Mobility and phone call behavior explain patterns in poverty at high-resolution across multiple settings

Abstract Call detail records (CDRs) from mobile phone metadata are a promising data source for mapping poverty indicators in low- and middle-income countries. These data provide information on social networks, call behavior, and mobility patterns in a population, which are correlated with measures o...

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Autores principales:	Jessica E. Steele, Carla Pezzulo, Maximilian Albert, Christopher J. Brooks, Elisabeth zu Erbach-Schoenberg, Siobhán B. O’Connor, Pål R. Sundsøy, Kenth Engø-Monsen, Kristine Nilsen, Bonita Graupe, Rajesh Lal Nyachhyon, Pradeep Silpakar, Andrew J. Tatem
Formato:	article
Lenguaje:	EN
Publicado:	Springer Nature 2021
Materias:	History of scholarship and learning. The humanities AZ20-999 Social Sciences H
Acceso en línea:	https://doaj.org/article/b1da642832ed4079982b877b89426c7b
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spelling	oai:doaj.org-article:b1da642832ed4079982b877b89426c7b2021-11-28T12:25:47ZMobility and phone call behavior explain patterns in poverty at high-resolution across multiple settings10.1057/s41599-021-00953-02662-9992https://doaj.org/article/b1da642832ed4079982b877b89426c7b2021-11-01T00:00:00Zhttps://doi.org/10.1057/s41599-021-00953-0https://doaj.org/toc/2662-9992Abstract Call detail records (CDRs) from mobile phone metadata are a promising data source for mapping poverty indicators in low- and middle-income countries. These data provide information on social networks, call behavior, and mobility patterns in a population, which are correlated with measures of socioeconomic status. CDRs are passively collected and provide information with high spatial and temporal resolution. Identifying features from these data that are generalizable and able to predict poverty and wealth beyond a single context could promote broader usage of mobile data, contribute to a reduction in the cost of socioeconomic data collection and processing, as well as complement existing census and survey-based methods of poverty estimation with improved temporal resolution. This is especially important within the context of the sustainable development goals (SDGs), where poverty and related health indicators are to be reduced significantly across subnational geographies by 2030. Here we utilize measures of cell phone user behavior derived from three CDR datasets within a Bayesian modeling framework to map poverty and wealth patterns across Namibia, Nepal, and Bangladesh. We demonstrate five metrics of user mobility and call behavior that are able to explain between 50% and 65% of the variance in socioeconomic status nationally for these three countries. These key metrics prove useful in very different contexts and can be readily provided as part of an existing CDR platform or software package. This paper provides a key contribution in this regard by identifying such metrics relevant to estimating poverty. We highlight the inclusion of ancillary data and local context as an important factor in understanding model outputs when targeting poverty alleviation strategies.Jessica E. SteeleCarla PezzuloMaximilian AlbertChristopher J. BrooksElisabeth zu Erbach-SchoenbergSiobhán B. O’ConnorPål R. SundsøyKenth Engø-MonsenKristine NilsenBonita GraupeRajesh Lal NyachhyonPradeep SilpakarAndrew J. TatemSpringer NaturearticleHistory of scholarship and learning. The humanitiesAZ20-999Social SciencesHENHumanities & Social Sciences Communications, Vol 8, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	History of scholarship and learning. The humanities AZ20-999 Social Sciences H
spellingShingle	History of scholarship and learning. The humanities AZ20-999 Social Sciences H Jessica E. Steele Carla Pezzulo Maximilian Albert Christopher J. Brooks Elisabeth zu Erbach-Schoenberg Siobhán B. O’Connor Pål R. Sundsøy Kenth Engø-Monsen Kristine Nilsen Bonita Graupe Rajesh Lal Nyachhyon Pradeep Silpakar Andrew J. Tatem Mobility and phone call behavior explain patterns in poverty at high-resolution across multiple settings
description	Abstract Call detail records (CDRs) from mobile phone metadata are a promising data source for mapping poverty indicators in low- and middle-income countries. These data provide information on social networks, call behavior, and mobility patterns in a population, which are correlated with measures of socioeconomic status. CDRs are passively collected and provide information with high spatial and temporal resolution. Identifying features from these data that are generalizable and able to predict poverty and wealth beyond a single context could promote broader usage of mobile data, contribute to a reduction in the cost of socioeconomic data collection and processing, as well as complement existing census and survey-based methods of poverty estimation with improved temporal resolution. This is especially important within the context of the sustainable development goals (SDGs), where poverty and related health indicators are to be reduced significantly across subnational geographies by 2030. Here we utilize measures of cell phone user behavior derived from three CDR datasets within a Bayesian modeling framework to map poverty and wealth patterns across Namibia, Nepal, and Bangladesh. We demonstrate five metrics of user mobility and call behavior that are able to explain between 50% and 65% of the variance in socioeconomic status nationally for these three countries. These key metrics prove useful in very different contexts and can be readily provided as part of an existing CDR platform or software package. This paper provides a key contribution in this regard by identifying such metrics relevant to estimating poverty. We highlight the inclusion of ancillary data and local context as an important factor in understanding model outputs when targeting poverty alleviation strategies.
format	article
author	Jessica E. Steele Carla Pezzulo Maximilian Albert Christopher J. Brooks Elisabeth zu Erbach-Schoenberg Siobhán B. O’Connor Pål R. Sundsøy Kenth Engø-Monsen Kristine Nilsen Bonita Graupe Rajesh Lal Nyachhyon Pradeep Silpakar Andrew J. Tatem
author_facet	Jessica E. Steele Carla Pezzulo Maximilian Albert Christopher J. Brooks Elisabeth zu Erbach-Schoenberg Siobhán B. O’Connor Pål R. Sundsøy Kenth Engø-Monsen Kristine Nilsen Bonita Graupe Rajesh Lal Nyachhyon Pradeep Silpakar Andrew J. Tatem
author_sort	Jessica E. Steele
title	Mobility and phone call behavior explain patterns in poverty at high-resolution across multiple settings
title_short	Mobility and phone call behavior explain patterns in poverty at high-resolution across multiple settings
title_full	Mobility and phone call behavior explain patterns in poverty at high-resolution across multiple settings
title_fullStr	Mobility and phone call behavior explain patterns in poverty at high-resolution across multiple settings
title_full_unstemmed	Mobility and phone call behavior explain patterns in poverty at high-resolution across multiple settings
title_sort	mobility and phone call behavior explain patterns in poverty at high-resolution across multiple settings
publisher	Springer Nature
publishDate	2021
url	https://doaj.org/article/b1da642832ed4079982b877b89426c7b
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Mobility and phone call behavior explain patterns in poverty at high-resolution across multiple settings

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