Emergent Regularities of Interpersonal Victimization: An Agent-Based Investigation

Abstract

Objectives: Apply computational agent-based modeling to explore the generative sufficiency of several mechanisms derived from the field of environmental criminology in explaining commonly observed patterns of interpersonal victimization. Method: Controlled simulation experiments compared patterns of simulated interpersonal victimization to three empirically derived regularities of crime using established statistical techniques: (1) spatial clustering (nearest neighbor index), (2) repeat victimization (Gini coefficient), and (3) journeys to crime (Pearson's coefficient of skewness). Results: Large, statistically significant increases in spatial clustering, repeat victimization, and journey to crime skewness are observed when virtual offenders operate according to mechanisms proposed by the routine activity approach, rational choice perspective, and geometry/pattern theories of crime. Conclusion: This research provides support for several propositions of environmental criminology in explaining why interpersonal victimization tends to be spatially concentrated, experienced by a small number of repeat victims, and why aggregate journey to crime curves tend to follow a distance decay relationship. By extending previous work in agent-based modeling of property victimization, it also demonstrates that the same core mechanisms are sufficient to generate plausible patterns of crime when examining fundamentally different types of offending.