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Mobility patterns predict increasing polarization between neighborhoods
Karl Vachuska1, Meghann Norden-Bright1
1Department of Sociology, University of Wisconsin-Madison, United States.
Social Science Research
|May 22, 2026
Summary
Neighborhood mobility patterns significantly predict political polarization. Mobility connections to disadvantaged areas increase Republican voting, widening the political divide.
Area of Science:
- Social Sciences
- Political Science
- Urban Studies
Background:
- Political polarization is a growing concern in the United States.
- Understanding the drivers of political shifts is crucial for democratic health.
- Traditional analyses often focus on residential characteristics, potentially overlooking dynamic factors.
Purpose of the Study:
- To investigate the relationship between neighborhood mobility patterns and political polarization.
- To examine how mobility ties to disadvantaged or advantaged neighborhoods influence voting shifts.
- To assess the role of mobility-based disadvantage in predicting political alignment over time.
Main Methods:
- Utilized everyday mobility data from SafeGraph.
- Analyzed election results at the census block group level for the 2016 and 2020 presidential elections.
- Measured neighborhood disadvantage through residential data and mobility connections.
Main Results:
- Predominantly White neighborhoods with mobility links to disadvantaged areas showed increased Republican voting.
- Conversely, neighborhoods connected to more advantaged areas saw decreased Republican support.
- Mobility-based disadvantage increasingly explains variations in voting patterns, with the Republican vote gap expanding significantly by 2020.
Conclusions:
- Neighborhood mobility patterns are strong predictors of political polarization, often surpassing static residential characteristics.
- Mobility ties play a critical role in shaping political landscapes and contributing to widening political divides.
- The findings highlight the dynamic nature of political alignment influenced by sociospatial connections.
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