地理学报 ›› 2021, Vol. 76 ›› Issue (4): 853-869.doi: 10.11821/dlxb202104006

• 城市研究 • 上一篇    下一篇

基于腾讯迁徙大数据的中国城市网络研究

王录仓1(), 刘海洋1, 刘清2   

  1. 1.西北师范大学地理与环境科学学院,兰州 730070
    2.兰州大学资源环境学院,兰州 730000
  • 收稿日期:2019-07-26 修回日期:2020-10-18 出版日期:2021-04-25 发布日期:2021-06-25
  • 作者简介:王录仓(1967-), 男, 甘肃天水人, 博士, 教授, 博士生导师, 研究方向为城乡发展与规划。E-mail: Wanglc007@nwnu.edu.cn
  • 基金资助:
    国家自然科学基金项目(41261042)

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 Published:2021-04-25 Online:2021-06-25
  • Supported by:
    National Natural Science Foundation of China(41261042)

摘要:

随着经济全球化和区域一体化的快速发展,城市间的交流日益密切,要素的流动性和互赖性促进了城市网络的形成,并成为一种新的区域组织模式和空间结构。本文基于2018年腾讯人口迁徙数据,构建了372×372关系数据矩阵,并从不同交通方式出发,系统刻画了中国城市网络格局。研究表明:① 网络关联度高的城市主要集中在胡焕庸线以东区域,尤其是长三角、珠三角、京津冀、成渝等城市群地区的聚集程度最高,成为中国城市网络格局的主控力量;而西北半壁的城市处于被支配地位。② 依据迁徙量,将中国城市网络划分为国家级、大区级、区域级、地方级和基座级网络。城市网络结构随交通方式而变化,当网络等级下移时,节点城市趋于增多,网络密度加大,但网络范围趋于缩小,网络等级与迁徙路径存在着密切的关联性,国家级网络与空运相关,区域级网络与铁路运输相呼应,地方级网络与汽车运输相关。③ 不同运输方式的经济时空距离决定了网络结构,是引致网络随路径不同而产生分异的基本因素。

关键词: 城市网络, 腾讯人口迁徙大数据, 分异, 中国

Abstract:

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