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中国城市流动人口回流意愿的空间分异及影响因素 被引量:56

Spatial variation of migrant population’s return intention and its determinants in China’s prefecture and provincial level cities
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摘要 基于国家卫计委2016年流动人口动态监测数据(CMDS),运用相关GIS空间统计工具探讨了中国279个地级及以上城市流动人口回流意愿空间分异问题,进而考察流动人口回流意愿的影响因素。研究发现:①相比居留意愿及户籍迁移意愿,中国城市流动人口回流意愿偏低(6.17%),其中,绝大多数流动人口(74.05%)的回流去向地是返回原居住地。城市规模、城市等级与流动人口回流意愿呈非对称"U型"格局。②流动人口回流意愿总体上呈现出集聚分布模式,并具有显著的空间分异特征,长江三角洲、京津冀等城市群流动人口的回流意愿较高,成渝和哈长城市群流动人口回流意愿较低;东部地区流动人口回流意愿最高,东北地区的回流意愿最低;华南、中南和部分华东地区为回流意愿热点区,东北和华北地区为回流意愿的冷点区。③回流意愿受流动人口内部因素与流入地外部因素的共同影响,同时作为内部因素和外部因素的教育水平及经济发展变量对流动人口回流意愿的影响方向不同。④流动人口的家庭联系、社会网络、住房及经济因素是塑造流动人口空间格局的主要力量,流入地家庭规模及住房拥有率等对流动人口回流意愿产生抑制作用,非流入地家庭规模、流动次数、家庭收入支出比等对回流意愿产生促进作用。 In China, population migration has an influence on the level of economic and social development in various regions. In 2016, the scale of migrant population reached 245 million,which became an important factor affecting population changes. From the perspective of migrant populations’ destinations, with the control of population scale in some big cities in China, and the sustained economic growth in the central and western regions, the problem of population reflux is attracting more and more attention from the society. The spatial differentiation and influencing factors of migrant populations’ return intention are vital issues which are urgent to be investigated. Based on data from the 2016 China Migrant Population Dynamic Survey(CMDS), this paper discusses the phenomenon about spatial differentiation of migrant populations’ return intention in 279 prefecture and provincial level cities in China,using GIS spatial analysis methods such as Moran’s I coefficient, Getis-Ord Gi*. Besides, we investigate the influencing factors of migrant populations’ return intention. Study comes to the following conclusions:(1) Compared with the residence intention and hukou transfer intention,the return intention of migrant population in China is lower(6.17%), and the majority of migrants(74.05%) are eager to take their own home towns as refluxing destination. The scale of cities, the level of cities and the return intention of migrant population present an asymmetric "U"-shaped pattern.(2) The spatial distribution of return intention presents an aggregation pattern with marked spatial differentiation. The return intention of migrant population in Yangtze River Delta Urban Agglomeration, Beijing-Tianjin-Hebei Urban Agglomeration and Zhongyuan Urban Agglomeration is higher than that in Chengdu-Chongqing Urban Agglomeration and Harbin-Changchun Urban Agglomeration. Among the four main geographical divisions, the return intention of urban floating population in the eastern region is the highest while that of the northeast region is the lowest. South China, central-south China and part of East China are hot spots of return intention while Northeast and North China are in a weak corner.(3) The return intention is influenced by both internal factors of migrant population and external factors of in-flow area. Educational level and economic development are both internal and external factors that simultaneously play different roles in return intention.(4) Family connection, social networks, housing and economic factors of migrant population are main forces that shape the spatial pattern of return intention. Family scale in in-flow area and home-ownership rates inhibit return intention while variables such as family scale in nonin-flow area, migrant times and the family’s income and expenditure proportion exert a positive influence on return intention. Based on the above conclusions, this paper proposes strategies for relevant departments on the management of migrant population.
作者 古恒宇 覃小玲 沈体雁 GU Hengyu;QIN Xiaoling;SHEN Tiyan(School of Government, Peking University, Beijing 100871, China;School of Geography, South China Normal University, Guangzhou 510631, China)
出处 《地理研究》 CSSCI CSCD 北大核心 2019年第8期1877-1890,共14页 Geographical Research
基金 国家社会科学基金重大项目(17ZDA055) 国家自然科学基金重大项目(71733001) 中国科协创新战略研究院项目(CXY-ZKQN-2019-041)
关键词 流动人口 回流意愿 空间分异 影响因素 城市 migrant population return intention spatial differentiation influencing factor cities
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