地理研究 ›› 2015, Vol. 34 ›› Issue (3): 525-540.doi: 10.11821/dlyj201503011

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中国城市尺度科学知识网络与技术知识网络结构的时空复杂性

李丹丹1,4(), 汪涛2(), 魏也华3, 袁丰1   

  1. 1. 中国科学院南京地理与湖泊研究所, 南京 210008
    2. 南京师范大学地理科学学院江苏省地理信息资源开发与利用协同创新中心, 南京 210023
    3. 犹他大学地理系及公共与国际事务研究院, 盐湖城 841129155, 美国
    4. 中国科学院大学, 北京 100049
  • 收稿日期:2014-07-30 修回日期:2014-11-20 出版日期:2015-03-10 发布日期:2015-03-26
  • 作者简介:

    作者简介:李丹丹(1988- ),女,河南焦作人,博士研究生,主要从事区域创新及知识网络研究。E-mail:ddli@niglas.ac.cn

  • 基金资助:
    国家自然科学基金项目(41471103,41201111,41329001);国家软科学项目(2013GXS4D116);教育部人文社会科学重点研究基地重大项目(11JJDZH005)

Spatial and temporal complexity of scientific knowledge network and technological knowledge network on China's urban scale

Dandan LI1,4(), Tao WANG2(), Dennis WEI Yehua3, Feng YUAN1   

  1. 1. Nanjing Institute of Geography & Limnology, CAS, Nanjing 210008, China
    2. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, School of Geography Science, Nanjing Normal University, Nanjing 210023, China
    3. Department of Geography and IPIA, University of Utah, Salt Lake City, Utah 841129155, USA
    4. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2014-07-30 Revised:2014-11-20 Online:2015-03-10 Published:2015-03-26

摘要:

知识在产业集聚、区域创新中的地位越来越突出,城市知识储量及其在区域知识网络中的地位对城市的综合竞争力有重要影响。学术论文合作与专利合作是知识溢出的体现形式,是科学和技术发展的重要成果,也是度量区域创新能力的主要指标。以2000-2009年中国生物技术领域合著论文和共同申请专利的信息为原始数据,分别构建中国城市间科学知识网络(scientific knowledge network,SKN)与技术知识网络(technological knowledge network,TKN)。运用复杂网络与地学空间分析方法,从整体网络结构特征、择优链接性、中心城市及其自我网络的空间特征等方面进行分析,探究知识溢出的时空复杂性。研究表明:①SKN和TKN具有无标度网络特征;SKN节点度数的异质性高于TKN。②两种网络均呈异配性,即城市选择合作对象存在明显择优链接性,知识溢出具有粘着性和空间依赖性。③SKN中心城市具有明显的等级结构,空间分布总体呈“大分散小集聚”特点;TKN中心城市层级结构不明显,尚未形成明显极化中心。④SKN中心城市自我网络的合作空间,由最初的沿海省会城市间的合作转向长三角、珠三角、京津冀等区域间和沿海城市与内陆城市间的合作,区域间知识溢出明显;TKN中心城市自我网络仍多分布于沿海城市和少数中西部省会城市,区域间知识溢出不明显。⑤SKN中心城市及其自我网络的时空演变存在等级扩散和传染扩散的现象,符合时空梯度推移规律,且空间等级梯度逐渐向扁平化转变;TKN中心城市及其自我网络的时空演变以等级扩散为主,时空梯度推移现象不明显。研究结论为量化知识溢出及知识溢出网络结构的时空演化过程提供新的研究视角,对城市创新政策的制定有一定借鉴意义。

关键词: 知识溢出, 知识网络, 网络结构, 城市创新网络

Abstract:

With the rise of the knowledge-based economy in the 1980s, knowledge (including code and tacit knowledge) as the backbone of innovation has become a key factor affecting production process. Cities have gathered not only a large number of professionals, universities and research institutions, but also a great many producers and consumers, which provides the premise for the innovation actions. City's knowledge storage and its position in the regional knowledge network play an important role in comprehensive competitiveness. Published papers and patents are main outcomes of innovation, which are used to evaluate the urban innovation capability. Moreover, co-publications and co-patents are not only the form of knowledge spillover, but also the key indicators to measure regional innovation. Taking the co-publication and co-patent in the field of biotechnology in China during 2000-2009 as the original data, we built scientific knowledge network (SKN) and technological knowledge network (TKN) between cities. From the perspective of complex networks and geospatial analysis, we explored the temporal and spatial complexity of knowledge spillovers combining the indicators of whole network structure, ego network, power-law, hubs and so on. The results show that: firstly, the nodes degree distribution of SKN and TKN is consistent with the power-law distribution, which means that the both networks not only have a scale-free network structure, but also present a preferential attachment rule when the cities choose the cooperation partner. Secondly, central cities have an obvious hierarchical structure, and are featured by a "big scattered and small gathering" spatial pattern in SKN, while the TKN is not showing this feature. From the view of central city ego network, the cooperation develops between the coastal capital cities at first, and then turns to inter-regional cooperation, such as Yangtze River Delta, Pearl River Delta, and inter-regional knowledge spillovers is obvious in SKN. The central cities and its partners are still in the coastal city instead of western provincial capitals, and inter-regional knowledge spillovers are not significant in TKN. Thirdly, the temporal evolution of central cities and its ego-network presents hierarchical diffusion and contagious diffusion, and conforms to law of grades process in SKN. The TKN is dominated by hierarchical diffusion. Finally, this study draws conclusions on the temporal and spatial complexity of innovation network, which has a positive impact on quantifying spatial knowledge spillovers and measuring its space-time evolution. Besides, the results clarify the status of each city in innovation networks, which provides a new perspective for the cities to formulate innovative policies.

Key words: knowledge spillover, knowledge network, network structure, urban innovation system