Multivariate random forest prediction of poverty and malnutrition prevalence

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dc.creatorBrowne, Chris
dc.creatorMatteson, David S.
dc.creatorMcBride, Linden
dc.creatorHu, Leiqiu
dc.creatorLiu, Yanyan
dc.creatorSun, Ying
dc.creatorWen, Jiaming
dc.creatorBarrett, Christopher B.
dc.date2021-09-14
dc.date2024-05-22T12:11:10Z
dc.date2024-05-22T12:11:10Z
dc.date.accessioned2026-06-27T15:01:48Z
dc.descriptionAdvances in remote sensing and machine learning enable increasingly accurate, inexpensive, and timely estimation of poverty and malnutrition indicators to guide development and humanitarian agencies’ programming. However, state of the art models often rely on proprietary data and/or deep or transfer learning methods whose underlying mechanics may be challenging to interpret. We demonstrate how interpretable random forest models can produce estimates of a set of (potentially correlated) malnutrition and poverty prevalence measures using free, open access, regularly updated, georeferenced data. We demonstrate two use cases: contemporaneous prediction, which might be used for poverty mapping, geographic targeting, or monitoring and evaluation tasks, and a sequential nowcasting task that can inform early warning systems. Applied to data from 11 low and lower-middle income countries, we find predictive accuracy broadly comparable for both tasks to prior studies that use proprietary data and/or deep or transfer learning methods.
dc.identifierhttps://hdl.handle.net/10568/142847
dc.identifier.urihttp://hdl.handle.net/123456789/92559
dc.languageen
dc.publisherPublic Library of Science
dc.relationhttps://doi.org/10.1002/aepp.13175
dc.rightsOpen Access
dc.sourceBrowne, Chris; Matteson, David S.; McBride, Linden; Hu, Leiqiu; Liu, Yanyan; Sun, Ying; Wen, Jiaming; Barrett, Christopher B. 2021. Multivariate random forest prediction of poverty and malnutrition prevalence. PLoS ONE 16(9): e0255519 https://doi.org/10.1371/journal.pone.0255519
dc.subjectmodels
dc.subjectdata
dc.subjectforecasting
dc.subjectsurveys
dc.subjectremote sensing
dc.subjecttechnology
dc.subjectmachine learning
dc.subjectcapacity development
dc.subjectmalnutrition
dc.subjectnutrition
dc.subjectpoverty
dc.titleMultivariate random forest prediction of poverty and malnutrition prevalence
dc.typeJournal Article

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Instituto Internacional de Investigación sobre Políticas Alimentarias (IFPRI)