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THE SPATIAL CONCENTRATION OF INNOVATION AND ITS MECHANISMS:A LITERATURE REVIEW AND PROSPECT |
SUN Yu-kang, LI Guo-ping, YUAN Wei-wei, SUN Tie-shan |
School of Government, Peking University, Beijing 100871, China |
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Abstract Focused on the questions such as whether innovation activities are spatial concentrated, where innovation agglomeration locates and how the innovation agglomeration forms, this article analyses the spatial characteristics of innovation activities from aggregation, localization, and networking. A lot of research proved that innovation activities have remarkably agglomerated in space distribution. Some scholars measured the concentration degree of innovation activity by the quantitative indexes. Innovation cluster is very sensitive to the space distance and has a strong dependence on proximity. At the same time, innovation cluster has the characteristics of localization. At the regional level, innovation activities have a strong local embeddedness which rely on the regional characteristic of the element such as culture, system, organization. Regional innovation system particularly emphasis on the role of "innovation environment". At the city level, some scholars have discussed the relationship between the innovation cluster and the city size, industrial structure, urban internal spatial structure. The close relationship between different innovation clusters make innovation activities have strong tendency to be networking in space. Currently, the use of social network analysis in research of innovation network to analyze the structure, performance and spatial characteristics of innovation network is becoming the concern of our scholars. After that, the mechanisms which impact spatial agglomeration of innovation are induced into five types:knowledge spillover, sharing services, skilled labour matching, uncertainty and competition. Then, we discussed the problems in current research. In the future, multi-scale spatial analysis on innovation spatial agglomeration should be put forward. We also should strengthen the work on modeling and empirical studies.
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