Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning
Increasingly plentiful data and powerful predictive algorithms heighten the promise of data science for humanitarian and development programming. We advocate for embrace of, and investment in, machine learning methods for poverty and malnutrition targeting, mapping, monitoring, and early warning whi...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Agricultural and Applied Economics Association
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/141111 |
| _version_ | 1855519339944345600 |
|---|---|
| author | Mcbride, Linden Barrett, Christopher B. Browne, Christopher Hu, Leiqiu Liu, Yanyan Matteson, David S. Sun, Ying Wen, Jiaming |
| author_browse | Barrett, Christopher B. Browne, Christopher Hu, Leiqiu Liu, Yanyan Matteson, David S. Mcbride, Linden Sun, Ying Wen, Jiaming |
| author_facet | Mcbride, Linden Barrett, Christopher B. Browne, Christopher Hu, Leiqiu Liu, Yanyan Matteson, David S. Sun, Ying Wen, Jiaming |
| author_sort | Mcbride, Linden |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Increasingly plentiful data and powerful predictive algorithms heighten the promise of data science for humanitarian and development programming. We advocate for embrace of, and investment in, machine learning methods for poverty and malnutrition targeting, mapping, monitoring, and early warning while also cautioning that distinct objectives require distinct data and methods. In particular, we highlight the differences between poverty and malnutrition targeting and mapping, the differences between structural and stochastic deprivation, and the modeling and data challenges of early warning system development. Overall, we urge careful consideration of the purpose and use cases of machine learning informed models. |
| format | Journal Article |
| id | CGSpace141111 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Agricultural and Applied Economics Association |
| publisherStr | Agricultural and Applied Economics Association |
| record_format | dspace |
| spelling | CGSpace1411112025-10-26T13:02:00Z Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning Mcbride, Linden Barrett, Christopher B. Browne, Christopher Hu, Leiqiu Liu, Yanyan Matteson, David S. Sun, Ying Wen, Jiaming data humanitarian organizations machine learning capacity development early warning systems malnutrition poverty big data Increasingly plentiful data and powerful predictive algorithms heighten the promise of data science for humanitarian and development programming. We advocate for embrace of, and investment in, machine learning methods for poverty and malnutrition targeting, mapping, monitoring, and early warning while also cautioning that distinct objectives require distinct data and methods. In particular, we highlight the differences between poverty and malnutrition targeting and mapping, the differences between structural and stochastic deprivation, and the modeling and data challenges of early warning system development. Overall, we urge careful consideration of the purpose and use cases of machine learning informed models. 2022-06 2024-04-12T13:37:18Z 2024-04-12T13:37:18Z Journal Article https://hdl.handle.net/10568/141111 en https://doi.org/10.1371/journal.pone.0255519 Limited Access Agricultural and Applied Economics Association Mcbride, Linden; Barrett, Christopher B.; Browne, Christopher; Hu, Leiqiu; Liu, Yanyan; Matteson, David S.; Sun, Ying; and Wen, Jiaming. 2022. Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning. Applied Economic Perspectives and Policy 44(2): 879-892. https://doi.org/10.1002/aepp.13175 |
| spellingShingle | data humanitarian organizations machine learning capacity development early warning systems malnutrition poverty big data Mcbride, Linden Barrett, Christopher B. Browne, Christopher Hu, Leiqiu Liu, Yanyan Matteson, David S. Sun, Ying Wen, Jiaming Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning |
| title | Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning |
| title_full | Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning |
| title_fullStr | Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning |
| title_full_unstemmed | Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning |
| title_short | Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning |
| title_sort | predicting poverty and malnutrition for targeting mapping monitoring and early warning |
| topic | data humanitarian organizations machine learning capacity development early warning systems malnutrition poverty big data |
| url | https://hdl.handle.net/10568/141111 |
| work_keys_str_mv | AT mcbridelinden predictingpovertyandmalnutritionfortargetingmappingmonitoringandearlywarning AT barrettchristopherb predictingpovertyandmalnutritionfortargetingmappingmonitoringandearlywarning AT brownechristopher predictingpovertyandmalnutritionfortargetingmappingmonitoringandearlywarning AT huleiqiu predictingpovertyandmalnutritionfortargetingmappingmonitoringandearlywarning AT liuyanyan predictingpovertyandmalnutritionfortargetingmappingmonitoringandearlywarning AT mattesondavids predictingpovertyandmalnutritionfortargetingmappingmonitoringandearlywarning AT sunying predictingpovertyandmalnutritionfortargetingmappingmonitoringandearlywarning AT wenjiaming predictingpovertyandmalnutritionfortargetingmappingmonitoringandearlywarning |