| 转型期广州市居民职住模式的群体差异及其影响因素 |
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| 引用本文： | 张济婷,周素红.转型期广州市居民职住模式的群体差异及其影响因素[J].地理研究,2018,37(3):564-576. |
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| 作者姓名： | 张济婷 周素红 |
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| 作者单位： | 中山大学地理科学与规划学院,广东省城市化与地理环境空间模拟重点实验室,广州 510275 |
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| 基金项目： | 国家自然科学基金项目（41522104）;广东省自然科学基金项目（2017A030313228,2014A030312010） |
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| 摘 要： | 职住关系是城市研究领域重要的议题之一。体制改革后中国社会分层结构特殊,检验不同阶层居民在职住地选择偏好的差异,有助于理解居民职住格局形成的内部机制。利用广州市入户问卷调查、建成环境和人口普查等数据,采用两步聚类和多项logistic回归,对广州市居民进行阶层划分,对比居民职住模式的群体差异及其影响因素。结果表明：职住决策时,体制外工薪阶层追求低生活成本,受职住地建成环境影响显著;体制内阶层习惯于传统单位制下社会关系密切的社区,受邻里环境影响显著,还受个人属性影响;无固定工作者决策自由和平衡程度高,受少量建成环境因素影响。研究有助于了解居民职住格局形成的制度性机制,为优化居民职住格局提供思路。
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| 关 键 词： | 就业—居住平衡 职住模式 社会阶层 多项logistic回归 广州 |
| 收稿时间： | 2017-09-04 |
| 修稿时间： | 2017-12-07 |
The diversity of different groups' job-housing patterns and their impact factors under the background of institutional transformation: A case study of Guangzhou,China |
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| Jiting ZHANG,Suhong ZHOU.The diversity of different groups' job-housing patterns and their impact factors under the background of institutional transformation: A case study of Guangzhou,China[J].Geographical Research,2018,37(3):564-576. |
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| Authors: | Jiting ZHANG Suhong ZHOU |
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| Institution: | 1. School of Geography and Planning, Sun Yat-sen University, Guangdong Key Laboratory for Urbanization and Geo-simulation, Guangzhou 510275, China |
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| Abstract: | Job-housing relationship is one of the most important topics in urban studies. Because of the institutional transformation in China, people who work inside-system or live in urban areas may enjoy more welfare than those who work outside-system or live in rural areas, which makes the social differentiation unique. Dividing people into groups based on attributes related to institutional transformation and investigating what influences different groups' different job-housing situations can help to understand the internal mechanism of how the job-housing situations form in urban China. Mainly according to the result data of a questionnaire with a sample size of 1029, which was finished in Guangzhou in 2016, with the help of the point of interest and the road distribution data of Guangzhou in 2014, as well as the sixth census data of Guangzhou, two-step cluster and multinomial logistic regression are employed to figure out the mechanism. After clustering, three typical job-housing models were defined according to job-housing distance as well as the location of working and living place, and three social groups were defined according to residents' socioeconomic status. And then multinomial logistic regression was employed to compare the different reasons that influence different groups' job-housing situation. The results show that compared to the job-housing balance group, the outside-system group who have steady jobs try to achieve the highest comfort and convenience with the lowest living cost and they are willing to bear long commute for better living condition, so their job-housing models are influenced by the built environment mostly. When it comes to inside-system group, different from the outside-system group, on the one hand, they live in the unit community in the past, so they are used to the close-knit communities, and influenced by community environment obviously, on the other hand, they are influenced by social characters obviously, including marital status and the number of students in the family. The group of people without steady jobs are those who engage in business or have retired and get an informal job again. They have the highest freedom while deciding where to work and where to live, so their decisions about job-housing location are only slightly influenced by some factors about built environment, and the job-housing balance ratio of this group is the highest. This research tries to figure out the mechanism of how different social groups determine their job-housing location in the context of China's unique institutional transformation, and act as references to urban planners and policy makers while putting forward some advice to optimize it. |
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| Keywords: | job-housing balance job-housing model social groups multinomial logistic regression Guangzhou |
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