Crime Patterns Research Articles

Computer-aided analysis: Correlation between urban green spaces and crime rates

Abstract. Urban green spaces have been associated with various social and environmental benefits, including their potential impact on crime rates. This study investigates the correlation between urban green spaces and crime rates using a combination of Geographic Information Systems (GIS) and advanced statistical analysis. By analyzing data from a large metropolitan area, we examine how green space characteristics such as size, type, and maintenance influence crime patterns. Our findings indicate that well-maintained green spaces with diverse amenities can significantly reduce crime rates, whereas neglected areas may become hotspots for criminal activities. The study also explores temporal patterns of crime in relation to green spaces, revealing that adequate lighting and community presence are crucial for nighttime safety. Policy recommendations are provided to help urban planners and policymakers leverage green spaces to enhance urban safety. By integrating multiple analytical techniques, this research contributes to a comprehensive understanding of the role of green spaces in urban crime prevention and offers actionable insights for future urban planning.

Socioeconomic, Migration, and Political Upheaval Factors in Crime: Applying the Social Disorganization Theory in Turkey

ABSTRACTThis review attempts to provide a comprehensive investigation of the complex argument that socioeconomic conditions and crime are related through political instabilities and their impact on Turkey's economy, with a specific focus on social disorganization theory. By focusing on the Turkish labor market, this study examines the interplay of socioeconomic factors impacting crime rates and changing public views. This review highlights social disorganization and crime patterns via socioeconomic factors, migration, and political upheaval. By exploring the transformation of Turkey's labor market over many decades, this study aims to provide insights into the ongoing issues of the country.

Crime radiation theory: the co-production of crime patterns through opportunity creation and exploitation

AbstractConsiderable research shows that crime is concentrated at a few proprietary places: addresses and facilities. Emerging research suggests that proprietary places may radiate crime: activities at a place increase the risk of crime in the area around it. Weaknesses in the research create uncertainty about radiation, so we need more rigorous research. To conduct this research, we need a theory of crime radiation that operates at two spatial levels: the proprietary place and the area. This paper describes such a theory. Our theory states that crime radiation stems from the interaction between place management decisions at the place and offenders searching for opportunities in the area. Place managers create crime opportunities inside and outside their places. Offenders exploit place managers’ creations by deliberately searching for opportunities or by chancing upon the opportunities. The ways place managers and offenders interact gives rise to three types of crime radiation: hot dot, veiled dot, and cold dot. Finally, we propose questions crime scientists should answer to better understand crime radiation.

PREDICTIVE POLICING BY BHABINKAMTIBMAS AS A SOLUTION TO ADDRESS CRIMINALITY IN CILEMPUNG VILLAGE, PASIRJAYA, KARAWANG

The Karawang Regency faces a significant increase in the number of crimes, being a major concern due to its disruptive impact on the community as well as serious challenges for law enforcement and local governments. In order to respond to the various problems faced by Bhabinkamtibmas in the field, support and strengthening are needed with the right policing program. The research contributes to the field of public security by introducing a Predictive Policing framework tailored to the needs of Cilempung Village, Karawang, utilizing information technology and data analysis to identify crime patterns and predict potential crimes. The study provides insights into the factors influencing crime rates and offers practical strategies for implementing predictive polcing in local communities, ultimately aiming to enhance public safety and maintain social order.

Trends and Patterns of Crime against Scheduled Tribes in States of Northern and Western Regions of India

The paper discussed trends and patterns of crime against scheduled tribes in states of northern and western regions of India. It has found that highest crime against scheduled tribe(s) occurred in Rajasthan followed by Maharashtra and Gujrat. There are no incidences of crime against scheduled tribe(s) in Haryana. Incidences and victims of simple hurt, grievous hurt murder, assault on tribal women and attempt to commit murder are highest in Rajasthan followed by Maharashtra and Gujrat. Incidences and number of victims of sexual harassment is highest in Maharashtra followed by Rajasthan and Gujrat. Rajasthan has the highest incidences and victims of stalking followed by Maharashtra and Gujrat. Incidences of assault of children of scheduled tribes is highest in Maharashtra followed by Rajasthan and Gujrat. There are no incidences of kidnapping for ransom in any states of the regions. Incidences of kidnapping and abduction of women to compel her for marriage is highest in Rajasthan followed by Gujrat and Maharashtra. There are no incidences of procuration of minor girls of scheduled tribes in any state of the regions. Incidences of rape of women is higher in Rajasthan and Maharashtra. Rajasthan has the highest incidences of robbery followed by Maharashtra and Gujrat. Therefore, government of the concern states should implement proper policies for minimizing such crimes against scheduled tribes.

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.

Fraud events in the coolest of neighbourhoods: A case study from Gothenburg, Sweden

AbstractIn the current study credit card frauds are analysed. Concepts are taken from crime pattern theory and place-based criminology. The empirical material originates from the urban nightlife of Gothenburg, Sweden, and combines quantitative and qualitative data from different sources. Instead of focusing on the individual’s specific motives for committing crimes, the study has its interest in how place can play a part in fraud events and be a necessary condition in the crime modus operandi. In the conducted analysis, we can clearly see that place matters in the crime series studied, and that the place was not randomly chosen by the offenders.

Analysing non-linearities and threshold effects between street-level built environments and local crime patterns: An interpretable machine learning approach

Analysing non-linearities and threshold effects between street-level built environments and local crime patterns: An interpretable machine learning approach

A Machine Learning Approach to Analyzing Crime Concentration: The Case of New York City

Building upon prior work, we propose an alternative way to look at the pattern of spatial crime concentration and temporal stability of it. We first identify a high-crime cluster using the sample block groups in New York City by employing a k-means clustering method. We then examine the temporal stability of the high-crime cluster over time. We also longitudinally assess how our high-crime cluster classification is associated with the actual amount of crime while accounting for the measures of social and physical environments. We observed that about 6–12% of total areas are identified to be in the high-crime cluster. We also found that block groups identified to be high-crime cluster in one year are more likely to be that way in the next year. We hope future research may consider using data-driven approaches to expand understanding of spatial and temporal crime patterns.

Exploring Methods in Crime and Safety Analysis

Understanding the spatial distribution of crime patterns or poor safety perceptions equips us with valuable insights to guide resource allocation. In this special issue, we present examples of articles in environmental criminology, focusing on the use of spatial and temporal-based methods for crime and safety analysis. The contributions are from Sweden, Canada, New Zealand, and Pakistan, showcasing a diverse array of data and methods. Almost all articles are from sessions presented in the International Symposium in “Environmental Criminology and Crime Analysis” ECCA 2023, organized by the guest editor's university, and in “the Stockholm Criminology Symposium” that took place in Stockholm in June 2023. This introduction finishes with a summary of the articles of this collection.

Child Perpetrators and Victims: Criminal Policy Approaches to Sexual Violence in Indonesia through Restorative Justice

Criminal law embodies the principle of ultimum remedium, which means that prosecution is considered a final measure in law enforcement. This principle does not seek to eliminate punishment for offenders but rather aims to protect society from crimes by using prosecution as a last resort. Offenses are not only committed by adults but also by minors. With the rapid advancement of information and telecommunication technology, sexually explicit content has become widely available through electronic media. As a result, many children mimic behaviors they have seen or heard about, particularly since they are often given access to electronic devices such as smartphones and computers with internet access. This ease of access sometimes occurs without sufficient parental supervision, exposing children to inappropriate content. Given this context, the author is motivated to conduct research titled Criminal Policy for Addressing Sexual Violence Committed by Minors Against Minors in Indonesia. This study aims to explore effective legal measures and policies to address and mitigate such incidents. The research employs a qualitative method grounded in the theory of sanctions and the theory of legal effectiveness. By utilizing a criminological approach, the study seeks to analyze crime patterns associated with sexual violence perpetrated by minors. This includes examining the socio-cultural factors that contribute to such behaviors and evaluating the current legal framework's effectiveness in addressing these issues. Through this research, the author hopes to shed light on the complexities of juvenile delinquency in the context of sexual violence and to propose informed policies that can better protect minors from both committing and becoming victims of such offenses. The findings are expected to contribute to the development of a more effective criminal justice system that balances punitive measures with rehabilitative approaches, ultimately aiming to create a safer and more just society for all.

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.

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.

Assessing the Reality of Crimes in Jordanian Society: A Review Study on Jordanian Society from 2018-2022

The study aims to identify the reality of crime in Jordan during the period from 2018 to 2022 and analyze its impact on Jordanian society. The study relied on a review of the literature and available data to identify the levels of crime and its development in Jordan, recorded in the statistical reports issued by the Directorate of Public Security/Criminal Information Department for the years 2018-2022. The study relies on identifying the social and economic impacts of these crimes on society through evaluation and analysis of the reality of crime in Jordan, especially since it is an important topic that requires a comprehensive study. The objectives of this study are to understand the current situation in order to analyze its impact on Jordanian society. It depends on the available literature, through which it is possible to delve into the patterns of crimes recorded during that period within crime classifications such as violence, theft, fraud, drug trafficking, and others. Therefore, the reality of the development of these crimes develops over the course of the four years within many trends and factors that determine responsibility for the changes. crime rates affect the social and economic reality of crimes in Jordanian society, including the effects of many fears about insecurity, weak confidence in the security services, and the impact on the financial situation instability.

Addressing Urban Management Challenges for Sustainable Development: Analyzing the Impact of Neighborhood Deprivation on Crime Distribution in Chicago

Urban crime prevention remains a critical challenge intertwined with socioeconomic disparities. This study investigates the spatial nexus between urban deprivation and crime decline in Chicago from 2015 to 2022, addressing the question: How do changes in neighborhood deprivation relate to crime reduction patterns? Using comprehensive crime incident and census tract Area Deprivation Index (ADI) data, this study conducted exploratory spatial analysis, regression modeling, and local bivariate relationship analysis. The findings reveal persistent hotspots of concentrated deprivation on Chicago’s south and west sides, alongside a general citywide crime decline. However, the current research uncovered significant spatial heterogeneity in both deprivation patterns and crime reduction, challenging the achievement of equitable public safety outcomes. Surprisingly, while crime incidents in 2015 and 2022 significantly predicted crime change, ADI scores were not statistically significant predictors in our regression model. Local bivariate analysis exposed diverse relationships between ADI changes and crime rate changes across neighborhoods, with 72.2% of census tracts showing no statistically significant relationship. This underscores the complexity of urban crime dynamics and the importance of local context in understanding these patterns. Our research contributes a nuanced understanding of the intricate relationship between urban deprivation and crime patterns, providing valuable insights for policymakers, law enforcement agencies, and urban planners. These findings highlight the need for integrated, long-term strategies that address both crime prevention and socioeconomic disparities, ultimately fostering safer, more equitable urban environments and informing evidence-based interventions tailored to specific neighborhood contexts.

Crime dynamics in Edmonton’s train stations: analysing hot spots, harm spots and offender patterns

PurposeThe objectives of this study are to (a) identify spatial and temporal crime concentrations, (b) supplement the traditional place-based analysis that defines hot spots based on counted incidents with an analysis of crime severity and (c) add to the research of hot spots with an analysis of offender data.Design/methodology/approachThis study explores crime concentration in mass transit settings, focusing on Edmonton’s Light Rail Transit (LRT) stations in 2017–2022. Pareto curves are used to observe the degree of concentration of crime in certain locations using multiple estimates; trajectory analysis is then used to observe crime patterns in the data on both places and offenders.FindingsA total of 16.3% of stations accounted for 50% of recorded incidents. Train stations with high or low crime counts and severity remained as such consistently over time. Additionally, 3.6% of offenders accounted for 50% of incident count, while 5% accounted for 50% of harm. We did not observe differences in the patterns and distributions of crime concentrations when comparing crime counts and harm.Research limitations/implicationsHot spots and harm spots are synonymous in low-crime-harm environments: high-harm incidents are outliers, and their weight in the average crime severity score is limited. More sensitive severity measures are needed for high-frequenty, low-harm enviornments. Practical implicationsThe findings underscore the benefits of integrating offender data in place-based applied research.Originality/valueThe findings provide additional evidence on the utility of place-based criminology and potentially cost-effective interventions.

Analisis Anarkisme Remaja dalam Kejahatan Klitih di Kabupaten Bantul: Faktor, Karakteristik, dan Upaya Penegakan Hukum

The phenomenon of street violence that occurs in Bantul Regency, Yogyakarta, namely klitih, is a serious concern for the local community. This research aims to analyze the factors that influence juvenile involvement in crime, characteristics of perpetrators, crime patterns, and efforts to increase the effectiveness of law enforcement in dealing with these problems. The research method used is a normative approach with a qualitative descriptive approach. Secondary data was obtained from various literary sources and related legal documents. The results of the research show that social factors, the search for identity, hereditary school gangs, family, education, environment, as well as the influence of media and technology play a role in teenagers' involvement in klitih. The characteristics of the perpetrators include teenagers aged between 15 and 20 years, low educational background and a dysfunctional family environment. The klitih crime pattern involves acts of violence with the aim of showing courage in front of a peer group. Efforts to increase the effectiveness of law enforcement include improving coordination between law enforcement agencies, increasing security patrols, utilizing technology, and implementing comprehensive rehabilitation programs.

The importance of neighborhood offending networks for gun violence and firearm availability

Abstract The salience of neighborhoods in shaping crime patterns is one of sociology’s most robust areas of research. One way through which neighborhoods shape outcomes is through the creation and maintenance of social networks, patterns of interactions and relationships among neighborhood residents, organizations, groups, and institutions. This paper explores the relationship between network structures generated through acts of co-offending—when two or more individuals engage in an alleged crime together—and patterns of neighborhood gun violence and gun availability. Using arrest data from New York City, we create co-arrest networks between individuals arrested in the city between 2010 and 2015. We analyze these network patterns to, first, understand the overall structure of co-offending networks and, then, assess how they impact neighborhood levels of gun violence and gun availability. Results show that local and extra-local networks play a central role in predicting neighborhood levels of shootings: neighborhoods with a greater density of local ties have higher shootings rates, and neighborhoods that share social ties have similar rates of violence. In contrast, the network dynamics involved in gun recoveries are almost entirely local: co-offending patterns within neighborhoods are strongly associated with the level of gun recoveries, especially the clustering of co-offending networks indicative of groups. Contrary to previous research, spatial autocorrelation failed to predict either shootings or gun recoveries when demographic features were considered. Social-demographic characteristics seem to explain much of the observed spatial autocorrelation and the precise measurement of network properties might provide better measurements of the neighborhood dynamics involved in urban gun violence.

Spatiotemporal Analysis of Nighttime Crimes in Vienna, Austria

Studying the spatiotemporal dynamics of crime is crucial for accurate crime geography research. While studies have examined crime patterns related to weekdays, seasons, and specific events, there is a noticeable gap in research on nighttime crimes. This study focuses on crimes occurring during the nighttime, investigating the temporal definition of nighttime crime and the correlation between nighttime lights and criminal activities. The study concentrates on four types of nighttime crimes, assault, theft, burglary, and robbery, conducting univariate and multivariate analyses. In the univariate analysis, correlations between nighttime crimes and nighttime light (NTL) values detected in satellite images and between streetlight density and nighttime crimes are explored. The results highlight that nighttime burglary strongly relates to NTL and streetlight density. The multivariate analysis delves into the relationships between each nighttime crime type and socioeconomic and urban infrastructure variables. Once again, nighttime burglary exhibits the highest correlation. For both univariate and multivariate regression models the geographically weighted regression (GWR) outperforms ordinary least squares (OLS) regression in explaining the relationships. This study underscores the importance of considering the location and offense time in crime geography research and emphasizes the potential of using NTL in nighttime crime analysis.

Learning the rational choice perspective: A reinforcement learning approach to simulating offender behaviours in criminological agent-based models

Over the past 15 years, environmental criminologists have explored the application of agent-based models (ABMs) of crime events and various theoretical frameworks applied to understand them. Models have supported criminological theorising and, in some cases, been applied to make predictions about the impact of interventions devised to reduce crime. However, decision-making frameworks utilised in criminological ABMs have typically been implemented through traditional techniques such as condition-action rules. While these models have provided significant insights, they neglect a crucial component of theoretical accounts of offending, the notion that offenders are learning agents whose behavioural dynamics change over time and space. In response, this article presents an ABM of residential burglary in which offender agents utilise reinforcement learning (RL) to learn behaviours. This solution enables offender agents to learn from individual-level perceptions of the environment and, given these perceptions, develop behavioural responses that benefit themselves. The model includes conceptualisations of the Routine Activity Theory (RAT), Crime Pattern Theory (CPT) and a utility function, Target Attractiveness, which acts as a behavioural mould to nudge offender agents to learn behaviours in keeping with the Rational Choice Perspective (RCP). Trained behaviours are then tested by introducing crime prevention interventions into the model and examining the reactions of offender agents. In keeping with empirical studies of offending, experimental results demonstrate that offender agents utilising RL learn to offend at targets where rewards outweigh risks and effort, offend close to home, frequently victimise high-rewarding targets, and conversely learn to avoid offending in areas associated with high levels of risk and effort.