Lelieveld, J., & Chowdhury, S. (2023). Mortality attributable to ambient air pollution: A review of global estimates. GeoHealth, 7(`), e2022GH000711. doi:10.1029/2022GH000711
Gridded Population of the World (GPW) v3 (collection)
Gridded Population of the World (GPW) v4 (collection)

Pozzer, A., Dominici, F., Haines, A., Witt, C., Münzel, T., & Lelieveld, J. (2020). Regional and global contributions of air pollution to risk of death from COVID-19. Cardiovascular Research, 116(14), 2247-2253. doi:10.1093/cvr/cvaa288
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Pradhan, P., Kriewald, S., Costa, L., Rybski, D., Benton, T., Fischer, G., & Kropp, J. P. (2020). Urban food systems: how regionalization can contribute to climate change mitigation. Environmental Science & Technology, 54(17), 10551-10560. doi:10.1021/acs.est.0c02739
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Pratson, L. F. (2023). Assessing impacts to maritime shipping from marine chokepoint closures. Communications in Transportation Research, 3, 100083. doi:10.1016/j.commtr.2022.100083
Gridded Population of the World (GPW) v4.11 (population density)

Pratzer, M., Fernández-Llamazares, Á., Meyfroidt, P., Krueger, T., Baumann, M., Garnett, S. T., & Kuemmerle, T. (2023). Agricultural intensification, Indigenous stewardship and land sparing in tropical dry forests. Nature Sustainability, 6, 671-682. doi:10.1038/s41893-023-01073-0
Gridded Population of the World (GPW) v4.11 (population density)

Pricope, N. G., Daldegan, G. A., Zvoleff, A., Mwenda, K. M., Noon, M., & Lopez-Carr, D. (2023). Operationalizing an integrative socio-ecological framework in support of global monitoring of land degradation. Land Degradation & Development, 34(1), 109-124. doi:10.1002/ldr.4447
Anthropogenic Biomes of the World (collection)
Food Security (Food Insecurity Hotspots Data Set, v1)
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)
Satellite-Derived Environmental Indicators (Trends in Global Freshwater Availability from the Gravity Recovery and Climate Experiment (GRACE), v1)
NASA REMOTE SENSING (AVHRR GIMMS)
NASA REMOTE SENSING (MODIS)

Pu, Q., & Yoo, E.-H. (2020). Spatio-temporal modeling of PM2.5 concentrations with missing data problem: a case study in Beijing, China. International Journal of Geographical Information Science, 34(3), 423-447. doi:10.1080/13658816.2019.1664742
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (MODIS - MOD13A3)
NASA REMOTE SENSING (SRTM)

Pu, Q., & Yoo, E.-H. (2021). Ground PM2.5 prediction using imputed MAIAC AOD with uncertainty quantification. Environmental Pollution, 274, 116574. doi:10.1016/j.envpol.2021.116574
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MODIS)

Pu, Q., & Yoo, E.-H. (2022). A gap-filling hybrid approach for hourly PM2.5 prediction at high spatial resolution from multi-sourced AOD data. Environmental Pollution, 315, 120419. doi:10.1016/j.envpol.2022.120419
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MODIS)

Purnamasari, R., Wirapati, B. A., Alatas, H., & Nasiir, M. (2020). Estimating Small Area Poverty and Welfare Indicators in Timor-Leste Using Satellite Imagery Data. Retrieved from Washington DC: http://hdl.handle.net/10986/34614
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)
Global Roads (Global Roads Open Access Data Set (gROADS), v1) - 10.7927/H4VD6WCT
NASA REMOTE SENSING (MODIS - MOD13Q1)
REMOTE SENSING (Landsat)
REMOTE SENSING (VIIRS DNB)

Pyarali, K., Peng, J., Disse, M., & Tuo, Y. (2022). Development and application of high resolution SPEI drought dataset for Central Asia. Scientific Data, 9(1), 172. doi:10.1038/s41597-022-01279-5
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)

Python, A. (2020). Debunking Seven Terrorism Myths Using Statistics: Chapman and Hall/CRC Press.
Gridded Population of the World (GPW) v4.10 (documentation) - 10.7927/H4B56GPT

Python, A., Bender, A., Nandi, A. K., Hancock, P. A., Arambepola, R., Brandsch, J., & Lucas, T. C. D. (2021). Predicting non-state terrorism worldwide. Science Advances, 7(31), eabg4778. doi:10.1126/sciadv.abg4778
Gridded Population of the World (GPW) v4.10 (population density)
Poverty Mapping (Global Subnational Infant Mortality Rates, v1)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (DMSP-OLS)

Python, A., Illian, J. B., Jones‐Todd, C. M., & Blangiardo, M. (2019). A Bayesian approach to modelling subnational spatial dynamics of worldwide non‐state terrorism, 2010–2016. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(1), 323-344. doi:10.1111/rssa.12384
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ
REMOTE SENSING (DMSP-OLS)

Qader, S., Lefebvre, V., Tatem, A., Pape, U., Himelein, K., Ninneman, A., . . . Bird, T. (2021). Semi-automatic mapping of pre-census enumeration areas and population sampling frames. Humanities and Social Sciences Communications, 8(1), 3. doi:10.1057/s41599-020-00670-0
Gridded Population of the World (GPW) v4.10 (population density)
Global Rural-Urban Mapping Project (GRUMP) v1 (population count)

Qader, S. H., Lefebvre, V., Tatem, A. J., Pape, U., Jochem, W., Himelein, K., . . . Bird, T. (2020). Using gridded population and quadtree sampling units to support survey sample design in low-income settings. International Journal of Health Geographics, 19(1), 10. doi:10.1186/s12942-020-00205-5
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates) - 10.7927/H4F47M2C
Global Rural-Urban Mapping Project (GRUMP) v1 (population count) - 10.7927/H4VT1Q1H

Qiang, Y. (2019). Disparities of population exposed to flood hazards in the United States. Journal of Environmental Management, 232, 295-304. doi:10.1016/j.jenvman.2018.11.039
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Qin, K., Rao, L., Xu, J., Bai, Y., Zou, J., Hao, N., . . . Yu, C. (2017). Estimating ground level NO2 concentrations over Central-Eastern China using a satellite-based geographically and temporally weighted regression model. Remote Sensing, 9(9), 20pp. doi:10.3390/rs9090950
Gridded Population of the World (GPW) v4 (unspecified)
NASA REMOTE SENSING (OMI NO2)
REMOTE SENSING (SCIAMACHY)

Qiu, M., Zigler, C. M., & Selin, N. E. (2022). Impacts of wind power on air quality, premature mortality, and exposure disparities in the United States. Science Advances, 8(48), eabn8762. doi:10.1126/sciadv.abn8762
Gridded Population of the World (GPW) v4.11 (population count)

Qiu, T., Song, C., Zhang, Y., Liu, H., & Vose, J. M. (2020). Urbanization and climate change jointly shift land surface phenology in the northern mid-latitude large cities. Remote Sensing of Environment, 236, 111477. doi:10.1016/j.rse.2019.111477
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM
Global Rural-Urban Mapping Project (GRUMP) v1.01 (urban extent) - 10.7927/H4Z31WKF
NASA (MEaSUREs Vegetation Index and Phenology (VIP))
REMOTE SENSING (Landsat)

Qiu, Y., Zhao, X., Fan, D., Li, S., & Zhao, Y. (2022). Disaggregating population data for assessing progress of SDGs: methods and applications. International Journal of Digital Earth, 15(1), 2-29. doi:10.1080/17538947.2021.2013553
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)

Qu, W., Zhang, X., Wang, Y., & Fu, G. (2020). Atmospheric visibility variation over global land surface during 1973 – 2012: Influence of meteorological factors and effect of aerosol, cloud on ABL evolution. Atmospheric Pollution Research, 11(4), 730-743. doi:10.1016/j.apr.2020.01.002
Gridded Population of the World (GPW) v4 (collection)

Queiroz, H. A. A., Gonçalves, R. M., & Mishra, M. (2022). Characterizing global satellite-based indicators for coastal vulnerability to erosion management as exemplified by a regional level analysis from Northeast Brazil. Science of The Total Environment, 817, 152849. doi:10.1016/j.scitotenv.2021.152849
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (Landsat)
REMOTE SENSING (Sentinel-2 Multispectral Imager (MSI))

Quinn, S. E., Dyer, S. D., Fan, M., Keller, V. D. J., Johnson, A. C., & Williams, R. J. (2018). Predicting risks from down‐the‐drain chemicals in a developing country: Mexico and Linear Alkylbenzene Sulfonate (LAS) as a case study. Environmental Toxicology and Chemistry, 37(9), 2475-2486. doi:10.1002/etc.4181
Gridded Population of the World (GPW) v4 (population density)

Quintero, L. E., & Roberts, M. (2018). Explaining Spatial Variations in Productivity: Evidence from Latin America and the Caribbean. Retrieved from Washington: http://hdl.handle.net/10986/30287
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Quintero, L. E., & Roberts, M. (2023). Cities and productivity: Evidence from 16 Latin American and Caribbean countries. Journal of Urban Economics, 136, 103573. doi:10.1016/j.jue.2023.103573
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Rader, B., Astley, C. M., Sewalk, K., Delamater, P. L., Cordiano, K., Wronski, L., . . . Brownstein, J. S. (2022). Spatial modeling of vaccine deserts as barriers to controlling SARS-CoV-2. Communications Medicine, 2(1), 141. doi:10.1038/s43856-022-00183-8
Gridded Population of the World (GPW) v4.11 (population count)

Rader, B., Scarpino, S. V., Nande, A., Hill, A. L., Adlam, B., Reiner, R. C., . . . Kraemer, M. U. G. (2020). Crowding and the shape of COVID-19 epidemics. Nature Medicine, 26, 1829-1834. doi:10.1038/s41591-020-1104-0
Gridded Population of the World (GPW) v4.11 (population count)

Radford, A., Geddes, J. A., Gallagher, K., & Larson, B. A. (2021). Open-source methods for estimating health risks of fine particulate matter from coal-fired power plants: A demonstration from Karachi, Pakistan. Environmental Impact Assessment Review, 91, 106638. doi:10.1016/j.eiar.2021.106638
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (SRTM)

Rahimi, J., Fillol, E., Mutua, J. Y., Cinardi, G., Robinson, T. P., Notenbaert, A. M. O., . . . Butterbach-Bahl, K. (2022). A shift from cattle to camel and goat farming can sustain milk production with lower inputs and emissions in north sub-Saharan Africa’s drylands. Nature Food, 3(7), 523-531. doi:10.1038/s43016-022-00543-6
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
REMOTE SENSING (SPOT 4)
REMOTE SENSING (SPOT 5)

Raju, A., & Singh, A. (2017). Assessment of groundwater quality and mapping human health risk in Central Ganga Alluvial Plain, Northern India. Environmental Processes, 4(2), 375-397. doi:10.1007/s40710-017-0232-0
Gridded Population of the World (GPW) v4 (documentation) - 10.7927/H4D50JX4

Rakotoarinia, M. R., Seidou, O., Lapen, D. R., Leighton, P. A., Ogden, N. H., & Ludwig, A. (2023). Future land-use change predictions using Dyna-Clue to support mosquito-borne disease risk assessment. Environmental Monitoring and Assessment, 195(7), 815. doi:10.1007/s10661-023-11394-4
Gridded Population of the World (GPW) v4.11 (population density)

Ralimanana, H., Perrigo, A. L., Smith, R. J., Borrell, J. S., Faurby, S., Rajaonah, M. T., . . . Antonelli, A. (2022). Madagascar's extraordinary biodiversity: Threats and opportunities. Science, 378(6623), eadf1466. doi:10.1126/science.adf1466
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (TRMM)

Ramesh, T., Kalle, R., Milda, D., Gayathri, V., Thanikodi, M., Ashish, K., & Giordano, A. J. (2020). Patterns of livestock predation risk by large carnivores in India’s Eastern and Western Ghats. Global Ecology and Conservation, 24, e01366. doi:10.1016/j.gecco.2020.e01366
Gridded Population of the World (GPW) v4 (documentation) - 10.7927/H4D50JX4

Ramesh, T., Milda, D., Kalle, R., Gayathri, V., Thanikodi, M., Ashish, K., & Giordano, A. J. (2022). Drivers of human-megaherbivore interactions in the Eastern and Western Ghats of southern India. Journal of Environmental Management, 316, 115315. doi:10.1016/j.jenvman.2022.115315
Gridded Population of the World (GPW) v4 (population count)

Ramirez-Villegas, J., Khoury, C. K., Achicanoy, H. A., Diaz, M. V., Mendez, A. C., Sosa, C. C., . . . Zavala, C. (2022). State of ex situ conservation of landrace groups of 25 major crops. Nature Plants, 8, 491-499. doi:10.1038/s41477-022-01144-8
Gridded Population of the World (GPW) v4.11 (population density)

Ramirez-Villegas, J., Khoury, C. K., Achicanoy, H. A., Mendez, A. C., Diaz, M. V., Sosa, C. C., . . . Guarino, L. (2020). A gap analysis modelling framework to prioritize collecting for ex situ conservation of crop landraces. Diversity and Distributions, 26(6), 730-742. doi:10.1111/ddi.13046
Gridded Population of the World (GPW) v4.11 (population density)

Ran, Q., Lee, S.-Y., Zheng, D., Chen, H., Yang, S., Moore, J. C., & Dong, W. (2023). Potential health and economic impacts of shifting manufacturing from China to Indonesia or India. Science of The Total Environment, 855, 158634. doi:10.1016/j.scitotenv.2022.158634
Gridded Population of the World (GPW) v4.11 (population count)

Randazzo-Eisemann, Á., Arias-González, J. E., Velez, L., McField, M., & Mouillot, D. (2021). The last hotspots of structural complexity as conservation targets in the Mesoamerican Coral Reef. Biological Conservation, 256, 109021. doi:10.1016/j.biocon.2021.109021
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)

Rao, A., Dutta, D., Kalita, P., Ackerley, N., Silva, V., Raghunandan, M., . . . Dasgupta, K. (2020). Probabilistic seismic risk assessment of India. Earthquake Spectra, 36(1_suppl), 345-371. doi:10.1177/8755293020957374
Gridded Population of the World (GPW) v4.11 (population count)

Ravishankara, A. R., David, L. M., Pierce, J. R., & Venkataraman, C. (2020). Outdoor air pollution in India is not only an urban problem. Proceedings of the National Academy of Sciences, 117(46), 28640-28644. doi:10.1073/pnas.2007236117
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MISR)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (VIIRS DNB)

Ray, C., Matos, C., Preisser, M., & Ellsworth, M. (2020). Vulnerability Mapping in Oceania. Retrieved from http://sites.utexas.edu/climatesecurity/files/2020/05/LBJ_Oceania_Mapping.pdf
Gridded Population of the World (GPW) v4 (unspecified)

Raymond, C., & Mankin, J. S. (2019). Assessing present and future coastal moderation of extreme heat in the Eastern United States. Environmental Research Letters, 14(11), 114002. doi:10.1088/1748-9326/ab495d
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42

Reader, M. O., Eppinga, M. B., de Boer, H. J., Damm, A., Petchey, O. L., & Santos, M. J. (2022). The relationship between ecosystem services and human modification displays decoupling across global delta systems. Communications Earth & Environment, 3(1), 102. doi:10.1038/s43247-022-00431-8
Global Agricultural Lands (Pasture)
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
Last of the Wild v3 (Human Footprint, 2018 Release (2009)) - 10.7927/H46T0JQ4
Gridded Species Distribution (Amphibians 2015)
Gridded Species Distribution (Mammals 2015)

Reader, M. O., Eppinga, M. B., de Boer, H. J., Damm, A., Petchey, O. L., & Santos, M. J. (2023). Biodiversity mediates relationships between anthropogenic drivers and ecosystem services across global mountain, island and delta systems. Global Environmental Change, 78, 102612. doi:10.1016/j.gloenvcha.2022.102612
Global Agricultural Lands (Pasture) - 10.7927/H47H1GGR
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
Last of the Wild v3 (Human Footprint, 2018 Release (2009)) - 10.7927/H46T0JQ4

Reddington, C. L., Conibear, L., Knote, C., Silver, B. J., Arnold, S. R., & Spracklen, D. V. (2019). Exploring the impacts of anthropogenic emission sectors on PM2.5 and human health in South and East Asia. Atmospheric Chemistry and Physics, 19, 11887-11910. doi:10.5194/acp-19-11887-2019
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Reddington, C. L., Conibear, L., Robinson, S., Knote, C., Arnold, S. R., & Spracklen, D. V. (2021). Air pollution from forest and vegetation fires in Southeast Asia disproportionately impacts the poor. GeoHealth, 5(9), e2021GH000418. doi:10.1029/2021GH000418
Gridded Population of the World (GPW) v4 (land and water area) - 10.7927/H45M63M9
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
Poverty Mapping (Global Subnational Infant Mortality Rates, v2) - 10.7927/H4PN93JJ

Redington, A. L., Manning, A. J., Henne, S., Graziosi, F., Western, L. M., Arduini, J., . . . Young, D. (2023). Western European emission estimates of CFC-11, CFC-12 and CCl4 derived from atmospheric measurements from 2008 to 2021. Atmospheric Chemistry and Physics, 23(13), 7383-7398. doi:10.5194/acp-23-7383-2023
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Reed, F., Gaughan, A., Stevens, F., Yetman, G., Sorichetta, A., & Tatem, A. (2018). Gridded population maps informed by different built settlement products. Data, 3(3), 33. doi:10.3390/data3030033
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)

Reimann, L., Jones, B., Nikoletopoulos, T., & Vafeidis, A. T. (2021). Accounting for internal migration in spatial population projections—a gravity-based modeling approach using the Shared Socioeconomic Pathways. Environmental Research Letters, 16(7), 074025. doi:10.1088/1748-9326/ac0b66
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted)

Reimann, L., Merkens, J.-L., & Vafeidis, A. T. (2018). Regionalized Shared Socioeconomic Pathways: narratives and spatial population projections for the Mediterranean coastal zone. Regional Environmental Change, 18(1), 235-245. doi:10.1007/s10113-017-1189-2
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42
Global Rural-Urban Mapping Project (GRUMP) v1 (urban extent) - 10.7927/H4GH9FVG

Reitalu, T., Bjune, A. E., Blaus, A., Giesecke, T., Helm, A., Matthias, I., . . . Birks, H. J. B. (2019). Patterns of modern pollen and plant richness across northern Europe. Journal of Ecology, 107(4), 1662-1667. doi:10.1111/1365-2745.13134
Gridded Population of the World (GPW) v4 (population density)

Ren, H., Zhao, L., Zhang, A., Song, L., Liao, Y., Lu, W., & Cui, C. (2020). Early forecasting of the potential risk zones of COVID-19 in China's megacities. Science of The Total Environment, 729, 138995. doi:10.1016/j.scitotenv.2020.138995
Gridded Population of the World (GPW) v4.11 (population density)

Renner, I. W., Louvrier, J., & Gimenez, O. (2019). Combining multiple data sources in species distribution models while accounting for spatial dependence and overfitting with combined penalized likelihood maximization. Methods in Ecology and Evolution, 10(12), 2118-2128. doi:10.1111/2041-210x.13297
Gridded Population of the World (GPW) v4 (population density)

Renner, K., Schneiderbauer, S., Pruß, F., Kofler, C., Martin, D., & Cockings, S. (2018). Spatio-temporal population modelling as improved exposure information for risk assessments tested in the Autonomous Province of Bolzano. International Journal of Disaster Risk Reduction, 27, 470-479. doi:10.1016/j.ijdrr.2017.11.011
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)

Rey, D., Hammad, A. W., & Saberi, M. (2023). Vaccine allocation policy optimization and budget sharing mechanism using reinforcement learning. Omega, 115, 102783. doi:10.1016/j.omega.2022.102783
Gridded Population of the World (GPW) v4 (unspecified)

Rezaie, A. M., Ferreira, C. M., Walls, M., & Chu, Z. (2021). Quantifying the impacts of storm surge, sea level rise, and potential reduction and changes in wetlands in coastal areas of the Chesapeake Bay Region. Natural Hazards Review, 22(4), 04021044. doi:10.1061/(ASCE)NH.1527-6996.0000505
Gridded Population of the World (GPW) v4.11 (population density)

Rhodes, C. G., Loaiza, J. R., Romero, L. M., Gutiérrez Alvarado, J. M., Delgado, G., Rojas Salas, O., . . . Chaves, L. F. (2022). Anopheles albimanus (Diptera: Culicidae) ensemble distribution modeling: Applications for malaria elimination. Insects, 13(3), 221. doi:10.3390/insects13030221
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (NASADEM)
REMOTE SENSING (ALOS PALSAR)

Ridder, N. N., Ukkola, A. M., Pitman, A. J., & Perkins-Kirkpatrick, S. E. (2022). Increased occurrence of high impact compound events under climate change. npj Climate and Atmospheric Science, 5(1), 3. doi:10.1038/s41612-021-00224-4
Gridded Population of the World (GPW) v4.11 (population count)

Rigaud, K. K., de Sherbinin, A., Jones, B., Adamo, S., Maleki, D., Abu-Ata, N. E., . . . Mills, B. (2021). Groundswell Africa: Internal Climate Migration in West African Countries. Retrieved from Washington DC: https://openknowledge.worldbank.org/handle/10986/36404
Anthropogenic Biomes of the World v2 (2000) - 10.7927/H4D798B9
Food Security (Food Insecurity Hotspots Data Set, v1) - 10.7927/cx02-2587
Gridded Population of the World (GPW) v3 (population count) - 10.​7927/​H4639MPP
Gridded Population of the World (GPW) v4.10 (basic demographic characteristics) - 10.7927/H45H7D7F
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
Global Rural-Urban Mapping Project (GRUMP) v1.01 (urban extent) - 10.7927/H4Z31WKF
Low Elevation Coastal Zone (LECZ) (Urban-Rural Population and Land Area Estimates, v2) - 10.7927/H4MW2F2J
Low Elevation Coastal Zone (LECZ) (Urban-Rural Population and Land Area Estimates, v3) - 10.7927/d1x1-d702
Population Dynamics (Global One-Eighth Degree Population Projection Grids for the SSPs, v1) - 10.7927/H4RF5S0P
Population Estimation Service v3 - 10.7927/H4DR2SK5
Poverty Mapping (Global Subnational Infant Mortality Rates, v2) - 10.7927/H4PN93JJ

Rigaud, K. K., de Sherbinin, A., Jones, B., Adamo, S., Maleki, D., Arora, A., . . . Mills, B. (2021). Groundswell Africa : Internal Climate Migration in the Lake Victoria Basin Countries. Retrieved from Washington DC: https://openknowledge.worldbank.org/handle/10986/36403
Anthropogenic Biomes of the World v2 (2000) - 10.7927/H4D798B9
Food Security (Food Insecurity Hotspots Data Set, v1) - 10.7927/cx02-2587
Gridded Population of the World (GPW) v3 (population count) - 10.​7927/​H4639MPP
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
Poverty Mapping (Global Subnational Infant Mortality Rates, v2) - 10.7927/H4PN93JJ

Rigaud, K. K., de Sherbinin, A., Jones, B., Bergmann, J., Clement, V., Ober, K., . . . Midgley, A. (2018). Groundswell:  Preparing for Internal Climate Migration. Retrieved from Washington DC: http://hdl.handle.net/10986/29461
Gridded Population of the World (GPW) v3 (population count) - 10.​7927/​H4639MPP
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
Low Elevation Coastal Zone (LECZ) (Urban-Rural Population and Land Area Estimates, v2) - 10.7927/H4MW2F2J

Riggio, J., Kija, H., Masenga, E., Mbwilo, F., Van de Perre, F., & Caro, T. (2018). Sensitivity of Africa’s larger mammals to humans. Journal for Nature Conservation, 43, 136-145. doi:10.1016/j.jnc.2018.04.001
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted) - 10.7927/H4JQ0XZW

Rizvi, S. R., Killough, B., Cherry, A., Rattz, J., Lubawy, A., & Gowda, S. (2020). Data Cube application algorithms for the United Nation Sustainable Development Goals (UN-SDGS). Paper presented at the IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium.
Gridded Population of the World (GPW) v4 (unspecified)

Rizzati, M., De Cian, E., Guastella, G., Mistry, M. N., & Pareglio, S. (2022). Residential electricity demand projections for Italy: A spatial downscaling approach. Energy Policy, 160, 112639. doi:10.1016/j.enpol.2021.112639
Gridded Population of the World (GPW) v4.11 (population count)
Population Dynamics (Global 1-km Downscaled Population Projection Grids for the SSPs, v1) - 10.7927/q7z9-9r69

Roberts, M. (2018). The empirical determinants of city productivity. In M. M. Ferreyra & M. Roberts (Eds.), Raising the Bar for Productive Cities in Latin America and the Caribbean (pp. 89-115). Washington: World Bank.
Gridded Population of the World (GPW) v4 (population density)

Roberts, M., Blankespoor, B., Deuskar, C., & Stewart, B. P. (2017). Urbanization and development : is Latin America and the Caribbean different from the rest of the world? Retrieved from Washington: https://doi.org/10.1596/1813-9450-8019
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (population count)

Roberts, P., Hamilton, R., & Piperno, D. R. (2021). Tropical forests as key sites of the “Anthropocene”: Past and present perspectives. Proceedings of the National Academy of Sciences, 118(40), e2109243118. doi:10.1073/pnas.2109243118
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Robertson, D., Pokorný, P., Granvik, M., Wheeler, L., & Rumpf, C. (2021). Latitude variation of flux and impact angle of asteroid collisions with Earth and the Moon. The Planetary Science Journal, 2(3), 88. doi:10.3847/psj/abefda
Gridded Population of the World (GPW) v4.11 (population count)

Robinson, T. R., Rosser, N., & Walters, R. J. (2019). The spatial and temporal influence of cloud cover on satellite-based emergency mapping of earthquake disasters. Scientific Reports, 9(1), 12455. doi:10.1038/s41598-019-49008-0
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
NASA REMOTE SENSING (MODIS)

Rockström, J., Gupta, J., Qin, D., Lade, S. J., Abrams, J. F., Andersen, L. S., . . . Zhang, X. (2023). Safe and just Earth system boundaries. Nature, 619, 102-111. doi:10.1038/s41586-023-06083-8
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4PN93PB
Low Elevation Coastal Zone (LECZ) (Urban-Rural Population and Land Area Estimates, v3) - 10.7927/d1x1-d702
NASA REMOTE SENSING (GRACE)

Rohmer, J., Lincke, D., Hinkel, J., Le Cozannet, G., Lambert, E., & Vafeidis, A. T. (2021). Unravelling the importance of uncertainties in global-scale coastal flood risk assessments under sea level rise. Water, 13(6), 774. doi:10.3390/w13060774
Gridded Population of the World (GPW) v4 (unspecified)

Román, M. O., Stokes, E. C., Shrestha, R., Wang, Z., Schultz, L., Carlo, E. A. S., . . . Enenkel, M. (2019). Satellite-based assessment of electricity restoration efforts in Puerto Rico after Hurricane Maria. PLoS ONE, 14(6), e0218883. doi:10.1371/journal.pone.0218883
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (Black Marble -VIIRS)
REMOTE SENSING (Landsat 8)
REMOTE SENSING (Sentinel-2)

Romanello, M., Di Napoli, C., Drummond, P., Green, C., Kennard, H., Lampard, P., . . . Costello, A. (2022). The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels. The Lancet, 400(10363), 1619-1654. doi:10.1016/S0140-6736(22)01540-9
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)

Romanello, M., McGushin, A., Di Napoli, C., Drummond, P., Hughes, N., Jamart, L., . . . Hamilton, I. (2021). The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future. The Lancet, 398(10311), 1619-1662. doi:10.1016/S0140-6736(21)01787-6
Gridded Population of the World (GPW) v4.11 (unspecified)
NASA REMOTE SENSING (FIRMS)

Romanov, A. A., Tamarovskaya, A. N., Gloor, E., Brienen, R., Gusev, B. A., Leonenko, E. V., . . . Krikunov, E. E. (2022). Reassessment of carbon emissions from fires and a new estimate of net carbon uptake in Russian forests in 2001–2021. Science of The Total Environment, 846, 157322. doi:10.1016/j.scitotenv.2022.157322
Gridded Population of the World (GPW) v4 (documentation) - 10.7927/H4D50JX4
Gridded Population of the World (GPW) v4 (population count)
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)

Romero, D., Olivero, J., Real, R., & Guerrero, J. C. (2019). Applying fuzzy logic to assess the biogeographical risk of dengue in South America. Parasites & Vectors, 12(1), 428. doi:10.1186/s13071-019-3691-5
Gridded Population of the World (GPW) v4 (population density)

Romero-Blanco, A., Castro-Díez, P., Lázaro-Lobo, A., Molina-Venegas, R., Cruces, P., & Pyšek, P. (2023). Searching for predictors of the variability of impacts caused by non-native trees on regulating ecosystem services worldwide. Science of The Total Environment, 877, 162961. doi:10.1016/j.scitotenv.2023.162961
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW 

Romiti, F., Casini, R., Magliano, A., Ermenegildi, A., & De Liberato, C. (2022). Aedes albopictus abundance and phenology along an altitudinal gradient in Lazio region (central Italy). Parasites & Vectors, 15(1), 92. doi:10.1186/s13071-022-05215-9
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Ronchi, E., Wahlqvist, J., Gwynne, S., Kinateder, M., Benichou, N., Ma, C., . . . Kimball, A. (2020). WUI-NITY: A Platform for the Simulation of Wildland-Urban Interface Fire Evacuation. Retrieved from https://www.nfpa.org/News-and-Research/Data-research-and-tools/Wildland-Urban-Interface/WUINITY-a-platform-for-the-simulation-of-wildland-urban-interface-fire-evacuation
Gridded Population of the World (GPW) v4 (collection)

Rosa, L., Davis, K. F., Rulli, M. C., & D'Odorico, P. (2017). Environmental consequences of oil production from oil sands. Earth's Future, 5(2), 158-170. doi:10.1002/2016EF000484
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4BG2KXS

Rosa, L., Rulli, M. C., & Davis, K. F. (2018). The water‐energy nexus of hydraulic fracturing: a global hydrologic analysis for shale oil and gas extraction. Earth's Future, 6(5), 745-756. doi:10.1002/2018EF000809
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4BG2KXS

Roseman, C. A., & Argrow, B. M. (2020). Weather hazard risk quantification for sUAS safety risk management. Journal of Atmospheric and Oceanic Technology, 37(7), 1251-1268. doi:10.1175/JTECH-D-20-0009.1
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
Global High Resolution Urban Data from Landsat (HBASE) - 10.7927/H4DN434S

Rosenthal, N. (2023). Exposure, Vulnerability and Adaptation to Heat and Wildfire in the Southwestern United States. (Ph.D.). University of California, Los Angeles, Los Angeles. Retrieved from https://escholarship.org/uc/item/1zw5574v 
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)

Rosenthal, N., Benmarhnia, T., Ahmadov, R., James, E., & Marlier, M. E. (2022). Population co-exposure to extreme heat and wildfire smoke pollution in California during 2020. Environmental Research: Climate, 1(2), 025004. doi:10.1088/2752-5295/ac860e
Gridded Population of the World (GPW) v4.11 (population count)
REMOTE SENSING (VIIRS)

Rostami, M., Mohammadi, Y., Jalilian, A., & Nazparvar, B. (2017). Modeling spatio-temporal variations of substance abuse mortality in Iran using a log-Gaussian Cox point process. Spatial and Spatio-temporal Epidemiology, 22, 15-25. doi:10.1016/j.sste.2017.05.002
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Roth, M. M. H. (2023). Analyzing source-routed approaches for Low Earth Orbit Satellite Constellation Networks. Paper presented at the Proceedings of the 1st ACM Workshop on LEO Networking and Communication, Madrid, Spain. https://doi.org/10.1145/3614204.3616109
Gridded Population of the World (GPW) v4.11 (population density) 

Rougé, C., Reed, P. M., Grogan, D. S., Zuidema, S., Prusevich, A., Glidden, S., . . . Lammers, R. B. (2021). Coordination and control: Limits in standard representations of multi-reservoir operations in hydrological modeling. Hydrology and Earth System Sciences, 25, 1365-1388. doi:10.5194/hess-25-1365-2021
Gridded Population of the World (GPW) v4 (population density)

Routley, N. (2019). How Facebook is using machine learning to map the world population.  Retrieved from https://www.visualcapitalist.com/facebook-machine-learning-world-population-map/
Gridded Population of the World (GPW) v4 (unspecified)

Rowley, J. J. L., Callaghan, C. T., Cutajar, T., Portway, C., Potter, K., Mahony, S., . . . Woods, A. (2019). FrogID: Citizen scientists provide validated biodiversity data on frogs of Australia. Herpetological Conservation and Biology, 14(1), 155-170. Retrieved from http://www.herpconbio.org/Volume_14/Issue_1/Rowley_etal_2019.pdf
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Ru, Y., Blankespoor, B., Wood-Sichra, U., Thomas, T. S., You, L., & Kalvelagen, E. (2023). Estimating local agricultural gross domestic product (AgGDP) across the world. Earth System Science Data, 15(3), 1357-1387. doi:10.5194/essd-15-1357-2023
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (DMSP-OLS)

Ruan, L., He, T., Xiao, W., Chen, W., Lu, D., & Liu, S. (2022). Measuring the coupling of built-up land intensity and use efficiency: An example of the Yangtze River Delta urban agglomeration. Sustainable Cities and Society, 87, 104224. doi:10.1016/j.scs.2022.104224
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (VIIRS NTL)

Ruberanziza, E., Owada, K., Clark, N. J., Umulisa, I., Ortu, G., Lancaster, W., . . . Soares Magalhães, R. J. (2019). Mapping soil-transmitted helminth parasite infection in Rwanda: Estimating endemicity and identifying at-risk populations. Tropical Medicine and Infectious Disease, 4(2), 93. doi:10.3390/tropicalmed4020093
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4D50JX4

Rubinyi, S., Blankespoor, B., & Hall, J. W. (2021). The utility of built environment geospatial data for high-resolution dasymetric global population modeling. Computers, Environment and Urban Systems, 86, 101594. doi:10.1016/j.compenvurbsys.2021.101594
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - data set unspecified)

Rudbeck, A. V., Sun, M., Tietje, M., Gallagher, R. V., Govaerts, R., Smith, S. A., . . . Eiserhardt, W. L. (2022). The Darwinian shortfall in plants: phylogenetic knowledge is driven by range size. Ecography, 2022(8), e06142. doi:10.1111/ecog.06142
Gridded Population of the World (GPW) v4.11 (population density)

Rufí-Salís, M., Brunnhofer, N., Petit-Boix, A., Gabarrell, X., Guisasola, A., & Villalba, G. (2020). Can wastewater feed cities? Determining the feasibility and environmental burdens of struvite recovery and reuse for urban regions. Science of The Total Environment, 737, 139783. doi:10.1016/j.scitotenv.2020.139783
Gridded Population of the World (GPW) v4 (unspecified)

Rufí-Salís, M., Parada, F., Arcas-Pilz, V., Petit-Boix, A., Villalba, G., & Gabarrell, X. (2020). Closed-loop crop cascade to optimize nutrient flows and grow low-impact vegetables in cities. Frontiers in Plant Science, 11(1793). doi:10.3389/fpls.2020.596550
Gridded Population of the World (GPW) v4 (collection)

Rufí-Salís, M., Petit-Boix, A., Villalba, G., Sanjuan-Delmás, D., Parada, F., Ercilla-Montserrat, M., . . . Gabarrell, X. (2020). Recirculating water and nutrients in urban agriculture: An opportunity towards environmental sustainability and water use efficiency? Journal of Cleaner Production, 261, 121213. doi:10.1016/j.jclepro.2020.121213
Gridded Population of the World (GPW) v4 (collection)

Ruhnau, O., Lundström, L., Dürr, L., & Hunecke, F. (2023, 6-8 June 2023). Empirical weather dependency of heat pump load: Disentangling the effects of heat demand and efficiency. Paper presented at the 2023 19th International Conference on the European Energy Market (EEM), Lappeenranta, Finland.
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Rupa Rajulapati, C., Mohamed Abdelmoaty, H., Nerantzaki, S. D., & Michael Papalexiou, S. (2022). Changes in the risk of extreme temperatures in megacities worldwide. Climate Risk Management, 36, 100433. doi:10.1016/j.crm.2022.100433
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Russ, J. (2018). Essays on the Impact of Weather on Economic Activity. (Ph.D.). The George Washington University, Washington DC. Retrieved from https://scholarspace-etds.library.gwu.edu/etd/vq27zn652 
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42
GRAND v1 (collection)
NASA REMOTE SENSING (MODIS - MOD17A3)

Russ, J. (2020). Water runoff and economic activity: The impact of water supply shocks on growth. Journal of Environmental Economics and Management, 101, 102322. doi:10.1016/j.jeem.2020.102322
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42
REMOTE SENSING (DMSP-OLS)

Russ, J., Zaveri, E., Desbureaux, S., Damania, R., & Rodella, A.-S. (2022). The impact of water quality of GDP growth: Evidence from around the world. Water Security, 17, 100130. doi:10.1016/j.wasec.2022.100130
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42
REMOTE SENSING (DMSP-OLS)

Russell-Smith, J., Yates, C., Vernooij, R., Eames, T., van der Werf, G., Ribeiro, N., . . . Johnston, S. (2021). Opportunities and challenges for savanna burning emissions abatement in southern Africa. Journal of Environmental Management, 288, 112414. doi:10.1016/j.jenvman.2021.112414
Gridded Population of the World (GPW) v4 (documentation) - 10.7927/H45Q4T5F
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)
REMOTE SENSING (Sentinel-2)

Rutz, C., Loretto, M.-C., Bates, A. E., Davidson, S. C., Duarte, C. M., Jetz, W., . . . Cagnacci, F. (2020). COVID-19 lockdown allows researchers to quantify the effects of human activity on wildlife. Nature Ecology & Evolution, 4, 1156-1159. doi:10.1038/s41559-020-1237-z
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Ryan, S. J., Carlson, C. J., Mordecai, E. A., & Johnson, L. R. (2019). Global expansion and redistribution of Aedes-borne virus transmission risk with climate change. PLoS Neglected Tropical Diseases, 13(3), e0007213. doi:10.1371/journal.pntd.0007213
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Ryan, S. J., Lippi, C. A., Villena, O. C., Singh, A., Murdock, C. C., & Johnson, L. R. (2023). Mapping current and future thermal limits to suitability for malaria transmission by the invasive mosquito Anopheles stephensi. Malaria Journal, 22(1), 104. doi:10.1186/s12936-023-04531-4
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
Population Dynamics (Global One-Eighth Degree Population Base Year and Projection Grids Based on the SSPs, v1.01) - 10.7927/m30p-j498

Ryeland, J., Derham, T. T., & Spencer, R. J. (2021). Past and future potential range changes in one of the last large vertebrates of the Australian continent, the emu Dromaius novaehollandiae. Scientific Reports, 11(1), 851. doi:10.1038/s41598-020-79551-0
Gridded Population of the World (GPW) v4 (unspecified)
Last of the Wild v2 (Global Human Footprint (Geographic))

Sabūnas, A., Mori, N., Fukui, N., Miyashita, T., & Shimura, T. (2020). Impact assessment of climate change on storm surge and sea level rise around Viti Levu, Fiji. Frontiers in Climate, 2(19). doi:10.3389/fclim.2020.579715
Gridded Population of the World (GPW) v4.11 (population density)

Sachit, M. S., Shafri, H. Z. M., Abdullah, A. F., Rafie, A. S. M., & Gibril, M. B. A. (2022). Global spatial suitability mapping of wind and solar systems using an explainable AI-based approach. ISPRS International Journal of Geo-Information, 11(8), 422. doi:10.3390/ijgi11080422
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)

Sachs, J., Moya, D., Giarola, S., & Hawkes, A. (2019). Clustered spatially and temporally resolved global heat and cooling energy demand in the residential sector. Applied Energy, 250, 48-62. doi:10.1016/j.apenergy.2019.05.011
Gridded Population of the World (GPW) v4 (population count)
Gridded Population of the World (GPW) v4 (population density)

Sacks, J. D., Lloyd, J. M., Zhu, Y., Anderton, J., Jang, C. J., Hubbell, B., & Fann, N. (2018). The Environmental Benefits Mapping and Analysis Program – Community Edition (BenMAP–CE): A tool to estimate the health and economic benefits of reducing air pollution. Environmental Modelling & Software, 104, 118-129. doi:10.1016/j.envsoft.2018.02.009
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Saeed, W., Haqiqi, I., Kong, Q., Huber, M., Buzan, J. R., Chonabayashi, S., . . . Hertel, T. W. (2022). The poverty impacts of labor heat stress in West Africa under a warming climate. Earth's Future, 10(11), e2022EF002777. doi:10.1029/2022EF002777
Gridded Population of the World (GPW) v4.11 (admin unit center points)

Sahu, S. K., Chen, L., Liu, S., Ding, D., & Xing, J. (2020). The impact of aerosol direct radiative effects on PM2.5-related health risk in Northern Hemisphere during 2013–2017. Chemosphere, 254, 126832. doi:10.1016/j.chemosphere.2020.126832
Gridded Population of the World (GPW) v4 (population count)

Sahu, S. K., Liu, S., Liu, S., Ding, D., & Xing, J. (2021). Ozone pollution in China: Background and transboundary contributions to ozone concentration & related health effects across the country. Science of The Total Environment, 761, 144131. doi:10.1016/j.scitotenv.2020.144131
Gridded Population of the World (GPW) v4 (unspecified)

Saikawa, E., Wu, Q., Zhong, M., Avramov, A., Ram, K., Stone, E. A., . . . Yokelson, R. (2020). Garbage burning in South Asia - How important is it to regional air quality? Environmental Science & Technology, 54(16), 9928-9938. doi:10.1021/acs.est.0c02830
Gridded Population of the World (GPW) v4.11 (population count)

Sajeev, P., Martin, R. V., Snider, G., Weagle, C., van Donkelaar, A., Brauer, M., . . . Zhang, Q. (2017). Anthropogenic fugitive, combustion and industrial dust is a significant, underrepresented fine particulate matter source in global atmospheric models. Environmental Research Letters, 12(4), 7pp. doi:10.1088/1748-9326/aa65a4
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates) - 10.7927/H4F47M2C

Sala, S., Crenna, E., Secchi, M., & Pant, R. (2017). Global Normalisation Factors for the Environmental Footprint and Life Cycle Assessment. Retrieved from Ispra: https://doi.org/10.2760/88930
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Salaree, A., Howe, B. M., Huang, Y., Weinstein, S. A., & Sakya, A. E. (2023). A numerical study of SMART cables potential in marine hazard early warning for the Sumatra and Java regions. Pure and Applied Geophysics, 180, 1717-1749. doi:10.1007/s00024-022-03004-0
Gridded Population of the World (GPW) v4.11 (population density)

Salvi, A., Williamson, M., & Draper, J. (2020). On the beaten path: Violence against civilians and simulated conflict along road networks. In E. Deutschmann, J. Lorenz, L. G. Nardin, D. Natalini, & A. F. X. Wilhelm (Eds.), Computational Conflict Research (pp. 183-199). Cham: Springer International Publishing.
Gridded Population of the World (GPW) v4.11 (population count)

Samaniego, L., Thober, S., Kumar, R., Wanders, N., Rakovec, O., Pan, M., . . . Marx, A. (2018). Anthropogenic warming exacerbates European soil moisture droughts. Nature Climate Change, 8(5), 421-426. doi:10.1038/s41558-018-0138-5
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Samoilys, M. A., Halford, A., & Osuka, K. (2019). Disentangling drivers of the abundance of coral reef fishes in the Western Indian Ocean. Ecology and Evolution, 9(7), 4149-4167. doi:10.1002/ece3.5044
Gridded Population of the World (GPW) v4 (population density)

Sampedro, J., Cui, R. Y., McJeon, H., Smith, S. J., Hultman, N., He, L., . . . Cazcarro, I. (2021). Quantifying the reductions in mortality from air-pollution by cancelling new coal power plants. Energy and Climate Change, 2, 100023. doi:10.1016/j.egycc.2020.100023
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates)

Samy, A. M., Alkishe, A. A., Thomas, S., Wang, L., & Zhang, W. (2018). Mapping the potential distributions of etiological agent, vectors, and reservoirs of Japanese Encephalitis in Asia and Australia. Acta Tropica, 188, 108-117. doi:10.1016/j.actatropica.2018.08.014
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (DMSP-OLS)

Sannigrahi, S., Kumar, P., Molter, A., Zhang, Q., Basu, B., Basu, A. S., & Pilla, F. (2021). Examining the status of improved air quality in world cities due to COVID-19 led temporary reduction in anthropogenic emissions. Environmental Research, 196, 110927. doi:10.1016/j.envres.2021.110927
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (TROPOMI)

Santos, J., & Meneses, B. M. (2017). An integrated approach for the assessment of the Aedes aegypti and Aedes albopictus global spatial distribution, and determination of the zones susceptible to the development of Zika virus. Acta Tropica, 168, 80-90. doi:10.1016/j.actatropica.2017.01.015
Gridded Population of the World (GPW) v4 (population count)

Sapena, M., Kühnl, M., Wurm, M., Patino, J. E., Duque, J. C., & Taubenböck, H. (2022). Empiric recommendations for population disaggregation under different data scenarios. PLoS ONE, 17(9), e0274504. doi:10.1371/journal.pone.0274504
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
REMOTE SENSING (Landsat)

Sarkar, A., Panda, J., Kant, S., & Mukherjee, A. (2022). Influence of smoke aerosols on low-level clouds over the Indian region during winter. Atmospheric Research, 278, 106358. doi:10.1016/j.atmosres.2022.106358
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (CALIOP Lidar)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (OMI)
NASA REMOTE SENSING (SRTM)

Sarkar, S., Mukherjee, A., Chakraborty, M., Quamar, M. T., Duttagupta, S., & Bhattacharya, A. (2023). Prediction of elevated groundwater fluoride across India using multi-model approach: insights on the influence of geologic and environmental factors. Environmental Science and Pollution Research, 30, 31998-32013. doi:10.1007/s11356-022-24328-3
Gridded Population of the World (GPW) v4.11 (population count)

Sarkar, S., Mukherjee, A., Gupta, S. D., Bhanja, S. N., & Bhattacharya, A. (2022). Predicting regional-scale elevated groundwater nitrate contamination risk using machine learning on natural and human-induced factors. ACS ES&T Engineering, 2(4), 689-702. doi:10.1021/acsestengg.1c00360
Gridded Population of the World (GPW) v4.10 (population count)

Sarmah, S., Singha, M., Wang, J., Dong, J., Deb Burman, P. K., Goswami, S., . . . Niu, S. (2021). Mismatches between vegetation greening and primary productivity trends in South Asia – A satellite evidence. International Journal of Applied Earth Observation and Geoinformation, 104, 102561. doi:10.1016/j.jag.2021.102561
Gridded Population of the World (GPW) v4.11 (population count)
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (GRACE)
NASA REMOTE SENSING (MODIS)

Sartorius, B., Cano, J., Simpson, H., Tusting, L. S., Marczak, L. B., Miller-Petrie, M. K., . . . Pullan, R. L. (2021). Prevalence and intensity of soil-transmitted helminth infections of children in sub-Saharan Africa, 2000–18: a geospatial analysis. The Lancet Global Health, 9(1), e52-e60. doi:10.1016/S2214-109X(20)30398-3
Gridded Population of the World (GPW) v4.11 (population count)

Sarzotti, E., Pignocchino, G., Pezzoli, A., & Besana, A. (2023). NO2 Concentrations and COVID-19 in Local Systems of Northwest Italy. In G. Brunetta, P. Lombardi, & A. Voghera (Eds.), Post Un-Lock: From Territorial Vulnerabilities to Local Resilience (pp. 83-98). Cham: Springer International Publishing.
Gridded Population of the World (GPW) v4.11 (population count)
REMOTE SENSING (Sentinel-5P)

Sasikumar, K., Nath, D., Nath, R., & Chen, W. (2020). Impact of extreme hot climate on COVID-19 outbreak in India. GeoHealth, 4(12), e2020GH000305. doi:10.1029/2020GH000305
Gridded Population of the World (GPW) v4.11 (population density map)
Satellite-Derived Environmental Indicators (Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, v1)

Saunders, J. K., Aagaard, B. T., Baltay, A. S., & Minson, S. E. (2020). Optimizing earthquake early warning alert distance strategies using the July 2019 Mw 6.4 and Mw 7.1 Ridgecrest, California, earthquakes. Bulletin of the Seismological Society of America, 110(4), 1872-1886. doi:10.1785/0120200022
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Saunders, J. K., Minson, S. E., & Baltay, A. S. (2022). How low should we alert? Quantifying intensity threshold alerting strategies for earthquake early warning in the United States. Earth's Future, 10(3), e2021EF002515. doi:10.1029/2021EF002515
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Say, D., Kuyper, B., Western, L., Khan, M. A. H., Lesch, T., Labuschagne, C., . . . Shallcross, D. (2020). Emissions and marine boundary layer concentrations of unregulated chlorocarbons measured at Cape Point, South Africa. Environmental Science & Technology, 54(17), 10514-10523. doi:10.1021/acs.est.0c02057
Gridded Population of the World (GPW) v4 (population density)
REMOTE SENSING (DMSP-OLS)

Scarpelli, T. R., Jacob, D. J., Grossman, S., Lu, X., Qu, Z., Sulprizio, M. P., . . . Gordon, D. (2022). Updated Global Fuel Exploitation Inventory (GFEI) for methane emissions from the oil, gas, and coal sectors: evaluation with inversions of atmospheric methane observations. Atmospheric Chemistry and Physics, 22, 3235-3249. doi:10.5194/acp-22-3235-2022
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4F47M65

Scarpelli, T. R., Jacob, D. J., Maasakkers, J. D., Sulprizio, M. P., Sheng, J.-X., Rose, K., . . . Janssens-Maenhout, G. (2020). A global gridded (0.1° x 0.1°) inventory of methane emissions from oil, gas, and coal exploitation based on national reports to the United Nations Framework Convention on Climate Change. Earth System Science Data, 12, 563-575. doi:10.5194/essd-12-563-2020
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted) - 10.7927/H4JQ0XZW

Schar, D., Zhao, C., Wang, Y., Larsson, D. G. J., Gilbert, M., & Van Boeckel, T. P. (2021). Twenty-year trends in antimicrobial resistance from aquaculture and fisheries in Asia. Nature Communications, 12(1), 5384. doi:10.1038/s41467-021-25655-8
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Scherer, L., & Pfister, S. (2016). Dealing with uncertainty in water scarcity footprints. Environmental Research Letters, 11(5), 9 pp. doi:10.1088/1748-9326/11/5/054008
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)

Scherer, L. A., Verburg, P. H., & Schulp, C. J. E. (2018). Opportunities for sustainable intensification in European agriculture. Global Environmental Change, 48, 43-55. doi:10.1016/j.gloenvcha.2017.11.009
Gridded Population of the World (GPW) v4 (population count)

Schild, J. E. M., Vermaat, J. E., de Groot, R. S., Quatrini, S., & van Bodegom, P. M. (2018). A global meta-analysis on the monetary valuation of dryland ecosystem services: The role of socio-economic, environmental and methodological indicators. Ecosystem Services, 32, 78-89. doi:10.1016/j.ecoser.2018.06.004
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4D50JX4

Schiller, G., & Roscher, J. (2023). Impact of urbanization on construction material consumption: A global analysis. Journal of Industrial Ecology, 27(3), 1021-1036. doi:10.1111/jiec.13392
Gridded Population of the World (GPW) v4.10 (documentation)

Schlosser, C. A., Frankenfeld, C., Eastham, S. D., Gao, X., Gurgel, A., McCluskey, A., . . . Reilly, J. (2022). Assessing Compounding Risks Across Multiple Systems and Sectors: A Socio-Environmental Systems Risk-Triage Approach. Retrieved from Cambridge, MA: https://globalchange.mit.edu/publication/17873
Air Quality Data for Health-Related Applications (Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, v1) - 10.7927/0rvr-4538
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
Gridded Population of the World (GPW) v4.11 (land and water area)
NASA REMOTE SENSING (Worldview)

Schmidt, C., Kumar, R., Yang, S., & Büttner, O. (2020). Microplastic particle emission from wastewater treatment plant effluents into river networks in Germany: Loads, spatial patterns of concentrations and potential toxicity. Science of The Total Environment, 737, 139544. doi:10.1016/j.scitotenv.2020.139544
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted)

Schmidt, E., Dorosh, P. A., Kedir Jemal, M., & Smart, J. (2020). Urbanization and structural transformation. In P. A. Dorosh & B. Minten (Eds.), Ethiopia's Agrifood System: Past Trends, Present Challenges, and Future Scenarios (pp. 379-422). Washington DC: International Food Policy Research Institute (IFRPI).
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4HX19NJ
Global Rural-Urban Mapping Project (GRUMP) v1 (population density)

Schrodt, F., de la Barreda Bautista, B., Williams, C., Boyd, D. S., Schaepman-Strub, G., & Santos, M. J. (2020). Integrating Biodiversity, Remote Sensing, and Auxiliary Information for the Study of Ecosystem Functioning and Conservation at Large Spatial Scales. In J. Cavender-Bares, J. A. Gamon, & P. A. Townsend (Eds.), Remote Sensing of Plant Biodiversity (pp. 449-484). Cham: Springer International Publishing.
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ
REMOTE SENSING (many)

Schug, F., Frantz, D., Okujeni, A., van der Linden, S., & Hostert, P. (2020). Mapping urban-rural gradients of settlements and vegetation at national scale using Sentinel-2 spectral-temporal metrics and regression-based unmixing with synthetic training data. Remote Sensing of Environment, 246, 111810. doi:10.1016/j.rse.2020.111810
Gridded Population of the World (GPW) v4.10 (population density UN WPP-adjusted)
REMOTE SENSING (Sentinel-2 Multispectral Imager (MSI))

Schug, F., Frantz, D., van der Linden, S., & Hostert, P. (2021). Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLoS ONE, 16(3), e0249044. doi:10.1371/journal.pone.0249044
Gridded Population of the World (GPW) v4.10 (population density UN WPP-adjusted)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)
REMOTE SENSING (Sentinel-1)
REMOTE SENSING (Sentinel-2)

Schultes, A., Piontek, F., Soergel, B., Rogelj, J., Baumstark, L., Kriegler, E., . . . Luderer, G. (2020). Economic damages from on-going climate change imply deeper near-term emission cuts. Retrieved from https://mpra.ub.uni-muenchen.de/id/eprint/103655
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42

Schutte, S., Vestby, J., Carling, J., & Buhaug, H. (2021). Climatic conditions are weak predictors of asylum migration. Nature Communications, 12(1), 2067. doi:10.1038/s41467-021-22255-4
Gridded Population of the World (GPW) v4 (population density)

Schuyler, Q., Roman, L., Lawson, T. J., & Hardesty, B. D. (2023). Modelling the relative risk of plastic pollution to wildlife when data are scarce: an applied approach in the Mekong and Ganges river basins. Environmental Research Letters, 18(7), 074019. doi:10.1088/1748-9326/acdca7
Gridded Population of the World (GPW) v4.11 (population density)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
REMOTE SENSING (VIIRS NTL)

Schwarz, M., Landmann, T., Cornish, N., Wetzel, K.-F., Siebert, S., & Franke, J. (2020). A spatially transferable drought hazard and drought risk modeling approach based on remote sensing data. Remote Sensing, 12(2), 237. doi:10.3390/rs12020237
Gridded Population of the World (GPW) v4.10 (population density)
NASA REMOTE SENSING (MODIS)

Schwenk, J., Zussman, T., Stachelek, J., & Rowland, J. C. (2022). rabpro: global watershed boundaries, river elevation profiles, and catchment statistics. The Journal of Open Source Software, 7(73), 4237. doi:10.21105/joss.04237
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MODIS)

Scoccimarro, E., Cattaneo, O., Gualdi, S., Mattion, F., Bizeul, A., Risquez, A. M., & Quadrelli, R. (2023). Country-level energy demand for cooling has increased over the past two decades. Communications Earth & Environment, 4(1), 208. doi:10.1038/s43247-023-00878-3
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Seekell, D. A., Lapierre, J. F., & Cheruvelil, K. S. (2018). A geography of lake carbon cycling. Limnology and Oceanography Letters, 3(3), 49-56. doi:10.1002/lol2.10078
Global Agricultural Inputs (phosphorous fertilizer application) - 10.7927/H4FQ9TJR
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ
Human Appropriation of Net Primary Productivity (HANPP) (Global Patterns in Net Primary Productivity, v1) - 10.7927/H40Z715X

Seiler, J., Harttgen, K., Kneib, T., & Lang, S. (2021). Modelling children's anthropometric status using Bayesian distributional regression merging socio-economic and remote sensed data from South Asia and Sub-Saharan Africa. Economics & Human Biology, 40, 100950. doi:10.1016/j.ehb.2020.100950
Gridded Population of the World (GPW) v3 (population density) - 10.7927/H4XK8CG2
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS NTL)

Selby, R., Wamboga, C., Erphas, O., Mugenyi, A., Jamonneau, V., Waiswa, C., . . . Lehane, M. (2019). Gambian human African trypanosomiasis in North West Uganda. Are we on course for the 2020 target? PLoS Neglected Tropical Diseases, 13(8), e0007550. doi:10.1371/journal.pntd.0007550
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates) - 10.7927/H4F47M2C

Sellers, S., Ebi, K. L., & Hess, J. (2019). Climate change, human health, and social stability: Addressing interlinkages. Environmental Health Perspectives, 127(4), 045002. doi:10.1289/EHP4534
Gridded Population of the World (GPW) v4 (population density)

Sha, Z., Bai, Y., Li, R., Lan, H., Zhang, X., Li, J., . . . Xie, Y. (2022). The global carbon sink potential of terrestrial vegetation can be increased substantially by optimal land management. Communications Earth & Environment, 3(1), 8. doi:10.1038/s43247-021-00333-1
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4PN93PB

Shaad, K. (2018). Evolution of river-routing schemes in macro-scale models and their potential for watershed management. Hydrological Sciences Journal, 63(7), 1062-1077. doi:10.1080/02626667.2018.1473871
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H46T0JKB

Shaddick, G., Thomas, M. L., Amini, H., Broday, D., Cohen, A., Frostad, J., . . . Brauer, M. (2018). Data integration for the assessment of population exposure to ambient air pollution for global burden of disease assessment. Environmental Science & Technology, 52(16), 9069-9078. doi:10.1021/acs.est.8b02864
Gridded Population of the World (GPW) v4 (population count)

Shaddick, G., Thomas, M. L., Green, A., Brauer, M., van Donkelaar, A., Burnett, R., . . . Prüss‐Ustün, A. (2018). Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution. Journal of the Royal Statistical Society: Series C (Applied Statistics), 67(1), 231-253. doi:10.1111/rssc.12227
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates)

Shaddick, G., Thomas, M. L., Mudu, P., Ruggeri, G., & Gumy, S. (2020). Half the world’s population are exposed to increasing air pollution. npj Climate and Atmospheric Science, 3(1), 23. doi:10.1038/s41612-020-0124-2
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Shah, H. A., Carrasco, L. R., Hamlet, A., & Murray, K. A. (2022). Exploring agricultural land-use and childhood malaria associations in sub-Saharan Africa. Scientific Reports, 12(1), 4124. doi:10.1038/s41598-022-07837-6
Gridded Population of the World (GPW) v4.11 (population density)

Shah, R. F., Zhang, S., Li, K., Baker, L., Sox-Harris, A., & Kamal, R. N. (2020). Physical and occupational therapy use and cost after common hand procedures. The Journal of Hand Surgery, 45(4), 289-297.e281. doi:10.1016/j.jhsa.2019.09.008
Gridded Population of the World (GPW) v4 (population density) map

Shahab, A., Bohnett, E., Ahmad, B., Rashid, A., Hayat, M., & Alam, N. (2023). Ecological impact assessment of dam construction: A case study of Diamer Basha Dam Gilgit-Baltistan, Pakistan. River Research and Applications, 39(6), 1160-1172. doi:10.1002/rra.4131
Gridded Population of the World (GPW) v4.11 (population density map)

Shahrokhi, N., Rayner, P. J., Silver, J. D., & Thomas, S. (2023). Urban-scale variational flux inversion for CO Using TROPOMI total-column retrievals: A case study of Tehran. Atmospheric Environment, 311, 120009. doi:10.1016/j.atmosenv.2023.120009
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D
REMOTE SENSING (TROPOMI)

Shahrokhishahraki, N., Rayner, P. J., Silver, J. D., Thomas, S., & Schofield, R. (2022). High-resolution modeling of gaseous air pollutants over Tehran and validation with surface and satellite data. Atmospheric Environment, 270, 118881. doi:10.1016/j.atmosenv.2021.118881
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D
REMOTE SENSING (TROPOMI)

Shana, S. S., Sreenath, K. R., Sumithra, T. G., Krishnaveny, S. M. S., Joshi, K. K., Nameer, P. O., & Gopalakrishnan, A. (2023). A global-scale ecological niche modeling of the emerging pathogen Serratia marcescens to aid in its spatial ecology. Current Microbiology, 80(2), 59. doi:10.1007/s00284-022-03159-y
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
Millennium Ecosystem Assessment (MA) (Rapid Land Cover Change, v1) - 10.7927/H4G44N76

Shankar Acharya, K. (2020). Modelling and Assessing Linkages Between Urban Growth and Road Network for Southeast Asian Cities. (Ph.D.). Nanyang Technological University, Singapore. Retrieved from https://hdl.handle.net/10356/140554 
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - data set unspecified)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (Landsat)

Sharma, B. M., Melymuk, L., Bharat, G. K., Přibylová, P., Sáňka, O., Klánová, J., & Nizzetto, L. (2018). Spatial gradients of polycyclic aromatic hydrocarbons (PAHs) in air, atmospheric deposition, and surface water of the Ganges River basin. Science of The Total Environment, 627, 1495-1504. doi:10.1016/j.scitotenv.2018.01.262
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Sharma, E., Molden, D., Rahman, A., Khatiwada, Y. R., Zhang, L., Singh, S. P., . . . Wester, P. (2019). Introduction to the Hindu Kush Himalaya Assessment. In P. Wester, A. Mishra, A. Mukherji, & A. B. Shrestha (Eds.), The Hindu Kush Himalaya Assessment: Mountains, Climate Change, Sustainability and People (pp. 1-16). Cham: Springer International Publishing.
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Sharma, G., Sinha, B., Pallavi, Hakkim, H., Chandra, B. P., Kumar, A., & Sinha, V. (2019). Gridded emissions of CO, NOx, SO2, CO2, NH3, HCl, CH4, PM2.5, PM10, BC and NMVOC from open municipal waste burning in India. Environmental Science & Technology, 53(9), 4765-4774. doi:10.1021/acs.est.8b07076
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Sharma, P., Thapa, R. B., & Matin, M. A. (2020). Examining forest cover change and deforestation drivers in Taunggyi District, Shan State, Myanmar. Environment, Development and Sustainability, 22, 5521-5538. doi:10.1007/s10668-019-00436-y
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4HX19NJ

Sharma, S., Blagrave, K., Magnuson, J. J., O’Reilly, C. M., Oliver, S., Batt, R. D., . . . Woolway, R. I. (2019). Widespread loss of lake ice around the Northern Hemisphere in a warming world. Nature Climate Change, 9, 227-231. doi:10.1038/s41558-018-0393-5
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted) - 10.7927/H4JQ0XZW
NASA REMOTE SENSING (Distance to the Nearest Coast - https://oceancolor.gsfc.nasa.gov/docs/distfromcoast/)

Shashaani, S., Guikema, S. D., Zhai, C., Pino, J. V., & Quiring, S. M. (2018). Multi-stage prediction for zero-inflated hurricane induced power outages. IEEE Access, 6, 62432-62449. doi:10.1109/ACCESS.2018.2877078
Gridded Population of the World (GPW) v4 (population density)

Shearer, F. M., Huang, Z., Weiss, D. J., Wiebe, A., Gibson, H. S., Battle, K. E., . . . Moyes, C. L. (2016). Estimating geographical variation in the risk of zoonotic Plasmodium knowlesi infection in countries eliminating malaria. PLoS Neglected Tropical Diseases, 10(8), e0004915. doi:10.1371/journal.pntd.0004915
Gridded Population of the World (GPW) v4 (collection)

Shearer, F. M., Longbottom, J., Browne, A. J., Pigott, D. M., Brady, O. J., Kraemer, M. U. G., . . . Golding, N. (2018). Existing and potential infection risk zones of yellow fever worldwide: a modelling analysis. The Lancet Global Health, 6(3), e270-e278. doi:10.1016/S2214-109X(18)30024-X
Gridded Population of the World (GPW) v4 (population count)

Shekar, P. R., & Mathew, A. (2023). Integrated assessment of groundwater potential zones and artificial recharge sites using GIS and Fuzzy-AHP: A case study in Peddavagu watershed, India. Environmental Monitoring and Assessment, 195(7), 906. doi:10.1007/s10661-023-11474-5
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (SRTM)

Shen, F., Yang, L., He, X., Zhou, C., & Adams, J. M. (2020). Understanding the spatial–temporal variation of human footprint in Jiangsu Province, China, its anthropogenic and natural drivers and potential implications. Scientific Reports, 10(1), 13316. doi:10.1038/s41598-020-70088-w
Gridded Population of the World (GPW) v4 (population density)
REMOTE SENSING (DMSP-OLS)

Shen, H., Jiang, Y., Li, T., Cheng, Q., Zeng, C., & Zhang, L. (2020). Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data. Remote Sensing of Environment, 240, 111692. doi:10.1016/j.rse.2020.111692
Gridded Population of the World (GPW) v4.10 (population density)
NASA REMOTE SENSING (MODIS - MOD11A1)
NASA REMOTE SENSING (MODIS - MOD13A2)
NASA REMOTE SENSING (MODIS - MCD12Q1)

Shen, X., Cai, C., & Li, H. (2020). Socioeconomic restrictions slowdown COVID-19 far more effectively than favorable weather-evidence from the satellite. Science of The Total Environment, 748, 141401. doi:10.1016/j.scitotenv.2020.141401
Gridded Population of the World (GPW) v4 (population density)
REMOTE SENSING (TROPOMI)

Shen, X., Liu, M., Hanson, J. O., Wang, J., Locke, H., Watson, J. E. M., . . . Ma, K. (2023). Countries’ differentiated responsibilities to fulfill area-based conservation targets of the Kunming-Montreal Global Biodiversity Framework. One Earth, 6(5), 548-559. doi:10.1016/j.oneear.2023.04.007
Gridded Population of the World (GPW) v4.11 (national identifier grid)

Shen, Y., Chen, W., & Liu, J. (2022). Joint placement of gateways and controllers in SDN-Enabled Space-Ground Integration Network. Paper presented at the Proceedings of the Asia Conference on Electrical, Power and Computer Engineering, Shanghai, China. https://doi.org/10.1145/3529299.3531486
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Shen, Y., Nielsen, K., Revel, M., Liu, D., & Yamazaki, D. (2023). Res-CN (Reservoir dataset in China): hydrometeorological time series and landscape attributes across 3254 Chinese reservoirs. Earth System Science Data, 15(7), 2781-2808. doi:10.5194/essd-15-2781-2023
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (Landsat)
REMOTE SENSING (Sentinel)

Sheng, J.-X., Jacob, D. J., Maasakkers, J. D., Sulprizio, M. P., Zavala-Araiza, D., & Hamburg, S. P. (2017). A high-resolution (0.1° × 0.1°) inventory of methane emissions from Canadian and Mexican oil and gas systems. Atmospheric Environment, 158, 211-215. doi:10.1016/j.atmosenv.2017.02.036
Gridded Population of the World (GPW) v4 (population density)

Sherman, L., Proctor, J., Druckenmiller, H., Tapia, H., & Hsiang, S. M. (2023). Global High-Resolution Estimates of the United Nations Human Development Index Using Satellite Imagery and Machine-learning. Retrieved from https://doi.org/10.3386/w31044
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
REMOTE SENSING (DMSP-OLS)

Shevade, V. S., & Loboda, T. V. (2019). Oil palm plantations in Peninsular Malaysia: Determinants and constraints on expansion. PLoS ONE, 14(2), e0210628. doi:10.1371/journal.pone.0210628
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
Global Rural-Urban Mapping Project (GRUMP) v1 (settlement points)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (Landsat)

Shi, Y., Matsunaga, T., Yamaguchi, Y., Li, Z., Gu, X., & Chen, X. (2018). Long-term trends and spatial patterns of satellite-retrieved PM2.5 concentrations in South and Southeast Asia from 1999 to 2014. Science of The Total Environment, 615(Supplement C), 177-186. doi:10.1016/j.scitotenv.2017.09.241
Gridded Population of the World (GPW) v4 (population count)

Shi, Y., Zhao, A., Matsunaga, T., Yamaguchi, Y., Zang, S., Li, Z., . . . Gu, X. (2018). Underlying causes of PM2.5-induced premature mortality and potential health benefits of air pollution control in South and Southeast Asia from 1999 to 2014. Environment International, 121, 814-823. doi:10.1016/j.envint.2018.10.019
Gridded Population of the World (GPW) v4 (population density)

Shi, Y., Zhao, Y., Li, H., Liu, H., Wang, L., Liu, J., . . . Han, C. (2023). Association between exposure to ambient PM2.5 and the health status in the mobile population from 338 cities in China. Environmental Science and Pollution Research, 30, 63716–63726. doi:10.1007/s11356-023-26453-z
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Shi, Y., Zhu, Y., Gong, S., Pan, J., Zang, S., Wang, W., . . . Bai, Y. (2022). PM2.5-related premature deaths and potential health benefits of controlled air quality in 34 provincial cities of China during 2001–2017. Environmental Impact Assessment Review, 97, 106883. doi:10.1016/j.eiar.2022.106883
Gridded Population of the World (GPW) v4.11 (population count)

Shield, S. A., Quiring, S. M., Pino, J. V., & Buckstaff, K. (2021). Major impacts of weather events on the electrical power delivery system in the United States. Energy, 218, 119434. doi:10.1016/j.energy.2020.119434
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Shiogama, H., Fujimori, S., Hasegawa, T., Takahashi, K., Kameyama, Y., & Emori, S. (2021). How many hot days and heavy precipitation days will grandchildren experience that break the records set in their grandparents’ lives? Environmental Research Communications, 3(6), 061002. doi:10.1088/2515-7620/ac0395
Gridded Population of the World (GPW) v4.11 (population count)

Shisong, C., Wenji, Z., Hongliang, G., Deyong, H., You, M., Wenhui, Z., & Shanshan, L. (2018). Comparison of remotely sensed PM2.5 concentrations between developed and developing countries: results from the US, Europe, China, and India. Journal of Cleaner Production, 182, 672-681. doi:10.1016/j.jclepro.2018.02.096
Gridded Population of the World (GPW) v4 (population count)

Shmuel, A., & Heifetz, E. (2022). Global wildfire susceptibility mapping based on machine learning models. Forests, 13(7), 1050. doi:10.3390/f13071050
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)

Shmuel, A., & Heifetz, E. (2023). Developing novel machine-learning-based fire weather indices. Machine Learning: Science and Technology, 4(1), 015029. doi:10.1088/2632-2153/acc008
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MODIS)

Shmuel, A., & Heifetz, E. (2023). Empirical evidence of reduced wildfire ignition risk in the presence of strong winds. Fire, 6(9), 338. doi:10.3390/fire6090338
Gridded Population of the World (GPW) v4.11 (population density)

Shmuel, A., & Heifetz, E. (2023). A machine-learning approach to predicting daily wildfire expansion rate. Fire, 6(8), 319. doi:10.3390/fire6080319
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MODIS)

Shoji, Y., Fuke, H., Hamada, K., Iijima, I., Ikeda, C., Izutsu, N., . . . Yoshida, T. (2017). Marine search-and-recovery operation of scientific balloons in Japan. Journal of Astronomical Instrumentation, 6(2), 13 pp. doi:10.1142/S2251171717400050
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4HX19NJ

Shou, Y.-X., Gao, W., & Lu, F. (2020). A statistical study of pre-summer hourly extreme rainfall over the Pearl River Delta metropolitan region during 2008–2017. International Journal of Climatology, 40(9), 4242-4258. doi:10.1002/joc.6457
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (VIIRS)

Shrestha, N., Tiwari, A., & Paudel, P. K. (2021). Assessing conservation priorities of endemic seed plants in the central Himalaya (Nepal): A complementarity and phylogenetic diversity approach. Biological Conservation, 261, 109274. doi:10.1016/j.biocon.2021.109274
Gridded Population of the World (GPW) v4.11 (population density)

Shrestha, S., Thapa, A., Bista, D., Robinson, N., Sherpa, A. P., Acharya, K. P., . . . Lama, S. (2021). Distribution and habitat attributes associated with the Himalayan red panda in the westernmost distribution range. Ecology and Evolution, 11(9), 4023-4034. doi:10.1002/ece3.7297
Gridded Population of the World (GPW) v4.11 (population count)
Last of the Wild v2 (Global Human Footprint (Geographic))

Shyamsundar, P., Cohen, F., Boucher, T. M., Kroeger, T., Erbaugh, J. T., Waterfield, G., . . . Zhang, X. X. (2022). Scaling smallholder tree cover restoration across the tropics. Global Environmental Change, 76, 102591. doi:10.1016/j.gloenvcha.2022.102591
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Silva, R. C. G., Gronwall, J., van der Kwast, J., Danert, K., & Foppen, J. W. (2020). Estimating domestic self-supply groundwater use in urban continental Africa. Environmental Research Letters, 15(10), 1040b1042. doi:10.1088/1748-9326/ab9af9
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM
Global High Resolution Urban Data from Landsat (HBASE) - 10.7927/H4DN434S

Simmonds, P. G., Rigby, M., McCulloch, A., Vollmer, M. K., Henne, S., Mühle, J., . . . Etheridge, D. M. (2018). Recent increases in the atmospheric growth rate and emissions of HFC-23 (CHF3) and the link to HCFC-22 (CHClF2) production. Atmospheric Chemistry and Physics, 18(6), 4153-4169. doi:10.5194/acp-18-4153-2018
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4HX19NJ

Simons, D., Attfield, L. A., Jones, K. E., Watson-Jones, D., & Kock, R. (2023). Rodent trapping studies as an overlooked information source for understanding endemic and novel zoonotic spillover. PLoS Neglected Tropical Diseases, 17(1), e0010772. doi:10.1371/journal.pntd.0010772
Gridded Population of the World (GPW) v4.11 (population density)

Singh, A., Raju, A., Chandniha, S. K., Singh, L., Tyagi, I., Karri, R. R., & Kumar, A. (2023). Hydrogeochemical characterization of groundwater and their associated potential health risks. Environmental Science and Pollution Research, 30, 14993-15008. doi:10.1007/s11356-022-23222-2
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Singh, M., Sood, S., & Collins, C. M. (2022). Fire dynamics of the Bolivian Amazon. Land, 11(9), 1436. doi:10.3390/land11091436
Gridded Population of the World (GPW) v4 (population count)
Land Use and Land Cover (LULC) (Global Human Modification of Terrestrial Systems, v1)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)

Singha, C., & Swain, K. C. (2021). Spatial analyses of Cyclone Amphan induced flood inundation mapping using Sentinel-1A SAR images through GEE Cloud. Paper presented at the Computer Vision and Robotics 2021, Singapore.
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (SMAP)
REMOTE SENSING (Sentinel-1 SAR)
REMOTE SENSING (VIIRS)

Singha, M., Dong, J., Ge, Q., Metternicht, G., Sarmah, S., Zhang, G., . . . Xiao, X. (2021). Satellite evidence on the trade-offs of the food-water–air quality nexus over the breadbasket of India. Global Environmental Change, 71, 102394. doi:10.1016/j.gloenvcha.2021.102394
Gridded Population of the World (GPW) v4 (population count)
Satellite-Derived Environmental Indicators (Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, v1)
NASA REMOTE SENSING (GRACE)
NASA REMOTE SENSING (MODIS - MOD09A1)

Sinha, P., Gaughan, A. E., Stevens, F. R., Nieves, J. J., Sorichetta, A., & Tatem, A. J. (2019). Assessing the spatial sensitivity of a random forest model: Application in gridded population modeling. Computers, Environment and Urban Systems, 75, 132-145. doi:10.1016/j.compenvurbsys.2019.01.006
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Sivakumar, V. L., Radha Krishnappa, R., & Nallanathel, M. (2021). Drought vulnerability assessment and mapping using Multi-Criteria decision making (MCDM) and application of Analytic Hierarchy process (AHP) for Namakkal District, Tamilnadu, India. Materials Today: Proceedings, 43(Part 2), 1592-1599. doi:10.1016/j.matpr.2020.09.657
Gridded Population of the World (GPW) v4.11 (population count)
REMOTE SENSING (Sentinel-2)

Small, C., & Sousa, D. (2016). Humans on Earth; Global extents of anthropogenic land cover from remote sensing. Anthropocene, 14, 1-33. doi:10.1016/j.ancene.2016.04.003
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (VIIRS)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (IKONOS)
REMOTE SENSING (MERIS)
REMOTE SENSING (MODIS Global Cropland)
REMOTE SENSING (ALOS PALSAR)
REMOTE SENSING (Landsat Vegetation Continuous Fields)
REMOTE SENSING (TanDEM-X (TDX))

Small, C., Sousa, D., Yetman, G., Elvidge, C., & MacManus, K. (2018). Decades of urban growth and development on the Asian megadeltas. Global and Planetary Change, 165, 62-89. doi:10.1016/j.gloplacha.2018.03.005
Gridded Population of the World (GPW) v4 (population count)
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS DNB)

Smith, D. A. (2017). Visualising world population density as an interactive multi-scale map using the global human settlement population layer. Journal of Maps, 13(1), 117-123. doi:10.1080/17445647.2017.1400476
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)

Smith, T. P., Flaxman, S., Gallinat, A. S., Kinosian, S. P., Stemkovski, M., Unwin, H. J. T., . . . Pearse, W. D. (2021). Temperature and population density influence SARS-CoV-2 transmission in the absence of nonpharmaceutical interventions. Proceedings of the National Academy of Sciences, 118(25), e2019284118. doi:10.1073/pnas.2019284118
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Smith, T. P., Stemkovski, M., Koontz, A., & Pearse, W. D. (2022). AREAdata: A worldwide climate dataset averaged across spatial units at different scales through time. Data in Brief, 43, 108438. doi:10.1016/j.dib.2022.108438
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Snethlage, J., de Miguel, A., Daniels, E., & Froebich, J. (2019). Water Stress and Water Vulnerability Indicators and Maps (M20). Retrieved from http://amsacta.unibo.it/6089/1/Del%201.2%20final.pdf
Gridded Population of the World (GPW) v4 (unspecified)

Sofiev, M., Winebrake, J. J., Johansson, L., Carr, E. W., Prank, M., Soares, J., . . . Corbett, J. J. (2018). Cleaner fuels for ships provide public health benefits with climate tradeoffs. Nature Communications, 9(1), 12pp. doi:10.1038/s41467-017-02774-9
Gridded Population of the World (GPW) v4 (population count)

Sogacheva, L., Rodriguez, E., Kolmonen, P., Virtanen, T. H., Saponaro, G., de Leeuw, G., . . . van der A, R. J. (2018). Spatial and seasonal variations of aerosols over China from two decades of multi-satellite observations. Part II: AOD time series for 1995-2017 combined from ATSR ADV and MODIS C6.1 for AOD tendencies estimation. Atmospheric Chemistry and Physics, 18, 16631-16652. doi:10.5194/acp-18-16631-2018
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Advanced Along Track Scanning Radiometer (AATSR))
REMOTE SENSING (Along Track Scanning Radiometer - ATSR-2)

Soininen, E. M., Barrio, I. C., Bjørkås, R., Björnsdóttir, K., Ehrich, D., Hopping, K. A., . . . Speed, J. D. M. (2021). Location of studies and evidence of effects of herbivory on Arctic vegetation: a systematic map. Environmental Evidence, 10(1), 25. doi:10.1186/s13750-021-00240-0
Gridded Population of the World (GPW) v4.11 (population density)
Last of the Wild v3 (Human Footprint, 2018 Release (2009))

Solbrig, J. E., Miller, S. D., Zhang, J., Grasso, L., & Kliewer, A. (2020). Assessing the stability of surface lights for use in retrievals of nocturnal atmospheric parameters. Atmospheric Measurement Techniques, 13(1), 165-190. doi:10.5194/amt-13-165-2020
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)

Solimini, A., Filipponi, F., Fegatelli, D. A., Caputo, B., De Marco, C. M., Spagnoli, A., & Vestri, A. R. (2021). A global association between Covid-19 cases and airborne particulate matter at regional level. Scientific Reports, 11(1), 6256. doi:10.1038/s41598-021-85751-z
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)
Gridded Population of the World (GPW) v4.11 (population count)

Sonea, A., & Westerholt, R. (2021). Geographic and temporal access to basic banking services offered through post offices in Wales. Applied Spatial Analysis and Policy, 14, 879-905. doi:10.1007/s12061-021-09386-3
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - methodology)

Song, C., Kwan, M.-P., Song, W., & Zhu, J. (2017). A comparison between spatial econometric models and random forest for modeling fire occurrence. Sustainability, 9(5), 21pp. doi:10.3390/su9050819
Gridded Population of the World (GPW) v4 (population density)

Song, C., Kwan, M.-P., & Zhu, J. (2017). Modeling fire occurrence at the city scale: A comparison between geographically weighted regression and global linear regression. International Journal of Environmental Research and Public Health, 14(4), 23pp. doi:10.3390/ijerph14040396
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)

Song, J., Tong, X., Wang, L., Zhao, C., & Prishchepov, A. V. (2019). Monitoring finer-scale population density in urban functional zones: A remote sensing data fusion approach. Landscape and Urban Planning, 103580. Retrieved from https://doi.org/10.1016/j.landurbplan.2019.05.011
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - methodology)

Song, S., Chen, K., Huang, T., Ma, J., Wang, J., Mao, X., . . . Zhou, Z. (2023). New emission inventory reveals termination of global dioxin declining trend. Journal of Hazardous Materials, 443, 130357. doi:10.1016/j.jhazmat.2022.130357
Gridded Population of the World (GPW) v4.11 (population density)

Song, Y., Wang, X., Wright, G., Thatcher, D., Wu, P., & Felix, P. (2019). Traffic volume prediction with segment-based regression kriging and its implementation in assessing the impact of heavy vehicles. IEEE Transactions on Intelligent Transportation Systems, 20(1), 232-243. doi:10.1109/TITS.2018.2805817
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Song, Y., Wright, G., Wu, P., Thatcher, D., McHugh, T., Li, Q., . . . Wang, X. (2018). Segment-based spatial analysis for assessing road infrastructure performance using monitoring observations and remote sensing data. Remote Sensing, 10(11), 1696. doi:10.3390/rs10111696
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
NASA REMOTE SENSING (MODIS - MOD11A2)
NASA REMOTE SENSING (MODIS - MOD13A2)

Song, Y., Xu, Y., Chen, B., He, Q., Tu, Y., Wang, F., & Cai, J. (2022). Dynamic population mapping with AutoGluon. Urban Informatics, 1(1), 13. doi:10.1007/s44212-022-00017-x
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)

Song, Y., Zajic, C. J., Hwang, T., Hakkenberg, C. R., & Zhu, K. (2021). Widespread mismatch between phenology and climate in human-dominated landscapes. AGU Advances, 2(4), e2021AV000431. doi:10.1029/2021AV000431
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (AVHRR)
NASA REMOTE SENSING (MODIS)

Song, Z., Chen, B., & Huang, J. (2022). Combining Himawari-8 AOD and deep forest model to obtain city-level distribution of PM2.5 in China. Environmental Pollution, 297, 118826. doi:10.1016/j.envpol.2022.118826
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (Himawari-8)

Song, Z., Chen, B., Zhang, P., Guan, X., Wang, X., Ge, J., . . . Wang, Y. (2022). High temporal and spatial resolution PM2.5 dataset acquisition and pollution assessment based on FY-4A TOAR data and deep forest model in China. Atmospheric Research, 274, 106199. doi:10.1016/j.atmosres.2022.106199
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (Advanced Geosynchronous Radiation Imager (AGRI))

Soofi, M., Qashqaei, A. T., Mousavi, M., Hadipour, E., Filla, M., Kiabi, B. H., . . . Waltert, M. (2022). Quantifying the relationship between prey density, livestock and illegal killing of leopards. Journal of Applied Ecology, 59(6), 1536-1547. doi:10.1111/1365-2664.14163
Gridded Population of the World (GPW) v4.11 (population density)

Soofi, M., Qashqaei, A. T., Trei, J.-N., Shokri, S., Selyari, J., Ghasemi, B., . . . Waltert, M. (2022). A novel application of hierarchical modelling to decouple sampling artifacts from socio-ecological effects on poaching intensity. Biological Conservation, 267, 109488. doi:10.1016/j.biocon.2022.109488
Gridded Population of the World (GPW) v4.11 (population density)

Sorichetta, A., Bird, T. J., Ruktanonchai, N. W., zu Erbach-Schoenberg, E., Pezzulo, C., Tejedor, N., . . . Tatem, A. J. (2016). Mapping internal connectivity through human migration in malaria endemic countries. Scientific Data, 3(160066), 16 pp. doi:10.1038/sdata.2016.66
Gridded Population of the World (GPW) v4 (collection)

Sorichetta, A., Hornby, G. M., Stevens, F. R., Gaughan, A. E., Linard, C., & Tatem, A. J. (2015). High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020. Scientific Data, 2, 150045. doi:10.1038/sdata.2015.45
Gridded Population of the World (GPW) v1
Gridded Population of the World (GPW) v2
Gridded Population of the World (GPW) v3 (collection)
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)
NASA REMOTE SENSING (MODIS NPP)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (VIIRS)
REMOTE SENSING (MERIS GlobCover)

Souter, N. J., Shaad, K., Sor, R., & Vollmer, D. (2023). Freshwater health index assessment of the Tonle Sap basin. Cambodian Journal of Natural History, 2023(1), 34-61. Retrieved from https://www.fauna-flora.org/app/uploads/2023/07/FFI_June2023_Cambodian_Journal_Natural_History.pdf
Gridded Population of the World (GPW) v4.11 (population density)

Southerland, V. A., Anenberg, S. C., Harris, M., Apte, J. S., Hystad, P., van Donkelaar, A., . . . Roy, A. (2021). Assessing the distribution of air pollution health risks within cities: A neighborhood-scale analysis leveraging high-resolution data sets in the Bay Area, California. Environmental Health Perspectives, 129(3), 037006. doi:10.1289/EHP7679
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)

Speed, J. D. M., Bendiksby, M., Finstad, A. G., Hassel, K., Kolstad, A. L., & Prestø, T. (2018). Contrasting spatial, temporal and environmental patterns in observation and specimen based species occurrence data. PLoS ONE, 13(4), e0196417. doi:10.1371/journal.pone.0196417
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Spiker, M. L., Welling, J., Hertenstein, D., Mishra, S., Mishra, K., Hurley, K. M., . . . Lee, B. Y. (2023). When increasing vegetable production may worsen food availability gaps: A simulation model in India. Food Policy, 116, 102416. doi:10.1016/j.foodpol.2023.102416
Gridded Population of the World (GPW) v4 (population count)

Spinosa, A. (2022). Wider urban zones: use of topology and nighttime satellite images for delimiting urban areas. Review of Regional Research, 42, 141-159. doi:10.1007/s10037-022-00169-y
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (VIIRS NTL)

Sreekar, R., Zeng, Y., Zheng, Q., Lamba, A., Teo, H. C., Sarira, T. V., & Koh, L. P. (2022). Nature-based climate solutions for expanding the global protected area network. Biological Conservation, 269, 109529. doi:10.1016/j.biocon.2022.109529
Gridded Population of the World (GPW) v4 (population density)

Sreeparvathy, V., & Srinivas, V. V. (2022). A Bayesian fuzzy clustering approach for design of precipitation gauge network using merged remote sensing and ground-based precipitation products. Water Resources Research, 58(2), e2021WR030612. doi:10.1029/2021WR030612
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (IMERG)
NASA REMOTE SENSING (SRTM)

Ssentongo, P., Fronterre, C., Geronimo, A., Greybush, S. J., Mbabazi, P. K., Muvawala, J., . . . Schiff, S. J. (2021). Pan-African evolution of within- and between-country COVID-19 dynamics. Proceedings of the National Academy of Sciences, 118(28), e2026664118. doi:10.1073/pnas.2026664118
Gridded Population of the World (GPW) v4.11 (population density)

Staffell, I., & Pfenninger, S. (2018). The increasing impact of weather on electricity supply and demand. Energy, 145, 65-78. doi:10.1016/j.energy.2017.12.051
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42

Stahl, T., Clark, M. K., Zekkos, D., Athanasopoulos-Zekkos, A., Willis, M., Medwedeff, W., . . . Jin, J. (2017). Earthquake science in resilient societies. Tectonics, 36(4), 749-753. doi:10.1002/2017TC004604
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Stanley, T. A., Kirschbaum, D. B., Sobieszczyk, S., Jasinski, M. F., Borak, J. S., & Slaughter, S. L. (2020). Building a landslide hazard indicator with machine learning and land surface models. Environmental Modelling & Software, 129, 104692. doi:10.1016/j.envsoft.2020.104692
Gridded Population of the World (GPW) v3 (population count) - 10.​7927/​H4639MPP
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Stanojević, G. B., Miljanović, D. N., Doljak, D. L., Ćurčić, N. B., Radovanović, M. M., B., M.-M. S., & Olena, H. (2019). Spatio-temporal variability of annual PM2.5 concentrations and population exposure assessment in Serbia for the period 2001-2016. Journal of the Geographical Institute "Jovan Cvijic", 69(3), 197-211. doi:10.2298/IJGI1903197S
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4PN93PB
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Staten, P. W., Grise, K. M., Davis, S. M., Karnauskas, K., & Davis, N. (2019). Regional widening of tropical overturning: Forced change, natural variability, and recent trends. Journal of Geophysical Research: Atmospheres, 124(12), 6104-6119. doi:10.1029/2018jd030100
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)

Steenhuisen, F., & Wilson, S. J. (2019). Development and application of an updated geospatial distribution model for gridding 2015 global mercury emissions. Atmospheric Environment, 211, 138-150. doi:10.1016/j.atmosenv.2019.05.003
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted) - 10.7927/H4JQ0XZW
Gridded Population of the World (GPW) v4.10 (population density UN WPP-adjusted) - 10.7927/H49884ZR

Sterner, R. W., Keeler, B., Polasky, S., Poudel, R., Rhude, K., & Rogers, M. (2020). Ecosystem services of Earth’s largest freshwater lakes. Ecosystem Services, 41, 101046. doi:10.1016/j.ecoser.2019.101046
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted)

Sterzel, T., Lüdeke, M. K. B., Walther, C., Kok, M. T. J., Sietz, D., & Lucas, P. L. (2020). Typology of coastal urban vulnerability under rapid urbanization. PLoS ONE, 15(1), e0220936. doi:10.1371/journal.pone.0220936
Gridded Population of the World (GPW) v3 (population count)
Gridded Population of the World (GPW) v4 (collection)
Natural Disaster Hotspots (cyclone hazard frequency and distribution)
Natural Disaster Hotspots (cyclone mortality risks)
Natural Disaster Hotspots (flood hazard frequency and distribution)
Natural Disaster Hotspots (flood mortality risks)
NASA REMOTE SENSING (SRTM)

Stevens, V. L., De Risi, R., Le Roux-Mallouf, R., Drukpa, D., & Hetényi, G. (2020). Seismic hazard and risk in Bhutan. Natural Hazards, 104, 2339-2367. doi:10.1007/s11069-020-04275-3
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)

Stirling, E. L., Pope, R. J., Graham, A. M., Chipperfield, M. P., & Arnold, S. R. (2020). Quantifying the transboundary contribution of nitrogen oxides to UK air quality. Atmospheric Science Letters, 21(2), e955. doi:10.1002/asl.955
Gridded Population of the World (GPW) v4.10 (population count)

Stock, A., Haupt, A. J., Mach, M., & Micheli, F. (2018). Mapping ecological indicators of human impact with statistical and machine learning methods: Tests on the California coast. Ecological Informatics, 48, 37-47. doi:10.1016/j.ecoinf.2018.07.007
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ
NASA REMOTE SENSING (MODIS)

Stowell, J. D., Ngo, C., Jimenez, M. P., Kinney, P. L., & James, P. (2023). Development of a global urban greenness indicator dataset for 1,000+ cities. Data in Brief, 48, 109140. doi:10.1016/j.dib.2023.109140
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (Landsat) via Google Earth Engine

Strindberg, S., Maisels, F., Williamson Elizabeth, A., Blake, S., Stokes Emma, J., Aba’a, R., . . . Wilkie David, S. (2018). Guns, germs, and trees determine density and distribution of gorillas and chimpanzees in Western Equatorial Africa. Science Advances, 4(4), eaar2964. doi:10.1126/sciadv.aar2964
Gridded Population of the World (GPW) v4 (population density)
Last of the Wild v2 Global Human Influence Index (Geographic)
NASA REMOTE SENSING (SRTM)

Stringer, L. C., Mirzabaev, A., Benjaminsen, T. A., Harris, R. M. B., Jafari, M., Lissner, T. K., . . . Tirado-von der Pahlen, C. (2021). Climate change impacts on water security in global drylands. One Earth, 4(6), 851-864. doi:10.1016/j.oneear.2021.05.010
Gridded Population of the World (GPW) v4.11 (population density)

Strømme, A., Fylkesnes, G. K., Denselow, J., Mangan, R., Podieh, P., & Kamøy, K. (2022). Stop the War on Children: The Forgotten Ones. Retrieved from https://resourcecentre.savethechildren.net/pdf/stop-the-war-on-children-the-forgotten-ones.pdf/
Gridded Population of the World (GPW) v4.11 (unspecified)

Stuhlmacher, M. (2020). Patch to Landscape and Back Again: Three Case Studies of Land System Architecture Change and Environmental Consequences from the Local to Global Scale. (Ph.D.). Arizona State University, Tempe, AZ. Retrieved from http://hdl.handle.net/2286/R.I.57273 
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4PN93PB
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (Landsat)
REMOTE SENSING (VIIRS NTL)

Stuhlmacher, M., Georgescu, M., Turner, B. L., Hu, Y. n., Goldblatt, R., Gupta, S., . . . Clinton, N. (2022). Are global cities homogenizing? An assessment of urban form and heat island implications. Cities, 126, 103705. doi:10.1016/j.cities.2022.103705
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4PN93PB
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (Landsat)
REMOTE SENSING (VIIRS NTL)

Stylianou, K. (2018). Nutritional and Environmental Impacts of Foods on Human Health. (Ph.D.). University of Michigan, Ann Arbor. Retrieved from https://deepblue.lib.umich.edu/handle/2027.42/147641 
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted) - 10.7927/H4JQ0XZW

Su, W., Hu, Q., Liu, C., Chen, Y., Lin, J., & Zhang, C. (2022). Inferring global surface HCHO concentrations from multisource hyperspectral satellites and their application to HCHO-related global cancer burden estimation. Environment International, 170, 107600. doi:10.1016/j.envint.2022.107600
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (Ozone Mapping and Profiler Suite (OMPS))
REMOTE SENSING (Environmental Trace Gases Monitoring Instrument (EMI))

Sun, D., Ellepola, G., Herath, J., & Meegaskumbura, M. (2023). Ecological barriers for an amphibian pathogen: A narrow ecological niche for Batrachochytrium salamandrivorans in an Asian chytrid hotspot. Journal of Fungi, 9(9). doi:10.3390/jof9090911
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Sun, H., Dickens, B. L., Jit, M., Cook, A. R., & Carrasco, L. R. (2020). Mapping the cryptic spread of the 2015–2016 global Zika virus epidemic. BMC Medicine, 18(1), 399. doi:10.1186/s12916-020-01845-x
Gridded Population of the World (GPW) v4 (population count)

Sun, H., Shin, Y. M., Xia, M., Ke, S., Wan, M., Yuan, L., . . . Archibald, A. T. (2022). Spatial resolved surface ozone with urban and rural differentiation during 1990–2019: A space–time Bayesian neural network downscaler. Environmental Science & Technology, 56(11), 7337-7349. doi:10.1021/acs.est.1c04797
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Sun, L., Chen, J., Li, Q., & Huang, D. (2020). Dramatic uneven urbanization of large cities throughout the world in recent decades. Nature Communications, 11(1), 5366. doi:10.1038/s41467-020-19158-1
Gridded Population of the World (GPW) v4.11 (admin unit center points)
NASA REMOTE SENSING (MODIS - MOD13A1)

Sun, S., Song, Z., Chen, B., Wang, Y., Ran, X., Fang, Y., . . . Wang, H. (2023). Current and future potential soil organic carbon stocks of vegetated coastal ecosystems and their controls in the Bohai Rim Region, China. CATENA, 225, 107023. doi:10.1016/j.catena.2023.107023
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Sun, X., Wandelt, S., & Hansen, M. (2020). Airport road access at planet scale using population grid and Openstreetmap. Networks and Spatial Economics, 20, 273-299. doi:10.1007/s11067-019-09480-7
Gridded Population of the World (GPW) v4 (population density)

Sun, X., Wandelt, S., Husemann, M., & Stumpf, E. (2021). Operational considerations regarding on-demand air mobility: A literature review and research challenges. Journal of Advanced Transportation, 2021, 3591034. doi:10.1155/2021/3591034
Gridded Population of the World (GPW) v4 (collection)

Sun, X., Wandelt, S., & Zhang, A. (2021). Comparative accessibility of Chinese airports and high-speed railway stations: A high-resolution, yet scalable framework based on open data. Journal of Air Transport Management, 92, 102014. doi:10.1016/j.jairtraman.2020.102014
Gridded Population of the World (GPW) v4 (population count)

Sun, Y., Nan, Z., Yang, W., & Li, L. (2023). Projecting China’s future water footprints and water scarcity under socioeconomic and climate change pathways using an integrated simulation approach. Climate Services, 30, 100385. doi:10.1016/j.cliser.2023.100385
Gridded Population of the World (GPW) v4.11 (population density)

Sung, K. Y. (2020). The Limits of Location Privacy in Mobile Devices. (Ph.D.). University of Massachusetts Amherst, Amherst, MA. Retrieved from https://scholarworks.umass.edu/dissertations_2/1957 
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Sung, K. Y., Huang, J., Corner, M. D., & Levine, B. N. (2020). Re-identification of mobile devices using real-time bidding advertising networks. Paper presented at the Proceedings of the 26th Annual International Conference on Mobile Computing and Networking, London, United Kingdom. https://doi.org/10.1145/3372224.3419205
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Suryo, V. N. S., Grüter, B., Diepolder, J., Ruseno, N., & Holzapfel, F. (2020). Departure trajectory optimization for noise abatement procedure in Soekarno-Hatta International Airport. Jurnal Teknologi Dirgantara, 18(2), 141-158. Retrieved from http://jurnal.lapan.go.id/index.php/jurnal_tekgan/article/view/3376
Gridded Population of the World (GPW) v4.11 (population count)

Suthar, G., Singhal, R. P., Khandelwal, S., & Kaul, N. (2023). Spatiotemporal variation of air pollutants and their relationship with land surface temperature in Bengaluru, India. Remote Sensing Applications: Society and Environment, 32, 101011. doi:10.1016/j.rsase.2023.101011
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)

Suwal, M. K., & Huettmann, F. (2020). A rather short story of shared GIS data layers in the Hindu Kush-Himalayas: State of the art, justifications and urgent suggestions for a sustainable global data governance with open access and open source coming to the rescue. In G. R. Regmi & F. Huettmann (Eds.), Hindu Kush-Himalaya Watersheds Downhill: Landscape Ecology and Conservation Perspectives (pp. 521-563). Cham: Springer International Publishing.
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
Last of the Wild v2 (Global Human Footprint (Geographic)) - 10.7927/H4M61H5F
Last of the Wild v2 Global Human Influence Index (Geographic) - 10.7927/H4BP00QC
Last of the Wild v2 Last of the Wild (Geographic) - 10.7927/H4348H83
Natural Disaster Hotspots (cyclone hazard frequency and distribution) - 10.7927/H4CZ353K

Swain, K. C., Singha, C., & Nayak, L. (2020). Flood susceptibility mapping through the GIS-AHP technique using the cloud. ISPRS International Journal of Geo-Information, 9(12), 720. doi:10.3390/ijgi9120720
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)
Global High Resolution Urban Data from Landsat (GMIS)
Global High Resolution Urban Data from Landsat (HBASE)
NASA REMOTE SENSING (ASTER GDEM)
NASA REMOTE SENSING (SMAP)
NASA REMOTE SENSING (TRMM)
REMOTE SENSING (Landsat)

Swain, M., Nadimpalli, R. R., Mohanty, U. C., Guhathakurta, P., Gupta, A., Kaginalkar, A., . . . Niyogi, D. (2023). Delay in timing and spatial reorganization of rainfall due to urbanization- analysis over India’s smart city Bhubaneswar. Computational Urban Science, 3(1), 8. doi:10.1007/s43762-023-00081-2
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (TRMM)
REMOTE SENSING (Landsat)

Swain, S., Mishra, S. K., Pandey, A., & Kalura, P. (2022). Inclusion of groundwater and socio-economic factors for assessing comprehensive drought vulnerability over Narmada River Basin, India: A geospatial approach. Applied Water Science, 12(2), 14. doi:10.1007/s13201-021-01529-8
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (SRTM)

Szyniszewska, A. M., Leppla, N. C., Huang, Z., & Tatem, A. J. (2016). Analysis of seasonal risk for importation of the Mediterranean fruit fly, Ceratitis capitata (Diptera: Tephritidae), via air passenger traffic arriving in Florida and California. Journal of Economic Entomology, 409(6), 2317-2328. doi:10.1093/jee/tow196
Gridded Population of the World (GPW) v4 (unspecified)

Takagi, H. (2019). Statistics on typhoon landfalls in Vietnam: Can recent increases in economic damage be attributed to storm trends? Urban Climate, 30, 100506. doi:10.1016/j.uclim.2019.100506
Gridded Population of the World (GPW) v4 (population density)

Takahashi, E. A., Masoud, L., Mukbel, R., Guitian, J., & Stevens, K. B. (2020). Modelling habitat suitability in Jordan for the cutaneous leishmaniasis vector (Phlebotomus papatasi) using multicriteria decision analysis. PLoS Neglected Tropical Diseases, 14(11), e0008852. doi:10.1371/journal.pntd.0008852
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Tan, C., Guo, B., Kuang, H., Yang, H., & Ma, M. (2018). Lake area changes and their influence on factors in arid and semi-arid regions along the Silk Road. Remote Sensing, 10(4), 19pp. doi:10.3390/rs10040595
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)

Tan, Y., Wang, Q., & Zhang, Z. (2023). Coupling the linear mixed effects model with random forest improves hourly PM2.5 estimation from Himawari-8 AOD over the Yangtze River Delta. Atmospheric Pollution Research, 14(5), 101739. doi:10.1016/j.apr.2023.101739
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS - MOD13A2)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (Himawari-8)

Tan, Z. D., Carrasco, L. R., & Taylor, D. (2020). Spatial correlates of forest and land fires in Indonesia. International Journal of Wildland Fire, 29(12), 1088-1099. doi:10.1071/WF20036
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
NASA REMOTE SENSING (MODIS - MCD14ML)

Tanalgo, K. C., Tabora, J. A. G., de Oliveira, H. F. M., Haelewaters, D., Beranek, C. T., Otálora-Ardila, A., . . . Hughes, A. C. (2022). DarkCideS 1.0, a global database for bats in karsts and caves. Scientific Data, 9(1), 155. doi:10.1038/s41597-022-01234-4
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (Black Marble -VIIRS)

Tananantayot, J., Agger, C., Ash, E., Aung, S. S., Baker-Whatton, M. C., Bisi, F., . . . Ngoprasert, D. (2022). Where will the dhole survive in 2030? Predicted strongholds in mainland Southeast Asia. Conservation Science and Practice, 4(11), e12831. doi:10.1111/csp2.12831
Gridded Population of the World (GPW) v4 (population density)

Tang, D., Liu, D., Tang, Y., Seyler, B. C., Deng, X., & Zhan, Y. (2019). Comparison of GOCI and Himawari-8 aerosol optical depth for deriving full-coverage hourly PM2.5 across the Yangtze River Delta. Atmospheric Environment, 217, 116973. doi:10.1016/j.atmosenv.2019.116973
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (GOCI)
REMOTE SENSING (Himawari-8)

Tang, Y., Duan, H., & Yu, S. (2023). Mitigating climate change to alleviate economic inequality under the Paris Agreement. iScience, 26(1), 105734. doi:10.1016/j.isci.2022.105734
Gridded Population of the World (GPW) v4.11 (population density)

Tang, Y., Duan, H., & Yu, S.-Y. (2023). Protocol for estimating the impact of climate change on economic growth and inequality under climate policies. STAR Protocols, 4(3), 102527. doi:10.1016/j.xpro.2023.102527
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Tanner, M., & Ratzke, L. (2022). Deforestation, Institutions, and Property Rights: Evidence from land titling to indigenous peoples and local communities in Ecuador. Retrieved from https://cafscioteca.azurewebsites.net/handle/123456789/1995
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Tarín-Carrasco, P., Im, U., Geels, C., Palacios-Peña, L., & Jiménez-Guerrero, P. (2021). Contribution of fine particulate matter to present and future premature mortality over Europe: A non-linear response. Environment International, 153, 106517. doi:10.1016/j.envint.2021.106517
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)

Tarín-Carrasco, P., Im, U., Geels, C., Palacios-Peña, L., & Jiménez-Guerrero, P. (2022). Reducing future air-pollution-related premature mortality over Europe by mitigating emissions from the energy sector: assessing an 80 % renewable energies scenario. Atmospheric Chemistry and Physics, 22, 3945-3965. doi:10.5194/acp-22-3945-2022
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)

Tarín-Carrasco, P., Im, U., Palacios-Peña, L., & Jiménez-Guerrero, P. (2021). Impacts of Fine Particulate Matter and Climate Change on Human Health Over Europe—Present and Future Scenarios. Paper presented at the Air Pollution Modeling and its Application XXVII, Berlin, Heidelberg.
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)

Tarín-Carrasco, P., Morales-Suárez-Varela, M., Im, U., Brandt, J., Palacios-Peña, L., & Jiménez-Guerrero, P. (2019). Isolating the climate change impacts on air pollution-related-pathologies over Europe - A modelling approach on cases and costs. Atmospheric Chemistry and Physics, 19(14), 9385-9398. doi:10.5194/acp-19-9385-2019
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Tariq, S., Mariam, A., ul‐Haq, Z., & Mehmood, U. (2022). Spatial and temporal variations in PM2.5 and associated health risk assessment in Saudi Arabia using remote sensing. Chemosphere, 308(Part 2), 136296. doi:10.1016/j.chemosphere.2022.136296
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (MISR)
NASA REMOTE SENSING (SeaWiFS)

Tatem, A. J. (2022). Small area population denominators for improved disease surveillance and response. Epidemics, 40, 100597. doi:10.1016/j.epidem.2022.100597
Gridded Population of the World (GPW) v4.11 (admin unit center points)
Gridded Population of the World (GPW) v4.11 (population density)

Tatem, A. J. (2022). Small area population denominators for improved disease surveillance and response. Epidemics, 41, 100641. doi:10.1016/j.epidem.2022.100641
Gridded Population of the World (GPW) v4.11 (admin unit center points)
Gridded Population of the World (GPW) v4.11 (population density)

Taube, J. C., Rest, E. C., Lloyd-Smith, J. O., & Bansal, S. (2023). The global landscape of smallpox vaccination history and implications for current and future orthopoxvirus susceptibility: a modelling study. The Lancet Infectious Diseases, 23(4), 454-462. doi:10.1016/S1473-3099(22)00664-8
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)

Taubenböck, H., Gerten, C., Rusche, K., Siedentop, S., & Wurm, M. (2019). Patterns of Eastern European urbanisation in the mirror of Western trends – Convergent, unique or hybrid? Environment and Planning B: Urban Analytics and City Science, 46(7), 1206-1225. doi:10.1177/2399808319846902
Gridded Population of the World (GPW) v4 (population count)
Gridded Population of the World (GPW) v4 (population density)
REMOTE SENSING (TanDEM-X (TDX))
REMOTE SENSING (Landsat)

Taylor, C., Robinson, T. R., Dunning, S., Rachel Carr, J., & Westoby, M. (2023). Glacial lake outburst floods threaten millions globally. Nature Communications, 14(1), 487. doi:10.1038/s41467-023-36033-x
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Taylor, C. M., Klein, C., Parker, D. J., Gerard, F., Semeena, V. S., Barton, E. J., & Harris, B. L. (2022). “Late-stage” deforestation enhances storm trends in coastal West Africa. Proceedings of the National Academy of Sciences, 119(2), e2109285119. doi:10.1073/pnas.2109285119
Gridded Population of the World (GPW) v4.11 (admin unit center points)
NASA REMOTE SENSING (MODIS - MYD11C3)
NASA REMOTE SENSING (TRMM)
REMOTE SENSING (Landsat)
REMOTE SENSING (Meteosat)

Taylor, P., Rahman, J., O'Sullivan, J., Podger, G., Rosello, C., Parashar, A., . . . Coombe, M. (2021). Basin futures, a novel cloud-based system for preliminary river basin modelling and planning. Environmental Modelling & Software, 141, 105049. doi:10.1016/j.envsoft.2021.105049
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Taylor, P., Stewart, J., Rahman, J., Parashar, A., Pollino, C., & Podger, G. (2017). Basin Futures: Supporting water planning in data poor basins Paper presented at the MODSIM2017, 22nd International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, Hobart, Tasmania, Australia. http://mssanz.org.au/modsim2017/L5/taylor.pdf
Gridded Population of the World (GPW) v4 (population count) - 10.7927/h4x63jvc

Teichmann, C., Bülow, K., Otto, J., Pfeifer, S., Rechid, D., Sieck, K., & Jacob, D. (2018). Avoiding extremes: Benefits of staying below +1.5 °C compared to +2.0 °C and +3.0 °C global warming. Atmosphere, 9(4), 19pp. doi:10.3390/atmos9040115
Gridded Population of the World (GPW) v4 (population density)

Tellman, B., Sullivan, J. A., Kuhn, C., Kettner, A. J., Doyle, C. S., Brakenridge, G. R., . . . Slayback, D. A. (2021). Satellite imaging reveals increased proportion of population exposed to floods. Nature, 596(7870), 80-86. doi:10.1038/s41586-021-03695-w
Gridded Population of the World (GPW) v4 (population density) - 10.7927//H4NP22DQ
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)

Tennant, E., & Gilmore, E. A. (2020). Government effectiveness and institutions as determinants of tropical cyclone mortality. Proceedings of the National Academy of Sciences, 117(46), 28692-28699. doi:10.1073/pnas.2006213117
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted) - 10.7927/H4JQ0XZW
Population Dynamics (Global Population Count Grid Time Series Estimates, v1) - 10.7927/H4CC0XNV
Poverty Mapping (Global Subnational Infant Mortality Rates, v1) - 10.7927/H4PZ56R2

Tepley, A. J., Parisien, M.-A., Wang, X., Oliver, J. A., & Flannigan, M. D. (2022). Wildfire evacuation patterns and syndromes across Canada's forested regions. Ecosphere, 13(10), e4255. doi:10.1002/ecs2.4255
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
Last of the Wild v3 (Human Footprint, 2018 Release (2009))

Tessler, Z. D., Vörösmarty, C. J., Overeem, I., & Syvitski, J. P. M. (2018). A model of water and sediment balance as determinants of relative sea level rise in contemporary and future deltas. Geomorphology, 305, 209-220. doi:10.1016/j.geomorph.2017.09.040
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Thiery, W., Visser, A. J., Fischer, E. M., Hauser, M., Hirsch, A. L., Lawrence, D. M., . . . Seneviratne, S. I. (2020). Warming of hot extremes alleviated by expanding irrigation. Nature Communications, 11(1), 290. doi:10.1038/s41467-019-14075-4
Gridded Population of the World (GPW) v4.10 (population density UN WPP-adjusted) - 10.7927/H49884ZR
NASA REMOTE SENSING (MODIS)

Thomas, N. P., Anyamba, A., Tubbs, H., & Bishnoi, B. (2022). Evaluation of extreme soil moisture conditions during the 2020 Sahel floods and implications for disease outbreaks. Geophysical Research Letters, 49(19), e2022GL099872. Retrieved from https://doi.org/10.1029/2022GL099872
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (SMAP)

Thomas, T. S., You, L., Wood-Sichra, U., Ru, Y., Blankenspoor, B., & Kalvelagen, E. (2019). Generating Gridded Agricultural Gross Domestic Product for Brazil: A Comparison of Methodologies. Retrieved from Washington DC: http://documents.worldbank.org/curated/en/677071566217273585/Generating-Gridded-Agricultural-Gross-Domestic-Product-for-Brazil-A-Comparison-of-Methodologies
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted) - 10.7927/H4JQ0XZW
Socioeconomic Downscaled Projections (Global 15 x 15 Minute Grids of the Downscaled GDP Based on the SRES B2 Scenario, v1) - 10.7927/H4NC5Z4X

Thompson, J. J., Morato, R. G., Niebuhr, B. B., Alegre, V. B., Oshima, J. E. F., de Barros, A. E., . . . Ribeiro, M. C. (2021). Environmental and anthropogenic factors synergistically affect space use of jaguars. Current Biology, 31(15), 3457-3466. doi:10.1016/j.cub.2021.06.029
Gridded Population of the World (GPW) v4.11 (population density)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)

Thompson, K. A., Renaudin, M., & Johnson, M. T. J. (2016). Urbanization drives the evolution of parallel clines in plant populations. Proceedings of the Royal Society B: Biological Sciences, 283(1845), 20162180. doi:10.1098/rspb.2016.2180
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
REMOTE SENSING (Landsat)

Thompson, M. (2022). Using Local, Global, and Simulated Earthquakes to Inform Earthquake Resilience Efforts in the Pacific Northwest. (Ph.D.). University of Washington, Seattle. Retrieved from http://hdl.handle.net/1773/48393 
Gridded Population of the World (GPW) v4.11 (population count)

Thompson, S., Vehkaoja, M., Pellikka, J., & Nummi, P. (2021). Ecosystem services provided by beavers Castor spp. Mammal Review, 51(1), 25-39. doi:10.1111/mam.12220
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM

Thomson, D. R. (2022). Designing and Implementing Gridded Population Surveys Retrieved from http://gridpopsurvey.com/wp-content/uploads/2022/09/Designing_and_Implementing_Gridded_Population_Surveys_2022.pdf
Gridded Population of the World (GPW) v4.11 (collection)

Thomson, D. R., Gaughan, A. E., Stevens, F. R., Yetman, G., Elias, P., & Chen, R. S. (2021). Evaluating the accuracy of gridded population estimates in slums: A case study in Nigeria and Kenya. Urban Science, 5(2), 48. doi:10.3390/urbansci5020048
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
POPGRID
REMOTE SENSING (RapidEye)
REMOTE SENSING (Sentinel-2)

Thomson, D. R., Kools, L., & Jochem, W. C. (2018). Linking synthetic populations to household geolocations: A demonstration in Namibia. Data, 3(3), 30. doi:10.3390/data3030030
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates)

Thomson, D. R., Leasure, D. R., Bird, T., Tzavidis, N., & Tatem, A. J. (2022). How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia. PLoS ONE, 17(7), e0271504. doi:10.1371/journal.pone.0271504
Gridded Population of the World (GPW) v4.11 (collection)

Thomson, D. R., Rhoda, D. A., Tatem, A. J., & Castro, M. C. (2020). Gridded population survey sampling: a systematic scoping review of the field and strategic research agenda. International Journal of Health Geographics, 19(1), 34. doi:10.1186/s12942-020-00230-4
Gridded Population of the World (GPW) v4 (collection)

Thomson, D. R., Stevens, F. R., Chen, R. S., Yetman, G., Sorichetta, A., & Gaughan, A. E. (2022). Improving the accuracy of gridded population estimates in cities and slums to monitor SDG 11: Evidence from a simulation study in Namibia. Land Use Policy, 123, 106392. doi:10.1016/j.landusepol.2022.106392
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (VIIRS NTL)

Thomson, D. R., Stevens, F. R., Ruktanonchai, N. W., Tatem, A. J., & Castro, M. C. (2017). GridSample: an R package to generate household survey primary sampling units (PSUs) from gridded population data. International Journal of Health Geographics, 16(1), 25. doi:10.1186/s12942-017-0098-4
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)

Thornhill, I., Chautard, A., & Loiselle, S. (2018). Monitoring biological and chemical trends in temperate still waters using citizen science. Water, 10(7), 839. doi:10.3390/w10070839
Gridded Population of the World (GPW) v4 Preliminary release 2 (population density)

Thornton, P., Chang, Y., Loboguerrero, A. M., & Campbell, B. (2023). Perspective: What might it cost to reconfigure food systems? Global Food Security, 36, 100669. doi:10.1016/j.gfs.2022.100669
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4PN93PB

Thornton, P. K., Kristjanson, P., Förch, W., Barahona, C., Cramer, L., & Pradhan, S. (2018). Is agricultural adaptation to global change in lower-income countries on track to meet the future food production challenge? Global Environmental Change, 52, 37-48. doi:10.1016/j.gloenvcha.2018.06.003
Gridded Population of the World (GPW) v4 (data quality indicators) - 10.7927/H49C6VBN

Tiago, P., Ceia-Hasse, A., Marques, T. A., Capinha, C., & Pereira, H. M. (2017). Spatial distribution of citizen science casuistic observations for different taxonomic groups. Scientific Reports, 7(1), 9pp. doi:10.1038/s41598-017-13130-8
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
NASA REMOTE SENSING (SRTM)

Tian, F., Wu, B., Zeng, H., Watmough, G. R., Zhang, M., & Li, Y. (2022). Detecting the linkage between arable land use and poverty using machine learning methods at global perspective. Geography and Sustainability, 3(1), 7-20. doi:10.1016/j.geosus.2022.01.001
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
Poverty Mapping (Global Subnational Infant Mortality Rates, v2) - 10.7927/H4PN93JJ

Tian, J., & Zhang, Y. (2020). Detecting changes in irrigation water requirement in Central Asia under CO2 fertilization and land use changes. Journal of Hydrology, 583, 124315. doi:10.1016/j.jhydrol.2019.124315
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (SRTM)

Tidemann, C., Engerer, N., Markham, F., Doran, B., & Pezzey, J. C. V. (2019). Spatial disaggregation clarifies the inequity in distributional outcomes of household solar PV installation. Journal of Renewable and Sustainable Energy, 11(3), 035901. doi:10.1063/1.5097424
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)

Tini, G., Trapani, D., Duso, B. A., Beria, P., Curigliano, G., Pelicci, P. G., & Mazzarella, L. (2022). Quantifying geographical accessibility to cancer clinical trials in different income landscapes. ESMO Open, 7(3), 100515. doi:10.1016/j.esmoop.2022.100515
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Tinti, F., Kasmaee, S., Elkarmoty, M., Bonduà, S., & Bortolotti, V. (2018). Suitability evaluation of specific shallow geothermal technologies using a GIS-based multi criteria decision analysis implementing the analytic hierarchic process. Energies, 11(2), 457. doi:10.3390/en11020457
Gridded Population of the World (GPW) v4 (population density)

Tiwari, A. D., Pokhrel, Y., Kramer, D., Akhter, T., Tang, Q., Liu, J., . . . Lakshmi, V. (2023). A synthesis of hydroclimatic, ecological, and socioeconomic data for transdisciplinary research in the Mekong. Scientific Data, 10(1), 283. doi:10.1038/s41597-023-02193-0
Gridded Population of the World (GPW) v4 (unspecified)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
Population Dynamics (Global One-Eighth Degree Population Base Year and Projection Grids Based on the SSPs, v1.01)
REMOTE SENSING (VIIRS NTL)

To Duy, T., Herrmann, M., Estournel, C., Marsaleix, P., Duhaut, T., Bui Hong, L., & Trinh Bich, N. (2022). The role of wind, mesoscale dynamics, and coastal circulation in the interannual variability of the South Vietnam Upwelling, South China Sea – answers from a high-resolution ocean model. Ocean Science, 18, 1131-1161. doi:10.5194/os-18-1131-2022
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
REMOTE SENSING (AVISO)
REMOTE SENSING (SMOS)

Tong, D., Geng, G., Zhang, Q., Cheng, J., Qin, X., Hong, C., . . . Davis, S. J. (2021). Health co-benefits of climate change mitigation depend on strategic power plant retirements and pollution controls. Nature Climate Change, 11, 1077-1083. doi:10.1038/s41558-021-01216-1
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Tong, Q. (2023). Data Analysis and Machine Learning for Enhancing Resilience to Fire, from Ignition Mapping to Structural and Systems Modeling. (Ph.D.). Johns Hopkins University, Baltimore MD. Retrieved from https://jhir.library.jhu.edu/handle/1774.2/68962 
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)

Tracy, J. L., Trabucco, A., Lawing, A. M., Giermakowski, J. T., Tchakerian, M., Drus, G. M., & Coulson, R. N. (2018). Random subset feature selection for ecological niche models of wildfire activity in Western North America. Ecological Modelling, 383, 52-68. doi:10.1016/j.ecolmodel.2018.05.019
Gridded Population of the World (GPW) v4 (population density)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
REMOTE SENSING (Landsat)

Trimmer, J. T., Cusick, R. D., & Guest, J. S. (2017). Amplifying progress toward multiple development goals through resource recovery from sanitation. Environmental Science & Technology, 51(18), 10765-10776. doi:10.1021/acs.est.7b02147
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Trimmer, J. T., & Guest, J. S. (2018). Recirculation of human-derived nutrients from cities to agriculture across six continents. Nature Sustainability, 1(8), 427-435. doi:10.1038/s41893-018-0118-9
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
Gridded Population of the World (GPW) v4 (land and water area)

Trimmer, J. T., Margenot, A., Cusick, R. D., & Guest, J. S. (2019). Aligning product chemistry and soil context for agronomic reuse of human-derived resources. Environmental Science & Technology, 53(11), 6501-6510. doi:10.1021/acs.est.9b00504
Gridded Population of the World (GPW) v3 (admin boundaries)
Gridded Population of the World (GPW) v4 (national identifier grid)

Trimmer, J. T., Miller, D. C., & Guest, J. S. (2019). Resource recovery from sanitation to enhance ecosystem services. Nature Sustainability, 2, 681-690. doi:10.1038/s41893-019-0313-3
Gridded Population of the World (GPW) v3 (admin boundaries)
Gridded Population of the World (GPW) v4 (national identifier grid)

Trinn, C., & Naumann, L. (2023). Guns and lightning: Power law distributions in intrastate conflict intensity dynamics. Conflict Management and Peace Science, 40(4), 373-397. doi:10.1177/07388942221092126
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Trisolini, M., & Colombo, C. (2020). Modeling Re-Entry Break-Up Uncertainties with Continuity Equation and Gaussian Mixture Models Interpolation. Retrieved from Milan: http://hdl.handle.net/11311/1146009
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4PN93PB

Tsori, Y., & Granek, R. (2022). Spatio-temporal spread of COVID-19: Comparison of the inhomogeneous SEPIR model and data from South Carolina. PLoS ONE, 17(6), e0268995. doi:10.1371/journal.pone.0268995
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Tuholske, C., Caylor, K., Evans, T., & Avery, R. (2019). Variability in urban population distributions across Africa. Environmental Research Letters, 14(8), 085009. doi:10.1088/1748-9326/ab2432
Gridded Population of the World (GPW) v4 (documentation) - 10.7927/H4B56GPT
POPGRID

Tuholske, C., Gaughan, A. E., Sorichetta, A., de Sherbinin, A., Bucherie, A., Hultquist, C., . . . Yetman, G. (2021). Implications for tracking SDG indicator metrics with gridded population data. Sustainability, 13(13), 7329. doi:10.3390/su13137329
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
POPGRID

Turschwell, M. P., Tulloch, V. J. D., Sievers, M., Pearson, R. M., Andradi-Brown, D. A., Ahmadia, G. N., . . . Brown, C. J. (2020). Multi-scale estimation of the effects of pressures and drivers on mangrove forest loss globally. Biological Conservation, 247, 108637. doi:10.1016/j.biocon.2020.108637
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
Last of the Wild v2 (Global Human Footprint (Geographic))

Uddin, K., & Matin, M. A. (2021). Potential flood hazard zonation and flood shelter suitability mapping for disaster risk mitigation in Bangladesh using geospatial technology. Progress in Disaster Science, 11, 100185. doi:10.1016/j.pdisas.2021.100185
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
REMOTE SENSING (Sentinel-1 SAR)

Ummel, K. (2016). Impact of CCL’s Proposed Carbon Fee and Dividend Policy: A High-resolution Analysis of the Financial Effect on U.S. Households. Retrieved from https://citizensclimatelobby.org/wp-content/uploads/2016/02/Household-Impact-Study-Ummel.pdf
Gridded Population of the World (GPW) v4 (collection)

UN ESCAP. (2022). Asia-Pacific Disaster Report 2022 for ESCAP Subregions - Pathways to Adaptation and Resilience in South and South-West Asia. Retrieved from Bangkok: https://www.unescap.org/kp/2022/asia-pacific-disaster-report-2022-escap-subregions-pathways-adaptation-and-resilience-south
Gridded Population of the World (GPW) v4.11 (unspecified)

UN ESCAP. (2022). Asia-Pacific Riskscape @1.5C: Subregional Pathways for Adaptation and Resilience: Asia Pacific Disaster Report 2022 for ESCAP Subregions: Summary for Policymakers. Retrieved from https://hdl.handle.net/20.500.12870/4448
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

UN ESCAP. (2022). Pathways to Adaptation and Resilience in East and North-East Asia. Retrieved from Bangkok: https://www.unescap.org/kp/2021/asia-pacific-disaster-report-2021
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)

Unfried, K., Kis-Katos, K., & Poser, T. (2022). Water scarcity and social conflict. Journal of Environmental Economics and Management, 113, 102633. doi:10.1016/j.jeem.2022.102633
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates)
Global Rural-Urban Mapping Project (GRUMP) v1 (urban extent)
Satellite-Derived Environmental Indicators (Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, v1)
NASA REMOTE SENSING (GRACE)

UNICEF. (2015). Unless We Act Now:  The Impact of Climate Change on Children. Retrieved from New York: http://www.unicef.org/publications/index_86337.html
Gridded Population of the World (GPW) v4 Preliminary Release 2 (collection)

UNICEF. (2019). 29 million babies born into conflict in 2018 [Press release]. Retrieved from https://www.unicef.org/press-releases/29-million-babies-born-conflict-2018
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Ustaoglu, E., & Kabadayı, M. E. (2021). Reconstruction of residential land cover and spatial analysis of population in Bursa Region (Turkey) in the mid-nineteenth century. Land, 10(10), 1077. doi:10.3390/land10101077
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)

Uttajug, A., Ueda, K., Honda, A., & Takano, H. (2022). Estimation of hospital visits for respiratory diseases attributable to PM10 from vegetation fire smoke and health impacts of regulatory intervention in Upper Northern Thailand. Scientific Reports, 12(1), 18515. doi:10.1038/s41598-022-23388-2
Gridded Population of the World (GPW) v4.11 (population count)

van Daalen, K. R., Romanello, M., Rocklöv, J., Semenza, J. C., Tonne, C., Markandya, A., . . . Lowe, R. (2022). The 2022 Europe report of the Lancet Countdown on health and climate change: towards a climate resilient future. The Lancet Public Health, 7(11), e942-e965. doi:10.1016/S2468-2667(22)00197-9
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MODIS)

van den Bosch, M., Beyer, D. E., Erb, J. D., Gantchoff, M. G., Kellner, K. F., MacFarland, D. M., . . . Belant, J. L. (2022). Identifying potential gray wolf habitat and connectivity in the eastern USA. Biological Conservation, 273, 109708. doi:10.1016/j.biocon.2022.109708
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

van den Bosch, M., Kellner, K. F., Beyer Jr., D. E., Erb, J. D., MacFarland, D. M., Norton, D. C., . . . Belant, J. L. (2023). Gray wolf range in the western Great Lakes region under forecasted land use and climate change. Ecosphere, 14(8), e4630. doi:10.1002/ecs2.4630
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Van der Weyde, L. K., Tobler, M. W., Gielen, M. C., Cozzi, G., Weise, F. J., Adams, T., . . . Flyman, M. V. (2022). Collaboration for conservation: Assessing countrywide carnivore occupancy dynamics from sparse data. Diversity and Distributions, 28(6), 917-929. doi:10.1111/ddi.13386
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

van der Wiel, K., Kapnick, S. B., & Vecchi, G. A. (2017). Shifting patterns of mild weather in response to projected radiative forcing. Climatic Change, 140(3), 649-658. doi:10.1007/s10584-016-1885-9
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H46T0JKB

van der Wiel, K., Stoop, L. P., van Zuijlen, B. R. H., Blackport, R., van den Broek, M. A., & Selten, F. M. (2019). Meteorological conditions leading to extreme low variable renewable energy production and extreme high energy shortfall. Renewable and Sustainable Energy Reviews, 111, 261-275. doi:10.1016/j.rser.2019.04.065
Gridded Population of the World (GPW) v4 (population density)

van Donkelaar, A., Hammer, M. S., Bindle, L., Brauer, M., Brook, J. R., Garay, M. J., . . . Martin, R. V. (2021). Monthly global estimates of fine particulate matter and their uncertainty. Environmental Science & Technology, 55(22), 15287-15300. doi:10.1021/acs.est.1c05309
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MISR)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SeaWiFS)

van Donkelaar, A., Martin, R. V., Li, C., & Burnett, R. T. (2019). Regional estimates of chemical composition of fine particulate matter using a combined geoscience-statistical method with information from satellites, models, and monitors. Environmental Science & Technology, 53(5), 2595-2611. doi:10.1021/acs.est.8b06392
Gridded Population of the World (GPW) v4 (unspecified)
NASA REMOTE SENSING (MISR)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SeaWiFS)

van Soesbergen, A., & Mulligan, M. (2018). Uncertainty in data for hydrological ecosystem services modelling: Potential implications for estimating services and beneficiaries for the CAZ Madagascar. Ecosystem Services, 33(Part B), 175-186. doi:10.1016/j.ecoser.2018.08.005
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (TRMM)

van Vliet, J., Verburg, P. H., Grădinaru, S. R., & Hersperger, A. M. (2019). Beyond the urban-rural dichotomy: Towards a more nuanced analysis of changes in built-up land. Computers, Environment and Urban Systems, 74, 41-49. doi:10.1016/j.compenvurbsys.2018.12.002
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)

van Weezel, S. (2020). Local warming and violent armed conflict in Africa. World Development, 126, 104708. doi:10.1016/j.worlddev.2019.104708
Gridded Population of the World (GPW) v4 (population density)

Vandamme, E., Manners, R., Adewopo, J., Thiele, G., Friedmann, M., & Thornton, P. (2022). Strategizing research and development investments in climate change adaptation for root, tuber and banana crops in the African Great Lakes Region: A spatial prioritisation and targeting framework. Agricultural Systems, 202, 103464. doi:10.1016/j.agsy.2022.103464
Gridded Population of the World (GPW) v4.10 (population density)

Vanstreels, R. E. T., Durant, A., Santos, A. P., Santos, R. G., Sarmiento, A. M. S., Rossi, S., . . . Uhart, M. M. (2023). Exploring the relationship between environmental drivers and the manifestation of fibropapillomatosis in green turtles (Chelonia mydas) in eastern Brazil. PLoS ONE, 18(8), e0290312. doi:10.1371/journal.pone.0290312
Gridded Population of the World (GPW) v4.11 (population density)

Vanstreels, R. E. T., Gallo, L., Serafini, P. P., Santos, A. P., Egert, L., & Uhart, M. M. (2021). Ingestion of plastics and other debris by coastal and pelagic birds along the coast of Espírito Santo, Eastern Brazil. Marine Pollution Bulletin, 173, 113046. doi:10.1016/j.marpolbul.2021.113046
Gridded Population of the World (GPW) v4.11 (admin unit center points)
Gridded Population of the World (GPW) v4.11 (population density)

Vanstreels, R. E. T., Parsons, N. J., Sherley, R. B., Stander, N., Strauss, V., Kemper, J., . . . Ludynia, K. (2023). Factors determining the number of seabirds impacted by oil spills and the success of their rehabilitation: Lessons learned from Namibia and South Africa. Marine Pollution Bulletin, 188, 114708. doi:10.1016/j.marpolbul.2023.114708
Gridded Population of the World (GPW) v4 (population density) map

Varaprasad, V., Kanawade, V. P., & Narayana, A. C. (2021). Spatio-temporal variability of near-surface air pollutants at four distinct geographical locations in Andhra Pradesh State of India. Environmental Pollution, 268(Part B), 115899. doi:10.1016/j.envpol.2020.115899
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (OMI)

Varis, O., Taka, M., & Tortajada, C. (2022). Global human exposure to urban riverine floods and storms. River, 1(1), 80-90. doi:10.1002/rvr2.1
Gridded Population of the World (GPW) v4 (unspecified)

Vashchenko, V. M., Korduba, I. B., Loza, Y. A., Patlashenko, Z. I., Bannikov, O. O., & Kryzska, Y. M. (2018). Tornado statistics in Ukraine based on new data. Geofizicheskiy Zhurnal, 40(3), 199-213. doi:10.24028/gzh.0203-3100.v40i3.2018.137206
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Veilleux, J., & Dinar, S. (2021). A geospatial analysis of water-related risk to international security: an assessment of five countries. GeoJournal, 86, 185-238. doi:10.1007/s10708-019-10045-x
Gridded Population of the World (GPW) v4 (population density)

Veliev, N., Ivanov, A., & Biktimirov, S. (2020). Active Debris Removal for Mega-constellation Reliability. In K.-U. Schrogl (Ed.), Handbook of Space Security: Policies, Applications and Programs (pp. 1043-1061). Cham: Springer International Publishing.
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - data set unspecified)

Velpuri, N. M., Mateo-Sagasta, J., & Mohammed, O. (2023). Spatially Explicit Wastewater Generation and Tracking (SEWAGE-TRACK) in the Middle East and North Africa region. Science of The Total Environment, 875, 162421. doi:10.1016/j.scitotenv.2023.162421
Gridded Population of the World (GPW) v4.11 (collection)
NASA REMOTE SENSING (TRMM)

Venter, Z. S., Aunan, K., Chowdhury, S., & Lelieveld, J. (2020). COVID-19 lockdowns cause global air pollution declines. Proceedings of the National Academy of Sciences, 117(32), 18984-18990. doi:10.1073/pnas.2006853117
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (TROPOMI)

Venter, Z. S., Aunan, K., Chowdhury, S., & Lelieveld, J. (2021). Air pollution declines during COVID-19 lockdowns mitigate the global health burden. Environmental Research, 192, 110403. doi:10.1016/j.envres.2020.110403
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Venter, Z. S., Cramer, M. D., & Hawkins, H. J. (2018). Drivers of woody plant encroachment over Africa. Nature Communications, 9(1), 2272. doi:10.1038/s41467-018-04616-8
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS MCD45A1.051)

Ventura, Y., Rodríguez, Y., Odériz, I., Chávez, V., Mori, N., Felix, A., . . . Silva, R. (2022). New assessment of wave energy in relation to geomorphological and demographic characteristics on the Pacific Coast of Baja California, Mexico. Frontiers in Marine Science, 9, 872707. doi:10.3389/fmars.2022.872707
Gridded Population of the World (GPW) v4 (population density) - 10.7927//H4NP22DQ

Verbavatz, V., & Barthelemy, M. (2020). Access to mass rapid transit in OECD urban areas. Scientific Data, 7(1), 301. doi:10.1038/s41597-020-00639-3
Gridded Population of the World (GPW) v4 (population density)

Vergopolan, N., Xiong, S., Estes, L., Wanders, N., Chaney, N. W., Wood, E. F., . . . Sheffield, J. (2021). Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields. Hydrology and Earth System Sciences, 25, 1827-1847. doi:10.5194/hess-25-1827-2021
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Vernimmen, R., & Hooijer, A. (2023). New LiDAR-based elevation model shows greatest increase in global coastal exposure to flooding to be caused by early-stage sea-level rise. Earth's Future, 11(1), e2022EF002880. doi:10.1029/2022EF002880
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
NASA REMOTE SENSING (ICESat-2)

Vestby, J., Brandsch, J., Larsen, V. B., Landsverk, P., & Tollefsen, A. F. (2022). Predicting (de-)escalation of sub-national violence using gradient boosting: Does it work? International Interactions, 48(4), 841-859. doi:10.1080/03050629.2022.2021198
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Vetter-Gindele, J., Braun, A., Warth, G., Bui, T. T. Q., Bachofer, F., & Eltrop, L. (2019). Assessment of household solid waste generation and composition by building type in Da Nang, Vietnam. Resources, 8(4), 171. doi:10.3390/resources8040171
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (population density)

Viana, D. F., Zamborain-Mason, J., Gaines, S. D., Schmidhuber, J., & Golden, C. D. (2023). Nutrient supply from marine small-scale fisheries. Scientific Reports, 13(1), 11357. doi:10.1038/s41598-023-37338-z
Gridded Population of the World (GPW) v4 (population count)

Villarreal, M. L., Haire, S. L., Iniguez, J. M., Cortés Montaño, C., & Poitras, T. B. (2019). Distant neighbors: recent wildfire patterns of the Madrean Sky Islands of southwestern United States and northwestern Mexico. Fire Ecology, 15(1), 2. doi:10.1186/s42408-018-0012-x
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
NASA REMOTE SENSING (LIS)

Vishnu, S., Risser, M. D., O’Brien, T. A., Ullrich, P. A., & Boos, W. R. (2023). Observed increase in the peak rain rates of monsoon depressions. npj Climate and Atmospheric Science, 6(1), 111. doi:10.1038/s41612-023-00436-w
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
REMOTE SENSING (CHIRPS-2)

Visioni, D., Slessarev, E., MacMartin, D., Mahowald, N., M., Goodale, C., L., & Xia, L. (2020). What goes up must come down: impacts of deposition in a sulfate geoengineering scenario. Environmental Research Letters, 15(9), 094063. doi:10.1088/1748-9326/ab94eb
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Vivian, J. (2021). Energy in buildings and districts. In D. Borge-Diez & E. Rosales-Asensio (Eds.), Energy Services Fundamentals and Financing (pp. 81-108): Academic Press.
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted) - 10.7927/H4JQ0XZW

Vodonos, A., & Schwartz, J. (2021). Estimation of excess mortality due to long-term exposure to PM2.5 in continental United States using a high-spatiotemporal resolution model. Environmental Research, 196, 110904. doi:10.1016/j.envres.2021.110904
Gridded Population of the World (GPW) v4.11 (population count)

Vohra, K., Vodonos, A., Schwartz, J., Marais, E. A., Sulprizio, M. P., & Mickley, L. J. (2021). Global mortality from outdoor fine particle pollution generated by fossil fuel combustion: Results from GEOS-Chem. Environmental Research, 195, 110754. doi:10.1016/j.envres.2021.110754
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4PN93PB

Vojta, M., Plach, A., Thompson, R. L., & Stohl, A. (2022). A comprehensive evaluation of the use of Lagrangian particle dispersion models for inverse modeling of greenhouse gas emissions. EGUsphere, 15, 8295-8323. doi:10.5194/gmd-15-8295-2022
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

von Uexkull, N., Croicu, M., Fjelde, H., & Buhaug, H. (2016). Civil conflict sensitivity to growing-season drought. Proceedings of the National Academy of Sciences, 113(44), 12391–12396. doi:10.1073/pnas.1607542113
Gridded Population of the World (GPW) v4 (collection)
REMOTE SENSING (DMSP-OLS)

Wahl, F. (2017). Does European development have Roman roots? Evidence from the German Limes. Journal of Economic Growth, 22(313–349). doi:10.1007/s10887-017-9144-0
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (DMSP-OLS)

Wahlqvist, J., Ronchi, E., Gwynne, S. M. V., Kinateder, M., Rein, G., Mitchell, H., . . . Kuligowski, E. (2021). The simulation of wildland-urban interface fire evacuation: The WUI-NITY platform. Safety Science, 136, 105145. doi:10.1016/j.ssci.2020.105145
Gridded Population of the World (GPW) v4 (collection)

Waktola, D. K. (2021). Geographic Aspects of Hydropolitics: The Case of Nile and Mekong River Basins. In A. M. Melesse, W. Abtew, & S. A. Moges (Eds.), Nile and Grand Ethiopian Renaissance Dam: Past, Present and Future (pp. 117-142). Cham: Springer International Publishing.
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (SRTM)

Walker, W. S., Gorelik, S. R., Cook-Patton, S. C., Baccini, A., Farina, M. K., Solvik, K. K., . . . Griscom, B. W. (2022). The global potential for increased storage of carbon on land. Proceedings of the National Academy of Sciences, 119(23), e2111312119. doi:10.1073/pnas.2111312119
Gridded Population of the World (GPW) v4.11 (population count)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
NASA REMOTE SENSING (MODIS)

Walsh, M. G., Sawleshwarkar, S., Hossain, S., & Mor, S. M. (2020). Whence the next pandemic? The intersecting global geography of the animal-human interface, poor health systems and air transit centrality reveals conduits for high-impact spillover. One Health, 11, 100177. doi:10.1016/j.onehlt.2020.100177
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
Gridded Species Distribution (Mammals 2015)
Poverty Mapping (Global Subnational Infant Mortality Rates, v2) - 10.7927/H4PN93JJ

Walton, D., Arrighi, J., Van Aalst, M., & Claudet, M. (2021). The Compound Impact of Extreme Weather Events and COVID-19. Retrieved from Geneva: https://www.ifrc.org/media/49590
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Wang, A., Tao, H., Wu, Q., Huang, J., Zhang, B., & Wang, Y. (2022). Increasing urban and rural population exposures to warm-season concurrent hot days and nights on the North China Plain. International Journal of Climatology, 42(15), 7938-7950. doi:10.1002/joc.7685
Gridded Population of the World (GPW) v4 (unspecified)

Wang, C., Tang, C., Fu, B., Lü, Y., Xiao, S., & Zhang, J. (2022). Determining critical thresholds of ecological restoration based on ecosystem service index: A case study in the Pingjiang catchment in southern China. Journal of Environmental Management, 303, 114220. doi:10.1016/j.jenvman.2021.114220
Gridded Population of the World (GPW) v4.11 (population density)

Wang, F., Zheng, B., Zhang, J., Zhou, Y., & Jia, M. (2022). Potential heat-risk avoidance from nationally determined emission reductions targets in the future. Environmental Research Letters, 17(5), 055007. doi:10.1088/1748-9326/ac66f4
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Wang, H., He, X., Liang, X., Choma, E. F., Liu, Y., Shan, L., . . . Evans, J. S. (2020). Health benefits of on-road transportation pollution control programs in China. Proceedings of the National Academy of Sciences, 117(41), 25370-25377. doi:10.1073/pnas.1921271117
Gridded Population of the World (GPW) v4 (unspecified)

Wang, J., Gao, D., Shi, W., Du, J., Huang, Z., & Liu, B. (2023). Spatio-temporal changes in ecosystem service value: Evidence from the economic development of urbanised regions. Technological Forecasting and Social Change, 193, 122626. doi:10.1016/j.techfore.2023.122626
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (Landsat)

Wang, L., Bi, J., Meng, X., Geng, G., Huang, K., Li, J., . . . Liu, Y. (2020). Satellite-based assessment of the long-term efficacy of PM2.5 pollution control policies across the Taiwan Strait. Remote Sensing of Environment, 251, 112067. doi:10.1016/j.rse.2020.112067
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
NASA REMOTE SENSING (ASTER GDEM)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)

Wang, L., Fan, H., & Wang, Y. (2020). Improving population mapping using Luojia 1-01 nighttime light image and location-based social media data. Science of The Total Environment, 730, 139148. doi:10.1016/j.scitotenv.2020.139148
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - data set unspecified)
REMOTE SENSING (Luojia 1-01)

Wang, L., Li, Q., Qiu, Q., Hou, L., Ouyang, J., Zeng, R., . . . Liu, Y. (2022). Assessing the ecological risk induced by PM2.5 pollution in a fast developing urban agglomeration of southeastern China. Journal of Environmental Management, 324, 116284. doi:10.1016/j.jenvman.2022.116284
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
NASA REMOTE SENSING (ASTER GDEM)
NASA REMOTE SENSING (MODIS)

Wang, L., & Wu, J. T. (2018). Characterizing the dynamics underlying global spread of epidemics. Nature Communications, 9(1). doi:10.1038/s41467-017-02344-z
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates) - 10.7927/H4F47M2C
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)

Wang, L., Xu, B., Zhao, J., Li, C., Zeng, Y., Niu, Y., . . . Shi, Z.-H. (2023). Socioecological predicament on global steeply sloped cropland. Earth's Future, 11(3), e2022EF003165. doi:10.1029/2022EF003165
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
NASA REMOTE SENSING (ASTER GDEM)
NASA REMOTE SENSING (MODIS)

Wang, L., Yao, T., Chai, C., Cuo, L., Su, F., Zhang, F., . . . Wang, Y. (2021). TP-River: Monitoring and quantifying total river runoff from the Third Pole. Bulletin of the American Meteorological Society, 102(5), E948-E965. doi:10.1175/bams-d-20-0207.1
Gridded Population of the World (GPW) v4.11 (unspecified)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)
REMOTE SENSING (Sentinel-2)

Wang, L., Zhao, Y., Shi, Y., Liu, H., Li, H., Liu, J., . . . Han, C. (2023). The interaction effects between exposure to ambient PM2.5 and economic development on the settlement intention for floating population in China. Environmental Science and Pollution Research, 30, 67217-67226. doi:10.1007/s11356-023-27043-9
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Wang, M., Jiang, S., Ren, L., Xu, C.-Y., Menzel, L., Yuan, F., . . . Yang, X. (2021). Separating the effects of climate change and human activities on drought propagation via a natural and human-impacted catchment comparison method. Journal of Hydrology, 603, 126913. doi:10.1016/j.jhydrol.2021.126913
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (GRACE)
REMOTE SENSING (DMSP-OLS)

Wang, M. Y., Yim, S. H. L., Dong, G. H., Ho, K. F., & Wong, D. C. (2020). Mapping ozone source-receptor relationship and apportioning the health impact in the Pearl River Delta region using adjoint sensitivity analysis. Atmospheric Environment, 222, 117026. doi:10.1016/j.atmosenv.2019.117026
Gridded Population of the World (GPW) v4 (unspecified)

Wang, Q., Gao, X., Li, Q., Lan, T., Huang, R., & Deng, O. (2022). Spatially explicit reconstruction of the population distribution in the Tuojiang River Basin during 1911–2010 using random forest regression. Regional Environmental Change, 22(1), 13. doi:10.1007/s10113-021-01872-1
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (ASTER GDEM)

Wang, Q., Gu, J., & Wang, X. (2020). The impact of Sahara dust on air quality and public health in European countries. Atmospheric Environment, 748, 141421. doi:10.1016/j.atmosenv.2020.117771
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MODIS)

Wang, S., Ren, Z., Liu, X., & Yin, Q. (2022). Spatiotemporal trends of life expectancy, economic growth, and air pollution: A 134 countries investigation based on Bayesian modeling. Social Science & Medicine, 293, 114660. doi:10.1016/j.socscimed.2021.114660
Gridded Population of the World (GPW) v4 (unspecified)
Satellite-Derived Environmental Indicators (Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, v1)

Wang, S., Vogt, R. D., Carstensen, J., Lin, Y., Feng, J., & Lu, X. (2022). Riverine flux of dissolved phosphorus to the coastal sea may be overestimated, especially in estuaries of gated rivers: Implications of phosphorus adsorption/desorption on suspended sediments. Chemosphere, 287(Part 3), 132206. doi:10.1016/j.chemosphere.2021.132206
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Wang, S. S.-C., Qian, Y., Leung, L. R., & Zhang, Y. (2021). Identifying key drivers of wildfires in the contiguous US using machine learning and game theory interpretation. Earth's Future, 9(6), e2020EF001910. doi:10.1029/2020EF001910
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)

Wang, S. S.-C., Qian, Y., Leung, L. R., & Zhang, Y. (2022). Interpreting machine learning prediction of fire emissions and comparison with FireMIP process-based models. Atmospheric Chemistry and Physics, 22(5), 3445-3468. doi:10.5194/acp-22-3445-2022
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MODIS)

Wang, T., & Sun, F. (2022). Global gridded GDP data set consistent with the shared socioeconomic pathways. Scientific Data, 9(1), 221. doi:10.1038/s41597-022-01300-x
Gridded Population of the World (GPW) v4.11 (unspecified)
Global Rural-Urban Mapping Project (GRUMP) v1 (population density)

Wang, W., Parrish, D. D., Wang, S., Bao, F., Ni, R., Li, X., . . . Su, H. (2022). Long-term trend of ozone pollution in China during 2014-2020: Distinct seasonal and spatial characteristics and ozone sensitivity. Atmospheric Chemistry and Physics, 22, 8935-8949. doi:10.5194/acp-22-8935-2022
Gridded Population of the World (GPW) v4.11 (population density)

Wang, W., Yang, S., Stanley, H. E., & Gao, J. (2019). Local floods induce large-scale abrupt failures of road networks. Nature Communications, 10(1), 2114. doi:10.1038/s41467-019-10063-w
Gridded Population of the World (GPW) v4 (population density)

Wang, X., Fu, T.-M., Zhang, L., Cao, H., Zhang, Q., Ma, H., . . . Henze, D. K. (2021). Sensitivities of ozone air pollution in the Beijing–Tianjin–Hebei area to local and upwind precursor emissions using adjoint modeling. Environmental Science & Technology, 55(9), 5752-5762. doi:10.1021/acs.est.1c00131
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
REMOTE SENSING (TROPOMI)

Wang, X., Ma, W., Hua, T., & Li, D. (2017). Variation in vegetation greenness along China's land border. Science China Earth Sciences, 60(11), 2025-2032. doi:10.1007/s11430-016-9078-8
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (SPOT VGT)

Wang, X., Meng, X., & Long, Y. (2022). Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways. Scientific Data, 9(1), 563. doi:10.1038/s41597-022-01675-x
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)

Wang, X., Xiao, X., Zou, Z., Dong, J., Qin, Y., Doughty, R. B., . . . Li, B. (2020). Gainers and losers of surface and terrestrial water resources in China during 1989–2016. Nature Communications, 11(1), 3471. doi:10.1038/s41467-020-17103-w
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (GRACE)

Wang, Y., Fu, B., Liu, Y., Li, Y., Feng, X., & Wang, S. (2021). Response of vegetation to drought in the Tibetan Plateau: Elevation differentiation and the dominant factors. Agricultural and Forest Meteorology, 306, 108468. doi:10.1016/j.agrformet.2021.108468
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)

Wang, Y., Gardoni, P., Murphy, C., & Guerrier, S. (2019). Predicting fatality rates due to earthquakes accounting for community vulnerability. Earthquake Spectra, 35(2), 513-536. doi:10.1193/022618eqs046m
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42

Wang, Y., Gardoni, P., Murphy, C., & Guerrier, S. (2021). Empirical predictive modeling approach to quantifying social vulnerability to natural hazards. Annals of the American Association of Geographers, 111(5), 1559-1583. doi:10.1080/24694452.2020.1823807
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4PN93PB

Wang, Y., Liang, Z., Ding, J., Shen, J., Wei, F., & Li, S. (2022). Prediction of urban thermal environment based on multi-dimensional nature and urban form factors. Atmosphere, 13(9), 1493. doi:10.3390/atmos13091493
Gridded Population of the World (GPW) v4.10 (population density)
REMOTE SENSING (DMSP-OLS)

Wang, Y., Liao, H., Chen, H., & Chen, L. (2023). Future projection of mortality from exposure to PM2.5 and O3 under the carbon neutral pathway: Roles of changing emissions and population aging. Geophysical Research Letters, 50(15), e2023GL104838. doi:10.1029/2023GL104838
Gridded Population of the World (GPW) v4.11 (population count)

Wang, Y., Zhao, N., Wu, C., Quan, J., & Chen, M. (2023). Future population exposure to heatwaves in 83 global megacities. Science of The Total Environment, 888, 164142. doi:10.1016/j.scitotenv.2023.164142
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Wang, Y., Zhao, N., Yin, X., Wu, C., Chen, M., Jiao, Y., & Yue, T. (2023). Global future population exposure to heatwaves. Environment International, 178, 108049. doi:10.1016/j.envint.2023.108049
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Wang, Y., Zhou, Y., Franz, K., Zhang, X., Ding, K. J., Jia, G., & Yuan, X. (2021). An agent-based framework for high-resolution modeling of domestic water use. Resources, Conservation and Recycling, 169, 105520. doi:10.1016/j.resconrec.2021.105520
Gridded Population of the World (GPW) v4 (unspecified)
REMOTE SENSING (VIIRS)

Wang, Y. V., Gardoni, P., Murphy, C., & Guerrier, S. (2020). Worldwide predictions of earthquake casualty rates with seismic intensity measure and socioeconomic data: A fragility-based formulation. Natural Hazards Review, 21(2), 04020001. doi:10.1061/(ASCE)NH.1527-6996.0000356
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Wanghe, K., Guo, X., Ahmad, S., Tian, F., Nabi, G., Igorevich Strelnikov, I., . . . Zhao, K. (2022). FRESF model: An ArcGIS toolbox for rapid assessment of the supply, demand, and flow of flood regulation ecosystem services. Ecological Indicators, 143, 109264. doi:10.1016/j.ecolind.2022.109264
Gridded Population of the World (GPW) v4.11 (population density)

Ward, P. J., Blauhut, V., Bloemendaal, N., Daniell, J. E., de Ruiter, M. C., Duncan, M., . . . Winsemius, H. C. (2020). Review article: Natural hazard risk assessments at the global scale. Natural Hazards and Earth System Sciences, 20, 1069-1096. doi:10.5194/nhess-20-1069-2020
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Watson, O. J., Sumner, K. M., Janko, M., Goel, V., Winskill, P., Slater, H. C., . . . Parr, J. B. (2019). False-negative malaria rapid diagnostic test results and their impact on community-based malaria surveys in sub-Saharan Africa. BMJ Global Health, 4(4), e001582. doi:10.1136/bmjgh-2019-001582
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Watts, M. J., Sarto i Monteys, V., Mortyn, P. G., & Kotsila, P. (2021). The rise of West Nile Virus in Southern and Southeastern Europe: A spatial–temporal analysis investigating the combined effects of climate, land use and economic changes. One Health, 13, 100315. doi:10.1016/j.onehlt.2021.100315
Gridded Population of the World (GPW) v4.11 (population count)

Watts, N., Amann, M., Arnell, N., Ayeb-Karlsson, S., Beagley, J., Belesova, K., . . . Costello, A. (2021). The 2020 report of The Lancet Countdown on health and climate change: responding to converging crises. The Lancet, 397(10269), 129-170. doi:10.1016/S0140-6736(20)32290-X
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)

Watts, N., Amann, M., Arnell, N., Ayeb-Karlsson, S., Belesova, K., Boykoff, M., . . . Montgomery, H. (2019). The 2019 report of The Lancet Countdown on health and climate change: ensuring that the health of a child born today is not defined by a changing climate. The Lancet, 394(10211), 1836-1878. doi:10.1016/S0140-6736(19)32596-6
Gridded Population of the World (GPW) v4.10 (population count)
Gridded Population of the World (GPW) v4.10 (basic demographic characteristics)
NASA REMOTE SENSING (MODIS Active Fires)
NASA REMOTE SENSING (MODIs SSTs)

Wayland, J. J. (2018). Natural Resources, Civil Conflict, and the Political Ecology of Scale. (Ph.D. Ph.D.). University of Maryland, College Park, MD. Retrieved from https://doi.org/10.13016/atbq-zmvw 
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (Landsat)

Weagle, C. L., Saint-Louis, R., Dumas-Lefebvre, É., Chavanne, C., Dumont, D., & Chang, R. Y. W. (2022). Sea-air transfer of a tracer dye observed during the Tracer Release Experiment with implications for airborne contaminant exposure. Marine Pollution Bulletin, 182, 113945. doi:10.1016/j.marpolbul.2022.113945
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Weber, E. M., Seaman, V. Y., Stewart, R. N., Bird, T. J., Tatem, A. J., McKee, J. J., . . . Reith, A. E. (2018). Census-independent population mapping in northern Nigeria. Remote Sensing of Environment, 204, 786-798. doi:10.1016/j.rse.2017.09.024
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)
Global Rural-Urban Mapping Project (GRUMP) v1 (population count) - 10.7927/H4VT1Q1H
REMOTE SENSING (DigitalGlobe)

Weber, R., & Kornher, L. (2019). Can One Improve Now-casts of Crop Prices in Africa?  Google Can. Retrieved from Bonn: https://www.zef.de/uploads/tx_zefportal/Publications/ZEF_DP_271.pdf
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM

Wei, C., Chen, W., Lu, Y., Blaschke, T., Peng, J., & Xue, D. (2022). Synergies between urban heat island and urban heat wave effects in 9 global mega-regions from 2003 to 2020. Remote Sensing, 14(1), 70. doi:10.3390/rs14010070
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)

Wei, F., Liang, Z., Ma, W., Shen, J., Wang, Y., Liu, D., & Li, S. (2022). Dominant factors in the temporal and spatial distribution of precipitation change in the Beijing-Tianjin-Hebei urban agglomeration. Remote Sensing, 14(12), 2880. doi:10.3390/rs14122880
Gridded Population of the World (GPW) v4.10 (population density)
NASA REMOTE SENSING (MODIS)

Wei, F., Wang, S., Fu, B., Wang, L., Liu, Y. Y., & Li, Y. (2019). African drylands ecosystem changes controlled by soil water. Land Degradation & Development, 30(13), 1564-1573. doi:10.1002/ldr.3342
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS - MCD12C1)

Wei, S., Lin, Y., Zhang, H., Wan, L., Lin, H., & Wu, Z. (2021). Estimating Chinese residential populations from analysis of impervious surfaces derived from satellite images. International Journal of Remote Sensing, 42(6), 2303-2326. doi:10.1080/01431161.2020.1841322
Gridded Population of the World (GPW) v4 (collection)
REMOTE SENSING (Landsat) via Google Earth Engine

Wei, Y., Danesh Yazdi, M., Ma, T., Castro, E., Liu, C. S., Qiu, X., . . . Schwartz, J. (2023). Additive effects of 10-year exposures to PM2.5 and NO2 and primary cancer incidence in American older adults. Environmental Epidemiology, 7(4). doi:10.1097/EE9.0000000000000265
Air Quality Data for Health-Related Applications (Daily and Annual NO2 Concentrations for the Contiguous United States, 1-km Grids, v1) - 10.7927/f8eh-5864
Air Quality Data for Health-Related Applications (Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, v1) - 10.7927/0rvr-4538
Air Quality Data for Health-Related Applications (Daily and Annual PM2.5, O3, and NO2 Concentrations at ZIP Codes for the Contiguous U.S., v1) - 10.7927/9yp5-hz11
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)

Weidner, T., Yang, A., Forster, F., & Hamm, M. W. (2022). Regional conditions shape the food–energy–land nexus of low-carbon indoor farming. Nature Food, 3, 206-216. doi:10.1038/s43016-022-00461-7
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Weiss, L., Ludwig, W., Heussner, S., Canals, M., Ghiglione, J.-F., Estournel, C., . . . Kerhervé, P. (2021). The missing ocean plastic sink: Gone with the rivers. Science, 373(6550), 107-111. doi:10.1126/science.abe0290
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Welle, P. D., Small, M. J., Doney, S. C., & Azevedo, I. L. (2017). Estimating the effect of multiple environmental stressors on coral bleaching and mortality. PLoS ONE, 12(5), e0175018. doi:10.1371/journal.pone.0175018
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
NASA REMOTE SENSING (MODIS)

Wen, S., Wang, A., Tao, H., Malik, K., Huang, J., Zhai, J., . . . Su, B. (2019). Population exposed to drought under the 1.5 °C and 2.0 °C warming in the Indus River Basin. Atmospheric Research, 218, 296-305. doi:10.1016/j.atmosres.2018.12.003
Gridded Population of the World (GPW) v4 (unspecified)

Westerveld, J. J. L., van den Homberg, M. J. C., Nobre, G. G., van den Berg, D. L. J., Teklesadik, A. D., & Stuit, S. M. (2021). Forecasting transitions in the state of food security with machine learning using transferable features. Science of The Total Environment, 786, 147366. doi:10.1016/j.scitotenv.2021.147366
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
NASA REMOTE SENSING (GPM IMERG)
NASA REMOTE SENSING (TRMM)

Westphal, D. (2022). Genetic Impacts of Deforestation on Mouse Lemurs. (Ph.D.). City University of New York, New York. Retrieved from https://academicworks.cuny.edu/gc_etds/4839 
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
Gridded Population of the World (GPW) v4.11 (documentation) - 10.7927/H45Q4T5F
NASA REMOTE SENSING (IceSat-1)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)

Wetterwald, J., & Smirnov, Y. (2019). Using network analysis to evaluate the cascading impacts of crises on service and market systems. Contributing Paper to Global Assessment Report on Disaster Risk Reduction (GAR 2019). Retrieved from https://www.unisdr.org/we/inform/publications/65950
Gridded Population of the World (GPW) v4 (collection)

Whalen, M. A., Whippo, R. D. B., Stachowicz, J. J., York, P. H., Aiello, E., Alcoverro, T., . . . Duffy, J. E. (2020). Climate drives the geography of marine consumption by changing predator communities. Proceedings of the National Academy of Sciences, 117(45), 28160-28166. doi:10.1073/pnas.2005255117
Gridded Population of the World (GPW) v4.10 (population density)
NASA REMOTE SENSING (MODIS)

Widmer, K., Beloconi, A., Marnane, I., & Vounatsou, P. (2022). ETC HE Report 2022/8: Review and Assessment of Available Information on Light Pollution in Europe. Retrieved from Kjeller, Norway: https://www.eionet.europa.eu/etcs/etc-he/products/etc-he-products/etc-he-report-2022-8-review-and-assessment-of-available-information-on-light-pollution-in-europe
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS NTL)

Wieringa, J. G. (2022). Comparing predictions of IUCN Red List categories from machine learning and other methods for bats. Journal of Mammalogy, 103(3), 528-539. doi:10.1093/jmammal/gyac005
Gridded Population of the World (GPW) v4 (population count)

Wild, F., & Stadelmann, D. (2022). Coastal proximity and individual living standards: Econometric evidence from georeferenced household surveys in sub-Saharan Africa. Review of Development Economics, 26(4), 1883-1901. doi:10.1111/rode.12901
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Williams, B. A., Venter, O., Allan, J. R., Atkinson, S. C., Rehbein, J. A., Ward, M., . . . Watson, J. E. M. (2020). Change in terrestrial human footprint drives continued loss of intact ecosystems. One Earth, 3(3), 371-382. doi:10.1016/j.oneear.2020.08.009
Gridded Population of the World (GPW) v4 (population density)
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
REMOTE SENSING (DMSP-OLS)

Williams, M. A., Faiad, S., Claar, D. C., French, B., Leslie, K. L., Oven, E., . . . Wood, C. L. (2022). Life history mediates the association between parasite abundance and geographic features. Journal of Animal Ecology, 91(5), 996-1009. doi:10.1111/1365-2656.13693
Gridded Population of the World (GPW) v4 (population count)

Williams, R. (2019). The Geography of Secession. (Ph.D.). University of North Carolina, Chapel Hill. Retrieved from https://doi.org/10.17615/1ysh-sk68 
Gridded Population of the World (GPW) v3 (population count)
Gridded Population of the World (GPW) v4 (population density)
REMOTE SENSING (DMSP-OLS)

Willner, S. N., Levermann, A., Zhao, F., & Frieler, K. (2018). Adaptation required to preserve future high-end river flood risk at present levels. Science Advances, 4(1), 8pp. doi:10.1126/sciadv.aao1914
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Willner, S. N., Otto, C., & Levermann, A. (2018). Global economic response to river floods. Nature Climate Change, 8, 594-598. doi:10.1038/s41558-018-0173-2
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Wilmot, T. Y., Hallar, A. G., Lin, J. C., & Mallia, D. V. (2021). Expanding number of Western US urban centers face declining summertime air quality due to enhanced wildland fire activity. Environmental Research Letters, 16(5), 054036. doi:10.1088/1748-9326/abf966
Gridded Population of the World (GPW) v4.11 (population count)
Gridded Population of the World (GPW) v4.11 (Population density)
NASA REMOTE SENSING (OMI)

Wilmoth, J. (2016). Strengthening the demographic evidence base for the post-2015 development agenda. Paper presented at the 49th Session of the UN Commission on Population and Development, New York. http://www.un.org/en/development/desa/population/commission/pdf/49/CPD2016_SubstantiveBriefing_SGReport_Bravo_22March2016.pdf
Gridded Population of the World (GPW) v4 (map)

Wilson, A. G., Wilson, S., Alavi, N., & Lapen, D. R. (2021). Human density is associated with the increased prevalence of a generalist zoonotic parasite in mammalian wildlife. Proceedings of the Royal Society B: Biological Sciences, 288(1961), 20211724. doi:10.1098/rspb.2021.1724
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Wilson, B., & Paradise, T. (2018). Assessing the impact of Syrian refugees on earthquake casualty estimations in southeast Turkey. Natural Hazards and Earth System Sciences, 18, 257-269. doi:10.5194/nhess-18-257-2018
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)

Wilson, K., & Wakefield, J. (2020). Pointless spatial modeling. Biostatistics, 21(2), e17-e32. doi:10.1093/biostatistics/kxy041
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM

Wilson, T., Grossman, I., Alexander, M., Rees, P., & Temple, J. (2022). Methods for small area population forecasts: State-of-the-art and research needs. Population Research and Policy Review, 41(3), 865-898. doi:10.1007/s11113-021-09671-6
Gridded Population of the World (GPW) v4 (collection)

Wimberly, M. C. (2023). Geospatial Environmental Data for Planetary Health Applications. In T.-H. Wen, T.-W. Chuang, & M. Tipayamongkholgul (Eds.), Earth Data Analytics for Planetary Health (pp. 123-141). Singapore: Springer Nature Singapore.
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)

Winchester, S. (2020). Does Quality Matter? An Evaluation of the Relationship Between United Nations Peacekeepers and Civil War Violence. (Ph.D.). Norman Paterson School of International Affairs, Carleton University, Ottawa. Retrieved from https://doi.org/10.22215/etd/2020-13989 
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Wingate, V. R., Akinyemi, F. O., & Speranza, C. I. (2023). Archetypes of remnant West African forest patches, their main characteristics and geographical distribution. Applied Geography, 158, 103024. doi:10.1016/j.apgeog.2023.103024
Gridded Population of the World (GPW) v4 (population density) - 10.7927//H4NP22DQ
Global Roads (Global Roads Open Access Data Set (gROADS), v1)
Land Use and Land Cover (LULC) (Global Human Modification of Terrestrial Systems, v1)
NASA REMOTE SENSING (MODIS - MYD13A1)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (ALOS-1)
REMOTE SENSING (ALOS-2)
REMOTE SENSING (Landsat)

Winskill, P., Slater, H. C., Griffin, J. T., Ghani, A. C., & Walker, P. G. T. (2017). The US President's Malaria Initiative, Plasmodium falciparum transmission and mortality: A modelling study. PLoS Medicine, 14(11), e1002448. doi:10.1371/journal.pmed.1002448
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Winstead, D. J., & Jacobson, M. G. (2022). Food resilience in a dark catastrophe: A new way of looking at tropical wild edible plants. Ambio, 51, 1949-1962. doi:10.1007/s13280-022-01715-1
Gridded Population of the World (GPW) v4.11 (population density)
Population Estimation Service v3

Wohner, C., Ohnemus, T., Zacharias, S., Mollenhauer, H., Ellis, E. C., Klug, H., . . . Mirtl, M. (2021). Assessing the biogeographical and socio-ecological representativeness of the ILTER site network. Ecological Indicators, 127, 107785. doi:10.1016/j.ecolind.2021.107785
Gridded Population of the World (GPW) v4.11 (population density)

Woinarski, J. C. Z., Braby, M. F., Burbidge, A. A., Coates, D., Garnett, S. T., Fensham, R. J., . . . Murphy, B. P. (2019). Reading the black book: The number, timing, distribution and causes of listed extinctions in Australia. Biological Conservation, 239, 108261. doi:10.1016/j.biocon.2019.108261
Gridded Population of the World (GPW) v4 (population density)

Wolf, C., Levi, T., Ripple, W. J., Zárrate-Charry, D. A., & Betts, M. G. (2021). A forest loss report card for the world’s protected areas. Nature Ecology & Evolution, 5, 520-529. doi:10.1038/s41559-021-01389-0
Gridded Population of the World (GPW) v4.11 (population density)

Wolff, C., Vafeidis, A. T., Muis, S., Lincke, D., Satta, A., Lionello, P., . . . Hinkel, J. (2018). A Mediterranean coastal database for assessing the impacts of sea-level rise and associated hazards. Scientific Data, 5, 180044. doi:10.1038/sdata.2018.44
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
Global Rural-Urban Mapping Project (GRUMP) v1 (population count)
NASA REMOTE SENSING (SRTM)

Wong, K. L. M., Brady, O. J., Campbell, O. M. R., & Benova, L. (2018). Comparison of spatial interpolation methods to create high-resolution poverty maps for low- and middle-income countries. Journal of the Royal Society Interface, 15(147), 20180252. doi:10.1098/rsif.2018.0252
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D
NASA REMOTE SENSING (MODIS - MOD11C1)
NASA REMOTE SENSING (MODIS - MOD13Q1)
REMOTE SENSING (DMSP-OLS)

Wong, K. L. M., Brady, O. J., Campbell, O. M. R., Jarvis, C. I., Pembe, A., Gomez, G. B., & Benova, L. (2019). Current realities versus theoretical optima: quantifying efficiency and sociospatial equity of travel time to hospitals in low-income and middle-income countries. BMJ Global Health, 4(4), e001552. doi:10.1136/bmjgh-2019-001552
Gridded Population of the World (GPW) v4 (population density)

Woods, J., James, N., Kozubal, E., Bonnema, E., Brief, K., Voeller, L., & Rivest, J. (2022). Humidity’s impact on greenhouse gas emissions from air conditioning. Joule, 6(4), 726-741. doi:10.1016/j.joule.2022.02.013
Gridded Population of the World (GPW) v4.11 (population count)

Woolway, R. I., Huang, L., Sharma, S., Lee, S.-S., Rodgers, K. B., & Timmermann, A. (2022). Lake ice will be less safe for recreation and transportation under future warming. Earth's Future, 10(10), e2022EF002907. doi:10.1029/2022EF002907
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

World Bank. (2018). Solar Resource and PV Potential of Malawi: Solar Resource Atlas. Retrieved from Washington DC: http://documents.worldbank.org/curated/en/974121547193692233/Solar-Resource-and-PV-Potential-of-Malawi-Solar-Resource-Atlas
Gridded Population of the World (GPW) v4 (population density)

Worqlul, A. W., Dile, Y. T., Jeong, J., Adimassu, Z., Lefore, N., Gerik, T., . . . Clarke, N. (2019). Effect of climate change on land suitability for surface irrigation and irrigation potential of the shallow groundwater in Ghana. Computers and Electronics in Agriculture, 157, 110-125. doi:10.1016/j.compag.2018.12.040
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
NASA REMOTE SENSING (MODIS)

Wright, P. G. R., Croose, E., Hunter, S. B., MacPherson, J., Çoraman, E., Yarotskiy, V., . . . Radonjic, M. (2023). Can social media be used to inform the distribution of the marbled polecat, Vormela peregusna? Mammal Research, 68, 295-304. doi:10.1007/s13364-023-00680-8
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Wu, B., Yang, C., Wu, Q., Wang, C., Wu, J., & Yu, B. (2023). A building volume adjusted nighttime light index for characterizing the relationship between urban population and nighttime light intensity. Computers, Environment and Urban Systems, 99, 101911. doi:10.1016/j.compenvurbsys.2022.101911
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - data set unspecified)

Wu, D., Lin, J., C., Oda, T., & Kort, E. (2020). Space-based quantification of per capita CO2 emissions from cities. Environmental Research Letters, 15(3), 035004. doi:10.1088/1748-9326/ab68eb
Gridded Population of the World (GPW) v4.10 (basic demographic characteristics) - 10.7927/H45H7D7F
NASA REMOTE SENSING (OCO-2)

Wu, J., He, J., & Christakos, G. (2022). Chapter 13 - Syntheses of CTDA techniques with DIA models. In J. Wu, J. He, & G. Christakos (Eds.), Quantitative Analysis and Modeling of Earth and Environmental Data (pp. 449-475): Elsevier.
Gridded Population of the World (GPW) v4 (unspecified)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)

Wu, J., Junaid, M., Wang, Z., Sun, W., & Xu, N. (2020). Spatiotemporal distribution, sources and ecological risks of perfluorinated compounds (PFCs) in the Guanlan River from the rapidly urbanizing areas of Shenzhen, China. Chemosphere, 245, 125637. doi:10.1016/j.chemosphere.2019.125637
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Wu, J., Wang, Y., Liang, J., & Yao, F. (2021). Exploring common factors influencing PM2.5 and O3 concentrations in the Pearl River Delta: Tradeoffs and synergies. Environmental Pollution, 285, 117138. doi:10.1016/j.envpol.2021.117138
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (TROPOMI)

Wu, Q., Ke, L., Wang, J., Pavelsky, T. M., Allen, G. H., Sheng, Y., . . . Song, C. (2023). Satellites reveal hotspots of global river extent change. Nature Communications, 14(1), 1587. doi:10.1038/s41467-023-37061-3
Gridded Population of the World (GPW) v4.11 (population density)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (Landsat)

Wu, R., Liu, F., Tong, D., Zheng, Y., Lei, Y., Hong, C., . . . Zhang, Q. (2019). Air quality and health benefits of China’s emission control policies on coal-fired power plants during 2005–2020. Environmental Research Letters, 14(9), 094016. doi:10.1088/1748-9326/ab3bae
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)

Wu, R., Podgorski, J., Berg, M., & Polya, D. A. (2021). Geostatistical model of the spatial distribution of arsenic in groundwaters in Gujarat State, India. Environmental Geochemistry and Health, 43, 2649-2664. doi:10.1007/s10653-020-00655-7
Gridded Population of the World (GPW) v4.11 (population density)

Wu, R., Tessum, C. W., Zhang, Y., Hong, C., Zheng, Y., Qin, X., . . . Zhang, Q. (2021). Reduced-complexity air quality intervention modeling over China: the development of InMAPv1.6.1-China and a comparison with CMAQv5.2. Geoscientific Model Development, 14(12), 7621-7638. doi:10.5194/gmd-14-7621-2021
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - data set unspecified)

Wu, W., Yao, M., Yang, X., Hopke, P. K., Choi, H., Qiao, X., . . . Zhang, J. (2021). Mortality burden attributable to long-term ambient PM2.5 exposure in China: Using novel exposure-response functions with multiple exposure windows. Atmospheric Environment, 246, 118098. doi:10.1016/j.atmosenv.2020.118098
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)
Satellite-Derived Environmental Indicators (Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, v1)

Wu, X., Degefu, D. M., Yuan, L., Liao, Z., He, W., An, M., & Zhang, Z. (2019). Assessment of water footprints of consumption and production in transboundary river basins at country-basin mesh-based spatial resolution. International Journal of Environmental Research and Public Health, 16(5), 703. doi:10.3390/ijerph16050703
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Wu, Y., Chen, X., Wen, L., Li, Z., Peng, M., Wu, H., & Xie, L. (2022). Linking human activity to spatial accumulation of microplastics along mangrove coasts. Science of The Total Environment, 825, 154014. doi:10.1016/j.scitotenv.2022.154014
Gridded Population of the World (GPW) v4 (population density) - 10.7927//H4NP22DQ

Wu, Y., Di, B., Luo, Y., Grieneisen, M. L., Zeng, W., Zhang, S., . . . Zhan, Y. (2021). A robust approach to deriving long-term daily surface NO2 levels across China: Correction to substantial estimation bias in back-extrapolation. Environment International, 154, 106576. doi:10.1016/j.envint.2021.106576
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (OMI NO2)

Wu, Y., Mooring, T. A., & Linz, M. (2021). Policy and weather influences on mobility during the early US COVID-19 pandemic. Proceedings of the National Academy of Sciences, 118(22), e2018185118. doi:10.1073/pnas.2018185118
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4PN93PB

Wu, Z., Zhao, H., Hao, J., & Wu, G. (2022). Climatological characteristics and aerosol loading trends from 2001 to 2020 based on MODIS MAIAC data for Tianjin, North China Plain. Sustainability, 14(3), 1072. doi:10.3390/su14031072
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MODIS)

Wuepper, D., Borrelli, P., Mueller, D., & Finger, R. (2020). Quantifying the soil erosion legacy of the Soviet Union. Agricultural Systems, 185, 102940. doi:10.1016/j.agsy.2020.102940
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (ASTER GDEM)
NASA REMOTE SENSING (MODIS)

Wuepper, D., Le Clech, S., Zilberman, D., Mueller, N., & Finger, R. (2020). Countries influence the trade-off between crop yields and nitrogen pollution. Nature Food, 1(11), 713-719. doi:10.1038/s43016-020-00185-6
Gridded Population of the World (GPW) v4 (unspecified)

Xanke, J., & Liesch, T. (2022). Quantification and possible causes of declining groundwater resources in the Euro-Mediterranean region from 2003 to 2020. Hydrogeology Journal, 30, 379-400. doi:10.1007/s10040-021-02448-3
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (GRACE)

Xia, H., Adamo, S. B., de Sherbinin, A., & Jones, B. (2019). The influence of environmental change (crops and water) on population redistribution in Mexico and Ethiopia. Applied Sciences, 9(23), 5219. doi:10.3390/app9235219
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Xia, N., Cheng, L., & Li, M. (2019). Mapping urban areas using a combination of remote sensing and geolocation data. Remote Sensing, 11(12), 1470. doi:10.3390/rs11121470
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H46T0JKB
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)
REMOTE SENSING (VIIRS)

Xia, N., Cheng, L., & Li, M. (2021). Transnational accessibility between residential areas based on multimodal transport system. ISPRS International Journal of Geo-Information, 10(3), 136. doi:10.3390/ijgi10030136
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC

Xia, Q.-Q., Chen, Y.-N., Zhang, X.-Q., & Ding, J.-L. (2022). Spatiotemporal changes in ecological quality and its associated driving factors in Central Asia. Remote Sensing, 14(14), 3500. doi:10.3390/rs14143500
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
NASA REMOTE SENSING (MODIS)

Xiang, Y., Huang, C., Huang, X., Zhou, Z., & Wang, X. (2021). Seasonal variations of the dominant factors for spatial heterogeneity and time inconsistency of land surface temperature in an urban agglomeration of central China. Sustainable Cities and Society, 75, 103285. doi:10.1016/j.scs.2021.103285
Gridded Population of the World (GPW) v4 (unspecified)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (DMSP-OLS)

Xiao, L., Lang, Y., & Christakos, G. (2018). High-resolution spatiotemporal mapping of PM2.5 concentrations at Mainland China using a combined BME-GWR technique. Atmospheric Environment, 173(Supplement C), 295-305. doi:10.1016/j.atmosenv.2017.10.062
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)

Xiao, Q., Geng, G., Liang, F., Wang, X., Lv, Z., Lei, Y., . . . He, K. (2020). Changes in spatial patterns of PM2.5 pollution in China 2000–2018: Impact of clean air policies. Environment International, 141, 105776. doi:10.1016/j.envint.2020.105776
Gridded Population of the World (GPW) v4 (unspecified)
NASA REMOTE SENSING (MODIS - MOD13A3)

Xiao, Q., Liang, F., Ning, M., Zhang, Q., Bi, J., He, K., . . . Liu, Y. (2021). The long-term trend of PM2.5-related mortality in China: The effects of source data selection. Chemosphere, 263, 127894. doi:10.1016/j.chemosphere.2020.127894
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - data set unspecified)
NASA REMOTE SENSING (MODIS - MOD13A3)
NASA REMOTE SENSING (OMI NO2)

Xiao, Q., Zheng, Y., Geng, G., Chen, C., Huang, X., Che, H., . . . Zhang, Q. (2021). Separating emission and meteorological contributions long-term PM2.5 trends over eastern China during 2000–2018. Atmospheric Chemistry and Physics, 21, 9475-9496. doi:10.5194/acp-21-9475-2021
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (MODIS)

Xiao, X., Wang, Q., Guan, Q., Zhang, Z., Yan, Y., Mi, J., & Yang, E. (2023). Quantifying the nonlinear response of vegetation greening to driving factors in Longnan of China based on machine learning algorithm. Ecological Indicators, 151, 110277. doi:10.1016/j.ecolind.2023.110277
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS - MODQ13Q1)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (DMSP-OLS)

Xie, X., Hao, M., Ding, F., Helman, D., Scheffran, J., Wang, Q., . . . Jiang, D. (2022). Exploring the direct and indirect impacts of climate variability on armed conflict in South Asia. iScience, 25(11), 105258. doi:10.1016/j.isci.2022.105258
Gridded Population of the World (GPW) v4.10 (population density)

Xie, Y., Huang, J., & Ming, Y. (2019). Robust regional warming amplifications directly following the anthropogenic emission. Earth's Future, 7(4), 323-369. doi:10.1029/2018ef001068
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Xie, Y.-F., Yang, L., Deng, R., Chen, M.-H., Luan, X.-F., Gottardi, E., & Zhang, Z.-X. (2018). Changes in the range of the medicinal herb Eriocaulon buergerianum Koern. (Eriocaulaceae) under climate change. Plant Biology, 20(4), 771-779. doi:10.1111/plb.12836
Gridded Population of the World (GPW) v4 (population density)
Last of the Wild v2 (Global Human Footprint (Geographic))
Last of the Wild v2 Global Human Influence Index (Geographic)

Xin, Y. (2022). Forest Change and Oil Palm Expansion in Indonesia: Biophysical and Socioeconomic Analysis. (Ph.D.). University of Maryland, College Park, MD. Retrieved from https://doi.org/10.13016/bt6m-qkwu 
Gridded Population of the World (GPW) v4 (population density) - 10.7927//H4NP22DQ

Xin, Y., Sun, L., & Hansen, M. C. (2022). Oil palm reconciliation in Indonesia: Balancing rising demand and environmental conservation towards 2050. Journal of Cleaner Production, 380(Part 2), 135087. doi:10.1016/j.jclepro.2022.135087
Gridded Population of the World (GPW) v4 (population density)

Xing, C., Liu, C., Lin, J., Tan, W., & Liu, T. (2024). VOCs hyperspectral imaging: A new insight into evaluate emissions and the corresponding health risk from industries. Journal of Hazardous Materials, 461, 132573. doi:10.1016/j.jhazmat.2023.132573
Gridded Population of the World (GPW) v4.11 (population count)

Xiong, J., Li, K., Cheng, W., Ye, C., & Zhang, H. (2019). A method of population spatialization considering parametric spatial stationarity: Case study of the southwestern area of China. ISPRS International Journal of Geo-Information, 8(11), 495. doi:10.3390/ijgi8110495
Gridded Population of the World (GPW) v4 (population density)
REMOTE SENSING (DMSP-OLS)

Xu, C., Kohler, T. A., Lenton, T. M., Svenning, J.-C., & Scheffer, M. (2020). Future of the human climate niche. Proceedings of the National Academy of Sciences, 117(21), 11350-11355. doi:10.1073/pnas.1910114117
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
NASA REMOTE SENSING (MODIS - MOD17A3)

Xu, J., Chen, J., Liu, Y., & Fan, F. (2020). Identification of the geographical factors influencing the relationships between ecosystem services in the Belt and Road region from 2010 to 2030. Journal of Cleaner Production, 275, 124153. doi:10.1016/j.jclepro.2020.124153
Gridded Population of the World (GPW) v4 (population count)

Xu, J., Morris, P. J., Liu, J., & Holden, J. (2018). Hotspots of peatland-derived potable water use identified by global analysis. Nature Sustainability, 1(5), 246-253. doi:10.1038/s41893-018-0064-6
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper - population density)

Xu, J., Yao, M., Wu, W., Qiao, X., Zhang, H., Wang, P., . . . Zhang, J. (2021). Estimation of ambient PM2.5-related mortality burden in China by 2030 under climate and population change scenarios: A modeling study. Environment International, 156, 106733. doi:10.1016/j.envint.2021.106733
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

Xu, L., Chen, N., Zhang, X., & Chen, Z. (2019). Spatiotemporal changes in China's terrestrial water storage from GRACE satellites and its possible drivers. Journal of Geophysical Research: Atmospheres, 124(22), 11976-11993. doi:10.1029/2019jd031147
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted)
NASA REMOTE SENSING (GRACE)

Xu, M., Cao, C., & Jia, P. (2020). Mapping fine-scale urban spatial population distribution based on high-resolution stereo pair images, points of interest, and land cover data. Remote Sensing, 12(4), 608. doi:10.3390/rs12040608
Gridded Population of the World (GPW) v4 (collection)
REMOTE SENSING (ZiYuan3)

Xu, X., Tan, M., Liu, X., Wang, X., & Xin, L. (2023). Stability and changes in the spatial distribution of China's population in the past 30 years based on census data spatialization. Remote Sensing, 15(6), 1674. doi:10.3390/rs15061674
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS NTL)

Xu, Y., Ho, H. C., Knudby, A., & He, M. (2021). Comparative assessment of gridded population data sets for complex topography: a study of Southwest China. Population and Environment, 42, 360-378. doi:10.1007/s11111-020-00366-2
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Xu, Y., Zhou, J., Liu, T., Liu, P., Wu, Y., Lai, Z., . . . Chen, X.-G. (2022). Assessing the risk of spread of zika virus under current and future climate scenarios. Biosafety and Health, 4(3), 193-204. doi:10.1016/j.bsheal.2022.03.012
Gridded Population of the World (GPW) v4.11 (population density)
Population Dynamics (Global One-Eighth Degree Population Base Year and Projection Grids Based on the SSPs, v1.01)

Xu, Z., Jin, J., Yang, T., Wang, Y., Huang, J., Pan, X., . . . Li, G. (2023). Outdoor light at night, genetic predisposition and type 2 diabetes mellitus: A prospective cohort study. Environmental Research, 219, 115157. doi:10.1016/j.envres.2022.115157
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS NTL)

Xue, T., Geng, G., Li, J., Han, Y., Guo, Q., Kelly, F. J., . . . Zhu, T. (2021). Associations between exposure to landscape fire smoke and child mortality in low-income and middle-income countries: a matched case-control study. The Lancet Planetary Health, 5(9), e588-e598. doi:10.1016/S2542-5196(21)00153-4
Gridded Population of the World (GPW) v4 (unspecified)
NASA REMOTE SENSING (MERRA-2)
NASA REMOTE SENSING (MODIS - MCD64A1)

Xue, T., Zhu, T., Zheng, Y., & Zhang, Q. (2019). Declines in mental health associated with air pollution and temperature variability in China. Nature Communications, 10(1), 2165. doi:10.1038/s41467-019-10196-y
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS - MOD13A3)
NASA (AppEEARS)

Yalew, A. W. (2020). Overview of the Ethiopian Economy. In Economic Development under Climate Change : Economy-Wide and Regional Analysis for Ethiopia (pp. 15-25). Wiesbaden: Springer Fachmedien Wiesbaden.
Gridded Population of the World (GPW) v4 (collection)

Yan, J., Jia, S., Lv, A., & Zhu, W. (2019). Water resources assessment of China's transboundary river basins using a machine learning approach. Water Resources Research, 55(1), 632-655. doi:10.1029/2018WR023044
Gridded Population of the World (GPW) v4 (population count)

Yang, B., Borgert, B. A., Alto, B. W., Boohene, C. K., Brew, J., Deutsch, K., . . . Cummings, D. A. T. (2021). Modelling distributions of Aedes aegypti and Aedes albopictus using climate, host density and interspecies competition. PLoS Neglected Tropical Diseases, 15(3), e0009063. doi:10.1371/journal.pntd.0009063
Gridded Population of the World (GPW) v4.11 (population density)

Yang, H. (2022). Warming hotspots induced by more eddies. Nature Climate Change, 12, 889-890. doi:10.1038/s41558-022-01488-1
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
REMOTE SENSING (Global Ocean Gridded L4 Sea Surface Heights and Derived Variables NRT)

Yang, H., Tao, W., Liu, Y., Qiu, M., Liu, J., Jiang, K., . . . Tao, S. (2019). The contribution of the Beijing, Tianjin and Hebei region's iron and steel industry to local air pollution in winter. Environmental Pollution, 245, 1095-1106. doi:10.1016/j.envpol.2018.11.088
Gridded Population of the World (GPW) v4 (unspecified)

Yang, H., Tao, W., Wang, Y., Liu, Y., Liu, J., Zhang, Y., & Tao, S. (2019). Air quality and health impacts from the updated industrial emission standards in China. Environmental Research Letters, 14(12), 124058. doi:10.1088/1748-9326/ab54fa
Gridded Population of the World (GPW) v4 (population count)

Yang, J., Chen, Y., Wilson, J. P., Tan, H., Cao, J., & Xu, Z. (2020). Land cover pattern simulation using an eigenvector spatial filtering method in Hubei Province. Earth Science Informatics, 13, 989-1004. doi:10.1007/s12145-020-00483-4
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
NASA REMOTE SENSING (SRTM)

Yang, L., Shen, F., Zhang, L., Cai, Y., Yi, F., & Zhou, C. (2021). Quantifying influences of natural and anthropogenic factors on vegetation changes using structural equation modeling: A case study in Jiangsu Province, China. Journal of Cleaner Production, 280, 124330. doi:10.1016/j.jclepro.2020.124330
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (DMSP-OLS)

Yang, S., Büttner, O., Jawitz, J. W., Kumar, R., Rao, P. S. C., & Borchardt, D. (2019). Spatial organization of human population and wastewater treatment plants in urbanized river basins. Water Resources Research, 55(7), 6138-6152. doi:10.1029/2018wr024614
Gridded Population of the World (GPW) v4.10 (population count UN WPP-adjusted) - 10.7927/H4JQ0XZW

Yang, Y., & Tang, J. (2023). Substantial differences in compound long-duration dry and hot events over China between transient and stabilized warmer worlds at 1.5°C global warming. Earth's Future, 11(3), e2022EF002994. doi:10.1029/2022EF002994
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Yao, F., Livneh, B., Rajagopalan, B., Wang, J., Crétaux, J.-F., Wada, Y., & Berge-Nguyen, M. (2023). Satellites reveal widespread decline in global lake water storage. Science, 380(6646), 743-749. doi:10.1126/science.abo2812
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (ICESat-1)
NASA REMOTE SENSING (ICESat-2)
REMOTE SENSING (CryoSat-2)
REMOTE SENSING (Envisat)
REMOTE SENSING (Jason)
REMOTE SENSING (SARAL)
REMOTE SENSING (Sentinel-3)

Yao, J. (2021). Electricity Consumption and Temperature: Evidence from Satellite Data. Retrieved from https://www.imf.org/en/Publications/WP/Issues/2021/02/05/Electricity-Consumption-and-Temperature-Evidence-from-Satellite-Data-50031
Gridded Population of the World (GPW) v4 (population count)
REMOTE SENSING (DMSP-OLS)

Yarragunta, Y., Srivastava, S., Mitra, D., & Chandola, H. C. (2021). Source apportionment of carbon monoxide over India: a quantitative analysis using MOZART-4. Environmental Science and Pollution Research, 28, 8722-8742. doi:10.1007/s11356-020-11099-y
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MOPITT CO)

Ye, B., Saito, T., Hirano, T., Dong, Z., Do, V. T., & Chiba, S. (2020). Human-geographic effects on variations in the population genetics of Sinotaia quadrata (Gastropoda: Viviparidae) that historically migrated from continental East Asia to Japan. Ecology and Evolution, 10(15), 8055-8072. doi:10.1002/ece3.6456
Gridded Population of the World (GPW) v4.11 (population density)
Last of the Wild v3 (Human Footprint, 2018 Release (2009))
Land Use and Land Cover (LULC) (Development Threat Index, v1)
Population Dynamics (Global Estimated Net Migration Grids By Decade, v1)

Yew, Y., Arcos González, P., & Castro Delgado, R. (2020). Real-time impact analysis and response using a new disaster metrics: 2018 Sulawesi (Indonesia) earthquake and tsunami. Prehospital and Disaster Medicine, 35(1), 76-82. doi:10.1017/S1049023X19005247
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Yew, Y. Y., Castro Delgado, R., Heslop, D. J., & Arcos González, P. (2019). The Yew Disaster Severity Index: A new tool in disaster metrics. Prehospital and Disaster Medicine, 34(1), 8-19. doi:10.1017/S1049023X18001115
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Yim, S. H. L., Wang, M. Y., Gu, Y., Yang, Y., Dong, G. H., & Li, Q. (2019). Effect of urbanization on ozone and resultant health effects in the Pearl River Delta region of China. Journal of Geophysical Research: Atmospheres, 124(21), 11568-11579. doi:10.1029/2019jd030562
Gridded Population of the World (GPW) v4 (unspecified)

Yin, C., Yang, F., Wang, J., & Ye, Y. (2020). Spatiotemporal distribution and risk assessment of heat waves based on apparent temperature in the One Belt and One Road Region. Remote Sensing, 12(7), 1174. doi:10.3390/rs12071174
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)
REMOTE SENSING (AVHRR GIMMS NDVI)
REMOTE SENSING (DMSP-OLS)

Yin, H., Brauer, M., Zhang, J., Cai, W., Navrud, S., Burnett, R., . . . Liu, Z. (2021). Population ageing and deaths attributable to ambient PM2.5 pollution: A global analysis of economic cost. The Lancet Planetary Health, 5(6), e356-e367. doi:10.1016/S2542-5196(21)00131-5
Gridded Population of the World (GPW) v4.11 (population count)

Yin, J., Gentine, P., Slater, L., Gu, L., Pokhrel, Y., Hanasaki, N., . . . Schlenker, W. (2023). Future socio-ecosystem productivity threatened by compound drought–heatwave events. Nature Sustainability, 6, 259-272. doi:10.1038/s41893-022-01024-1
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
NASA REMOTE SENSING (GRACE)
NASA REMOTE SENSING (GRACE-FO)

Yin, J., Gentine, P., Zhou, S., Sullivan, S. C., Wang, R., Zhang, Y., & Guo, S. (2018). Large increase in global storm runoff extremes driven by climate and anthropogenic changes. Nature Communications, 9(1), 4389. doi:10.1038/s41467-018-06765-2
Gridded Population of the World (GPW) v4.10 (population density)
Global Reservoir and Dam (GRanD) v1.01 (reservoirs)

Yin, K., Wu, J., Wang, W., Lee, D.-H., & Wei, Y. (2023). An integrated resilience assessment model of urban transportation network: A case study of 40 cities in China. Transportation Research Part A: Policy and Practice, 173, 103687. doi:10.1016/j.tra.2023.103687
Gridded Population of the World (GPW) v4.11 (population density)

Yin, S. (2021). Decadal trends of MERRA-estimated PM2.5 concentrations in East Asia and potential exposure from 1990 to 2019. Atmospheric Environment, 264, 118690. doi:10.1016/j.atmosenv.2021.118690
Gridded Population of the World (GPW) v3 (unspecified)
Gridded Population of the World (GPW) v4 (unspecified)
NASA REMOTE SENSING (MERRA-2)

Yin, S. (2022). Decadal changes in PM2.5-related health impacts in China from 1990 to 2019 and implications for current and future emission controls. Science of The Total Environment, 834, 155334. doi:10.1016/j.scitotenv.2022.155334
Gridded Population of the World (GPW) v3 (unspecified)
Gridded Population of the World (GPW) v4 (unspecified)

Yin, S. (2023). Decadal changes in premature mortality associated with exposure to outdoor PM2.5 in mainland Southeast Asia and the impacts of biomass burning and anthropogenic emissions. Science of The Total Environment, 854, 158775. doi:10.1016/j.scitotenv.2022.158775
Gridded Population of the World (GPW) v3 (unspecified)
Gridded Population of the World (GPW) v4 (unspecified)
NASA REMOTE SENSING (MODIS)

Yin, S. (2023). Effect of biomass burning on premature mortality associated with long-term exposure to PM2.5 in Equatorial Asia. Journal of Environmental Management, 330, 117154. doi:10.1016/j.jenvman.2022.117154
Gridded Population of the World (GPW) v3 (unspecified)
Gridded Population of the World (GPW) v4 (unspecified)
NASA REMOTE SENSING (MODIS)

Yin, S. (2023). Spatiotemporal variation of PM2.5-related preterm birth in China and India during 1990–2019 and implications for emission controls. Ecotoxicology and Environmental Safety, 249, 114415. doi:10.1016/j.ecoenv.2022.114415
Gridded Population of the World (GPW) v3 (unspecified)
Gridded Population of the World (GPW) v4 (unspecified)

Yin, X., Li, P., Feng, Z., Yang, Y., You, Z., & Xiao, C. (2021). Which gridded population data product Is better? Evidences from Mainland Southeast Asia (MSEA). ISPRS International Journal of Geo-Information, 10(10), 681. doi:10.3390/ijgi10100681
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)

You, H., Huang, Y., Qin, Z., Chen, J., & Liu, Y. (2022). Forest tree species classification based on Sentinel-2 images and auxiliary data. Forests, 13(9), 1416. doi:10.3390/f13091416
Gridded Population of the World (GPW) v4.11 (admin unit center points)
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (Sentinel-2)

Yovovich, V., Robinson, N., Robinson, H., Manfredo, M. J., Perry, S., Bruskotter, J. T., . . . Elbroch, L. M. (2023). Determining puma habitat suitability in the Eastern USA. Biodiversity and Conservation, 32, 921-941. doi:10.1007/s10531-022-02529-z
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW

Yoza-Mitsuishi, N., Sun, R., & Mathys, P. (2019). Spectrum sharing between WLANs and fixed microwave links in 6 and 13 GHz bands: a case study. Paper presented at the 2019 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN).
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65
NASA REMOTE SENSING (ASTER GDEM)

Yu, B., Cui, B., Zang, Y., Wu, C., Zhao, Z., & Wang, Y. (2021). Long-term dynamics of different surface water body types and their possible driving factors in China. Remote Sensing, 13(6), 1154. doi:10.3390/rs13061154
Gridded Population of the World (GPW) v4.11 (Unspecified)
REMOTE SENSING (Landsat)

Yu, B., Lian, T., Huang, Y., Yao, S., Ye, X., Chen, Z., . . . Wu, J. (2019). Integration of nighttime light remote sensing images and taxi GPS tracking data for population surface enhancement. International Journal of Geographical Information Science, 33(4), 687-706. doi:10.1080/13658816.2018.1555642
Gridded Population of the World (GPW) v4 (collection)

Yu, H., Wu, D., Piao, X., Zhang, T., Yan, Y., Tian, Y., . . . Xuefeng, C. (2021). Reduced impacts of heat extremes from limiting global warming to under 1.5 ℃ or 2 ℃ over Mediterranean regions. Environmental Research Letters, 16(1), 014034. doi:10.1088/1748-9326/abd132
Gridded Population of the World (GPW) v4.11 (population density)

Yu, P., Xu, R., Coelho, M. S. Z. S., Saldiva, P. H. N., Li, S., Zhao, Q., . . . Guo, Y. (2021). The impacts of long-term exposure to PM2.5 on cancer hospitalizations in Brazil. Environment International, 154, 106671. doi:10.1016/j.envint.2021.106671
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)

Yu, Q., You, L., Wood-Sichra, U., Ru, Y., Joglekar, A. K. B., Fritz, S., . . . Yang, P. (2020). A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps. Earth System Science Data, 12, 3545-3572. doi:10.5194/essd-12-3545-2020
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Yuan, B., Wang, Z., & Li, J. (2020). Social vulnerability and infant mortality in space dimension: an investigation of the world’s most underdeveloped West Africa coastal area. International Journal of Human Rights in Healthcare, 13(3), 239-248. doi:10.1108/IJHRH-10-2019-0077
Gridded Population of the World (GPW) v4 (unspecified)
Poverty Mapping (Global Subnational Infant Mortality Rates, v2) - 10.7927/H4PN93JJ
West Africa Coastal Vulnerability Mapping (Social Vulnerability Indices, v1) - 10.7927/H4H41PCK

Yuan, Z., Jiang, S., Sheng, H., Liu, X., Hua, H., Liu, X., & Zhang, Y. (2018). Human perturbation of the global phosphorus cycle: Changes and consequences. Environmental Science & Technology, 52(5), 2438-2450. doi:10.1021/acs.est.7b03910
Gridded Population of the World (GPW) v4 (population count)

Zacarias, D., & Loyola, R. (2019). Climate change impacts on the distribution of venomous snakes and snakebite risk in Mozambique. Climatic Change, 152(1), 195-207. doi:10.1007/s10584-018-2338-4
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Zamrsky, D., Oude Essink, G. H. P., & Bierkens, M. F. P. (2018). Estimating the thickness of unconsolidated coastal aquifers along the global coastline. Earth System Science Data, 10(3), 1591-1603. doi:10.5194/essd-10-1591-2018
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM

Zani, N. B., Lonati, G., Mead, M. I., Latif, M. T., & Crippa, P. (2020). Long-term satellite-based estimates of air quality and premature mortality in Equatorial Asia through Deep Neural Networks. Environmental Research Letters, 15(10), 104088. doi:10.1088/1748-9326/abb733
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (MOPITT CO)
NASA REMOTE SENSING (OMI)

Zanoni, M. G., Majone, B., & Bellin, A. (2022). A catchment-scale model of river water quality by Machine Learning. Science of The Total Environment, 838(Part 3), 156377. doi:10.1016/j.scitotenv.2022.156377
Gridded Population of the World (GPW) v4.10 (population density) - 10.7927/H4DZ068D

Zaveri, E., Russ, J., & Damania, R. (2020). Rainfall anomalies are a significant driver of cropland expansion. Proceedings of the National Academy of Sciences, 117(19), 10225-10233. doi:10.1073/pnas.1910719117
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
Global Reservoir and Dam (GRanD) v1.01 (dams)
NASA REMOTE SENSING (AVHRR)
NASA REMOTE SENSING (MODIS - MCD12Q1)
REMOTE SENSING (MERIS)
REMOTE SENSING (SPOT VGT)

Zermoglio, F., Ryan, S. J., & Swaim, M. (2019). Shifting Burdens: Malaria Risks in a Hotter Africa. Retrieved from Washington DC: https://www.climatelinks.org/resources/shifting-burdens-malaria-risks-hotter-africa
Gridded Population of the World (GPW) v4.11 (unspecified)

Zhai, C., Bretthauer, K., Mejia, J., & Pedraza-Martinez, A. (2021). Disaster mitigation: Leveraging community involvement to improve water access in sub-Saharan Africa. IFOR Newsletter, 16(1), 6-9. Retrieved from https://ifors.org/newsletter/ifors-news-march2021.pdf
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Zhan, Y., Luo, Y., Deng, X., Grieneisen, M. L., Zhang, M., & Di, B. (2018). Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment. Environmental Pollution, 233, 464-473. doi:10.1016/j.envpol.2017.10.029
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS)

Zhan, Y., Luo, Y., Deng, X., Zhang, K., Zhang, M., Grieneisen, M. L., & Di, B. (2018). Satellite-based estimates of daily NO2 exposure in China using hybrid random forest and spatiotemporal kriging model. Environmental Science & Technology, 52(7), 4180-4189. doi:10.1021/acs.est.7b05669
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (OMI NO2)
NASA REMOTE SENSING (MODIS)
NASA REMOTE SENSING (SRTM)

Zhang, A., Wang, J., Jiang, Y., Chen, Y., & Shi, P. (2018). Spatiotemporal changes of hazard intensity-adjusted population exposure to multiple hazards in Tibet during 1982–2015. International Journal of Disaster Risk Science, 9(4), 541-554. doi:10.1007/s13753-018-0194-5
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)

Zhang, C., Liao, H., Strobl, E., Li, H., Li, R., Jensen, S. S., & Zhang, Y. (2021). The role of weather conditions in COVID-19 transmission: A study of a global panel of 1236 regions. Journal of Cleaner Production, 292, 125987. doi:10.1016/j.jclepro.2021.125987
Digital Elevation Data Collection (DEDC) (Altimeter Corrected Elevations (ACE2), v2)
Gridded Population of the World (GPW) v4.11 (basic demographic characteristics)
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (OMI NO2)

Zhang, D., Du, L., Wang, W., Zhu, Q., Bi, J., Scovronick, N., . . . Liu, Y. (2021). A machine learning model to estimate ambient PM2.5 concentrations in industrialized highveld region of South Africa. Remote Sensing of Environment, 266, 112713. doi:10.1016/j.rse.2021.112713
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates)
NASA REMOTE SENSING (ASTER GDEM)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Sentinel-2)

Zhang, G. (2020). Spatial and temporal patterns in volunteer data contribution activities: A case study of eBird. ISPRS International Journal of Geo-Information, 9(10), 597. doi:10.3390/ijgi9100597
Gridded Population of the World (GPW) v4.11 (population density)

Zhang, G., Wang, H., Gan, T. Y., Zhang, S., Shi, L., Zhao, J., . . . Song, S. (2022). Climate change determines future population exposure to summertime compound dry and hot events. Earth's Future, 10(11), e2022EF003015. doi:10.1029/2022EF003015
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
Population Dynamics (Global 1-km Downscaled Population Base Year and Projection Grids Based on the SSPs, v1.01) - 10.7927/q7z9-9r69

Zhang, H., Di, B., Liu, D., Li, J., & Zhan, Y. (2019). Spatiotemporal distributions of ambient SO2 across China based on satellite retrievals and ground observations: Substantial decrease in human exposure during 2013-2016. Environmental Research, 179(Part A), 108795. doi:10.1016/j.envres.2019.108795
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (OMI SO2)

Zhang, H., Dou, Y., Ye, L., Zhang, C., Yao, H., Bao, Z., . . . Chen, Y. (2022). Realizing the full reservoir operation potential during the 2020 Yangtze river floods. Scientific Reports, 12(1), 2822. doi:10.1038/s41598-022-06801-8
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Zhang, H., & Smith, J. W. (2023). A data-driven and generalizable model for classifying outdoor recreation opportunities at multiple spatial extents. Landscape and Urban Planning, 240, 104876. doi:10.1016/j.landurbplan.2023.104876
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Zhang, J., Xu, W., Liao, X., Zong, S., & Liu, B. (2021). Global mortality risk assessment from river flooding under climate change. Environmental Research Letters, 16(6), 064036. doi:10.1088/1748-9326/abff87
Gridded Population of the World (GPW) v4.11 (population count)

Zhang, L., & Du, Y. (2023). Cascading failure model and resilience enhancement scheme of space information networks. Reliability Engineering & System Safety, 237, 109379. doi:10.1016/j.ress.2023.109379
Gridded Population of the World (GPW) v4 (population density) - 10.7927//H4NP22DQ

Zhang, L., Wilson, J. P., MacDonald, B., Zhang, W., & Yu, T. (2020). The changing PM2.5 dynamics of global megacities based on long-term remotely sensed observations. Environment International, 142, 105862. doi:10.1016/j.envint.2020.105862
Gridded Population of the World (GPW) v4 (collection)
Satellite-Derived Environmental Indicators (Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, v1)

Zhang, L., Wilson, J. P., Zhao, N., Zhang, W., & Wu, Y. (2022). The dynamics of cardiovascular and respiratory deaths attributed to long-term PM2.5 exposures in global megacities. Science of The Total Environment, 842, 156951. doi:10.1016/j.scitotenv.2022.156951
Gridded Population of the World (GPW) v4.11 (population count)
Satellite-Derived Environmental Indicators (Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, v1)

Zhang, L., Yang, H., Wang, Y., Zhuang, H., Chen, W., Lin, Z., . . . Wang, Y. (2021). Blue footprint: Distribution and use of indigo-yielding plant species Strobilanthes cusia (Nees) Kuntze. Global Ecology and Conservation, 30, e01795. doi:10.1016/j.gecco.2021.e01795
Global Agricultural Lands (Cropland)
Gridded Population of the World (GPW) v4.11 (population density)
Last of the Wild v3 (Human Footprint, 2018 Release (2009))

Zhang, L., Yang, L., Zohner, C., M., Crowther, T., W., Li, M., Shen, F., . . . Zhou, C. (2022). Direct and indirect impacts of urbanization on vegetation growth across the world’s cities. Science Advances, 8(27), eabo0095. doi:10.1126/sciadv.abo0095
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)

Zhang, L., Zhao, N., Zhang, W., & Wilson, J. P. (2022). Changes in long-term PM2.5 pollution in the urban and suburban areas of China's three largest urban agglomerations from 2000 to 2020. Remote Sensing, 14(7), 1716. doi:10.3390/rs14071716
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MODIS)

Zhang, Q., Zheng, Z., Wu, Z., Cao, Z., & Luo, R. (2022). Using multi-source geospatial information to reduce the saturation problem of DMSP/OLS nighttime light data. Remote Sensing, 14(14), 3264. doi:10.3390/rs14143264
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (Landsat)
REMOTE SENSING (VIIRS Cloud Mask)

Zhang, Q.-Q., Xing, C., Cai, Y.-Y., Yan, X.-T., & Ying, G.-G. (2021). How much do human and livestock actually contribute to steroids emission and surface water pollution from past to the future: A global research. Science of The Total Environment, 772, 145558. doi:10.1016/j.scitotenv.2021.145558
Gridded Population of the World (GPW) v4.11 (population count)

Zhang, R., Wang, Y., He, Q., Chen, L., Zhang, Y., Qu, H., . . . Burrows, J. P. (2017). Enhanced trans-Himalaya pollution transport to the Tibetan Plateau by cut-off low systems. Atmospheric Chemistry and Physics, 17(4), 3083-3095. doi:10.5194/acp-17-3083-2017
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4HX19NJ
Global Rural-Urban Mapping Project (GRUMP) v1 (population density) - 10.7927/H4R20Z93
REMOTE SENSING (SCIAMACHY)

Zhang, S., Chen, Y., Luo, Y., Liu, B., Ren, G., Zhou, T., . . . Chang, M. (2022). Revealing the circulation pattern most conducive to precipitation extremes in Henan Province of North China. Geophysical Research Letters, 49(7), e2022GL098034. doi:10.1029/2022GL098034
Gridded Population of the World (GPW) v4.11 (population count)

Zhang, S., Mi, T., Wu, Q., Luo, Y., Grieneisen, M. L., Shi, G., . . . Zhan, Y. (2022). A data-augmentation approach to deriving long-term surface SO2 across Northern China: Implications for interpretable machine learning. Science of The Total Environment, 827, 154278. doi:10.1016/j.scitotenv.2022.154278
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (OMI SO2)

Zhang, T., & Cheng, C. (2021). Temporal and spatial evolution and influencing factors of public sentiment in natural disasters—a case study of Typhoon Haiyan. ISPRS International Journal of Geo-Information, 10(5), 299. doi:10.3390/ijgi10050299
Gridded Population of the World (GPW) v4.11 (population density)

Zhang, T., Liu, P., Sun, X., Zhang, C., Wang, M., Xu, J., . . . Huang, L. (2020). Application of an advanced spatiotemporal model for PM2.5 prediction in Jiangsu Province, China. Chemosphere, 246, 125563. doi:10.1016/j.chemosphere.2019.125563
Gridded Population of the World (GPW) v4.10 (admin unit center points)
NASA REMOTE SENSING (MODIS NDVI)
NASA REMOTE SENSING (SRTM)

Zhang, W., Brandt, M., Penuelas, J., Guichard, F., Tong, X., Tian, F., & Fensholt, R. (2019). Ecosystem structural changes controlled by altered rainfall climatology in tropical savannas. Nature Communications, 10(1), 671. doi:10.1038/s41467-019-08602-6
Gridded Population of the World (GPW) v4.10 (population density)
NASa REMOTE SENSING (MODIS)

Zhang, W., Fensholt, R., & Brandt, M. (2023). Projected rainfall-driven expansion of woody cover in African drylands. Geophysical Research Letters, 50(15), e2023GL103932. doi:10.1029/2023GL103932
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
NASA REMOTE SENSING (MODIS)

Zhang, W., Li, Y., Li, Z., Wei, X., Ren, T., Liu, J., & Zhu, Y. (2020). Impacts of climate change, population growth, and urbanization on future population exposure to long-term temperature change during the warm season in China. Environmental Science and Pollution Research, 27, 8481-8491. doi:10.1007/s11356-019-07238-9
Gridded Population of the World (GPW) v4.11 (admin unit center points)

Zhang, W., Randall, M., Jensen, M. B., Brandt, M., Wang, Q., & Fensholt, R. (2021). Socio-economic and climatic changes lead to contrasting global urban vegetation trends. Global Environmental Change, 71, 102385. doi:10.1016/j.gloenvcha.2021.102385
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)

Zhang, W., Zhang, L., Li, Y., Tian, Y., Li, X., Zhang, X., . . . Chen, L. (2018). Neglected environmental health impacts of China's supply-side structural reform. Environment International, 115, 97-103. doi:10.1016/j.envint.2018.03.006
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted) - 10.7927/H4SF2T42

Zhang, X., He, P., Guo, L., & Meng, F. (2023). Potential carbon sequestration and economic value assessment of the relict plant Ginkgo biloba L. based on the Maximum Entropy Model. Forests, 14(8), 1618. doi:10.3390/f14081618
Gridded Population of the World (GPW) v4.11 (population density)
Last of the Wild v3 (Human Footprint, 2018 Release (2009))
Land Use and Land Cover (LULC) (Global Human Modification of Terrestrial Systems, v1)

Zhang, X., Ou, X., Yang, X., Qi, T., Nam, K.-M., Zhang, D., & Zhang, X. (2017). Socioeconomic burden of air pollution in China: Province-level analysis based on energy economic model. Energy Economics, 68, 478-489. doi:10.1016/j.eneco.2017.10.013
Gridded Population of the World (GPW) v4 (population count)

Zhang, Y., Chen, N., Du, W., Li, Y., & Zheng, X. (2021). Multi-source sensor based urban habitat and resident health sensing: A case study of Wuhan, China. Building and Environment, 198, 107883. doi:10.1016/j.buildenv.2021.107883
Gridded Population of the World (GPW) v4.11 (population density)

Zhang, Y., Dalal, R. C., Bhattacharyya, R., Meyer, G., Wang, P., Menzies, N. W., & Kopittke, P. M. (2021). Effect of long-term no-tillage and nitrogen fertilization on phosphorus distribution in bulk soil and aggregates of a Vertisol. Soil and Tillage Research, 205, 104760. doi:10.1016/j.still.2020.104760
Gridded Population of the World (GPW) v4.10 (population count) - 10.7927/H4PG1PPM

Zhang, Y., Eastham, S. D., Lau, A. K. H., Fung, J., & Selin, N. E. (2021). Global air quality and health impacts of domestic and international shipping. Environmental Research Letters, 16(8), 084055. doi:10.1088/1748-9326/ac146b
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4F47M65

Zhang, Y., Zhao, J., Yan, Y., Shi, Y., & Yu, Q. (2021). Relationship of population migration, crop production pattern, and socioeconomic development: evidence from the early 21st century. Environmental Research Letters, 16(7), 074045. doi:10.1088/1748-9326/ac0e66
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Zhang, Y., Zhou, D., Li, Z., & Qi, L. (2020). Spatial and temporal dynamics of social-ecological resilience in Nepal from 2000 to 2015. Physics and Chemistry of the Earth, Parts A/B/C, 120, 102894. doi:10.1016/j.pce.2020.102894
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS - MCD12Q1)
REMOTE SENSING (VIIRS)

Zhang, Z., Liu, J., Xiao, C., & Chen, G. (2023). Making waves: Enhancing sustainability and resilience in coastal cities through the incorporation of seawater into urban metabolism. Water Research, 242, 120140. doi:10.1016/j.watres.2023.120140
Gridded Population of the World (GPW) v4 (collection)

Zhao, C., Chen, J., Su, G., & Yuan, H. (2020). Assessment of the climate change adaptation capacity of urban agglomerations in China. Mitigation and Adaptation Strategies for Global Change, 25, 221-236. doi:10.1007/s11027-019-09874-5
Gridded Population of the World (GPW) v4 (population density)

Zhao, C., Wang, Y., Tiseo, K., Pires, J., Criscuolo, N. G., & Van Boeckel, T. P. (2021). Geographically targeted surveillance of livestock could help prioritize intervention against antimicrobial resistance in China. Nature Food, 2, 596-602. doi:10.1038/s43016-021-00320-x
Gridded Population of the World (GPW) v4.11 (population density)

Zhao, F., Veldkamp, T. I. E., Frieler, K., Schewe, J., Ostberg, S., Willner, S., . . . Yamazaki, D. (2017). The critical role of the routing scheme in simulating peak river discharge in global hydrological models. Environmental Research Letters, 12(7), 14pp. doi:10.1088/1748-9326/aa7250
Gridded Population of the World (GPW) v4 (collection)

Zhao, F., Wu, Y., Yin, X., Sun, K., Ma, S., Zhang, S., . . . Chen, J. (2022). Projected changes in population exposure to drought in China under CMIP6 forcing scenarios. Atmospheric Environment, 282, 119162. doi:10.1016/j.atmosenv.2022.119162
Gridded Population of the World (GPW) v4 (unspecified)

Zhao, G., Bates, P., Neal, J., & Pang, B. (2021). Design flood estimation for global river networks based on machine learning models. Hydrology and Earth System Sciences, 25(11), 5981-5999. doi:10.5194/hess-25-5981-2021
Gridded Population of the World (GPW) v4 (population density)

Zhao, H., Geng, G., Liu, Y., Liu, Y., Zheng, Y., Xue, T., . . . Zhang, Q. (2022). Reduction of global life expectancy driven by trade-related transboundary air pollution. Environmental Science & Technology Letters, 9(3), 212-218. doi:10.1021/acs.estlett.2c00002
Gridded Population of the World (GPW) v4.11 (population count)

Zhao, H., Gui, K., Ma, Y., Wang, Y., Wang, Y., Wang, H., . . . Zhang, X. (2021). Climatology and trends of aerosol optical depth with different particle size and shape in Northeast China from 2001 to 2018. Science of The Total Environment, 763, 142979. doi:10.1016/j.scitotenv.2020.142979
Gridded Population of the World (GPW) v4 (unspecified)
NASA REMOTE SENSING (MISR)
NASA REMOTE SENSING (MODIS)

Zhao, H., Wu, R., Liu, Y., Cheng, J., Geng, G., Zheng, Y., . . . Zhang, Q. (2023). Air pollution health burden embodied in China's supply chains. Environmental Science and Ecotechnology, 16, 100264. doi:10.1016/j.ese.2023.100264
Gridded Population of the World (GPW) v4.11 (population count)

Zhao, J., Chen, H., Liang, Q., Xia, X., Xu, J., Hoey, T., . . . Zhou, X. (2022). Large-scale flood risk assessment under different development strategies: the Luanhe River Basin in China. Sustainability Science, 17(4), 1365-1384. doi:10.1007/s11625-021-01034-6
Gridded Population of the World (GPW) v4.11 (population density) - 10.7927/H49C6VHW
NASA REMOTE SENSING (MODIS)
REMOTE SENSING (Landsat)

Zhao, M., Cheng, C., Zhou, Y., Li, X., Shen, S., & Song, C. (2022). A global dataset of annual urban extents (1992-2020) from harmonized nighttime lights. Earth System Science Data, 14, 517-534. doi:10.5194/essd-14-517-2022
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MODIS - MOD44W)
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS NTL)

Zhao, N., Cao, G., Zhang, W., Samson, E. L., & Chen, Y. (2020). Remote sensing and social sensing for socioeconomic systems: A comparison study between nighttime lights and location-based social media at the 500 m spatial resolution. International Journal of Applied Earth Observation and Geoinformation, 87, 102058. doi:10.1016/j.jag.2020.102058
Gridded Population of the World (GPW) v4.10 (population density UN WPP-adjusted) - 10.7927/H49884ZR
NASA REMOTE SENSING (MODIS - MCD12Q1)
REMOTE SENSING (VIIRS DNB)

Zhao, N., Zhang, W., Liu, Y., Samson, E. L., Chen, Y., & Cao, G. (2019). Improving nighttime light imagery with location-based social media data. IEEE Transactions on Geoscience and Remote Sensing, 57(4), 2161-2172. doi:10.1109/TGRS.2018.2871788
Gridded Population of the World (GPW) v4.10 (population density UN WPP-adjusted) - 10.7927/H49884ZR
REMOTE SENSING (DMSP-OLS)
REMOTE SENSING (VIIRS DNB)

Zhao, S., Liu, Y., Zhang, R., & Fu, B. (2020). China's population spatialization based on three machine learning models. Journal of Cleaner Production, 256, 120644. doi:10.1016/j.jclepro.2020.120644
Gridded Population of the World (GPW) v4 (unspecified)
NASA REMOTE SENSING (SRTM)
REMOTE SENSING (VIIRS)

Zhao, X., Allen, R. J., & Thomson, E. S. (2021). An implicit air quality bias due to the state of pristine aerosol. Earth's Future, 9(9), e2021EF001979. doi:10.1029/2021EF001979
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted)
NASA REMOTE SENSING (MERRA-2)

Zhao, Z., Yue, Y., Liu, X., Li, C., Ma, W., & Liu, Q. (2023). The patterns and driving forces of dengue invasions in China. Infectious Diseases of Poverty, 12(1), 42. doi:10.1186/s40249-023-01093-0
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5
Land Use and Land Cover (LULC) (Global Human Modification of Terrestrial Systems, v1) - 10.7927/edbc-3z60

Zheng, B., Geng, G., Ciais, P., Davis, S. J., Martin, R. V., Meng, J., . . . Zhang, Q. (2020). Satellite-based estimates of decline and rebound in China’s CO2 emissions during COVID-19 pandemic. Science Advances, 6(49), eabd4998. doi:10.1126/sciadv.abd4998
Gridded Population of the World (GPW) v4.11 (population count UN WPP-adjusted) - 10.7927/H4PN93PB
REMOTE SENSING (TROPOMI)

Zheng, B., Zhang, Q., Geng, G., Chen, C., Shi, Q., Cui, M., . . . He, K. (2021). Changes in China's anthropogenic emissions and air quality during the COVID-19 pandemic in 2020. Earth System Science Data, 13(6), 2895-2907. doi:10.5194/essd-13-2895-2021
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Zheng, D., Yin, G., Liu, M., Hou, L., Yang, Y., Van Boeckel, T. P., . . . Li, Y. (2022). Global biogeography and projection of soil antibiotic resistance genes. Science Advances, 8(46), eabq8015. doi:10.1126/sciadv.abq8015
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
Last of the Wild v2 Global Human Influence Index (Geographic)
Land Use and Land Cover (LULC) (Development Threat Index, v1)
Land Use and Land Cover (LULC) (Global Human Modification of Terrestrial Systems, v1)
PEST-CHEMGRIDS
NASA REMOTE SENSING (Compilation of Global Soil Microbial Biomass Carbon, Nitrogen, and Phosphorus Data - ORNL)

Zhong, C., Guo, H., Swan, I., Gao, P., Yao, Q., & Li, H. (2023). Evaluating trends, profits, and risks of global cities in recent urban expansion for advancing sustainable development. Habitat International, 138, 102869. doi:10.1016/j.habitatint.2023.102869
Gridded Population of the World (GPW) v4 (population density) - 10.7927//H4NP22DQ
Global Rural-Urban Mapping Project (GRUMP) v1.02 (urban extent polygons) - 10.7927/np6p-qe61
Global High Resolution Urban Data from Landsat (GMIS) - 10.7927/H4P55KKF

Zhong, J., Zhang, X., Gui, K., Liao, J., Fei, Y., Jiang, L., . . . Zhou, Z. (2022). Reconstructing 6-hourly PM2.5 datasets from 1960 to 2020 in China. Earth System Science Data, 14, 3197-3211. doi:10.5194/essd-14-3197-2022
Gridded Population of the World (GPW) v4.11 (population count)
NASA REMOTE SENSING (MODIS NDVI)

Zhong, X., Zhao, Y., Sha, J., Liang, H., & Wu, P. (2022). Spatiotemporal variations of air pollution and population exposure in Shandong Province, eastern China, 2014–2018. Environmental Monitoring and Assessment, 194(2), 114. doi:10.1007/s10661-022-09769-0
Gridded Population of the World (GPW) v4.11 (population count)

Zhong, Y., Dubois, N., Xiong, J., Deng, C., Zhang, H., Xiao, W., . . . Li, Y. (2022). Jet transitions caused multiple abrupt droughts in the Asian summer monsoon margin during Holocene times. Palaeogeography, Palaeoclimatology, Palaeoecology, 601, 111106. doi:10.1016/j.palaeo.2022.111106
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted) - 10.7927/H4F47M65

Zhou, B., Thies, S., Gudipudi, R., Lüdeke, M. K. B., Kropp, J. P., & Rybski, D. (2020). A Gini approach to spatial CO2 emissions. PLoS ONE, 15(11), e0242479. doi:10.1371/journal.pone.0242479
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Zhou, D., Bonafoni, S., Zhang, L., & Wang, R. (2018). Remote sensing of the urban heat island effect in a highly populated urban agglomeration area in East China. Science of The Total Environment, 628–629, 415-429. doi:10.1016/j.scitotenv.2018.02.074
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted)
NASA REMOTE SENSING (MODIS)

Zhou, D., Sheng, M., Wu, J., Li, J., & Han, Z. (2022). Gateway placement in integrated satellite-terrestrial networks: Supporting communications and Internet of Remote Things. IEEE Internet of Things Journal, 9(6), 4421-4434. doi:10.1109/JIOT.2021.3103965
Gridded Population of the World (GPW) v4.11 (population count)

Zhou, J., Yang, Y., & Webster, C. (2019). Legacies of European ‘Belt and Road’? Visualizing transport accessibility and its impacts on population distribution. Regional Studies, Regional Science, 6(1), 451-454. doi:10.1080/21681376.2019.1652111
Gridded Population of the World (GPW) v4 (population density) map

Zhou, L., Shen, G., Wu, Y., Brown, R., Chen, T., & Wang, C. (2018). Urban form, growth, and accessibility in space and time: Anatomy of land use at the parcel-level in a small to medium-sized American city. Sustainability, 10(12), 4572. doi:10.3390/su10124572
Gridded Population of the World (GPW) v4.10 (land and water area) - 10.7927/H4Z03642

Zhou, M., Liu, H., Peng, L., Qin, Y., Chen, D., Zhang, L., & Mauzerall, D. L. (2022). Environmental benefits and household costs of clean heating options in northern China. Nature Sustainability, 5, 329-338. doi:10.1038/s41893-021-00837-w
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)

Zhou, R., Meng, Z., & Bai, L. (2022). Differences in tornado activities and key tornadic environments between China and the United States. International Journal of Climatology, 42(1), 367-384. doi:10.1002/joc.7248
Gridded Population of the World (GPW) v4 (population density) - 10.7927//H4NP22DQ

Zhou, S., Chen, Y., Paytan, A., Li, H., Wang, F., Zhu, Y., . . . Zhang, R. (2021). Non-marine sources contribute to aerosol methanesulfonate over coastal seas. Journal of Geophysical Research: Atmospheres, 126(21), e2021JD034960. doi:10.1029/2021JD034960
Gridded Population of the World (GPW) v4.11 (population density)
NASA REMOTE SENSING (MODIS)

Zhou, X., Peng, W., Guo, Y., Chen, P., Ren, Q., Feng, X., . . . Huang, Q. (2023). Relationships between urban expansion and socioenvironmental indicators across multiple scales of watersheds: a case study among watersheds running through China. Environmental Science and Pollution Research, 30, 75752–75767. doi:10.1007/s11356-023-27639-1
Gridded Population of the World (GPW) v4 (population count) - 10.7927/H4X63JVC
NASA REMOTE SENSING (MODIS - MYD10A1)

Zhou, X., Yang, Y., & Sheng, Z. (2019). Reconstructed natural runoff helps to quantify the relationship between upstream water use and downstream water scarcity in China's river basins. Hydrology and Earth System Sciences, 23(5), 2491-2505. doi:10.5194/hess-23-2491-2019
Gridded Population of the World (GPW) v4 (population count)

Zhou, Y., Li, X., Chen, W., Meng, L., Wu, Q., Gong, P., & Seto, K. C. (2022). Satellite mapping of urban built-up heights reveals extreme infrastructure gaps and inequalities in the Global South. Proceedings of the National Academy of Sciences, 119(46), e2214813119. doi:10.1073/pnas.2214813119
Gridded Population of the World (GPW) v4 (population density)
REMOTE SENSING (Sentinel-1 Ground Range Detected)

Zhou, Y., Ma, M., Shi, K., & Peng, Z. (2020). Estimating and interpreting fine-scale gridded population using random forest regression and multisource data. ISPRS International Journal of Geo-Information, 9(6), 369. doi:10.3390/ijgi9060369
Gridded Population of the World (GPW) v1
Gridded Population of the World (GPW) v4 (Doxsey-Whitfield et al. paper)
Global Rural-Urban Mapping Project (GRUMP) v1

Zhu, C., Tian, H., & Hao, J. (2020). Global anthropogenic atmospheric emission inventory of twelve typical hazardous trace elements, 1995–2012. Atmospheric Environment, 220, 117061. doi:10.1016/j.atmosenv.2019.117061
Gridded Population of the World (GPW) v4 (unspecified)

Zhu, D., Zhou, Q., Liu, M., & Bi, J. (2021). Non-optimum temperature-related mortality burden in China: Addressing the dual influences of climate change and urban heat islands. Science of The Total Environment, 782, 146760. doi:10.1016/j.scitotenv.2021.146760
Gridded Population of the World (GPW) v4.11 (population density UN WPP-adjusted)
REMOTE SENSING (DMSP-OLS)

Zhu, E., Wang, Y., & Yuan, X. (2023). Changes of terrestrial water storage during 1981–2020 over China based on dynamic-machine learning model. Journal of Hydrology, 621, 129576. doi:10.1016/j.jhydrol.2023.129576
Gridded Population of the World (GPW) v4 (population count)
NASA REMOTE SENSING (GRACE)
NASA REMOTE SENSING (MODIS)

Zhu, G., Hu, W., Liu, Y., Cao, J., Ma, Z., Deng, Y., . . . Wang, H. (2019). Health burdens of ambient PM2.5 pollution across Chinese cities during 2006–2015. Journal of Environmental Management, 243, 250-256. doi:10.1016/j.jenvman.2019.04.119
Gridded Population of the World (GPW) v4 (population density)

Zhu, L., & Quiring, S. M. (2022). Exposure to precipitation from tropical cyclones has increased over the continental United States from 1948 to 2019. Communications Earth & Environment, 3(1), 312. doi:10.1038/s43247-022-00639-8
Gridded Population of the World (GPW) v4 (admin unit center points with population estimates) - 10.7927/H4F47M2C
Population Dynamics (Global Population Density Grid Time Series Estimates, v1)

Zhu, S., Dai, Q., Zhao, B., & Shao, J. (2020). Assessment of population exposure to urban flood at the building scale. Water, 12(11), 3253. doi:10.3390/w12113253
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)

Zhu, Y., Liu, Y., Liu, X., & Wang, H. (2023). Carbon mitigation and health effects of fleet electrification in China’s Yangtze River Delta. Environment International, 180, 108203. doi:https://doi.org/10.1016/j.envint.2023.108203
Gridded Population of the World (GPW) v4.11 (population count)

Zhu, Y., Price, O. R., Kilgallon, J., Qi, Y., Tao, S., Jones, K. C., & Sweetman, A. J. (2018). Drivers of contaminant levels in surface water of China during 2000–2030: Relative importance for illustrative home and personal care product chemicals. Environment International, 115, 161-169. doi:10.1016/j.envint.2018.03.013
Gridded Population of the World (GPW) v4 (population count UN WPP-adjusted)
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ
Global Rural-Urban Mapping Project (GRUMP) v1 (urban extent)

Zhu, Y., Zhan, Y., Wang, B., Li, Z., Qin, Y., & Zhang, K. (2019). Spatiotemporally mapping of the relationship between NO2 pollution and urbanization for a megacity in Southwest China during 2005–2016. Chemosphere, 220, 155-162. doi:10.1016/j.chemosphere.2018.12.095
Gridded Population of the World (GPW) v4 (population density)
NASA REMOTE SENSING (MODIS - MOD13Q1)
NASA REMOTE SENSING (SRTM)

Zhuang, H., Liu, X., Yan, Y., Ou, J., He, J., & Wu, C. (2021). Mapping multi-temporal population distribution in China from 1985 to 2010 using Landsat images via deep learning. Remote Sensing, 13(17), 3533. doi:10.3390/rs13173533
Gridded Population of the World (GPW) v4 (collection)
Global Rural-Urban Mapping Project (GRUMP) v1 (collection)
REMOTE SENSING (Landsat)

Ziv, G., Mullin, K., Boeuf, B., Fincham, W., Taylor, N., Villalobos-Jiménez, G., . . . Beckmann, M. (2016). Water quality is a poor predictor of recreational hotspots in England. PLoS ONE, 11(11), e0166950. doi:10.1371/journal.pone.0166950
Gridded Population of the World (GPW) v4 (population density) - 10.7927/H4NP22DQ

Zoraghein, H., & O'Neill, B. C. (2020). A spatial population downscaling model for integrated human-environment analysis in the United States. Demographic Research, 43(54), 1563-1606. doi:10.4054/DemRes.2020.43.54
Gridded Population of the World (GPW) v4.11 (population count)
Global Rural-Urban Mapping Project (GRUMP) v1 (Balk et al 2006)

Zoraghein, H., Pinchoff, J., Balk, D., Montgomery, M. R., & Engin, H. (2022). People and infrastructure: multi-scale assessment of coastal and fluvial flood exposure in India. Environmental Research Communications, 4(12), 121009. doi:10.1088/2515-7620/aca9aa
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Zou, X., Hou, S., Zhang, W., Liu, K., Yu, J., Pang, H., & Liu, Y. (2020). An increase of ammonia emissions from terrestrial ecosystems on the Tibetan Plateau since 1980 deduced from ice core record. Environmental Pollution, 262, 114314. doi:10.1016/j.envpol.2020.114314
Gridded Population of the World (GPW) v4 (population density)

Zscheischler, J., Mahecha, M. D., Avitabile, V., Calle, L., Carvalhais, N., Ciais, P., . . . Reichstein, M. (2017). Reviews and syntheses: An empirical spatiotemporal description of the global surface–atmosphere carbon fluxes: opportunities and data limitations. Biogeosciences, 14(15), 3685-3703. doi:10.5194/bg-14-3685-2017
Gridded Population of the World (GPW) v4 (population density UN WPP-adjusted) - 10.7927/H4HX19NJ

Zuidema, S. (2022). Interventions Towards Sustainable Watershed Management as Demonstrated by Hydrologic Simulation. (Ph.D.). University of New Hampshire, Retrieved from https://scholars.unh.edu/dissertation/2700 
Gridded Population of the World (GPW) v4 (population density)

Zuo, J., Zhang, L., Chen, B., Liao, J., Hashim, M., Sutrisno, D., . . . Sani, D. A. (2023). Assessment of coastal sustainable development along the maritime silk road using an integrated natural-economic-social (NES) ecosystem. Heliyon, 9(6), e17440. doi:10.1016/j.heliyon.2023.e17440
Gridded Population of the World (GPW) v4.11 (population count) - 10.7927/H4JW8BX5

Zuo, X., Chen, Y., Ohno-Machado, L., & Xu, H. (2021). How do we share data in COVID-19 research? A systematic review of COVID-19 datasets in PubMed Central Articles. Briefings in Bioinformatics, 22(2), 800-811. doi:10.1093/bib/bbaa331
Gridded Population of the World (GPW) v4.11 (population density)