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地理研究    2018, Vol. 37 Issue (12): 2567-2575     DOI: 10.11821/dlyj201812016
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
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
北京大学城市规划与设计学院,深圳 518055
Multi-dimensional analysis of housing segregation:A case study of Shenzhen, China
ZHANG Yu(),TONG De(),Ian MacLACHLAN
School of Urban Planning and Design, Peking University, Shenzhen 518055, Guangdong, China
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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。

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张瑜
仝德
IanMacLACHLAN
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 wordshousing segregation    Clustering Index    Centralization Index    Concentration Index    Shenzhen
收稿日期: 2018-05-31      出版日期: 2018-12-24
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)
引用本文:   
张瑜, 仝德, IanMacLACHLAN . 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[J]. 地理研究, 2018, 37(12): 2567-2575.
ZHANG Yu, TONG De, Ian MacLACHLAN . Multi-dimensional analysis of housing segregation:A case study of Shenzhen, China[J]. GEOGRAPHICAL RESEARCH, 2018, 37(12): 2567-2575.
链接本文:  
http://www.dlyj.ac.cn/CN/10.11821/dlyj201812016      或      http://www.dlyj.ac.cn/CN/Y2018/V37/I12/2567
Fig. 1  1979-2015年深圳市按户籍状况人口构成
Fig. 2  多维度居住空间分异内涵示意图
Fig. 3  研究区行政区划及街道分布示意
排序 相异指数 多维度指数
集聚—分散度 中心—边缘度 极化—均质度
深圳市 0.684 深圳市 1.058 深圳市 0.693 深圳市 0.250
1 光明新区 0.352 盐田区 1.204 宝安区 0.797 龙岗区 0.406
2 宝安区 0.317 光明新区 1.187 罗湖区 0.755 罗湖区 0.386
3 南山区 0.264 宝安区 1.064 盐田区 0.750 盐田区 0.329
4 福田区 0.257 南山区 1.046 龙岗区 0.699 光明新区 0.245
5 龙岗区 0.231 福田区 1.020 坪山新区 0.693 福田区 0.183
6 盐田区 0.203 龙岗区 1.017 福田区 0.472 南山区 0.154
7 罗湖区 0.187 罗湖区 1.007 南山区 0.472 宝安区 0.135
8 坪山新区 0.040 坪山新区 1.000 光明新区 0.357 坪山新区 0.089
Tab. 1  深圳市分维度居住空间分异统计指标
Fig. 4  2010年深圳市各区分行业在岗职工人数
Fig. 5  聚类分析树状图
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