Quantifying segregation in an integrated urban physical-social space

Abstract

<jats:p>Our knowledge of how cities bring together different social classes is still limited. Much effort has been devoted to investigating residential segregation, mostly over well-defined social groups (e.g. race). Little is known of how mobility and human communications affect urban social integration. The dynamics of spatial and social-network segregation and individual variations along these two dimensions are largely untapped. In this article, we put forward a computational framework based on coupling large-scale information on human mobility, social-network connections and people’s socio-economic status (SES), to provide a breakthrough in our understanding of the dynamics of spatio-temporal and social-network segregation in cities. Building on top of a social similarity measure, the framework can be used to depict segregation dynamics down to the individual level, and also provide aggregate measurements at the scale of places and cities, and their evolution over time. By applying the methodology in Singapore using large-scale mobile phone and socio-economic datasets, we find a relatively higher level of segregation among relatively wealthier classes, a finding that holds for both social and physical space. We also highlight the interplay between the effect of distance decay and homophily as forces that determine communication intensity, defining a notion of characteristic ‘homophily distance’ that can be used to measure social segregation across cities. The time-resolved analysis reveals the changing landscape of urban segregation and the time-varying roles of places. Segregations in physical and social space are weakly correlated at the individual level but highly correlated when grouped across at least hundreds of individuals. The methodology and analysis presented in this paper enable a deeper understanding of the dynamics of human segregation in social and physical space, which can assist social scientists, planners and city authorities in the design of more integrated cities.</jats:p>

The Relationship between Ethno-Linguistic Composition of Social Networks and Activity Space: A Study Using Mobile Phone Data

“…Social networks are on the one hand the medium for information that influences the choice of place of residence, but also on the other hand a source of social support. The choice to live in close proximity to co-ethnic groups can mitigate the cultural shock on arrival and help people to adapt to the host society (van Kempen & Özüekren, 1998;Xu, Belyi, Santi, & Ratti, 2019). Alternatively, is the result of different sets of possible residential options for ethnic groups and their descendants (Krysan & Crowder, 2017).…”

Mobile Phone Data for Urban Climate Change Adaptation: Reviewing Applications, Opportunities and Key Challenges

“…al. [61] combined information on human mobility from mobile phone data with social-network connections and people's socio-economic status in order to depict dynamics of human segregation, which also suggest here that seemingly homogeneous urban populations groups are not equally at risk to suffer harm from climate-related hazards.…”

Analysis of mobility homophily in Stockholm based on social network data

“…Some even credit the circulation of people throughout a city with the superlinear production of income and innovation [ 2 ]. However, a growing body of literature suggests that this circulation of people may be quite irregular: researchers have used high-granularity geospatial mobility datasets from Twitter or mobile phone data to identify individuals’ trajectories around cities and found that people are significantly more likely to colocate with others “like” them across various socioeconomic dimensions [ 3 – 5 ].…”

“…Further, studying location-connectivity as opposed to person-connectivity bypasses some of the obstacles that come with more traditional approaches. One such obstacle is that existing literature on segregation in activity spaces either relies on travel diaries and surveys [ 11 , 12 ], or on dense geospatial datasets that come from social media or mobile phones like the Twitter dataset that we use [ 3 – 5 , 7 , 8 ]. Survey-based methods produce rich data but are limited by sample size constraints and the reliability of respondents.…”