地理研究 ›› 2014, Vol. 33 ›› Issue (5): 887-898.doi: 10.11821/dlyj201405008

• 论文 • 上一篇    下一篇

基于六普数据的中国流动人口住房状况的空间格局

林李月1, 朱宇1, 梁鹏飞2, 肖宝玉1   

  1. 1. 福建师范大学地理科学学院, 湿润亚热带生态地理过程省部共建教育部重点实验室, 福州350007;
    2. 福建省测绘地理信息局, 福州350003
  • 收稿日期:2013-05-09 修回日期:2013-11-12 出版日期:2014-05-10 发布日期:2014-05-10
  • 通讯作者: 朱宇(1961- ),男,汉族,福建闽清人,博士,研究员,博士生导师,研究方向为人口迁移、城镇化与区域发展.E-mail:zhu300@fjnu.edu.cn E-mail:zhu300@fjnu.edu.cn
  • 作者简介:林李月(1985- ),女,汉族,福建霞浦人,博士,助理研究员,研究方向为人口迁移与城乡发展。E-mail:lly30@fjnu.edu.cn
  • 基金资助:
    福建省公益类科研院所基本科研专项一般项目(2013R06);福建省社会科学基金项目(2010C16)

The spatial patterns of housing conditions of the floating population in China based on the sixth census data

LIN Liyue1, ZHU Yu1, LIANG Pengfei2, XIAO Baoyu1   

  1. 1. School of Geographical Sciences, Fujian Normal University, Fujian Key Laboratory of Subtropical Resources abd Environment, Fuzhou, Fujian, 350007, China;
    2. Fujian Bureau of Surveying, Mapping and Geoinformationy, Fuzhou, Fujian, 350001, China
  • Received:2013-05-09 Revised:2013-11-12 Online:2014-05-10 Published:2014-05-10

摘要: 住房是流动人口融入城市、实现市民化过程中必须解决的关键问题。基于2010 年第六次人口普查数据,采用住房拥有率、租住房率、住房面积指数、住房不受干扰指数、住房质量指数和住房费用指数6 个指标考察流动人口的住房状况,并综合运用数理统计、空间自相关和系统聚类法揭示流动人口住房状况的属性特征、空间分布与集聚类型。研究发现,与城镇常住人口相比,流动人口的住房状况较差。从空间分布看,流动人口住房状况的各项指标具有显著的空间正相关,在空间分布上不仅存在集聚现象,而且有明显的集聚中心。研究结果还表明,流动人口住房条件综合状况可划分为较好、中等、中等偏下、较差4 级类型区,在全国尺度上的空间分布除个别类型外具有团块聚合的结构特征。在考虑社会公平的前提下,应分类解决不同类型区域流动人口的住房问题。

关键词: 流动人口, 住房状况, 空间格局, 六普数据, 中国

Abstract: China's rapid urbanization and economic development have given rise to the fast growth of the floating population, and housing is a key issue in the process of their integration into the destination cities. This paper intends to explore this topic by analyzing the spatial patterns of housing conditions of the floating population. Based on the sixth census data, the paper selects six indicators to measure housing conditions of the floating population: the home-ownership rate, the rental-housing rate, the floor area index, the housing facilities index (constructed by summing up the situation of five variables: availability of running water, washroom, bathroom, kitchen, and the type of fuel), the index of privacy (constructed by summing up the situation of two variables: the function of the dwelling and the number of the dwelling's floors), and the housing consumption index. It uses the methods of Spatial Autocorrelation Analysis and Hierarchical Cluster to examine the spatial distribution and agglomeration patterns of the floating population's housing conditions. The results of the calculation show that compared with urban permanent residents, members of the floating population are much more likely to live in rental homes;their housing conditions are generally worse;and their rental expenses are higher. The spatial variation of the homeownership rate, the rental-housing rate, and the housing facilities index is mainly manifested as north-south differences;the floor area index, and the index of privacy show marked difference between eastern and western China. The low-value centers of the housing consumption index are located in Inner Mongolia, Shaanxi, Hubei and Anhui provinces, while the high-value centers are located in Beijing. Furthermore the results of Spatial Autocorrelation Analysis demonstrate that there is a significant positive spatial correlation in the indicators of the floating population's housing conditions on a national scale, and identify the phenomenon of their spatial clustering and the centers of such spatial clustering. The analysis of Hierarchical Clustering identifies the housing conditions of the floating population into four distinctive groups, and suggests that the housing conditions of the floating population in the inner and east parts of China are better than those in the outer and west parts, and such a spatial variation extends from the north to the south. Finally, on the basis of the above findings, the paper puts forward some policy suggestions for improving the housing conditions of the floating population.

Key words: the floating population, housing conditions, spatial patterns, sixth census data, China