Author(s)

Azad Abdulhafedh^*

Department of Civil and Environmental Engineering, University of Missouri, Columbia, MO, USA.

Spatial autocorrelation is a measure of the correlation of an observation with other observations through space. Most statistical analyses are based on the assumption that the values of observations are independent of one another. Spatial autocorrelation violates this assumption, because observations at near-by locations are related to each other, and hence, the consideration of spatial autocorrelations has been gaining attention in crash data modeling in recent years, and research have shown that ignoring this factor may lead to a biased estimation of the modeling parameters. This paper examines two spatial autocorrelation indices: Moran’s Index; and Getis-Ord G_i^* statistic to measure the spatial autocorrelation of vehicle crashes occurred in Boone County roads in the state of Missouri, USA for the years 2013-2015. Since each index can identify different clustering patterns of crashes, therefore this paper introduces a new hybrid method to identify the crash clustering patterns by combining both Moran’s Index and G_i^* statistic. Results show that the new method can effectively improve the number, extent, and type of crash clustering along roadways.

Spatial Autocorrelation, Moran’s Index, Getis-Ord G_i^* Statistic, Vehicle Crashes

Abdulhafedh, A. (2017) A Novel Hybrid Method for Measuring the Spatial Autocorrelation of Vehicular Crashes: Combining Moran’s Index and Getis-Ord G_i^* Statistic. Open Journal of Civil Engineering, 7, 208-221. doi: 10.4236/ojce.2017.72013.