High Crime Areas Research Articles

Exploring the usefulness of the INLA model in predicting levels of crime in the City of Johannesburg, South Africa

Crime prediction serves as a valuable tool for deriving insightful information that can inform policy decisions at both operational and strategic tiers. This information can be used to identify high-crime areas, and optimise resource allocation and personnel management for crime prevention. Traditionally, techniques such as the Poisson model and regression analysis have been widely used for crime prediction. However, recent statistical advancements have introduced Integrated Nested Laplace Approximations (INLA) as a promising alternative for spatial and temporal data analysis. This study focuses on crime prediction using the INLA model. Specifically, the first-order autoregressive model under the INLA modelling framework is employed on longitudinal data for crime predictions in different regions of the City of Johannesburg, South Africa. The model parameters and hyperparameters considering space and time are estimated through the INLA model. In this work, the suitability and performance of the INLA model for crime prediction is assessed, which effectively captures spatial and temporal patterns. This study contributes to research by first introducing a novel approach for South African crime prediction. Secondly, it develops a model using no demographic information other than clustering attributes as an exogenous variable. Thirdly, it quantifies prediction uncertainty. Finally, it addresses data scarcity through demonstrating how INLA can provide reliable crime predictions, where conventional methods are limited. Based on our findings, the INLA model ranked areas by crime levels, obtaining a 29.3% Mean Absolute Percentage Error (MAPE) and 0.8 R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} value for crime predictions. These findings and contributions presents the potential of INLA in advancing evidence-based decision-making for crime prevention.

Crime Activity Detection in Surveillance Videos Based on Developed Deep Learning Approach

In modern communities, lots of offenders are prone to recidivism, hence, there is a requirement to inhibit such criminals, especially from impending socioeconomically disadvantaged and high-crime areas that experience elevated levels of criminal activity, involving drug-related offenses, violence, theft, and other forms of anti-social behavior. Consequently, surveillance cameras have been installed in relevant institutions, and further personnel have been provided to monitor videos using various surveillance apparatus. However, relying solely on monitoring with the naked eye and manual video processing falls short of accurately evaluating the footage acquired via such cameras. To handle the issues of conventional systems, there is a need for a system that is able to classify acquired images while supporting surveillance personnel actively. Therefore, in this paper, a deep-learning approach is developed to build a crime detection system. This developed approach includes various layers necessary to perform feature extraction and classification processes and make the system capable of efficiently and accurately detecting crime activities from surveillance video frames. Besides the proposed crime activity detection system, two deep-learning approaches (EfficientNet-B7, and MobileNet-V2) are trained and assessed on the popular UCF Crime and DCSASS datasets. Generally, the proposed detection system encompasses dataset preparation and pre-processing, splitting the pre-processed crime activity image dataset, and implementing the proposed deep learning approach and other pre-trained approaches.

Literature Review of Deep-Learning-Based Detection of Violence in Video.

Physical aggression is a serious and widespread problem in society, affecting people worldwide. It impacts nearly every aspect of life. While some studies explore the root causes of violent behavior, others focus on urban planning in high-crime areas. Real-time violence detection, powered by artificial intelligence, offers a direct and efficient solution, reducing the need for extensive human supervision and saving lives. This paper is a continuation of a systematic mapping study and its objective is to provide a comprehensive and up-to-date review of AI-based video violence detection, specifically in physical assaults. Regarding violence detection, the following have been grouped and categorized from the review of the selected papers: 21 challenges that remain to be solved, 28 datasets that have been created in recent years, 21 keyframe extraction methods, 16 types of algorithm inputs, as well as a wide variety of algorithm combinations and their corresponding accuracy results. Given the lack of recent reviews dealing with the detection of violence in video, this study is considered necessary and relevant.

COVID-19 and Immigrant Status: A Qualitative Study of Malawian Immigrants Living in South Africa

Migration to South Africa is motivated by the pursuit of employment, safety, and improved living conditions. However, immigrants encounter significant challenges, such as restricted access to essential services, which were exacerbated by the COVID-19 pandemic. This paper investigates the impact of the COVID-19 pandemic on Malawian immigrants in South Africa, addressing the lack of attention given to this immigrant population by highlighting their vulnerabilities. Using a qualitative exploratory and descriptive approach, in-depth interviews were conducted with 24 Malawi immigrants who were over 18 years old and had established residency in South Africa before the onset of the COVID-19 pandemic. Five key stakeholders were also interviewed for additional perspectives and to ensure triangulation and improve data reliability. The interviews were transcribed verbatim and analyzed using thematic analysis strategies and coding with Nvivo12 software. The study highlighted the exacerbated struggles of Malawian immigrants in South Africa amid the COVID-19 pandemic, uncovering systemic discrimination in healthcare, marked by longer wait times and reluctance from health workers to treat undocumented immigrants. The study also revealed a dire security situation, with immigrants living in high-crime areas and feeling particularly targeted due to their foreign status, a situation worsened by the pandemic’s economic effects. Additionally, the economic downturn induced by COVID-19 significantly impacted employment opportunities, with many immigrants facing prolonged unemployment and job losses, especially in sectors where they traditionally found work. The detailed accounts of participants highlight not only the multifaceted challenges imposed by the pandemic but also the critical need for inclusive policies and support systems that ensure healthcare access, safety, and economic resilience for immigrants, particularly during global health emergencies. Future research should focus on effective interventions for socioeconomic integration and well-being, particularly for immigrants from other African countries.

Rape Crime in India: A Spatio-temporal Analysis

Analysing crime spatiotemporally involves studying the spatial and temporal patterns of criminal incidents. It helps identify high crime areas and times, aiding law enforcement in resource allocation. Using National Crime Records Bureau's (NCRB), crime in India reports, this study analysed the spatial-temporal patterns of rape crime in India to see how incidents of rape crime are distributed in the country. Arc GIS (10.4) software was employed to map these patterns. The results shows that the spatial pattern of rape crime in India displays a picture of irregular distribution.

Heat Vision Based Light Automation

The Human Detection project utilizing thermal sensors aims to detect individuals or groups in the surrounding environment and respond accordingly. For instance, it can adjust lighting in a room or on a street to accommodate the presence of an individual instead of illuminating everything. Thermal sensors can also detect the presence of people and cars, making street lighting smart and adapting to the lighting situation accordingly. This technology can save up to 70% of energy costs while maintaining safe streets in high-crime areas and creating room for photovoltaic cells. Accurate occupancy tracking and temperature control are crucial in any organization or society, particularly in crowded places like hospitals or malls. To address this issue, we are introducing a data-driven electronic and sensor-based system that can detect the overall environment's temperature. This system is not a simple machine that gives a single output related to human temperature, but it provides real-time data to detect other parameters for multiple applications.

Neighborhood Crime and Externalizing Behavior in Toddlers: A Longitudinal Study With Neonatal fMRI and Parenting

Prenatal exposure to neighborhood crime has been associated with weaker neonatal frontolimbic connectivity; however, associations with early childhood behavior remain unclear. We hypothesized that living in a high-crime neighborhood would be related to higher externalizing symptoms at age 1 and 2 years, over and above other adversities, and that neonatal frontolimbic connectivity and observed parenting behaviors at 1 year would mediate this relationship. Participants included 399 pregnant women, recruited as part of the Early Life Adversity, Biological Embedding, and Risk for Developmental Precursors of Mental Disorders (eLABE) study. Geocoded neighborhood crime data was obtained from Applied Geographic Solution. A total of 319 healthy, non-sedated neonates underwent scanning using resting-state functional magnetic resonance imaging (fMRI) on a Prisma 3T scanner and had≥10 minutes of high-quality data. Infant-Toddler Socioemotional Assessment Externalizing T scores were available for 274 mothers of 1-year-olds and 257 mothers of 2-year-olds. Observed parenting behaviors were available for 202 parent-infant dyads at 1 year. Multilevel and mediation models tested longitudinal associations. Living in a neighborhood with high violent (β= 0.15, CI= 0.05-0.27, p= .004) and property (β= 0.10, CI= 0.01-0.20, p= .039) crime was related to more externalizing symptoms at 1 and 2 years, controlling for other adversities. Weaker frontolimbic connectivity was also associated with higher externalizing symptoms at 1 and 2 years. After controlling for other adversities, parenting behaviors mediated the specific association between crime and externalizing symptoms, but frontolimbic connectivity did not. These findings provide evidence that early exposure to neighborhood crime and weaker neonatal frontolimbic connectivity may influence later externalizing symptoms, and suggest that parenting may be an early intervention target for families in high-crime areas. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. While citing references scientifically relevant for this work, we also actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our reference list. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work.

Swarm Intelligence Response Methods Based on Urban Crime Event Prediction

Cities attract a large number of inhabitants due to their more advanced industrial and commercial sectors and more abundant and convenient living conditions. According to statistics, more than half of the world’s population resides in urban areas, contributing to the prosperity of cities. However, it also brings more crime risks to the city. Crime prediction based on spatiotemporal data, along with the implementation of multiple unmanned drone patrols and responses, can effectively reduce a city’s crime rate. This paper utilizes machine learning and data mining techniques, predicts crime incidents in small geographic areas with short timeframes, and proposes a random forest algorithm based on oversampling, which outperforms other prediction algorithms in terms of performance. The research results indicate that the random forest algorithm based on oversampling can effectively predict crimes with an accuracy rate of up to 95%, and an AUC value close to 0.99. Based on the crime prediction results, this paper proposes a multi-drone patrol response strategy to patrol and respond to predicted high-crime areas, which is based on target clustering and combined genetic algorithms. This strategy may help with the pre-warning patrol planning within an hourly range. This paper aims to combine crime event predictions with crowd-sourced cruise responses to proactively identify potential crimes, providing an effective solution to reduce urban crime rates.

Crime mapping and hot spot analysis using geospatial data in Jalingo metropolis of Taraba State, Nigeria

Crime mapping and hot spot analysis are topical and significant research across the fields of criminology, data digging, city planning and law enforcement for crime control. This study seeks to identify high crime areas which require an officer presence and predicting the possible response demand to increase the efficiency of response officer patrols. Crime type and patterns exist on a spatial level; these patterns and type can be grouped geographically by physical location, and analyzed contextually based on the region in which crime occurs. The study aimed at analyzing crime hotspots using geospatial data in Jalingo metropolis by identifying the different kinds of crime in the study area, determine the spatio-temporal variation of crime occurrences in the study area and determine the high crime density area (hotspot).This study proposed a method to identifying level crime, localize crime hotspots, identify relationship between spatiotemporal crime patterns and social trends, and analyze the resulting data for the purposes of knowledge discovery and anomaly detection. Several types of crime were analyzed in this dataset, including burglary, bribery, forgery ,murder ,rape ,theft drug abuse, assault, and robbery analysis, several interesting findings were drawn about crime in Jalingo metropolis, including: Crime mapping and hotspots with steadily increasing crime levels, hotspots with unstable crime levels, synchronous changes in crime trends throughout Jalingo metropolis as a whole, individual months in which certain hotspots behaved anomalously, and a strong relationship between crime hotspot locations. This type of statistical and correlative analysis of crime patterns will help law enforcement agencies predict criminal activity, allocate resources, and promote community awareness to reduce overall crime rates in Jalingo metropolis. Policing approaches like patrolling and response to crime incidents can be more effective if proper crime mapping and crime hot spot analysis data are available for policing is used to make decisions on crime control in the study area. Predictive policing in crime management use information such as historical crime data to predict crime patterns and response demand. Historical crime data is used to identify high crime areas through kernel density estimation. It is also used to anticipate the levels of response demand. Both of these factors are used to determine how to direct police patrols. This study revealed how kernel density estimation is used for crime mapping and hot spot.

범죄율 감소를 위한 도시재생 계획기법 연구

Urban regeneration plays a pivotal role in enhancing urban safety and residents' quality of life while being closely linked to reducing urban crime rates through transformative changes in city spaces. The improvement of infrastructure, parks, and public facilities connected to residential areas has a profound impact on addressing urban crime issues within communities.
 This study focuses on Mitte, a district in the German capital Berlin, where successful crime rate reduction was achieved through urban regeneration initiatives in high-crime areas. The research conducts a comprehensive case analysis of urban regeneration plans and strategies to identify the specific attributes contributing to crime reduction and the strategic approaches used for crime prevention environment design. The analysis of Mitte's Leopolaplatz area reveals two distinct features of crime prevention environment design implemented through urban regeneration: physical space rejuvenation and enhanced social integration. The rejuvenation of physical spaces involves a clear distinction between private and public areas. The findings of the analysis are as follows: Firstly, urban regeneration of private spaces results in an improvement in residents' sense of ownership and increased surveillance. Secondly, the regeneration of public spaces strengthens their identity and image, promoting a more organic form of surveillance. Lastly, the physical urban regeneration of both private and public spaces contributes to a safer urban environment. The implementation of community strengthening programs further enhances community cohesion.

The association between lifetime trauma exposure typologies and mental health outcomes among veterans

We know little about veterans’ lifetime trauma exposure patterns and how such patterns are associated with mental health outcomes. This study sought to identify lifetime trauma exposure typologies among veterans and examine associations between these typologies and mental health outcomes. It used baseline data from a national longitudinal mail-based survey of 3,544 veterans and oversampled for women (51.6%) and veterans living in high crime areas (67.6%). Most veterans (94.2%) reported trauma exposure, and 80.1% reported exposure to two or more traumas. Prevalence of mental health outcomes was: 27.7% anxiety, 31.3% depression, 37.9% posttraumatic stress disorder, 44.4% alcohol use disorder, 10.4% suicide attempt, and 33.5% mental health comorbidity. Latent class analysis was used to identify patterns of lifetime trauma exposure and logistic regression was used to examine the odds of mental health outcomes as a function of class membership. Five lifetime trauma exposure typologies emerged: (1) low trauma; (2) high combat and community violence; (3) intimate partner violence trauma; (4) high global physical assault; and (5) high trauma. Classes showed differential associations with mental health outcomes. Findings have implications for clinical practice including informing providers’ mental health treatment plans to correspond to each veteran's trauma exposure typology.

A Crime Prediction Model/Tool for Hotspot Mapping

The hotspot map is a popular analytics tool used to help law enforcement and crime reduction resources determine where to focus. Essentially, the hotspot map is used as a data-driven crime map to identify high-crime areas and places where police and other criminal activities should be used. The project uses crime data from a period before (already past) a certain date to create heat maps and test their accuracy in predicting what will happen next. Compare hotspot mapping accuracy with maps used to measure crime issues (thematic mapping of the census output area, spatial ellipses, grid thematic mapping, and core density estimation) and by crime type – the four crime types (crime, crime, car theft, and car theft)..

Types of community violence exposure and clinical outcomes: Evidence of differing associations among emerging adults.

Community violence (CV) is an important public health concern. The literature has largely focused on CV exposure among higher-risk, urban youth, while the impact of CV on emerging adults in university settings remains poorly understood, even though this developmental period is associated with heightened risk. Much of the extant research has utilized a cumulative approach to study CV, thus, little is known about how different dimensions of CV (i.e., direct, witnessed, learned about) may be uniquely related to functioning. The goals of this study were to: (a) examine the associations between cumulative CV and both adverse (i.e., posttraumatic stress symptoms [PTSS], anger, emotion dysregulation) and adaptive (i.e., resilience) clinical outcomes and (b) investigate the distinct ties between the three CV dimensions and these constructs. This study included 547 emerging adults from two universities that are located in two U.S. cities with high crime rates (Mage = 20.31, SD = 2.08, range = 18-28; 80.8% female; 57.8% white). Cumulative CV was related to higher levels of PTSS as well as resilience. For the specific CV dimensions, direct CV was positively linked with PTSS, anger, and emotion dysregulation, whereas witnessed CV inversely corresponded with emotion dysregulation and resilience. Learning about CV was unrelated to clinical outcomes. Students in high crime areas are at heightened risk for CV, which may contribute to both psychological difficulties and resilience. Findings signal the need to better understand how to foster resilience among emerging adults exposed to violence in their communities. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

“Because anyone can misuse this (a gun) and they can use it for weapon”: A Photovoice Exploration of Reasons Why Female Adolescents Feel Unsafe in Rural and Suburban Communities

Adolescents' perceptions of safety vary by community contexts. While much is known about how violence activities and disadvantaged neighborhood environments harm youth's safe feelings in urban and high-crime areas, few studies have evaluated the extent to which environmental factors impact adolescents' perceptions of safety in rural areas. Moreover, although females tend to perceive their community as unsafe and are more likely to be impacted by violence and crimes than males, there is a dearth of research on what neighborhood features and characteristics can cause safety concerns among girls. To bridge the literature gaps, this study has utilized photovoice to explore risk factors contributing to female adolescents' unsafe perceptions in rural and suburban areas. While seven girls were recruited in West Central Florida using both online and in-person strategies, five of them (12–14 years of age) completed a training session, photo-taking activities, and the follow-up photo discussions. Photo discussions were guided by a revised SHOWeD framework and audio recorded. Abridged transcripts were used to match quotes with corresponding photos. A code book was developed for coding process. Thematic analysis was conducted, and four main themes were identified. In this study a total of 44 photos were collected from 5 female participants living in the rural and suburban communities. The main themes associated their unsafe feelings included the following: (1) traffic-related concerns, such as car crashes, speeding, in-vehicle distractions, improper road design, and unsafe road sharing; (2) disadvantaged community environments, such as broken infrastructure and constructions; (3) human trafficking and sexual offenders; and (4) gun-related issues, such as misuse of a gun as weapon and mass shootings. This photovoice study indicates researchers and professionals should focus on traffic issues and negative impacts on adolescents' safe feelings in rural and suburban areas. Media reports on gun violence and human trafficking may cause safety concerns among girls living in low-crime communities. Therefore targeted health promotion programs aiming to improve community traffic safety, facilitate clean physical environments, and limit the exposure to media reports on gun violence and human trafficking should be developed to increase female adolescents' perceptions of safety in rural and suburban areas.

Analyzing physical and socio-economic factors for property crime incident in Addis Ababa, Ethiopia

Property crime has become a challenge in major cities of developing countries including Addis Ababa, Ethiopia. However, factors contributing to property crime have not been carefully examined. Therefore, this paper presents the physical and socio-economic factors that clearly have a substantial impact on property crime. In this study, both primary and secondary data were used. Recorded property crime was collected from Addis Ababa police commission. Property crime incidence locations were georefrenced using Google Earth as well as handheld Global Positioning System. Then, a property crime surface map and pattern were created using point pattern analysis and inverse distance weighted interpolation technique. The study area was then classified into low crime and hotspot areas based on the frequency of crime events as well as the created crime surface map. A total of 200 respondents (100 in high crime area and 100 in low crime area) were selected using purposive sampling techniques. Five key informants were selected purposely from senior police officers. Data were collected using questionnaires and in-depth interview. A binary logistic regression model was employed to analyze the collected data. The data analysis showed that physical features mainly commercial center, road with no street light and river courses were positively correlated at significant level with the committing of property crime. On the other hand, public participation in crime prevention, witness and crime events reporting influenced negatively the committing of property crime. In addition, unemployment, family background, education level, age, time, season and police patrolling activities determine the occurrence of property crime events. The finding depicts that the identified physical and socio-economic factors can influence the patterns and rate of crime incidents in the study area. Therefore, the law enforcement officers should consider these influential factors to deploy police officers and to reduce property crime problems.

Informing regional taphonomy research using retrospective forensic anthropology cases in the Western Cape, South Africa

The post-mortem interval (PMI) is important in death investigations as it can assist in reconstructing the context of the case and the identification of the unknown individual. However, in some instances the PMI is challenging to estimate due to the absence of regional taphonomy standards. To conduct accurate and locally relevant forensic taphonomy research, investigators require insight into the regional hotspot recovery areas. Thus, forensic cases examined by the Forensic Anthropology Cape Town (FACT) in the Western Cape (WC) province of South Africa between 2006 and 2018 (n = 172 cases; n = 174 individuals) were retrospectively reviewed. In our study, a considerable number of individuals did not have the PMI estimations (31 %; 54/174) and the ability to estimate a PMI was significantly associated with skeletal completeness, unburnt remains, absence of clothing and the absence of entomological evidence (p < 0.05 for each). Significantly fewer cases had the PMI estimated after FACT was formalised in 2014 (p < 0.0001). One third of cases with PMI estimations were made with large open-ended ranges, making them less informative. Factors significantly associated with these broad PMI ranges were fragmented remains, absence of clothing and absence of entomological evidence (p < 0.05 for each). Most decedents (51 %; 87/174) were discovered in police precincts from high crime areas, yet a considerable number (47 %; 81/174) were also found in low crime and sparsely populated areas regularly used for recreational activities. Common sites of body discovery were vegetated areas (23 %; 40/174), followed by the roadside (15 %; 29/174), aquatic environments (11 %; 20/174) and farms (11 %; 19/174). Decedents were discovered exposed (35 %; 62/174), covered with items such as bedding or shrubs (14 %; 25/174) or buried (10 %; 17/174). Our data highlight gaps in forensic taphonomy studies and clearly informs what regional research is needed. Overall, our study demonstrates how forensic case information can inform regional forensic taphonomy studies to locate common areas and contexts for the discovery of decomposed bodies and encourages similar studies in other areas of the world.

Going above and beyond: assessing the characteristics of officers who complete additional in-service training

ABSTRACT As a result of recent controversial police-citizen encounters, both practitioners and scholars have become increasingly interested in officer training. Despite prior research having identified various potential benefits of in-service training programs, little is known about factors that might impact the likelihood that an officer will participate in additional in-service training beyond what is mandated of them. Utilizing survey data from close to 600 patrol officers employed at a large metropolitan police department, the current inquiry examines the relationship between officer characteristics and additional training completion. Results indicate that officers’ promotional aspirations, role orientation, length of tenure, working in a high crime area, and education levels all had significant positive relationships with their likelihood of participating in additional in-service training. This study closes with a discussion of the findings and their implications towards future research and police practice.

Does crime trigger genetic risk for type 2 diabetes in young adults? A G x E interaction study using national data

BackgroundLiving in neighborhoods perceived as disordered exacerbates genetic risk for type 2 diabetes (T2D) among older adults. It is unknown whether this gene-neighborhood interaction extends to younger adults. The present study aims to investigate whether crime, an objectively measured indicator of neighborhood disorder, triggers genetic risk for T2D among younger adults, and whether this hypothesized triggering occurs through exposure to obesity. MethodsData were from the Wave I (2008) National Longitudinal Study of Adolescent to Adult Health. A standardized T2D polygenic score was created using 2014 GWAS meta-analysis results. Weighted mediation analyses using generalized structural equation models were conducted in a final sample of 7606 adults (age range: 25–34) to test the overall association of T2D polygenic scores with T2D, and the mediating path through obesity exposure in low, moderate, and high county crime-rate groups. Age, sex, ancestry, educational degree, household income, five genetic principal components, and county-level concentrated advantage and population density were adjusted. ResultsThe overall association between T2D polygenic score and T2D was not significant in low-crime areas (p = 0.453), marginally significant in moderate-crime areas (p = 0.064), and statistically significant in high-crime areas (p = 0.007). The mediating path through obesity was not significant in low or moderate crime areas (ps = 0.560 and 0.261, respectively), but was statistically significant in high-crime areas (p = 0.023). The indirect path through obesity accounted for 12% of the overall association in high-crime area. ConclusionA gene-crime interaction in T2D was observed among younger adults, and this association was partially explained by exposure to obesity.

Police and Crown Prosecutor Use of Restorative Justice and Diversion for Adults and Youth in a High-Crime Area

This research assesses the use of diversion and restorative justice (RJ) referrals by patrol officers and Crown prosecutors in a high-crime urban community. Of interest was the influence on referral of legal factors such as prior criminal history and offence severity and the potential impact of extra-legal factors such as age, gender, and Indigenous ethnicity. Data on 1,000 eligible offenders during a 6-month study period were analyzed. About 15% of eligible cases were referred. Police and Crown were not as aggressive as desirable, with the police passing on some eligible low-severity cases later referred by prosecutors. As prior criminal history increased referrals declined, with a more pronounced impact at the patrol officer level. In bivariate analysis, age, gender, and marital, student, and unemployment status exerted small effects, but these were non-significant in multivariate analysis. No evidence of ethnic disparity was found. Crime types of less serious violence and lower property, as well as prior convictions, had moderate bivariate effects, and these persisted in multivariate analysis. Although most cases referred had a limited criminal history and involved minor crimes, police and prosecutors did refer a few involving serious violent offences and prior convictions, showing potential for greater use of RJ.

EQUALITY IN THE STREETS Using Proportionality Analysis to Regulate Street Policing

EQUALITY IN THE STREETS Using Proportionality Analysis to Regulate Street Policing