Crime Data Research Articles

A novel bounded ℤ-valued autoregressive model with its application on crime data

This paper tackles the challenge of autoregressive modelling for bounded Z -valued count time series, a topic that has been largely overlooked in the existing literature. To bridge this gap, we introduce the trinomial difference bounded Z -valued autoregressive (TDBZAR) model utilizing the trinomial difference thinning operator, and we explore its stochastic properties in detail. Two notable advantages of the TDBZAR model are that the incorporated trinomial difference thinning operator ensures the conditional expectation takes a linear form and allows its 1-step transition probability to be expressed as the convolution of two independent trinomial difference distributions. This significantly simplifies the computation of both the conditional least squares (CLS) and conditional maximum likelihood (CML) estimates, making them more practical and straightforward. Second, we examine the two-step CLS and CML estimates, establishing their asymptotic properties. Third, we compare the performance of these estimators through simulation studies. Finally, we apply the proposed model to a real dataset to investigate which type of crime (rape or sex offense) was more prevalent in police reports from January 1990 to December 1998.

Bayesian analysis of urban theft crime in 674 Chinese cities

Current academic research on fitting the volume of urban theft crimes at a macro size is limited, especially from the urban functionality perspective. Given this gap, this study utilizes a Bayesian model to conduct a fitting analysis of theft crime data from 674 cities in China from 2018 to 2020. This research aims to explore novel pathways for theft crime fitting. Results indicate that the size of urban functionality, particularly points of interest (POIs), exhibits excellent performance in fitting theft crimes, with POIs related to public services and commercial activities demonstrating the most significant fitting effects. This research successfully identifies effective indicators for crime fitting, thereby offering a new perspective and supplement to theft crime research. This study holds significant value for gaining a profound understanding of criminal phenomena and explaining the causes and mechanisms underlying the differences in theft crimes among various cities in China.

Evaluating The Quality of K-Medoids Clustering on Crime Data in Indonesia

Evaluating The Quality of K-Medoids Clustering on Crime Data in Indonesia

No Place Like Home? Local Crime and Older Adults’ Time at Home

ABSTRACT Home is a locus of everyday activity among a growing population of Americans who are “aging in place,” and for whom leaving the home is generally thought to benefit quality of life and well-being. Sociological and criminological theory has often assumed that higher levels of local crime constrain individuals’ activities to the residential environment, although few studies have empirically tested this assumption. We use longitudinal smartphone-based GPS data and ecological momentary assessments from 409 older adults in the Chicago Health and Activity Space in Real-Time study, linked with administrative crime data, to test this relationship through a series of multilevel linear regression models. Our findings suggest that older adults living in higher crime areas spend less time at home, on average, compared to older adults who live in lower crime areas. This association is especially evident among older adults who experience higher levels of unsafety at home. We discuss the implication that neighborhood characteristics can permeate the boundaries of the home, adversely affecting an already vulnerable population in ways that may exacerbate inequality in community engagement, collective efficacy, and health. Our findings prompt a more nuanced understanding of what leaving the home represents among the aging population.

A doubly stochastic point process approach for spatio-temporal dynamics of crime data

Crime, in general, is at the base of crucial issues for many societies living in large cities worldwide. Indeed, crime and neighbourhood disorder may negatively impact the health of urban residents. Thus, the crime rate reduction is at the core of many local policies driven by active plans supported by police action and local authorities. Considering crime reports as a spatio-temporal point pattern, we propose spatio-temporal log-Gaussian Cox processes as a modelling framework for crimes in space and time. We model the spatial and temporal variation through generalized parametric additive and linear models, and a Gaussian space-time process approximates the residual variation. The inference is performed via Markov chain Monte Carlo through MALA algorithms. We provide short-term forecasts of future crimes and suggest a surveillance system that operates by reporting predictive probabilities. Our data come from the reported crimes in the locality of Kennedy (Bogota) over several years and several types of crimes. The police department may use our method to help allocate police resources and design crime prevention strategies and policies, such as surveillance, in specific zonal planning units.

Unveiling Environmental Crime Trends and Intensity in the EU Countries Through a Sustainability Lens

AbstractEnvironmental crime poses a significant threat to global ecosystems, biodiversity, and human well-being, encompassing activities such as pollution, illegal waste disposal, trade in protected species, and natural resource degradation. Understanding the dynamics of these crimes is essential for devising effective mitigation strategies and promoting sustainability at micro, meso, and macro level. The aim of the study is to examine environmental crimes in the EU, focusing on their current trends, patterns, and Intensity, as well as the impact on sustainability across its three dimensions. Fourteen EU countries environmental crime data from 2016 to 2021were analysed to reveal variations and trends. Geographic information systems (GIS) utilized to identify countries with elevated environmental crime rates, emphasizing the need for interventions in pollution control, waste management, and resource conservation. Results indicate high environmental pollution intensity in Italy, Sweden, and Denmark. Moreover, Italy emerges as the epicentre of illegal waste dumping, with notable cases also present in Belgium and France. Additionally, France, Slovakia, and Italy show alarming levels of illegal wildlife trade. Furthermore, France and Sweden exhibit the highest density of natural resource degradation cases. In addition, the study unveiled a positive correlation ($$\:b>0$$) between environmental pollution, trade in protected species, and natural resource degradation with population growth, while waste disposal exhibited a negative correlation ($$\:b<0$$). Moreover, trade in protected species and resource degradation correlated negatively ($$\:b<0$$) with the poverty ratio. Addressing environmental crimes aligns with the pursuit of green justice, recognizing the interconnectedness of environmental, social, and economic factors. The findings offer valuable insights for policymakers, environmentalists, and communities, guiding interventions towards a sustainable and resilient future.

Empirical and Experimental Insights into Data Mining Techniques for Crime Prediction: A Comprehensive Survey

This survey paper presents a comprehensive analysis of crime prediction methodologies, exploring the various techniques and technologies utilized in this area. The paper covers the statistical methods, machine learning algorithms, and deep learning techniques employed to analyze crime data, while also examining their effectiveness and limitations. We propose a methodological taxonomy that classifies crime prediction algorithms into specific techniques. This taxonomy is structured into four tiers, including methodology category, methodology sub-category, methodology techniques, and methodology sub-techniques. Empirical and experimental evaluations are provided to rank the different techniques. The empirical evaluation assesses the crime prediction techniques based on three criteria, while the experimental evaluation ranks the algorithms that employ the same sub-technique, the different sub-techniques that employ the same technique, the different techniques that employ the same methodology sub-category, the different methodology sub-categories within the same category, and the different methodology categories. The combination of methodological taxonomy, empirical evaluations, and experimental comparisons allows for a nuanced and comprehensive understanding of crime prediction algorithms, aiding researchers in making informed decisions. Finally, the paper provides a glimpse into the future of crime prediction techniques, highlighting potential advancements and opportunities for further research in this field.

The Influence of Weather Temperature on Crime Occurrence in Babylon Province

Objectives: This study investigates the influence of temperature fluctuation on specific types of crimes in Babylon in Iraq. It also analyzes how varying temperatures leave an impact on crime patterns and trajectories. Methods: The study uses quantitative analysis, descriptive statistics, and Geographic Information Systems (GIS) to examine temperature and crime data. Statistical techniques, including correlation and regression analysis, are applied to assess the relationships between temperature variables and crime rates. Results: The study results showed that there is a strong affinity between temperatures and crime rates. Murder crimes reach their peak in hot summer months. The study also showed that theft crimes, including burglary, increase during cold winter months. Statistical analysis validates these findings, highlighting a direct correlation between temperature and murder rates and an inverse correlation with theft rates. Conclusions: This study confirms the huge impact of temperature fluctuation on criminal behavior in Babylon, Iraq. Recognizing these seasonal variations may help construct effective crime prevention strategies. Additionally, the study explores multiple theoretical perspectives, underlining the importance of adopting a multidisciplinary approach to comprehensively grasp the intricate relationship between climate and crime.

Crime Data Analysis Using Naive Bayes Classification and Least Square Estimation with MapReduce

Crime Data Analysis Using Naive Bayes Classification and Least Square Estimation with MapReduce

Data-driven dynamic police patrolling: An efficient Monte Carlo tree search

Crime is responsible for major financial losses and serious harm to the well-being of individuals, and, hence, a crucial task of police operations is effective patrolling. Yet, in existing decision models aimed at police operations, microscopic routing decisions from patrolling are not considered, and, furthermore, the objective is limited to surrogate metrics (e.g., response time) instead of crime prevention. In this paper, we thus formalize the decision problem of dynamic police patrolling as a Markov decision process that models microscopic routing decisions, so that the expected number of prevented crimes are maximized. We experimentally show that standard solution approaches for our decision problem are not scalable to real-world settings. As a remedy, we present a tailored and highly efficient Monte Carlo tree search algorithm. We then demonstrate our algorithm numerically using real-world crime data from Chicago and show that the decision-making by our algorithm offers significant improvements for crime prevention over patrolling tactics from current practice. Informed by our results, we finally discuss implications for improving the patrolling tactics in police operations.

Legal gun ownership as an important factor of state security: Reality or Myth?

The question of whether legal gun ownership is a positive security factor in the Czech Republic is subject to expert debate and depends on several factors, including available crime data, public attitudes, and the legal framework. Some argue that legal gun ownership can dissuade criminals because they know victims may be armed. Many advocates argue that the right to own guns is a fundamental right that should be protected. Sometimes, it is difficult to clearly demonstrate that legal gun ownership directly contributes to crime reduction. Statistical data can be interpreted in different ways. In contrast, the presence of guns can in some situations escalate conflicts that could otherwise be resolved nonviolently. In the Czech Republic, legal gun ownership is relatively strictly regulated. Citizens must meet the conditions established by law, including criminal integrity and passing a theoretical-practical examination of professional competence. This regulation aims to ensure that only responsible and qualified individuals own guns. Therefore, the presented article discusses legal gun ownership as an internal factor of state security. Using statistical data, it analyses the amount of violent crime committed with firearms in relation to the possibility of holding and carrying a gun in the conditions of the Czech Republic and in selected EU countries. Furthermore, with the help of a questionnaire survey, it identifies that legal gun ownership can be considered a positive safety factor in certain situations, if it is associated with strict regulation and a responsible attitude of gun owners. The resulting effect on security depends on a combination of legal frameworks, gun culture, and effective law enforcement.

URBANIZATION AND SOCIAL CRIME: A STUDY ON SOCIO-ECONOMIC DIMENSIONS OF THE MANIPUR CENTRAL VALLEY

Geography is concerned to provide accurate, orderly, and rational description and interpretation of the earth's surface. The study of population dynamics and its impact on social crime is crucial for understanding regional development and planning. The present study examines the dynamics of population growth, urbanization, and social crime in Manipur's Central Valley. Utilizing, the data from the primary and secondary sources (such: NCRB, Superintendent of Police, Manipur central Valley, and Census of India). The research adopts technique, such as Decadal Growth method, Principal component Analysis. Over a 70-year period from 1951 to 2021, the region witnessed a consistent rise in population, growing from 3,87,523 to 18,77,416. Despite this growth, the pace has decelerated, reflecting a shift from high to moderate growth rates due to factors such as birth rates, mortality rates, and migration patterns. Urban population data from the 2011 Census reveals significant regional disparities, with Imphal West being the most urbanized and Bishnupur the least. Principal Component Analysis (PCA) of social crime data identifies three key patterns: "Anomie Violence," "Marginalized Deviance," and "Economic Strain Deviance," each associated with different regions and types of crime. A comparison of social crime patterns with urban population sizes highlights varying correlations, suggesting complex interactions between demographic changes and crime rates. This analysis underscores the need for targeted urban planning and crime prevention strategies in response to evolving socio-economic dimensions.

Variable selection and prediction performance of penalized two-part regression with community-based crime data application

Variable selection and prediction performance of penalized two-part regression with community-based crime data application

Exploring the Impact of Clery Sexual Assault Timely Warnings on a Campus Community

ABSTRACT The Clery Act requires higher education institutions to issue timely warnings in response to immediate safety concerns and to provide annual reports of campus crime data to the campus community. Despite 40 years of Clery-mandated timely warnings and reporting, rates of sexual violence on college campuses have not declined. Buidling on previous schoalrship about Clery Act requirements, we document more detailed challenges associated with complying with the Clery Act. Through a case study methodology, including interviews with university administrators and students, we examined the impact of a timely warning alert related to sexual assault on a campus community. We illustrate that campus community members feel confused about the intentions of timely warning alerts and uncertain about what to do with the information provided by the alerts. Additionally, the alerts created fear and anxiety among some members of the campus community and a chilling effect on reporting sexual violence. We provide implications for policymakers and researchers related to Clery compliance.

Predicting Crime Hotspots using Machine Learning Techniques

This research delves into the application of machine learning algorithms for forecasting crime hotspots by leveraging historical data of public property crime in a major coastal city in southeast China. The study conducts a comparative analysis, emphasizing the predictive efficacy of various machine learning models. Results indicate that the LSTM model surpasses other methods including KNN, random forest, support vector machine, naive Bayes, and convolutional neural networks when utilizing solely historical crime data. Moreover, integrating built environment data such as points of interest (POIs) and urban road network density as covariates into the LSTM model enhances predictive accuracy. These findings bear significance for shaping policing strategies and implementing measures for crime prevention and control.

Time of week intensity estimation from partly interval censored data with applications to police patrol planning

Law enforcement agencies are tasked with crime prevention and crime reduction under limited resources. Having an accurate temporal estimate of the crime rate would be valuable to achieve such a goal. However, estimation is usually complicated by the interval censored nature of crime data. We cast the problem of intensity estimation as a Poisson regression using an EM algorithm to estimate the parameters. Two special penalties are added that provide smoothness over the time of day and day of week. This approach provides accurate intensity estimates and can also uncover day of week clusters that share the same intensity patterns. Both simulated and real crime data gathered from the city of Cincinnati and the city of Dallas are used to demonstrate the effectiveness of the proposed model.

The Politics and Pitfalls of Policing High‐Volume Crime: Responding to the Shoplifting Epidemic

AbstractIt has been reported that shoplifting has reached ‘epidemic’ levels. Comparing police record crime data with industry estimates reveals that potentially just 2.4 per cent of thefts are reported to and recorded by the police. This is creating a large intelligence gap that emboldens thieves, enables organised criminal networks to go undetected, and makes it difficult for the police to prioritise retail crime and detect the most prolific and serious offenders. This article outlines some of the political issues with recording high‐volume crime and the resource implications of increased reporting. The article presents the case for the development of a national retail crime intelligence bureau to improve reporting and close the intelligence gap on shoplifting.

Mathematical Approaches to Crime Data Analysis: Enhancing Evaluation and Prevention Strategies

Crime data analysis is crucial for assessment and prevention. This research study examines mathematical techniques to crime data analysis to find effective ways to identify crime trends and design effective preventative methods. The report opens with an overview of the situation, noting rising crime rates and the need for new analysis. Crime data analysis's historical backdrop, mathematical and statistical methods, technical advances, and theoretical frameworks are covered in a thorough literature review. The study covers data collection, preprocessing, analytical technique selection, model assessment, and validation. Crime data interpretation is tested using predictive modelling, geographical analysis, clustering, classification, and network analysis. Case studies show successful implementations and compare methods. The discussion part discusses the results, crime prevention techniques, and limits and obstacles. Future research suggestions are given. This study summarizes major themes, policy consequences, and future prospects, adding to crime data analytic expertise. This study uses mathematical methods to help law enforcement and policymakers improve crime prevention. Keywords: - Crime Data Analysis, Mathematical Approaches, Predictive Modelling, Spatial Analysis, Crime Prevention Strategies

Mapping the unseen: Dissecting property crime dynamics in urban Malaysia through spatial analysis

AbstractExtensive research over the years has illuminated how urban land use and development intricately influence property crime dynamics. Yet, there remains a notable paucity of simultaneous explorations of spatial–temporal patterns and their correlation with urban geography. This study aims to bridge this gap by employing advanced spatial analytical techniques, providing a nuanced understanding of how property crime is distributed and how it evolves within the urban fabric of Malaysia. This research rigorously investigates the spatial–temporal patterns of property crime within the urban context of Kuala Lumpur and Selangor in Malaysia, utilizing a novel combination of mean center and standard deviational ellipse methodologies. Concentrating on the regions of Selangor, the Federal Territory of Kuala Lumpur, and the Federal Territory of Putrajaya, the study meticulously analyzes property crime data spanning from 2015 to 2020. This analysis uncovers pivotal insights into the distribution and trends of criminal activities. The integration of these spatial analytical techniques not only facilitates the identification of prevalent hotspots and directional tendencies in property crimes but also probes into their association with urban land use and developmental patterns. The outcomes of this study shed light on the intricate interplay between the incidence of crime and urban spatial configurations, thereby yielding significant theoretical and practical ramifications. The research makes a substantial contribution to the disciplines of criminological geography and urban planning, underscoring the indispensable role of advanced spatial analysis in devising robust crime prevention mechanisms and bolstering the safety of urban environments.

Estimating the Reliability of Crime Data in Geographic Areas

Abstract Crime data are problematic: Crimes that are never reported undermine its validity and differences in police recording practices affect its reliability. However, the true extent of these problems is not well known, with existing studies suffering from a number of methodological limitations. We examine the quality of police recorded crime data and survey-based crime estimates recorded in England and Wales using a robust latent trait model that effectively represents the competing sources of error. We find that whilst crime rates derived from police data systematically underestimate the true extent of crime, they are substantially more reliable than estimates from survey data. Reliability is lower for violence and criminal damage and is getting worse over time.