Simulation for theory testing and experimentation: An example using routine activity theory and street robbery

Authored by Elizabeth R. Groff

Date Published: 2007-06

DOI: 10.1007/s10940-006-9021-z

Sponsors: National Institute of Justice

Platforms: Repast Python

Model Documentation: Other Narrative Flow charts

Model Code URLs: Model code not found

Abstract

Achieving a better understanding of the crime event in its spatio-temporal context is an important research area in criminology with major implications for improving policy and developing effective crime prevention strategies. However, significant barriers related to data and methods exist for conducting this type of research. The research requires micro-level data about individual behavior that is difficult to obtain and methods capable of modeling the dynamic, spatio-temporal interaction of offenders, victims, and potential guardians at the micro level. This paper presents simulation modeling as a method for addressing these challenges. Specifically, agent-based modeling, when integrated with geographic information systems, offers the ability to model individual behavior within a real environment. The method is demonstrated by operationalizing and testing routine activity theory as it applies to the crime of street robbery. Model results indicate strong support for the basic premise of routine activity theory; as time spent away from home increases, crime will increase. The strength of the method is in providing a research platform for translating theory into models that can be discussed, shared, tested and enhanced with the goal of building scientific knowledge.

Tags

Simulation Agent-based models theory testing Geographic information systems Experiment