Li Zhenlong, Huang Xiao, Ye Xinyue, Jiang Yuqin, Martin Yago, Ning Huan, Hodgson Michael E, Li Xiaoming
Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, SC, USA.
Department of Geosciences, University of Arkansas, AR, USA.
ArXiv. 2021 Feb 8:arXiv:2102.03991v3.
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 launched visualization platform and open-sourced PCI datasets at various geographic levels, are expected to support research fields requiring knowledge in human spatial interactions.
场所连通性受人类活动塑造，通过地点间空间相互作用的强度来量化。几十年来，空间科学家一直在研究场所连通性、应用和度量方法。社交媒体日益普及，提供了一种新的数据流，其中空间社交互动度量在很大程度上不存在隐私问题，易于评估且具有一致性。在本研究中，我们基于地理标记推文所揭示的地点间空间相互作用，引入了一种全球多尺度场所连通性指数（PCI），作为一种时空连续且易于实施的度量方法。在美国县级层面展示的多尺度PCI，与SafeGraph人口流动记录（在美国人口中渗透率为10%）以及Facebook的社交连通性指数（SCI，一种基于社交网络的流行连通性指数）呈现出强烈的正相关关系。我们发现PCI具有很强的边界效应，并且通常遵循距离衰减规律，尽管在人口密度更大的城市化程度更高的县这种效应较弱。我们的调查进一步表明，PCI在解决需要场所连通性知识的现实世界问题方面具有巨大潜力，通过两个应用实例来说明：1）在疫情早期对新冠病毒空间传播进行建模；2）对飓风疏散目的地选择进行建模。PCI的方法和背景知识，连同已推出的可视化平台以及各级地理层面的开源PCI数据集，有望为需要人类空间相互作用知识的研究领域提供支持。
