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<p>Residential segregation in US cities threatens economic opportunity and social cohesion. Most segregation estimates rely on aggregate data with arbitrary boundaries such as Census tracts. Sensitivity to boundary choices is well theorized but rarely measured. Here, leveraging redistricting software, we simulate millions of alternative Census tract maps that satisfy Census guidelines, compute segregation for each and estimate probabilistic distributions of common indices. This approach yields new estimates of racial segregation across US cities and measures the aggregation-induced variability hidden in conventional estimates. We find that variability is largely driven by the number of spatial units: simulated segregation in small cities varies widely across plans but converges in larger cities. We find no systematic bias: values calculated using official tract definitions closely track the mean of simulated alternative boundary definitions. While our findings confirm the practical robustness of Census tracts, we provide a general framework for diagnosing and correcting spatial aggregation error in other contexts.</p>

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City racial segregation statistics are robust to aggregation bias

  • 期刊论文

  • 发表日期：

    2026年6月16日

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  • Jacob R. Brown,
  • Christopher T. Kenny,
  • Tyler Simko

摘要

Residential segregation in US cities threatens economic opportunity and social cohesion. Most segregation estimates rely on aggregate data with arbitrary boundaries such as Census tracts. Sensitivity to boundary choices is well theorized but rarely measured. Here, leveraging redistricting software, we simulate millions of alternative Census tract maps that satisfy Census guidelines, compute segregation for each and estimate probabilistic distributions of common indices. This approach yields new estimates of racial segregation across US cities and measures the aggregation-induced variability hidden in conventional estimates. We find that variability is largely driven by the number of spatial units: simulated segregation in small cities varies widely across plans but converges in larger cities. We find no systematic bias: values calculated using official tract definitions closely track the mean of simulated alternative boundary definitions. While our findings confirm the practical robustness of Census tracts, we provide a general framework for diagnosing and correcting spatial aggregation error in other contexts.