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Multivariate random forest prediction of poverty and malnutrition prevalence

Author

Listed:
  • Chris Browne
  • David S Matteson
  • Linden McBride
  • Leiqiu Hu
  • Yanyan Liu
  • Ying Sun
  • Jiaming Wen
  • Christopher B Barrett

Abstract

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.

Suggested Citation

  • Chris Browne & David S Matteson & Linden McBride & Leiqiu Hu & Yanyan Liu & Ying Sun & Jiaming Wen & Christopher B Barrett, 2021. "Multivariate random forest prediction of poverty and malnutrition prevalence," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-23, September.
  • Handle: RePEc:plo:pone00:0255519
    DOI: 10.1371/journal.pone.0255519
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    Cited by:

    1. Ola Hall & Francis Dompae & Ibrahim Wahab & Fred Mawunyo Dzanku, 2023. "A review of machine learning and satellite imagery for poverty prediction: Implications for development research and applications," Journal of International Development, John Wiley & Sons, Ltd., vol. 35(7), pages 1753-1768, October.
    2. Aziza Usmanova & Ahmed Aziz & Dilshodjon Rakhmonov & Walid Osamy, 2022. "Utilities of Artificial Intelligence in Poverty Prediction: A Review," Sustainability, MDPI, vol. 14(21), pages 1-39, October.
    3. Binh Tang & Yanyan Liu & David S. Matteson, 2022. "Predicting poverty with vegetation index," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 930-945, June.
    4. Beltramo, Theresa P. & Calvi, Rossella & De Giorgi, Giacomo & Sarr, Ibrahima, 2023. "Child poverty among refugees," World Development, Elsevier, vol. 171(C).
    5. Linden McBride & Christopher B. Barrett & Christopher Browne & Leiqiu Hu & Yanyan Liu & David S. Matteson & Ying Sun & Jiaming Wen, 2022. "Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 879-892, June.

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