## 3 Results  
### Spatial Correlation Network of UAs in the Context of COVID-19  
This paper integrated the acquired Baidu migration big data; calculated the UA network connection strength, network density, and the urban centrality of each node; adopted the natural split breakpoint method to divide the acquired 27,493 pairs of OD (Origin-Destination) data of the UAs; and then established the OD network matrix of the nineteen UAs by means of the social network analysis method, and the spatial correlation network diagrams in three time periods are drawn by ArcGIS 10.8 software (Figure 3).  
UAs in China are predominantly concentrated in Southeast China, with fewer instances in the northwest. These variations reflect significant disparities in classification and network structures among the UAs. Before COVID-19, the UAs with a high level of development display a more advanced stage of development with tight network connections and higher network density. The central cities exert significant attraction and influence, leading to predominantly first-class and second-class connections within the UAs. This results in a network structure characterized by a remarkable hierarchy and polycentric attributes, with the central cities radiating from a prominent center, thereby forming a closely interlinked network with surrounding cities. Certain cities exhibited pronounced centrality, influencing neighboring cities to establish closely connected network structures. In contrast, the UAs in the growth stage of development demonstrate slightly lower network densities during their mature development stage. The network links between central cities were  
Figure 2: Research logic diagram.  
primarily second-level linkage flows, with fewer first-level linkage flows, resulting in relatively weaker overall network connections. Conversely, the UAs in the primary stage of development are still in their nascent stages of development. City connections are primarily based on third-level and fourth-level linkage flows, yielding lower network density and linkage intensity, signifying that these UAs are still in their developmental stages and have not yet established a stable network structure.  
During the normalization stage of COVID-19, the network structure of UAs is significantly impacted by COVID-19, particularly in the UAs with a high level of development. The mature UAs of the Yangtze River Delta, Pearl River Delta, and Beijing-Tianjin-Hebei experience more pronounced effects compared to other UAs. The impacts are predominantly observed in long-distance third-level and fourth-level linkage flows, resulting in a notable overall decrease in their network connections. This manifested as a discernible weakening of the central effect of the central cities and the sub-central cities.  
Figure 3: Network structure of UAs in China at different time periods under COVID-19.  
After COVID-19, the network structures of the UAs were under reconstruction. The UAs with a high level of development witnessed significant recovery in network connectivity and hierarchy, with the network structures becoming more stabilized, and hierarchical trends becoming apparent. The network structures of the UAs largely regained their pre-epidemic levels. Beijing-Tianjin-Hebei and Chengdu-Chongqing exhibited first-level and second-level linkage flows surpassing pre-epidemic levels in terms of quantity and values, signifying stronger network links between major cities in these UAs. The UAs in the growth stage of development and in the primary stage of development also displayed signs of recovery, albeit at a relatively slower pace.