A mobility-based framework for measuring socio-spatial segregation in 30 U.S. metropolitan areas

Although “segregation” is widely used and often treated as a clear condition, what it actually means for a place to be “highly segregated” remains ambiguous. Studies invoke the term across diverse social and spatial contexts, from income to race, or from residence to facility access, without a shared definition of what segregation entails. This ambiguity obscures comparisons across cities and dimensions, risks misdiagnosing the sources of inequality, and undermines targeted interventions. To address this conceptual and empirical gap, we propose a socio-spatial dimensional framework that defines segregation as the intersection between social dimensions (who differs) and spatial axes (how that difference is structured and experienced). The social dimensions correspond to the Social Vulnerability Index (SVI), including Socioeconomic Status (SES), Household Composition & Disability (HCD), Minority Status & Language (MSL), and Housing Type (HT). The spatial axes capture metropolitan unevenness and local clustering. Using mobility data from 45 million mobile devices, we construct this mobility-based framework, Social Segregation Index (SSI), for the 30 largest U.S. metropolitan areas. Results show that “high segregation” has no single meaning: rankings of metropolitan areas change dramatically by social dimensions, with severity and clustering strongest along SES and MSL but weaker for HCD and HT. Extreme multi-axis segregation is rare yet concentrated in a few urban cores. By clarifying who differs and how in space, our framework transforms the vague notion of segregation into a measurable, comparable, and policy-relevant socio-spatial condition.