中国省际人口流动网络演化及其影响因素研究

Study on the Evolution and the Influencing Factors of Inter-provincial Population Flow Network in China

基于2005年、2015年省际人口流动的相关数据,借助社会网络分析方法,进行了人口流动的网络结构特征研究,并剖析了人口流动网络的影响因素,研究结果表明:①整个网络的关联性增强,小世界性发育典型;以广东为主,浙江、江苏、安徽、四川为副的“一主四副”的网络结构进一步凸显,等级层次性加强;从人口流动网络的关联性视角看,相邻省份间具有较为活跃的人口流动,体现出显著的地理邻近性;整个人口流动的空间格局体现了“东入内出”的模式,空间不均衡性加剧。②通过QAP回归发现:开放水平、收入水平、地理邻近性对省际人口流动网络具有显著的影响;人均GDP、人力资本、第三产业只对省际人口流入网络具有显著影响,对人口流出网络并没有发挥显著的作用;失业水平对人口流动网络的影响经历了由显著到不显著的演化;第二产业对人口流动网络的影响经历了由不显著到显著的演化过程。

This paper takes the relevant data of inter-provincial population flow in 2005 and 2015,using social network analysis method,to study the network structure characteristics of population flow,and analyzes the influencing factors of the population flow network.The results of the study show that:①the correlation of the whole network is enhanced,which presents a typical"small world"network characteristics;the"one primary and four secondary"network structure is further highlighted with Guangdong as the primary place,Zhejiang,Jiangsu,Anhui and Sichuan as secondary places,which means the level of hierarchy is strengthened;from the point of the relevance of the population flow network,there is a relatively active population flow among the neighboring provinces,which shows significant geographical proximity;the spatial pattern of the whole population flow reflects the pattern of"inland population flowing to the east",which results in the aggravation of spatial imbalance.②QAP regression analysis shows that opening up level,income level and geographical proximity have a significant impact on inter-provincial population flow network;per capita GDP,human capital and the third industry have a significant impact on the inter-provincial inflow network,but have not played a significant role in the outflow network.The impact of unemployment level on the population flow network has experienced the evolution from significant to not significant;the impact of the second industry on the population flow network has experienced a change from not significant to significant.