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Mobility-Based Segregation in U.S. Metropolitan Areas.

Yongjun Zhang1, Siwei Cheng2

  • 1Department of Sociology and Institute for Advanced Computational Science, Stony Brook University, Stony Brook, NY, USA.

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Summary
This summary is machine-generated.

This study introduces a new way to measure segregation using mobility data, revealing how daily movements create distinct racial, ethnic, and income isolation in activity spaces. It highlights significant own-group isolation and varied exposure within communities.

Keywords:
Activity spaceHuman mobility dataIncome segregationIntergroup exposureRacial segregation

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Area of Science:

  • Sociology
  • Urban Studies
  • Data Science

Background:

  • Traditional segregation measures often rely on residential proximity, potentially overlooking the nuances of daily movement and interaction.
  • Understanding segregation requires examining not only where people live but also where they go and who they encounter in their daily activities.

Purpose of the Study:

  • To develop and validate a novel, mobility-based measure of racial, ethnic, and income segregation within individuals' activity spaces.
  • To differentiate between local residential environments and connected communities shaped by daily travel patterns.
  • To analyze the sources of segregation by decomposing measures into within- and between-community components.

Main Methods:

  • Utilized large-scale Global Positioning System (GPS) daily movement data from mobile devices in U.S. metropolitan areas.
  • Developed a mobility-based conceptualization of group exposure, shifting from traditional distance-based metrics.
  • Integrated daily mobility data with U.S. Census data on community characteristics to measure mobility connectedness and segregation.

Main Results:

  • Mobility-based segregation measures capture distinct dimensions compared to traditional distance-based approaches.
  • Individuals exhibit significant own-group isolation within their activity spaces, as evidenced by everyday movement patterns.
  • Substantial heterogeneity in local mobility exposure exists even within communities of similar demographic composition, particularly for minority groups.

Conclusions:

  • Mobility-based segregation measures offer a more comprehensive understanding of experienced segregation in activity spaces.
  • Large-scale human movement data, combined with novel segregation metrics, provides valuable insights into complex social inequalities.
  • Findings underscore the importance of considering daily mobility in research on racial, ethnic, and income segregation.