Abstract
In this paper, we introduce two methods to forecast apartment burglaries that are based on repeat and near repeat victimization. While the first approach, the “heuristic method” generates buffer areas around each new apartment burglary, the second approach concentrates on forecasting near repeat chain links. These near repeat chain links are events that follow a near repeat pair of an originating and (near) repeat event that is close in space and in time. We name this approach the “near repeat chain method”. This research analyzes apartment burglaries from November 2013 to November 2016 in Vienna, Austria. The overall research goal is to investigate whether the near repeat chain method shows better prediction efficiencies (using a capture rate and the prediction accuracy index) while producing fewer prediction areas. Results show that the near repeat chain method proves to be the more efficient compared to the heuristic method for all bandwidth combinations analyzed in this research.
Highlights
Scientific studies showed that crime events are spatially concentrated (Guerry 1833; Quetelet 1835)
Assessing space–time clustering using the Near Repeat Calculator (NRC) we investigate whether there is a pattern of repeat and/or near repeat victimization for apartment burglaries in Vienna
The temporal distance starts from 0 days, which enumerates apartment burglaries that occur on the same day as a previous apartment burglary at the Temporal distance 0–1 day 0–3 days 0–5 days 0–7 days 0–9 days
Summary
Scientific studies showed that crime events are spatially concentrated (Guerry 1833; Quetelet 1835). An increasing number of studies have revealed that crime events are spatially and temporally clustered (e.g., Polvi et al 1991; Sagovsky and Johnson 2007). In the case of police patrols, there may be only marginal gains of deterring disorderly and criminal behavior to be made by increasing the amount of time that officers spend at particular locations (Koper 1995). For such reasons, the optimal deployment of resources may involve a more dynamic allocation strategy, whereby resources are only deployed for short amounts of time before being moved to other locations (Koper 1995; Telep et al 2014)
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