Innovation has obvious spatial characteristics. As the key carrier of technological innovation activities, innovative space (IS) serves as a critical spatial platform and operational environment for the diffusion of innovation elements, including technology transfer. A network-based approach can effectively reveal the regional development patterns of ISs and the rules of knowledge exchange. This study integrates spatial interaction theory with complex network methods to empirically analyze the Guangdong–Hong Kong–Macao Greater Bay Area using large-scale patent transfer data. First, the radiation zones (RZs) of the ISs are identified using Wilson's maximum entropy model, and the radiation effect of the IS is characterized by the radiation radius and intensity. Second, a multi-layer innovation association network is constructed by integrating ISs, RZs and other external micro-innovation entities. The characteristics of regional innovation linkages are analyzed, revealing the interaction patterns of ISs. Finally, considering the interaction patterns and radiation effects of ISs, ISs are classified into four types, namely, Hub-type, Outward-type, Inward-type and Island-type. Based on the identification of the dominant industries of the IS themselves, further determine their industrial sectors, thereby revealing the industrial heterogeneity characteristics of different types of IS, and ultimately proposing differentiated development strategies for each type. These findings provide empirical evidence and policy recommendations for promoting the coordinated development of regional ISs.
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Xing et al. (2026) studied this question.
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