Patrol Operations Research Articles

Service Time, Dispatch Time, and Demand for Police Services: Helping More by Serving Less

The management of police patrol operations is a complex endeavor. On the one hand police are the principal public agency responsible for implementing crime control policy, while on the other hand they respond to the multitude of other problems which befall urban residents. Police are trained in the use of deadly force and other instruments of violence, but they are most commonly confronted with mundane, non-threatening problems. One of the dilemmas facing police administrators is how to balance the critical and non-critical demands for police service. Most would place threats to life and limb, whether the result of crime or accidental misfortune, in the former category, while relegating what turns out to be the majority of police duties to the non-critical category. Included among this latter group are verbal disputes among neighbors or relatives (although either of these may erupt into violent attacks), uncivil behavior in the form of homeless derelicts or teenagers occupying their street corner turf, and such criminal events as burglaries discovered by householders returning from an extended vacation. The variety of events which precipitate the mobilization of police includes some incidents which clearly require a rapid police response, others which may be resolved as well, if not better, without police intervention, and, what is probably the largest group, those incidents in which it is unclear whether action by the police is either necessary or desirable. Given this task environment, it is safe to say only that law enforcement agencies must be flexible enough to expect the proverbial unexpected. Not only are police unsure what their next encounter with clients on the street will produce, they are also uncertain about when they will be mobilized and where they will be sent. The vast *majority of mobilizations are reactive, in which police respond to calls for assistance from urban residents.' A relatively small number of police encounters with citizens are the result of officer-initiated activity. Opera-

Police supervision on the street: An analysis of supervisor/officer interaction during the shift

This article examines a major assumption of street supervisors in police agencies—that control of police officer behavior is dependent on strict hierarchical organization. The intellectual roots of this assumption are examined, and an emerging perspective developed from direct observation of patrol operations is presented. The research questions are addresses with data that were collected from observations of patrol activities in 60 residential neighborhoods in 24 agencies. The analysis, which is referenced to officers' shifts of duty, shows that the effect of supervision is in the direction posited by the authors of traditional police administration texts, but the magnitude of the effect is not overly strong. The findings are used to inform the evaluation of a common policy option directed at improving officer performance through supervision.

Management Science Roundup

—“Assessment of energy R&D programs for the Appalachian region”; —“From Mexico: A wheat distribution model”; —“Management science model used in Bell system corporate planning”; —“News: Gravitational models in product distribution”; —“News: Ship combat system”; —“News: Medical treatment and evacuation”; —“News: Police patrol operations”; —“News: Pricing of products in the solar array manufacturing industry”; —“Seen Elsewhere” with five overviews: (1) A Rachet Model of Advertising Carryover Effects (Journal of Marketing, February 1976, Vol. 13, No. 3, pp. 76–79) by Leonard J. Parsons (Claremont Graduate School); (2) Image inputs to a Probabilistic Model: Predicting Retail Potential (Journal of Marketing, July 1976, Vol. 40, No. 3, pp. 48–53) by Thomas J. Stanley and Murphy A. Sewall (State University of New York at Albany), (3) The State of Practice in Planning Systems (Sloan Management Review, Winter 1977, Vol. 18, No. 2, pp. 1–24) by H. Igor Ansoff (Vanderbilt University), (4) Production Performance and Customer Satisfaction: A New Concept (Journal of Marketing April 1976, Vol. 40, No. 2, pp. 11–16) by John E. Swan and Linda Jones Combs (University of Arkansas), (5) Articles from SIMSNIPS, February 1977 and May 1977: A Study of Buffer Management Policies for Data Management Systems, Simulating a Virtual Data Machine, Honeywell Time-Sharing System, Dynamics of a Crash Victim, Patient Admission as a Markov Decision Process, Gasoline-Consumption Model, Simulation of Alternate Pediatric Hospital Care Units, Volume III, Air Defense System, Dayton Aircraft Cabin Fire Model, Reference Scene, Growth, Water Planning, Wheat Yield, Undersea Oil Sites, Computer Workload, Airport Parking, Driver-Vehicle Behavior at an Intersection, Hotel Management, Fire Station Location.

Response speeds and response times of urban police patrol cars in Ottawa, Canada

When planning or simulating the operations of radio patrol cars, it is necessary to know the response time and response speed. In the present work the response speed was measured for downtown and surburban cars in order to facilitate computer simulations of patrol operations. Recorders were installed in the cars and records of dispatch delay, travel time, distance, response time, service time, and percentage of time per shift answering calls, were kept. Curves (which may be of general application) were fitted to the data for each parameter. The most probable average speed of the downtown car was 16 mph and of the suburban cars, 25 mph and 27 mph. Because of different average distances, however, the response times were comparable. Good agreement was found berween average travel times calculated from a formula given by R.C. Larson and travel times found experimentally in the present work.

On Quantitative Approaches to Urban Police Patrol Problems

This paper reviews the use of "hazard " formulas for allocating police patrol personnel and indicates the need for new quantitative methods to assist decision makers. Mathematical models of patrol activity can be structured to provide insight to such planning and management prob lems as optimal design of patrol sectors, formulation of preventive patrol strategies, evaluation of technological and administrative innova tions, and precinct-by-precinct allocation of personnel. Several illustra tive models are discussed, with emphasis placed on the diversity of modeling applications in patrol operations and on the insights provided by use of the models.

On the Modeling of Police Patrol Operations

An analytical approach to several operational problems of urban police departments is introduced. In particular, the focus is on urban police patrol forces and the two important activities of a patrol car (or patrol unit) 1) answering a call for police service, 2) performing crime preventive patrol. After reviewing the traditional allocation method, a patrol travel time model and a preventive patrol model are developed. The first depicts the time required for a patrol unit to travel from its position at time of dispatch until arrival at the scene of the incident. The second relates the frequency of patrols to physical parameters and can be used to estimate the probability that a patrolling unit will intercept a crime while in progress. Applications of the models are discussed.

Australian Patrol Operations, Strazeele, July 1918

Australian Patrol Operations, Strazeele, July 1918

Australian Patrol Operations, Strazeele, July, 1918

Australian Patrol Operations, Strazeele, July, 1918