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New Dataset Reveals Everyday Racial/Ethnic Environments Are Less Extreme Than Traditional Measures Suggest
Insights from the Field
geolocation data
measurement gap
daily life exposure
political representation
Methodology
PSR&M
7 R files
6 other files
2 datasets
Dataverse
Defining Racial and Ethnic Context with Geolocation Data was authored by Andrew Reeves and Ryan T. Moore. It was published by Cambridge in PSR&M in 2020.

Political scientists long claimed that racial and ethnic context profoundly shapes individual attitudes. However, conventional measures rely on imprecise geographic containers to define this exposure.

This paper introduces a novel dataset—tracking over 400 people via GPS—with unprecedented detail about daily life environments.

🔍 Data & Methods

The study analyzes more than 2.6 million GPS records from individuals across various regions. This represents an innovative shift away from static census data toward dynamic, real-time location tracking that captures nuanced everyday experiences.

📊 Key Findings

When compared to traditional geographic measures, this precise dataset demonstrates a significant discrepancy: seemingly diverse areas appear much more homogeneous than their statistical boundaries suggest.

🌍 Why It Matters

The findings reveal how standard static methods tend to exaggerate differences in racial and ethnic exposure. This suggests major reinterpretations of existing research on political representation and public opinion are needed.

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Political Science Research & Methods
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