Displaying 1 - 7 of 7
Operations
Chen, Lingling; Wu, Zhifeng; Tu, Wei; Cao, Zheng. 2020. Applying LUR model to estimate spatial variation of PM2.5 in the Greater Bay Area, China. [Spatiotemporal Analysis of Air Pollution and Its Application in Public Health] Ed(s): Li, Lixin; Zhou, Xiaolu; Tong, Weitian. pp: 207-215
Related URL(s): https://www.elsevier.com/books/spatiotemporal-analysis-of-air-pollution-and-its-…
Uses Remote Sensing: yes
SEDAC Data Collection(s):
gpw-v4
(Book Section)
export
Macdonald, Bobbie Neil James. 2020. Room to Move: Political Accountability of “Lawmakers” in the Kenya National Assembly, 1998-2019. [Political Science] Weinstein, Jeremy M. pp: 151
Related URL(s): https://searchworks.stanford.edu/view/13680879
Uses Remote Sensing: yes
SEDAC Data Collection(s):
gpw-v3
gpw-v4
(Thesis)
export
Schrodt, Franziska; de la Barreda Bautista, Betsabe; Williams, Christopher; Boyd, Doreen S.; Schaepman-Strub, Gabriela; Santos, Maria J. 2020. Integrating Biodiversity, Remote Sensing, and Auxiliary Information for the Study of Ecosystem Functioning and Conservation at Large Spatial Scales. [Remote Sensing of Plant Biodiversity] Ed(s): Cavender-Bares, Jeannine; Gamon, John A.; Townsend, Philip A. pp: 449-484 DOI: https://doi.org/10.1007/978-3-030-33157-3_17
Uses Remote Sensing: yes
SEDAC Data Collection(s):
gpw-v4
(Book Section)
export
Breckner, Miriam. 2019. Climatic and Geographic Determinants of Economic Development. [Economics] Sunde, Uwe. Ph.D.(ediss:23734): 155
Related URL(s): https://edoc.ub.uni-muenchen.de/23734/
Uses Remote Sensing: yes
SEDAC Data Collection(s):
gpw-v3
gpw-v4
(Thesis)
export
Williams, Rob. 2019. The Geography of Secession. [Political Science] Crecenzi, Mark J. C. Ph.D.: 162 DOI: https://doi.org/10.17615/1ysh-sk68
Uses Remote Sensing: yes
SEDAC Data Collection(s):
gpw-v3
gpw-v4
(Thesis)
export
Gebremichael, Esayas. 2018. Assessing Land Deformation and Sea Encroachment in the Nile Delta: A Radar Interferometric and Modeling Approach. [Geosciences] Sultan, Mohamed. Ph.D.(3371): 108
Related URL(s): https://scholarworks.wmich.edu/dissertations/3371
Uses Remote Sensing: yes
SEDAC Data Collection(s):
gpw-v4
(Thesis)
export
Jackson, Benjamin. 2018. The GREAT-ER Model as a Tool for Chemical Risk Assessment and Management for Chinese River Catchments. [Faculty of Science and Technology, Lancaster Environment Centre] Ph.D.(126264): 299
Related URL(s): http://eprints.lancs.ac.uk/126264/
Uses Remote Sensing: yes
SEDAC Data Collection(s):
gpw-v4
(Thesis)
export
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