地理研究 ›› 2016, Vol. 35 ›› Issue (1): 95-107.doi: 10.11821/dlyj201601009

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基于微观企业数据的产业空间集聚特征分析——以杭州市区为例

李佳洺1,2(), 张文忠1,2, 李业锦3, 杨勋凤2,4, 余建辉1,2()   

  1. 1. 中国科学院区域可持续发展分析与模拟重点实验室,北京 100101
    2. 中国科学院地理科学与资源研究所,北京 100101
    3. 首都师范大学资源环境与旅游学院,北京 100048
    4. 中国科学院大学,北京 100049
  • 收稿日期:2015-06-26 修回日期:2015-10-21 出版日期:2016-01-23 发布日期:2016-01-23
  • 作者简介:

    作者简介:李佳洺(1984- ),男,山西晋城人,博士,助理研究员,主要研究方向为经济地理与区域发展。E-mail: lijm.12b@igsnrr.ac.cn

  • 基金资助:
    国家自然科学基金重点项目(41230632);国家自然科学基金项目(41201169,41001105);北京市教委科研计划项目(KM201510028013)

The characteristics of industrial agglomeration based on micro-geographic data

Jiaming LI1,2(), Wenzhong ZHANG1,2, Yejin LI3, Xunfeng YANG2,4, Jianhui YU1,2()   

  1. 1. Key Laboratory of Regional Sustainable Development Modeling, CAS, Beijing 100101, China
    2. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    3. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
    4. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2015-06-26 Revised:2015-10-21 Online:2016-01-23 Published:2016-01-23

摘要:

与基于面状地理数据的研究不同,以微观企业点数据为基础,采用基于距离的产业集聚的研究方法,最终完成一个完整的从企业地址信息处理,到大量企业之间距离运算和处理,再到集聚参照设定,最后进行产业空间集聚分析的研究过程。以杭州市2013年工商登记数据为基础,通过对不同类型产业的研究,认为生产性服务业和高科技制造业集聚趋势较为明显,而传统的零售业和制造业在城市空间上没有形成集聚。进一步分析企业规模对空间集聚趋势的影响表明,金融服务和商务服务业的集聚由较小规模企业主导,零售业的集聚由较大规模企业主导,信息服务业的集聚由中等规模企业主导,制造业的集聚整体上由中小规模企业主导。

关键词: 产业集聚, 微观数据, 企业规模, 杭州

Abstract:

: Our research goal is to test the spatial agglomeration according to industries and firms sizes at the city level, which is based on a unique plant-level data set of Hangzhou. In the paper, we employ a new method based on the distance and firm point data to explore industrial agglomeration in the city. The result from this method shows great differences with that from the method dealing with the surface data based on administration boundary such as the Moran's I and Getis-Ord Gi*. On the basis of firm's data provided by Hangzhou Trade and Industry Bureau, a complete process has been finished from making spatial data. We only use text firm address information to process spatial data, then to construct counterfactuals. Finally, the results are interpreted in this research. We select nine representative industries to reveal the discrepancy of agglomeration characteristics among industries. The finding shows that the spatial agglomeration of knowledge-intensive industries is significant, while most of enterprises from traditional labor and capital-intensive industries are approximately to the random distribution in urban areas. Specifically, the spatial agglomeration degree of finance, information service and high-tech and equipment manufacturing is obviously higher than the average degree of service and manufacturing industries; on the contrary, the agglomeration degree of consumer services and manufacturing industries, such as retail and food processing, fails to pass the counterfactuals. Although the degree of agglomeration of textile and apparel and heavy industry is higher than the counterfactuals in the range of 15~40 km, such a large distance means most of enterprises are dispersed in the suburbs. It is worth noting that most of business service are dispersed in industrial space rather than clustered at a small scale as the producer service should be. This unusual result probably means that business service is under development in Hangzhou. Besides, we analyze the further impact of establishment size on industrial agglomeration. Generally, the spatial agglomeration of manufacturing industries has been driven by the larger establishments, whereas service industries are mixed. While the spatial agglomeration of finance and business is also driven by small establishments, the agglomeration of large retails is more important than that of small ones. In the field of information service, it seems that industrial spatial agglomeration is driven by neither large nor small enterprises. Actually, the contribution from the agglomeration of a large number of medium-sized enterprises in the range between 0~3 km is dominant for service cluster. To manufacturing industries, it is clear that small enterprises dominate the spatial agglomeration, but the agglomeration of large ones is also important at a certain scale.

Key words: industrial agglomeration, micro-geographic data, company size, Hangzhou