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Deer–vehicle collisions (DVCs) are steadily increasing across North America. The increase is particularly pronounced in urban green spaces where deer (Odocoileus spp.) populations and road densities are high. In the greater city of Edmonton, Alberta, Canada, 333 DVCs occurred from 2002 to 2004. To identify landscape and traffic correlates of these collisions, we built 3 statistical models. The first model assessed the importance of local variables and was based on a spatial precision of the nearest intersection to which collisions were referenced. The second model was based on landscape characteristics and used the nearest township intersection to aggregate collisions. For each of the models, we generated an equivalent number of random locations in a geographic information system (GIS) and examined several independent variables at 4 spatial scales (using 100-m, 200-m, 400-m, and 800-m radius buffers). We used multivariate logistic regression to determine which landscape and traffic factors increased the probability of a DVC. The third model used ordinal regression to assess correlates with collision frequency. Our first (High Precision) model showed that DVCs occurred in areas with high speed limits and low densities of roads within an 800-m buffer. The second (Aggregate) model found DVCs more likely to occur in areas close to water and the combination of high road densities and non-forested vegetation of high productivity within 800 m. The third (Hotspot) model identified only high traffic speed as a correlate of collision frequency. A temporal analysis of the collision data found that DVCs peaked in mid- November. We conclude that rates of DVCs could be reduced and road safety improved by lowering speed limits during peak seasons, particularly in areas where road density is high (i.e., interchanges) and where non-forested vegetation occurs in close proximity to roads. Several aspects of our analyses and results may have applications in other jurisdictions where DVCs occur.