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Trajectory Data Analyses for Pedestrian Space-time Activity Study
Published on: February 25, 2013
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Human mobility patterns are associated with experienced partisan segregation in US metropolitan areas
Yongjun Zhang1, Siwei Cheng2, Zhi Li2,3
1Department of Sociology and Institute for Advanced Computational Science, Stony Brook University, Stony Brook, USA. Yongjun.Zhang@stonybrook.edu.
Scientific Reports
|June 16, 2023
Summary
Daily activities reveal partisan segregation in American politics, distinct from racial or income divides. This segregation is higher in residential areas but lower when people visit places outside their communities.
Area of Science:
- Political Geography
- Computational Social Science
- Urban Studies
Background:
- Partisan sorting is well-documented in residential settings.
- Limited research exists on partisan segregation in daily activity spaces.
- Advances in spatial computation and GPS data enable new mobility analyses.
Purpose of the Study:
- To measure experienced partisan segregation in activity spaces using mobility data.
- To differentiate partisan segregation from racial and income segregation.
- To explore factors influencing experienced partisan segregation.
Main Methods:
- Utilized global positioning system (GPS) data from smartphones to track daily mobility flows.
- Measured place-level partisan segregation based on visitor composition.
- Assessed community-level experienced partisan segregation based on visited locations.
Main Results:
- Experienced partisan segregation varies by geography, location type, and time.
- Partisan segregation is distinct from racial and income segregation.
- Segregation is lower in activity spaces outside residential areas, but residential and activity segregation are correlated.
- Higher partisan segregation is experienced in predominantly Black, liberal, low-income, non-immigrant, transit-dependent, and central city communities.
Conclusions:
- Daily mobility patterns reveal significant partisan segregation in activity spaces.
- This segregation is influenced by community characteristics and geographic context.
- Understanding activity space segregation offers new insights into political polarization.
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