Characteristics and prediction of urban interaction networks from the perspective of traffic flow and text information flow

The degree of urban development depends on the degree of closeness between cities, which is reflected in the strength of interactions between cities, such as traffic and information flows. In this research, we compare and analyze the characteristics of urban interaction networks (UINs) at two spatial scales in Shandong Province and the whole country from two different perspectives of traffic flow and information flow and validate the spatial interaction characteristics reflected in the material space traffic flow from the perspective of textual spatial information flow. The UIN is constructed based on Tencent migration big data, and the assortative coefficient method is introduced to explore the assortative and interaction characteristics between core cities and edge cities in the traffic flow network. Introducing deep learning methods on a larger scale, the GCN_CD model is proposed for semi-supervised classification of nodes to realize community discovery for both traffic flow and information flow networks. The spatial interaction intensity prediction model GAT _ d β is constructed taking into account geographic features, which improves the prediction accuracy. The results show that from the perspectives of traffic flow and text information flow, the urban interactive network in Shandong Province has the characteristics of Scale-Free and Small-World, showing certain homogeneity and strong spatial interaction. Shandong Province’s UIN formed Jinan, Qingdao, and other cities as the core, satellite cities around, the east and west ends of the remote echo structure. From the perspective of traffic flow, the national urban interactive network presents a jumping distribution, with a single core city as the dominant distribution structure. From the perspective of information flow, the dividing line between the eastern and western communities is obvious, and the internal aggregation of the community is strong. Distance attenuation effects have an impact on the strength of spatial interactions.