Urban mobility network centrality predicts social resilience

2026-02-20

AI Summary

This study addresses the pronounced disparities in social resilience across urban places under external shocks and seeks to identify their key determinants. Leveraging large-scale human mobility data, we construct an urban place mobility network and, for the first time, link eigenvector centrality from graph theory to social resilience. We propose a “wells-and-pools” analogy model to elucidate the distinct functional mechanisms of core versus peripheral places in sustaining visitation volume and population diversity. Empirical results demonstrate that this centrality measure improves explanatory power for place segregation and visitation changes by over 80% compared to conventional metrics. Following the shock, 36.28%–53.01% of places exhibited reduced segregation, while 21.04%–38.55% saw increased visitation—outcomes significantly surpassing those predicted by traditional indicators.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Application Domains: Humanities & Computational Social Science
• Humans and AI: Understanding People, Theories, Concepts and Methods

Application Category

• Social Networks and Social Media: Computational social science
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

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 types of urban venues, we reveal that eigenvector network centrality tends to indicate the provision of essential services and robustly predicts social resilience across varied urban shocks. Specifically, centrality elevates the explanatory power by more than 80% in predicting both segregation and mobility change, compared with more intuitive features. Furthermore, compared to peripheral venues, core venues featuring shorter visit distances, broader neighborhood visitation, shorter visitor dwell times, and steadier popularity throughout the day. Such patterns imply a dual social mechanism: core venues sustain social ties through frequent informal interaction, while peripheral ones facilitate deeper engagement around specialized interests and their corresponding social circles. By bridging urban mobility research with economic theories that distinguish staple from discretionary products, we propose a well-and-pool analogy that suggests how people spend their varying urban mobility budgets.

Problem

• social resilience
• urban mobility
• exogenous shocks
• venue segregation
• human mobility

Innovation

• eigenvector centrality
• social resilience
• urban mobility network
• human mobility data
• well-and-pool analogy