• 论文 •

### 区域产业布局模式识别:指标体系与实证检验

1. 北京大学城市与环境学院, 北京 100871
• 出版日期:2010-03-20 发布日期:2010-03-20
• 通讯作者: 曹广忠,副教授。caogzh@urban.pku.edu.cn E-mail:caogzh@urban.pku.edu.cn
• 作者简介:刘涛,(1987- ),男,安徽宿州人,硕士研究生,研究方向为城市地理与城市规划。
• 基金资助:
国家科技支撑计划重点项目课题(2006BAJ11B06)资助。

### Industrial Layout Pattern Recognition:Index System and Empirical Test

LIU Tao, CAO Guang-zhong, JIANG Yi-dong, GAO Xiao-wen

1. Department of Urban and Environmental Sciences, Peking University, Beijing 100871
• Online:2010-03-20 Published:2010-03-20

Abstract: Regional industrial layout patterns have drawn the attention of many geographers. Focusing on specific industry and (or)specific area, however, most of previous researches are lacking of continuity and comparability. A rational, comparable, and feasible index system is essential for describing and evaluating layout patterns of various industries in different areas. This paper tries to establish such an index system and estimate its rationality and feasibility through the followed case study. The index system is constructed to represent the industrial layout patterns from perspectives of centricity, inequality, spatial agglomeration, and correlation between observed industry and other industries and economic factors. Firstly, the proportion of employment in the center to that in the region and to that in the sub-center, are recommended to represent the centricity of the industrial layout. Secondly, the authors set up indices of ubiquity, measured by the proportion of subregions with one or more enterprises of the observed industry, and concentration ratio to represent the inequality of industrial distribution among subregions. The concentration ratio can also be estimated excluding the center. Thirdly, spatial agglomeration of an industry is measured by distances between the center and sub-centers and other industrial agglomeration areas, and distances among industrial agglomeration areas without regard to the center. Finally, it is also important for recognition of industrial layout patterns to investigate their relationship with the spatial distribution of other industries, population, land use and other socio-economic factors, which can be measured by Pearson correlation coefficient, similarity coefficient or grey relational degree. Taking Nanchong as a case, we classify 2-digital industries from the aforesaid perspectives respectively with quantitative analysis methods. At last, four typical comprehensive patterns of industrial layout are concluded. The case study provides evidence for rationality and flexibility of the index system, which can also be used in industrial layout pattern recognition and classification in all kinds of areas, at different times and on multi-scales.

• F061.5