|Presenters' slides and posters - International Crime and Intelligence Analysis Conference, 25-26 February, Manchester (UK)|
WHAT WORKS CLASSES
27 September 2016
7-10th November 2016
4th October 2016
6th December 2016
Date to be confirmed
7th July 2016
12th July 2016
15th November 2016
5th-16th September 2016
ICIAC 2011 Seminar Stream 5A
Predictive crime mapping (I)
Abstracts and slides
Disrupting the optimal forager: predictive risk mapping and domestic burglary reduction in Trafford, Greater Manchester
Vincent Jones, Intelligence Hub Manager, Greater Manchester Police
This approach presents an adaptation from a scientific study (Johnson and Bowers, 2004; Bowers et al., 2004), which included an examination of the propensity of offenders to return to a familiar area and the potential placement of a ‘capable guardian’ into areas at the right time to disrupt the offending pattern of the ‘optimal forager’. Traditionally within Trafford, the application of ‘blobology’ was a more appropriate description of the method employed to dictate the deployment of resources. This was simply putting dots on maps which gave no areas for patrols to focus on in terms of priority and limited the ability of Inspectors to monitor the movement of their resources.
The project involved using geographical mapping of previous domestic burglary locations and the creation of buffers, which were date dependant on the colour coded areas. The areas identified were used for Police and external resource deployment to provide a ‘capable guardian’ presence within areas at the key times, there by deterring repeat offences of domestic burglary. This approach was originally run for a 12month period (12/05/10-10/05/11) with results demonstrating a clear reduction of domestic burglaries and the more effective and efficient use of resources. Trafford BCU showed a 26.6% (n-327 domestic burglaries) reduction compared to the previous 12months prior to implementation, considerably outperforming its most similar groups within Greater Manchester Police and nationally.
As well as these quantitative results, there has also been increased and improved intelligence coming into the Trafford Hub on a daily basis due to the new approach. Any intelligence that is now being submitted by officers is added with a simple cipher stating the relevant area (i.e. orange/red/yellow etc) allowing local intelligence officers to identify individuals who could be of note, particularly ones who are not known to the Division but being stopped within key areas for recent offending.
Another success of the approach has been increased senior management control of resources both internally and externally. Now simple taskings can be given to police and partnership resources to deploy in specific risk areas. In the current economical climate, improved efficiency and effectiveness of any available resources will be crucial, particularly when the theory being used has been created from scientific research and findings.
The results strongly indicate that success can be achieved by adopting and adapting a scientific approach into a ‘real world’ situation. This latterly includes a broadening of the application to include offences other than just burglary dwelling, i.e. offending patterns for vehicle crime appropriate to the optimal forager has been factored into the risk mapping and producing similar volume reduction in that crime type.
The improved and focused deployment of resources away from the previous ad-hoc method will lead to reductions in offences. It’s not yet understood the level, if any, of diffusion or the impact with which this project has had on associated crime types. However, the methodology in this project, demonstrates a simple cost-effective approach to producing patrol plans using scientific research to aid the reduction of domestic burglary.
The predictive policing challenges of near repeat armed street robberies
Cory Haberman, Center for Security and Crime Science, Temple University, USA
New research methodologies like the near repeat phenomenon provide police with a potentially powerful predictive technique, if law enforcement possesses the capacity to capitalize on identified patterns in time. The current study examines armed street robbery data from Philadelphia, PA USA in order to identify and quantify the existence of multiple-event near repeat chains. We then explore the impact of near repeat chains on the temporal stability of micro-level armed street robbery hot spots. Our findings will interest all police personnel involved in day-to-day tactical decisions or considering the implementation of crime prediction or forecasting methods. In short, our findings are supportive of long-term opportunity reduction measures, and we will discuss the complex organizational and analytical capacities required by police organizations to effectively use predictive policing techniques.
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