Poverty from space: using high-resolution satellite imagery for estimating economic well-being

Ryan Engstrom, Jonathan Samuel Hersh, David Locke Newhouse, Ryan Engstrom, Jonathan Samuel Hersh and David Locke Newhouse
Authors registered in the RePEc Author Service: David Locke Newhouse

No 8284, Policy Research Working Paper Series from The World Bank

Abstract: Can features extracted from high spatial resolution satellite imagery accurately estimate poverty and economic well-being? This paper investigates this question by extracting object and texture features from satellite images of Sri Lanka, which are used to estimate poverty rates and average log consumption for 1,291 administrative units (Grama Niladhari divisions). The features that were extracted include the number and density of buildings, prevalence of shadows, number of cars, density and length of roads, type of agriculture, roof material, and a suite of texture and spectral features calculated using a nonoverlapping box approach. A simple linear regression model, using only these inputs as explanatory variables, explains nearly 60 percent of poverty headcount rates and average log consumption. In comparison, models built using night-time lights explain only 15 percent of the variation in poverty or income. The predictions remain accurate when restricting the sample to poorer Gram Niladhari divisions. Two sample applications, extrapolating predictions into adjacent areas and estimating local area poverty using an artificially reduced census, confirm the out-of-sample predictive capabilities.

Keywords: Inequality; Food Security; Railways Transport; Transport Services (search for similar items in EconPapers)
Date: 2017-12-19
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Citations: View citations in EconPapers (24)

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Related works:
Journal Article: Poverty from Space: Using High Resolution Satellite Imagery for Estimating Economic Well-being (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:wbk:wbrwps:8284

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