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

Chen, Huanfa; Cheng, Tao; Ye, Xinyue

doi:10.1080/13658816.2018.1525493

Cited by 24 publications

(12 citation statements)

References 49 publications

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“…Chen et al (2017) propose a heuristic online Bayesian ant-colony optimization algorithm which generates routes that are efficient, flexible, unpredictable, scalable, and robust. In a subsequent paper (Chen et al 2019), the authors present a street network police districting problem where the territorial atomic units are street segments rather than r-districts. The objective is to generate patrol sectors that are balanced in terms of workload, which is defined as a combination of multiple factors, i.e., risk, area, and diameter, following previous contributions in the literature (Liberatore et al 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Equity in the Police Districting Problem: Balancing Territorial and Racial Fairness in Patrolling Operations

2021

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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.

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Section: Literature Reviewmentioning

confidence: 99%

Equity in the Police Districting Problem: Balancing Territorial and Racial Fairness in Patrolling Operations

2021

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“…In addition, the use of network-based predictive hotspot mapping is also well-motivated considering its practical utility. For instance, street-network-based crime prediction could effectively guide the police patrolling in cities (Chen et al, 2018, Chen et al, 2017. Traffic demand (e.g., taxi-hailing) forecasting alongside the road network contributes to optimising the urban transportation resources allocation and improving traffic efficiency (Tang et al, 2016).…”

Section: Network-level Predictive Hotspot Mapping and Motivationsmentioning

confidence: 99%

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

Zhang

Cheng

2020

Computers, Environment and Urban Systems

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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-theart benchmark by 12% and 25% in terms of the mean hit rate at 10% and 20% coverage level, respectively.

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“…In constraint (10), it is guaranteed that in each shift only one type of patrol vehicle can be activated in place j. Finally, in constraint (11), positive and binary variables are determined.…”

Section: Constraintsmentioning

confidence: 99%

“…Observation and monitoring of police cause drivers to commit crimes and to deal with accidents more rapidly, which leads to prevention of heavy traffic due to accidents. The main objective of police forces is to increase road safety, which is possible by controlling and dealing with drivers and preventing crimes and faults resulting in accidents and injuries [20,11]. Organization for Economic Cooperation and Development (OECD) [38] defined traffic police regulations as "field of activities performed to control the drivers' behavior through prevention, rewarding, and punishment to obtain security.…”

Section: Introductionmentioning

confidence: 99%

Location-allocation of traffic police patrols in the suburban network

poor

Sabouhi

Bozorgi-Amiri

et al. 2019

IJOR

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Nowadays, due to population increase and expansion of the transportation networks, the monitoring of this network, control, and prevention of accidents and crimes are very important. The main tasks of traffic patrols, like other emergency-security facilities, are the monitoring and prevention of crime as well as handling and fining in the case of committing crimes. Traffic patrols should be present in the scene and also in high traffic congestion, to reduce accidents and crimes. This paper presents a bi-objective and stochastic optimization model to design an emergency-security system. This network includes traffic patrol vehicles and manpower in patrol vehicles. The objective of the proposed model is to maximize the number of vehicles passing through patrols and minimize the costs according to different scenarios. To solve the model, the epsilonconstraint method is used which simultaneously determines the location of the patrols, allocation of demand points to patrols, and determining the number of existing manpower in patrols. To evaluate and analyze the proposed model, a numerical example is used.

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

Cited by 24 publications

References 49 publications

Equity in the Police Districting Problem: Balancing Territorial and Racial Fairness in Patrolling Operations

Equity in the Police Districting Problem: Balancing Territorial and Racial Fairness in Patrolling Operations

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

Location-allocation of traffic police patrols in the suburban network

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