Spatial heterogeneity in machine learning-based poverty mapping:Where do models underperform?OA
Spatial heterogeneity in machine learning-based poverty mapping:Where do models underperform?
Yating Ru;Elizabeth Tennant;David S.Matteson;Christopher B.Barrett
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,USACharles H.Dyson School of Applied Economics and Management,Cornell University,Ithaca,NY 14853,USADepartment of Statistics and Data Science,Cornell University,Ithaca,NY 14853,USACharles 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
Poverty mappingMachine learningSpatial modelsEast Africa
Poverty mappingMachine learningSpatial modelsEast Africa
《地理学与可持续性（英文）》 2026 (2)
44-58,15
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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