Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/259481 
Year of Publication: 
2021
Series/Report no.: 
ADB Economics Working Paper Series No. 643
Publisher: 
Asian Development Bank (ADB), Manila
Abstract: 
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.
Subjects: 
population mapping
big data
random forest estimation
Philippines
Thailand
JEL: 
C19
D30
O15
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Working Paper

Files in This Item:





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.