Spatial Analysis of Weather Crash Patterns

Abstract

Spatial statistical techniques can be an effective tool for analyzing patterns and autocorrelation in crash data, especially weather-related crashes. Since weather is a geographic phenomenon, it tends to show distinct geographic patterns affecting certain locations more than others. Accordingly, “weather-related” crashes may also display similar distinct patterns or clustering. The objective of this research was to use spatial statistical techniques to identify significant patterns of weather-related crashes. Weather-related crashes, defined as those crashes which occurred in adverse weather conditions, were analyzed using the Getis-Ord Gi* (d) statistic. The statistic reveals spatial patterns for weather-related crashes which are clustered at different locations depending upon weather conditions (snow, rain, and fog). The results also show geographic areas (counties) of statistically significant high and low relative crash rates for each weather condition. Furthermore, the resulting patterns of crashes w...