## 3 Results  
### Results of IDEMATEL  
#### 3.1.1 Results of Constructing the Influencing Factor Set  
This paper uses \"coastal historical districts\", \"block wind resistance\", \"typhoon disasters\", \"wind resilience\", and \"disaster prevention management\" as keywords to search for literature in the Web of Science database and Science Direct database, screen out the influencing factors of wind resilience of coastal historical districts, and summarize the influencing factor indicators that appear frequently in the research, as shown in Table 2.  
Based on the preliminary selection of 20 indicators of factors affecting the wind resilience of coastal historical districts, 100 experts in the field of historical district protection and disaster prevention and reduction were invited to \"merge\", \"delete\", and \"supplement\" in the form of questionnaires, and the expert semantics were informatized, as shown in Appendix A. A total of 100 questionnaires were distributed, and 95 were finally collected. According to the experts' suggestions, the four indicators of \"resilience concept\", \"coastal city\", \"protection and renewal\", and \"risk assessment\" with a negligible impact on the wind resilience of coastal historical districts were eliminated. At the same time, to avoid the same meaning as other existing indicators, the experts suggested deleting the four indicators with repeated concepts, \"impact intensity\", \"strong typhoon\", \"preventive protection\", and \"hazard investigation\". The second round of questionnaires was designed based on the feedback from the first round of questionnaires. A Likert five-level scale was used to evaluate the importance of the evaluation indicators of the wind resilience influencing factors of coastal historical districts determined in the second round of questionnaires (1-5 importance rating), as shown in Appendix B. A total of 95 questionnaires were distributed, and 89 were effectively collected, with an efficiency of 93.7%. Invalid questionnaires included four questionnaires with serious missing items and two questionnaires with careless filling. The reliability analysis of the measurement was performed using SPSS 27.0, according to Formula (1), and the overall Cronbach's \(\alpha\) coefficient was 0.798. The reliability coefficients of all indicators were greater than 0.7. By comparing the standards in Table 3, it can be seen that the reliability of the questionnaire results is good. According to Formulas (2) and (3), the Kendall synergy coefficient and asymptotic significance of expert opinions were calculated, as shown in Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline
**Number** & **Indicator** & **Frequency** & **Number** & **Indicator** & **Frequency** \\ \hline
1 & Windproof design & 103 & 11 & Block direction & 52 \\
2 & Block form & 98 & 12 & Hidden danger identification & 51 \\
3 & Building Structure & 87 & 13 & Resilience Concept & 44 \\
4 & Typhoon level & 80 & 14 & Safety Education & 35 \\
5 & Emergency Plan & 76 & 15 & Impact intensity & 29 \\
6 & Typhoon wind speed & 72 & 16 & Preventive protection & 26 \\
7 & Architectural composition & 63 & 17 & Risk assessment & 23 \\
8 & Typhoon direction & 61 & 18 & Protection and renewal & 21 \\
9 & Strong typhoon & 57 & 19 & Hazard investigation & 19 \\
10 & Block density & 55 & 20 & Coastal cities & 15 \\ \hline \end{tabular}
\end{table} 
Table 2: High-frequency influencing factor indicators. \begin{table}
\begin{tabular}{c c c} \hline
**Test Statistics** & **Kendall** & **Asymptotic Significance** \\ \hline Evaluation indicators & 0.634 & 0.001 \\ \hline \end{tabular}
\end{table} 
Table 3: Kendall synergy coefficient of expert opinions. Since the asymptotic significance of the questionnaire results is far less than 0.01, it proves that the experts' judgment on the suitability of the indicators passed the consistency test. We summarized the indicator scores and used the calculated coefficient of variation and average as the basis for indicator screening.