春节返乡人流的空间特征及其对城市消费经济的影响

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