Mapping non-monetary poverty at multiple geographical scales
提出一种整合调查与遥感数据的小区域多尺度方法，通过贝叶斯Beta模型和基准算法，在多个空间分辨率下一致估计贫困率，并以孟加拉国为例验证其有效性。
Abstract Poverty mapping is a powerful tool to study the geography of poverty. The choice of the spatial resolution is central as poverty measures defined at a coarser level may mask their heterogeneity at finer levels. We introduce a small area multi-scale approach integrating survey and remote sensing data that leverages information at different spatial resolutions and accounts for hierarchical dependencies, preserving estimates coherence. We map poverty rates by proposing a Bayesian Beta-based model equipped with a new benchmarking algorithm accounting for the double-bounded support. A simulation study shows the effectiveness of our proposal and an application on Bangladesh is discussed.