Multivariate random forest prediction of poverty and malnutrition prevalence.

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 tra...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Chris Browne, David S Matteson, Linden McBride, Leiqiu Hu, Yanyan Liu, Ying Sun, Jiaming Wen, Christopher B Barrett
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e90e4f8fad114eea9a676e4cc99a8b9b
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e90e4f8fad114eea9a676e4cc99a8b9b
record_format	dspace
spelling	oai:doaj.org-article:e90e4f8fad114eea9a676e4cc99a8b9b2021-12-02T20:04:42ZMultivariate random forest prediction of poverty and malnutrition prevalence.1932-620310.1371/journal.pone.0255519https://doaj.org/article/e90e4f8fad114eea9a676e4cc99a8b9b2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255519https://doaj.org/toc/1932-6203Advances 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.Chris BrowneDavid S MattesonLinden McBrideLeiqiu HuYanyan LiuYing SunJiaming WenChristopher B BarrettPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0255519 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Chris Browne David S Matteson Linden McBride Leiqiu Hu Yanyan Liu Ying Sun Jiaming Wen Christopher B Barrett Multivariate random forest prediction of poverty and malnutrition prevalence.
description	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.
format	article
author	Chris Browne David S Matteson Linden McBride Leiqiu Hu Yanyan Liu Ying Sun Jiaming Wen Christopher B Barrett
author_facet	Chris Browne David S Matteson Linden McBride Leiqiu Hu Yanyan Liu Ying Sun Jiaming Wen Christopher B Barrett
author_sort	Chris Browne
title	Multivariate random forest prediction of poverty and malnutrition prevalence.
title_short	Multivariate random forest prediction of poverty and malnutrition prevalence.
title_full	Multivariate random forest prediction of poverty and malnutrition prevalence.
title_fullStr	Multivariate random forest prediction of poverty and malnutrition prevalence.
title_full_unstemmed	Multivariate random forest prediction of poverty and malnutrition prevalence.
title_sort	multivariate random forest prediction of poverty and malnutrition prevalence.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/e90e4f8fad114eea9a676e4cc99a8b9b
work_keys_str_mv	AT chrisbrowne multivariaterandomforestpredictionofpovertyandmalnutritionprevalence AT davidsmatteson multivariaterandomforestpredictionofpovertyandmalnutritionprevalence AT lindenmcbride multivariaterandomforestpredictionofpovertyandmalnutritionprevalence AT leiqiuhu multivariaterandomforestpredictionofpovertyandmalnutritionprevalence AT yanyanliu multivariaterandomforestpredictionofpovertyandmalnutritionprevalence AT yingsun multivariaterandomforestpredictionofpovertyandmalnutritionprevalence AT jiamingwen multivariaterandomforestpredictionofpovertyandmalnutritionprevalence AT christopherbbarrett multivariaterandomforestpredictionofpovertyandmalnutritionprevalence
_version_	1718375577137709056

Multivariate random forest prediction of poverty and malnutrition prevalence.

Ejemplares similares