Search Results (25)

American citizens are significantly more likely to experience property crime victimization than violent crime victimization. During a staffing crisis, police prioritize limited resources in combating serious crime; however, property crimes remain impactful to the community. Therefore, agencies need to consider innovative ways to control property crime, such as “super-cocooning” strategies that alert residents to recent offenses. These strategies intend to empower the community to implement guardianship and crime prevention measures. For these strategies to be effective, they require public buy-in and support. The present study implements a preregistered information provision survey experiment (N = 2412), similar to the strategy of super-cocooning, to assess whether the public is more likely to support such strategies to combat property crime. Although the sample held overall high support of this strategy, exposure to a super-cocooning door hanger prime produced no significant changes in perceived effectiveness. However, there was observed racial heterogeneity in the treatments: non-White respondents assigned to the treatment relative to White respondents experienced significantly increased support of super-cocooning strategies. Implications for light-footprint crime control strategies, particularly during a staffing crisis, are discussed. Full article

With rapid urbanization, the spatial layout and functional characteristics of urban facilities have a strong correlation with the spatial distribution of criminal activities. Using Changsha City as a case study, this research analyzes 2023 urban crime data, Point of Interest (POI) data, and socioeconomic data. The Multi-scale Geographically Weighted Regression (MGWR) model and clustering analysis are applied to examine how different types of urban facilities influence the spatial heterogeneity of crimes and propose tailored urban planning recommendations and crime prevention strategies. The findings reveal the following: (1) The spatial distribution of crimes in Changsha’s central urban area demonstrates significant spatial heterogeneity. Property crimes dominate in frequency and spatial distribution, primarily clustering around commercial hubs and transport nodes, while violent crimes are more common in scenic areas and open spaces with high pedestrian flow. (2) The impact of built facilities on crime exhibits spatial variability. Facilities such as Financial Services Facilities (FSF) and Shopping facilities (SHF) significantly contribute to property crime in core urban areas, while Scientific, educational, and cultural facilities (SEC) suppress crime in university towns. Scenic spots and facilities (SPF) are associated with violent crimes near scenic site entrances and transport hubs. (3) Facility resource allocation and preventive strategies should be optimized based on dominant factors in different areas to enhance security management efficiency through precise and differentiated planning, fostering sustainable urban safety systems. This study provides insights into the spatial patterns of crime distribution and its dominant factors from the perspective of urban facilities, offering a scientific basis for improving urban crime management and facility planning. Full article

Most policies to combat gang criminal behavior are rooted in deterrence and punitive strategies. This is fueled by moral panic, a get tough on crime rhetoric, and a lack of understanding for the psychological factors that may influence this behavior. Further, the extant literature has consistently observed that gang membership is associated with increased criminal behavior. In an effort to promote and shift away from punitive approaches in response to gang delinquency, the current study investigates the role psychopathic traits have in violent and property offending, longitudinally, in a sample of gang-involved youth. The study implemented count mixed effect models to investigate the topic longitudinally in waves 3, 5, 7, 8, 9, 10, and 11, while controlling for other variables with violent and property offending frequency. The current study found that some psychopathic traits are associated with offending behavior, longitudinally, in gang members and youth with a history of gang involvement. The findings suggest that gang intervention strategies should include empirically supported programs for treating psychopathic traits in gang identified youth to reduce involvement in delinquent behavior. Further, practitioners, researchers, and policymakers should collaborate to develop more empirically supported strategies to reduce and prevent gang delinquent behavior from an empathetic lens. Full article

The occurrence of criminal activities has the potential to hinder socioeconomic advancement, preventing individuals from investing in human capital and pursuing employment opportunities. Our investigation focuses on the hypothesis that the NEET (not in education, employment, or training) rate is related to crime levels. Through an econometric analysis based on regional data, we examine the impact of crimes against property and against persons on NEET rates within central-northern and southern Italy, while controlling for prevalent determinants of the NEET phenomenon. Our findings reveal that, compared with prevailing discouragement factors such as youth unemployment and lack of interest in tertiary education, crime exerts a more pronounced influence on elevating NEET rates. This effect is particularly evident in the relatively less developed southern regions, where violent crimes, although relatively uncommon, may disproportionately contribute to feelings of apprehension and uncertainty regarding future prospects. Full article

Background: This study explores how arrests changed in response to the COVID-19 pandemic across race. Daily life changed for everyone across the country with the onset of the pandemic, and early works have shown that crime changed in this period. Method: Official arrest data were pulled from the Indiana State Police database for several violent and property crimes covering 26 counties. Data were gathered from 2017 to 2021 for a comparison of pre-COVID-19 versus after the onset of COVID-19 (2020–2021). An OLS regression was run to assess differences in these patterns of arrests across Black and White populations. Results: This analysis finds that Black homicide, White homicide, and total Black violent crime arrests were significantly related to COVID-19 measures after controlling for other variables. The COVID-19 measures indicate that these crimes saw an increase in arrest after the onset of the pandemic and that these effects may not have been identical across race. Conclusions: The COVID-19 pandemic was linked to crime across race in the state of Indiana. Moving forward, it is important to uncover how crime changed across race in other locales and exactly what mechanisms may have driven these changes. Full article

In all countries, cities and their suburbs are the most densely populated areas. They are also the places visited by the largest number of tourists and one-day visitors, who inevitably run the risk of becoming victims of crime. It is, therefore, important, not only at national but also at the international level, to know the structure of urban crime and identify urban areas that differ in terms of their criminogenic situation. This requires a geographical approach and regionalisation based on the quantitative data that can offer it. This paper presents the results of a study using big data regarding violent crime, property crime and infringements against public order registered by the police in 2020 in the territories of three major Lithuanian cities and their suburbs (n = 149,239). Events in open spaces were separately addressed. A series of experiments were carried out using several spatial clustering methods. The automatic zoning procedure method that gave the best statistical results was then tested with different combinations of parameters. In each city, seven types of areas of urban crime were identified. Maps of crime areas (regions) were created for each city. The results of the regionalisation have been interpreted from a socio-geographical point of view and conform with previous sociological urban studies. Seven types of areas of crime have been identified, which are present in all the cities studied and, according to a preliminary assessment, roughly correspond to the socio-demographic and urban zones of each city. The maps of crime areas can be applied for crime prevention planning and communication, real estate valuation, strategic urban development planning and other purposes. Full article

This paper investigates whether perceptions of neighborhood physical disorder—measured by vacant lots, vacant buildings, and overgrown vegetation—influence perceptions of crime and perceptions of the frequency or magnitude of crime events. We use ordinal logistic regression to analyze individual-level and contextual-level variables derived from a survey of 401 randomly selected residents in seven New Orleans neighborhoods. Findings show that as perceptions of physical disorder increase, so does the likelihood of perceiving violent crime as more of a neighborhood problem than property crime. As residents perceive greater physical disorder, their perception of crime level decreases. Finally, residents who perceive greater physical disorder are more likely to perceive crime levels to be stable to increasing over the previous five years. The category of “physical disorder” is inherently subjective, and future research should distinguish between social disorder and physical disorder in the evaluation of perceptions of crime. Our major contribution is to empirically demonstrate that the influence of physical disorder on crime perceptions depends upon the crime type and temporal scale under investigation. Full article

Crime statistics indicate that the total number of annually reported crimes decreased steadily between 2008 and 2019 in South Africa. However, annually reported robberies have been steadily increasing over the same period. Additionally, South Africa remains plagued by high income inequality. This paper presents a system dynamics simulation model describing the relationships between education inequality, income inequality and robbery in South Africa. The model employs strain theory to project the effectiveness of interventions aimed at increasing South African education levels to decrease income inequality and thereby reduce robbery. The model explored robbery prevention interventions aimed at increasing South African education levels to reduce the current high level of income inequality. The results suggest that reducing robbery incidents through improved education levels will require a long time to become effective but will have long-lasting effects. Furthermore, the results indicate that combinations of interventions generate more substantial effects than the sum of effects produced by interventions applied in isolation. Full article

Thousands of buildings in Cleveland, Ohio were demolished or rehabilitated since the Great Recession in the 2000s. Recent evidence suggests removing vacant and decaying buildings reduces violent and firearm-involved crime. This study examines the dose-response relationship between demolitions, rehabilitations, and crime. We use Bayesian spatiotemporal models to estimate the association of interest for five types of crime outcomes: violent crimes, violent crimes involving a firearm, drug crimes, and crimes often associated with building vacancy. We estimate associations in quarterly time periods from 2012 through 2017 in 569 hexagons approximately the size of a neighborhood (2000 feet, approximately 610 m, in diameter), stratified by vacancy level. Across vacancy levels, the majority of our models do not identify statistically significant associations between demolition and rehabilitation dose and crime incidence. However, in some cases, we identify positive associations between demolition and crime. These associations generally appeared at higher levels of demolition (2 or 3 or more demolitions) in areas characterized by medium to high levels of vacancy. We also find that the presence of a property rehabilitation is associated with an increase in drug crimes in areas with medium levels of vacancy. Full article

Numerous studies have established the link between ADHD and antisocial behavior, one of the most serious functional impairments caused by the disorder. However, research on protective factors that mitigate this link is still lacking. The Salutogenic Model of Health offers the “Sense of Coherence” (SOC), establishing that individuals who see their lives as logical, meaningful, and manageable are more resistant to various risk factors and diseases. The present study examines for the first time whether SOC is also a protective factor against different ADHD-related types of antisocial behaviors (severe/mild violent behavior, verbal violence, property crimes, public disorder, and drug abuse). A total of 3180 participants aged 15–50 completed online questionnaires assessing the level of ADHD symptoms, antisocial behaviors, and SOC. Structural equation modeling was applied to examine the research hypothesis. An interaction between ADHD symptoms and SOC was found in predicting each type of antisocial behavior (beta = −0.06–−0.17, p < 0.01). The link between ADHD symptoms and antisocial behavior was significantly weaker for high than low SOC participants, regardless of age group. The current study found that people with high SOC are protected against the effect of ADHD symptoms on one of the most serious functional impairments, antisocial behavior. These findings suggest that SOC is a protective factor from the adverse effects of ADHD symptoms, justifying further prospective and intervention studies. Full article

Urban crimes are a severe threat to livable and sustainable urban environments. Many studies have investigated the patterns, causes, and strategies for curbing the occurrence of urban crimes. It is found that neighborhood socioeconomic status, physical environment, and ethnic composition all might play a role in the occurrence of urban crimes. Inspired by the recent interest in exploring urban crime patterns with spatial data analysis techniques and the development of Bayesian hierarchical analytical approaches, we attempt to explore the inherently intricate relationships between urban assaultive violent crimes and the neighborhood socioeconomic status, physical environment, and ethnic composition in Paterson, NJ, using census data of the American Community Survey, alcohol and tobacco sales outlet data, and abandoned property listing data from 2013. Analyses are set at the census block group level. Urban crime data are obtained from the Paterson Police Department. Instead of examining relationships at a global level with both non-spatial and spatial analyses, we examine in depth the potential locally varying relationships at the local level through a Bayesian hierarchical spatially varying coefficient model. At both the global and local analysis levels, it is found that median household income is decisively negatively related to urban crime occurrence. Percentage of African Americans and Hispanics, number of tobacco sales outlets, and number of abandoned properties are all positively related with urban crimes. At the local level of analysis, however, the different factors have varying influence on crime occurrence throughout the city of Paterson, with median household income having the broadest influence across the city. The practice of applying a Bayesian hierarchical spatial analysis framework to understand urban crime occurrence and urban neighborhood characteristics enables urban planners, stakeholders, and public safety officials to engage in more active and targeted crime-reduction strategies. Full article

Crime prevention and governance play critical roles in public security management. Liangshan Yi Autonomous Prefecture in Sichuan Province has a high crime rate, and spatio-temporal analysis of crime in this region could assist with public security management. Therefore, Liangshan Prefecture was selected as the research object in this study. The spatial crime data were obtained from China Judgments Online, and property crime, violent crime, and special crime (i.e., pornography, gambling, drugs, and guns) were analyzed. The findings were as follows. In terms of time characteristics (month, day, and hour), property crime tended to occur in autumn and winter, in the early month, on Wednesdays and Fridays, and at early morning. Violent crime tended to occur in winter and spring, on Mondays and Thursdays, and at night. Special crime occurred in spring and autumn, on Tuesdays, and in the daytime. In terms of spatial features, the central region of Liangshan Prefecture was the focal area for crime. There were obvious low-aggregation areas in the western region for special crime. The eastern region exhibited a high incidence of various crimes. Regarding the spatio-temporal evolution characteristics from 2013 to 2019, there were some obvious hotspots of violent and property crime in downtown and surrounding townships of Xichang City, which is the capital of Liangshan Prefecture. During the study period, the incidence of special crime has an obvious downward trend which shows that there are more new cold spots. Full article

This project report outlines the findings of an initial exploratory study of the impact of the coronavirus (COVID-19) pandemic on the demand, capacity, and capability of the police within the United Kingdom. Freedom of information requests provide data regarding employees affected by COVID-19, including those working from home. A survey of police employees adds richness by exploring the departments and specialist capabilities affected. Preliminary results indicate a shift in demand away from property-related and violent crime, to online criminality, and disorders such as anti-social behavior and breaches of coronavirus legislation. Combined with high volumes of absent employees throughout 2020, the study postulates a reduction in police satisfaction, trust, and confidence may have occurred in the response to cyber investigation and policing anti-social behavior. Investment in agile technology to increase workforce flexibility and improved contingency planning are identified as requirements to prepare for future pandemics and avoid repetition. Full article

Criminal recidivism is a major global concern. There is a well-known association between substance use disorders and offending. Yet, little is known about crime-specific recidivism. The aim of this study was to investigate the relationship between specific substance use and crime-specific recidivism. The study is based on 4207 Swedish prison clients with substance use assessed with Addiction Severity Index interviews between 2001 and 2006. Clients were followed for an average of 2.7 years. Risk factors for criminal recidivism were assessed with the Cox regression analysis. Sixty-eight percent of the clients returned to the criminal justice system. Apart from well-known risk factors such as male gender and young age, amphetamine, injection drug use, prior prosecution for violent and property crime, as well as homelessness and psychiatric problems, were risk factors for criminal recidivism. Sedatives and cannabis were, in this setting, negative risk factors for general recidivism. Age, heroin and injection drug use elevated the risks of recidivism to property and drug crime. Alcohol was associated with violent recidivism. When analysing different categories of crime separately, risk factors differed substantially. This further highlights the need for crime-specific research. Identifying crime-specific risk factors should be an important part of improving rehabilitation into society after imprisonment and hopefully decrease recidivism. Full article

The occurrence of street crime is affected by socioeconomic and demographic characteristics and is also influenced by streetscape conditions. Understanding how the spatial distribution of street crime is associated with different streetscape features is significant for establishing crime prevention and city management strategies. Conventional data sources that quantify people on the street and streetscape characteristics, such as questionnaires, field surveys, or manual audits, are labor-intensive, time-consuming, and unable to cover a large area with a sufficient spatial resolution. Emerging cell phone and social media data have been used to measure ambient population, but they cannot distinguish between the street and indoor populations. This study addresses these limitations by combining Baidu Street View (BSV) images, deep learning algorithms, and spatial statistical regression models to examine the influences of people on the street and in the streetscape physical environment on street crime in a large Chinese city. First, we collected fine-grained street view images from the Baidu Map website. Then, we constructed a Faster R-CNN network to detect discrete elements with distinct outlines (such as persons) in each image. From this, we counted the number of people on the street in every BSV image and finally obtained the community-level total amounts. Additionally, the PSPNet network was developed for pixel-wise semantic segmentation to determine the proportions of other streetscape features such as buildings in each BSV image, based on which we obtained their community-level averages. The quantitative measurement of people on the street and a set of streetscape features that had potential influences on crime were finally derived by combining the outputs of two deep learning networks. To account for the spatial autocorrelation effect and distributional characteristics of crime data, we constructed a set of spatial lag negative binomial regression models to investigate how three types of street crime (i.e., total crime, property crime, and violent crime) were affected by the number of people on the street and the streetscape-built conditions. The models also controlled the effect of socioeconomic and demographic factors, land use features, the formal surveillance level, and transportation facilities. The models with people on the street and streetscape environment features had noticeable performance improvements, demonstrating the necessity for accounting for the effect of these factors when understanding street crime. Specifically, the number of people on the street had significantly positive impacts on the total street crime and street property crime. However, no statistically significant impact was found on street violent crime. The average proportions of the paths, buildings, and trees were associated with significantly lower street crime among physical streetscape features. Additionally, the statistical significances of most control variables conformed to previous research findings. This study is the first to combine Street View images and deep learning algorithms to retrieve the number of people on the street and the features of the visual streetscape environment to understand street crime. Full article