SBIR Phase I: Predictive Analytics and Optimization for Crime Reduction and Police Service Improvement

This SBIR Phase I project proposes to develop a system that, based on data analysis of past crimes, forecasts the type, location and timing of probable future crimes and automatically suggests appropriate police unit deployment. The proposed research is innovative in two fundamental ways: First, the system will view crime occurrences and police unit deployment as a holistic problem and create both a crime forecast and the appropriate resource allocation for maximum efficiency. Second, recent advances in Robust Optimization will be used to handle any uncertainties during the resource allocation process and produce a deployment plan that yields good results under scenarios that deviate from the forecast or the allocation plan. The system will use state-of-the-art Predictive Analytics and Robust Optimization in combination or embedded within each other in order to achieve a much higher degree of effectiveness in crime prevention and to design optimal unit deployment for time horizons between one and thirty days. The proposed approach is a substantial improvement over currently available systems, which forecast crime using predictive analytics and then pass the results to either a different scheduling system or a human to design the unit deployment without any consideration for uncertainty or forecast error. The broader impact of this project is to lower the crime index of police departments (PDs) by determining a series of locations and times where crimes of specific types are probable, with an acceptable degree of certainty, and suggesting deployment of appropriate units. Most PDs do not have enough resources to deploy in the vicinity of every probable crime; they need tools to help them select unit deployment by responding to the set of crimes that will have the most effect on the crime index on aggregate. They are few such tools available in the market today; most of them concentrate on creating weekly or monthly predictions, or on tracking known criminals. Some of the more advanced systems require expensive hardware and well-trained personnel to operate, putting them beyond the reach of smaller Police Departments. The proposed system will be highly customizable and capable of connecting with almost any type of data repository, while being affordable through on site or hosted access. Therefore, it will improve crime prevention rates, decrease response time and increase the effectiveness of budget-constrained Police Departments. In the long term it will also enable strategic unit deployment, and efficient personnel planning.