PROGRESS IN GEOGRAPHY ›› 2017, Vol. 36 ›› Issue (11): 1359-1367.doi: 10.18306/dlkxjz.2017.11.005

• Orginal Article • Previous Articles     Next Articles

Multi-level spatial structure analysis of urban agglomeration in the Beijing-Tianjin-Hebei region based on spatial clustering algorithms

Xun ZHANG1,3(), Jianzhang CHEN1, Jinchuan HUANG2,3,4,*(), Chongchong YU1, Xiuxin CHEN1   

  1. 1. Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
    2. Key Laboratory of Regional Sustainable Development Modeling, CAS, Beijing 100101, China
    3. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    4. College of Resources and Environmental Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
  • Online:2017-12-07 Published:2017-12-07
  • Contact: Jinchuan HUANG;
  • Supported by:
    Research Project of Humanities and Social Science on Youth Fund of the Ministry of Education, No.15YJCZH224;Beijing Natural Science Foundation, No.9164025;Opening Fund of Capital Circulation Industry Research Base, No.JD-YB-2017-024;Scientific Research Project of Beijing Educational Committee, No.KM201510011010


With the main focus of regional competition shifting from cities to urban agglomerations, it is important to analyze the spatial structure and direction of coordinated development in urban agglomerations. This is especially significant in the Beijing-Tianjin-Hebei urban agglomeration, which is a core urban agglomeration in China. Machine learning algorithms are relatively new methods for addressing geographical problems. Clustering method, as unsupervised learning, is useful for classifying geographical units without the need for priori knowledge. Using data from 156 counties in the Beijing-Tianjin-Hebei urban agglomeration, this study applied four clustering algorithms: the K-means, density-based spatial clustering of applications with noise (DBSCAN), Chameleon, and self-organizing map (SOM) methods, for classifying counties and districts in the Beijing-Tianjin-Hebei urban agglomeration from the perspectives of economic centrality, traffic centrality, information centrality, and population centrality. GDP of the counties in 2014 was used to represent economic centrality; density of road networks in counties and attraction factor, calculated by the unsold train tickets in different time periods of the year, represent traffic centrality; Sina Weibo check-in data were used to represent information centrality; and county/district population represents population centrality. The result classifies the urban agglomeration into several levels. Respectively, K-means algorithm classifies counties into five levels; DBSCAN algorithm classifies counties into six levels; Chameleon algorithm classifies counties into six levels; and SOM algorithm classifies counties into five levels. SOM is the most applicable algorithm for the division of the urban agglomeration because the structure of counties is stable. This study further analyzed the spatial structure of the urban agglomeration with the central place theory, which points out that an agglomeration should contain certain number of counties in every level. The result of the SOM algorithm matches the central place theory. This research shows that there were remarkable gaps between different levels of the urban agglomeration. The central area of Beijing, as the core of the region, has strong radiation effect on the surrounding areas, but its functions are shared by the nearby counties. Moreover, the second and third level central cities distribute evenly and play an important role in regional development.

Key words: spatial clustering, spatial structure, Beijing-Tianjin-Hebei Region Agglomeration