Practical geospatial and sociodemographic predictors of human mobility

Abstract Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resol...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Corrine W. Ruktanonchai, Shengjie Lai, Chigozie E. Utazi, Alex D. Cunningham, Patrycja Koper, Grant E. Rogers, Nick W. Ruktanonchai, Adam Sadilek, Dorothea Woods, Andrew J. Tatem, Jessica E. Steele, Alessandro Sorichetta
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9e47a7f8c9d7423098bb8b5855e8376f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:9e47a7f8c9d7423098bb8b5855e8376f
record_format	dspace
spelling	oai:doaj.org-article:9e47a7f8c9d7423098bb8b5855e8376f2021-12-02T18:46:59ZPractical geospatial and sociodemographic predictors of human mobility10.1038/s41598-021-94683-72045-2322https://doaj.org/article/9e47a7f8c9d7423098bb8b5855e8376f2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94683-7https://doaj.org/toc/2045-2322Abstract Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe typical seasonal subnational mobility in Kenya pre-COVID-19, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings in non-pandemic settings. To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates explaining human movement patterns, while accounting for spatial and temporal autocorrelations. Typical pre-pandemic mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, which is important in establishing how mobility patterns changed post-pandemic. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly explained mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.Corrine W. RuktanonchaiShengjie LaiChigozie E. UtaziAlex D. CunninghamPatrycja KoperGrant E. RogersNick W. RuktanonchaiAdam SadilekDorothea WoodsAndrew J. TatemJessica E. SteeleAlessandro SorichettaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Corrine W. Ruktanonchai Shengjie Lai Chigozie E. Utazi Alex D. Cunningham Patrycja Koper Grant E. Rogers Nick W. Ruktanonchai Adam Sadilek Dorothea Woods Andrew J. Tatem Jessica E. Steele Alessandro Sorichetta Practical geospatial and sociodemographic predictors of human mobility
description	Abstract Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe typical seasonal subnational mobility in Kenya pre-COVID-19, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings in non-pandemic settings. To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates explaining human movement patterns, while accounting for spatial and temporal autocorrelations. Typical pre-pandemic mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, which is important in establishing how mobility patterns changed post-pandemic. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly explained mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.
format	article
author	Corrine W. Ruktanonchai Shengjie Lai Chigozie E. Utazi Alex D. Cunningham Patrycja Koper Grant E. Rogers Nick W. Ruktanonchai Adam Sadilek Dorothea Woods Andrew J. Tatem Jessica E. Steele Alessandro Sorichetta
author_facet	Corrine W. Ruktanonchai Shengjie Lai Chigozie E. Utazi Alex D. Cunningham Patrycja Koper Grant E. Rogers Nick W. Ruktanonchai Adam Sadilek Dorothea Woods Andrew J. Tatem Jessica E. Steele Alessandro Sorichetta
author_sort	Corrine W. Ruktanonchai
title	Practical geospatial and sociodemographic predictors of human mobility
title_short	Practical geospatial and sociodemographic predictors of human mobility
title_full	Practical geospatial and sociodemographic predictors of human mobility
title_fullStr	Practical geospatial and sociodemographic predictors of human mobility
title_full_unstemmed	Practical geospatial and sociodemographic predictors of human mobility
title_sort	practical geospatial and sociodemographic predictors of human mobility
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9e47a7f8c9d7423098bb8b5855e8376f
work_keys_str_mv	AT corrinewruktanonchai practicalgeospatialandsociodemographicpredictorsofhumanmobility AT shengjielai practicalgeospatialandsociodemographicpredictorsofhumanmobility AT chigozieeutazi practicalgeospatialandsociodemographicpredictorsofhumanmobility AT alexdcunningham practicalgeospatialandsociodemographicpredictorsofhumanmobility AT patrycjakoper practicalgeospatialandsociodemographicpredictorsofhumanmobility AT granterogers practicalgeospatialandsociodemographicpredictorsofhumanmobility AT nickwruktanonchai practicalgeospatialandsociodemographicpredictorsofhumanmobility AT adamsadilek practicalgeospatialandsociodemographicpredictorsofhumanmobility AT dorotheawoods practicalgeospatialandsociodemographicpredictorsofhumanmobility AT andrewjtatem practicalgeospatialandsociodemographicpredictorsofhumanmobility AT jessicaesteele practicalgeospatialandsociodemographicpredictorsofhumanmobility AT alessandrosorichetta practicalgeospatialandsociodemographicpredictorsofhumanmobility
_version_	1718377695669125120

Practical geospatial and sociodemographic predictors of human mobility

Ejemplares similares