主管单位：中国科学技术协会
主办单位：中国地理学会
承办单位：华东师范大学
世界地理研究

世界地理研究 ›› 2025, Vol. 34 ›› Issue (4): 127-138.DOI: 10.3969/j.issn.1004-9479.2025.04.20230166

• 城市与产业 • 上一篇    

春节返乡人流的空间特征及其对城市消费经济的影响

李民健1(), 秦萧1,2(), 甄峰1,2   

  1. 1.南京大学建筑与城市规划学院，南京 210093
    2.江苏省智慧城市规划与数字治理工程研究中心，南京 210093
  • 收稿日期:2023-03-28 修回日期:2023-08-20 出版日期:2025-04-15 发布日期:2025-04-27
  • 通讯作者: 秦萧
  • 作者简介:李民健（1999—），男，硕士研究生，研究方向为城市大数据，E-mail：MF21360076@smail.nju.edu.cn。
  • 基金资助:
    国家自然科学基金面上项目(52078246)

Spatial feature of returning people flows in the Spring Festival and its impact on urban consumption: An empirical study of 93 major cities in China

Minjian LI1(), Xiao QIN1,2(), Feng ZHEN1,2   

  1. 1.School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China
    2.Jiangsu Engineering Research Center of Smart City Planning and Digital Governance, Nanjing 210093, China
  • Received:2023-03-28 Revised:2023-08-20 Online:2025-04-15 Published:2025-04-27
  • Contact: Xiao QIN

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摘要：

在中国快速城市化的进程中，人口城市化相对滞后，流动人口在城市常住人口中占据较大比例。现有研究普遍认为流动人口个体的消费水平偏低，但尚未量化流动人口整体对城市消费经济的影响。在国家深入推进“以人为核心”的新型城镇化、着力推动恢复和扩大消费的背景下，识别各城市流动人口对于消费经济的影响维度与程度，将成为结合城市实际制定差异化发展政策的必要前提。研究选取全国人口和经济规模较大、中心性较强的93个主要城市，基于百度迁徙数据，应用创新性返乡人流计算方法，以春节实际返乡人流规模表征流动人口规模，探索返乡人流的空间特征，并构建多元线性回归模型评估春节返乡人流对消费经济的影响程度。研究发现：①返乡人流规模与返乡目的地城市数量存在正相关关系，珠三角、长三角居于领先位置，平均返乡距离可表征城市吸引范围的大小，整体均值为549 km。②自然地形地物与区域经济格局共同影响着返乡目的地城市的标准差椭圆扁率。③地区生产总值、常住人口规模、第三产业比重具有显著正相关关系，而返乡人流规模、标准差椭圆扁率具有显著负相关关系，其中地区生产总值、常住人口规模、返乡人流规模的影响程度最大。最后，研究基于各城市的常住人口规模和返乡人流规模对于城市消费经济的正负效应差异，分类提出政策优化建议。

关键词: 百度迁徙数据, 春运, 大数据, 城市消费, 标准差椭圆

Abstract:

In the process of rapid urbanization in China, population urbanization is relatively lagging behind, so the floating population occupies a large proportion of the urban permanent population. In the context of people-oriented new urbanization and promoting the recovery and expansion of consumption, it is necessary to identify the dimension and degree of the impact of the floating population on the consumption economy, which helps formulate differentiated development policies based on the actual conditions of cities. This study selected 93 major cities with large population or economic scale and strong centrality. Based on Baidu migration data, the scale of floating population was represented by the actual scale of returning people during the Spring Festival by an innovative calculating method. This study explored the spatial feature of returning people flows, and quantified the impact of returning people flows on the consumption by constructing a multiple linear regression model. There are 3 main conclusions. ① a positive correlation is found between the number of returning people flows and the destination cities, and the cities in Pearl River Delta or Yangtze River Delta lead the way. The average distance of returning is 549 km, which reflects the size of the urban attractive area. ② natural terrain features and regional economic patterns together affect the standard deviation elliptic oblateness of destination cities. ③ there is a significant positive correlation among GDP, the size of urban permanent population, the proportion of tertiary industry and the total retail sales of consumer goods, and there is a significant negative correlation among the size of returning people flows, the standard deviation elliptic oblateness and the total retail sales of consumer goods. Moreover, GDP, the size of urban permanent population and returning people flows have the most prominent influence. Finally, this study puts forward optimization suggestions, based on the positive or negative effect of urban permanent population and returning people flows in each city.

Key words: Baidu migration data, Spring Festival travel rush, big data, urban consumption, standard deviation ellipse