Highlights A methodology is proposed to establish efficient police patrol routes by identifying crime and crash hotspots. Dispatch times between police patrol routes with and without focus on hotspots are compared. Results show a reduction of up to 44% in police dispatch time when DDACTS principles are applied.
Abstract Applying Data-Driven Approaches to Crime and Traffic Safety (DDACTS) can help police departments allocate limited resources more efficiently. By focusing on hazardous areas, highly visible traffic law enforcement simultaneously can reduce both crime and crashes. Most studies have focused on the reduction of crime and crashes after applying new patrol routes, but few have documented how to improve or change police dispatch time. The objective of this study was to compare the police dispatch time between two conditions: (1) police patrol routes with organized hotspots, and (2) police patrol route patterns without focusing on hotspots. A secondary objective consisted of developing a procedure to describe the calculation of the change in dispatch time. This study used data obtained from the College Station Police Department. Crime and crash data were collected between January 2005 and September 2010, which included 65,461 offense reports and 14,712 crash reports. The study procedure includes four steps: (1) geocoding the data, (2) defining the hotspots, (3) organizing the best patrol routes, and (4) estimating the effectiveness. ArcGIS was used for the data analysis. The results indicate that using DDACTS principles can potentially reduce police dispatch time by 13% and 17% when the top five and top ten hotspot routes respectively are included in the analysis. The procedure can be used by law enforcement agencies to estimate whether or not the DDACTS protocols can be an effective tool for reducing law enforcement dispatch times when crash and crime data are analyzed simultaneously.
Using geographical information systems to organize police patrol routes effectively by grouping hotspots of crash and crime data
Journal of Transport Geography ; 30 ; 138-148
2013-01-01
11 pages
Article (Journal)
Electronic Resource
English
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