Designing efficient and balanced police patrol districts on an urban street network

In police planning, a territory is often divided into several patrol districts with balanced workloads, in order to repress crime and provide better police service. Conventionally, in this districting problem, there is insufficient consideration of the impacts of street networks. In this study, we propose a street-network police districting problem (SNPDP) that explicitly uses streets as basic underlying units. This model defines the workload as a combination of different attributes and seeks an efficient and balanced design of districts. We also develop an efficient heuristic to generate high-quality districting plans in an acceptable time. The capability of the algorithm is demonstrated in comparison to an exact linear programming solver on simulated datasets. The SNPDP model is successfully implemented and tested in a case study in London, and the generated police districts have different characteristics that are consistent with the crime risk and land use distribution. Besides, we demonstrate that SNPDP is superior to an aggregation grid-based model regarding the solution quality. This model has the potential to generate street-based districts with balanced workloads for other districting problems, such as school districting and health care districting

Developing Police Patrol Strategies Based on the Urban Street Network

In urban areas, crime and disorder have been long-lasting problems that spoil the economic and emotional well-being of residents. A significant way to deter crime, and maintain public safety is through police patrolling. So far, the deployment of police forces in patrolling has relied mainly on expert knowledge, and is usually based on two-dimensional spatial units, giving insufficient consideration to the underlying urban structure and collaboration among patrol officers. This approach has led to impractical and inefficient police patrol strategies, as well as a workload imbalance among officers. Therefore, it is of essential importance to devise advanced police patrol strategies that incorporate urban structure, the collaboration of the patrol officers, and a workload balance. This study aims to develop police patrol strategies that would make intelligent use of the street network layout in urban areas. The street network is a key component in urban structure and is the domain in which crime and policing take place. By explicitly considering street network configurations in their operations, police forces are enabled to provide timely responses to emergency calls and essential coverage to crime hotspots. Although some models have considered street networks in patrolling to some extent, challenges remain. First, most existing methods for the design of police districts use two-dimensional units, such as grid cells, as basic units, but using streets as basic units would lead to districts that are more accessible and usable. Second, the routing problem in police patrolling has several unique characteristics, such as patrollers potentially starting from different stations, but most existing routing strategies have failed to consider these. Third, police patrolling strategies should be validated using real-world scenarios, whilst most existing strategies in the literature have only been tested in small hypothetical instances without realistic settings. In this thesis, a framework for developing police patrol strategies based on the urban street network is proposed, to effectively cover crime hotspots, as well as the rest of the territory. This framework consists of three strategies, including a districting model, a patrol routing strategy for repeated coverage, and a patrol routing strategy for infrequent coverage. Various relevant factors have been considered in the strategy design, including the underlying structure of the street network and the collaboration among patrollers belonging to different stations. Moreover, these strategies have been validated by the patrolling scenarios in London. The results demonstrate that these strategies outperform the current corresponding benchmark strategies, which indicates that they may have considerable potential in future police operations

Shared-Use Bus Priority Lanes On City Streets: Case Studies in Design and Management, MTI Report 11-10

This report examines the policies and strategies governing the design and, especially, operations of bus lanes in major congested urban centers. It focuses on bus lanes that operate in mixed traffic conditions; the study does not examine practices concerning bus priority lanes on urban highways or freeways. Four key questions addressed in the paper are: How do the many public agencies within any city region that share authority over different aspects of the bus lanes coordinate their work in designing, operating, and enforcing the lanes? What is the physical design of the lanes? What is the scope of the priority use granted to buses? When is bus priority in effect, and what other users may share the lanes during these times? How are the lanes enforced? To answer these questions, the study developed detailed cases on the bus lane development and management strategies in seven cities that currently have shared-use bus priority lanes: Los Angeles, London, New York City, Paris, San Francisco, Seoul, and Sydney. Through the case studies, the paper examines the range of practices in use, thus providing planners and decision makers with an awareness of the wide variety of design and operational options available to them. In addition, the report highlights innovative practices that contribute to bus lanes’ success, where the research findings make this possible, such as mechanisms for integrating or jointly managing bus lane planning and operations across agencies

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

Local Public Services in Wisconsin: Alternatives for Municipalities with a Focus on Privatization

Both rural and urban municipal officials, faced with increased local resistance to higher taxes, increasing expenditure needs, weakening financial support from higher levels of government, and the growing pressure to "do more with less" have accelerated their search for alternative ways of delivering local public services. The downsizing of government has been brought to the forefront of public discussion in part due to the general conservative shift at the federal and state level and the need to maintain a balanced budget at the local level. Related private sector trends downsizing middle management as a means to become "leaner and meaner," reducing duplication and waste, and increasing earnings, profit levels, and returns to investors. At the same time many local public officials are faced with rising costs to maintain an aging infrastructure, accommodating the needs of special populations, satisfying rules and regulations imposed by higher levels of government, funding new investments to meet the demands of a growing economy in some instances, or maintaining critical services in the face declining economies. In short, the rules of the game for effective management of local governments have changed.

Equity in the Police Districting Problem: balancing territorial and racial fairness in patrolling operations

Objectives The Police Districting Problem concerns the definition of patrol districts that distribute police resources in a territory in such a way that high-risk areas receive more patrolling time than low-risk areas, according to a principle of territorial fairness. This results in patrolling configurations that are efficient and effective at controlling crime but that, at the same time, might exacerbate racial disparity in police stops and arrests. In this paper, an Equitable Police Districting Problem that combines crime-reduction effectiveness with racial fairness is proposed. The capability of this model in designing patrolling configurations that find a balance between territorial and racial fairness is assessed. Also, the trade-off between these two criteria is analyzed. Methods The Equitable Police Districting Problem is defined as a mixed-integer program. The objective function is formulated using Compromise Programming and Goal Programming. The model is validated on a real-world case study on the Central District of Madrid, Spain, and its solutions are compared to standard patrolling configurations currently used by the police. Results A trade-off between racial fairness and crime control is detected. However, the experiments show that including the proposed racial criterion in the optimization of patrol districts greatly improves racial fairness with limited detriment to the policing effectiveness. Also, the model produces solutions that dominate the patrolling configurations currently in use by the police. Conclusions The results show that the model successfully provides a quantitative evaluation of the trade-off between the criteria and is capable of defining patrolling configurations that are efficient in terms of both racial and territorial fairness

Determining optimal police patrol deployments: a simulation-based optimisation approach combining agent-based modelling and genetic algorithms

One of the most important tasks faced by police agencies concerns the strategic deployment of patrols in order to respond to calls whilst also deterring crime. Current deployment strategies typically lack robustness as they are often based on tradition. As police agencies are encouraged to improve the effectiveness and efficiency of their services, it is essential to devise advanced patrol deployments that are based on recent scientific evidence. Most existing models of patrol deployments are too simplistic, and are thus unable to provide a realistic representation of the complexity of patrol activities. Furthermore, past studies have tended to focus on individual aspects of patrol deployment such as efficiency, reactive effectiveness or proactive effectiveness, rather than consider them all together as part of the same problem. This thesis proposes to develop a decision-support tool for informing better patrol deployment designs. This tool consists of a simulation-based optimisation approach combining two key components: (1) an agent-based model (ABM) of patrol activities used to evaluate the performance of the system under a given deployment configuration and (2) a genetic algorithm (GA) which seeks to speed up the search for optimal deployments. While the developed framework is designed to be applicable to any police force, a case study is provided for the city of Detroit in order to demonstrate its potential. The developed decision-support tool shows considerable potential in informing more cost-effective patrol deployments. First, the ABM of patrol activities allows for exploration of the impact of various deployment decisions that police agencies are unable to experiment with in the real world. Second, the GA makes it possible to optimise patrol deployments by identifying 'good' solutions, which provide faster responses to incidents and deter crime in key areas, in reasonable time

Graph deep learning model for network-based predictive hotspot mapping of sparse spatio-temporal events

The predictive hotspot mapping of sparse spatio-temporal events (e.g., crime and traffic accidents) aims to forecast areas or locations with higher average risk of event occurrence, which is important to offer insight for preventative strategies. Although a network-based structure can better capture the micro-level variation of spatio-temporal events, existing deep learning methods of sparse events forecasting are either based on area or grid units due to the data sparsity in both space and time, and the complex network topology. To overcome these challenges, this paper develops the first deep learning (DL) model for network-based predictive mapping of sparse spatio-temporal events. Leveraging a graph-based representation of the network-structured data, a gated localised diffusion network (GLDNet) is introduced, which integrating a gated network to model the temporal propagation and a novel localised diffusion network to model the spatial propagation confined by the network topology. To deal with the sparsity issue, we reformulate the research problem as an imbalance regression task and employ a weighted loss function to train the DL model. The framework is validated on a crime forecasting case of South Chicago, USA, which outperforms the state-of-the-art benchmark by 12% and 25% in terms of the mean hit rate at 10% and 20% coverage level, respectively

BIDding on cities: Applying the Business Improvement District model for urban sustainability

There is a growing expectation in the field of sustainable development that cities are the most suitable scale for addressing global environmental issues, particularly through their ability to mobilize local actors. Business improvement districts (BIDs) are a form of public-private partnership (PPP) in cities typically established by associations of private actors that aim to generate value in communities by jointly investing in physical improvements and local services. The model is gaining attention in Sweden, with one BID established in the Sofielund neighborhood of Malmö currently integrating sustainable development concepts into its core strategy to experiment with solutions for reducing socioeconomic inequalities and the area’s environmental impacts. Since BID Sofielund is seeking to learn new methods for incorporating sustainability and because the nexus between BIDs and sustainability has not been adequately addressed in the academic literature, this research utilizes an exploratory approach in a multiple-case study design focusing on BID Sofielund and four reference cases to investigate how BIDs engage with sustainability through the projects and processes they carry out and develops potential explanations for why they might choose to do so. By plotting BID activities in a sustainability framework, this study found that BIDs contribute to sustainable development through strategies including providing a platform for collaborative governance, promoting energy efficiency in buildings, investing in capital improvement projects that enhance public spaces, and filling gaps in social service provision. The study identified multiple contributors to why BIDs engage in sustainability and assembled a general framework consisting of both internal and external drivers that must be considered to fully understand BID sustainability activities, however more research is needed. From an academic standpoint, the knowledge produced furthers the discussion on BIDs in a sustainability context and it provides practical value for BID practitioners as they seek to measure performance in new ways and enhance their effectiveness through sustainability-driven strategies