Compiling Granular Population Data Using Geospatial Information

Katharina Fenz, Thomas Mitterling (), Arturo M. Martinez (), Joseph Albert Nino M. Bulan (), Ron Lester S. Durante (), Marymell A. Martillan (), Mildred B. Addawe () and Isabell Roitner-Fransecky ()
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Katharina Fenz: World Data Lab, Vienna, Austria
Thomas Mitterling: World Data Lab, Vienna, Austria
Arturo M. Martinez: Asian Development Bank (ADB), Metro Manila, Philippines
Joseph Albert Nino M. Bulan: ADB, Metro Manila, Philippines
Ron Lester S. Durante: ADB, Metro Manila, Philippines
Marymell A. Martillan: ADB, Metro Manila, Philippines
Mildred B. Addawe: ADB, Metro Manila, Philippines
Isabell Roitner-Fransecky: Data Scientist, World Data Lab, Vienna, Austria

Asian Development Review (ADR), 2024, vol. 41, issue 01, 263-300

Abstract: Detailed data on the distribution of human populations are valuable inputs to research and decision making. This study aims at compiling data on population density that are more granular than government-published estimates and assessing different methods and model specifications. As a first step, we combine government-published data with publicly available data like land cover classes, elevation, slope, and nighttime lights, and then apply a random forest approach to estimate population density in the Philippines and Thailand at the 100 meter (m) by 100m level. Second, we use different specifications of random forest and Bayesian model averaging (BMA) techniques to forecast grid-level population density and evaluate their predictive power. The use of a random forest model showed that reasonable forecasts of grid-level population growth rates are achievable. The results of this study contribute to the assessment of methods like random forest and BMA in forecasting population distributions.

Keywords: geospatial data; machine learning; Philippines; population distribution; Thailand (search for similar items in EconPapers)
JEL-codes: C49 C80 J11 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0116110524500021

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