# A New Approach to Identifying Crash Hotspot Intersections (CHIs) Using Spatial Weights Matrices
## 4 Results and Discussion
### Results
#### 4.1.2 The Results of Intersection Hotspot Analysis (Getis-Ord Gi*) of Spencer City  
We used intersection hotspot analysis (IHA, as discussed in Section 2.3.3) based on geo-processing tools in ArcGIS by taking the input parameters listed in Table 4 to calculate the Getis-Ord Gi*, \(z\)-score, \(p\)-value, and Gi*-bin (confidence level bin) for each intersection to identify the CHIs of Spencer city (2008-2014). Note that the \"Distance Band\", \"Weights Matrix File\", and \"Input Field,\" as the key input parameters, were generated in Sections 2.3.1 and 2.3.2. In general, when the confidence level exceeds 95%, we consider it to have significant statistical significance. As discussed in Section 2.3.3, we formed a randomization null hypothesis about the spatial distribution of the number of crashes at intersections. If an intersection's positive \(p\)-value is \(>\)0.05 and its \(Gi\)'_ZScore_ is \(>\)1.96, then the spatial distribution pattern of the number of crashes on the intersection is a random distribution with a probability of less than 5%, and the spatial distribution on the intersection is clustered (positive correlation, hot spot) with a probability of greater than 95%. An intersection that meets the above conditions is considered a CHI at the 95% confidence level. From this perspective, we can identify the CHIs that indicate a positive spatial autocorrelation of the number of crashes. The intersections among the CHIs all have a high frequency of crashes, and their neighbor intersections also have a high frequency of crashes. The calculated \(Gi\)'_ZScores_ and \(p\)-values of CHIs (2008-2014) are listed in Table 5. \begin{table}
\begin{tabular}{c c} \hline \hline
**Input Parameter** & **Input Value** \\ \hline Input Feature Class & intersection \\ Input Field & num\_crash \\ Output Feature Class & c:\(\backslash\)data\(\backslash\)crash\(\backslash\)h\(\backslash\)stop\(\backslash\)intersections.shp \\ Conceptualization of Spatial Relationships & get\_spatial\_weights\_from\_file \\ Standardization & true \\ Distance Band or Threshold Distance & 683.64 \\ Weights Matrix File & c:\(\backslash\)data\(\backslash\)INDSWMI.swm \\ Apply False Discovery Rate Correction & no\_dr \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Input parameters of intersection hotspot analysis. Furthermore, the expected Gi*ZScore has a positive correlation with the number of crashes for each intersection. Based on the linear regression, we used the number of crashes as independent variable \(x\) and the Gi*ZScore as independent variable \(y\) in the output feature shapefile to draw a scatter plot, shown in Figure 10. The scatter plot indicates that there is a linear relationship between the number of crashes and the Gi*ZScore. The CHIs in the output feature shapefile of IHA at the 95% confidence level are colored in red in Figure 11 using GIS visualization. Figure 11 clearly demonstrates that the CHIs are clustered along the roads including Grand Ave, S Grand Ave, 11 th St SE, and 1 st Ave E. Based on the spatial distribution of CHIs, transportation authorities can develop targeted mitigation strategies to effectively reduce the number of crashes.