Urban mobility network centrality predicts social resilience
Lin Chen, Fengli Xu, Esteban Moro, Pan Hui, Yong Li, and James Evans

TL;DR
This study demonstrates that eigenvector centrality in urban mobility networks effectively predicts social resilience during shocks, highlighting the importance of core venues in maintaining social cohesion and diversity in cities.
Contribution
It introduces a novel application of network centrality measures to predict social resilience in urban venues during shocks, supported by large-scale mobility data from multiple cities.
Findings
Eigenvector centrality predicts social resilience with over 80% explanatory power.
Core venues with shorter visits and broader reach are crucial for social stability.
Patterns of venue visitation suggest dual mechanisms of social interaction and engagement.
Abstract
Cities thrive on social interactions that foster well-being, innovation, and prosperity; yet, exogenous shocks such as pandemics, hurricanes, and wildfires can severely disrupt them. Different urban venues exhibit widely divergent response patterns, raising key questions about what factors contribute to these differences and how we can anticipate and respond. Understanding these questions is crucial for safeguarding social resilience, the capacity of urban venues to maintain both visitation and diversity. In this study, we analyze large-scale human mobility data from 15 US cities covering more than 103 million residents across three distinct urban shocks. Despite a general trend of declining visitation and weakened social mixing, 36.28%-53.01% of venues exhibit reduced segregation, and 21.04%-38.55% of venues exhibit increased visitation. By constructing a mobility network interlinking…
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Taxonomy
TopicsHuman Mobility and Location-Based Analysis · Complex Network Analysis Techniques · Urban Transport and Accessibility
