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.

This article develops and assesses the concept of triple neighborhood disadvantage. We argue that a neighborhood’s well-being depends not only on its own socioeconomic conditions but also on the conditions of neighborhoods its residents visit and are visited by, connections that form through networks of everyday urban mobility. We construct measures of mobility-based disadvantage using geocoded patterns of movement estimated from hundreds of millions of tweets sent by nearly 400,000 Twitter users over 18 months. Analyzing nearly 32,000 neighborhoods and 9,700 homicides in 37 of the largest U.S. cities, we show that neighborhood triple disadvantage independently predicts homicides, adjusting for traditional neighborhood correlates of violence, spatial proximity to disadvantage, prior homicides, and city fixed effects. Not only is triple disadvantage a stronger predictor than traditional measures, it accounts for a sizable portion of the association between residential neighborhood disadvantage and homicides. In turn, potential mechanisms such as neighborhood drug activity, interpersonal friction, and gun crime prevalence account for much of the association between triple disadvantage and homicides. These findings implicate structural mobility patterns as an important source of triple (dis)advantage for neighborhoods and have implications for a broad range of phenomena beyond crime, including community capacity, gentrification, transmission in a pandemic, and racial inequality.

Do public policies on gay and lesbian rights affect the incidence of hate crimes based on sexual orientation? We propose that legal inequalities increase hate crimes because they provide discursive opportunities for bias, discrimination, and violence. Legal equality, however, will reduce violence. Using annual panel data from 2000 to 2012, a period of substantial policy change, we analyze how three state policies affect reported hate crimes: same-sex partnerships, employment non-discrimination, and hate crime laws. Hate crime and employment non-discrimination laws that include sexual orientation reduce hate crime incidence. Partnership recognition increases reported hate crimes, though it may not increase actual crime incidence. Because incidence is spatially correlated, policy changes in one state yield spillover benefits in other states. These results provide some of the first quantitative evidence that public policies affect hate crimes based on sexual orientation. Findings confirm the roles of institutional heterosexism and discursive opportunities in producing hate crimes.

Race and class disparities in COVID-19 cases are well documented, but pathways of possible transmission by neighborhood inequality are not. This study uses administrative data on COVID-19 cases for roughly 2000 census tracts in Wisconsin, Seattle/King County, and San Francisco to analyze how neighborhood socioeconomic (dis)advantage predicts cumulative caseloads through February 2021. Unlike past research, we measure a neighborhood’s disadvantage level using both its residents’ demographics and the demographics of neighborhoods its residents visit and are visited by, leveraging daily mobility data from 45 million mobile devices. In all three jurisdictions, we find sizable disparities in COVID-19 caseloads. Disadvantage in a neighborhood’s mobility network has greater impact than its residents’ socioeconomic characteristics. We also find disparities by neighborhood racial/ethnic composition, which can be explained, in part, by residential and mobility-based disadvantage. Neighborhood conditions measured before a pandemic offer substantial predictive power for subsequent incidence, with mobility-based disadvantage playing an important role.

This study estimates the effect of neighborhood disadvantage on bachelor's degree attainment with data from a long-term follow-up of the Project on Human Development in Chicago Neighborhoods. We focus on heterogeneous effects by race and class as well as individual and neighborhood mechanisms that might explain observed patterns, including parents' educational expectations, collective efficacy, social relationships, and neighborhood violence. Using newly developed methods for estimating longitudinal treatment effects, we find that cumulative neighborhood disadvantage in adolescence is strongly associated with lower bachelor's attainment among high-income blacks and Latinos. We find no effect for whites and at most a modest effect among low-and middle-income blacks/Latinos. A sensitivity analysis suggests that the estimated effect for high-income blacks/Latinos is plausibly causal. These results support an advantage-leveling model of neighborhood effects and add important nuance for research considering how and for whom neighborhoods influence life chances.