## 4 Results
### Mixing Patterns Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Type**} & \multicolumn{2}{c}{**Information**} & \multicolumn{2}{c}{**Investment**} & \multirow{2}{*}{**High-Speed Rail**} & \multirow{2}{*}{**Highway**} \\  & **In-Degree** & **Out-Degree** & & & & \\ \hline Global & \(-0.0162\)*** & \(-0.0774\)*** & \(-0.2289\)** & \(-0.1993\)*** & \(0.1678\)** & \(0.1761\)*** \\ BTHUA & \(-0.0419\)*** & \(-0.0593\)*** & \(-0.1016\)** & \(-0.2174\)*** & \(-0.1494\)*** & \(-0.1384\)* \\ YRDUA & \(0.0284\)*** & \(0.0362\)*** & \(-0.1317\)*** & \(-0.2391\)*** & \(-0.0437\)*** & \(-0.1898\)* \\ GBA & \(-0.0897\)*** & \(-0.0805\)*** & \(-0.3609\)*** & \(-0.0434\)*** & \(-0.3737\)*** & \(-0.3056\)*** \\ CCUA & \(-0.0664\)*** & \(-0.0667\)*** & \(-0.7356\)*** & \(-0.1138\)** & \(-0.0589\)*** & \(-0.1282\)*** \\ MRYRUA & \(-0.0766\)*** & \(-0.1110\)*** & \(-0.5421\)*** & \(-0.0195\) & \(-0.0894\)*** & \(0.2436\)*** \\ CPUA & \(0.2528\)*** & \(0.0155\)*** & \(0.2334\)** & \(-0.2482\)*** & \(0.1968\)*** & \(-0.095\)*** \\ GPUA & \(-0.2064\)*** & \(-0.1734\)*** & \(-0.1217\) & \(-0.9987\)*** & \(-0.3773\)*** & \(-0.2625\)*** \\ \hline \end{tabular} \(p\)-value \(<0.01\)***, \(p\)-value \(<0.05\)***, \(p\)-value \(<0.1\)*.  
\end{table} 
Table 6: The node degree assortativity coefficient of the four types of urban networks. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Information**} & \multicolumn{2}{c}{**Investment**} & \multirow{2}{*}{**High-Speed Rail**} & \multirow{2}{*}{**Highway**} \\  & & & & \\ Global & \(-0.0458\)*** & \(-0.2878\)*** & \(-0.0092\)* & \(-0.1178\)*** \\ BTHUA & \(-0.0373\)*** & \(-0.1197\)*** & \(-0.0688\)*** & \(0.1482\)*** \\ YRDUA & \(-0.0046\)*** & \(-0.1295\)*** & \(-0.0331\)*** & \(-0.1932\)*** \\ GBA & \(-0.0497\)*** & \(-0.3968\)*** & \(-0.2290\)*** & \(-0.1553\)*** \\ CCUA & \(-0.0184\)*** & \(-0.7549\)*** & \(-0.0250\)* & \(-0.0547\)*** \\ MRYRUA & \(-0.0240\)*** & \(-0.4771\)*** & \(0.0216\) & \(-0.0095\)*** \\ CPUA & \(-0.0312\)*** & \(-0.1055\) & \(0.0067\)* & \(-0.1976\)*** \\ GPUA & \(-0.0349\)*** & \(-0.9985\)*** & \(-0.0329\)** & \(-0.2302\)*** \\ \hline \end{tabular} \(p\)-value \(<0.01\)***, \(p\)-value \(<0.05\)*, \(p\)-value \(<0.1\)*.  
\end{table} 
Table 7: The node betweenness centrality assortativity coefficient of the four types of urban networks. show a weak correlation between GDP and permanent resident population. Among the four types of networks, only the information network exhibits significant assortativity in both the GDP level and the permanent resident population, indicating that information flow between cities tends to occur between cities with similar socioeconomic attributes. In contrast, the investment network shows significant disassortativity only in terms of GDP, suggesting that capital tends to flow from cities with higher GDP to those with more average GDP on a national scale. The statistical results for the high-speed rail and highway networks indicate that economic disparities between cities do not significantly affect intercity transportation links on a global scale. For the urban networks of the seven urban agglomerations, the assortativity coefficients for the Beijing-Tianjin-Hebei, Yangtze River Delta, Greater Bay Area, and Chengdu-Chongqing urban agglomerations show no correlation in the information network, indicating that information exchange between cities is not solely based on socioeconomic attributes. However, other urban agglomerations demonstrate strong disassortativity, implying that the socioeconomic development levels of these urban agglomerations influence the information connections between cities. For the investment network, most urban agglomerations show significant disassortative features, indicating that cities are more likely to connect with cities that have differing economic or permanent resident populations.