Matthew Quick

Analyzing Local Spatio-Temporal Patterns of Police Calls-for-Service Using Bayesian Integrated Nested Laplace Approximation

This research investigates spatio-temporal patterns of police calls-for-service in the Region of Waterloo, Canada, at a fine spatial and temporal resolution. Modeling was implemented via Bayesian Integrated Nested Laplace Approximation (INLA). Temporal patterns for two-hour time periods, spatial patterns at the small-area scale, and space-time interaction (i.e., unusual departures from overall spatial and temporal patterns) were estimated. Temporally, calls-for-service were found to be lowest in the early morning (02:00–03:59) and highest in the evening (20:00–21:59), while high levels of calls-for-service were spatially located in central business areas and in areas characterized by major roadways, universities, and shopping centres. Space-time interaction was observed to be geographically dispersed during daytime hours but concentrated in central business areas during evening hours. Interpreted through the routine activity theory, results are discussed with respect to law enforcement resource demand and allocation, and the advantages of modeling spatio-temporal datasets with Bayesian INLA methods are highlighted.

Open area and road density as land use indicators of young offender residential locations at the small-area level: A case study in Ontario, Canada

This research explores associations between land use types and young offender residential location in the Regional Municipality of York, Ontario, Canada, at a small-area level. Employing a Bayesian spatial modelling approach, we found that after controlling for socio-economic risk factors, proportion of open area land use was positively associated, and road density negatively associated, with residential location of young offenders. Map decomposition, which visualises the contribution of each risk factor to total young offender risk, demonstrated that open area land use contributed more risk in rural areas than urban, and that road density contributed less risk in urban areas than rural. We propose explanations for these results focused on social disorganisation theory and accessibility to structured leisure activities and apply findings to inform law enforcement and land use planning. Results provide a criminological perspective not often considered in planning and urban studies research and contrast land use policies generally motivated by public health and the environment.

Time-varying relationships between land use and crime: A spatio-temporal analysis of small-area seasonal property crime trends:

Neighborhood land use composition influences the geographical patterns of property crime. Few studies, however, have investigated if, and how, the relationships between land use and crime change over time. This research applies a Bayesian spatio-temporal regression model to analyze 12 seasons of property crime at the small-area scale. Time-varying regression coefficients estimate the seasonally varying relationships between land use and crime and distinguish both time-constant and season-specific effects. Seasonal property crime trends are commonly hypothesized to be associated with fluctuating routine activity patterns around specific land uses, but past studies do not quantify the time-varying effects of neighborhood characteristics on small-area crime risk. Results show that, accounting for sociodemographic contexts, parks are more positively associated with property crime during spring and summer seasons, and eating and drinking establishments are more positively associated during autumn and winter seasons. Land use is found to have a more substantial impact on spatial, rather than spatio-temporal, crime patterns. Proposed explanations for results focus on seasonal activity patterns and corresponding spatio-temporal interactions with the built environment. The theoretical and analytical implications of this modeling approach are discussed. This research advances past cross-sectional spatial analyses of crime by identifying built environment characteristics that simultaneously shape both where and when crime occurs.

Analyzing the Influence of Ethnic Composition and Immigrant Residents on the Spatial Distribution of Violent Crime

The spatial distribution of violent crime is influenced by small-area characteristics. The social disorganization theory proposes that neighbourhood-scale characteristics, including ethnic composition and immigrant residents, indirectly influence crime through social control. Recent spatial demographic changes in urban areas, including increased immigration and ethnic heterogeneity in city peripheries, have motivated reconsiderations of social disorganization. Using exploratory spatial data analysis and spatial regression methods, this research identifies violent crime hotspots and analyzes the influence of ethnic composition and immigrant resident concentration on violent crime in Toronto, Ontario, at the census tract scale. Results suggest that violent crime hotspots are located in downtown and north Toronto and that ethnic heterogeneity is positively associated with violent crime rate while immigrant resident concentration is negatively associated. This research provides novel insight into the spatial dimensions of crime and the effects of spatial demographic changes on violent crime and social disorganization in contemporary cities.