ADB亚洲开发银行：基于机器学习的贫困地图中的空间异质性：模型在哪里表现不佳？（英文版）

Recent studies harnessing geospatial big data and machine learning have significantly advanced poverty mapping, enabling granular and timely welfare estimates in traditionally datascarce regions. While much of the existing research has focused on overall out-of-sample predictive performance, there is a lack of understanding regarding where such models underperform and whether key spatial relationships might vary across places. This study investigates spatial heterogeneity in machine learning-