The Social Integration of American Cities: Network Measures of Connectedness Based on Everyday Mobility Across Neighborhoods

Abstract

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.

Urban mobility and resilience: exploring Boston’s urban mobility network through twitter data

“…Social segregation limits housing opportunities and has serious detrimental impact on educational outcomes (Condron et al 2013), it creates concentrated poverty (Massey and Fischer 2006) and crime (Peterson and Krivo 2010). On the other hand, social integration creates greater solidarity and trust (Angell 1947), improves longevity and well-being in communities (Moen et al 1989), benefits social mobility (Peach 2005), helps communities to be resilient to perturbations (Folke et al 2010), and provides access to coping mechanisms (Ramirez-Valles et al 2010) as well as lowering conflict and crime (Crutchfield et al 1982).…”

Normalised degree variance

“…The degree variance is the original characteristic (Snijders 1981). Several other measures of heterogeneity have been proposed (Bell 1992;Jacob et al 2017;Safaei et al 1011), but the degree variance remains one of the most widespread and easiest to interpret, aiding, for example, in analysing degree distributions of networks from genetics (Horvath and Dong 2008), physics (Cao et al 2015), chemistry (Randić et al 2016), sociology (Phillips et al 2019), neuroscience , and policy (Mochtak and Diviak 2019;Mäkinen et al 2019), among others.…”

Neighborhood street activity and greenspace usage uniquely contribute to predicting crime

“…Virtually all smartphones include a GPS chip, as well as applications that can retrieve the device's physical coordinates. This allows for the recording of human mobility with high granularity and volume (31). By identifying park visits within such data, our study interrogates the impact of realized access to parks, as distinct from their potential of use captured by more traditional sources like park area or land use.…”