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

• Published in: Scientific reports Vol. 11; no. 1; p. 14694
• Main Authors: Li, Zhenlong, Huang, Xiao, Ye, Xinyue, Jiang, Yuqin, Martin, Yago, Ning, Huan, Hodgson, Michael E., Li, Xiaoming
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 19.07.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705/1046; 692/699/255; 704/4111; Connectivity; COVID-19; COVID-19 - epidemiology; Cyclonic Storms; Humanities and Social Sciences; Humans; Hurricanes; Models, Theoretical; multidisciplinary; Pandemics; Science; Science (multidisciplinary); Social behavior; Social Interaction; Social Media; Social networks; Social organization; Spatial Analysis; United States; World problems; United States; United States > US
• DOI: 10.1038/s41598-021-94300-7
• ISSN: 2045-2322

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.