Brantingham, Patricia

Originally appeared in the 2011 European Intelligence and Security Informatics Conference (12-14 September 2011, Athens, Greece). <p>In the current study we develop a Criminal Movement Model (CriMM) to investigate the relationship between simulated travel routes of offenders along the physical road network and the actual locations of their crimes in the same geographic space. With knowledge of offenders' home locations and the locations of major attractors, we are able to model the routes that offenders are likely to take when travelling from their home to an attractor by employing variations of Dijkstra's shortest path algorithm. With these routes plotted, we then compare them to the locations of crimes committed by the same offenders. This model was applied to five attractor locations within the Greater Vancouver Regional District (GVRD) in the province of British Columbia, Canada. Information about offenders in these cities was obtained from five years worth of real police data. After performing a small-scale analysis for each offender to investigate how far off their shortest path they go to commit crimes, we found that a high percentage of crimes were located along the paths taken by offenders in the simulations. Aggregate analysis was also performed to observe travel patterns in different areas of the cities and how they relate to the amount of crime in each neighbourhood. The results are discussed in relation to both theory and potential policy implications.

Objectives: The main objective of this study was to see if the characteristics of offenders’ crimes exhibit spatial patterning in crime neutral areas by examining the relationship between simulated travel routes of offenders along the physical road network and the actual locations of their crimes in the same geographic space. <p>Method: This study introduced a Criminal Movement model (CriMM) that simulates travel patterns of known offenders. Using offenders’ home locations, locations of major attractors (e.g., shopping centers), and variations of Dijkstra’s shortest path algorithm we modeled the routes that offenders are likely to take when traveling from their home to an attractor. We then compare the locations of offenders’ crimes to these paths and analyze their proximity characteristics. This process was carried out using data on 7,807 property offenders from five municipalities in the Greater Vancouver Regional District (GVRD) in British Columbia, Canada. <p>Results: The results show that a great proportion of crimes tend to be located geographically proximal to the simulated travel paths with a distance decay pattern characterizing the distribution of distance measures. <p>Conclusion: These results lend support to Crime Pattern Theory and the idea that there is an underlying pattern to crimes in crime neutral areas.