Shannon Streliofff

Examining Street Level Robbery Predictors in Durham, Ontario using Statistical and Risk Terrain Modeling ©2015

The overall goal of this paper was to identify potential predictors of street level robbery and to determine the spatial distribution of these variables within the Regional Municipality of Durham, Ontario. Street level robbery and associated geographical factors were identified and used to develop a risk terrain model in the study. Risk terrain modeling is a multi-criteria technique which explores the combination of crime-related geographic factors using geographic information systems to assist in mitigating future crime events. This method produces a mapping surface which represents the frequency of crime predictor variables. A negative binomial regression was conducted to examine the ways in which potential street level robbery predictor variables affect crime outcome. Statistically significant predictor variables at the alpha values of 0.01 and 0.001 were combined into an equalized vector grid to highlight the frequency of street level robbery predictor variables within the Durham region. The implications of the analysis outlined risk factors of street level robbery within the Regional Municipality of Durham, the variation of their distribution based on the urban and rural landscapes, and the influence of risk factor optimization upon street level robbery.