地理研究 ›› 2019, Vol. 38 ›› Issue (7): 1678-1693.doi: 10.11821/dlyj020171231

• 论文 • 上一篇    下一篇

中国城市间人口流动空间格局的网络分析——以国庆-中秋长假和腾讯迁徙数据为例

潘竟虎(), 赖建波   

  1. 西北师范大学地理与环境科学学院,兰州 730070
  • 收稿日期:2017-12-25 修回日期:2019-05-09 出版日期:2019-07-20 发布日期:2019-07-12
  • 作者简介:

    作者简介:潘竟虎(1974-),男,甘肃嘉峪关人,博士,教授,主要从事空间经济分析研究。E-mail: panjh_nwnu@nwnu.edu.cn

  • 基金资助:
    国家自然科学基金项目(41661025);甘肃省高等学校科研项目(2016A-001);西北师范大学青年教师科研能力提升计划(NWNU-LKQN-16-7)

Research on spatial pattern of population mobility among cities: A case study of "Tencent Migration" big data in "National Day-Mid-Autumn Festival" vacation

Jinghu PAN(), Jianbo LAI   

  1. College of Geography and Environmental Science of Northwest Normal University, Lanzhou 730070, China
  • Received:2017-12-25 Revised:2019-05-09 Online:2019-07-20 Published:2019-07-12

摘要:

“腾讯迁徙”大数据基于位置服务,实时、动态、完整、系统地描述了用户日常出行活动的轨迹。通过采集“腾讯迁徙”数据平台中2017年国庆-中秋长假期间国内299个城市之间的逐日人口流动数据,分“出行期、旅途期、返程期”3个时间段,利用复杂网络分析方法,从人口流动集散层级、集散网络体系的分层集聚、人口流动空间格局、网络空间特征等角度分析各时间段城市间的人口流动特征与空间格局。结果表明,腾讯迁徙大数据直观地揭示了国庆-中秋期间中国各地级城市间人口的迁移规律,3个时段人口的净流入均呈现十字形骨架支撑的菱形分布,人流集散中心主要集中在京津冀、长三角、珠三角和成渝四大城市群,与城市等级有较强的一致性。人口流动集散体系呈明显的分层集聚,城市行政级别的高低与人口流动影响力存在一定的正相关关系,大部分城市人口流动处于“相对平衡”状态。人口流动空间格局呈现出明显的核心-边缘结构,大理-鹤岗一线是人口流动强度空间分布的显著分界线,以此线为界,城市网络呈现东密西疏的分布特征和东部并联、西部串联的网络关联特征。人口流动网络总体表现出“小世界”网络特征,局部具有较明显的“社区”结构特征,聚为2个国家级、2个区域级和3个地区级社区。

关键词: 人口流动, 腾讯迁徙, 城市网络, 国庆-中秋假期, 中国

Abstract:

Population migration, social check-in, vehicle navigation, and other spatial behavior big data have become vital carriers characterizing users' spatial behavior. The big data used in this paper were collected from the locations provided by hundreds of millions intelligent mobile phone users through Location Based Service (LBS) Tencent Migration data platform, and were displayed by means of real-time heat map which indicates user’s moving trajectory in China. "Tencent Migration" big data can real-timely, dynamically, completely and systematically record population flow routes using LBS device. Through gathering residents daily mobility among 299 cities in China during the period of "National Day-Mid-Autumn Festival" (NDMAF) vacation (from September 30 to October 8) in 2017 in "Tencent Migration" and defining three periods with "travel period, journey period, return period", this paper is designed to analyze and explore the characteristics and spatial patterns of daily flow mobility cities from the perspective of population daily mobility distribution levels, flow distribution layers network aggregation, spatial patterns and characteristics of the complex structure of the flow network. Results show that "Tencent migration" big data clearly discovers the temporal-spatial pattern of population mobility in China during the period of NDMAF. The net inflow of population showed a diamond-shaped pattern with cross frame support in each period, with the four nodes of Beijing, Shanghai, Guangzhou and Xi’an. Main mobility assembling centers are distributed in the urban agglomerations of Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta and Chengdu-Chongqing, and those centers have strong coherence with those urban hierarchies. There is a positive correlation between the level of urban administration and the influence of population flow. Most cities are in a state of "relative equilibrium" in the population flow, and clear hierarchical structure and level distinction can be identified. Spatial patterns of population mobility present obvious core-periphery structures. The Dali-Hegang line exhibits a significant network of spatial differences in terms of boundary divisions. In this context, the spatial distribution of urban network could be summarized as "dense in the East and sparse in the West", and the core linkages of urban network could be characterized as "parallel in the East and series in the West". The whole network exhibits a typical "small world" network characteristic, which shows that China's urban population flow network has high connectivity and accessibility during the period of NDMAF. The network has a distinct "community" structure in the local area, including 2 national communities, 2 regional communities and 3 local-level communities.

Key words: population flow, "Tencent migration", urban network, National Day - Mid-Autumn Festival vacation, China