Crime Data Research Articles

A Study on the Actual Status of Juvenile Fraud Crimes and Countermeasures

Adolescence is a critical period of development marked by significant physical, emotional, and social changes. However, unstable emotions, conflicts with older generations, and a tendency for risky behaviors make adolescents especially vulnerable to criminal activities. This study examines the increasing occurrence of digital fraud and gambling crimes among adolescents, including the emergence of sexual crimes involving deepfake technology. An analysis of juvenile crime data from 2000 to 2022 reveals that fraudulent crimes have doubled, particularly in online environments. Adolescents frequently use platforms such as online games, virtual currency exchanges, and messaging apps to commit fraud for financial gain. Crimes involving deepfake technology, where artificial intelligence is used to create fake explicit content, are also on the rise. These crimes are driven by economic challenges and psychological factors such as impulsive decision-making, poor moral judgment, and the anonymity provided by digital platforms. The lack of specific legal measures to address deepfake-related crimes has further intensified the issue. Adolescents often become both perpetrators and victims, emphasizing the need for comprehensive solutions. This study proposes an integrated approach that combines stronger legal penalties with preventive and rehabilitative education. By addressing the root causes and focusing on prevention, society can better protect young people from digital crimes. The research also calls for adopting successful international prevention models in South Korea to create a safer and healthier environment for adolescents.

Assessing the impact of safety interventions on open drug markets in a small Swedish municipality using synthetic control methods

Abstract Objectives We analyze the impact of the “Stoppa droghandeln” (“Stop the drug trade”) program, a set of safety interventions aimed at reducing crime, particularly drug-related crimes, in a small Swedish municipality (Österåker) from 2018 to 2023. Methods We use a count-based synthetic control approach to compare police crime data before and after beginning the safety interventions (“Stop the drug trade”). This quasi-experimental design was used to infer the causal effects of “Stop the drug trade” on crime, using an evaluation framework including several robustness tests with different crime types. Cluster detection analysis was also used to monitor potential changes in the geography of open drug markets. Results In 2023, Österåker performed better than 82% of all Swedish municipalities for drug-related offenses. Estimates indicate that 3221 crimes were prevented in Österåker, including 589 less drug-related offenses. However, the non-significant p-values suggest that Österåker’s crime reduction did not outperform most other municipalities. While this could be due to other municipalities implementing their own interventions, the magnitude and consistency of the treatment effects across crime types in Österåker make it unlikely that these results occurred by chance. In terms of geography, clusters of drug-related offenses were stable, and after the interventions, they became more concentrated in particular places. Conclusions This study’s findings underscore the potential impact of a blend of social and situational crime prevention interventions to reduce drug-related offenses and other crimes in a small municipality. The Österåker case demonstrates that strategies should target specific types of crime and monitor possible shifts in crime concentration across different parts of the study area over time. The synthetic control method helped isolate the impact of safety interventions on drug-related crime, demonstrating its value as a tool for policy evaluation.

The impact of COVID-19 lockdown on fraud in the UK

The COVID-19 pandemic profoundly altered socio-economic activities, human behaviors, and crime patterns. However, less is known about how the pandemic and associated restrictions affected cyber-enabled and traditional fraud. Here, we conducted a retrospective analysis using police-recorded crime data in the UK to examine the impact of COVID-19 restrictions and changes in human activity on fraud. Results indicate that following the onset of the lockdown, the number of recorded fraud cases increased by 28.5%, contrasting with traditional property crimes, which dropped by 28.1%. However, the lockdown did not have a significant impact on the long-term trend of fraud. With the lifting of restrictions, fraud gradually regressed to levels approaching those before the pandemic. By inspecting the effects of different government responses and changes in population mobility on various types of fraud, we found that more stringent restrictions were associated with larger increases in most types of cyber-enabled fraud, except for those that rely on offline activities, whereas the impact on traditional fraud was mixed and contingent upon specific opportunity structures. These findings overall align with the assumptions of routine activity theory and provide clear support for its applicability in fraud and cybercrime.

Analyzing Urban Crime Through Street View Imagery: Insights from Urban Micro Built Environment and Perceptions

Understanding the relationship between urban crime and the built environment is crucial for developing effective crime prevention strategies, particularly in the context of rapid urban development and city planning. As cities grow, urbanization leads to environments that either promote or inhibit criminal activity, making it essential to explore the interactions between urban design and crime. This study investigates the impact of micro built environment (MBE) elements and place perceptions on crime occurrences in Toronto using street view imagery (SVI) data and machine learning models. We used logistic regression models and an XGBoost (Version 1.7.5) classifier to assess the significance of MBE and perception variables in classifying crime and non-crime intersections. Our findings reveal that intersections with criminal activity tend to be related to more mobility-related features, such as roads and vehicles, and fewer natural elements, such as vegetation. The “beautiful” and “depressing” perceptions emerged as the most significant variables in explaining crime events, surpassing the commonly studied “safety” perception. The XGBoost model achieved 86% accuracy, indicating that MBE and perception variables are strong predictors of crime risk. These findings suggest that enhancing vegetation and improving street aesthetics could serve as effective crime prevention measures in urban environments. However, limitations include the general nature of the perception model and the reliance on aggregated crime data. Future research should incorporate local perceptions and fine-scale crime data to provide more tailored insights for urban planning and crime prevention

Forecasting Rate of Crime Using Machine Learning

Criminalities and predicting are indispensable when it comes to crime identification and prevention, and recognition of areas with a high probability of criminal occurrences, as well as simulation of crime scenes. Finally, and based on the methods of data mining, patterns can be obtained from large amount of unstructured data that can help in predicting future crimes. Crime is still a social issue that impacts the population on all the main aspects including quality of life, economic growth, and image of the nation, and thus, calls for more elaborate and effective systems and strategies for the safety of the population. With the help of the DM techniques proposed below, the goal is to design a system that is capable of identifying, studying, and these predicting types of crimes in certain areas. In particular, the current paper focuses on the methodological approaches of crime analysis and crime forecast. Thus, this work examined temporal and spatial features of crime to establish patterns and trends necessary to predict subsequent occurrences. Classification, clustering and association rule mining are utilized mainly in crime data classification process, in order to make distinctions and identify relationships between certain crime characteristics. Quantitative methods of analysis which include regression analysis, decision trees and artificial neural networks forecast future formulations of criminal incidences

Examining Use of Force Outcomes in Police Calls for Service: Evidence From Cincinnati

This study explores the correlates of use of force (UOF) in police responses to calls for service (CFS), drawing on situational, community, and organizational perspectives. Linear probability models were used to analyze CFS and UOF records for Cincinnati Police Department (CPD) from October 2014 to July 2021, along with sociodemographic and crime data aggregated to census tracts. The type of CFS, as well as quicker response times and police-initiated actions, were associated with UOF. Broader situational variables (including nighttime, alcohol consumption, and high activity periods), tract-level crime and sociodemographic characteristics, and year showed little statistical association. The study finds modest evidence of organizational influences on UOF outcomes, in the form of variation by police district.

Crime Type Identification Using High-Order Deep Residual Network with Multiple Attention Algorithm

ABSTRACT Crime type identification is crucial for improving public safety through more accurate prevention and efficient responses. However, practical applications often suffer from a significant lack of effective samples features, making it difficult to focus on the most informative aspects during identification. This study addresses these challenges by proposing a novel crime type identification method that leverages a deep neural network enhanced with multiple attention mechanisms. The approach includes a tailored data processing method involving target encoding to convert categorical data into numerical form, L 2 normalizer to standardize data and ensure balanced feature contribution, and variance threshold feature selection to remove low-variance features. Additionally, a High-Order Deep Residual Network with Multiple Attention (HO-ResNet-MA) is developed, featuring an optimized Huta68 block (Huta-6(8)-MA ResBlock) with an enhanced Contextual Transformer (CoT) unit for local attention and a queue-and-exclusion layer for global attention. To validate the effectiveness of the proposed method, homicide reports data and Chicago crimes data are processed and fed into the crime type identification model, resulting in accuracies of over 84.1% and 99.5%, respectively. This study makes contributions to the field of crime analysis by validating the practical applicability of these approaches, and enhancing the efficiency of public safety workers.

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.

Comprehensive Analysis of Detection Methods for Over-the-counter Codeine: A Systematic Review

Abstract Concerns have been raised about how readily available over-the-counter (OTC) codeine formulations may be contributing to the expanding opioid epidemic. Focusing on the analytical methods used to find and measure codeine in various sample types, this systematic review provides a thorough analysis of OTC codeine abuse and misuse. It also includes case studies that highlight the seriousness of the problem by describing codeine-related deaths and intoxications. A wider view of the issue is provided by the crime statistics in this paper that relate to codeine and related drugs in India from 2017 to 2021. A thorough electronic search covering the years 2012–2022 was carried out from February 2023 to April 2023 to compile this review. Google Scholar, Science Direct, and PubMed were just a few of the search engines used. While crime statistics for India were sourced from the National Crime Records Bureau website, case reports were gathered from the Journal of Medical Case Reports and Wiley Online Library. Studies examining OTC codeine, its abuse, and the analytical methods used for its detection and quantification were all covered by our inclusion criteria. Case reports involving codeine seizures, fatalities, and intoxications were also included, along with review and research papers. On the other hand, studies with little connection to OTC codeine, books, documents, clinical trials, meta-analyses, non-English papers, and publications with only abstracts were disregarded. With the help of this systematic review, we located 531 studies in databases, 83 of which satisfied our inclusion requirements. Our research is organized into sections that cover crime data, case studies of codeine-related overdoses or deaths, and detection methods. For researchers, medical professionals, and policymakers actively engaged in the fight against codeine abuse and the societal harms it causes, this review is an invaluable resource.

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.

Spatial variation of bus stop crime response to changes in the surrounding environment and transit level of service

Increasing concerns over the significant number of crimes occurring at and around bus stops have motivated the analysis of intervening factors to propose solutions to this problem. However, models found so far overlook important features of crime data: spatial dependence and spatial heterogeneity. In addition, crime predictor data has shown to be multicollinear in previous studies. To tackle these issues, this paper analyzes mobile phone thefts and robberies at 19,329 bus stops in São Paulo (Brazil) based on Geographically Weighted Regression (GWR), using components retained from a Principal Component Analysis (PCA) as explanatory variables. A comparison is carried out between GWR and a non-spatial Transformed Linear Regression (TLR), and a Negative Binomial Regression (NBR) with uncorrelated predictors. Effects on crime from 9 PCs, representing central areas, bus transit level of service, transport infrastructure, land use and sociodemographic features, were proven to have high spatial variability. Changes in the surrounding environment can cause higher or lower increases in mobile phone thefts and robberies at stops according to their spatial location. Results showed that GWR performs better than NBR and TLR in predicting bus stop crime, thus compensating for a loss of information associated with PCA. In addition, GWR was able to completely incorporate the spatial dependence found in the non-spatial model, which covered the nearest 96 neighboring stops. The proposed method can effectively detect critical bus stops and regions, contributing to crime prevention. GWR coupled with PCA can also help identify the best locations to install new bus stops.