地理学报 ›› 2018, Vol. 73 ›› Issue (8): 1462-1477.doi: 10.11821/dlxb201808006

• 城市与区域发展 • 上一篇    下一篇

中国城际技术转移网络的空间格局及影响因素

刘承良1,2,3(),管明明1,段德忠1()   

  1. 1. 华东师范大学城市与区域科学学院,上海 200241
    2. 华东师范大学全球创新与发展研究院,上海 200062
    3. 华东师范大学崇明生态研究院,上海 200062
  • 收稿日期:2017-12-19 出版日期:2018-08-15 发布日期:2018-07-31
  • 基金资助:
    国家自然科学基金项目(41571123, 41471108)

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

摘要:

基于2015年专利交易数据,融合数据挖掘、社会网络、空间分析等方法,从节点、关联、模块及影响因素4个方面揭示中国城际技术转移的空间格局及其影响因素:① 技术转移整体强度偏低,空间极化严重,长三角、珠三角、京津冀城市群成为技术转移的活跃地带。② 北京、深圳、上海、广州是全国技术转移网络的“集线器”,发挥城际技术流的集散枢纽和中转桥梁作用,中西部大部分城市处于网络边缘,整个网络发育典型的核心—边缘式和枢纽—网络式结构。③ 技术关联的空间层级和马太效应凸显,形成以北京、上海、广深为顶点的“三角形”技术关联骨架结构,技术流集聚在东部地带经济发达的城市之间和具有高技术能级的城市之间,中西部技术结网不足,呈现碎片化。④ 技术转移网络形成明显的四类板块(子群),具明显自反性和溢出效应,其空间聚类既有“近水楼台先得月”式块状集聚,也有“舍近求远”式点状“飞地”镶嵌。⑤ 城际技术流呈现等级扩散、接触扩散、跳跃扩散等多种空间扩散模式,其流向表现出经济指向性和行政等级指向性特征。⑥ 城市经济发展水平、对外开放程度、政策支持等主体属性和地理、技术、社会、产业邻近性的城市主体关系均会影响其技术转移强度。

关键词: 技术转移, 空间扩散, 社会网络, 多维邻近性, 技术关联, 中国

Abstract:

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