Analysis of mobility homophily in Stockholm based on social network data

Heine, Cate; Márquez, Cristina; Santi, Paolo; Sundberg, Marcus; Nordfors, Miriam; Ratti, Carlo

doi:10.1371/journal.pone.0247996

Cited by 20 publications

(10 citation statements)

References 30 publications

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“…Unlike previous studies 23 , 35 , we do not observe universal assortativity patterns over all cities in these networks. In some of the cities, such as Detroit, the strong diagonal component features strong segregation patterns, meaning that people tend to commute to neighborhoods with similar annual household incomes as their home neighborhood, and they tend to form social ties with people living in neighborhoods with similar income, as also found in 24 . In cities like Boston, patterns of mobility and online social ties are less assortative with higher likelihood for diverse, off-diagonal connections.…”

Section: Resultsmentioning

confidence: 91%

“…Furthermore, spatial segregation by income also fragments social networks, which can hinder progress and can deepen inequalities 16 – 20 . Given the importance of this problem, a growing community has investigated the patterns of mobility in cities to better understand mixing potentials across disparate and diverse neighborhoods 21 – 24 , which may increase economic prosperity 25 . Yet, less is known whether mobility mixing has any imprint on the social connections of people.…”

Section: Introductionmentioning

confidence: 99%

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Universal patterns of long-distance commuting and social assortativity in cities

Bokányi

Juhász

Karsai

et al. 2021

Sci Rep

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Millions commute to work every day in cities and interact with colleagues, partners, friends, and strangers. Commuting facilitates the mixing of people from distant and diverse neighborhoods, but whether this has an imprint on social inclusion or instead, connections remain assortative is less explored. In this paper, we aim to better understand income sorting in social networks inside cities and investigate how commuting distance conditions the online social ties of Twitter users in the 50 largest metropolitan areas of the United States. An above-median commuting distance in cities is linked to more diverse individual networks, moreover, we find that longer commutes are associated with a nearly uniform, moderate reduction of overall social tie assortativity across all cities. This suggests a universal relation between long-distance commutes and the integration of social networks. Our results inform policy that facilitating access across distant neighborhoods can advance the social inclusion of low-income groups.

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Section: Resultsmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Universal patterns of long-distance commuting and social assortativity in cities

Bokányi

Juhász

Karsai

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Unlike previous studies (Dong et al 2020;Morales et al 2019), we do not observe universal assortativity patterns over all cities in these networks. In some of the cities, such as Detroit, the strong diagonal component features strong segregation patterns, meaning that people tend to commute to neighborhoods with similar annual household incomes as their home neighborhood, and they tend to form social ties with people living in neighborhoods with similar income, as also found in (Heine et al 2021). In cities like Boston, patterns of mobility and online social ties are less assortative with higher likelihood for diverse, off-diagonal connections.…”

Section: Resultsmentioning

confidence: 80%

Universal role of commuting in the reduction of social assortativity in cities

Bokányi,

Juhász,

Karsai

et al. 2021

Preprint

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Millions commute to work every day in cities and interact with colleagues, customers, providers, friends, and strangers. Commuting facilitates the mixing of people from distant and diverse neighborhoods, but whether this has an imprint on social inclusion or instead, connections remain assortative is less explored. In this paper, we aim to better understand income sorting in social networks inside cities and investigate how commuting distance conditions the online social ties of Twitter users in the 50 largest metropolitan areas of the United States. Home and work locations are identified from geolocated tweets that enable us to infer the socio-economic status of individuals. Our results show that an above-median commuting distance in cities is associated with more diverse individual networks in terms of connected peers and their income. The degree that distant commutes link neighborhoods of different socio-economic backgrounds greatly varies by city size and structure. However, we find that above-median commutes are associated with a nearly uniform, moderate reduction of social tie assortativity across the top 50 US cities suggesting a universal role of commuting in integrating disparate social networks in cities. Our results inform policy that facilitating access across distant neighborhoods can advance the social inclusion of low-income groups.

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“…For instance, the theory of homophily states that people who share characteristics (e.g., age, sexuality, hobbies) tend to connect and interact with each other online and to congregate offline ("birds of a feather flock together"; e.g., McPherson et al, 2001). Inspired by this theory, Heine et al (2021) used geotagged tweets of residents in Stockholm to analyze how their movements around the city connected them to different neighborhoods. The authors found that the socioeconomic similarity of neighborhoods in Stockholm (i.e., similarity in residents' income, education level, and immigration history) could predict how individuals moved through those areas (i.e., residents of socioeconomically similar neighborhoods were likely to adopt similar movement patterns throughout the city).…”

Section: How the Use Of Mobilities Data Has Been Informed By Psycholo...mentioning

confidence: 99%

Integrating Insights About Human Movement Patterns From Digital Data Into Psychological Science

Hinds¹,

Brown²,

Smith

et al. 2021

Curr Dir Psychol Sci

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Understanding people’s movement patterns has many important applications, from analyzing habits and social behaviors, to predicting the spread of disease. Information regarding these movements and their locations is now deeply embedded in digital data generated via smartphones, wearable sensors, and social-media interactions. Research has largely used data-driven modeling to detect patterns in people’s movements, but such approaches are often devoid of psychological theory and fail to capitalize on what movement data can convey about associated thoughts, feelings, attitudes, and behavior. This article outlines trends in current research in this area and discusses how psychologists can better address theoretical and methodological challenges in future work while capitalizing on the opportunities that digital movement data present. We argue that combining approaches from psychology and data science will improve researchers’ and policy makers’ abilities to make predictions about individuals’ or groups’ movement patterns. At the same time, an interdisciplinary research agenda will provide greater capacity to advance psychological theory.

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Analysis of mobility homophily in Stockholm based on social network data

Cited by 20 publications

References 30 publications

Universal patterns of long-distance commuting and social assortativity in cities

Universal patterns of long-distance commuting and social assortativity in cities

Universal role of commuting in the reduction of social assortativity in cities

Integrating Insights About Human Movement Patterns From Digital Data Into Psychological Science

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