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

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

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 feasibi...

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Bibliographic Details
Main Authors: Engstrom, Ryan, Newhouse, David, Soundararajan, Vidhya
Format: Working Paper
Language:English
Published: World Bank, Washington, DC 2019
Subjects:
POPULATION DENSITY
SATELLITE IMAGERY
MACHINE LEARNING
SMALL AREA ESTIMATION
CENSUS DATA
HOUSEHOLD SURVEYS
MIGRATION
Online Access:http://documents.worldbank.org/curated/en/920771552394454183/Estimating-Small-Area-Population-Density-Using-Survey-Data-and-Satellite-Imagery-An-Application-to-Sri-Lanka
http://hdl.handle.net/10986/31402
id okr-10986-31402
recordtype oai_dc
spelling okr-10986-314022022-09-19T12:18:05Z Estimating Small Area Population Density Using Survey Data and Satellite Imagery : An Application to Sri Lanka Engstrom, Ryan Newhouse, David Soundararajan, Vidhya POPULATION DENSITY SATELLITE IMAGERY MACHINE LEARNING SMALL AREA ESTIMATION CENSUS DATA HOUSEHOLD SURVEYS MIGRATION 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. 2019-03-14T20:49:21Z 2019-03-14T20:49:21Z 2019-03 Working Paper http://documents.worldbank.org/curated/en/920771552394454183/Estimating-Small-Area-Population-Density-Using-Survey-Data-and-Satellite-Imagery-An-Application-to-Sri-Lanka http://hdl.handle.net/10986/31402 English Policy Research Working Paper;No. 8776 CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Policy Research Working Paper South Asia Sri Lanka
repository_type Digital Repository
institution_category Foreign Institution
institution Digital Repositories
building World Bank Open Knowledge Repository
collection World Bank
language English
topic POPULATION DENSITY
SATELLITE IMAGERY
MACHINE LEARNING
SMALL AREA ESTIMATION
CENSUS DATA
HOUSEHOLD SURVEYS
MIGRATION
spellingShingle POPULATION DENSITY
SATELLITE IMAGERY
MACHINE LEARNING
SMALL AREA ESTIMATION
CENSUS DATA
HOUSEHOLD SURVEYS
MIGRATION
Engstrom, Ryan
Newhouse, David
Soundararajan, Vidhya
Estimating Small Area Population Density Using Survey Data and Satellite Imagery : An Application to Sri Lanka
geographic_facet South Asia
Sri Lanka
relation Policy Research Working Paper;No. 8776
description 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.
format Working Paper
author Engstrom, Ryan
Newhouse, David
Soundararajan, Vidhya
author_facet Engstrom, Ryan
Newhouse, David
Soundararajan, Vidhya
author_sort Engstrom, Ryan
title Estimating Small Area Population Density Using Survey Data and Satellite Imagery : An Application to Sri Lanka
title_short Estimating Small Area Population Density Using Survey Data and Satellite Imagery : An Application to Sri Lanka
title_full Estimating Small Area Population Density Using Survey Data and Satellite Imagery : An Application to Sri Lanka
title_fullStr Estimating Small Area Population Density Using Survey Data and Satellite Imagery : An Application to Sri Lanka
title_full_unstemmed Estimating Small Area Population Density Using Survey Data and Satellite Imagery : An Application to Sri Lanka
title_sort estimating small area population density using survey data and satellite imagery : an application to sri lanka
publisher World Bank, Washington, DC
publishDate 2019
url http://documents.worldbank.org/curated/en/920771552394454183/Estimating-Small-Area-Population-Density-Using-Survey-Data-and-Satellite-Imagery-An-Application-to-Sri-Lanka
http://hdl.handle.net/10986/31402
_version_ 1764474247838695424

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