The flow of high-level talents in China is becoming more and more active and has a profound impact on the innovation and development of cities. Based on the research on the spatial distribution of the school and work stages of high-level talent in China, we study the structure and evolution of China’s high-level talent mobility network by using the data on the flow trajectory of talent among cities in China. The results show that: the spatial distribution of talents in the (...) two stages has greater consistency, but there is a change characteristic of “large dispersion, small agglomeration.” Compared with the school stage, the network in the work stage has much more connections; the centrality gap of cities has narrowed. “Core-periphery” structure of two networks is relatively significant and similar. The core layer in the two networks has not changed, but the subcore layer and the subedge layer have undergone changes. In addition, talents mainly flow to core and subcore cities. The space structure of network is more complex in the work stage, and it is expanding to the Guangdong-Hong Kong-Macao Greater Bay Area in southeastern China. (shrink)

Urban networks have been widely examined using infrastructure connection and firm connection data. In particular, urban networks constructed based on firm connection data have been used to depict the circulation of capital, information, personnel, and products between cities. Existing studies on firm connection networks rely on either inter- or intrafirm relationships. However, there exist various important extra-firm relationships, such as those between firms and governments, research institutions, and nonprofit organizations. This study innovatively incorporates the extra-firm relationships between governments and firms (...) into urban network construction to provide new insights into the field of urban network research. Using Northeast China as a case study, we construct government procurement activity connection networks for both central projects and local projects at the regional and national scales. Social network analysis and regression analysis are used to analyze the network characteristics and factors associated with the network structure. The results show that three provincial capitals hold dominant positions across all the networks. While these capitals serve as GPA suppliers in the region, they are mainly GPA consumers nationally. The factors of spatial proximity, economic development, population, and provincial boundaries were found to be significantly associated with GPA connections. Our study provides important insights into the role of cities in firm-government supply-demand networks. This research can be used to help formulate effective strategies to improve a city’s competitiveness and cities’ cooperation in the GPA supply-demand network. (shrink)