Violent Crime Data Research Articles

Neighborhood violence and socioeconomic deprivation influence associations between acute air pollution and temperature on childhood asthma in New York city

Ambient air pollution, temperature, and social stressor exposures are linked with asthma risk, with potential synergistic effects. We examined associations for acute pollution and temperature exposures, with modification by neighborhood violent crime and socioeconomic deprivation, on asthma morbidity among children aged 5–17 years year-round in New York City. Using conditional logistic regression in a time-stratified, case-crossover design, we quantified percent excess risk of asthma event per 10-unit increase in daily, residence-specific exposures to PM2.5, NO2, SO2, O3, and minimum daily temperature (Tmin). Data on 145,834 asthma cases presenting to NYC emergency departments from 2005 to 2011 were obtained from the New York Statewide Planning and Research Cooperative System (SPARCS). Residence- and day-specific spatiotemporal exposures were assigned using the NYC Community Air Survey (NYCCAS) spatial data and daily EPA pollution and NOAA weather data. Point-level NYPD violent crime data for 2009 (study midpoint) was aggregated, and Socioeconomic Deprivation Index (SDI) scores assigned, by census tract. Separate models were fit for each pollutant or temperature exposure for lag days 0–6, controlling for co-exposures and humidity, and mutually-adjusted interactions (modification) by quintile of violent crime and SDI were assessed. We observed stronger main effects for PM2.5 and SO2 in the cold season on lag day 1 [4.90% (95% CI: 3.77–6.04) and 8.57% (5.99–11.21), respectively]; Tmin in the cold season on lag day 0 [2.26% (1.25–3.28)]; and NO2 and O3 in the warm season on lag days 1 [7.86% (6.66–9.07)] and 2 [4.75% (3.53–5.97)], respectively. Violence and SDI modified the main effects in a non-linear manner; contrary to hypotheses, we found stronger associations in lower-violence and -deprivation quintiles. At very high stressor exposures, although asthma exacerbations were highly prevalent, pollution effects were less apparent—suggesting potential saturation effects in socio-environmental synergism.

Comparing Mobility and Spatial Pathways in the Hourly Prediction of Violent Crime

Much research has documented the contagiousness of violence. Some of this work has focused on contagiousness as operationalized by the spread across geographical space, while other work has examined the spread within social networks. While the latter body of work struggles with incomplete network data, the former constitutes a theoretical mismatch with how violence should spread. Theory instead strongly suggests that violence contagion should diffuse through everyday mobility networks rather than just adjacently through geographical space. Beyond contagion itself, I argue that neighborhoods connected through mobility networks should serve as useful short-term sensors in predicting imminent violence because these sets of residents tend to experience shared environmental exposures, which may induce synchrony in the likelihood of violence. I explore this topic and these relationships using violent crime data from the three largest U.S. cities: New York City, Los Angeles, and Chicago. Using two-way fixed effects models, I test whether or not violence in mobility-connected alter neighborhoods in the preceding hour predicts violence in an ego neighborhood in the next hour. Across all three jurisdictions, I find that recent violence in the neighborhoods to which a neighborhood is connected through mobility ties can strongly predict that neighborhood’s odds of subsequent violence. Furthermore, spatial proximity has no significant effect on the likelihood of violent crime after controlling for mobility ties. I conclude by arguing that mobility patterns are an important pathway in the prediction of violence.

Community Characteristics and the Geographic Distribution of Intimate Partner Violence: A Partial Test of Social Disorganization Theory.

Shaw and McKay's social disorganization theory has provided a framework to examine the relationship between community-level structural variables and neighborhood crime. Although empirical support for the theory has been widely demonstrated using property and violent crime data, the body of literature examining the theory's applicability to intimate partner violence (IPV) is more limited. Further, much of the existing literature in this area applies the theory's macro-level variables to individual outcomes instead of assessing community effects. Using negative binomial regression to analyze incident data from the Austin (TX) Police Department and demographic information from the United States Census Bureau, this study assesses the relationship between concentrated disadvantage, racial/ethnic heterogeneity, residential instability, and the geographic distribution of IPV incidents in a major U.S. city with no racial or ethnic majority. The independent variables of interest were constructed using principal axis factoring, and a spatial lag variable was included in the model to control for spatial clustering. The analysis showed that concentrated disadvantage was significantly, positively associated with annual counts of IPV incidents in neighborhoods, as was the control variable total crime reports. These results demonstrate that the geographic distribution of IPV is influenced by community factors. They underscore the importance of considering community-wide prevention and intervention efforts in tandem with individual services to those impacted by IPV.

Towards a National Definition and Database for Nonfatal Shooting Incidents.

After a decades-long decline, criminal gun violence has increased dramatically in many parts of the USA. Most victims survive their gunshot wounds; however, research and data collection focus primarily on fatal events. In fact, there is no official national definition of a nonfatal shooting incident, nor a repository of these data. This definitional oversight inhibits data-informed policy and practice. The current study involves two data sources: fatal and nonfatal shooting incidents recorded in an internal metropolitan police database and official Uniform Crime Reporting (UCR) violent crime data. Shooting incidents in the police database were matched to incidents in the UCR data to determine how they were officially categorized and reported to the FBI. The majority (82.0%) of nonfatal shooting incidents in the UCR data were recorded as Aggravated Assault-Gun, while 16.5% were classified as a violent crime other than an Aggravated Assault-Gun. The UCR data were missing 1.5% of the nonfatal shooting incidents documented by the police database. Almost four-fifths (79.7%) of all Aggravated Assault-Gun incidents in the UCR data did not meet the suggested definition of a nonfatal shooting incident. Overall, official crime statistics are not a good data source for nonfatal shooting incidents. A holistic response to criminal gun violence requires comprehensive, valid, and reliable data collection on all shooting incidents, especially those incidents in which a person is injured by gunfire. Establishing a national definition for a nonfatal shooting incident is the first important step toward effective gun violence prevention and reduction.

Comparing measurements of violent crime in local communities: a case study in Islington, London

ABSTRACT Police-recorded crime data are prone to measurement error, affecting our understanding of the nature of crime. Research has responded to this problem using data from surveys and emergency services. These data sources are not error-free, and data from different sources are not always easily comparable. This study compares violent crime data recorded by police, ambulance services, two surveys and computer simulations in Islington, London. Different data sources show remarkably different results. However, crime estimates become more similar, but still show different distributions, when crime rates are calculated using workday population as the denominator and log-transformed. Normalising crime rates by workday population controls for the fact that some data sources reflect offences’ location while others refer to victims’ residence, and log-transforming rates mitigates the biasing effect associated with some multiplicative forms of measurement error. Comparing multiple data sources allows for more accurate descriptions of the prevalence and distribution of crime.

A College Teacher’s Introduction to Jupyter Notebooks

This introduction to Jupyter Notebooks (JNBs) is an outcome of a year of learning, creating, and classroom testing JNBs in a variety of settings: a sabbatical with an urban violence reduction organization; an after school distance learning program for low to middle income high school students in Chicago; a general education computer-lab based math course; and finally two upper division courses for applied math majors at a liberal arts college. The examples include line graphs, histograms, and a pie chart of violent crime data created for a community organization, a high school/general education lab on computer music, a visualization of volumes computable by triple integrals, and creation of a .gif animation of a numerical solution to a 2-Dimensional PDE. In addition to executing and displaying output of computer code, JNBs can import data from files and external websites, interpret equations written in LaTeX, display PowerPoints in pdf format, and show videos posted on other websites. JNBs can also save output data, figures, and animations to files in a variety of formats. At a general education level, JNBs can enrich student learning experience by helping students overcome math anxiety through inclusion of the arts. At the undergraduate math major level, JNBs can create interesting visualizations/animations and support service learning projects. The appendix explains how to get started using JNBs. The learning curve for creating the JNBs described in this article is not that steep, and the low-hanging fruit from using JNBs for a wide-range of teaching contexts and for community service learning projects is plentiful.

Abstract 11417: Association Between Community-Level Violent Crime and Cardiovascular Mortality in Chicago - A Longitudinal Analysis

Introduction: Prior studies suggest an association between neighborhood crime levels and cardiovascular (CV) risk factors and disease but have been limited by cross-sectional designs. We investigated whether longitudinal changes in violent crime are associated with CV mortality at the community level in a large US city - Chicago. Methods: Chicago is divided into 77 community areas. Using the Illinois Department of Public Health Division of Vital Records, we obtained age-adjusted mortality rates by community area for heart disease, stroke, and coronary artery disease deaths from 2000-2014 aggregated at 5-year intervals. Annual violent crime data were obtained from the City of Chicago Police Data Portal and were geocoded to the community area level. Violent crime was defined as assault, battery, criminal sexual assault, robbery, and homicide. Using a two-way fixed effects estimator, a robust longitudinal estimation method that accounts for measured or unmeasured time-invariant confounders, we assessed the association between longitudinal changes in violent crime and CV mortality after accounting for changes in demographic (proportion of racial/ethnic minorities) and economic (household income, unemployment, housing vacancy, education attainment) variables for each community area. Results: Between 2000 and 2014, the median violent crime rate in Chicago decreased from 3620 per 100,000 [IQR 2256, 7777] in the 2000-2004 period to 2390 [IQR 1507, 5745] in the 2010-2014 period (p=0.005 for trend). In the fixed effects model, after accounting for changes in demographic and economic variables, a 1% increase in community-area violent crime was independently associated with a 0.21% (95% CI 0.09, 0.33) increase in heart disease deaths (p=0.0006) and a 0.19% (95% CI 0.04, 0.33) increase in deaths from coronary artery disease (p=0.01). There was no significant association between change in violent crime and stroke deaths [0.17% (95% CI -0.08, 0.42; p=0.017)]. Conclusions: From 2000 to 2014, an increase in violent crime at the community level was associated with an increase in heart disease and coronary artery disease deaths in Chicago. Detecting rises in community-level violent crime may identify areas with worsening CV health independent of other factors.

Using Crime Data to Assess Longitudinal Relationships Between Community Violent Crime and Aggressive Behavior Among At-Risk Youth

The goal of this study was to assess longitudinal, predictive relationships between community violent crime and reactive and proactive aggression. Community violent crime data were gathered from local law enforcement agencies and combined with an existing dataset of at-risk youth. Aggression was assessed by parents using the Reactive and Proactive Aggression Questionnaire (RPQ). Data were examined over four time points. Autoregressive cross-lagged modeling was used to test two models: one for proactive aggression and one for reactive aggression. Results revealed a positive relationship between community violent crime and proactive aggression, whereas the model including reactive aggression had poor model fit. Therefore, results support reactive and proactive aggression as distinct constructs. Findings also demonstrate that publicly accessible violent crime data can be used to predict children’s behavior over time. Finally, results have important implications for preventive interventions for at-risk youth.

Geographic disparities in violent crime during the COVID-19 lockdown in Miami-Dade County, Florida, 2018-2020.

ObjectivesThis study uses two cluster detection techniques to identify clusters of violent crime during the 3 months of the 2020 COVID-19 lockdown in Miami-Dade County compared to that during an equivalent period in 2018 and 2019.MethodsViolent crime data from the Miami-Dade Central Records Bureau were analyzed. The Local Indicators of Spatial Association statistics and a space‐time permutation statistic were used to identify clusters of violent crimes and outliers, and Global Moran’s I tool was used to assess spatial patterning in violent crime. Neighborhood disadvantage data were obtained from the American Community Survey 5-year estimates linked with arrest locations.ResultsViolent crime arrests fell by 7.1% in 2020. Arrests were concentrated in predominantly Black disadvantaged neighborhoods in the northern part, and similar results were produced for core clusters by the two cluster techniques with positive global Moran’s I for all study years. Although accounting for only 17% of the county population, nearly half of violent crime arrests were for Black or African American. Males comprised most violent crime arrests.ConclusionsCrime prevention and intervention efforts should be focused on both high-risk places and offenders.

Crime Concentration and Temporal Stability in Spatial Patterns of Crime in Niš, Serbia

The research objectives were to examine the concentration of crime and its temporal patterns in Nis and compare the related research results in spatial criminology. Longitudinal property and violent crime data were geocoded and assigned to three types of grid cells. Descriptive statistics were used to examine crime concentration, and spatial stability of crime was tested using a spatial point pattern test. The findings suggest an intense concentration of all crime types within all areal units and years of study. The higher rates of concentration were found in the analysis of less common robbery and violent crime. Findings of spatial pattern analysis suggest moderate to a high degree of similarity in spatial patterns among all crime types. However, sensitivity analysis indicates significant changes in the spatial distribution of both property and violent crime. Therefore, an alternative method is applied. Theoretical implications are discussed, highlighting routine activities and crime pattern theory. The study aims to instigate more spatial analysis of crime and evidence-based criminal policies within the region.

Investigating Clustering and Violence Interruption in Gang-Related Violent Crime Data Using Spatial–Temporal Point Processes With Covariates

Reported gang-related violent crimes in Los Angeles, California, from 1/1/14 to 12/31/17 are modeled using spatial–temporal marked Hawkes point processes with covariates. We propose an algorithm to estimate the spatial-temporally varying background rate nonparametrically as a function of demographic covariates. Kernel smoothing and generalized additive models are used in an attempt to model the background rate as closely as possible in an effort to differentiate inhomogeneity in the background rate from causal clustering or triggering of events. The models are fit to data from 2014 to 2016 and evaluated using data from 2017, based on log-likelihood and superthinned residuals. The impact of nonrandomized violence interruption performed by The City of Los Angeles Mayor’s Office of Gang Reduction Youth Development (GRYD) Incident Response (IR) Program is assessed by comparing the triggering associated with GRYD IR Program events to the triggering associated with sub-sampled non-GRYD events selected to have a similar spatial–temporal distribution. The results suggest that GRYD IR Program violence interruption yields a reduction of approximately 18.3% in the retaliation rate in locations more than 130 m from the original reported crimes, and a reduction of 14.2% in retaliations within 130 m.

Measuring small-area violent crime: a comparison of observed versus model-estimated crime rates and preterm birth

PurposeResearch examining the association between crime and health outcomes has been hampered by a lack of reliable small-area (e.g., census tract or census block group) crime data. Our objective is to assess the accuracy of synthetically estimated crime indices for use in health research by using preterm birth as a case study. MethodsWe used violent crime data reported by 47 law enforcement agencies in 15 counties in Atlanta, Georgia and compared them with commercially estimated crime rates from the same year to assess (1) how two measures of crime were correlated and (2) if the associations between violent crime rate indices and preterm birth (PTB) varied as a function of the source of crime index. To assess the association between violent crime and PTB, we used multilevel logistic regression and controlled for potential individual- and neighborhood-level confounders. ResultsViolent crime, both estimated and observed, was positively correlated with poverty, neighborhood proportion Black, and neighborhood deprivation index; however, the association was stronger using estimated rates as compared with observed crime rates. The association between living in a high violent crime neighborhood and PTB was only consistent for white women across the two crime indices after covariate adjustment. For Black women, the association between living in a high violent crime neighborhood and PTB is systematically underestimated across all models when the estimated crime rate is used. ConclusionsThere is evidence that model-estimated crime rates are not reliable proxies for crime in an urban area even when appropriate confounders are adjusted for.

The \u2018Exposed\u2019 Population, Violent Crime in Public Space and the Night-time Economy in Manchester, UK

The daily rhythms of the city, the ebb and flow of people undertaking routines activities, inform the spatial and temporal patterning of crime. Being able to capture citizen mobility and delineate a crime-specific population denominator is a vital prerequisite of the endeavour to both explain and address crime. This paper introduces the concept of an exposed population-at-risk, defined as the mix of residents and non-residents who may play an active role as an offender, victim or guardian in a specific crime type, present in a spatial unit at a given time. This definition is deployed to determine the exposed population-at-risk for violent crime, associated with the night-time economy, in public spaces. Through integrating census data with mobile phone data and utilising fine-grained temporal and spatial violent crime data, the paper demonstrates the value of deploying an exposed (over an ambient) population-at-risk denominator to determine violent crime in public space hotspots on Saturday nights in Greater Manchester (UK). In doing so, the paper illuminates that as violent crime in public space rises, over the course of a Saturday evening, the exposed population-at-risk falls, implying a shifting propensity of the exposed population-at-risk to perform active roles as offenders, victims and/or guardians. The paper concludes with a discussion of the theoretical and policy relevance of these findings.

Alcohol Advertising and Violence

Numerous studies have found associations between alcohol outlet density and violence, but it is unknown whether alcohol advertisements visible outside outlets are also associated with violent crime. Baltimore City, MD enacted restrictions on retail alcohol establishment advertising practices as of June 5, 2017. This study examines the association between alcohol advertisements visible outside off-premise alcohol outlets and violent crime before this restriction. Outlet observations (n=683) were conducted in summer 2015, and violent crime data (n=24,085) were from June 5, 2015, through June 4, 2017. The number of violent crimes per square mile within 1,000 feet of outlets was summed using kernel density estimation. In 2018-2019, authors used mixed models with a Simes-Benjamini-Hochberg correction for multiple testing. Roughly half (47%, n=267) of the outlets with complete data (n=572) had alcohol advertisements visible from the exterior. Outlets with alcohol advertisements had 15% more violent crimes per square mile within 1,000 feet (eβ=1.15, 95% CI=1.07, 1.25, q<0.001) after adjusting for neighborhood context. All associations between alcohol advertisements and specific types of violent crime were significant, with the association strongest for homicides (eβ=1.28, 95% CI=1.13, 1.46, q<0.001). There was no association between cigarette advertisements and violent crime (eB=1.08, 95% CI=0.92, 1.26, q=0.43). Alcohol advertisements visible outside off-premise outlets were associated with increased violent crime over and above the association between the outlets themselves and violent crime. Reducing alcohol advertising visible from the street may decrease risk of violent crime that is associated with alcohol outlets.

482: Neighborhood violent crime is associated with high perceived stress in pregnancy

Stress can adversely affect pregnancy outcomes. However, stress measures are not readily available for large population-based registries. While neighborhood measures of social stressors, such as violence, are publically accessible, whether they correlate with perceived stress in pregnancy is unknown. We sought to determine whether neighborhood violent crime rates were associated with perceived stress in a largely minority, urban pregnancy cohort. From the Motherhood & Microbiome cohort (n=2000), we performed a secondary analysis of 1,309 Philadelphia residents for whom neighborhood violent crime and Cohen’s Perceived Stress Scale (PSS-14) data were available. We downloaded violent crime data (aggravated assault with and without firearm, robbery with and without firearm, rape, and homicide) from the City of Philadelphia and calculated annual census tract rates per 10,000 residents. We then dichotomized PSS at a score of 30. We used log-binomial regression models to calculate risk ratios of high stress per IQR increment of violent crime. Demographics are presented in Table 1. Young, black, obese, and single women lived in neighborhoods with higher levels of violent crime. Young, black, smoking, and single women were more likely to have high stress. Violent crime rates and stress scores were weakly, but significantly correlated (r= 0.14, P < 0.0001). Unadjusted associations between crime and high stress were significant (RR 1.34, 95% CI: 1.18-1.51) (Table 2). After consideration of potential confounding variables, only adjustment for race/ethnicity substantially attenuated the results (RR 1.17, 95% CI: 1.00-1.35) (P=0.045), yet race-stratified models had similar magnitudes of association between crime and stress as unadjusted models. Neighborhood violent crime exposure may contribute to stress in pregnancy. Our study identified one potential area-level stressor that may be used in large epidemiologic pregnancy studies when personal-level stress data are not available.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Chicago Youths' Exposure to Community Violence: Contextualizing Spatial Dynamics of Violence and the Relationship With Psychological Functioning.

This study explores where and when community violence exposure (CVE) matters for psychological functioning in a sample of low-income, racial/ethnic minority youth (M) age=16.17, 55% female, 69% Black, and 31% Non-Black/Latinx) living in Chicago. CVE was measured with violent crime data that were geocoded in terms of distance from youths' home and school addresses, and then calculated in terms of three distinct spatial dynamics: chronicity, pervasiveness, and spatial proximity. These measures reflect indirect/objective CVE across different conceptualizations of time, space, and neighborhood context. We tested the relationship between each CVE measure and trait anxiety and behavioral and cognitive dysregulation while controlling for youth-reported, direct violent victimization (e.g., being attacked) to examine how indirect/objective CVE occurring within youths' neighborhood contexts matters beyond direct/subjective violence exposure. Results revealed that long-term chronic, pervasive, and spatially proximal CVE was related to higher levels of behavioral dysfunction. In contrast, CVE within home- and school-based neighborhoods interacted to predict trait anxiety; youth living in low-crime neighborhoods and attending schools in high-crime neighborhoods had the highest rates of trait anxiety. Measuring CVE within both home and school neighborhoods at specific spatial measurements and time frames is critical to understand and prevent the consequences of CVE.

Analyzing the Relationship between Perception of Safety and Reported Crime in an Urban Neighborhood Using GIS and Sketch Maps

This study analyzes the perception of safety among residents of Main South neighborhood in Worcester, MA, USA and compares it to reported crimes. This neighborhood is the focus of a community-based crime reduction project funded by the Bureau of Justice Assistance, the policy development arm of the U.S. Department of Justice. We collected social disorder and violent crime data from the Worcester Police Department and conducted 129 household surveys to understand residents’ perception of safety in the neighborhood and trust in community institutions. The surveys included a map on which residents indicated where they felt unsafe. The goal of this research was twofold: (1) to use geographic information systems (GIS) to analyze the differences in perception of neighborhood safety by gender and length of residency in the neighborhood and (2) to explore the relationship between reported crime and perception of safety in the community. Findings indicate that the strength of the correlation between perceived safety and reported crime varies and that gender and length of residency are significant factors that shape perceptions of safety. Implications of this research suggest the need for comprehensive community-based development initiatives to offer differentiated strategies that address a broad range of safety perceptions and crime experiences among a diverse group of residents.

Violent crime against children with disabilities: A nationwide prospective birth cohort-study

BackgroundThe rate of violent victimization against children with disabilities is thought to be lower than the rate for children without disabilities but several studies shows otherwise. ObjectiveThe study focuses on examining violent crime against children with disabilities and explaining differences in victimization, in order to elucidate to what extent types of disability, family disadvantages, gender, high-risk behavior, location and indicator of ethnic minority (e.g. non-Danish citizens) influence adolescents’ risk of violent victimization. Previous population studies in this area lack scientifically sound research methodology and results are weak or inconclusive. MethodData is based on a national study of reported violent crime against children in Denmark aged between 7 and 18 years, using total birth cohorts (N = 678,000). Data on types of disability were collected from the Danish national inpatient register. Violent-crime data were extracted from Danish police records. Within the birth cohorts studied, 3.5% of children had experienced a violent crime. A discrete-time Cox model was used for the statistical analysis, which included an extended list of potential risk factors to adjust for confounding. ResultsChildren with disabilities are more likely to be victims of a reported violent crime than non-disabled children – ADHD odds ratio: 2.7 (2.6–2.8), mental retardation: 2.7 (2.6–2.7), autism 2.6 (2.5–2.7), loss of hearing 1.4 (1.2–1.5), brain injury: 1.8 (1.7–1.9), physical disabilities 1.4 (1.2–1.5), and blindness 2.0 (1.4–2.8). Speech disability, epilepsy, stuttering, and dyslexia were not associated with increased risk of violent victimization, when adjusted for confounding risk factors and age. ConclusionsThe results of our study provide empirical insight into the first-time prevalence of victimization among children with disability, and into the predicative association between family disadvantages and victimization.

Methods for Evaluating the Association Between Alcohol Outlet Density and Violent Crime.

The objective of this analysis was to compare measurement methods-counts, proximity, mean distance, and spatial access-of calculating alcohol outlet density and violent crime using data from Baltimore, Maryland. Violent crime data (n=11,815) were obtained from the Baltimore City Police Department and included homicides, aggravated assaults, rapes, and robberies in 2016. We calculated alcohol outlet density and violent crime at the census block (CB) level (n=13,016). We then weighted these CB-level measures to the census tract level (n=197) and conducted a series of regressions. Negative binomial regression was used for count outcomes and linear regression for proximity and spatial access outcomes. Choropleth maps, partial R2 , Akaike's Information Criterion, and root mean squared error guided determination of which models yielded lower error and better fit. The inference depended on the measurement methods used. Eight models that used a count of alcohol outlets and/or violent crimes failed to detect an association between outlets and crime, and 3 other count-based models detected an association in the opposite direction. Proximity, mean distance, and spatial access methods consistently detected an association between outlets and crime and produced comparable model fits. Proximity, mean distance, and spatial access methods yielded the best model fits and had the lowest levels of error in this urban setting. Spatial access methods may offer conceptual strengths over proximity and mean distance. Conflicting findings in the field may be in part due to error in the way that researchers measure alcohol outlet density.

The Spatial and Social Patterning of Property and Violent Crime in Toronto Neighbourhoods: A Spatial-Quantitative Approach

Criminal activities are often unevenly distributed over space. The literature shows that the occurrence of crime is frequently concentrated in particular neighbourhoods and is related to a variety of socioeconomic and crime opportunity factors. This study explores the broad patterning of property and violent crime among different socio-economic stratums and across space by examining the neighbourhood socioeconomic conditions and individual characteristics of offenders associated with crime in the city of Toronto, which consists of 140 neighbourhoods. Despite being the largest urban centre in Canada, with a fast-growing population, Toronto is under-studied in crime analysis from a spatial perspective. In this study, both property and violent crime data sets from the years 2014 to 2016 and census-based Ontario-Marginalisation index are analysed using spatial and quantitative methods. Spatial techniques such as Local Moran’s I are applied to analyse the spatial distribution of criminal activity while accounting for spatial autocorrelation. Distance-to-crime is measured to explore the spatial behaviour of criminal activity. Ordinary Least Squares (OLS) linear regression is conducted to explore the ways in which individual and neighbourhood demographic characteristics relate to crime rates at the neighbourhood level. Geographically Weighted Regression (GWR) is used to further our understanding of the spatially varying relationships between crime and the independent variables included in the OLS model. Property and violent crime across the three years of the study show a similar distribution of significant crime hot spots in the core, northwest, and east end of the city. The OLS model indicates offender-related demographics (i.e., age, marital status) to be a significant predictor of both types of crime, but in different ways. Neighbourhood contextual variables are measured by the four dimensions of the Ontario-Marginalisation Index. They are significantly associated with violent and property crime in different ways. The GWR is a more suitable model to explain the variations in observed property crime rates across different neighbourhoods. It also identifies spatial non-stationarity in relationships. The study provides implications for crime prevention and security through an enhanced understanding of crime patterns and factors. It points to the need for safe neighbourhoods, to be built not only by the law enforcement sector but by a wide range of social and economic sectors and services.