An Approach to Determining the Spatially Contiguous Zone of a Self-Organized Urban Agglomeration

Abstract

The evolution of the urban agglomeration is a significant development in urban geography. Determining its spatial range for effective measurement remains a challenge for researchers. In previous studies, determining spatial range has primarily been done through distinguishing the cities that should belong to urban agglomerations from among other cities by using various indicators. Both the selection of indicators and the standards used for calculation and identification have been based on subjective choices, and have not considered spatial distribution or morphology. The urban agglomeration can be regarded as a self-organized space, and spatial features of the fractal can be regarded as one of the morphological characterizations of spatial self-organization. From the perspective of the assumption that the space of urban agglomerations is molecule like assembled, and through the extraction and analysis of spatial fractals, we present an objective method to determine the “spatially contiguous zone” of urban agglomeration, particularly the spatial range in which the urban agglomeration is able to exercise jurisdiction within the radius of its capacity, rather than in the administrative division. Our method is applied in this paper to the Beijing–Tianjin–Hebei urban agglomeration and produced the following results: (1) the existence of spatial fractals and the theory of space unit molecule like self-organization or assembly in the morphology of urban agglomerations has been proved; and (2) a spatially contiguous zone could be identified for the urban agglomeration has been confirmed. Compared with previous methods used for determining space, this method is centered on the spatial morphology of urban agglomerations; the recognition of a spatially contiguous zone liberates the geographical limits of the result from city boundary restrictions. Concurrently, by considering the linkages within the city as a self-organizing black box, we can circumvent the one-sidedness involved with the selection of indicators that has biased previous studies, thereby avoiding having to focus on the specific mechanism of urban dynamics, and coming much closer to its self-organizing dynamic inner nature. This approach will prove to be a useful reference for the identification of spatial ranges in future studies.