地理学报 ›› 2017, Vol. 72 ›› Issue (4): 589-602.doi: 10.11821/dlxb201704003

• 城镇化研究 • 上一篇    下一篇

基于住宅价格视角的居住分异耦合机制与时空特征——以南京为例

宋伟轩1,2(), 毛宁3, 陈培阳4(), 袁亚琦5, 汪毅6   

  1. 1. 中国科学院南京地理与湖泊研究所,南京 210008
    2. 中国科学院流域地理学重点实验室,南京 210008
    3. 北京师范大学地理学与遥感科学学院,北京 100875
    4. 苏州大学建筑学院,苏州 215123
    5. 南京大学地理与海洋科学学院,南京 210023
    6. 南京市规划设计研究院,南京 210005
  • 收稿日期:2016-06-06 修回日期:2016-10-21 出版日期:2017-04-20 发布日期:2017-05-09
  • 作者简介:

    作者简介:宋伟轩(1981-), 男, 吉林敦化人, 博士, 副研究员, 研究方向为城市社会地理。E-mail: wxsong@niglas.ac.cn

  • 基金资助:
    国家自然科学青年基金项目(41201161, 41501168)

Coupling mechanism and spatial-temporal pattern of residential differentiation from the perspective of housing prices:A case study of Nanjing

Weixuan SONG1,2(), Ning MAO3, Peiyang CHEN4(), Yaqi YUAN5, Yi WANG6   

  1. 1. Nanjing Institute of Geography and Limnology, CAS, Nanjing 210008, China
    2. Key Laboratory of Watershed Geographic Sciences, CAS, Nanjing 210008, China
    3. School of Geography, Beijing Normal University,Beijing 100875, China
    4. School of Architecture, Soochow University, Suzhou 215123, Jiangsu, China
    5. School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China
    6. Nanjing Academy of Urban Planning & Design, Nanjing 210005, China
  • Received:2016-06-06 Revised:2016-10-21 Online:2017-04-20 Published:2017-05-09
  • Supported by:
    National Natural Science Foundation of China, No.41201161, No.41501168

摘要:

城市住宅价格空间分异,是居住空间资源非均衡配置的市场化表达,映射出不同阶层社会群体对城市住宅的选择倾向与需求差异,与居住空间分异在机制和格局上存在一定耦合关联。以南京主城区商品房社区为研究对象,构建起住宅价格特征变量指标体系,采用地理加权回归模型,分析导致房价空间差异的主要因素、组合关系及时空演变特征。研究发现:社区服务档次、学区资源、环境区位、景观稀缺等能够体现居住群体经济实力、生活方式与文化品位的因素,是影响房价分异的主导要素并随时间不断强化;南京房价总体上呈现“圈层+扇形+飞地”的空间结构,高房价主要分布在城市中心、名校学区、高档封闭社区和山水景观别墅区;房价分异与居住分异在作用机制和空间格局上表现出显著的关联耦合特征。城市房价空间分异不止于表达,同时也是推动居住空间分异与再分化的重要驱动机制,并能够预判未来一段时期内城市居住空间分异格局演变的基本走势,可以为城市社会空间研究提供具有前瞻性的观察视角和分析工具。

关键词: 住宅价格, 居住空间分异, 地理加权回归模型, 封闭社区, 南京

Abstract:

Spatial differentiation of urban housing prices is the marketization expression of the non-equilibrium allocation of residential space resources, which reflects the contrast between social groups belonging to different social classes in selection preference and demand for housing. There is a certain degree of correlational coupling between urban housing prices' spatial differentiation and residential spatial differentiation with respect to the mechanism and the pattern. The paper chooses 1204 commercial housing communities in Nanjing's urban center as its research object and constructs a characteristic variable index system of housing prices. The GWR model is used to analyze major factors that lead to spatial differentiation in housing prices, as well as their combination relationship and spatial-temporal dynamics. Results demonstrate that: (1) there are various factors affecting housing prices, their subsequent spatial differentiation, and they are likely to evolve over time. The dominant factors are the level of the community, school district resources, quality of the landscape, access to leisure facilities, and so on. These may mainly reflect housing consumers' earning capacity, values, residential environment demands, life style and cultural tastes. (2) The pattern of housing prices in Nanjing generally shows the spatial pattern of "circle + fan-shaped + enclave". High priced housing mainly includes new apartments and gated communities in the inner city, communities in elite primary and secondary school districts, high-grade gated communities close to the Ming City Wall, housing in the center of Hexi New Town, and landscaped villas in the urban periphery. (3) Differing types and strata of housing communities attract and gather consumer groups with specific economic and social attributes, which makes housing price differentiation notable correlational coupling with residential differentiation in acting mechanisms and spatial patterns. As the degree of urban housing marketization is continuously deepening and the cultural characteristics and residential preference of social classes become increasingly mature, the spatial differentiation of urban housing prices is more than just an expression. It is also an important driving mechanism to promote residential spatial differentiation and re-differentiation, and can also predict basic trends pertaining to urban residential spatial differentiation in the future. Therefore, the spatial differentiation of urban housing prices can provide a prospective observational and analytical tool for the study of urban social space. It can further make up the time-lag defect which arises in traditional social space research using census data, as well as helps to predict and identify future trends regarding urban social spatial differentiation.

Key words: housing prices, residential differentiation, GWR model, gated community, Nanjing