Neighborhood isolation in Chicago: Violent crime effects on structural isolation and homophily in inter-neighborhood commuting networks

Graif, Corina; Lungeanu, Alina; Yetter, Alyssa M.

doi:10.1016/j.socnet.2017.01.007

Cited by 53 publications

(45 citation statements)

References 80 publications

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“…While the effects of urban environment characteristics, socio-economic conditions, and mobility have been empirically tested separately 9 , 49 , 60 – 62 , to the best of our knowledge, this is the first study to support with large-scale data the association of crime with socio-economic conditions, the built environment, and mobility. However, we find that these aspects do not play the same role across cities, and only some of them contribute to the crime prediction model.…”

Section: Resultsmentioning

confidence: 92%

“…This connectivity is not only influenced by distance but also by geographical barriers, roads, traffic, and public transportation. Moreover, it could be interpreted as a proxy of spatial mismatch and isolation, which was empirically found to be connected with crime 60 . To build the connectivity matrix we use the TimeGeo model, which simulates a reliable Origin-Destination matrix between regions and it is validated towards transportation surveys (see Supplementary Note 3.1 ).…”

Section: Resultsmentioning

confidence: 99%

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Socio-economic, built environment, and mobility conditions associated with crime: a study of multiple cities

Nadai

Letouzé

et al. 2020

Sci Rep

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Nowadays, 23% of the world population lives in multi-million cities. In these metropolises, criminal activity is much higher and violent than in either small cities or rural areas. Thus, understanding what factors influence urban crime in big cities is a pressing need. Seminal studies analyse crime records through historical panel data or analysis of historical patterns combined with ecological factor and exploratory mapping. More recently, machine learning methods have provided informed crime prediction over time. However, previous studies have focused on a single city at a time, considering only a limited number of factors (such as socio-economical characteristics) and often at large in a single city. Hence, our understanding of the factors influencing crime across cultures and cities is very limited. Here we propose a Bayesian model to explore how violent and property crimes are related not only to socio-economic factors but also to the built environmental (e.g. land use) and mobility characteristics of neighbourhoods. To that end, we analyse crime at small areas and integrate multiple open data sources with mobile phone traces to compare how the different factors correlate with crime in diverse cities, namely Boston, Bogotá, Los Angeles and Chicago. We find that the combined use of socio-economic conditions, mobility information and physical characteristics of the neighbourhood effectively explain the emergence of crime, and improve the performance of the traditional approaches. However, we show that the socio-ecological factors of neighbourhoods relate to crime very differently from one city to another. Thus there is clearly no “one fits all” model.

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Section: Resultsmentioning

confidence: 92%

Section: Resultsmentioning

confidence: 99%

Socio-economic, built environment, and mobility conditions associated with crime: a study of multiple cities

Nadai

Letouzé

et al. 2020

Sci Rep

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“…Nevertheless, the neighborhood isolation explanation has relied on the implicit assumption that social interactions are limited to one’s neighborhood of residence ( 11 , 12 ). In an increasingly interconnected and mobile society, this assumption is questionable ( 13 ).…”

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confidence: 99%

“…To date, studies relevant to this fundamental question have relied on three types of data. First, studies have examined commuting ties, which focuses on adults’ travel between home and work ( 12 , 16 ). However, commuting does not include neighborhoods experienced through leisure, errand activities, or visits to friends and family, all of which affect the extent of isolation.…”

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confidence: 99%

Urban mobility and neighborhood isolation in America’s 50 largest cities

Wang

Phillips

Small

et al. 2018

Proc. Natl. Acad. Sci. U.S.A.

288

250

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SignificanceLiving in disadvantaged neighborhoods is widely assumed to undermine life chances because residents are isolated from neighborhoods with greater resources. Yet, residential isolation may be mitigated by individuals spending much of their everyday lives outside their home neighborhoods, a possibility that has been difficult to assess on a large scale. Using new methods to analyze urban mobility in the 50 largest American cities, we find that residents of primarily black and Hispanic neighborhoods—whether poor or not—are far less exposed to either nonpoor or white middle-class neighborhoods than residents of primarily white neighborhoods. Although residents of disadvantaged neighborhoods regularly travel as far and to as many different neighborhoods as those from advantaged neighborhoods, their relative isolation and segregation persist.

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“…Mears and Bhati (2006), for instance, found that the crime rates of compositionally homophilous neighborhoods are associated (regardless of proximity), inferring that mobility-based ties among these neighborhoods drive their correlated outcomes. In one of the few studies that empirically examines the consequences of indirect exposures, Graif, Lungeanu, and Yetter (2017) found evidence that the crime rates of places to which neighborhood residents commute are associated with focal neighborhood crime. These studies suggest that aggregate links in the eco-network have consequences for the experience of any given location, regardless of an individual's own exposures.…”

Section: Introductionmentioning

confidence: 99%

Beyond Activity Space: Detecting Communities in Ecological Networks

Calder

Browning

2020

Annals of the American Association of Geographers

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Emerging research suggests that the extent to which activity spaces -the collection of an individual's routine activity locations -overlap provides important information about the functioning of a city and its neighborhoods. To study patterns of overlapping activity spaces, we draw on the notion of an ecological network, a type of two-mode network with the two modes being individuals and the geographic locations where individuals perform routine activities. We describe a method for detecting "ecological communities" within these networks based on shared activity locations among individuals. Specifically, we identify latent activity pattern profiles, which, for each community, summarize its members' probability distribution of going to each location, and community assignment vectors, which, for each individual, summarize his/her probability distribution of belonging to each community. Using data from the Adolescent Health and Development in Context (AHDC) Study, we employ latent Dirichlet allocation (LDA) to identify activity pattern profiles and communities. We then explore differences across neighborhoods in the strength, and within-neighborhood consistency of community assignment.We hypothesize that these aspects of the neighborhood structure of ecological community membership capture meaningful dimensions of neighborhood functioning likely to co-vary with economic and racial composition. We discuss the implications of a focus on ecological communities for the conduct of "neighborhood effects" research more broadly.

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Neighborhood isolation in Chicago: Violent crime effects on structural isolation and homophily in inter-neighborhood commuting networks

Cited by 53 publications

References 80 publications

Socio-economic, built environment, and mobility conditions associated with crime: a study of multiple cities

Socio-economic, built environment, and mobility conditions associated with crime: a study of multiple cities

Urban mobility and neighborhood isolation in America’s 50 largest cities

Beyond Activity Space: Detecting Communities in Ecological Networks

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