Acta Geographica Sinica ›› 2018, Vol. 73 ›› Issue (8): 1462-1477.doi: 10.11821/dlxb201808006

• Urban and Regional Development • Previous Articles     Next Articles

Spatial pattern and influential mechanism of interurban technology transfer network in China

LIU Chengliang1,2,3(),GUAN Mingming1,DUAN Dezhong1()   

  1. 1. School of Urban and Regional Science, East China Normal University, Shanghai 200241, China
    2. Institute for Global Innovation and Development, East China Normal University, Shanghai 200062, China
    3. Institute of Eco-Chongming, East China Normal University, Shanghai 200062, China
  • Received:2017-12-19 Online:2018-08-15 Published:2018-07-31
  • Supported by:
    National Natural Science Foundation of China, No.41571123, No.41471108


On the basis of patent transaction data in 2015, spatial pattern of interurban technology transfer network in China was portrayed by integrating big data mining, social network, and GIS, from the perspectives of nodal strength and centrality, linkage intensity, and modular divisions. Then, its key influencing factors were identified as well using the Negative Binominal Regression Analysis. Some findings were ontained as follows. First of all, the intensity of interurban technology transfers in China is not well distributed with obvious polarization. Those cities with higher-level technology transfers are concentrated in the three urban clusters, namely, the Yangtze River Delta, the Pearl River Delta and Beijing-Tianjin-Hebei urban agglomeration. Secondly, a typical core-periphery structure with hub-and-spoke organization is evidently observed, which consists of several hubs and the majority of cities with far lower technology transfers. Beijing, Shenzhen, Shanghai and Guangzhou are acting as the pivot of the technology transfer network and playing a critical role in aggregating and dispersing technology flows. Thirdly, technology linkage intensities of urban pairs appear to be significantly uneven with hierarchies, centralizing in the three edges from Beijing to Shanghai, from Shanghai to Guangzhou and Shenzhen, and from Beijing to Guangzhou and Shenzhen, which shapes a triangle pattern. Fourthly, the technology transfer network is divided into four communities or plates, with prominent reflexivity and spillover effects, which is resulted from geographical proximity and technological complementary. Last but not least, spatial flows of technology are co-organized by a variety of spatial diffusion modes such as hierarchical diffusion, contact diffusion and leapfrog diffusion, owing to economic and administrative powers. They are greatly influenced by urban economic scale, foreign linkage, policy making, as well as multiple proximity factors related to geographical, technological, social and industrial proximities.

Key words: technology transfer, spatial diffusion, social network analysis, multiple proximity, technical association, China