PROGRESS IN GEOGRAPHY ›› 2019, Vol. 38 ›› Issue (6): 840-850.doi: 10.18306/dlkxjz.2019.06.005

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Identification of urban agglomeration boundary based on POI and NPP/VIIRS night light data

Liang ZHOU1,2(), Qi ZHAO1,3, Fan YANG4   

  1. 1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
    2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    3. School of Geographical Sciences, Nanjing Normal University, Nanjing 210023, China
    4. Tencent Beijing Technology Co. Ltd, Beijing 100836, China
  • Received:2018-07-18 Revised:2019-02-16 Online:2019-06-28 Published:2019-06-27
  • Supported by:
    National Natural Science Foundation of China, No. 41701173;Science Foundation for the Excellent Youth Scholars of Ministry of Education of China, No. 17YJCZH268;Gansu Feitian Scholar Youth Expert Support Program.


Identification of urban agglomeration boundary is the key to the smart and compact development of urban agglomerations. It also contributes to the development of national spatial governance system and the ability of spatial governance. Taking the Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta urban agglomerations as an example and by combining the Suomi National Polar-orbiting Partnership / Visible Infrared Imaging Radiometer Suite (NPP /VIIRS) night light images and points of interest (POI) data, this study aimed to accurately identify the actual physical boundaries of the three urban agglomerations and to analyze the spatial features of the agglomeration space by density-based curve threshold method and object-oriented fractal network evolution algorithm. The results show that: 1) The curve threshold method based on POI density and the NPP/VIIRS fractal network evolution algorithm both recognize that the boundaries of urban agglomeration are smaller than the administrative boundaries of the national urban agglomeration planning, and the identified ranges are 20.90%-24.40% of the planned ranges. 2) The areas of urban agglomeration extracted by POI and night light data are very close and can be compared and verified with each other. The areas of urban agglomerations extracted by POI data are larger, which to a great extent reflect the overall boundary of urban agglomeration instead of internal details. The urban agglomeration areas extracted by NPP/VIIRS images are more fragmented, which can identify the core areas of urban agglomerations. 3) Overlay of urban agglomerations extracted from the POI and the light data shows that in addition to the core zones, there are many isolated point areas in the three urban agglomerations, indicating that the three major urban agglomerations in China are still at a rapid development stage. The intensity of inter-city contact needs to be further strengthened.

Key words: multi-source data, spatial planning, urban scale, urban structure, urban agglomeration