Abstract
Traditional hot spot and cluster analysis techniques based on the Euclidean distance may not be adequate for assessing high-risk locations related to crashes. This is because crashes occur on transportation networks where the spatial distance is network-based. Therefore, this research aims to conduct spatial analysis to identify clusters of high- and low-risk crash locations. Using vulnerable road users’ crash data of San Francisco, the first step in the workflow involves using Ripley’s K-and G-functions to detect the presence of clustering patterns and to identify their threshold distance. Next, the threshold distance is incorporated into the Getis-Ord Gi* method to identify local hot and cold spots. The analysis demonstrates that the network-constrained G-function can effectively define the appropriate threshold distances for spatial correlation analysis. This workflow can serve as an analytical template to aid planners in improving their threshold distance selection for hot spot analysis as it employs actual road-network distances to produce more accurate results, which is especially relevant when assessing discrete-data phenomena such as crashes.
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