## 4 Results
### Comprehensive Analysis Findings  
Figure 3 visually illustrates the changes in the network structure for each scenario as the proportion of vertiport placement increases. All scenarios exhibit distinct vertiport placement patterns. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **Network Strategy Effect** & **Ratio Effect** \\  & **(_p_-Value)** & **(_p_-Value)** \\ \hline Global Efficiency & 0.0097 & 0.0000 \\ Average Shortest Path Length & 0.0053 & 0.0000 \\ Algebraic Connectivity & 0.0276 & 0.0051 \\ Clustering Coefficient & 0.0067 & 0.0000 \\ Degree Assortativity & 0.0005 & 0.0039 \\ Transitivity & 0.0378 & 0.0000 \\ Modularity & 0.0540 & 0.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Statistical results of network indicators based on two-way ANOVA. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ratio** & \begin{tabular}{c} **Betweenness UAM** \\ **Network** \\ \end{tabular} & \
\begin{tabular}{c} **Degree UAM** \\ **Network** \\ \end{tabular} &
\begin{tabular}{c} **Vulnerable UAM** \\ **Network** \\ \end{tabular} \\ \hline
5\% & 0.65703 & 0.68810 & 0.66245 \\
10\% & 0.31077 & 0.30245 & 0.28555 \\
15\% & 0.12937 & 0.14362 & 0.12440 \\
20\% & 0.07310 & 0.07711 & 0.06092 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Network modularity results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ratio** & \begin{tabular}{c} **Betweenness UAM** \\ **Network** \\ \end{tabular} & \
\begin{tabular}{c} **Degree UAM** \\ **Network** \\ \end{tabular} &
\begin{tabular}{c} **Vulnerable UAM** \\ **Network** \\ \end{tabular} \\ \hline
5\% & 0.94994 & 0.92884 & 0.89724 \\
10\% & 0.94004 & 0.93193 & 0.88948 \\
15\% & 0.92365 & 0.92317 & 0.85936 \\
20\% & 0.90299 & 0.89032 & 0.81274 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Network degree assortativity results. In the degree scenario, the network is formed over the widest range, indicating a more evenly distributed connectivity pattern. The vulnerability scenario falls in an intermediate range among the three but displays more distinct connection lines compared to the other scenarios. The betweenness scenario shows the narrowest connectivity range, with broader inter-vertiport connections forming within this confined area. These findings highlight that vertiport allocation strategies can significantly influence future network characteristics, demonstrating the importance of strategic planning in determining the overall network structure. Figure 4 shows a comparative analysis of efficiency, connectivity, and average shortest path length. The results indicate that as the proportion of vertiport placement increases, all metrics show improvement; efficiency and connectivity indices increase, while the average shortest path length decreases. From the perspective of efficiency and average shortest path length, the vulnerability scenario proves to be the most advantageous. In terms of connectivity, the degree scenario performs the best. Additionally, the vulnerability scenario demonstrates higher connectivity than the betweenness scenario. These findings suggest that vulnerability should be a key consideration in developing vertiport allocation strategies as it plays a crucial role in enhancing both efficiency and network integration. The network structural metrics presented in Tables 6-9 indicate that vertiport placement are expected to significantly reshape the existing network structure. Even when disregarding the unique characteristics of urban air mobility (UAM), which differ from conventional transportation modes, substantial changes are anticipated. Figure 3: Simulation (5–20%) networks by each scenario. Therefore, we recognize that traditional methodologies for location allocation, such as objective function-based approaches, expert judgment, and multi-criteria evaluation techniques, have limitations at this stage due to the high uncertainty of UAM characteristics.