GEOGRAPHICAL RESEARCH ›› 2021, Vol. 40 ›› Issue (2): 477-494.doi: 10.11821/dlyj020200074

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ng urban functional areas based on multi-source data: A case study of Beijing

YANG Zhenshan1,2(), SU Jinhua3, YANG Hang1,2, ZHAO Yonghong4   

  1. 1. Key Lab of Regional Sustainable Development of Modelling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
    3. School of Statistics, Renmin University of China, Beijing 100872, China
    4. School of Tourism & Research Institute of Human Geography, Xi'an International Studies University, Xi'an 710128, China
  • Received:2020-02-06 Accepted:2020-09-02 Online:2021-02-10 Published:2021-04-10


It has been a trend for the study of urban functional areas to employ multi-source at a finer tempo and/or spatial scale to contribute a niche understanding of the structure and content of the area. Drawing on advancement of technological development of new type and new sources of geo-coded data, the paper proposes a set of indicators to understand urban functional areas, including the intensity of the use of the area, the difference in the use between day and night, and the degree of multifunctionality. The empirical research is taken in Beijing and these indicators are derived by integrating the mobile phone signaling data of 14400 grid areas in the urban area in 2017 and 380975 points of interest (POI) data from Gaode map in 2016. The main research conclusions are as follows. (1) Every standard area in this paper has a square of 250 m×250 m. The number of Beijing's daytime active areas is three times that of nighttime active areas. The nighttime use intensity of restaurant, living, and other service facilities is higher, and the daytime use intensity of financial, tourism, and public service facilities is higher. (2) Tourism (28.2%), residence (12.1%), transportation (11.4%) are the three types of functional areas with the largest proportion of Beijing area, and the smallest area proportion is financial function district (2.8%), showing that the spatial characteristics of tourism, finance, and public functional areas gather, and other functional areas' distribution presents scattered characteristics. (3) Residential, restaurants, living, and other functional services are strongly dependent on each other, while tourism and enterprise functional services show strong exclusivity to other functional types. Except for the tourism functional areas, there appears an obviously different functional mixing pattern in the central urban area of Beijing. In this paper, the area with the highest function mixing degree (greater than 0.98) is defined as the highly mixed functional area, which accounted for 24.6% of the study area. The result of the functional area divisions has a strong practical significance for Beijing urban planning, and also provides an effective method and a richer perspective for the future in-depth study of urban functional areas.

Key words: urban functional area, multi-source data, population density, mixed functional area, Beijing