Compiling Granular Population Data Using Geospatial Information

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 100[Formula: see text]m 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