Acta Geographica Sinica ›› 2018, Vol. 73 ›› Issue (8): 1421-1432.doi: 10.11821/dlxb201808003

• Urban and Regional Development • Previous Articles     Next Articles

The geography of knowledge complexity and its influence in Chinese cities

ZHANG Yiou(),GU Renxu()   

  1. School of Urban and Regional Science, East China Normal University, Shanghai 200241, China
  • Received:2017-11-29 Online:2018-08-15 Published:2018-07-31


Knowledge is one of the key drivers of long-term economic growth. Additionally, it is widely accepted that not all knowledge has the same value. Nevertheless, too often in the field of economic geography and cognate, we have been obsessed with counting knowledge inputs and outputs rather than assessing the quality of knowledge produced. To fill this gap, this article focuses on the knowledge complexity and its significant role in regional innovation. To begin with, based on the "bimodal network models" of Hidalgo and Hausmann, designed to measure the complexity of knowledge, the article investigates 14349355 pieces of patent records from the State Intellectual Property Office of P.R.China to identify the technological structure of Chinese cities in terms of the patent classes between 1986 and 2015. Furthermore, the article explores the evolution of knowledge complexity in Chinese cities and how the spatial diffusion of knowledge is linked to complexity using visualizing tools such as Gephi and ArcGIS. The results show that: (1) The cities with the most complex technological structures are not necessarily those with the highest rates of patenting, which suggests that the emphasis on both quality and quantity will definitely contribute to a better understanding of the distribution of knowledge production in China; (2) Knowledge complexity is unevenly distributed across China and the evolution process shows that the hot spots are obviously southward, indicating the characteristics of geographical agglomeration and the great differences between zones; (3) More complex patents are less likely to be transferred than those with less complexity in terms of the data of patent transference and the pattern is also verified by correlational analysis, Linear Probability Model and logistic regression approach of STATA. Therefore, upgrading regional knowledge structure from low complexity to high complexity will contribute to reshaping regional advantages respectively.

Key words: knowledge complexity, bimodal network model, knowledge flow, patents, cities, China