A simulated annealing approach to police district design (original) (raw)

Police Districting Problem: Literature Review and Annotated Bibliography

2020

The police districting problem concerns the efficient and effective design of patrol sectors in terms of performance attributes. Effectiveness is particularly important as it directly influences the ability of police agencies to stop and prevent crime. However, in this problem, a homogeneous distribution of workload is also desirable to guarantee fairness to the police agents and an increase in their satisfaction. This chapter provides a systematic review of the literature related to the police districting problem, whose history dates back to almost 50 years ago. Contributions are categorized in terms of attributes and solution methodology adopted. Also, an annotated bibliography that presents the most relevant elements of each research is given.

Performance as a Constraint to Determine Optimum Allocation of Police Patrols Using Stochastic Simulation

Our research analyzes actual operating strategies of a public safety Emergency Response System (ERS) in a large city in Mexico integrating a sixth police district into previously published research composed of five districts out of a total of eight in the city. The research procedure firstly characterizes the demand for service and processes associated with the patrols' response and utilization during the attention of historic calls. Subsequently, we created a stochastic simulation model to emulate current ERS's patrols deployment strategies. After validating the model, we then generated a scenario with the performance's constraint of three minutes maximum patrol response time. Lastly, the minimum numbers of police back up patrols, required to provide the ideal response time for each police quadrant in every district, were obtained. Results reflect that the minimum required numbers of back up police patrols to provide an acceptable service level are viable.

Designing police patrol districts on street network

2017

This paper deals with the police districting problem on the street network. Traditionally, design of police patrol sectors is based on grids or census blocks, which may generate districts that are difficult to cover. This problem may be alleviated using a network-based model. This paper formulates a Street Network-based Police Districting Problem (SNPDP) model, which simultaneously considers the connectivity, workload efficiency and balance of districts. An algorithm framework is also proposed to solve this problem. This provides a conceptual based toward finding suitable districting plans for patrol sectors.

Determining Ideal Number of Police Patrols to Meet Reference Response Time Using Stochastic Simulation

Mexico has experienced a drastic insecurity environment in the last decade due to multiple national and international factors. In this regard, public safety Emergency Response Systems (ERS) have the potential of effectively combat and deter crime through rapid and coordinated strategies. Utilizing stochastic simulation, our research focuses on determining an ideal number of police patrols to be allocated to a public safety Emergency Response System (ERS) in order to comply with a maximum international reference response time as a strategy to deter and combat crime in a large city in Mexico. The city´s ERS is composed by eight police districts, and this research incorporates the analysis of only half of the 7 th police district to previously published results of six districts, given that this particular district is integrated by eight police quadrants, as opposed to only four adjacent quadrants found in a regular police district. Simulation scenarios include actual and proposed operating strategies of a police quadrant considering one dedicated patrol per patrolling zone plus an additional number of back up patrols. Results identify a feasible level of ideal back up patrols in all evaluated police districts. Recommendations are provided to reconsider redistricting strategies to assist the patrol deployment strategy.