Within-Neighborhood Dynamics: Disadvantage, Collective Efficacy, and Homicide Rates in Chicago

Abstract

Research on neighborhood structural conditions like concentrated disadvantage and crime largely focuses on between-neighborhood differences; for example, places with more disadvantage are expected to experience higher homicide rates. However, empirical research often does not consider within-neighborhood dynamics of structural stability and change. Furthermore, several recent studies have found cross-sectional associations between structural variables and crime outcomes can vary significantly across units, violating a key assumption of global modeling strategies. The current work explores if and how historical changes in disadvantage influence neighborhood collective efficacy and homicide rates, net of the level of disadvantage at a given time point. Collective efficacy theoretically mediates the relationship between conditions and crime, and is hypothesized to be the mechanism through which structural change influences homicide rates. It is also hypothesized that spatial variation in cross-sectional associations between disadvantage and social outcomes can be explained by accounting for within-neighborhood changes in disadvantage. Using a sample of Chicago neighborhoods and ordinary least squares and geographically weighted regression models, I find that within-neighborhood changes in disadvantage significantly predict neighborhood collective efficacy, though the effects of this change on homicide rates are not completely mediated by collective efficacy. Within-neighborhood change completely accounts for spatial variation in cross-sectional associations, offering one explanation of prior research findings. Within-neighborhood structural changes appear to disrupt collective efficacy and contribute to higher homicide rates than predicted by the level of disadvantage alone.

Community Boosts Immunity? Exploring the Relationship Between Social Capital and COVID-19 Social Distancing

“…The interpretation of GWR coefficients is largely consistent with that of OLS models (Becker, 2019 ). Explicitly, the regression coefficient of a particular variable—for example, social capital—at a specific location in the model indicates the change in the magnitude in mobility compared to the baseline given a one-unit change.…”

“…OLS is limited in that it presumes local effects are consistent across all observations (Fotheringham et al, 2003 ). To establish how well social capital predicts social distancing across the US, we supplement these models with GWR (Becker, 2019 ).…”

“…First, we reported summary statistics (minimum, three quartiles, and maximum) of the local coefficient estimates. Second, we mapped out select coefficients for each form of mobility analyzed (Becker, 2019 ). To address multiple hypothesis testing, we adopted the Fotheringham-Byrne correction to adjust the P-value for visualization (Byrne et al, 2009 ).…”

“…To unlock the variation in the association of organizational and network measures of social capital to distancing, we employ geographically weighted regression (GWR) along with traditional ordinary least squares (OLS) regression. Commonly viewed as an exploratory tool (O'Sullivan & Unwin, 2010), GWR allows us to generate empirically determined coefficients for each observation, producing a more nuanced assessment of the association between social capital and distancing from a local perspective (Becker, 2019;Fotheringham et al, 2003). Based on such analysis we build a nationwide picture of the relationships between social distancing to social capital.…”

Analyzing Socioeconomic Factors and Health Disparity of COVID-19 Spatiotemporal Spread Patterns at Neighborhood Levels in San Diego County

“…To unlock the spatial variation in the associations of the selected socioeconomic factors to COVID-19, we employed GWR along with Spatial Autocorrelation. Commonly viewed as an exploratory tool (O’Sullivan and Unwin 2010), GWR allowed us to generate empirically determined coefficients for each observation, producing a more nuanced assessment of the association between our dependent variable (COVID-19 cases) and independent variables (selected social factors) at a local level (Becker 2019; Fotheringham, Brunsdon, and Charlton 2003). …”