Abstract:

Increasing globalization and informatization has enhanced the intercity exchange of information, materials, and energy. Cities no longer represent isolated systems. Instead, they are closely linked to each other, forming regional or global city network systems. Therefore, the study of urban networks has attracted massive attention in human geography and urban planning. In particular, the emergence of the concept of “space of flow” provides a new perspective and paradigm for the interpretation of regional spatial structure. Based on the data collected from domestic and foreign journal database published from 2000 to 2018, this paper uses social network analysis method and spatial structure index method to explore the evolution process of the overall characteristics, organizational structure, and the spatial pattern of the knowledge network in Guangdong-Hong Kong-Macao Greater Bay Area. Furthermore, it identified the evolution trend of factors influencing the knowledge network in the Bay Area. The results also revealed the following: 1) Over the duration of the research, publications in the Greater Bay Area significantly increased. The pattern of the knowledge network gradually evolved from the “single power” represented by Guangzhou to “simultaneous development” that included Guangzhou, Shenzhen, and Hong Kong. Although Hong Kong is at the core of the knowledge network, it establishes close knowledge cooperation primarily with Guangzhou and Shenzhen due to administrative barriers. 2) The knowledge network of the Guangdong-Hong Kong-Macao Greater Bay Area represents a “core-edge” structure with the knowledge connection in the western region significantly lower than that in the eastern region. The knowledge network densities and spatial structure indices of the Guangdong-Hong Kong-Macao Greater Bay Area suggest an increasing volatility. In 2016, the knowledge network density of the Bay Area attained the maximum value, indicating the development and maturity of the overall knowledge connection of the Guangdong-Hong Kong-Macao Greater Bay Area. In addition, the spatial structure indices demonstrate an alleviation of polarization characteristics of knowledge networks in the Bay Area, despite persistent significant imbalance. 3) The demand of the knowledge activity actors such as universities and scientific research institutions in the Bay Area is the internal driving force promoting knowledge cooperation among cities. The knowledge environment and the knowledge connection channels are the external driving forces of the regional knowledge cooperation network. The influence of endogenous and exogenous factors is responsible for the output of knowledge cooperation, resulting in the development of the knowledge network in the Guangdong-Hong Kong-Macao Greater Bay Area. This study provides a reference for the development of innovative collaborative paths in the Guangdong-Hong Kong-Macao Greater Bay Area by refining the characteristics of Bay Area’s knowledge network.

Key words: knowledge network, evolutionary trend, influence mechanism, regional collaboration, collaborative innovation, the Guangdong-Hong Kong-Macao Greater Bay Area