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This study asks the question: If we take a small neighborhood and introduce a barrier, how will the neighborhood change? Will it be better protected and flourish, or will it decay and die or perhaps will there be no change at all? What determines the outcome? This work tries to answer these questions by creating an Agent-Based Model (ABM) to test different scenarios and observe the results. Urban environments, both natural and built, are complex systems, containing a multitude of people, landscapes and buildings. Simple changes in street-lighting and sidewalks, the addition of trees and green scapes or the enforcement of “broken-window” policies impact local neighborhoods [1]. Measuring behavior changes on a neighborhood level are difficult to quantify, but by using ABM methods we can build our neighborhood, populate it with a variety of actors and watch their interaction with each other, and with introduced stimuli. Our simulation introduces a barrier (highway) with varying permeability into a mixed use neighborhood loosely based on Richmond’s Jackson Ward. Several metrics (such as property value, crime rates, etc.) were used to determine if the neighborhood was under duress, or thriving. In real-world terms we built a roadway though the neighborhood and observed the “severance effect” as our actors’ adapted to reduced mobility and remained within their accessible range.

The systemic model posits that informal social control directly reduces crime victimization and social networks indirectly reduce crime victimization through informal social control. While empirical testing of the systemic model advanced the theory, important analytical issues remain. First, social networks are inconsistently conceptualized and measured. Second, the conceptual relationship between social networks and informal social control remains unclear. This study addresses these issues by testing an alternative to the systemic model, including new constructs and hypotheses. The goal is to develop better indicators for the model and refine the theory, rethinking and deepening the existing theory about neighborhood effects on crime victimization. The data come from the 2002-2003 Seattle Neighborhoods and Crime Survey (N=2,200). Structural equation modeling (SEM), a multivariate statistical technique, was used to analyze these data. The SEM included five latent constructs (neighboring, neighborhood and non-neighborhood social networks, collective efficacy, and crime victimization) and six social structural variables (racially homogeneous neighborhood, resident tenure, household income, family disruption, male, and non-white ethnicity). One of my 9 hypotheses was supported; the remaining hypotheses were partly supported. The results support my argument that the systemic model is too simplistic, but the relationships among the variables are not exactly as I hypothesized. The results provide insight into the complexities of the systemic model and areas for future research.