| 基于空间滤波方法的中国省际人口迁移驱动因素 |
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| 引用本文： | 古恒宇,沈体雁,刘子亮,孟鑫.基于空间滤波方法的中国省际人口迁移驱动因素[J].地理学报,2019,74(2):222-237. |
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| 作者姓名： | 古恒宇 沈体雁 刘子亮 孟鑫 |
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| 作者单位： | 北京大学政府管理学院,北京,100871;华南师范大学经济与管理学院,广州,510006 |
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| 基金项目： | 国家社会科学基金重大项目(17ZDA055);国家自然科学基金项目(71473008);国家自然科学基金重大项目(71733001) |
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| 摘 要： | 人口迁移数据中往往存在较强的网络自相关性,以往基于最小二乘估计的重力模型与迁移数据的拟合度较低,而改进后的泊松重力模型仍存在过度离散的缺陷,以上问题均导致既有人口迁移模型中的估计偏差。本文构建了特征向量空间滤波（ESF）负二项重力模型,基于2015年全国1%人口抽样调查数据,研究2010-2015年中国省际人口迁移的驱动因素。结果表明：① 省际人口迁移流间存在显著的空间溢出效应,ESF能有效地提取数据中的网络自相关性以降低模型的估计偏差,排序在前1.4%的特征向量即可提取较强的网络自相关信息。② 省际人口迁移流之间存在明显的过度离散现象,考虑到数据离散的负二项重力模型更适用于人口迁移驱动因素的估计。③ 网络自相关性会导致模型对距离相关变量估计的上偏与大部分非距离变量估计的下偏,修正后的模型揭示出以下驱动因素：区域人口特征、社会网络、经济发展、教育水平等因素是引发省际人口迁移的重要原因,而居住环境与公路网络等因素也逐渐成为影响人口迁移重要的“拉力”因素。④ 与既有研究相比,社会网络因素(迁移存量、流动链指数)对人口迁移的影响日益增强,而空间距离对人口迁移的影响进一步呈现弱化趋势。
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| 关 键 词： | 省际人口迁移 空间滤波 负二项重力模型 驱动因素 中国 |
| 收稿时间： | 2017-11-09 |
Driving mechanism of interprovincial population migration flows in China based on spatial filtering |
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| GU Hengyu,SHEN Tiyan,LIU Ziliang,MENG Xin.Driving mechanism of interprovincial population migration flows in China based on spatial filtering[J].Acta Geographica Sinica,2019,74(2):222-237. |
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| Authors: | GU Hengyu SHEN Tiyan LIU Ziliang MENG Xin |
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| Institution: | 1. School of Government, Peking University, Beijing 100871, China2. School of Economics and Management, South China Normal University, Guangzhou 510006, China |
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| Abstract: | 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. |
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| Keywords: | interprovincial population migration flows spatial filtering negative binomial gravity model driving mechanism China |
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