## 3 Results
### Results of IDEMATEL
#### 3.1.2 Calculate the Comprehensive Influence Matrix  
The Delphi method is used to preliminarily determine the correlation between the influencing factors, and the direct influence relationship matrix \(W\) is determined according to Formula (6). \[W= \begin{bmatrix}0&0&0&0&0&2&0&1&1&1&2&0\\ 0&0&0&3&0&4&0&3&1&0&0&0\\ 0&0&0&2&0&2&0&3&2&0&0&0\\ 2&2&2&0&0&1&0&3&1&0&2&0\\ 0&0&0&0&0&3&0&0&0&0&0&0\\ 0&1&0&1&2&0&0&2&1&1&1&0\\ 0&0&0&0&4&3&0&1&3&2&4&3\\ 0&1&1&1&1&3&0&0&0&0&2&0\\ 0&1&0&0&3&2&1&0&0&1&3&0\\ 1&1&1&0&3&0&0&0&0&0&4&3\\ 1&1&0&0&3&3&0&0&0&0&0&1\\ 0&0&0&0&2&0&0&0&1&3&0\end{bmatrix} \]  
After determining the direct influence relationship matrix, the normalization is performed using Formulas (7) and (8). The row sum maximum method is employed to normalize and calculate the normalized influence matrix \(F\). \[F= \begin{bmatrix}0.000&0.000&0.000&0.000&0.100&0.000&0.050&0.050&0.050&0.100& 0.000\\ 0.000&0.000&0.000&0.150&0.000&0.200&0.000&0.150&0.050&0.000&0.000&0.000\\ 0.000&0.000&0.000&0.100&0.000&0.100&0.000&0.150&0.050&0.000&0.000&0.000\\ 0.100&0.100&0.100&0.000&0.000&0.050&0.000&0.150&0.050&0.100&0.000\\ 0.000&0.000&0.000&0.000&0.150&0.000&0.000&0.000&0.000&0.000&0.000\\ 0.000&0.000&0.000&0.000&0.100&0.000&0.000&0.000&0.000&0.000&0.000\\ 0.000&0.050&0.050&0.050&0.150&0.000&0.000&0.000&0.000&0.100&0.000\\ 0.000&0.050&0.000&0.000&0.150&0.100&0.050&0.000&0.000&0.050&0.150&0.000\\ 0.050&0.050&0.050&0.050&0.000&0.150&0.000&0.000&0.000&0.000&0.000&0.050\\ 0.000&0.000&0.000&0.000&0.100&0.000&0.000&0.000&0.000&0.000&0.000\\ 0.000&0.000&0.000&0.000&0.100&0.000&0.000&0.000&0.000&0.000&0.000\\ \end{bmatrix} \]  
First, sum the rows and columns of the initial direct influence matrix \(S\) and compare their sizes. Based on Formula (9), calculate the normalized influence matrix [36]. Finally, the comprehensive influence matrix \(T\) can be computed. \[T=\begin{bmatrix}0.012&0.028&0.009&0.020&0.052&0.168&0.003&0.076&0.063&0.063&0.1 45&0.017\\ 0.024&0.053&0.031&0.188&0.065&0.293&0.004&0.221&0.082&0.021&0.076&0.008\\ 0.017&0.042&0.024&0.129&0.058&0.184&0.006&0.199&0.122&0.017&0.068&0.007\\ 0.115&0.140&0.117&0.054&0.069&0.186&0.004&0.221&0.087&0.020&0.168&0.012\\ 0.002&0.012&0.003&0.012&0.021&0.165&0.001&0.021&0.0103.1.3. The Results of the Centrality and Causation Degree  
The influence degree set (\(D\)) and the affected degree set (\(E\)) can be calculated according to Formula (10). The centrality set (\(B\)) and the causation degree set (\(C\)) can be calculated according to Formula (11) [37; 38; 39]. The results are shown in Table 6.  
According to the results in Table 6, a causal relationship diagram of the factors affecting the wind resilience of China's coastal historical districts was drawn with centrality B as the horizontal axis and causation C as the vertical axis, as shown in Figure 3.  
The results in Table 6 and Figure 4 show the influence degree values for block form (\(S_{2}\)), architectural composition (\(S_{4}\)), typhoon level (\(S_{7}\)), and emergency plan (\(S_{10}\)) are relatively high, indicating that these four factors have a significant impact on other factors in the system. The influence values of building structure (\(S_{5}\)), windproof design (\(S_{6}\)), typhoon direction (\(S_{8}\)), and hazard investigation (\(S_{11}\)) are also considerable, suggesting that these factors are more significantly affected by others.