中国城市群分布及范围的定量评价(英文)

编号 zgly0001585700

文献类型 期刊论文

文献题名 中国城市群分布及范围的定量评价(英文)

作者 张倩  胡云锋  刘纪远  刘越  任旺兵  李军 

作者单位 InstituteofGeographicSciencesandNaturalResourcesResearch  CAS  GraduateUniversityofChineseAcademyofSciences  DepartmentofUrbanPlanningandEnvironment  RoyalInstituteofTechnology-KTH  AcademyofMacroeconomicResearch  NationalDevelopm 

母体文献 Journal of Geographical Sciences 

年卷期 2012年01期

年份 2012 

分类号 TU982 

关键词 urbanclusters  GeographicInformationSystem(GIS)  computerizedidentification  spatialdistribu-tion  China 

文摘内容 Urban clusters are the expected products of high levels of industry and urbanization in a country, as well as being the basic units of participation in global competition. With respect to China, urban clusters are regarded as the dominant formation for boosting the Chinese urbanization process. However, to date, there is no coincident, efficient, and credible methodological system and set of techniques to identify Chinese urban clusters. This research investigates the potential of a computerized identification method supported by geographic information techniques to provide a better understanding of the distribution of Chinese urban clusters. The identification method is executed based on a geographic information database, a digital elevation model, and socio-economic data with the aid of ArcInfo Macro Language programming. In the method, preliminary boundaries are identified accord-ing to transportation accessibility, and final identifications are achieved from limiting city numbers, population, and GDP in a region with the aid of the rasterized socio-economic dataset. The results show that the method identifies nine Chinese urban clusters, i.e., Pearl River Delta, Lower Yangtze River Valley, Beijing-Tianjin-Hebei Region, Northeast China Plain, Middle Yangtze River Valley, Central China Plains, Western Taiwan Strait, Guanzhong and Chengdu-Chongqing urban clusters. This research represents the first study involving the computerized identification of Chinese urban clusters. Moreover, compared to other related studies, the study’s approach, which combines transportation accessibility and socio-economic characteristics, is shown to be a distinct, effective and reliable way of identifying urban clusters.