Geographical Research, Volume. 39, Issue 2, 370(2020)
Structural characteristics and power hierarchy are important elements of urban networks. This paper studied the selection of China's automobile industry supply chain system in 2012 by using "China automobile industry enterprise information Daquan", "China industrial enterprise database" and "China automobile supplier network", and analyzed the characteristics of China's urban network from the perspective of "industry-location". The results showed that: Firstly, based on the supply chain system of automobile industry, China's urban network showed obvious structural characteristics of "low density, multi-core, high clustering, less convergence". Secondly, there existed a "paradox" between the structural characteristics and power levels of cities in the urban network, which means that the network status depended not only on the number of linked cities, but also on the spatial attributes and capital capacity of the associated networks. Thirdly, the urban network power level included not only the leading core cities such as Shanghai and Chongqing, but also the central intensive cities such as Guangzhou and Wuhu, and the power gateway cities such as Suzhou and Chengdu. The result suggested that the "alter-based centrality" and "alter-based power" could not only effectively reveal the real power attribute of China's urban network nodes, but also kept more in line with the unbalanced law of geographical space of economic phenomena. Fourth, leading core cities, including Chongqing, Shanghai, Tianjin, Changchun, Beijing, Shiyan, etc., did not completely take over the six major automotive agglomerations in China, among which Yangtze River Delta region was at the highest level in the power hierarchy whereas the Pearl River Delta region was at the bottom. Finally, it should be noted that the supply chain system of automobile industry was only a special situation between "urban agents", and its research conclusions could not be infinitely copied and promoted, and could not replace the relevant conclusions of other factor flows.
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Xiaofei CHEN, Jiehui YANG, Enru WANG, Changhong MIAO.
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Received: Jan. 18, 2019
Accepted: --
Published Online: Oct. 17, 2020
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