July 2021 Global Human Settlement Layer: Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid, v1 (1975, 1990, 2000, 2014, 2015) PURPOSE To provide global data on human population, built up area, and degree of urbanization for the years 1975, 1990, 2000, and 2014/2015 in the World Geodetic System 1984 (WGS84) geographic coordinate system. DESCRIPTION The Global Human Settlement Layer: Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid data set provides gridded data on human population (GHS-POP), built-up area (GHS-BUILT), and degree of urbanization (GHS-SMOD) across four time periods: 1975, 1990, 2000, and 2014 (BUILT) or 2015 (POP, SMOD). GHS-BUILT describes the percent built-up area for each 30 arc-second grid cell (approximately 1 km at the equator) based on Landsat imagery from each of the four time periods. GHS-POP consists of census data from the 2010 round of global census from Gridded Population of the World, Version 4 (GPWv4): Revision 10 spatially-allocated within census units based on the percent built-up areas from GHS-BUILT. GHS-SMOD uses GHS-BUILT and GHS-POP in order to develop a standardized classification of degree of urbanization grid. The original data from the Joint Research Centre of the European Commission (JRC-EC) has been combined into a single data package in GeoTIFF format and reprojected from Mollweide Equal Area into WGS84 at 9 arc-second and 30 arc-second horizontal resolutions in order to support integration with a variety of global raster data sets. Data from the JRC-EC R2018 release of GHS-BUILT and R2019 release of GHS-SMOD were projected using the R2019 GHS-POP as a reference for snapping and extent. GHS-BUILT, GHS-POP, and GHS-SMOD are available in separate layers per individual epoch (1975, 1990, 2000, 2014/2015). The GHSL multitemporal collections are derived from new spatial data mining technologies that rely on a combination of fine-scale satellite image data streams, census data, and crowd sourced or volunteered geographic information sources: - GHS Built-up area layers (GHS-BUILT) depict the location and size of the building surface area, referred to as the building footprint area, expressed as a proportion of the building footprint area within the total size of the cell. GHS-BUILT measures human settlements over time regardless of administrative boundaries. GHS-BUILT are derived from Sentinel-1 satellite backscatter images, described in a scientific publication (Corbane et al., 2018a). - GHS Population area layers (GHS-POP) depict distribution of population, expressed as a continuous value representing the number of people per grid cell. GHS-POP disaggregates census or administrative units derived from CIESIN’s Gridded Population of the World, Version 4 (GPWv4): Revision 10 into grid cells using a method first described in (Freire et al., 2016), and in this case is informed by the distribution and density of building footprints as mapped in GHS-BUILT for each corresponding epoch. - GHS Settlement model layers (GHS-SMOD) depict settlement typologies expressed as an integer assigned to each settlement classification per grid cell. GHS-SMOD defines eight classes, through the porting of the Degree of Urbanization (DEGURBA) methodology, developed by EuroSAT, and applied through a set of decision rules that consider population and built-up area densities derived from the GHS-POP and GHS-BUILT data sets. ACCESSING THE DATA The data may be downloaded at https://sedac.ciesin.columbia.edu/data/set/ghsl-population-built-up-estimates-degree-urban-smod/data-download Permanent URL: https://doi.org/10.7927/h4154f0w Documentation for the data may be downloaded at: https://doi.org/10.7927/tg7r-n260 Documentation for the JRC-EC GHSL 2019 Data Package Technical Report may be downloaded at: https://ghsl.jrc.ec.europa.eu/documents/GHSL_Data_Package_2019.pdf DATA FORMAT The data archive consists of zip files containing all global tiles in GeoTIFF format. Each zip file contains: 1) the selected GeoTIFF tile(s) for the chosen epoch and resolution, 2) Readme.TXT file, and 3) PDF Documentation. NAMING CONVENTION Each product uses the following naming convention: "ghsl-population-built-up-estimates-degree-urban-smod_GHS----.tif" where: = The GHSL Product, either 'BUILT', 'POP', or 'SMOD' = Either 1975, 1990, 2000, or 2014/2015 = Either 9 or 30 arc-seconds. = The current version of the data release DATA VALUES GHSL version R2019 includes three collections in two different resolutions and four epochs for a total of 24 global raster files that shows human presence over time. The three collections have two resolutions available, 9 arc-seconds and 30 arc-seconds, for all epochs. GHS-BUILT - All GHS-BUILT products are 32 bit floating pixel types. - Band 1 value is a continuous variable representing the proportion of building footprint area per grid cell. GHS-POP - All GHS-POP products are 64 bit double-precision pixel types. - Band 1 value is a continuous variable of population counts per grid cell. GHS-SMOD - All GHS-SMOD products are 16 bit signed pixel types. - Band 1 value is an integer that represents a settlement classification: GHS-SMOD Classification: ——— Class 30: “Urban Centre grid cell”, if the grid cell belongs to an Urban Centre spatial entity; ——— Class 23: “Dense Urban Cluster grid cell”, if the grid cell belongs to a Dense Urban Cluster spatial entity; ——— Class 22: “Semi-dense Urban Cluster grid cell”, if the grid cell belongs to a Semi-dense Urban Cluster spatial entity; ——— Class 21: “Suburban or peri-urban grid cell”, if the grid cell belongs to an Urban Cluster cells at first hierarchical level but is not part of a Dense or Semi-dense Urban Cluster; ——— Class 13: “Rural cluster grid cell”, if the grid cell belongs to a Rural Cluster spatial entity; ——— Class 12: “Low Density Rural grid cell”, if the grid cell is classified as Rural grid cells at first hierarchical level, has more than 50 inhabitants and is not part of a Rural Cluster; ——— Class 11: “Very low density rural grid cell”, if the grid cell is classified as Rural grid cells at first hierarchical level, has less than 50 inhabitants and is not part of a Rural Cluster; ——— Class 10: “Water grid cell”, if the grid cell has 0.5 share covered by permanent surface water and is not populated nor built. SPATIAL EXTENT Global at Bounding Box: West -180 East 180 North 90 South -90 The data are provided in the WGS84 Geographic Coordinate System at a resolution of 9 arc-seconds and 30 arc-seconds. DISCLAIMER CIESIN follows procedures designed to ensure that data disseminated by CIESIN are of reasonable quality. If, despite these procedures, users encounter apparent errors or misstatements in the data, they should contact SEDAC User Services at ciesin.info@ciesin.columbia.edu. Neither CIESIN nor NASA verifies or guarantees the accuracy, reliability, or completeness of any data provided. CIESIN provides this data without warranty of any kind whatsoever, either expressed or implied. CIESIN shall not be liable for incidental, consequential, or special damages arising out of the use of any data provided by CIESIN. USE CONSTRAINTS This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided. RECOMMENDED CITATION(S) Dataset: Joint Research Centre (JRC), European Commission, and Center for International Earth Science Information Network (CIESIN), Columbia University. 2021. Global Human Settlement Layer: Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/h4154f0w. Accessed DAY MONTH YEAR. Scientific Publications: Florczyk A. J., C. Corbane, D. Ehrlich, S. Freire, T. Kemper, L. Maffenini. M. Melchiorri, M. Pesaresi, P. Politis, M. Schiavina, F. Sabo, and L. Zanchetta. GHSL Data Package 2019, EUR 29788 EN, Publications Office of the European Union, Luxembourg, 2019, ISBN 978-92-76-13186-1, JRC 117104. https://doi.org/10.2760/290498. https://publications.jrc.ec.europa.eu/repository/handle/JRC117104. REFERENCES Corbane, C., A. Florczyk, M. Pesaresi, P. Politis, and V. Syrris. 2018. GHS built-up grid, derived from Landsat, multitemporal (1975-1990-2000-2014), R2018A. European Commission, Joint Research Centre (JRC). https://doi.org/10.2905/jrc-ghsl-10007. https://data.europa.eu/89h/jrc-ghsl-10007. Corbane, C., M. Pesaresi, T. Kemper, P. Politis, A. Florczyk, V. Syrris, M. Melchiorri, F. Sabo, and P. Soille. 2019. Automated global delineation of human settlements from 40 years of Landsat satellite data archives. Big Earth Data 3, 140–169. https://doi.org/10.1080/20964471.2019.1625528. Corbane, C., M. Pesaresi. P. Politis, V. Syrris, A. J. Florczyk, P. Soille, L. Maffenini, A. Burger, V. Vasilev, D. Rodriguez, F. Sabo, L. Dijkstra, and T. Kemper. 2017. Big earth data analytics on Sentinel-1 and Landsat imagery in support to global human settlements mapping, Big Earth Data, 1:1-2, 118-144, https://doi.org/10.1080/20964471.2017.1397899. Corbane, C., P. Politis, V. Syrris, and M. Pesaresi. 2018. GHS built-up grid, derived from Sentinel-1 (2016), R2018A. European Commission, Joint Research Centre (JRC). https://doi.org/10.2905/jrc-ghsl-10008. http://data.europa.eu/89h/jrc-ghsl-10008. Freire S., K. MacManus, M. Pesaresi, E. Doxsey-Whitfield, and J. Mills. 2016. Development of new open and free multi-temporal global population grids at 250 m resolution. Proceedings of the 19th AGILE Conference on Geographic Information Science. Helsinki, Finland, June 14-17, 2016. https://agile-online.org/conference_paper/cds/agile_2016/shortpapers/152_Paper_in_PDF.pdf. Freire S., M. Schiavina. A. J. Florczyk, K. MacManus, M. Pesaresi, C. Corbane, O. Borkovska, J. Mills, L. Pistolesi, J. Squires, and R. Sliuzas. 2018. Enhanced data and methods for improving open and free global population grids: putting ‘leaving no one behind’ into practice, International Journal of Digital Earth, https://doi.org/10.1080/17538947.2018.1548656. Pesaresi, M., A. Florczyk, M. Schiavina, M. Melchiorri, and L. Maffenini. 2019. GHS settlement grid, updated and refined REGIO model 2014 in application to GHS-BUILT R2018A and GHS-POP R2019A, multitemporal (1975-1990-2000-2015), R2019A. European Commission, Joint Research Centre (JRC) [Dataset]. https://doi.org/10.2905/42E8BE89-54FF-464E-BE7B-BF9E64DA5218. http://data.europa.eu/89h/42e8be89-54ff-464e-be7b-bf9e64da5218. Pesaresi, M., V. Syrris, and A. Julea. 2016. A New Method for Earth Observation Data Analytics Based on Symbolic Machine Learning. Remote Sens. 2016, 8, 399. https://doi.org/10.3390/rs8050399. Schiavina, M., S. Freire, and K. MacManus. 2019. GHS population grid multitemporal (1975-1990- 2000-2015), R2019A. European Commission, Joint Research Centre (JRC) [Dataset] https://doi.org/10.2905/0C6B9751-A71F-4062-830B-43C9F432370F. http://data.europa.eu/89h/0c6b9751-a71f-4062-830b-43c9f432370f. United Nations’ World Population Prospects (UN WPP): The 2015 Revision. https://population.un.org/wpp/Publications/Files/WPP2015_DataBooklet.pdf.