Driving mechanism of interprovincial population migration flows in China based on spatial filtering

被引：0
|
作者：
Gu H. [1 ]
Shen T. [1 ]
Liu Z. [2 ]
Meng X. [1 ]
机构：
[1] School of Government, Peking University, Beijing
[2] School of Economics and Management, South China Normal University, Guangzhou
来源：
Dili Xuebao/Acta Geographica Sinica | 2019年 / 74卷 / 02期
基金：
中国国家自然科学基金; 国家自然科学基金重大项目;
关键词：
China; Driving mechanism; Interprovincial population migration flows; Negative binomial gravity model; Spatial filtering;
D O I：
10.11821/dlxb201902002
中图分类号：
学科分类号：
摘要：
According to previous studies, not only does the conditional gravity model based on ordinary least squares often bring about poor fitting of migration flows in reality, but also there exists overdispersion in the extended Poisson gravity model. Simultaneously, network autocorrelation usually exists in population migration data (e.g., the spatial interaction among migration flows). The problems mentioned above result in biased estimation. In order to capture network autocorrelation and deal with the issue of overdispersion, we build an eigenvector spatial filtering negative binomial gravity model (ESF NBGM) based on the data of 1% national population sample survey in 2015, to analyze the driving mechanism of interprovincial population migration flows in China. The results are as follows: (1) Positive spatial spillover effect exists in interprovincial population migration flows, and ESF can capture network autocorrelation in data, so as to reduce the estimated deviation of the model. Furthermore, eigenvectors ranking top 1.4% can properly interpret the spatial pattern of high network autocorrelation in data. (2) There exists overdispersion in China's interprovincial migration flows. Considering this problem, a negative binomial regression model is more suitable for the estimation of driving mechanism for population migration, together with statistical enhancement. (3) Network autocorrelation leads to overestimation of distance variables and underestimation of non-distance variables. The results of the improved model reveal that: chief factors the affect driving mechanism are regional population characters, social network, economic development and education level. Meanwhile, living environment and road network gradually become one of the most crucial pulling factors that influence migration flows. (4) Compared to previous studies, social network (i.e. migration stock) plays a more significant role in population migration flows, while the impact of spatial distance keeps weakening. © 2019, Science Press. All right reserved.
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页码：222 / 237
页数：15
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