|There is increasing interest in describing spatial characteristics of crime patterns with geographic information systems. However, systemic errors in crime data and static theories of patterns make it difficult to interpret these findings. Routine Activity (RA) Theory provides a useful framework for understanding the development of these patterns. Static RA identifies a number of agents whose interaction in microenvironments gives rise to crime events. In this paper we describe how we can move from a static RA model of crime events to a dynamic model of crime patterns by incorporating agent movement and learning. We identify a class of simulation models (RA/CA) that facilitate improved theory development, the identification of testable hypotheses, and "bench testing" of interventions prior to implementation.
Key words: crime patterns, routine activity theory, crime simulation
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