Poverty from Space: Using High Resolution Satellite Imagery for Estimating Economic Well-being
By Ryan N. Engstrom, Jonathan Hersh, David Newhouse. World Bank Economic Review, 2017
185 citations recorded by the source corpus.
Can features extracted from high spatial resolution satellite imagery accurately estimate poverty and economic well-being? The present study investigates this question by extracting both object and texture features from satellite images of Sri Lanka. These features are used to estimate poverty rates and average expected log consumption taken from small-area estimates derived from census data, for 1,291 administrative units. Features extracted include the number and density of buildings, the prevalence of building shadows (proxying building height), the number of cars, length of roads, type of agriculture, roof material, and several texture and spectral features. A linear regression model explains between 49 and 61 percent of the variation in average expected log consumption, and between 37 and 62 percent for poverty rates. Estimates remain accurate throughout the consumption distribution, and when extrapolating predictions into adjacent areas, although performance falls when using fewer households to calculate estimates of poverty and welfare.
Subfields: Global and Planetary Change, Transportation
View Paper: https://www.semanticscholar.org/paper/d1f80cacd11447420bc41cebaa722edf641926aa
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In Global and Planetary Change
In Transportation
Citations in the corpus
- The Impact of Machine Learning on Economics (2018, background): “…survey data to train models that can be used to produce estimates of economic outcomes at the level of the individual home, either within the U.S. or in developing countries where administrative data quality can be problematic (e.g. Jean et al. (2016), Engstrom et al. (2017), Naik et al. (2014)).”