Publication:
Estimating Small Area Population Density Using Survey Data and Satellite Imagery: An Application to Sri Lanka

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dc.contributor.authorEngstrom, Ryan
dc.contributor.authorNewhouse, David
dc.contributor.authorSoundararajan, Vidhya
dc.date.accessioned2019-03-14T20:49:21Z
dc.date.available2019-03-14T20:49:21Z
dc.date.issued2019-03
dc.description.abstractCountry-level census data are typically collected once every 10 years. However, conflict, migration, urbanization, and natural disasters can cause rapid shifts in local population patterns. This study uses Sri Lankan data to demonstrate the feasibility of a bottom-up method that combines household survey data with contemporaneous satellite imagery to track frequent changes in local population density. A Poisson regression model based on indicators derived from satellite data, selected using the least absolute shrinkage and selection operator, accurately predicts village-level population density. The model is estimated in villages sampled in the 2012/13 Household Income and Expenditure Survey to obtain out-of-sample density predictions in the nonsurveyed villages. The predictions approximate the 2012 census density well and are more accurate than other bottom-up studies based on lower-resolution satellite data. The predictions are also more accurate than most publicly available population products, which rely on areal interpolation of census data to redistribute population at the local level. The accuracies are similar when estimated using a random forest model, and when density estimates are expressed in terms of population counts. The collective evidence suggests that combining surveys with satellite data is a cost-effective method to track local population changes at more frequent intervals.en
dc.identifierhttp://documents.worldbank.org/curated/en/920771552394454183/Estimating-Small-Area-Population-Density-Using-Survey-Data-and-Satellite-Imagery-An-Application-to-Sri-Lanka
dc.identifier.doi10.1596/1813-9450-8776
dc.identifier.urihttps://hdl.handle.net/10986/31402
dc.languageEnglish
dc.publisherWorld Bank, Washington, DC
dc.relation.ispartofseriesPolicy Research Working Paper;No. 8776
dc.rightsCC BY 3.0 IGO
dc.rights.holderWorld Bank
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/igo
dc.subjectPOPULATION DENSITY
dc.subjectSATELLITE IMAGERY
dc.subjectMACHINE LEARNING
dc.subjectSMALL AREA ESTIMATION
dc.subjectCENSUS DATA
dc.subjectHOUSEHOLD SURVEYS
dc.subjectMIGRATION
dc.titleEstimating Small Area Population Density Using Survey Data and Satellite Imageryen
dc.title.subtitleAn Application to Sri Lankaen
dc.typeWorking Paperen
dc.typeDocument de travailfr
dc.typeDocumento de trabajoes
dspace.entity.typePublication
okr.crossref.titleEstimating Small Area Population Density Using Survey Data and Satellite Imagery: An Application to Sri Lanka
okr.date.disclosure2019-03-12
okr.doctypePublications & Research
okr.doctypePublications & Research::Policy Research Working Paper
okr.docurlhttp://documents.worldbank.org/curated/en/920771552394454183/Estimating-Small-Area-Population-Density-Using-Survey-Data-and-Satellite-Imagery-An-Application-to-Sri-Lanka
okr.guid920771552394454183
okr.identifier.doi10.1596/1813-9450-8776
okr.identifier.externaldocumentum090224b086a5c785_1_0
okr.identifier.internaldocumentum30895987
okr.identifier.reportWPS8776
okr.importedtrueen
okr.language.supporteden
okr.pdfurlhttp://documents.worldbank.org/curated/en/920771552394454183/pdf/WPS8776.pdfen
okr.region.administrativeSouth Asia
okr.region.countrySri Lanka
okr.statistics.combined1621
okr.statistics.dr920771552394454183
okr.statistics.drstats1061
okr.topicHealth, Nutrition and Population::Demographics
okr.topicPoverty Reduction::Migration and Development
okr.topicPoverty Reduction::Small Area Estimation Poverty Mapping
okr.topicScience and Technology Development::Earth Sciences & GIS
okr.unitPoverty and Equity Global Practice
relation.isSeriesOfPublication26e071dc-b0bf-409c-b982-df2970295c87
relation.isSeriesOfPublication.latestForDiscovery26e071dc-b0bf-409c-b982-df2970295c87
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