Predicting poverty using geospatial data in Thailand - Nattapong Puttanapong; Arturo M. Martinez; Mildred Addawe; Joseph Bulan; Ron Lester Durante; Marymell Martillan (Asian Development Bank (ADB), 2020)
Poverty statistics are conventionally compiled using data from household income and expenditure survey or living standards survey. 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. In particular, geospatial data examined in this study include night light intensity, land cover, vegetation index, land surface temperature, built-up areas, and points of interest. The study also compares the predictive performance of various econometric and machine learning methods such as generalized least squares, neural network, random forest, and support vector regression. Results suggest that intensity of night lights and other variables that approximate population density are highly associated with the proportion of an area's population who are living in poverty. The random forest technique yielded the highest level of prediction accuracy among the methods considered in this study, perhaps due to its capability to fit complex association structures even with small and mediumsized datasets. Moving forward, additional studies are needed to investigate whether the relationships observed here remain stable over time, and therefore, may be used to approximate the prevalence of poverty for years when household surveys on income and expenditures are not conducted, but data on geospatial correlates of poverty are available.
Keywords: C19; D31; I32; O15; 330; big data; computer vision; data for development; machine learning algorithm; multidimensional poverty; official statistics; poverty; SDG; Thailand