Optimizing Police Resources Deployment

Abstract

The Ottawa Police Service (OPS) deploys its resources based on the needs of predefined zones. However, the current zoning approach has been acknowledged as inefficient due to negative impacts on costs, proficiency, quality of services and time management. The zoning approach has also been acknowledged as inefficient due to its static nature, its inflexibility and its inability to adjust systematically according to the number of currently available police vehicles. It also cannot assist in addressing demand changes throughout the day in order to reduce call responses in neighbouring zones. Therefore, the demand variation could lead to a significant decrease in police efficiency, since those officers who have been allocated to other zones are not able to participate in events outside their zones without permission. It may cause a high volume of waiting calls and increased response time depending on the time of day, shifts, seasons, etc. Hence, the OPS needs to find a new model for resource deployment that can provide the same coverage but with better service quality. Resource allocation has always been a challenge for emergency services like police, fire emergency, and ambulance services since it has a direct impact on the efficiency and effectiveness of the service activities. The ambulance and fire emergency services have received research attention while the optimization of police resources remains largely ignored. While there are many similarities between ambulance and police deployment there are also significant differences that mean the direct transfer of ambulance models to police deployment is not feasible. This research addresses the lack of an effective tool for the deployment of police resources. We develop a simulation model that analyzes potential deployment plans in order to determine their effect on response times. The model has been developed in partnership with the Ottawa Police Service (OPS) and will address the obstacles, disadvantages, and geographical constraints of the existing allocation model. The OPS needs to align deployment with the service demand and their operational goals (response times, visibility, workload, compliance, etc.). Repositioning police vehicles in real time, helps in responding to future calls more effectively without adding more officers.