Crime Hot Spots Research Articles

Evaluating the Effectiveness of Predictive Policing in Nigeria: A Qualitative study of law Enforcement Practices in Lagos State

This qualitative study evaluates the effectiveness of predictive policing technologies by examining the implementation within law enforcement operations in Lagos State. The research aims to explore how predictive systems influence crime prevention, and decision-making processes in policing. Drawing on semi-structured interviews with NDLEA officers, the study reveals the perceived benefits and challenges of integrating predictive policing tools into traditional methods. Through thematic analysis of interviews and review of secondary sources, the research identifies key themes such as the tension between technology and human judgement, and the potential for over-reliance on system predictions. The findings indicate that while predictive policing can improve efficiency in crime hotspot identification, its efficacy is undermined by concerns over privacy, and fairness. The study concludes that for predictive policing to be effective, it must come with strong regulatory frameworks and ethical guidelines to ensure support from technologies. This research contributes to the growing discourse on the intersection of technology and law enforcement, offering insights into how predictive policing can evolve to better serve the people.

Maritime security and the inter-agency challenge: The case of Ghana

ABSTRACT Maritime security is a major international concern with the Gulf of Guinea recognised as one of the global hotspots of blue crime, such as piracy, kidnap for ransom, trafficking of narcotics, human and arms, and illegal fishing. The complex and complicated challenge of maritime security, a wicked problem, calls for inter-agency coordination, synergy of efforts and scaling up of responses. Given the complexity of maritime security threats, no single organisation has the institutional muscle to single-handedly deal with it. Drawing on evidence from Ghana, where the arrival of maritime security as a concept triggered a shift from single to a multi-agency approach to dealing with maritime issues, the article examines the potential of and challenges associated with inter-agency coordination. In line with recent international developments, African nations like Ghana aim to apply the concept of inter-agency coordination to tackle maritime insecurity in its waters. The paper assesses how inter-agency coordination could be used in an African maritime security governance context, while examining power imbalance, strife for agency autonomy and other obstacles that have to be addressed to ensure that the promises associated with the concept are fulfilled.

The Role of Artificial Intelligence in Criminal Justice

This study explores the transformative role of artificial intelligence (AI) in the criminal justice system, examining its applications, benefits, and potential challenges. AI technologies, including machine learning, predictive analytics, and natural language processing, are increasingly integrated into various facets of criminal justice, from law enforcement and legal processes to corrections and rehabilitation. Through a comprehensive literature review, this research analyzes how AI enhances the efficiency and effectiveness of criminal justice operations. Key findings reveal that AI can significantly improve crime prediction and prevention, aid in evidence analysis, streamline administrative tasks, and support decision-making processes. For instance, predictive policing models using AI can identify crime hotspots and allocate resources more effectively, while AI-driven tools can assist in analyzing large volumes of legal documents and evidence. However, the study also highlights critical concerns related to bias, fairness, transparency, and ethical implications. There is a growing need for frameworks that ensure AI applications in criminal justice are transparent, accountable, and aligned with ethical standards to prevent discrimination and protect civil liberties. The role of AI in criminal justice presents a dual-edged sword, offering significant advancements while posing substantial risks if not properly managed. This study provides a balanced perspective, offering insights for policymakers, practitioners, and researchers on leveraging AI for a more just and efficient criminal justice system. Future research directions are proposed to address the ethical challenges and to develop robust regulatory frameworks for AI in criminal justice.

Are There Non-Business Days for Crime? A Small-Area Bayesian Spatiotemporal Analysis of Crime Patterns

ABSTRACT Criminal behavior may be different on weekends and holidays compared to business days. Understanding the distinctive crime patterns on non-business days is useful for crime research and crime control. This study contributes to the literature by explicitly investigating the small-area spatiotemporal variation in five types of major crimes between business days and non-business days using a Bayesian modeling approach in Old Toronto, Canada. The results show that criminal activity varies between business days and non-business days, influenced by the types of crimes, geographic locations, and local neighborhood characteristics. Compared to business days, on non-business days, southern areas with high business and entertainment activity exhibit increased assault and robbery levels, while northern residential areas experience reduced activity of break and enter, auto theft, and theft over $5,000. Nonetheless, spatial crime hot spots generally remain consistent between the two date categories, with some hot spots presenting an exacerbation of criminal activity during non-business days. A few sociodemographic variables and built environment features are associated with the spatiotemporal variation in crime. These findings demonstrate the spatiotemporal variation in criminal behavior and crime patterns between business days and non-business days and highlight the need for customized crime control measures at the small area level.

Exploring Spatial Statistics for Cluster and Outlier Analysis in Crime Hotspots: A Geospatial Study of Coastal Zones in Kollam and Thiruvananthapuram

Exploring Spatial Statistics for Cluster and Outlier Analysis in Crime Hotspots: A Geospatial Study of Coastal Zones in Kollam and Thiruvananthapuram

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.

Factors that Facilitate and Hinder Implementation of a Problem Oriented Policing Intervention in Crime Hot Spots: Suggestions to Improve Implementation Based on a Field Experiment

Factors that Facilitate and Hinder Implementation of a Problem Oriented Policing Intervention in Crime Hot Spots: Suggestions to Improve Implementation Based on a Field Experiment

The Impact of Urban Streets Layout on Crimes: A Space Syntax Analysis of Hayatabad Peshawar

Urban population is on a rising trend and with that, the planning and design of cities and urban settings has a vital role to play in making sure that our cities remain pleasant, healthy, and safe. Crime prevention as a goal is often overlooked in urban planning and has a drastic effect on the quality of urban life. In this study, the street layout of Hayatabad, Peshawar is analyzed through axial map analysis using UCL Depthmap software based on Space Syntax theory to identify possible crime hotspots in the area. The possible hotspots in existing street layout of the area are identified based on the integration values from analysis. Thereafter, a proposed street layout, with addition of new streets, is presented that significantly improves the safety of existing street layout with regard to crime occurrence by improving the global integration of the layout.

Regional Crime Data Analysis and Insights Using Fb Prophet

Crime is one of the most dominate and formidable aspect of the society. Crimes are committed daily, which has made the lives of the common people restive. Therefore, preventing the crime from occurring can be achieved through a systematic approach by recognizing and evaluating the criminal trends specific to a certain location. Data analysis can assist us to explore former criminal incidents and help us analyze the patterns and hidden correlations which can be used for prevention of crime. Our system aims to analyze crime-prone regions by using the location and time of the crime. Data visualization is a powerful tool for investigating crime rates across the country. Heat maps can be useful for determining the hotspots of crime. The proposed system analyzes the data using the FBprophet model to forecast crime rates in different regions. The Facebook Prophet library, maintained by Facebook, provides forecasting of time series data. This application uses past data and web-scraped data from various sites in order to get detailed studies of various crimes in multiple places. It also enables you to get data about crimes in many places across India by using the pin codes. Government organizations and police departments can effectively utilize it to monitor crime events within a locality.

Multi-density crime predictor: an approach to forecast criminal activities in multi-density crime hotspots

Multi-density crime predictor: an approach to forecast criminal activities in multi-density crime hotspots

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.

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.

Implementation of K-Means Clustering in Recognizing Crime Hotspots and Traffic Issues Through GIS

The challenge of accurately identifying instances of crime and traffic issues has rendered the precise localization thereof difficult, thereby impeding the populace's access to information concerning areas of high risk and safety. Employing a Geographic Information System (GIS)-based mapping system utilizing the K-means clustering method, spatial data pertaining to crime and traffic concerns are grouped. The primary objective is to aid in the identification of high-risk areas concerning crime and traffic matters. The methodology employed in this study revolves around the application of the K-means clustering method to categorize spatial data relevant to crime and traffic issues. K-means clustering represents a non-hierarchical cluster analysis technique designed to partition data into multiple groups based on spatial similarities. Research findings elucidate that through the utilization of the K-means clustering method, three distinct sets of clusters predicated upon the intensity of crime and traffic issues emerge. Consequently, from these clustering outcomes, districts and specific locales falling within each cluster, denoted as moderately vulnerable (C1), vulnerable (C2), and highly vulnerable (C3), can be delineated. This system is poised to furnish recommendations to pertinent authorities for addressing areas exhibiting heightened intensity levels while concurrently facilitating the generation of reports and dissemination of information to the public via a dedicated website pertaining to areas at elevated risk of crime and traffic issues.

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.

Smart city planning focused on the US cities in need of policing innovations and public health safety technologies and strategies

The issue of firearm-related injuries, deaths and crime in the United States is of particular relevance. Every year, about 32,000 lives are lost as a result of firearm-related incidents, including self-inflicted injuries. Other 74,000 people suffer non-fatal gunshot wounds. Addressing this multi-faceted problem is possible through a comprehensive and integrated approach that encompasses public health initiatives, law enforcement efforts, and security measures. In the context of solving this problem, the concepts of “community-oriented policing”, “problem-oriented policing”, “broken windows theory”, “smart city social sustainability initiative” (multi-system improvement and meeting the needs of marginalised residents), “feedback methodology”, etc. are thoroughly considered. The author proposes the strategic deployment of mini-police stations in areas with a high crime rate as proper centres of police visibility and a deterrent to criminals. To realise this task, it is necessary to integrate the latest approaches to Smart Urban Development with innovative urban planning technologies that would increase the influence of law enforcement agencies in the process of protecting public health and safety in cities. In particular, the article proposes the following for implementation: identifying crime hotspots by means of advanced geographic information systems, applying machine learning algorithms to predict future crime trends, using advanced real-time surveillance technologies and firearms detection systems, applying geospatial analysis to optimise patrol routes, dynamically adjusting patrol routes depending on real-time data analytics, deploying mobile command centres. As a result, law enforcement agencies can effectively communicate to potential criminals that their actions will not go unnoticed. They may achieve significant success in improving the overall quality of life of residents in such areas. Also, they will create safer communities, and reduce the number of incidents involving the use of firearms.

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.

Using Information Technology for Comprehensive Analysis and Prediction in Forensic Evidence

With the escalation of cybercriminal activities, the demand for forensic investigations into these crimeshas grown significantly. However, the concept of systematic pre-preparation for potential forensicexaminations during the software design phase, known as forensic readiness, has only recently gainedattention. Against the backdrop of surging urban crime rates, this study aims to conduct a rigorous andprecise analysis and forecast of crime rates in Los Angeles, employing advanced Artificial Intelligence(AI) technologies. This research amalgamates diverse datasets encompassing crime history, varioussocio-economic indicators, and geographical locations to attain a comprehensive understanding of howcrimes manifest within the city. Leveraging sophisticated AI algorithms, the study focuses on scrutinizingsubtle periodic patterns and uncovering relationships among the collected datasets. Through thiscomprehensive analysis, the research endeavors to pinpoint crime hotspots, detect fluctuations infrequency, and identify underlying causes of criminal activities. Furthermore, the research evaluates theefficacy of the AI model in generating productive insights and providing the most accurate predictionsof future criminal trends. These predictive insights are poised to revolutionize the strategies of lawenforcement agencies, enabling them to adopt proactive and targeted approaches. Emphasizing ethicalconsiderations, this research ensures the continued feasibility of AI use while safeguarding individuals'constitutional rights, including privacy. The anticipated outcomes of this research are anticipated tofurnish actionable intelligence for law enforcement, policymakers, and urban planners, aiding in theidentification of effective crime prevention strategies. By harnessing the potential of AI, this researchcontributes to the promotion of proactive strategies and data-driven models in crime analysis andprediction, offering a promising avenue for enhancing public security in Los Angeles and othermetropolitan areas.

Internal and External Crime Hot Spots: From Neural to Micro-Geographical Networks

AbstractAntisocial behaviour arises from a complex interplay of innate and environmental factors, with the brain’s adaptability to shifting environmental demands playing a pivotal role. An important but scantly studied environmental factor – micro-geographic hot spots of crime – covers a broad array of problems that produce frequent triggers for antisocial behaviour. Despite the established influence of neural substrates and various environmental factors on antisocial behaviour, the impact of residing in high-risk, violent crime hot spots in Israel, as well as other global locales, remains understudied. This paper aims to elucidate the intricate interplay between neurobiological mechanisms and crime hot spots in the context of antisocial behaviour. Its objectives are twofold: first, to acquaint researchers with the existing literature on the subject; and second, to catalyse further research and robust discourse in this domain. The article commences by reviewing the behavioural manifestations of antisocial tendencies within the framework of crime hot spots. Subsequently, it delves into the influence of crime hot spots on neurocognitive substrates, particularly emphasizing their impact on developmental trajectories associated with antisocial tendencies and the expression of antisocial behaviours. In closing, the paper offers implications and conclusions pertinent to crime hot spots in Israel.

A Systematic Review on the use of Machine Learning and Deep Learning Techniques for Crime Prediction from Social Media

The goal of crime prediction is to help law enforcement authorities to prevent crimes before they happen by identifying and reducing potential future crimes. In present days, crime cases are rapidly occur so it is a challenging task to accurately predict and classify the future crimes, as criminal patterns are adaptable and constantly evolve, making it a challenging task. Sentiment analysis is essential for deciphering the feelings conveyed in text since social media has grown into a popular venue for individuals to communicate their thoughts, feelings, views, and comments. This analysis is particularly valuable for making informed decisions in business, politics and government agencies especially to identify crimes. However, it faces the difficulties like lexical diversity and dataset imbalance. In order to make better decisions and maybe lower the risk of predicting the suspects in a crime, early crime prediction has lately made use of Artificial Intelligence (AI) models like Deep Learning (DL) and Machine Learning (ML). When applied to crime data, ML and DL models may assist pinpoint possible crime hotspots and foretell when crimes may occur. Crime prediction and categorization using social media posts is covered in depth in this paper, which offers a comprehensive overview of several ML and DL frameworks. Initially, different ML and DL based crime prediction models designed by many researchers are examined in brief. The next step is to do a comparison research to learn about the shortcomings of those frameworks and provide an alternative method for effectively predicting crimes based on social media postings.

Assessing Crime History as a Predictor: Exploring Hotspots of Violent and Property Crime in Malmö, Sweden

Objectives: Assessing the predictive accuracy of using prior crime, place attributes, ambient population, community structural, and social characteristics, in isolation and combined when forecasting different violent and property crimes. Method: Using multilevel negative binomial regression, crime is forecasted into the subsequent year, in 50-m grid-cells. Incidence rate ratio (IRR), Prediction Accuracy Index (PAI), and Prediction Efficacy Index (PEI*) are interpreted for all combined crime generators and community characteristics. This study is partially a test of a crude version of the Risk Terrain Modeling technique. Results: Where crime has been in the past, the risk for future crime is higher. Where characteristics conducive to crime congregate, the risk for crime is higher. Community structural characteristics and ambient population are important for some crime types. Combining variables increases the accuracy for most crime types, looking at the IRR. Taking the geographical area into account, crime history in combination with both place- and neighborhood characteristics reaches similar accuracy as crime history alone for most crime types and most hotspot cutoffs. Conclusions: Crime history, place-, and neighborhood-level attributes are all important when trying to accurately forecast crime, long-term at the micro-place. Only counting past crimes, however, still does a really good job.