A global poverty map derived from satellite data

By Christopher D. Elvidge, Paul C. Sutton, Tilottama Ghosh, B. Tuttle, Kimberly E. Baugh, B. Bhaduri, E. Bright. Computational Geosciences, 2009

469 citations recorded by the source corpus.

Subfields: Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/fb0a4b13a79ace5db7c70bae5948c819cb3cef3f

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In Global and Planetary Change

Citations in the corpus

Shedding Light on the Global Distribution of Economic Activity (2010, background): “…of a disaggregated map is that economic data that are currently available only at national or state levels can be aggregated to various administrative units – national, state, sub-state, and municipal; or physical and ecological units, such as, watersheds, or soil and vegetation zones [5].”
Using Nighttime Satellite Imagery as a Proxy Measure of Human Well-Being (2013, methodology): “[18] conducted a novel study by creating the first spatially disaggregated 1 km 2 resolution map of population in poverty.”
Mapping Marginality Hotspots: Geographical Targeting for Poverty Reduction (2012, unknown): “Another approach is presented by Elvidge et al. (2009) who developed a disaggregated global poverty map using remote sensing data on population count5 and nighttime lights6.”
A review of regional science applications of satellite remote sensing in urban settings (2013, unknown): “…scale from the Defense Meteorological Satellite Program (DMSP) has been used to estimate economic activity and poverty and to map urbanization dynamics at a global scale (Doll, 2008; Doll, Muller, & Elvidge, 2000; Elvidge et al., 2001; Elvidge, Sutton, Ghosh, & Tuttle, 2009; Zhang & Seto, 2011).”
WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020, unknown): “On top of LandSat images, we follow Yeh et al. (2020) and append an additional eighth image channel for nighttime light intensity from a separate satellite, which correlates with poverty measures (Noor et al., 2008; Elvidge et al., 2009).”
Spectral Identification of Lighting Type and Character (2010, background): “Despite these flaws, a diverse set of global applications have been developed for DMSP nighttime lights ranging from estimation of light pollution levels [6], constructed surface densities [7], population distribution [8], poverty rates [9], electrification rates [10], and estimates of resource cons”