## 5 Experimental Results and Analysis
### Spatial Correlation Analysis of Traffic Black Spots
#### 5.3.1 Global Moran's \(I\)  
Global Moran's \(I\) is a commonly used statistical index that can be used to measure the overall spatial correlation of traffic black spots at road intersections in Kowloon Peninsula, Hong Kong. In this section, the study takes the Kowloon Peninsula of Hong Kong as the study area and the road intersections as the study units, and the MapGIS spatial statistics module is used to calculate spatial statistics and construct the Global Moran's \(I\) to analyze the global spatial correlation of the distribution of traffic black spots. The global \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation Indicators** & \multicolumn{4}{c}{**Model Algorithm Name**} \\ \hline  & **SVM** & **GBDT** & **Random Forest** & **Fully CNN** \\ \hline Accuracy & 81.14\% & 85.58\% & 87.19\% & 84.54\% \\ \hline Precision Rate & 74.12\% & 85.02\% & 86.12\% & 83.04\% \\ \hline Recall & 63.08\% & 74.32\% & 80.07\% & 73.54\% \\ \hline F1\_score & 41.86\% & 63.99\% & 75.06\% & 68.09\% \\ \hline Loss Mean & 6.513 & 4.636 & 3.725 & 4.884 \\ \hline Loss (max) & 7.346 & 5.795 & 4.928 & 5.973 \\ \hline Loss (min) & 5.658 & 3.741 & 2.738 & 3.634 \\ \hline Loss (std) & 0.269 & 0.430 & 0.335 & 0.421 \\ \hline \hline \end{tabular}
\end{table} 
Table 23: Comparison of the results of the four model constructions. [PERSON]'s \(I\) ranges from \(-1\) to \(1\). Positive values indicate positive correlation, which means that there is a positive correlation in the distribution of traffic black spots at road intersections in the Kowloon Peninsula region of Hong Kong, and the closer the value is to \(1\), the stronger the correlation is. Negative values indicate negative correlation, and the closer the value is to \(-1\), the stronger the correlation. A value close to \(0\) indicates no spatial correlation. In the process of constructing the global [PERSON]'s \(I\) for the traffic black spots of road intersections in the Kowloon Peninsula region of Hong Kong, the most crucial aspect is the setting of the parameter value of the conceptualization of spatial relationship, which mainly reflects the inherent relationship between the traffic black spots, and the common conceptualizations include the inverse distance, the undifferentiated area, and the connecting edges only, and so on. Based on the characteristics of road intersections, this study decides to adopt the inverse distance method: it shows that the closer the road intersections are spatially, the more likely they are to interact with each other and cause each other to become traffic black spots. In MapGIS, analysis based on [PERSON]'s index, z-value, and \(p\)-value can help us to assess the spatial autocorrelation of geospatial data. Table 24 illustrates the relationship between z-value, \(p\)-value, and confidence level. When \(p\) is less than \(0.1\) and the absolute value of \(Z\) is greater than \(1.65\), this indicates that there is a \(90\%\) probability that the distribution of traffic black spots at road intersections in the Kowloon Peninsula region of Hong Kong is correlated. When \(p\) is less than \(0.01\) and the absolute value of \(Z\) is greater than \(2.58\), this means that there is a \(99\%\) probability that there is a correlation between the distribution of traffic black spots at road intersections in the Kowloon Peninsula region of Hong Kong. Other cases can be recognized as accepting the null hypothesis, i.e., it is a random state. From Table 25, from the results of the construction of the global Moran's \(I\) of traffic black spots at road intersections in the Kowloon Peninsula region of Hong Kong, it can be seen that the global Moran's \(I\) is \(0.55\), which indicates that there is a positive correlation in the distribution of traffic black spots, and that this correlation may be due to the built-up environments of adjacent road intersections interacting with each other.