中国市域间日常人口流动特征及影响因素

doi:10.13249/j.cnki.sgs.2022.11.004

Abstract

Abstract:

Using Tencent location big data, this study analyzes the spatial pattern of population flow among 368 cities in China and identifies the influencing factors related to population inflow and outflow based on an exponential random graph model (ERGM). 1) From 2015 to 2018, the spatial distribution pattern of population flow was relatively stable, forming a rhombic spatial structure with Beijing, Shenzhen, Shanghai, Guangzhou, Chengdu, and Dongguan as the ‘center’. The densely populated nodes and channels are mainly concentrated to the east of the Hu Huanyong Line. The significance of this study lies in further determining the core cities and main pillars in the population flow network. 2) The urban subgroup structure obtained by community division shows obvious geographical proximity and inter-provincial differentiation among communities, which form not only small urban subgroups with the provincial capital city forming the core and bordered by the provincial boundary, but also large urban subgroups with a multi-center structure spanning provincial administrative boundaries. However, for most cities, the provincial boundary delimits the main flow circle, and population flow within the same province is more frequent. 3) The influencing factors of the population inflow and outflow networks determined by the ERGM model are consistent with the predictions of neoclassical economics. Market and economic factors such as population scale, urbanization level, time cost, and economic cost still play a leading role in population flow. 4) The attraction of a city to the floating population depends on its individual attributes, while the urban population outflow depends more on the external-network-related elements of the city. To a certain extent, this study verifies the predominance of the urban “pull” in the push-pull theory and the comprehensive effect of various distance factors.

Key words: population flow, Tencent location big data, ERGM, migrant

CLC Number:

C912.81

Shi Xiang, Wang Shijun, Wang Dongyan, Hao Feilong, Li Zhuowei. Characteristics and Influencing Factors of Daily Population Flow Among Cities in China[J].SCIENTIA GEOGRAPHICA SINICA, 2022, 42(11): 1889-1899.

Figures/Tables 4

Table 1

Fig. 1

Fig. 2

Table 2

References 43

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变量		计量单位	定义
网络结构效应	边缘数	无	人口流动网络中连接数
网络结构效应	互惠性	无	网络节点是否倾向于形成交互
节点属性效应	人口规模	万人	城市的户籍人口规模
	二产结构	%	第二产业增加值占国内生产总值比重
	三产结构	%	第三产业增加值占国内生产总值比重
	教育水平	元/人	人均技术支出
	科技水平	元/人	人均教育支出
	城市化水平	无	土地城市化、人口城市化、经济城市化和社会城市化的综合测度
	经济规模	元/人	人均GDP
外生网络效应	地理距离	km	地球表面上城市间最短距离
	时间距离	min	城市间所有铁路车次出行时间的平均值
	经济距离	元	城市间所有铁路车次出行票价的平均值
	制度距离	无	城市间是否跨越省级行政区（是=1，否=0）
	高铁连通度	无	城市间是否有高铁（是=1，否=0）
	高速连通度	无	城市间是否有高速（是=1，否=0）

	人口流出网络		人口流入网络
	（1）	（2）	（3）	（4）
注：括号内数值为P值，^* 、^、^*分别代表变量在10%，5%，1%水平下显著；空白为无此项。
网络结构效应
边缘数	−4.49149 （<1e-04）^***	−4.28501 （<1e-04）^***	−4.49183 （<1e-04）^***	−4.26362 （<1e-04）^***
互惠性	4.70789 （<1e-04）^***	4.60989 （<1e-04）^***	4.68381 （<1e-04）^***	4.65761 （<1e-04）^***
节点属性效应
经济规模		9.62969 （0.884887）		8.90485 （0.884142）
人口规模		−1.53909 （0.001178）^**		0.45459 （0.0011775）^**
二产结构		−8.81496 （0.89458）		−8.50411 （0.893914）
三产结构		−1.59955 （0.112897）		−0.46686 （0.1128975）
科技水平		−0.26842 （0.329531）		1.33565 （0.336121）
教育水平		2.57122 （0.582948）		2.63779 （0.557048）
城市化水平		0.92069 （0.012087）^*		0.92069 （0.0120885）^*
外部网络效应
高铁连通度		2.21651 （<1e-04）^***		0.16682 （<1e-04）^***
高速连通度		0.51851 （<1e-04）^***		0.19283 （<1e-04）^***
地理距离		−2.81816 （<1e-04）^***		−0.35489 （<1e-04）^***
时间距离		−1.92137 （<1e-04）^***		−0.21347 （<1e-04）^***
经济距离		−1.0689 （0.000467）^***		−0.08442 （0.0005）^***
制度距离		−1.85178 （<1e-04）^***		−0.05274 （<1e-04）^***
Akaike Inf. Crit.	23068	23745	23088	42794
Bayesian Inf. Crit.	23088	23891	23107	42920

Characteristics and Influencing Factors of Daily Population Flow Among Cities in China

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