Poverty in Thailand shows strong spatial dependence that existing administrative boundaries fail to capture, leading to policies that overlook local socioeconomic realities. This study proposes a data-driven regionalization framework to infer geographically coherent “policy regions” that better represent poverty dynamics. Using household-level data from the Thai People Map and Analytics Platform (TPMAP), we analyze spatial autocorrelation across multiple poverty factors through Moran’s statistics and principal component analysis, followed by spatially constrained hierarchical clustering to delineate coherent regions. Bayesian hierarchical and geographically weighted regression models are then employed to examine how education influences household income at provincial, regional, and national levels. Our results identified six regions that reflect more accurately poverty structures than official divisions. Northern and Northeastern Thailand emerge as the regions most affected by low education, income, and savings, while Central Thailand shows higher inequality. The inferred regions demonstrate that spatially contiguous provinces often share similar socioeconomic structures, suggesting that policy targeting should align with these patterns rather than provincial borders. Our findings provide a quantitative foundation for evidence-based regional planning, enabling policymakers to design differentiated yet regionally coordinated interventions. The approach illustrates how spatial statistical modeling can bridge the gap between data analysis and effective poverty-alleviation policy.
Gómez-Méndez et al. (Thu,) studied this question.