Compiling Granular Population Data Using Geospatial Information

Thomas Mitterling, Katharina Fenz, Arturo Martinez Jr (), Joseph Bulan, Mildred Addawe, Ron Lester Durante and Marymell Martillan
Additional contact information
Thomas Mitterling: World Data Lab
Katharina Fenz: World Data Lab
Arturo Martinez Jr: Asian Development Bank, Postal: No. 6 ADB Avenue, Mandaluyong, 1550 Philippines, http://www.adb.org
Joseph Bulan: Asian Development Bank, Postal: No. 6 ADB Avenue, Mandaluyong, 1550 Philippines, http://www.adb.org
Mildred Addawe: Asian Development Bank, Postal: No. 6 ADB Avenue, Mandaluyong, 1550 Philippines, http://www.adb.org
Ron Lester Durante: Asian Development Bank, Postal: No. 6 ADB Avenue, Mandaluyong, 1550 Philippines, http://www.adb.org
Marymell Martillan: Asian Development Bank, Postal: No. 6 ADB Avenue, Mandaluyong, 1550 Philippines, http://www.adb.org

No 643, ADB Economics Working Paper Series from Asian Development Bank

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.

Keywords: population mapping; big data; random forest estimation; Philippines; Thailand (search for similar items in EconPapers)
JEL-codes: C19 D30 O15 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2021-12-31
New Economics Papers: this item is included in nep-big, nep-cmp, nep-hea and nep-sea
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Persistent link: https://EconPapers.repec.org/RePEc:ris:adbewp:0643

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