Predicting Poverty Using Geospatial Data in Thailand
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This study examines an alternative approach in estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial distribution of poverty in Thailand.
Results suggest that intensity of night lights and other variables that approximate population density are highly associated with the proportion of population living in poverty. The random forest technique yielded the highest level of prediction accuracy among the methods considered, perhaps due to its capability to fit complex association structures even with small and medium-sized datasets.
Contents
- Introduction
- Literature Review
- Data
- Methods
- Analytical Results
- Conclusion
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Published Version
Puttanapong, Nattapong, Arturo Martinez Jr., Joseph Albert Nino Bulan, Mildred Addawe, Ron Lester Durante, and Marymell Martillan. 2022. "Predicting Poverty Using Geospatial Data in Thailand." ISPRS International Journal of Geo-Information 11 (5): 293. https://doi.org/10.3390/ijgi11050293.