Many cities are making significant financial investments in cycling infrastructure with the aim of making cycling safer for riders of all ages and abilities.
METHODS for evaluating cycling safety tend to summarize average change for a city or emphasize change on a single road segment. Few spatially explicit approaches are available to evaluate how patterns of safety change throughout a city due to cycling infrastructure investments or other changes. Our goal is to demonstrate a method for monitoring changes in the spatial-temporal distribution of cycling incidents across a city. Using cycling incident data provided by the Insurance Corporation of British Columbia, we first compare planar versus network constrained kernel density estimation for visualizing incident intensity across the street network of Vancouver, Canada. Second, we apply a change detection algorithm explicitly designed for detecting statistically significant change in kernel density estimates. The utility of network kernel density change detection is demonstrated through the comparison of cycling incident densities following the construction of two cycle tracks in the downtown core of Vancouver. The methods developed and demonstrated for this study provide city planners, transportation engineers and researchers a means of monitoring city-wide change in the intensity of cycling incidents following enhancements to cycling infrastructure or other significant changes to the transportation network.