Search

Characteristics of the urban environment influence where and when crime events occur; however, past studies often analyse cross-sectional data for one spatial scale and do not account for the processes and place-based policies that influence crime across multiple scales. This research applies a Bayesian cross-classified multilevel modelling approach to examine the spatiotemporal patterning of violent crime at the small-area, neighbourhood, electoral ward, and police patrol zone scales. Violent crime is measured at the small-area scale (lower-level units) and small areas are nested in neighbourhoods, electoral wards, and patrol zones (higher-level units). The cross-classified multilevel model accommodates multiple higher-level units that are non-hierarchical and have overlapping geographical boundaries. Results show that violent crime is positively associated with population size, residential instability, the central business district, and commercial, government-institutional, and recreational land uses within small areas and negatively associated with civic engagement within electoral wards. Combined, the three higher-level units explain approximately fifteen per cent of the total spatiotemporal variation of violent crime. Neighbourhoods are the most important source of variation among the higher-level units. This study advances understanding of the multiscale processes influencing spatiotemporal crime patterns and provides area-specific information within the geographical frameworks used by policymakers in urban planning, local government, and law enforcement. [ABSTRACT FROM AUTHOR]

This research analyzes the association between alcohol outlets and reported violent crime at the small-area level in the Region of Waterloo, Ontario, Canada. Alcohol outlets are hypothesized to be associated with high violent crime, primarily because they bring together large numbers of potential offenders and targets, and from a policy perspective, alcohol outlets are a modifiable land use risk factor that can be addressed through land use planning and policy. Results show that both on- and off-premise alcohol outlets are positively associated with reported violent crime after accounting for socioeconomic characteristics. Using small-area risk estimates resulting from Bayesian spatial regression models, map decomposition visualizes the geographic pattern of violent crime risk and the contribution of specific risk factors at the small-area scale, and violent crime hotspots are identified based on posterior probability of relative risk. Applying results to land use planning and policy, we identify small-areas that may be preferred for proposed alcohol outlets based on specific criteria such as low violent crime risk and low risk due to alcohol outlets. We also discuss how the results of this research inform law enforcement initiatives. [ABSTRACT FROM AUTHOR]

Objectives: Explore Bayesian spatio-temporal methods to analyse local patterns of crime change over time at the small-area level through an application to property crime data in the Regional Municipality of York, Ontario, Canada. Methods: This research represents the first application of Bayesian spatio-temporal modeling to crime trend analysis at a large map scale. The Bayesian model, fitted by Markov chain Monte Carlo simulation using WinBUGS, stabilized risk estimates in small (census dissemination) areas and controlled for spatial autocorrelation (through spatial random effects modeling), deprivation, and scarce data. It estimated (1) (linear) mean trend; (2) area-specific differential trends; and (3) (posterior) probabilities of area-specific differential trends differing from zero (i.e. away from the mean trend) for revealing locations of hot and cold spots. Results: Property crime exhibited a declining mean trend across the study region from 2006 to 2007. Variation of area-specific trends was statistically significant, which was apparent from the map of (95 % credible interval) differential trends. Hot spots in the north and south west, and cold spots in the middle and east of the region were identified. Conclusions: Bayesian spatio-temporal analysis contributes to a detailed understanding of small-area crime trends and risks. It estimates crime trend for each area as well as an overall mean trend. The new approach of identifying hot/cold spots through analysing and mapping probabilities of area-specific crime trends differing from the mean trend highlights specific locations where crime situation is deteriorating or improving over time. Future research should analyse trends over three or more periods (allowing for non-linear time trends) and associated (changing) local risk factors. [ABSTRACT FROM AUTHOR]

This paper adopts a Bayesian spatial modeling approach to investigate the distribution of young offender residences in York Region, Southern Ontario, Canada, at the census dissemination area level. Few geographic researches have analyzed offender (as opposed to offense) data at a large map scale (i.e., using a relatively small areal unit of analysis) to minimize aggregation effects. Providing context is the social disorganization theory, which hypothesizes that areas with economic deprivation, high population turnover, and high ethnic heterogeneity exhibit social disorganization and are expected to facilitate higher instances of young offenders. Non-spatial and spatial Poisson models indicate that spatial methods are superior to non-spatial models with respect to model fit and that index of ethnic heterogeneity, residential mobility (1 year moving rate), and percentage of residents receiving government transfer payments are, respectively, the most significant explanatory variables related to young offender location. These findings provide overwhelming support for social disorganization theory as it applies to offender location in York Region, Ontario. Targeting areas where prevalence of young offenders could or could not be explained by social disorganization through decomposing the estimated risk map are helpful for dealing with juvenile offenders in the region. Results prompt discussion into geographically targeted police services and young offender placement pertaining to risk of recidivism. We discuss possible reasons for differences and similarities between the previous findings (that analyzed offense data and/or were conducted at a smaller map scale) and our findings, limitations of our study, and practical outcomes of this research from a law enforcement perspective. [ABSTRACT FROM AUTHOR]