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Urban mobility network centrality predicts social resilience

Lin Chen, Fengli Xu, Esteban Moro, Pan Hui, Yong Li, James Evans

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.

Urban mobility network centrality predicts social resilience

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.
Paper Structure (15 sections, 5 equations, 4 figures, 2 tables)

This paper contains 15 sections, 5 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Results
  3. Discussion
  4. Methods
  5. Data Availability
  6. Code Availability
  7. Author contributions
  8. Competing Interests
  9. Additional information

Figures (4)

  • Figure 1: Social resilience of urban places during shocks.a, Illustrated calculation of mobility and segregation changes during shocks. b, Changes in segregation among 15 MSAs during 3 shocks (COVID-19, 2018 California Wildfire, and 2020 Hurricane ETA), with 36.28%-53.01% of venues manifesting reduced segregation. c, Changes in mobility among 15 MSAs during 3 shocks, with 21.04%-38.55% of places showing increased visitation. d, Proportions of places in the top 30% range of segregation increase, aggregated by sector and sorted in ascending order. The middle curves show the mean value across different MSAs. The upper-left subfigure shows the correlation between single-MSA patterns and the mean pattern. The node colors reflect the broader category each sector falls within. e, Proportion of places in the top 30% range of mobility reduction, aggregated by sector and sorted in ascending order. The middle curves show the mean value across different MSAs. The upper-left subfigure shows the correlation between single-MSA patterns and the mean pattern. The node colors reflect the broader category each sector falls within.
  • Figure 2: Uneven segregation and mobility changes across places can be predicted by their centrality in the mobility network.a, Constructed mobility network of places in the pre-disaster period, for the Philadelphia MSA. The node sizes reflect the total visitations to each sector. The node colors reflect the broader category each sector falls within. The value next to each sector/category reflects its average eigenvector centrality. b,d,f, Relationship between the network centrality of places and their relative segregation change (b) in Philadelphia during COVID-19, (d) in Riverside during the 2018 California Wildfire, and (f) in Miami during the 2020 Hurricane ETA. c,e,g, Relationship between the network centrality of places and their relative mobility change (c) in Philadelphia during COVID-19, (e) in Riverside during the 2018 California Wildfire, and (g) in Miami during the 2020 Hurricane ETA. Lines are fitted by OLS regression. Shaded regions indicate 95% CI.
  • Figure 3: OLS regression models for segregation and mobility changes during COVID-19. Pie charts show the relative percentage importance of each feature in Model 4 for the Philadelphia MSA.
  • Figure 4: Comparison between core and peripheral venues.a, Geographical distribution of core (red dots) and peripheral (blue dots) venues in the Philadelphia MSA. b-d, Comparison of distribution statistics between core and peripheral venues in 10 large MSAs. Each dot represents one MSA. e, Median visitation distances to core and peripheral venues before and during COVID-19. f, Median number of covered CBGs of core and peripheral venues before and during COVID-19. g, Median visitor dwell time at core and peripheral places before and during COVID-19. h, Entropy of hourly popularity of core and peripheral places before and during COVID-19. Error bars indicating 95% CI.