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A longstanding tradition of research linking neighborhood disadvantage to higher rates of violence is based on the characteristics of where people reside. This Essay argues that we need to look beyond residential neighborhoods to consider flows of movement throughout the wider metropolis. Our basic premise is that a neighborhood’s well-being depends not only on its own socioeconomic conditions but also on the conditions of neighborhoods that its residents visit and are visited by—connections that form through networks of everyday urban mobility. Based on the analysis of large-scale urban-mobility data, we find that while residents of both advantaged and disadvantaged neighborhoods in Chicago travel far and wide, their relative isolation by race and class persists. Among large U.S. cities, Chicago’s level of racially segregated mobility is the second highest. Consistent with our major premise, we further show that mobility-based socioeconomic disadvantage predicts rates of violence in Chicago’s neighborhoods beyond their residence-based disadvantage and other neighborhood characteristics, including during recent years that witnessed surges in violence and other broad social changes. Racial disparities in mobility-based disadvantage are pronounced—more so than residential neighborhood disadvantage. We discuss implications of these findings for theories of neighborhood effects on crime and criminal justice contact, collective efficacy, and racial inequality. 

Disadvantage in a neighborhood’s mobility network has a larger impact than its own economic status on COVID-19 incidence. 

While research on racial segregation in cities has grown rapidly over the last several decades, its foundation remains the analysis of the neighbourhoods where people reside. However, contact between racial groups depends not merely on where people live, but also on where they travel over the course of everyday activities. To capture this reality, we propose a new measure of racial segregation – the segregated mobility index (SMI) – that captures the extent to which neighbourhoods of given racial compositions are connected to other types of neighbourhoods in equal measure. Based on hundreds of millions of geotagged tweets sent by over 375,000 Twitter users in the 50 largest US cities, we show that the SMI captures a distinct element of racial segregation, one that is related to, but not solely a function of, residential segregation. A city’s racial composition also matters; minority group threat, especially in cities with large Black populations and a troubled legacy of racial conflict, appears to depress movement across neighbourhoods in ways that produce previously undocumented forms of racial segregation. Our index, which could be constructed using other data sources, expands the possibilities for studying dynamic forms of racial segregation including their effects and shifts over time. 

The social integration of a city depends on the extent to which people from different neighborhoods have the opportunity to interact with one another, but most prior work has not developed formal ways of conceptualizing and measuring this kind of connectedness. In this article, we develop original, network-based measures of what we call “structural connectedness” based on the everyday travel of people across neighborhoods. Our principal index captures the extent to which residents in each neighborhood of a city travel to all other neighborhoods in equal proportion. Our secondary index captures the extent to which travels within a city are concentrated in a handful of receiving neighborhoods. We illustrate the value of our indices for the 50 largest American cities based on hundreds of millions of geotagged tweets over 18 months. We uncover important features of major American cities, including the extent to which their connectedness depends on a few neighborhood hubs, and the fact that in several cities, contact between some neighborhoods is all but nonexistent. We also show that cities with greater population densities, more cosmopolitanism, and less racial segregation have higher levels of structural connectedness. Our indices can be applied to data at any spatial scale, and our measures pave the way for more powerful and precise analyses of structural connectedness and its effects across a broad array of social phenomena.