Risk assessment for enhanced service provision projects
Project 1 - Predictive Policing for Crime Reduction
Police forces play a key role in ensuring the public’s safety by responding to emergencies, performing neighbourhood patrols, monitoring criminal activity, making arrests, investigating crimes and testifying in court. With today’s society in a state of austerity major funding cuts are being implemented on public services including the police. It is vital that the police are still able to protect the public with the limited resources available and this means an increase in operational efficiency is required. This research investigates how the police response resources can be used to their highest efficiency, to ensure the public’s welfare, by investigating allocation to incidents and patrol planning.
Two major aspects which affect the efficiency of response to incidents are the location of officers when an incident occurs and which officer is selected to respond to an incident. These two problems are the focus of this research, which has yielded a computer aided dispatch algorithm and a computer aided patrol positioning algorithm. The computer aided dispatch algorithm automatically selects the most appropriate officer to attend an incident. This involves multiple criteria decision making as there are many factors to consider when choosing the most appropriate officer including availability, driving qualifications and predicted response times. The method of dispatch developed results in increased response times and better demand coverage as well as reductions in distance travelled. The computer aided patrol positioning algorithm generates the ideal officer positioning by considering demand coverage and areas of high crime, hotspots. Hotspots are found by analysing historical incident data and then using Kernel density estimation. The routes are then planned using a variation of the double standard location problem. This police response resource patrol positioning algorithm leads to decreased response times, increased demand coverage and problem area targeting.
This project has taken place in collaboration with Leicestershire police force. Hence it has benefited from finding real world problems within the police force and allowed an algorithm to be developed to cater for these problems and fit in with methods already in place. Also is has profited from use of actual data. Communicating with other police forces has confirmed this is a common problem so even though details of the processes may not a line completely with all police forces, the tool developed is general and can be applied to all police forces with the required information.