GEOGRAPHICAL RESEARCH ›› 2020, Vol. 39 ›› Issue (1): 92-102.doi: 10.11821/dlyj020181002

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Identifying the spatial range of urban agglomerations in China based on night light remote sensing and POI data

LIANG Ze1,2, HUANG Jiao1,2, WEI Feili1,2, SHEN Jiashu1,2, LI Shuangcheng1,2()   

  1. 1. College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
    2. Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China
  • Received:2018-09-14 Revised:2018-11-27 Online:2020-01-20 Published:2020-03-20
  • Contact: LI Shuangcheng E-mail:scli@urban.pku.edu.cn

Abstract:

Urban agglomeration, as an emerging phenomenon in many urbanized areas worldwide, is considered as a highly developed spatial form of integrated cities. Cities are highly linked within an urban agglomeration, which renders the agglomeration one of the most important carriers for global economic development. In recent years, the study of urban agglomeration has become an important agenda both for urban planning and urban sustainable development. However, in the research community, there is still a lack of a consensus with regard to how to delineate the urban agglomerations in geographic space. Particularly, in many urban planning cases, functional links among cities are often neglected, resulting in overestimated spatial ranges of the planned urban agglomerations. The aim of this paper is to develop a method for the identification of the spatial range of urban agglomerations by using night-light remote sensing data, digitally mapped points of interest (POI) and the "point-axis" theory in economic geography. Firstly, based on a review of the "point-axis" theory in economic geography, we developed a concept of "developing axis" with two basic characteristics and used the concept to describe the four development stages of urban agglomerations. Then, we calculated two indexes to quantify the intensity and its changes in socio-economic activities by combining nighttime light remote sensing images and POI data. After that, we conducted a clustering analysis on the two indexes to identify and extract the "point-axis cluster", and overlaid it with the administrative boundaries to obtain a set of candidate urban agglomerations. Finally, we used socio-economic statistic data and formulated criteria based on previous studies to select urban agglomerations. Using this method, a total of 14 urban agglomerations in China are identified. Among which, eight have spatial ranges match their planning documents. As for the mismatching urban agglomerations, three different types of mismatch are distinguished, which indicate that different types of problems need to be considered in the planning. The results show that the proposed method can overcome the restriction of administrative boundaries in the identification of the spatial range of urban agglomerations, objectively reflect the strength of social and economic links among cities, and help to identify potential urban agglomerations with a dynamic perspective. This research can provide useful implications and suggestions for urban agglomeration planning and management.

Key words: urban agglomeration, spatial range, point-axis theory, point-axis cluster, status-dynamic perspective, ISO clustering method