Crime Data Research Articles

Detecting spatial and spatio-temporal clusters of rape in India, 2011–2020

The study aims to identify the spatial and spatio-temporal patterns of rape in India from 2011 to 2020 using crime data aggregated at the district level. The study also tries to understand persistent patterns in the spatial variation of rape incidence across the country during the study period. The annual rape data from 2011 to 2020 were extracted from the National Crime Records Bureau. Retrospective spatial and temporal cluster analyses were performed using the purely spatial (Kulldorff’s and Mann–Whitney scan statistic) and space–time scan statistic (Kulldorff’s based on the discrete Poisson model). Cluster frequency analysis was performed. Results showed the spatial heterogeneity in the pattern of rape crime across the country as well as its localization among geographically contiguous regions across time and space. The detected primary spatial clusters (P < 0.05) persisted mainly in the Central, North, and North Eastern zones of the country over the ten year study period. The primary spatio-temporal cluster (LLR = 5560.09, P < 0.001) appeared between the time frame 2014 to 2016 and was concentrated mainly in the Northern zone. Thirty-seven districts of India (5.78%) contributed to spatial clusters in all years of the study period. The heterogeneous distribution of rape across India could be due to the spatio-temporal variations in the determinants of rape (such as socioeconomic status, demographics, urbanisation, and gender equality) across the country. Identification of the localized rape clusters could result in understanding the contextual factors of rape and, thereby be beneficial to national and regional rape control strategies.

Spatial Patterns of Violence Against Women and Children using Geographic Information System and Density-Based Clustering Algorithm

This paper underscores the importance of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm in data mining. In this research, data mining clustering methods were applied to investigate consummated felonies related to the "Anti-Violence Against Women and Their Children Act of 2004 - RA 9262" from 2018 to 2023. The criminal data processed from the Police Regional Office 6 of the Philippine National Police encompassed 248 attributes reflecting cases over the specified period. The significance of this study lies in its utilization of ArcGIS Pro software to process the provided data through clustering techniques, presenting a robust approach for detecting criminal activities and recognizing patterns to aid law enforcement in crime reduction efforts. Spatial data mining proves practical when dealing with geographic crime datasets, facilitating the analysis of large volumes of crime data. The DBSCAN algorithm was employed to cluster crime incidents centered on predefined events, with the resultant clusters used to identify hotspots. These clustering outcomes are then visualized using GIS, enabling real-time mapping of crime distribution for law enforcement agencies to comprehend and engage with effectively. The outcomes empower stakeholders to devise interventions tailored to specific locations, thereby contributing to a safer environment for women and children. The study illuminates the localized analysis of crime distribution, offering insights into the interconnected factors influencing criminal incidents and providing a framework for crafting targeted and efficient strategies for crime prevention, thereby enriching the broader dialogue on crime management and public safety.

Forecasting Criminal Activity Using Machine Learning Approaches

Predicting criminal activity has long been a challenge for law enforcement agencies worldwide. Traditional methods often rely on historical data and human intuition, which may be limited in their accuracy and scope. In recent years, machine learning techniques have emerged as promising tools for forecasting criminal activity by leveraging large-scale datasets and advanced algorithms. This paper presents a novel machine learning approach to forecasting criminal activity, focusing on the development and evaluation of predictive models using various data sources, including crime reports, demographic information, and environmental factors. We explore the application of supervised and unsupervised learning algorithms, such as decision trees, random forests, support vector machines, and neural networks, to identify patterns and trends in crime data. Furthermore, we discuss the challenges and ethical considerations associated with deploying predictive models in real-world law enforcement settings, emphasizing the importance of transparency, fairness, and accountability. Through empirical analysis and case studies, we demonstrate the potential of machine learning techniques to enhance crime prediction and prevention efforts, providing valuable insights for policymakers, law enforcement agencies, and researchers in the field of criminal justice.

Criminal Investigation System

The Criminal Investigation System with represents a significant advancement in the field of law enforcement technology. By leveraging the power of data and predictive analytics, it empowers investigators to solve cases faster, identify suspects more accurately, and ultimately, enhance public safe. Based on the historical crime data suggest that Hashing algorithm. The digital system functions by gathering and classifying different kinds of information, such as crime scene reports, witness statements, forensic evidence, and suspect profiles. It facilitates the work of an officer by allowing him to compile new, vital case details on demand and to monitor the case's progress online. Keywords: Criminal Investigation, System Law, enforcement technology ,Data Predictive, analytics, Solve cases, faster, Identify suspects public safety, Historical crime , data Hashing algorithm , Gathering information Classifying , information Crime ,scene reports, Witness statements ,Forensic evidence ,Suspect profiles , Officer.

Utilizing Machine Learning Ensemble Techniques for Crime Hotspot Analysis and Prediction

By combining the predictions of trained classifiers, the ensemble learning approach generates new examples through cooperative decision-making. Evidence from early analysis has demonstrated the empirical and logical superiority of ensemble classifiers over single component classifiers. It is still difficult to identify the right configuration for a given dataset, even with the presentation of many ensemble approaches. Many theories based on prediction have been developed to address the topic of machine learning crime prediction in India. The dynamic character of crimes becomes difficult to ascertain. Crime prediction aims to lower crime rates and discourage criminal action. This study provides an authentic and efficient way for determining acceptable crime predictions: the assemble-stacking based crime prediction method (SBCPM), which applies learning-based strategies to produce domain-specific configurations compared to another machine learning model. The result implies that performer models are generally not particularly successful. The ensemble model occasionally outperforms the others with the best correlation coefficient, the lowest average, and the lowest absolute errors. The proposed method generated accurate categorization on the test data. Compared to previous research that just employed violence-based crime records as a baseline, the model's prediction effect is demonstrated to be stronger. The results further shown that criminological theories are congruent with any real-world crime data. The recommended strategy also proved useful in predicting possible crimes. and show that the ensemble model has higher prediction accuracy when compared to the individual classifier.

Strengthening the Rule of Law Through Community Policing: Evidence From Liberia

How to improve security and strengthen the rule of law in fragile states? Community policing programs have long been at the forefront of policymakers’ efforts to address this challenge. These programs tend to be more expansive than those found in developed countries, focusing not only on building trust through meetings and foot patrols, but also on eliciting ‘coproduction’ from communities to supplement scarce police capacity and provide alternatives to vigilantism. I partnered with the Liberian National Police (LNP) to experimentally evaluate the effectiveness of this approach in Monrovia, Liberia, one of sub-Saharan Africa’s most crime-ridden cities. Drawing on a large-scale resident survey and administrative crime data, I find that the program improved relations between police and citizens, strengthened social norms against vigilantism, and mobilized communities to participate in the police’s “Watch Forum” initiative by forming and sustaining local security groups designed to facilitate cooperation with police. These changes were accompanied by a roughly 40% reduction in the incidence of mob violence. Despite these improvements, the program did not reduce the overall incident of crime, improve perceptions of security, or increase crime reporting.

Exploring the association between park size and crime

ABSTRACT This study explored the link between park size and crime risk in Alabama, analyzing 564 parks across 73 cities with populations over 10,000. Park dimensions were measured using Google Earth Pro, and crime data, covering violent and property crimes, were sourced from Applied Geographic Solutions. Additional data on population density, mental health prevalence, social vulnerability, and alcohol expenditure (indicative of affluence) were obtained from the U.S. Census Bureau, CDC, and ESRI. A multiple regression analysis revealed a significant negative association between park size and crime risk, meaning that larger park sizes tended to have lower crime rates. Key covariates—mental health, social vulnerability, and alcohol spending— were also significantly related to crime rates. Our findings have policy implications for local governments and community organizations seeking to reduce crime rates.

Software Reliability Model Estimation for an Indeterministic Crime Cluster through Reinforcement Learning

The software reliability model estimates the probability of data failure in a specific environment, significantly impacting reliability and trustworthiness. The paper study focuses on cluster crime data, i.e., indeterministic in Neutrosophic Logic, using a software reliability model. The study utilizes reinforcement learning, Neutrosophic logic, and non-homogeneous Poisson process crime data to estimate indeterministic cluster data in crime. The "Non-homogeneous Poisson Process with Neutrosophic Logic" technique performs well in evaluating and deterring crime based on crime data analysis. The crime cluster involving offenders correctly classified as failure to accomplish does better than uncertain cluster reliability estimation with least squares and logistic regression analysis. The method enables crime prediction and prevention by using concave growth models to create an uncertain crime cluster, penalizing the correct person.

Spatial Analysis Leveraging Geographic Information System and Kernel Density Estimation for Crime Prediction

This study explored the potential of spatial analysis techniques to predict crime hotspots and coldspots within the province of Antique, Philippines. The unique blend of geographical features (coastal and mountainous regions) and socio-economic characteristics in Antique presented a compelling opportunity for the research. Crime data from 2018 to 2023, encompassing direct assaults, homicides, murders, and parricides, was acquired from the Police Regional Office 6. Using Quantum Geographic Information System (QGIS) software and Kernel Density Estimation (KDE) algorithms, the researchers analyzed these crime records. The findings highlighted a positive trend – a decrease in direct assaults over the study period. However, homicide data presented a more concerning picture, with specific locations consistently reporting higher rates. While murder cases showed a decline, parricide incidents remained relatively low, with occasional fluctuations. In conclusion, this data-driven approach, utilizing advanced crime analysis tools, offers valuable insights for policymakers in Antique. By investing in such technology and adopting data-driven strategies, policymakers can work towards creating a safer and more secure future for their communities.

112 범죄 신고 데이터 분석을 통한 캠퍼스 내에서의 범죄취약요소 연구

This study is to examine the vulnerable environmental factors in university Crime Hot-Spot Area through campus field surveys and the correlation between safety system and statistical analysis. Seven campuses were used for current research wity using the data of 112 crime data with 1,793 crimes. We then analyzed Crime Hot-Spot Area through the Geographic Information System(GIS). For each university, vulnerable elements were derived in terms of the geographical characteristics of Crime Hot-Spot Area, the use of academic buildings, and the environmental design for crime prevention(CPTED). Crime Hot-Spot Area includes border areas(Edge effect) such as campus entrances, libraries, living halls, and student halls, as well as hospitals and parking lots on campus, nearby bus stops, and subway routes. Multiple regression analysis was conducted on seven independent variables related to vulnerable factors using the number of crime hot-spots as a dependent variable. The independent variables are the measured distance between the 112 report location and the CCTV, lighting, emergency bell, campus entrance, and general situation room. In addition, the correlation between the integration value of crime occurrence locations and the connection value through Space-Syntax analysis was also analyzed. The higher the number of Crime Hot-Spot Areas, the higher the integration value and the lower the connection value. Crimes have been concentrated on streets with high centrality and low connectivity on campus and streets with the characteristics of traveling in one direction. CCTV is a physical surveillance system known to be effective in crime prevention, but in this study, on the contrary, CCTV was installed closer as the number of occurrences in Crime Hot-Spot Areas increased. Lighting(CPTED surveillance index) was also related to crime occurrence in the analysis results of only one out of seven universities. Therefore, it was difficult to see lighting as an indicator related to campus crime prevention. It is important to analyze 112 police report data on important crimes such as theft, sexual violence, and violence that actually occurred on campus to derive accurate hotspot areas suitable for environmental characteristics. In the future, the results of this study that fit the characteristics of the university campus can be used.

Describing the geographical linkage between the UK Millennium Cohort Study and crime incidents in England and Wales

BackgroundEnhancing longitudinal cohort studies by linking routine external data to them is increasingly used to evaluate how local environments impact participants' outcomes (e.g. crime on adolescents' perception of security and victimisation). ObjectiveTo describe the geographical linkage between the UK Millennium Cohort Study (MCS) and street-level crime incidents reported to the Police in England and Wales, and to estimate crime count and rates around MCS participants' residences. MethodsEight years of monthly street-level police data were linked to the residential postcodes of MCS participants living in England and Wales in surveys 5, 6 and 7 to create individual-level variables of neighbourhood crime counts and rates (28,724 surveys and 11,365 individuals). Radial buffers around participants' residences were created at ages 11, 14 and 17. Crime counts and rates were created prior to the month of interview (at 1, 3, 6, 9, and 12 months prior). A homogenisation of crime categories reported in the police data was conducted to evaluate changes over time and areas. Multivariate models were used to study the association between MCS participants' demographic characteristics and derived measures of neighbourhood crime. ResultsWhile total crime rates and counts around MCS participants remain stable over the period, they hide heterogeneous upward and downward trends in specific sub-categories, with violence and sexual offences showing a larger increase. We observe a negative socioeconomic gradient between household income deciles, recorded at age 11, and subsequent exposure to neighbourhood crime. ConclusionLinking routine crime data to longitudinal studies, such as the MCS, which follow children and their families through a critical period of development, can provide a new resource to understand how local crime impacts child and adolescent outcomes.

What’s the safest way home? Mapping safe transport routes in South Africa with (and without) crime data

Safety is arguably the single biggest daily concern for commuters. Popular navigation software systems such as Google Maps, TomTom and Waze do not however currently give the same weight of consideration to crime risk when directing road users to the safest and shortest routes as they do existing traffic knowledge. In this study we propose three simple methods to delineate the safest and shortest route between any two locations in South Africa considering crime risk. The first method uses historical crime data to delineate the safest and shortest route; the second method, uses only the location of so-called ‘risky facilities’ to determine the safest and shortest route; while the third method uses a combination of both historical crime data and facility data to delineate the optimal route. We contrast the resultant routes delineating by these three methods and speculate on the policy implications of integrating this knowledge into future navigation software systems.

Differentiating risk: The association between relationship type and risk of repeat victimization of domestic abuse

Abstract Much of the literature on domestic abuse focuses on those in intimate partner relationships or ex-partners, however, in the UK the Home Office definition also includes those in familial relationships. The Domestic Abuse, Stalking, and Harassment and Honour-Based Violence Risk Assessment assumes homogeneous risk factors across all relationships. This paper therefore examines the risk factors for repeat victimization of domestic abuse by relationship type between the victim and perpetrator in a UK police force. Using police-recorded domestic abuse incident and crime data, a logistic regression model found that the most similar repeat victimization risk profiles for 14,519 victims were amongst partners and ex-partners, with both relationships demonstrating the greatest degree of gender asymmetry, compared with other familial relationships. Physical violence was the strongest predictor of repeat victimization and was a statistically significant predictor for ex-partners, partners, and all familial relationships. Coercive behaviour was also a significant predictor for all relationships apart from partners, but not at the same magnitude as physical abuse. Recognizing the difference in risk by relationship type may assist the police in deciding the most appropriate response and interventions to reduce the risk of further harm.

Clustering method for criminal crime acts using K-means and principal component analysis

Criminality is an act of violating the values and norms of society that causes a lot of harm. Much of the criminal data is often just a collection of data that has no information. Analysis of crime data is key in efforts to reduce crime rates that provide an overview of the incidence of crime, patterns, levels of vulnerability, and the level of security of an area. This research proposes data analysis that provides an understanding of crime using data mining techniques, especially the K-means cluster method, both traditional and with principal component analysis (PCA) dimension reduction. Before the PCA process, the values are transformed first with Z score normalization. From the processing through the davies bouldin index (DBI) performance test with 3 clusters, it is concluded that traditional K-means produces a DBI Index value of 0.019 and K-means PCA of 0.299. Meanwhile, to see the optimal cluster, several iterations were performed and resulted in the most optimal DBI index of 4 clusters in K-means of 0.014 and K-means PCA of 0.172. From the performance test value, it means that in the context of clustering the traditional criminal K-means data is declared more optimal than K-means PCA.

Spatial and Temporal Analysis: A GIS-Based Application Tool for Crime Monitoring and Clustering in Malawi

For the purposes of monitoring, evaluating, and conducting a geographical analysis of crime-related data, the study used geospatial technology to collect crime data based on spatial location and the Malawi Police Data Digest of 2019 and 2020. In a more generic sense, knowing the geographic patterns of crime in Malawi using GIS technology can help determine how to make and implement important decisions to reduce crimes in Malawi. The Malawi Police Service has established a number of database management systems to help with crime monitoring. Notwithstanding, it has not yet fully integrated Geographic Information Systems across all jurisdictions. Maps showing crime locations and crime hotspot zones are therefore not included in the crime data and statistics report provided by the Malawi Police Service. In this light, a lot of people have become victims of various forms of crimes in areas where those crimes are also prevalent. To collect, track, and analyze crime data in Malawi for this study, Geographical Information System (GIS) particularly network analysis techniques were used. Network Analysis was used to identify crime hotspots by analyzing crime data as a network of interconnected events and locations. The rationale behind this was to treat each crime event as a node in the network and the spatial relationships between the crimes as edges. By analyzing this network, patterns and relationships between crime events were revealed, allowing for the identification of crime hotspots. The study found that Lilongwe in the central region and the capital city registered the highest number of crimes seconded by Blantyre in the southern region and followed by Mangochi. Mzimba recorded high crimes in the northern region. In Malawi, the traditional systems of intelligence and criminal record keeping have failed to satisfy the demands of today's crime situation. Manual methods neither give accurate, dependable, or complete data 24 hours a day nor do they help in trend forecasting and decision assistance. It also leads to poorer productivity and inefficient workforce use. The appropriate application of information technology is the solution to this ever-increasing challenge.

Revising What We Know About Spatial Predictors of Violent Victimization: A Comprehensive Analysis of Differential Effects of Spatial Predictors of Robbery Victimization for Victims of Different Ethnic Backgrounds

ABSTRACT Despite abundant research on spatial predictors of violent victimization, few studies have explored the varying effects of these predictors on victims of different demographic backgrounds. This study seeks to bridge this gap by integrating perspectives from social disorganization, routine activity, risky lifestyles, and broken windows theories. Utilizing 2017–2019 crime data from Arlington, TX, the authors employ generalized spatial two-stage least-squares models to dissect the nuanced effects of place characteristics across different racial and ethnic groups. The findings highlight the importance of victim race/ethnicity in predicting robbery victimization within high-risk areas, uncovering the disparate effects the same place features have on people of different racial or ethnic backgrounds.

Criminological Insights: A Comprehensive Spatial Analysis of Crime Hot Spots of Property Offenses in Malaysia's Urban Centers

This study examines property crime trends in Selangor, Kuala Lumpur, and Putrajaya from 2015 to 2020, utilizing Geographic Information Systems (GIS) and comprehensive crime data from the Royal Malaysia Police. It focuses on the relationship between property crime rates and the delineation of police station jurisdictions across Selangor State, Kuala Lumpur Federal Territory (KLFT), and Putrajaya Federal Territory (PFT). The objective is to understand the dynamics of property crime, its evolution, and the influence of the National Transformation Program on crime prevention strategies. By assessing the spatial distribution of property crimes, this research aims to identify underlying trends and the efficacy of hot spot policing. The analysis is underpinned by an extensive review of secondary data from the Intelligence/Operations/Records Division of the Royal Malaysia Police Headquarters, enabling a deep dive into the spatial characteristics of property crime within these regions. The findings contribute to the broader understanding of crime prevention in Malaysia, underscoring the importance of GIS technology and the need for strategic interventions tailored to specific crime patterns. This study provides critical insights for policymakers and law enforcement to formulate evidence-based strategies, enhance public safety, and inform future crime prevention efforts.

Криминологическая обоснованность внесения изменений в уголовный закон: использование статистики и роль средств массовой информации

Introduction. The foundation of the state’s strategic activity in the fight against crime (criminal policy) is a set of initial data of a criminological and social nature, the objectivity (compliance with the truth) of which is often rightly questioned, and therefore the issues of searching and selecting sources for the formation of criminal policy in order to achieving the requirement of criminological validity. Methods. In work based on the dialectical-materialistic method of scientific knowledge, the legal-dogmatic method, formal-logical and systemic methods are used. Results. Despite the fact that the scale of criminologically significant information is quite large, an obstacle to the formation of a criminologically based criminal policy is the lack of the required volume of objective data on crime. Recognizing that certain indicators themselves must be correctly interpreted (including in their interrelation and interdependence), it can be argued that correct criminological forecasting requires an appropriate methodological basis that clearly indicates how it is necessary to process what is significant for the fight against crime information. Recognizing the problem of crime latency, it is worth emphasizing the need to find tools for objectifying crime data. In particular, it is worth paying attention to the promise of an integrated approach to the analysis of criminal phenomena, including, at a minimum, “cross-reconciliation” of data from various government bodies and non-state structures (subject to confidence in the objectivity of the information they provide). When making changes to the criminal law, the use of official statistics as a basic source of information is recognized as justified. Of particular importance is the issue of searching for alternative sources of information, which are now increasingly referred to as social expectations.

The Crime Prediction of Criminal Activity Based on Weather Changes Towards Quality of Life

Crime is a significant problem in society, and crime prevention is crucial. Factors such as politics, economics, culture, education, demographics, and employment have been identified as contributing to crime. Recent studies have also explored the relationship between weather and crime. Therefore, this research aims to identify the best-performing machine learning algorithm based on weather in Malaysia, using crime data from the Royal Malaysia Police and Meteorological Department from 2011 to 2020. Five machine learning algorithms were utilized, and the results showed that all algorithms had good prediction accuracy, with Gradient Boosted Trees performing the best, with an error rate of less than 23%. Location was found to be the most important feature in all the models. This study provides a valuable fundamental framework for environmental crime and social impact research scholars to conduct a more in-depth analysis of the prediction models. This study establishes a fundamental framework for scholars in environmental crime and social impact research to conduct in-depth analysis using prediction models, thereby contributing to a better understanding of the complex relationship between weather and crime, and aiding in the development of effective crime prevention strategies.

Diversifying Crime Datasets in Introductory Statistical Courses in Criminology

Contemporary criminology issues are increasingly global, cross-cultural, and multilingual. Moreover, students from different cultural and national backgrounds will need to apply data analytics in their respective contexts. Crime data used in statistical courses should reflect this diversity, and in turn enhance the equality and inclusivity of the teaching curriculum. Supported by evidence-based pedagogic principles and evaluations, researchers have identified strategies to enhance the teaching and learning of quantitative skills. Promoting students’ understanding of quantitative methods and their application in criminology requires that teaching materials reflect real-world problems and the diversity of today’s student population. To facilitate this aim, the article first describes over 40 open and accessible crime data sources across political, cultural, and linguistic borders in the Global South. Moreover, to support educators in their implementation and use of these datasets, the article presents three case studies of exemplar pedagogic activities using available data sources in an undergraduate Criminology program in the UK. Exemplar activities include (1) time series analysis of homicide in Asia; (2) bivariate analysis of trust in police and victimization in Algeria; and (3) mapping kidnappings in Mexico. We end by discussing the pedagogical and research implications of diversifying datasets and some future challenges.