The Characteristics and Influencing Factors of City-based Innovation Association Network in China——From the Perspective of High Tech Zones
Authors: Jiang Lili , Zhang Hong , Zhou Jia , Chu Nanchen , Li Fengjiao 
Published: February 2023

In this paper, we took 142 cities in China as the research object, and used the attribute data of High-Tech zones to construct comprehensive development quality index system. And then, we took the entropy method to calculate the quality index and modified the traditional gravity model. In this way, the innovation spatial correlation matrix was obtained. Combined with the parameters of social network analysis method (the network density, average path length and average clustering coefficient), we explored the comprehensive characteristics of the network. The characteristics of node centrality were analyzed by Degree Centrality, Closeness Centrality and Betweenness Centrality. Agglomeration subgroup analysis method and E-I index were also used to analyze the spatial clustering characteristics of innovation association network. The QAP analysis method is used to explore the influencing mechanism of innovation correlation.Above all, We can draw four areas of conclusion. First of all, from the aspect of association network structure, city-based innovation network is not closely connected, the number of redundant connection channels is low efficiency, and the network accessibility is weak. The city-based network shows a pattern of dense network in the eastern region and sparse connections in the western region. The spatial pattern of four-level innovation association network with Beijing, Shanghai and Shenzhen as the core hub and "Z" shape as the main axis is presented. Among them, the first-level network takes the central city as the leader to build the urban agglomeration connection framework; the second-level network extends connections in the East and central regions; the third-level network connections in the eastern region are gradually complex and dense; the fourth-level network intakes more marginal cities. In addition, from the perspective of centralities, the degree centrality of cities in East China is higher than that in West China, and the spillover effect is obvious. The core cities in hinterland are the key nodes of the regional innovation association network, playing the role of "bridge" in the network. The cities located in remote areas have difficulty in transferring innovation factors and are easily influenced by related cities. Moreover, we summarized four innovation cohesion subgroups, including the northern hinterland subgroup, the eastern coastal subgroup, the southern subgroup and the western cooperation subgroup. Among them, the first three showed the phenomenon of small group network, the northern hinterland subgroup and the southern subgroup cover the largest number of cities.The eastern coastal subgroup is the "central actor", and the western cooperation subgroup is the "marginal actor". Furthermore, the results of the influencing factors analysis show the differences in administrative level, economic development level, openness to the outside world, and investment in technology are conducive to the innovation association between cities, while the similarities in spatial adjacency and industrial structure will promote the strong innovation association between cities.