地理研究 ›› 2018, Vol. 37 ›› Issue (12): 2567-2575.doi: 10.11821/dlyj201812016

所属专题: 人口与城市研究

• 研究论文 • 上一篇    下一篇

非户籍与户籍人口居住空间分异的多维度解析——以深圳为例

张瑜(), 仝德(), IanMacLACHLAN   

  1. 北京大学城市规划与设计学院,深圳 518055
  • 收稿日期:2018-05-31 修回日期:2018-09-05 出版日期:2018-12-20 发布日期:2018-12-24
  • 作者简介:

    作者简介:张瑜(1994- ),女,山东省淄博市人,硕士,研究方向为城市与区域规划。E-mail: 1601213872@sz.pku.edu.cn

  • 基金资助:
    国家自然科学基金项目(41371167);深圳市哲学社会科学“十三五”规划课题(135B022)

Multi-dimensional analysis of housing segregation:A case study of Shenzhen, China

Yu ZHANG(), De TONG(), MacLACHLAN Ian   

  1. School of Urban Planning and Design, Peking University, Shenzhen 518055, Guangdong, China
  • Received:2018-05-31 Revised:2018-09-05 Online:2018-12-20 Published:2018-12-24
  • About author:

    Author: Shi Zhenqin (1988-), PhD, specialized in regional development and land space management in mountain areas. E-mail: kevinszq@163.com

    *Corresponding author: Deng Wei (1957-), Professor, specialized in mountain environment and regional development.

    E-mail: dengwei@imde.ac.cn

摘要:

在居住空间相异指数基础上,构建了集聚—分散度、中心—边缘度和极化—均质度指数,进一步挖掘由于人口聚居形态、居住区位和居住质量等方面差异导致的居住空间分异的多维内涵,及其所揭示出的社会经济空间现象、成因及空间治理重点。利用全国第六次人口普查数据开展深圳实证研究,在计算全市及各区分维指数的基础上,分析深圳人口居住空间相异指数特征及空间尺度差异,多维居住空间分异格局特征及成因,并通过聚类分析将深圳非户籍与户籍人口居住空间分异类型划分为三类,分类提出空间治理政策建议。从而为深入理解中国大城市日益出现的居住分异现象及机制提供新鲜视角和多样化测度方法,为解决其带来的社会及空间治理问题提供更有针对性的政策建议。

关键词: 居住空间分异, 集聚—分散度指数;, 中心—边缘度指数;, 极化—均质度指数;, 深圳市

Abstract:

Residential segregation has been a severe and widespread phenomenon in mega cities along with fast urbanization in China. Migrants from rural area flock into developed cities especially coastal regions for better job opportunities, which provide essential cheap labor for urban growth. However, their housing problems could not be resolved in formal housing either hindered by institutional barrier or unreachable housing price. The housing segregation gradually formed as locals reside in formal gated communities while migrants crowd in informal housing like urban villages, which is characterized with lower rent but substandard living conditions. The housing segregation in China derives from household registration system (hukou). The Index of Dissimilarity (ID) only emphasizes the unevenness of population distribution but could not fully manifest the segregation characteristics in density, location, proximity, etc. Inspired by the work of Massey Denton in multi-dimensional segregation, this article applies three measures of housing segregation (Clustering, Centralization, and Concentration) based on the ID to analyze the segregation between urban residents with and without hukou. It examines the multi-dimensional housing segregation based on hukou status using data from China’s 6th national census in 2010. The typical migrant city Shenzhen was chosen to conduct the case study, and the segregation index of three dimensions was calculated based on 55 sub-districts for comparison. The multi-dimensional segregation indexes showed that Shenzhen has high segregation problems at the city scale, but more homogeneous inside each district. The history, industrial structure and socioeconomic background of each district play a crucial role in the segregation. The outside-custom area provides more chances in labor-dense sectors and attracts more migrants to reside in a large scale, while the inside-custom regions are more advanced in informatics and financial sectors, which results in scattered spots of migrants housing. Cluster analysis reveals the three types of segregation, each of which has its unique processual mechanisms, and policy prescriptions. The study shows that the housing segregation has multiple dimensions and scales. Thus two sets of people could be featured by a single ID yet to be clustered or dispersed, central or peripheral, or concentrated or deconcentrated. Migrants may occupy continuous neighboring blocks in peripheral area, or densely reside in few scattered urban villages in inner city, or congregate in factory dorms alongside each industrial zone. Based on segregation patterns, locations and density, local governments should take different measures like redevelopment of targeted urban villages, large-scale public housing construction or cooperation with factories in worker dormitory improvement accordingly. This article contributes an innovative and comprehensive perspective to conceptualize housing segregation, and provides policy recommendations to deal with the social problems that arise from segregation in China. With the advancement of big data, more practical real-time housing management measures could be developed for practitioners to provide human-centric housing planning and avoid the housing polarization.

Key words: housing segregation, Clustering Index, Centralization Index, Concentration Index, Shenzhen