Measuring global multi-scale place connectivity using geotagged social media data
© 2021. The Author(s)..
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.
| Errataetall: | |
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| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:11 |
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| Enthalten in: |
Scientific reports - 11(2021), 1 vom: 19. Juli, Seite 14694 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Li, Zhenlong [VerfasserIn] |
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| Links: |
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| Themen: |
Journal Article |
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| Anmerkungen: |
Date Completed 23.07.2021 Date Revised 02.04.2024 published: Electronic UpdateOf: ArXiv. 2021 Feb 8;:. - PMID 33564697 Citation Status MEDLINE |
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| doi: |
10.1038/s41598-021-94300-7 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 500 | |a UpdateOf: ArXiv. 2021 Feb 8;:. - PMID 33564697 | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2021. The Author(s). | ||
| 520 | |a 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 | ||
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| 650 | 4 | |a Research Support, N.I.H., Extramural | |
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| 650 | 4 | |a Research Support, U.S. Gov't, Non-P.H.S. | |
| 700 | 1 | |a Huang, Xiao |e verfasserin |4 aut | |
| 700 | 1 | |a Ye, Xinyue |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Yuqin |e verfasserin |4 aut | |
| 700 | 1 | |a Martin, Yago |e verfasserin |4 aut | |
| 700 | 1 | |a Ning, Huan |e verfasserin |4 aut | |
| 700 | 1 | |a Hodgson, Michael E |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Xiaoming |e verfasserin |4 aut | |
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