Publication:
Poverty from Space: Using High-Resolution Satellite Imagery for Estimating Economic Well-Being

dc.contributor.author Engstrom, Ryan
dc.contributor.author Hersh, Jonathan
dc.contributor.author Newhouse, David
dc.date.accessioned 2017-12-21T20:28:23Z
dc.date.available 2017-12-21T20:28:23Z
dc.date.issued 2017-12
dc.description.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. en
dc.identifier http://documents.worldbank.org/curated/en/610771513691888412/Poverty-from-space-using-high-resolution-satellite-imagery-for-estimating-economic-well-being
dc.identifier.uri http://hdl.handle.net/10986/29075
dc.language English
dc.publisher World Bank, Washington, DC
dc.relation.ispartofseries Policy Research Working Paper;No. 8284
dc.rights CC BY 3.0 IGO
dc.rights.holder World Bank
dc.rights.uri http://creativecommons.org/licenses/by/3.0/igo
dc.subject POVERTY MEASUREMENT
dc.subject SATELLITE IMAGERY
dc.subject MACHINE LEARNING
dc.subject WELL-BEING
dc.subject POVERTY
dc.title Poverty from Space en
dc.title.subtitle Using High-Resolution Satellite Imagery for Estimating Economic Well-Being en
dc.type Working Paper en
dc.type Document de travail fr
dc.type Documento de trabajo es
dspace.entity.type Publication
okr.crossref.title Poverty from Space: Using High-Resolution Satellite Imagery for Estimating Economic Well-Being
okr.date.disclosure 2017-12-19
okr.doctype Publications & Research
okr.doctype Publications & Research :: Policy Research Working Paper
okr.docurl http://documents.worldbank.org/curated/en/610771513691888412/Poverty-from-space-using-high-resolution-satellite-imagery-for-estimating-economic-well-being
okr.identifier.doi 10.1596/1813-9450-8284
okr.identifier.externaldocumentum 090224b085464805_1_0
okr.identifier.internaldocumentum 29284908
okr.identifier.report WPS8284
okr.imported true en
okr.language.supported en
okr.pdfurl http://documents.worldbank.org/curated/en/610771513691888412/pdf/WPS8284.pdf en
okr.region.administrative South Asia
okr.region.country Sri Lanka
okr.statistics.combined 8531
okr.statistics.dr 610771513691888412
okr.statistics.drstats 3943
okr.topic Poverty Reduction :: Poverty Assessment
okr.topic Poverty Reduction :: Poverty Monitoring & Analysis
okr.topic Poverty Reduction :: Small Area Estimation Poverty Mapping
okr.topic Science and Technology Development :: Technology Innovation
okr.unit Poverty and Equity Global Practice
relation.isSeriesOfPublication 26e071dc-b0bf-409c-b982-df2970295c87
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