地理学报 ›› 2018, Vol. 73 ›› Issue (8): 1421-1432.doi: 10.11821/dlxb201808003

• 城市与区域发展 • 上一篇    下一篇

中国城市知识复杂性的空间特征及影响研究

张翼鸥(),谷人旭()   

  1. 华东师范大学城市与区域科学学院,上海 200241
  • 收稿日期:2017-11-29 出版日期:2018-08-15 发布日期:2018-07-31

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

摘要:

学者和政策制定者一致认为“知识”是推动经济长期增长的关键因素之一,且并非所有知识都具有相同价值。然而,经济地理学一直致力于计量知识的投入和产出,而忽视评估知识质量。为此,首先借鉴Hidalgo等的“双峰网络模型”构建了知识复杂性的测度模型,并根据中国知识产权局1986-2015年间的14349355条专利记录,依照122项专利分类识别中国城市的技术结构。将中国城市知识复杂性的分布、集聚特征和演化过程地图化,以探索知识的空间分布及其复杂性的关联。结果表明:① 具有最复杂技术结构的城市未必是那些专利申请率最高的城市,说明强调知识的数量与质量并重,有助于科学衡量中国知识的生产格局;② 知识复杂性在中国呈不均匀分布,其空间集聚显著,演化过程显示热点区域“南下”态势明显;③ 专利转移数据进一步证明,知识复杂性越高的城市,其知识的空间粘性越大,越不易流动,因此将区域知识结构从低复杂度转化为高复杂度将有助于地区重塑自身优势。

关键词: 知识复杂性, 知识流动, 双峰网络模型, 专利, 城市, 中国

Abstract:

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