Country-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.
Detalhes
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Autor
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Data do documento
2019/03/12
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TIpo de documento
Documento de trabalho sobre pesquisa de políticas
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No. do relatório
WPS8776
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Nº do volume
1
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Total Volume(s)
1
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País
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Região
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Data de divulgação
2019/03/12
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Disclosure Status
Disclosed
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Nome do documento
Estimating Small Area Population Density Using Survey Data and Satellite Imagery : An Application to Sri Lanka
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Palavras-chave
Population Density; Household Income and Expenditure Survey; satellite imagery; Poverty and Equity; international earth science information network; Indian Institute of Management; satellite data; high resolution imagery; log likelihood function; number of cars; relationship between population; local population; measures of population; forest cover change; technology and markets; service and infrastructure; data collection instruments; population growth rate; census data; human settlement; survey data; population estimate; village area; machine learning; forest model; small area; high-resolution satellite
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