基于创新联系的城市网络特征及影响因素研究——以粤港澳地区为例

Urban Network on Innovation Connection: Case Study of Guangdong-Hong Kong-Macao Area

摘要:

本研究依托世界知识专利组织(WIPO)中专利合作协定(PCT)数据,运用社会网络分析方法中的核心-边缘分析方法和网络密度、度数中心度与中间中心度等指标,构建了粤港澳地区城市之间的创新联系网络。结合与硅谷创新网络的对比,试图从创新合作的视角探讨粤港澳地区的创新联系网络特征,并探究了影响粤港澳地区城市创新联系的因素。研究发现：(1)近年来粤港澳地区城市创新联系逐渐增强,但网络联系紧密度与硅谷相比仍存在差距；(2)粤港澳地区的创新联系网络呈现核心-边缘结构,创新联系网络的中心度呈圈层式向外递减；(3)广州、深圳、香港、珠海、东莞等五大创新合作核心城市所组成的核心圈层网络发育程度较高,边缘城市倾向于与核心城市开展创新合作；(4)若将对外创新联系的影响力细化为对外交往能力和中介能力,网络中的核心城市在这两方面表现存在差异；(5)地理邻近性、技术邻近性、经济邻近性、网络邻近性、科技邻近性均对粤港澳地区的城市创新联系产生显著影响。本研究的意义在于,通过构建创新联系网络,从创新联系的视角进一步理解粤港澳地区城市之间的功能联系,并为该地区深化科技创新产业合作提供决策参考。

Abstract:

Based on the data of Patent Cooperation Treaty (PCT) in World Knowledge Patent Organization (WIPO), this study use the Core-periphery Analysis method, the index such as the network density, the degree centrality, and the betweenness centrality from the Social Network Analysis, to form an innovation connection network within Guangdong-Hong Kong-Macao Area. According to the comparison with Silicon Valley, this paper attempts to explore the characteristics and the influence factor of the innovation connection network of Guangdong—Hong Kong—Macao Area, from the perspective of innovation cooperation. The main findings include: (1) In recent years, urban innovation linkages within Guangdong—Hong Kong—Macao Area became closely. But the degree of the closed-connection was not as good as that in Silicon Valley; (2) The innovation connection network in Guangdong-Hong Kong-Macao Area has an obvious core-periphery structure, and the degree centrality declines as the city far away from the core circle of innovation connection. (3) The key circle which consisting of five core cities, Guangzhou, Shenzhen, Hong Kong, Zhuhai and Dongguan, is highly developed. The periphery cities tend to develop innovative cooperation relationship with core cities rather than with the other periphery cities. (4) By defining the influence capacity of innovation connection as external linkage capabilities and intermedia capabilities, the core cities of the network have different performances in these two capabilities. (5) Geographical proximity, technological proximity, economic proximity, network proximity and technological proximity all have a significant impact on the urban innovation network of this area. The geographical proximity shows negative significant impact, while the other four factors show positive significant impact.