Measuring global multi-scale place connectivity using geotagged social media data

Abstract Shaped by human movement, place connectivity is quantified by the strength of spatial interactions among locations. For decades, spatial scientists have researched place connectivity, applications, and metrics. The growing popularity of social media provides a new data stream where spatial...

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Autores principales:	Zhenlong Li, Xiao Huang, Xinyue Ye, Yuqin Jiang, Yago Martin, Huan Ning, Michael E. Hodgson, Xiaoming Li
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ac316bb1bbc34f3d81bca82712cb138e
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id	oai:doaj.org-article:ac316bb1bbc34f3d81bca82712cb138e
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spelling	oai:doaj.org-article:ac316bb1bbc34f3d81bca82712cb138e2021-12-02T16:26:30ZMeasuring global multi-scale place connectivity using geotagged social media data10.1038/s41598-021-94300-72045-2322https://doaj.org/article/ac316bb1bbc34f3d81bca82712cb138e2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94300-7https://doaj.org/toc/2045-2322Abstract Shaped by human movement, place connectivity is quantified by the strength of spatial interactions among locations. For decades, spatial scientists have researched place connectivity, applications, and metrics. The growing popularity of social media provides a new data stream where spatial social interaction measures are largely devoid of privacy issues, easily assessable, and harmonized. In this study, we introduced a global multi-scale place connectivity index (PCI) based on spatial interactions among places revealed by geotagged tweets as a spatiotemporal-continuous and easy-to-implement measurement. The multi-scale PCI, demonstrated at the US county level, exhibits a strong positive association with SafeGraph population movement records (10% penetration in the US population) and Facebook’s social connectedness index (SCI), a popular connectivity index based on social networks. We found that PCI has a strong boundary effect and that it generally follows the distance decay, although this force is weaker in more urbanized counties with a denser population. Our investigation further suggests that PCI has great potential in addressing real-world problems that require place connectivity knowledge, exemplified with two applications: (1) modeling the spatial spread of COVID-19 during the early stage of the pandemic and (2) modeling hurricane evacuation destination choice. The methodological and contextual knowledge of PCI, together with the open-sourced PCI datasets at various geographic levels, are expected to support research fields requiring knowledge in human spatial interactions.Zhenlong LiXiao HuangXinyue YeYuqin JiangYago MartinHuan NingMichael E. HodgsonXiaoming LiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-19 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhenlong Li Xiao Huang Xinyue Ye Yuqin Jiang Yago Martin Huan Ning Michael E. Hodgson Xiaoming Li Measuring global multi-scale place connectivity using geotagged social media data
description	Abstract Shaped by human movement, place connectivity is quantified by the strength of spatial interactions among locations. For decades, spatial scientists have researched place connectivity, applications, and metrics. The growing popularity of social media provides a new data stream where spatial social interaction measures are largely devoid of privacy issues, easily assessable, and harmonized. In this study, we introduced a global multi-scale place connectivity index (PCI) based on spatial interactions among places revealed by geotagged tweets as a spatiotemporal-continuous and easy-to-implement measurement. The multi-scale PCI, demonstrated at the US county level, exhibits a strong positive association with SafeGraph population movement records (10% penetration in the US population) and Facebook’s social connectedness index (SCI), a popular connectivity index based on social networks. We found that PCI has a strong boundary effect and that it generally follows the distance decay, although this force is weaker in more urbanized counties with a denser population. Our investigation further suggests that PCI has great potential in addressing real-world problems that require place connectivity knowledge, exemplified with two applications: (1) modeling the spatial spread of COVID-19 during the early stage of the pandemic and (2) modeling hurricane evacuation destination choice. The methodological and contextual knowledge of PCI, together with the open-sourced PCI datasets at various geographic levels, are expected to support research fields requiring knowledge in human spatial interactions.
format	article
author	Zhenlong Li Xiao Huang Xinyue Ye Yuqin Jiang Yago Martin Huan Ning Michael E. Hodgson Xiaoming Li
author_facet	Zhenlong Li Xiao Huang Xinyue Ye Yuqin Jiang Yago Martin Huan Ning Michael E. Hodgson Xiaoming Li
author_sort	Zhenlong Li
title	Measuring global multi-scale place connectivity using geotagged social media data
title_short	Measuring global multi-scale place connectivity using geotagged social media data
title_full	Measuring global multi-scale place connectivity using geotagged social media data
title_fullStr	Measuring global multi-scale place connectivity using geotagged social media data
title_full_unstemmed	Measuring global multi-scale place connectivity using geotagged social media data
title_sort	measuring global multi-scale place connectivity using geotagged social media data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ac316bb1bbc34f3d81bca82712cb138e
work_keys_str_mv	AT zhenlongli measuringglobalmultiscaleplaceconnectivityusinggeotaggedsocialmediadata AT xiaohuang measuringglobalmultiscaleplaceconnectivityusinggeotaggedsocialmediadata AT xinyueye measuringglobalmultiscaleplaceconnectivityusinggeotaggedsocialmediadata AT yuqinjiang measuringglobalmultiscaleplaceconnectivityusinggeotaggedsocialmediadata AT yagomartin measuringglobalmultiscaleplaceconnectivityusinggeotaggedsocialmediadata AT huanning measuringglobalmultiscaleplaceconnectivityusinggeotaggedsocialmediadata AT michaelehodgson measuringglobalmultiscaleplaceconnectivityusinggeotaggedsocialmediadata AT xiaomingli measuringglobalmultiscaleplaceconnectivityusinggeotaggedsocialmediadata
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Measuring global multi-scale place connectivity using geotagged social media data

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