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Spatial heterogeneity in machine learning-based poverty mapping:Where do models underperform?

Yating Ru Elizabeth Tennant David S.Matteson Christopher B.Barrett

地理学与可持续性（英文）2026，Vol.7Issue(2)：44-58,15.

地理学与可持续性（英文）2026，Vol.7Issue(2)：44-58,15.DOI:10.1016/j.geosus.2026.100413

Spatial heterogeneity in machine learning-based poverty mapping:Where do models underperform?

Yating Ru ¹Elizabeth Tennant ²David S.Matteson ³Christopher B.Barrett⁴

作者信息

1. Economic Research and Development Impact Department,Asian Development Bank,Mandaluyong City,Metro Manila 1550,Philippines||Department of City and Regional Planning,Cornell University,Ithaca,NY 14853,USA
2. Charles H.Dyson School of Applied Economics and Management,Cornell University,Ithaca,NY 14853,USA
3. Department of Statistics and Data Science,Cornell University,Ithaca,NY 14853,USA
4. Charles H.Dyson School of Applied Economics and Management,Cornell University,Ithaca,NY 14853,USA||Cornell Jeb E.Brooks School of Public Policy,Cornell University,Ithaca,NY 14853,USA
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摘要

关键词

Poverty mapping/Machine learning/Spatial models/East Africa

Key words

Poverty mapping/Machine learning/Spatial models/East Africa

引用本文复制引用

Yating Ru,Elizabeth Tennant,David S.Matteson,Christopher B.Barrett..Spatial heterogeneity in machine learning-based poverty mapping:Where do models underperform?[J].地理学与可持续性（英文）,2026,7(2):44-58,15.

基金项目

This work was supported by the Cornell Atkinson Center for Sustain-ability.We thank Cassian D'Cunha and the Cornell Center for Social Sci-ences for computational resources and support.We also thank Takaaki Masaki and Arturo Jr.M.Martinez for their insightful comments and suggestions.Finally,we appreciate the constructive feedback from the editor and anonymous reviewers,which strengthened the paper. （）

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