Simulating calls for service for an urban police department

Abstract

Police departments in the United States strive to schedule officers so that a number of benchmarks are met. The police administration is often asked to justify to local governing bodies the size of the police force. To assess the effects of force size and scheduling strategies on the ability to meet the benchmark goals, we develop a discrete-event simulation for the calls for service (CFS). Using actual call data from an urban police department in the United States, we fit distributions for call rates and service times for input to the simulation. The output of the model includes statistics related to the response delay, cross-sector calls, and officer utilization. The simulation model verifies intuitive notions about policing and reveals interesting properties in the system.

Understanding the concept of ‘demand’ in policing: a scoping review and resulting implications for demand management

“…Suggestions include defining demand as the need for police presence due to both crime and non-crime related activities and also for the purpose of prevention or deterrence as well as 'self-generated' demand arising from administrative processes and errors (Maxfield 1982, Elliott-Davies et al 2016). To measure these, researchers have suggested Boulton et al (2017) Calls for service Brooks et al (2011) Calls for service Camacho-Collados and Liberatore 2015Crime Risk Past crime data / interviews with service coordinators and agents involved in public safety operations to identify characteristics of 'good' patrol sector partition Camacho-Collados et al…”

“…Of special importance when attempting to model police demand are the many studies that seek to use different modelling approaches to improve resource allocation and maximise benefits in police work. The focus of these studies is often concerned with the optimisation of location-allocation problems as they relate to the locations and routes for police patrols (Brooks et al 2011, Zhang and Brown 2013, Adler et al 2014, De La Cruz 2014, or the design of police patrol districts (Aly and Litwhiler 1979, Carroll and Laurin 1981, D'Amico et al 2002, Curtin et al 2010, Zhang and Brown 2013, Zhang and Brown 2014. As mentioned in earlier sections, the understanding and modelling of police demand, and its dynamics and drivers, is rather limited.…”

Optimal Staffing Policy - A Service System with Stochastic Travel Times

“…There have been countless other applications of these theories in problems as diverse as highway traffic queues Evans et al (1964) or management of call centres (Aksin et al, 2007;Whitt, 2005). There are some examples of queueing theory within the problem of optimal staffing, such as Brooks et al (2011). They investigated the problem of care-athome services using a Markov decision process with queues aiming to balance the cost of staffing against the cost of rejection.…”

“…In our model we assume that the response service provider wishes to balance costs of staffing against the response times, since the regulator has incentivised that with targets. Another difference between our approach and that of Brooks et al (2011) is that the inclusion of different classes of patients resulted in the full problem becoming computationally intractable. Consequently, they can only provide heuristic bounds to guide the optimal policy.…”

Planning robust policing futures: modelling using multimethodology

“…Although a number of research papers report on the use of spatial modelling (Curtin et al, 2010;Zhang and Brown, 2013;Li et al, 2011;Barbosa and Petty, 2014) and discrete event modelling of operational level resource decisions in police patrol systems (Brooks et al, 2011;Srinivasan et al, 2013) there does not appear to be any research published on simulation modelling of more strategic and policy orientated patrol officer resourcing. This paper aims to demonstrate how group based multimethodological modelling can been used to support the development of a robust policy oriented system dynamics model for patrol officer resource evaluation.…”