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 that accounts for the double-bounded support. A simulation study shows the effectiveness of our proposal and an application on Bangladesh is discussed.

翻译：贫困制图是研究贫困地理分布的有力工具。空间分辨率的选择至关重要，因为在较粗略尺度上定义的贫困指标可能掩盖其在更精细尺度上的异质性。我们提出了一种整合调查数据与遥感数据的小区域多尺度方法，该方法利用不同空间分辨率的信息，并考虑层级依赖性以保持估计的一致性。通过提出一种基于贝叶斯贝塔分布的模型，并配备一种考虑双重边界约束的新型基准校准算法，我们实现了贫困率制图。模拟研究验证了我们方法的有效性，并以孟加拉国为例进行了应用分析。