Spatial analysis of crash data is to study the distribution of crash locations to identify the spatial patterns and their underlying causes. Spatial association indicators such as Getis G and Moran's I can measure the clustering of crash attributes of a set of geographic features at a global or a local scale. Kernel density estimation, Ripley's k-function and cross-k function analyze crash points by calculating crash intensity or the correlation between two distinct sets of points. Spatial regression methods explicitly consider spatial dependency of crash observations and spatial heterogeneity in the relationship between crashes and their contributing factors.
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