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

43 Pages Posted: 26 Mar 2019

See all articles by Ryan Engstrom

Ryan Engstrom

George Washington University - Department of Geography

David Locke Newhouse

World Bank

Vidhya Soundararajan

Indian Institute of Management (IIMB), Bangalore

Date Written: March 12, 2019

Abstract

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.

Suggested Citation

Engstrom, Ryan and Newhouse, David Locke and Soundararajan, Vidhya, Estimating Small Area Population Density Using Survey Data and Satellite Imagery: An Application to Sri Lanka (March 12, 2019). World Bank Policy Research Working Paper No. 8776, Available at SSRN: https://ssrn.com/abstract=3360133

Ryan Engstrom (Contact Author)

George Washington University - Department of Geography ( email )

1922 F St., NW
Washington
DC 20052
United States

David Locke Newhouse

World Bank ( email )

1818 H Street, NW
Washington, DC 20433
United States

Vidhya Soundararajan

Indian Institute of Management (IIMB), Bangalore ( email )

Bannerghatta Road
Bangalore, Karnataka 560076
India

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