Compiling granular population data using geospatial information - Thomas Mitterling; Katharina Fenz; Arturo Martinez; Joseph Bulan; Mildred Addawe; Ron Lester Durante; Marymell Martillan (Asian Development Bank (ADB), 2021)
Granular spatial information on the distributions of human population is relevant to a variety of fields like health, economics, and other areas of public sector planning. This paper applies ensemble methods and aims at assessing their applicability to analyzing and forecasting population density on a grid level. In a first step, we use a Random Forest approach to estimate population density in the Philippines and Thailand on a 100 meter by 100-meter level. Second, we use different specifications of Random Forest and Bayesian model averaging techniques to create forecasts of the grid-level population density in three Thailand provinces and evaluate their predictive power.
Keywords: C19; D30; O15; 330; population mapping; big data; random forest estimation; Philippines; Thailand