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Criminal incident prediction using a point-pattern-based density model
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DOI：10.1016/S0169-2070(03)00094-3.png)
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Law enforcement agencies need crime forecasts to support their tactical operations; namely, predicted crime locations for next week based on data from the previous week. Current practice simply assumes that spatial clusters of crimes or hot spots observed in the previous week will persist to the next week. This paper introduces a multivariate prediction model for hot spots that relates the features in an area to the predicted occurrence of crimes through the preference structure of criminals. We use a point-pattern-based transition density model for space-time event prediction that relies on criminal preference discovery as observed in the features chosen for past crimes. The resultant model outperforms the current practices, as demonstrated statistically by an application to breaking and entering incidents in Richmond, VA. (C) 2003 International Institute of Forecasters. Published by Elsevier B. V. All rights reserved.
Keyword:
point-pattern methods
crime forecasting
spatial transition density model
hot spot prediction model
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