Olmez_GISRUK_AH-pages-1-2.pdf

Detailed processes for emergency response work and demand and resource management can be recognisably modelled using system dynamics. The Emergency Service Incident Model (ESIM) successfully joins together the very short timescales associated with providing an emergency service to the public, to the far longer periods involved in planning for the recruitment and training of police patrol officers within Devon and Cornwall Police. A system dynamics model is presented which can help inform decisions on patrol officer staffing requirements. The impact of a range of policies can be studied to determine new and better ways to resolve the supply-demand equation that delivers frontline policing services to the public. A number of alternative policy interventions directed at improving police patrol officer incident resolution are highlighted in this study and related scenarios are tested. Results show that particular attention should be directed towards better management of the backlog of routine incidents.

Proceedings of the International Conference on Automated Planning and Scheduling, 2022

In the quest to achieve better response times in dense urban environments, law enforcement agencies are seeking AIdriven planning systems to inform their patrol strategies. In this paper, we present a framework, OFFICERS, for deployment planning that learns from historical data to generate deployment schedules on a daily basis. We accurately predict incidents using ST-ResNet, a deep learning technique that captures wide-ranging spatio-temporal dependencies, and solve a large-scale optimization problem to schedule deployment, significantly improving its scalability through a simulated annealing solver. Methodologically, our approach outperforms our previous works where prediction was done using Generative Adversarial Networks, and optimization was performed with the CPLEX solver. Furthermore, we show that our proposed framework is designed to be readily transferable between use cases, handling a wide range of both criminal and non-criminal incidents, with the use of deep learning and a general-purpose efficient solver, reducing dependence on context-specific details. We demonstrate the value of our approach on a police patrol case study, and discuss both the ethical considerations, and operational requirements, for deployment of a lightweight and responsive planning system.

1975

ness to sacrifice (vacations that never were, a promising career put in abeyance), this thesis would still be only a dream (or nightmare). Most of all, I am grateful for her sharing my life, sharing the burdens which otherwise would have been unbearable, and for gi~ing me 30 much of herself. I would therefore dedicate with love this thesis to her though, in fact, it is already at least as much hers as it is mine. Support for this work was provided by the National Science FOlmdation under grant GI38004 and by the Harry J.

Proceedings of the 2011 Winter Simulation Conference, 2011

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.

Salus

The increasing importance of proactive policing has highlighted the need to ensure that the police utilise their resources both efficiently and effectively. Traditionally, police agencies have allocated resources in response to their operational demands or requirements, with the majority of resources being distributed in response to political demands and public calls for service. In recent years there has been a greater emphasis by police to deliver services proactively, and to direct resources to specific geographic areas of high crime or to specific crimes, and to apply intelligence led targeted policing initiatives. The changing operating environment to a public service ethos of accountability and ‘do more with less’ means that historical methods of allocating police officers may not meet an agency's strategic goals. This paper examines if an economic approach to allocating police strategic resources is an appropriate and equitable method in a time of austerity. This greater ...

Operational Research, 2007

Violent crimes such as ram raids and armed robberies have a substantial impact on the physical, emotional and material wellbeing of victims. Therefore these crimes typically receive the highest priority from the police. In this paper we present prescriptive models to deploy police patrol units to improve their ability to respond timely to such crimes. These models can also help police administrators to assess the effectiveness of proposals that attempt to advance the moment when the police arrives on the scene. The models are illustrated by an application to real-life data from a Belgian police zone from which some general guidelines are derived.

Fire Technology, 2001

In order to adequately respond to emergencies, it is necessary to maintain a sufficient level of personnel as well as geographic coverage. Planning for geographic and manpower coverage can involve a wide variety of factors, including union restrictions, frequency and spatial distribution of calls, and road network accessibility. The focus of this paper is on manpower deployment across such agencies as police, fire, and EMS. Since the cost of manpower is the single largest cost factor in providing such services, it is also one of the central elements in efficient service provision. This paper discusses some of the differences in manpower planning among the different emergency services. It also presents details associated with several special models that have been developed in police and EMS planning in order to efficiently deploy manpower throughout each week. We conclude with comments on future needs for research and development in deployment planning and operations.

Urban Studies, 2021

The effective, efficient and equitable policing of urban areas rests on an appreciation of the qualities and scale of, as well as the factors shaping, demand. It also requires an appreciation of the factors shaping the resources deployed in their address. To this end, this article probes the extent to which policing demand (crime, anti-social behaviour, public safety and welfare) and deployment (front-line resource) are similarly conditioned by the social and physical urban environment, and by incident complexity. The prospect of exploring policing demand, deployment and their interplay is opened through the utilisation of big data and artificial intelligence and their integration with administrative and open data sources in a generalised method of moments (GMM) multilevel model. The research finds that policing demand and deployment hold varying and time-sensitive association with features of the urban environment. Moreover, we find that the complexities embedded in policing demand...

The problem of resourcing and staffing, or finding how much manpower is needed to meet demand, can be traced back to the times of the Roman Empire. We examine here the various means used by the Royal Canadian Mounted Police in trying to solve this problem efficiently. We also examine their latest attack in building a simulator to determine future demands on resources and we provide a solution to determine efficient staffing levels through an application of a scheduling algorithm using "rods". This algorithm is characterized as a rod-scheduling method which can be reduced to a linear program. It has been found that the previous methods used by police departments in Canada and the United States are extremely cumbersome. The methods suggested here correct this. Although the methods are very similar to those used before in other industries they haven't been applied to police work. What was previously done in policing is to optimize very simple constraints first and then try to fit the results to the needs of the user. In this work I have suggested first obtaining all legal inputs and then optimizing to obtain a final answer. The author uses this method to investigate different types of demand data. Furthermore, different integer programming techniques are investigated. This document is meant for different users. It is hoped it can be read by various police departments as well as administrators and academics. In light of this the tone of the thesis is conversational. Much of the mathematical work is in sections three to seven.