Improving the Creation of Hot Spot Policing Patrol Routes: Comparing Cognitive Heuristic Performance to an Automated Spatial Computation Approach

Hot spot policing involves the deployment of police patrols to places where high levels of crime have previously concentrated. The creation of patrol routes in these hot spots is mainly a manual process that involves using the results from an analysis of spatial patterns of crime to identify the areas and draw the routes that police officers are required to patrol. In this article we introduce a computational approach for automating the creation of hot spot policing patrol routes. The computational techniques we introduce created patrol routes that covered areas of higher levels of crime than an equivalent manual approach for creating hot spot policing patrol routes, and were more efficient in how they covered crime hot spots. Although the evidence on hot spot policing interventions shows they are effective in decreasing crime, the findings from the current research suggest that the impact of these interventions can potentially be greater when using the computational approaches that we introduce for creating hot spot policing patrol routes

Filaments of crime: Informing policing via thresholded ridge estimation

Objectives: We introduce a new method for reducing crime in hot spots and across cities through ridge estimation. In doing so, our goal is to explore the application of density ridges to hot spots and patrol optimization, and to contribute to the policing literature in police patrolling and crime reduction strategies. Methods: We make use of the subspace-constrained mean shift algorithm, a recently introduced approach for ridge estimation further developed in cosmology, which we modify and extend for geospatial datasets and hot spot analysis. Our experiments extract density ridges of Part I crime incidents from the City of Chicago during the year 2018 and early 2019 to demonstrate the application to current data. Results: Our results demonstrate nonlinear mode-following ridges in agreement with broader kernel density estimates. Using early 2019 incidents with predictive ridges extracted from 2018 data, we create multi-run confidence intervals and show that our patrol templates cover around 94% of incidents for 0.1-mile envelopes around ridges, quickly rising to near-complete coverage. We also develop and provide researchers, as well as practitioners, with a user-friendly and open-source software for fast geospatial density ridge estimation. Conclusions: We show that ridges following crime report densities can be used to enhance patrolling capabilities. Our empirical tests show the stability of ridges based on past data, offering an accessible way of identifying routes within hot spots instead of patrolling epicenters. We suggest further research into the application and efficacy of density ridges for patrolling.Comment: 17 pages, 3 figure

Analysing the police patrol routing problem : a review

Police patrol is a complex process. While on patrol, police officers must balance many intersecting responsibilities. Most notably, police must proactively patrol and prevent offenders from committing crimes but must also reactively respond to real-time incidents. Efficient patrol strategies are crucial to manage scarce police resources and minimize emergency response times. The objective of this review paper is to discuss solution methods that can be used to solve the so-called police patrol routing problem (PPRP). The starting point of the review is the existing literature on the dynamic vehicle routing problem (DVRP). A keyword search resulted in 30 articles that focus on the DVRP with a link to police. Although the articles refer to policing, there is no specific focus on the PPRP; hence, there is a knowledge gap. A diversity of approaches is put forward ranging from more convenient solution methods such as a (hybrid) Genetic Algorithm (GA), linear programming and routing policies, to more complex Markov Decision Processes and Online Stochastic Combinatorial Optimization. Given the objectives, characteristics, advantages and limitations, the (hybrid) GA, routing policies and local search seem the most valuable solution methods for solving the PPRP

Skillman Foundation Safety Strategy Review Executive Summary

Understanding that children cannot thrive if they do not feel safe in the places they live, play and study, in 2012, the Skillman Foundation added a safety strategy. The Foundation's investment in strategies and activities intended to increase safety is part of its overall investment in building pathways to success for Detroit's children. The Foundation also recognizes that movements toward safety were taking place among residents, community development practitioners and other stakeholders prior to 2012. These activities were key and provided the groundwork which informed, as well as worked alongside, the Foundation's investments in safety.With the goal of documenting the Foundation's safety grantmaking strategies and examining how these strategies are playing out in the target neighborhoods, the Skillman Foundation retained JFM Consulting Group (JFM), a Detroit-based planning, evaluation, and research firm to conduct a review of its safety strategy for the years between 2012 and 2015. As mentioned above, Skillman had not instituted an official safety component until 2012, however safety efforts had taken place prior to this time. This report provides the results of that review after 2012, as well as some context on safety efforts outside of the Foundation's direct investments

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

Police officer dynamic positioning for incident response and community presence

Police Forces are under a constant struggle to provide the best service possible with limited and decreasing resources. One area where service cannot be compromised is incident response. Resources which are assigned to incident response must provide attendance to the scene of an incident in a timely manner to protect the public . To ensure the possible demand is met maximum coverage location planning can be used so response officers are located in the most effective position for incident response. This is not the only concern of response officer positioning. Location planning must also consider targeting high crime areas, hotspots, as an officer presence in these areas can reduce crime levels and hence reduce future demand on the response officers. In this work hotspots are found using quadratic kernel density estimation with historical crime data. These are then used to produce optimal dynamic patrol routes for response officers to follow. Dynamic patrol routes result in reduced response times and reduced crime levels in hotspot areas resulting in a lower demand on response officers

Resources Package Modelling Supporting Border Surveillance Operations

The purpose of this work is to propose a military planning tool capable of providing logistical bases and patrol packages to most effectively support border surveillance. Presently, military patrols are employed along geographical borders to combat transnational crimes; acts such as drug trafficking, smuggling of goods and illegal natural resources exploitation. The patrols make temporary stops within specific time windows at specific places characterised by a high incidence of crime (hotspots). These hotspots have different criticalities within given time windows. To optimise the results, the proposed model allows additional stops in more critical hotspots. It achieves this using a mathematical optimisation model. Considering that there are not adequate logistical-military capacities (logistical bases and patrols) at all needed locations, developing a border surveillance plan that optimises resource use is imperative. The model was run using black hole-based optimisation and a real patrol mission’s database to ensure timely solutions. The solutions were then evaluated in terms of quality (number of bases and patrols, coverage efforts, and travel time) and computational processing time. Next, they were compared with solutions using the traditional method, thereby demonstrating the model’s robustness in providing timely surveillance schemes that ensure high coverage with minimum resources

Prescriptive Analytics in Urban Policing Operations

Problem definition: We consider the case of prescriptive policing, that is, the data-driven assignment of police cars to different areas of a city. We analyze key problems with respect to prediction, optimization, and evaluation as well as trade-offs between different quality measures and crime types. Academic/practical relevance: Data-driven prescriptive analytics is gaining substantial attention in operations management research, and effective policing is at the core of the operations of almost every city in the world. Given the vast amounts of data increasingly collected within smart city initiatives and the growing safety challenges faced by urban centers worldwide, our work provides novel insights on the development and evaluation of prescriptive analytics applications in an urban context. Methodology: We conduct a computational study using crime and auxiliary data on the city of San Francisco. We analyze both strong and weak prediction methods along with two optimization formulations representing the deterrence and response time impact of police vehicle allocations. We analyze trade-offs between these effects and between different crime types. Results: We find that even weaker prediction methods can produce Pareto-efficient outcomes with respect to deterrence and response time. We identify three different archetypes of combinations of prediction methods and optimization objectives that constitute the Pareto frontier among the configurations we analyze. Furthermore, optimizing for multiple crime types biases allocations in a way that generally decreases single-type performance along one outcome metric but can improve it along the other. Managerial implications: Although optimization integrating all relevant crime types is theoretically possible, it is practically challenging because each crime type requires a collectively consistent weight. We present a framework combining prediction and optimization for a subset of key crime types with exploring the impact on the remaining types to support implementation of operations-focused smart city solutions in practice