Open AccessMultivariate random forest prediction of poverty and malnutrition prevalence
Author(s) -
Chris Browne,
David S. Matteson,
Linden McBride,
Leiqiu Hu,
Yanyan Liu,
Ying Sun,
Jiaming Wen,
Christopher B. Barrett
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0255519
Subject(s) - multivariate statistics , multivariate analysis , malnutrition , poverty , random forest , geography , statistics , environmental health , mathematics , medicine , computer science , economics , machine learning , economic growth
Advances 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.