Elsevier

Social Networks

Volume 51, October 2017, Pages 40-59
Social Networks

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

https://doi.org/10.1016/j.socnet.2017.01.007Get rights and content

Highlights

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    We examined violence effects on external social isolation of urban neighborhoods.

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    Commuting networks among Chicago's neighborhoods were analyzed over twelve years.

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    Violence predicted residential neighborhood isolation from the citywide network.

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    Similarity in violence predicted inter-neighborhood ties, indicating homophily.

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    Violence homophily affected tie formation; neighborhood violence dissolved ties.

Abstract

Urban sociologists and criminologists have long been interested in the link between neighborhood isolation and crime. Yet studies have focused predominantly on the internal dimension of social isolation (i.e., increased social disorganization and insufficient jobs and opportunities). This study highlights the need to assess the external dimension of neighborhood isolation, the disconnectedness from other neighborhoods in the city. Analyses of Chicago's neighborhood commuting network over twelve years (2002–2013) showed that violence predicted network isolation. Moreover, pairwise similarity in neighborhood violence predicted commuting ties, supporting homophily expectations. Violence homophily affected tie formation most, while neighborhood violence was important in dissolving ties.

Section snippets

Neighborhood violence and network isolation

Violence may affect a neighborhood's isolation from the citywide commuting network in multiple ways. Directly, violence increases residents’ concerns about the safety of public transportation (e.g., gang presence or illegal drug activity on one's way to the bus stop) (Harding, 2010) and about the reliability of private transportation (e.g., stolen or disassembled cars). Increased victimization risk discourages residents’ use of transportation to search for jobs and affects their informal

Homophily in inter-neighborhood commuting ties

It is important to note that resources and institutions are not always absent from impoverished neighborhoods (e.g., childcare programs like Head Start) and when present, they do connect their clients to resources and institutions across the city (Small and McDermott, 2006, Murphy and Wallace, 2010, Tran et al., 2013). Even then, important questions remain about the quality of such connections. Are impoverished and violent neighborhoods connected to areas of better quality or to similarly

Data, measures, and methods

The measures in the current study are based on data from the Decennial Census, police records, and the Longitudinal Employer Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES) (Abowd et al., 2005). The LEHD is a U.S. Census Bureau program that matches Unemployment Insurance earning records with other administrative records from state and local authorities to additional census and survey data on the firms, worker, and household statistics. The unemployment insurance data

Measures

The dependent variable is defined as a commuting relation from the home community to the work community. While ties based only on commuting do not represent all possible ties between neighborhoods, they are likely related to a broader range of interpersonal interactions and resource exchanges across space. To compute the commuting relation we used LEHD's LODES database to calculate the number of residents from a home community that commute to a work community. Then, we normalized this value by

Main independent variables

We calculated neighborhood violent crime rate based on the number of violent incidents located in the neighborhood during each year between 2001 and 2012, divided by the number of neighborhood residents, as assessed by the 2000 decennial census. The types of violent incidents included were homicide, assault, battery, sexual assault, domestic violence, and robbery. We used violent crime as a predictor in two different ways. First, we used the violence rates in the “home” (or sending)

Control variables

Spatial proximity. McPherson et al. (2001: 429) named space as “the most basic source of homophily,” drawing on the principle of least effort – that it takes more effort to connect over larger distances. While new communication technologies have lowered the cost of forming and maintaining ties over long distances (Wellman, 1996), geographic space remains powerful in influencing the presence and strength of ties (Hipp and Perrin, 2009, Schaefer, 2012). In our analysis, we define spatial

ERGM/p* and TERGM

Standard regression methods assume that observations are independent. However, the relations between communities are inter-dependent – violating the independence assumption. To address this challenge, we use Exponential Random Graph Models or p* models (ERGMs/p*) (Frank and Strauss, 1986, Robins and Pattison, 2005, Robins et al., 2007, Wasserman and Pattison, 1996). A key feature of ERGM/p* is the ability to control for purely structural effects (also called endogenous effects). For example,

Inter-neighborhood commuting networks

Fig. 1, Fig. 2 graphically present the 77 community areas of Chicago and their commuting network. Both graphs of Fig. 1 show the geographic distribution of communities. The leftmost map also depicts the geographic boundaries and the CA names, whereas the rightmost map shows a simplified representation of communities as nodes (circles located at the latitude and longitude coordinates of the CA centroids) connected through the 2013 commuting ties (0.5% cutoff). The size of the nodes varies

Descriptives

Table 1 reports the means, standard deviations, ranges, and correlations of the core neighborhood level measures in this study for 2002 and 2013 in order to give a sense of the temporal bounds of the values. We use 0.5% in defining the ties, which represents about 11% of all possible ties in 2002 and 9% in 2013. First, Table 1 presents information on neighborhood violence. The mean violent crime rate decreased over the course of the years, following the general pattern of decreasing crime in

Multivariate ERGM/p* estimates: static and dynamic network models

Table 2 presents the results of ERGM/p* estimations of the likelihood of a commuting relation between two communities. Model 1 includes the endogenous effects (i.e., network structure), attribute effects (i.e., sender and receiver effects), spatial proximity and the difference in violent crime rate between the “home” and “work” community. Model 2 adds a control for transportation ties. Finally, Model 3 also controls for the difference in residential stability, diversity and density of local

Supplementary analyses

Different cutoffs and tie strength. In additional analyses, we eliminate the need to choose a particular threshold value in dichotomizing the ties by using a multiscale backbone extraction algorithm (Serrano et al., 2009) to redefine the commuting networks. This procedure preserves only links with weights that significantly deviate from a null model (which determines the expected distribution of link weights around a node if those weights were distributed randomly). We then re-estimated our

Discussion

This study investigated the role of violence in predicting neighborhood isolation and differential exclusion from the ecological network structure of commuting flows across Chicago over the course of twelve years. Results from analyses of commuting networks showed that violence is significantly associated with commuting connections to fewer communities. Cross-sectional analyses, net of controls, indicated that during most years, violence in the residential and work neighborhoods was associated

Contributions and implications

Urban sociologists and criminologists have long been interested in the extent to which neighborhood conditions are related to crime (Shaw and McKay, 1942). The focus has been predominantly on internal processes of social isolation as measured through social disorganization, weakened neighborhood social fabric, and the disappearance of local jobs, institutions, and opportunities (Wilson, 1987, Wilson, 1996). This article highlights the importance of assessing neighborhood isolation in a broader

Acknowledgments

The authors are grateful for funding from the National Science Foundation (NSF CNS-1544455), Penn State's Social Science Research Institute, Population Research Institute, and the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NIH R24-HD044943).

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