Acta Geographica Sinica ›› 2021, Vol. 76 ›› Issue (4): 853-869.doi: 10.11821/dlxb202104006

• Urban Studies • Previous Articles     Next Articles

China's city network based on Tencent's migration big data

WANG Lucang1(), LIU Haiyang1, LIU Qing2   

  1. 1. College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
    2. College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
  • Received:2019-07-26 Revised:2020-10-18 Online:2021-04-25 Published:2021-06-25
  • Supported by:
    National Natural Science Foundation of China(41261042)


With the rapid development of economic globalization and regional integration, the connection between cities is increasingly close. The mobility and interdependence of elements have promoted the formation of city network and become a new regional organization model and spatial structure. Based on Tencent's migration data, this paper constructs a 372×372 relational data matrix, and systematically depicts the city network pattern in China from different modes of transportation. The results show that cities with high network correlation degree are mainly concentrated in the area east of the Hu Huanyong Line, especially in the Yangtze River Delta, Pearl River Delta, Beijing-Tianjin-Hebei region, Chengdu-Chongqing region and other urban agglomeration areas, which have the highest concentration and become the main control power of China's city network pattern; and the cities in the northwest half are at a disadvantage status. According to the amount of migration, the Chinese city network is divided into national, large regional, regional, local and pedestal networks. The city network structure changes with the mode of transportation. When the network level moves down, the number of node cities tends to increase and the network density increases, but the network range tends to shrink. There is a close correlation between the network level and the migration path. National-level network related to air transportation, regional network related to railway transportation, and local network are relevant to automobile transportation. The economic space-time distance of different transportation modes determines the network structure, which is the basic factor that causes the network to differentiate with different paths.

Key words: city network, Tencent's migration big data, heteromorphism, China