SPATIAL AGGLOMERATION CHARACTERISTICS AND FORMATION MECHANISM OF KNOWLEDGE-INTENSIVE MANUFACTURING INDUSTRIES IN THREE MAJOR URBAN AGGLOMERATIONS IN CHINA: BASED ON BIG DATA ANALYSIS OF SCIENCE AND TECHNOLOGY-BASED ENTERPRISES
YU Ying-jie1,2, DU De-bin1,2, LI Qi-xiang1,2, XING He-xiang3
1. School of Geographic Sciences, East China Normal University, Shanghai 200241, China; 2. Institute of Global Innovation and Development, East China Normal University, Shanghai 200062, China; 3. Industrial Technology Research Institute, East China Normal University, Shanghai 200062, China
Abstract：The study examines the clustering characteristics and formation mechanisms of knowledgeintensive manufacturing industries at various scales. The results show that: 1)The computer industry has the highest agglomeration intensity, and the agglomeration intensity of each industry in Beijing-Tianjin-Hebei is higher than that in the Yangtze River Delta and Pearl River Delta regions. 2)At the city cluster scale, industries are primarily concentrated within a 100 km radius. 3)City-scale industries are mostly concentrated within a 40 km radius, with a small number of industries clustering in peripheral areas. The Beijing-TianjinHebei region exhibits a 'spreading type' agglomeration, while the Yangtze River Delta is of 'local type' and 'multi-centre type', and the Pearl River Delta is of 'multi-centre type'. 4)The scale of city clusters is determined by regional resource endowment and comparative advantages. The characteristics of agglomeration in each city scale vary depending on spatial differences in production costs, industrial policies, and local protectionism.
于英杰, 杜德斌, 李祺祥, 邢和祥. 中国三大城市群知识密集型制造业的空间集聚特征及形成机制——基于科技型企业大数据分析[J]. 人文地理, 2024, 39(4): 57-65,105.
YU Ying-jie, DU De-bin, LI Qi-xiang, XING He-xiang. SPATIAL AGGLOMERATION CHARACTERISTICS AND FORMATION MECHANISM OF KNOWLEDGE-INTENSIVE MANUFACTURING INDUSTRIES IN THREE MAJOR URBAN AGGLOMERATIONS IN CHINA: BASED ON BIG DATA ANALYSIS OF SCIENCE AND TECHNOLOGY-BASED ENTERPRISES. HUMAN GEOGRAPHY, 2024, 39(4): 57-65,105.
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