Predictive policing management: a brief history of patrol automation

Predictive policing has attracted considerably scholarly attention. Extending the promise of being able to interdict crime prior to its commission, it seemingly promised forms of anticipatory policing that had previously existed only in the realms of science fiction. The aesthetic futurism that attended predictive policing did, however, obscure the important historical vectors from which it emerged. The adulation of technology as a tool for achieving efficiencies in policing was evident from the 1920s in the United States, reaching sustained momentum in the 1960s as the methods of Systems Analysis were applied to policing. Underpinning these efforts resided an imaginary of automated patrol facilitated by computerised command and control systems. The desire to automate police work has extended into the present, and is evident in an emergent platform policing – cloud-based technological architectures that increasingly enfold police work. Policing is consequently datafied, commodified and integrated into the circuits of contemporary digital capitalism

Leveraging Wireless Broadband to Improve Police Land Mobile Radio Programming: Estimating the Resource Impact

Despite rapid growth in criminological studies of police technology, examinations of police land mobile radios are absent in the literature. This is troubling given the central role mobile radios serve in police operations and their significant management costs. The present study seeks to fill this gap by introducing the functionality of wireless broadband radio programming. Current practice requires a police officer to physically drive to a radio programming location to manage their mobile radio. Wireless programming remedies this burdensome reality, thereby saving officer time and cost. Geospatial analyses are used to estimate distance saved associated with wireless programming. We then conduct a number of calculations to determine time and cost savings related to the observed differences between existing and wireless radio programming within the context of the North Carolina State Highway Patrol. Results suggest wireless radio programming can save significant personnel and financial resources. Implications are discussed

An Online Decision-Theoretic Pipeline for Responder Dispatch

The problem of dispatching emergency responders to service traffic accidents, fire, distress calls and crimes plagues urban areas across the globe. While such problems have been extensively looked at, most approaches are offline. Such methodologies fail to capture the dynamically changing environments under which critical emergency response occurs, and therefore, fail to be implemented in practice. Any holistic approach towards creating a pipeline for effective emergency response must also look at other challenges that it subsumes - predicting when and where incidents happen and understanding the changing environmental dynamics. We describe a system that collectively deals with all these problems in an online manner, meaning that the models get updated with streaming data sources. We highlight why such an approach is crucial to the effectiveness of emergency response, and present an algorithmic framework that can compute promising actions for a given decision-theoretic model for responder dispatch. We argue that carefully crafted heuristic measures can balance the trade-off between computational time and the quality of solutions achieved and highlight why such an approach is more scalable and tractable than traditional approaches. We also present an online mechanism for incident prediction, as well as an approach based on recurrent neural networks for learning and predicting environmental features that affect responder dispatch. We compare our methodology with prior state-of-the-art and existing dispatch strategies in the field, which show that our approach results in a reduction in response time with a drastic reduction in computational time.Comment: Appeared in ICCPS 201

Towards a science of security games

Abstract. Security is a critical concern around the world. In many domains from counter-terrorism to sustainability, limited security resources prevent complete security coverage at all times. Instead, these limited resources must be scheduled (or allocated or deployed), while simultaneously taking into account the impor-tance of different targets, the responses of the adversaries to the security posture, and the potential uncertainties in adversary payoffs and observations, etc. Com-putational game theory can help generate such security schedules. Indeed, casting the problem as a Stackelberg game, we have developed new algorithms that are now deployed over multiple years in multiple applications for scheduling of secu-rity resources. These applications are leading to real-world use-inspired research in the emerging research area of “security games”. The research challenges posed by these applications include scaling up security games to real-world sized prob-lems, handling multiple types of uncertainty, and dealing with bounded rationality of human adversaries.

Optimising police dispatch for incident response in real-time

It is crucial that police forces operate in a cost efficient manner and, in the case of incident response, that the most efficient resources are allocated. The current procedure is that police response units are allocated manually by a dispatcher using a resource list and mapping software. The efficiency of this process can be improved by the use of integrated mathematical approaches embedded within an automatic framework, yielding the optimal selection framework developed in this paper. The framework combines mapping and routing algorithms, and a decision process to facilitate optimal officer selection for incident response. The decision process considers information such as quickest response time, predicted traffic conditions, driving qualifications, response unit availability and demand coverage. The selection framework has been tested and validated through simulation and has shown to increase the efficiency of response units through reduced response times, increased response unit availability, and greater demand coverage

Predictive police patrolling to target hotspots and cover response demand

Police forces are constantly competing to provide adequate service whilst faced with major funding cuts. The funding cuts result in limited resources hence methods of improving resource efficiency are vital to public safety. One area where improving the efficiency could drastically improve service is the planning of patrol routes for incident response officers. Current methods of patrolling lack direction and do not consider response demand. Police patrols have the potential to deter crime when directed to the right areas. Patrols also have the ability to position officers with access to high demand areas by pre-empting where response demand will arise. The algorithm developed in this work directs patrol routes in real-time by targeting high crime areas whilst maximising demand coverage. Methods used include kernel density estimation for hotspot identification and maximum coverage location problems for positioning. These methods result in more effective daily patrolling which reduces response times and accurately targets problem areas. Though applied in this instance to daily patrol operations, the methodology could help to reduce the need for disaster relief operations whilst also positioning proactively to allow quick response when disaster relief operations are required

CPC: Crime, Policing and Citizenship - Intelligent Policing and Big Data

Crime, Policing and Citizenship (CPC) – Space-Time Interactions of Dynamic Networks has been a major UK EPSRC-funded research project. It has been a multidisciplinary collaboration of geoinformatics, crime science, computer science and geography within University College London (UCL), in partnership with the Metropolitan Police Service (MPS). The aim of the project has been to develop new methods and applications in space-time analytics and emergent network complexity, in order to uncover patterning and interactions in crime, policing and citizen perceptions. The work carried out throughout the project will help inform policing at a range of scales, from the local to the city-wide, with the goal of reducing both crime and the fear of crime. The CPC project is timely given the tremendous challenging facing policing in big cities nationally and globally, as consequences of changes in society, population structure and economic well-being. It addresses these issues through an intelligent approach to data-driven policing, using daily reported crime statistics, GPS traces of foot and vehicular patrols, surveys of public attitudes and geo-temporal demographic data of changing community structure. The analytic focus takes a spatio-temporal perspective, reflecting the strong spatial and temporal integration of criminal, policing and citizen activities. Street networks are used throughout as a basis for analysis, reflecting their role as a key determinant of urban structure and the substrate on which crime and policing take place. The project has presented a manifesto for ‘intelligent policing’ which embodies the key issues arising in the transition from Big Data into actionable insights. Police intelligence should go beyond current practice, incorporating not only the prediction of events, but also how to respond to them, and how to evaluate the actions taken. Cutting-edge network-based crime prediction methods have been developed to accurately predict crime risks at street segment level, helping police forces to focus resources in the right places at the right times. Methods and tools have been implemented to support senior offices in strategic planning, and to provide guidance to frontline officers in daily patrolling. To evaluate police performance, models and tools have been developed to aid identification of areas requiring greater attention, and to analyse the patrolling behaviours of officers. Methods to understand and model confidence in policing have also been explored, suggesting strategies by which confidence in the police can be improved in different population segments and neighbourhood areas. A number of tools have been developed during the course of the project include data-driven methods for crime prediction and for performance evaluation. We anticipate that these will ultimately be adopted in daily policing practice and will play an important role in the modernisation of policing. Furthermore, we believe that the approaches to the building of public trust and confidence that we suggest will contribute to the transformation and improvement of the relationship between the public and police