Modeling the Dynamics of Street Robberies to Inform Policy and Prevention

Achieving a better understanding of the crime event in its context remains an important research area in criminology that has major implications for making better policy and developing effective crime prevention strategies. Agent-based models offer the ability to do just that. An agent-based simulation implemented in the framework of a computational laboratory offers the following advantages. First, agent-based models allow heterogeneity among individuals that more closely approximates the variety found in life. Second, the agents and the landscape can be held constant or systematically varied in order to provide a level of control impossible to attain using traditional social science methods. Third, the combination of heterogeneous agents and control enables the researcher to conduct a variety of experiments using different conditions or applying various prevention scenarios and then to evaluate outcomes for minimal cost compared to experiments in the real world.

This proposal addresses the issues encountered in earlier studies by designing and implementing an agent-based model for exploring the contextual aspects of individual crime events and how they culminate in emerging crime patterns. The initial research focuses on street robbery for three reasons. First, it is an instrumental crime and thus is more likely than expressive crimes to involve a decision process. Second, street robbery is more likely to occur on the street or some other exposed area than in a residence or business and thus involves the public intersection of offender and target in space and time. Third, robbery elicits a high level of fear among residents because of its unexpectedness and potential for serious injury. The model is informed by several of the opportunity theories in criminology and two geographical theories. Opportunity theories include routine activity theory, rational choice theory and environmental criminology. From geography we incorporate research on activity spaces.

The research specifically addresses the solicitation goal for Law Enforcement/Policing by creating a model to inform more effective strategies for law enforcement. In addition, the model examines the goal of Crime Prevention/Causes of Crime by offering: (1) a method for identifying situational characteristics that create the potential for crime to occur; and (2) the ability to test the impact of policy changes on crime rates. The situational elements of the convergence of offender and victim at a specific place and time can be simulated via agent-based modeling software. As the initial foray into this area, this study focuses on the development of a computational laboratory for modeling some simple, dynamic interactions between individuals from which aggregate crime rates and patterns of crime emerge. Once this model is built, it can be extended to serve as a laboratory for testing other facets of criminological theory and police practice at the micro level. In this first model, there is no attempt to incorporate the motivations behind either criminality or guardianship. More complex issues such as those will be addressed in later studies using the basic framework developed here. The results of this and subsequent experiments will inform crime prevention strategies and contribute to the body of knowledge in both environmental criminology and situational crime prevention.