Police Patrol Research Articles

Strengthening Equity and Inclusion in Urban Greenspace: Interrogating the Moral Management & Policing of 2SLGBTQ+ Communities in Toronto Parks.

There is growing recognition that greenspace provides invaluable benefits to health and wellbeing, and is essential infrastructure for promoting both social and environmental sustainability in urban settings. This paper contributes towards efforts to build 'just' and equitable urban sustainability, and more specifically greenspace management, by drawing attention to hostility and exclusion experienced by two-spirit, lesbian, gay, bisexual, transgender, queer, genderqueer, pansexual, transsexual, intersex and gender-variant (2SLGBTQ+) park occupants. There is evidence that access to greenspace is inequitable-despite ongoing media accounts of targeted violence and discriminatory police patrolling of 2SLGBTQ+ communities in urban parks, this population has not received adequate research attention. This paper examines systemic barriers that impede urban greenspace access among 2SLGBTQ+ communities, including how the threat of violence in greenspace limits opportunities for accessing benefits associated with naturalized settings. These themes are explored within the context of the City of Toronto, Canada. Our mixed-method approach draws upon key informant interviews, key document content analysis, and ground-truthing. Our findings reveal how queer corporeality, kinship and love subvert deeply entrenched heteronormative social values and understandings of sexuality, partnership, gender, and use of public space, challenging institutional understandings of morality and daily life. The paper concludes by reflecting on the state of 2SLGBTQ+ communities' relationships to greenspace, and potential ways forward in building greater inclusivity into the social fabric of park design and management.

Smart Patrolling Based on Spatial-Temporal Information Using Machine Learning

With the aim of improving security in cities and reducing the number of crimes, this research proposes an algorithm that combines artificial intelligence (AI) and machine learning (ML) techniques to generate police patrol routes. Real data on crimes reported in Quito City, Ecuador, during 2017 are used. The algorithm, which consists of four stages, combines spatial and temporal information. First, crimes are grouped around the points with the highest concentration of felonies, and future hotspots are predicted. Then, the probability of crimes committed in any of those areas at a time slot is studied. This information is combined with the spatial way-points to obtain real surveillance routes through a fuzzy decision system, that considers distance and time (computed with the OpenStreetMap API), and probability. Computing time has been analized and routes have been compared with those proposed by an expert. The results prove that using spatial–temporal information allows the design of patrolling routes in an effective way and thus, improves citizen security and decreases spending on police resources.

Prevalence and factors associated with post traumatic stress disorder among field police patrol officers serving in Kampala Metropolitan region

BackgroundOccupation groups like police officers and fire fighters are exposed to a number of traumatic events which put them at a risk of developing post-traumatic stress disorder (PTSD). Previous studies have found the prevalence of PTSD in police officers to vary between 7 and 19%. However, most of these studies have been undertaken in western setting with little research having been undertaken in sub-Saharan Africa including Uganda.ObjectiveTo determine the prevalence and factors associated with post-traumatic stress disorder among field police patrol officers serving in Kampala Metropolitan Police (KMP) North Region.MethodsThis was a cross sectional study that was conducted on 392 field police patrol officers serving in KMP North Region. Diagnosis of PTSD was undertaken using the Clinician Administered PTSD Scale for DSM-5. In order to assess for psychiatric comorbidities, the study used the Mini International Neuropsychiatric Interview (M.I.N.I.)ResultsIn this study, the prevalence of PTSD was 7.4%. An additional 62.5% had sub-threshold PTSD, which was defined as, the presence of at least one PTSD symptom but not meeting full criteria for PTSD diagnosis. The factors found to be significantly associated with PTSD were all related to the presence of psychiatric comorbidities, namely the presence of: a current major depressive episode (aOR = 4.7; 95% CI: 1.5- 14.8; p = .009); an alcohol use disorder (aOR = 5.1; 95% CI: 2.0–13.0; p = .001); and presence of dissociation symptoms (aOR = 6.7; 95% CI: 2.0–22.2; p = .002).ConclusionPTSD is one of the common psychiatric disorders experienced by serving police officers in Uganda. The tendency of PTSD in this group to co-occur with other psychiatric disorders means that any treatment program to address it should be part of a comprehensive multi-disorder mental health treatment programme in the police office.

An Examination of Existing Strategies to Address Street Robberies in the Nairobi Central Business District, Kenya, and their Effectiveness

Street robberies are a significant source of fear among the public because victims face an abrupt risk to life, loss of control, and an intrusion of individual space. Street robberies are a particularly dread initiating crime in view of the setting in which it is probably going to happen over the span of somebody's normal life. The 2005 National Crime Victimization Survey demonstrated that street robbers assaulted most victims on their approach to or from work, school, shopping, or running errands. The danger of damage and demise amid an assault further substantiates the open's dread of theft. This study sought to examination of existing strategies to address street robberies in the Nairobi Central Business District, and their Effectiveness. The study was underpinned by the Strain theory by Robert Merton, Social Disorganization and Routine Activity Theory by Cohen & Felson. The study adopted a descriptive research design and targeted persons who had been victims of street robbery within Nairobi CBD in the course of the one year preceding the study. Simple random sampling was used to sample (30%) 107 respondents out of a target population of 356 victims as per the police records within Nairobi CBD. Questionnaires were employed to collect quantitative data from the primary sample while key informant interview guides were employed to collect qualitative data from Key informants. Data collection instruments were piloted before the actual data collection process. Validity and reliability of the study questionnaire were ascertained before commencement of the study. Research findings revealed that majority of the respondents (90.1%) highlighted the national police service as one of the key organizations that deal with the crime of street robbery, 40.2% identified the courts and the rest (19.5%) identified officials from Nairobi city county security department. Majority of respondents (92.6%) indicated that there were street lights while 59.6% agreed that there were CCTV cameras on major streets and terminals, police patrols was confirmed by 54.2% of the respondents, presence of city county askaris (22.3%), and a few ((9.6%) of them pointed that they could see private security guards as strategies for addressing street robberies in Nairobi CBD. When asked on the effectiveness of the existing strategies to address street robberies, most (80.9%) of the respondents asserted that the strategies were not effective. In view of the study findings, the study recommended the development of policies to address street robberies with an insight into the factors encouraging the robberies, the location and crime patterns where the crime happens, and the profiles of both the offenders and victims. Keywords: Strategies, Street Robberies, Nairobi Central Business District, Effectiveness. DOI: 10.7176/JLPG/81-05 Publication date: November 30 th 2022

Tasks of the patrol police for crime prevention

Introduction. The paper, based on the analysis of domestic legislation, characterizes the tasks of the patrol police units in terms of crime prevention. The patrol police is considered one of the main units that performs tasks related to the prevention of criminal and administrative offenses, based on the norms of legislative and bylaw regulations. However, it should be noted that these norms have not yet acquired a systemic nature, with the help of which it would be possible to fully regulate the performance of the tasks of the patrol police in terms of crime prevention. This factor and certain shortcomings of the law enforcement activities of the patrol police actualize the research of such tasks. The purpose of the paper is to investigate the tasks of the patrol police as a subject of crime prevention, to divide them, and to provide proposals for normative legal acts. The results. The importance of the proper performance of tasks by the patrol police regarding the prevention of criminal and administrative offenses necessitates the need for appropriate regulatory and legal support. It is expedient to enshrine at the level of a subordinate regulatory legal act - Regulations on the Department of the Patrol Police the task of crime prevention by supplementing Section II "The main tasks of the Department" paragraph 5 with the following content: "Organization and implementation of preventive and preventive activities aimed at preventing the commission of criminal and administrative offenses, identifying and taking measures to eliminate the causes and conditions that contribute to their commission". Conclusions. The tasks of the patrol police as a subject of preventive activities, taking into account the general tasks of crime prevention, it is advisable to divide them into two groups - the task of identifying and eliminating the causes and conditions of the entire set of offenses (or individual groups) and the task of exerting a preventive influence on a specific person (circles persons) who is inclined to commit a tort or has started to commit it (has committed it).

CARESim: An integrated agent-based simulation environment for crime analysis and risk evaluation (CARE)

It requires innovations to accurately predict crimes and offending risks due to the complex nature of human behaviors within different social contexts. In recent years, theory-based and data-driven agent-based models have become useful tools in computational criminology with the abundance of public data, the rise of computing powers, and extensive explorations of related theories and modeling tools. In this paper, we present CARESim, an integrated simulation environment to predict the risks of high-risk individuals committing street violent crimes. The simulation environment combines agent-based modeling with a real geographical information system and incorporates public data (e.g., environmental data, crime data, census data, and transportation data) to reproduce authentic human mobility. This simulation environment is capable of replicating crime patterns and evaluating policies. We demonstrate the use of this environment and illustrate how key factors, such as social networks, the neighborhood, the weather, police patrol strategies, the population, and citizens’ behaviors, impact the risk of street-level violent offending. Furthermore, the environment can support the optimization of patrol strategies and offer evaluations of different social setups that could be unfeasible or expensive to test in real life.

“Hot street” of crime detection in London borough and lockdown impacts

ABSTRACT In recent years, the police intervention strategy “Hot spots policing” has been effective in combating crimes. However, as cities are under the intense pressure of increasing crime and scarce police resources, police patrols are expected to target more accurately at finer geographic units rather than ballpark “hot spot” areas. This study aims to develop an algorithm using geographic information to detect crime patterns at street level, the so-called “hot street”, to further assist the Criminal Investigation Department (CID) in capturing crime change and transitive moments efficiently. The algorithm applies Kernel Density Estimation (KDE) technique onto street networks, rather than traditional areal units, in one case study borough in London; it then maps the detected crime “hot streets” by crime type. It was found that the algorithm could successfully generate “hot street” maps for Law Enforcement Agencies (LEAs), enabling more effective allocation of police patrolling; and bear enough resilience itself for the Strategic Crime Analysis (SCA) team’s sustainable utilization, by either updating the inputs with latest data or modifying the model parameters (i.e. the kernel function, and the range of spillover). Moreover, this study explores contextual characteristics of crime “hot streets” by applying various regression models, in recognition of the best fitted Geographically Weighted Regression (GWR) model, encompassing eight significant contextual factors with their varied effects on crimes at different streets. Having discussed the impact of lockdown on crime rates, it was apparent that the land-use driven mobility change during lockdown was a fundamental reason for changes in crime. Overall, these research findings have provided evidence and practical suggestions for crime prevention to local governors and policy practitioners, through more optimal urban planning (e.g. Low Traffic Neighborhoods), proactive policing (e.g. in the listed top 10 “Hot Streets” of crime), publicizing of laws and regulations, and installations of security infrastructures (e.g. CCTV cameras and traffic signals).

The Emergency Care Competence Needed for Police Patrol Officers According to the Experts

The Emergency Care Competence Needed for Police Patrol Officers According to the Experts

Factors Influencing Drug Abuse among Youth (15-24 Years) in Kisenyi Slum, Kampala District of Uganda

Introduction: Globally, youths are at risk of engaging in drug abuse. In Uganda, the prevalence of drug abuse among youths is 70.1% which is a lot higher than that of East Africa which is approximated at 45.5%. The study examined the factors influencing drug abuse among the youth in Kisenyi slum of Kampala district. It specifically assessed the relationship between socio-cultural factors, economic factors, and individual factors on drug abuse among youth. Methods: The study adopted a cross-sectional survey design in which a quantitative data collection approach was applied. Questionnaires were used to collect data from 239 youths. Data was cleaned, coded and entered into SPSS software for analysis. The results generated were in the form of descriptive statistics, Pearson chi-square analyses and binary logistics regressions. Results: The study found that the following factors had a statistically significant relationship with drug abuse in Kisenyi slum; religious service attendance (AOR=0.12; 95%CI=0.02-0.68; p=0.03*), family structure (AOR=20.04; 95%CI= 1.75-29.66; p<0.001*), family member smoking cigarettes/tobacco (AOR=0.83; 95%CI= 0.21-3.35; p=0.001*), a friend who drinks alcohol (AOR=1.21; 95%CI= 0.50-2.95; p=0.050*), a friend who smokes cigarettes/tobacco (AOR=0.47; 95%CI= 0.06-3.75; p=0.003*), television watching (AOR=3.56; 95%CI= 1.19-10.71; p<0.001*), social media access (AOR=1.51; 95%CI= 0.51-4.42; p=0.002*), type of house lived in (AOR=3.64; 95%CI= 0.91-14.5; p<0.001*), source of livelihood (AOR=0.88; 95%CI= 0.06-13.39; p=0.014*), drug availability (AOR=0.18; 95%CI= 0.03-1.20; p=0.029*), cost of drugs (AOR=5.92; 95%CI= 1.81-19.40; p<0.001*), perceptions on drugs (AOR=0.70; 95%CI= 0.17-2.88; p=0.002*), and convenience in talking (AOR=1.41; 95%CI= 0.48-4.15; p=0.002*). Conclusion: It can be concluded that socio-cultural, economic and individual factors have a statistically significant influence on drug abuse among youths in Kisenyi Slum, Kampala district. The study recommends engagement of religious leaders to include drug abuse and its effects in their sermons, regular police patrols to remand street children, rolling out business, technical vocational education and training (BITVET) to provide skills to youths, support youth businesses with seed capital and identification of youth champions to sensitize youths on drug abuse, its dangers and how to overcome addiction.

Directions for the implementation of foreign experience in operative and investigative counteraction to intentional murders by criminal police units (on the example of Canada and the USA)

Recently, scientific interest in the study of foreign experience in combating criminal illegality among domestic scientists is constantly growing, which indicates an effort to investigate the best preventive practices of separate countries and the search for ways of their implementation in Ukraine. However, with regard to the direct study of the foreign experience of operative and investigative countermeasures against intentional murders by police units of various foreign countries, there is a lack of such studies today, which increases the relevance of the chosen direction and gives prospects for determining effective practices using a targeted approach to combating deliberate murders and defining such a model of operative and investigative activity. The choice of an appropriate strategy by a particular state and the formation of an appropriate preventive program or a program to combat criminal wrongdoing is determined not only by the current criminogenic situation in this country and in the world in general, but also by the relevant historical, cultural and religious traditions. When considering the specifics of combating criminal wrongdoing in a particular country, it is necessary to take into account the country's membership of the corresponding criminological system. Modern criminology distinguishes Anglo-American, Western European, East Asian, socialist, Muslim and mixed criminological systems.
 The reforms that have been taking place in Ukraine recently require restructuring and a significant increase in the level of efficiency and quality of law enforcement agencies. First of all, these requirements apply to operational units, patrol police and community police officers. Successful implementation of the tasks assigned to these divisions is impossible without taking into account the best examples of the experience of countries such as Canada and the USA. The purpose of the article is to analyze the international experience of operational and investigative prevention of intentional homicides by criminal police units, as well as legislation that provides for the development of an effective system of prevention of intentional homicides in Ukraine. The task of the research is the analysis of various programs to combat intentional homicides that operate in different countries of the world, as well as the international experience of operative and investigative prevention of intentional homicides, which can become the basis for the development of appropriate Ukrainian legislation in this area. The positive international experience of operative and investigative prevention of intentional murders is analyzed. The experience of the USA in combating criminal wrongdoing, in particular intentional murders, is considered, and three models of preventive activity are distinguished and analyzed: the model of social institutions, the model of individual safety, and the model of influence through the environment. It was concluded that conducting a comparative analysis of the prevention of intentional homicides in different countries is the basis for the development of the most promising directions for improving domestic legislation in this area.

Introduction: 2021 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research

The judges for the 2021 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research selected the five finalist papers featured in this special issue of the INFORMS Journal on Applied Analytics. The prestigious Wagner Prize—awarded for achievement in implemented operations research, management science, and advanced analytics—emphasizes the quality and originality of mathematical models along with clarity of written and oral exposition. This year’s winning application describes the design and deployment of Eva, the Greek COVID-19 testing system used as Greece was opening up for tourism in 2020. The remaining four papers describe the stochastic modeling and mixed-integer programming system used to optimize the Atlanta police patrol zones for better police balance and reduced response time to emergency calls; Lyft’s new priority dispatch system, which solves the ride-sharing productivity paradox whereby increases in efficiency do not benefit the drivers; the application of advanced analytics to assist local and federal law enforcement organizations in their efforts to disrupt sex-trafficking networks; and the development of a new after-sales service concept, which increases chip availability for ASML’s customers.

Data-Driven Optimization for Atlanta Police-Zone Design

We present a data-driven optimization framework for redesigning police patrol zones in an urban environment. The objectives are to rebalance police workload along geographical areas and to reduce response time to emergency calls. We develop a stochastic model for police emergency response by integrating multiple data sources, including police incident reports, demographic surveys, and traffic data. Using this stochastic model, we optimize zone-redesign plans using mixed-integer linear programming. Our proposed design was implemented by the Atlanta Police Department in March 2019. By analyzing data before and after the zone redesign, we show that the new design has reduced the response time to high-priority 911 calls by 5.8% and the imbalance of police workload among Atlanta’s zones by 43%.

PolRoute-DS: a Crime Dataset for Optimization-based Police Patrol Routing

It is a well-known fact that criminality is an open, yet important, issue in most urban centers worldwide. Especially in Brazil, creating solutions to reduce crime rates is a top priority. To reduce crime rates, many cities are adopting predictive policing techniques. Predictive policing techniques are highly based on extracting valuable knowledge from a massive dataset that contains information about times, locations, and types of past crimes. The extracted knowledge is then used to provide insights to police departments to define where the police must maintain its presence. These datasets may also be used for a critical predictive policing task: defining where police patrols should patrol. Such patrols are commonly defined to cover a series of crime hot spots (areas that present high criminality levels) and have some restrictions to be considered (number of available police officers, cars, etc). Thus, defining the route for each police vehicle is a complex optimization problem, since in most cases, there are many hot spots and the existing resources are scarce, i.e., the amount of vehicles and police available is much smaller than necessary. Unfortunately, high-quality crime rates data are not easy to obtain. Aiming to tackle this problem, this article proposes the PolRoute-DS dataset, a dataset designed to foster the development and evaluation of police routing approaches in large urban centers. The PolRoute-DS combines the spatial structure of the city of interest (in the context of this article, the city of São Paulo) represented as a connected and directed graph of street segments with criminal data obtained from public sources. PolRoute-DS is available for public use under the Creative Commons By Attribution 4.0 International license (CSV and PostgreSQL versions) and can be downloaded at https://osf.io/mxrgu/.

POLICING FORENSIC EVIDENCE

POLICING FORENSIC EVIDENCE

How Concentrated Are Police on Crime? a Spatiotemporal Analysis of the Concentration of Police Presence and Crime

What were the spatiotemporal patterns of police patrol in a major European city across the pre-COVID year of 2019, how did these patterns change over time, and to what extent did the concentrations of patrol correspond to concentrations of crimes? We analyzed more than 77 million GPS signals from 130 police patrol cars showing where and when police patrols were present in police districts and street segments. We also plotted location, time and days of the week of the locations, and times of more than 50,000 recorded crimes. We calculated concentration ratios within both crimes and patrols relative to their distributions in time and space. We then compared the concentration ratios for crime to the concentration ratios for patrols. We concluded the analysis by comparing the extent to which concentrations of crime and patrol locations and times were overlapping. We found that police patrols, much like crime, were concentrated on a small proportion of street segments. Yet spatiotemporal police presence is unrelated to local levels of crime and crime concentration. Relative to temporal crime concentrations, police patrols were substantially under-concentrated from 1500 to 0100 h, all day on Fridays, and the entire months of June, July, August, and December. There was very little overlap in patrol concentrations with crime concentrations. After three decades of research showing crime prevention benefits of patrol concentrations on micro-level crime concentrations, police in one European city concentrate patrol presence at locations, times, days, and months where crime is not concentrated. Whether this conclusion can be reached in other cities will depend on replications of this study, both in Europe and other continents.

Essential, lonely and exploited: why mobile EU workers’ labour rights are not enforced

ABSTRACT Labour mobility within the European Union (EU) is still increasing, and often such workers carry out ‘essential’ work. Yet, while EU law accords certain labour and social rights, mobile workers de facto often have only limited rights. We study why especially these ‘essential’ workers are often exploited and focus on enforcement actors – control authorities checking upon and interest representation bodies pushing for workers’ rights. We thereby contribute to broader literature on the precarious character of EU free movement in practice. For one, we offer an analytical framework to grasp enforcement by using the dichotomy of police patrol and fire alarm, each breaking down for willingness and capabilities. For another, we stress the relevance of two factors hampering enforcement: While, as we argue, the demand for cheap labour by economies and societies of destination has an impact on the willingness to see rights guaranteed, the isolation of workers limits the capabilities of both police patrol and fire alarm. We illustrate our argument with empirical evidence from live-in care work and international road transport, mainly based upon semi-structured interviews with control authorities and interest representation bodies in Austria and Germany, and with additional interviews in Poland and Slovenia.

Job satisfaction among Swedish police officers: The role of work-related stress, gender-based and sexual harassment.

The aim of this cross-sectional study is to increase our understanding of job satisfaction in Swedish police officers by taking into account work-related stress, and sexual and gender-based harassment. Data were collected from 152 police officers working in vulnerable areas in Stockholm using sociodemographic questions, the Police Stress Identification Questionnaire (PSIQ), Sexual and gender-based harassment questions, and Job Descriptive Index (JDI). The obtained results indicated that male and female police officers reported the highest satisfaction in “people on your present job.” The lowest score of job satisfaction in both male and female police officers was related to “opportunity for promotion” and then “pay.” There were no significant differences in the subscales of job satisfaction between male and female police officers. The older and more experienced officers, the less satisfaction was reported in “job in general” and more satisfaction reported in “pay.” Comparing job satisfaction between patrol officers and those officers who worked in internal services showed police patrol officers had higher job satisfaction in “job in general,” “work in the present job,” “opportunity for promotion” and “supervision” compared to their counterparts in internal services. There were not any significant differences between the subscales of job satisfaction between male and female police officers. There was not any significant association between job satisfaction subscales and having experience of sexual or gender-based harassment. Among various subscales of police stressors, organizational stress was in negative relation with three domains of job satisfaction; “job in general,” “pay” and “supervision.” Also, hierarchical multiple regression analyses showed organizational stress was most often of predictive impact related to various job satisfaction domains in police officers.

범죄분석을 통한 안전도시 구축의 효율화 방안

This study is a study on the construction of a crime-safe city as a countermeasure by analyzing the occurrence of crimes due to the new urban concentration in the outskirts of the city in order to escape the urban concentration phenomenon. The subject of this study is Pyeongtaek, south Gyeonggi-do. In 2020, the number of crimes nationwide was 1,611,906, 3,108.7 per 100,000 people, and the Gyeonggi Nambu Provincial Police Agency had 379,794 crimes, which was 3,877.5 per 100,000, while Pyeongtaek City had 28,270, which was 5,510.4 per 100,000. This is more than 2,000 cases higher than the national crime rate per 100,000 people, and 1,500 cases higher than the crime rate in Gyeonggi-do. In other words, the study began with the need for an alternative for the safety and order maintenance and risk prevention of Pyeongtaek citizens due to crime. As a result, the problem with the occurrence of crimes in Pyeongtaek City is First, the occurrence of crimes due to insufficient security conditions. Second, there is a lack of CCTV, an infrastructure for crime prevention. Third, it is the deterioration of crime prevention activities through people-to-people cooperation, which is one of the police activities of the local community, which is outside the traditional police activities. Based on these problems, suggestions were made on how to increase the efficiency of the construction of a crime-safe city. First, the factors vulnerable to crime and improvement of the environment. Second, the strengthening of police patrol power in response to crime. Third, crime prevention is the duty of the police.

Reinforcement Learning Approach to Solve Dynamic Bi-objective Police Patrol Dispatching and Rescheduling Problem

Police patrol aims to fulfill two main objectives namely to project presence and to respond to incidents in a timely manner. Incidents happen dynamically and can disrupt the initially-planned patrol schedules. The key decisions to be made will be which patrol agent to be dispatched to respond to an incident and subsequently how to adapt the patrol schedules in response to such dynamically-occurring incidents whilst still fulfilling both objectives; which sometimes can be conflicting. In this paper, we define this real-world problem as a Dynamic Bi-Objective Police Patrol Dispatching and Rescheduling Problem and propose a solution approach that combines Deep Reinforcement Learning (specifically neural networks-based Temporal-Difference learning with experience replay) to approximate the value function and a rescheduling heuristic based on ejection chains to learn both dispatching and rescheduling policies jointly. To address the dual objectives, we propose a reward function that implicitly tries to maximize the rate of successfully responding to an incident within a response time target while minimizing the reduction in patrol presence without the need to explicitly set predetermined weights for each objective. The proposed approach is able to compute both dispatching and rescheduling decisions almost instantaneously. Our work serves as the first work in the literature that takes into account these dual patrol objectives and real-world operational consideration where incident response may disrupt existing patrol schedules.

OFFICERS: Operational Framework for Intelligent Crime-and-Emergency Response Scheduling

In the quest to achieve better response times in dense urban environments, law enforcement agencies are seeking AI-driven 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.