## 4 Results and Discussions
### Crash Hot Spot Analysis of Ethiopian Regions and Towns  
Crash hot spot analysis was done at a regional level as a polygon feature dataset by applying the computed crash severity values of each region in the attribute table. The incremental spatial autocorrelation toolbox of ArcGIS was used to choose the distance bandwidth and the results are shown in Figure 3. The maximum and first peak distance obtained from incremental spatial autocorrelation analysis was 352.441 kilometers, indicating that RTC incidents are highly clustered at a distance band of 352.441 kilometers with a statistical significance of 0.01 (the \(p\)-value obtained is 0.402% \(<\) 1%; thus, the confidence level is 99%). The bigger threshold distance values can be explained by the fact that the polygons in the analysis (i.e., the regions and administrative cities) have larger area coverages. For example, Oromia, Ethiopia's largest region, has an area of around 355,423 km\({}^{2}\). In distance computations for polygon features, feature centroids are considered. The weighted mean center of all feature components is used to compute the centroid for various portions of the polygon feature. The weighted mean center for point features is 1, length for line features and area for polygon features [61]. Thus, in the case of this analysis, features such as polygons with wider regions were expected to have a larger threshold distance. Furthermore, the default _Beginning Distance_ was applied in this study while executing the incremental spatial autocorrelation. The minimum distance for which each feature in the dataset has at least one neighbor is set as the default value for _Beginning Distance_. As a result, the beginning distance for incremental spatial autocorrelation analysis increases since larger regions (polygons) demands longer distances to reach at least one neighbor from their centroids. In fact, the higher beginning distance used to determine the first peak has a direct influence on the value of the threshold distance. This value was used as the threshold distance in Getis Ord \(Gi^{*}\) crash hot spot identification. Accordingly, crash hot spot identification and ranking of Ethiopian regions and federal territories were performed by considering the spatial patterns and spatial dependence of crashes. The result obtained from Getis Ord \(Gi^{*}\) crash hot spot analysis is presented in Figure 4 and Table 1. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
**OBJ\_ID** & **Crash Severity** & **Shape\_L. (m)** & **Shape\_A. (m\({}^{2}\))** & **GiZScore** & **GiPValue** & **NNeighbors** & **Region\_Names** \\ \hline
1 & 109,518 & 106,098 & 525,638,401 & 2.87230 & 0.00407 & 3 & Addis Ababa \\
8 & 30,661 & 6,403,850 & 355,423,484,208 & 2.55848 & 0.01051 & 3 & Oromia \\
9 & 11,192 & 2,806,015 & 117,263,152,867 & 0.85676 & 0.39158 & 3 & SNNP \\
2 & 19,831 & 2,689,742 & 153,434,579,103 & 0.49482 & 0.62073 & 5 & Amhara \\
11 & 8201 & 1,580,725 & 56,451,528,629 & \(-\)0.07804 & 0.93780 & 3 & Tigrai \\
4 & 1477 & 1,630,480 & 48,889,173,519 & \(-\)0.54855 & 0.58331 & 3 & B\_Gumuz \\
6 & 845 & 908,665 & 25,649,364,273 & \(-\)0.86238 & 0.38848 & 3 & Gambella \\
3 & 1693 & 1,920,341 & 95,242,894,663 & \(-\)0.94792 & 0.34317 & 5 & Afar \\
10 & 2179 & 3,677,531 & 2780,73,581,426 & \(-\)1.40998 & 0.15855 & 3 & Somali R. \\
5 & 2780 & 215,410 & 1,507,085,646 & \(-\)1.77287 & 0.07625 & 4 & Dire-Dawa \\
7 & 8201 & 93,895 & 394,011,903 & \(-\)1.77287 & 0.07625 & 4 & Harari \\ \hline \end{tabular}
\end{table} 
Table 1: Crash Hot Spot Result of Getis Ord \(Gi^{*}\). Figure 2: Number of deaths recorded in Ethiopia from 2010–2017. With confidence intervals of 99% and 95%, Addis Ababa and the Oromia region were identified as crash hotspots. Furthermore, with a 90% confidence interval, two federal territories, 'Dire-Dawa' and 'Harari,' were revealed as low clusters of crash severity areas or cold spots.