PROGRESS IN GEOGRAPHY ›› 2017, Vol. 36 ›› Issue (6): 677-684.doi: 10.18306/dlkxjz.2017.06.003

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Identification of the physical space of urban systems based on fractal analysis

Fei LIU(), Xinqi ZHENG*(), Qing HUANG   

  1. School of Information Engineering, China University of Geosciences (Beijing), Beijing 100083, China
  • Online:2017-06-20 Published:2017-06-20
  • Contact: Xinqi ZHENG;
  • Supported by:
    National Major Programs of International Cooperation and Exchanges of China, No.S2015ZR1018


Urban system study is an important field of urban geography. Identification of the physical space of urban systems is a precondition and basis for the research on urban systems. Existing research on urban system identification primarily took city as the minimum unit since urban systems are aggregations of towns and cities. They determine whether a city belong to an urban system through the comparison between values of selected indicators and the criteria set by subjective estimates, which unfortunately lacks consideration of spatial morphology. The morphology of the physical space of urban systems has self-similarity, which can be expressed by the feature of fractal. Through the analysis, identification, and extraction of fractals we can describe the spatial objects objectively based on morphology. In this study, by referencing the experience in identifying boundary of individual cities by fractal, we measured the spatial fractal feature of towns of urban systems from the perspective of spatially identifiable minimum unit at different scales, and proposed a method to identify the towns of urban systems. Through the change of identifiable minimum unit we obtained different scales of towns by which we derive their spatial fractal feature. Then, the range of spatial fractal features was identified, and the identification of urban systems that is the spatial distribution of urban systems was achieved by mapping the range to the space. Finally, this study applied the method to the Beijing-Tianjin-Hebei urban system and achieved the identification of the system based on Landsat images in 2016.

Key words: urban systems, physical space, space identification, spatial morphology, fractal