多中心山地城市住房价格空间格局研究——以重庆主城区为例  

Spatial Pattern and Determinant Factors of Housing Price in Polycentric Mountainous Cities——the Case of Chongqing City Proper

作　　者：王梦玮[1] 刘勇[1] 刘秀华[1] 

机构地区：[1]西南大学资源环境学院,重庆400715

出　　处：《西南大学学报（自然科学版）》2016年第5期133-138,共6页Journal of Southwest University(Natural Science Edition)

基　　金：国家自然科学基金项目"典型山地城市多中心开发的生态效应研究"(41101568);重庆市自然科学基金项目"基于智能体模型的山地城市多中心格局模拟"(cstcjjA00008)

摘　　要：利用重庆市主城区2014年以小区为单元共5410个样本点的二手商品房房价数据,通过克里格空间插值方法并运用GWR模型揭示重庆主城二手房住宅价格空间格局和影响因子.研究表明,重庆市主城住房价格空间格局表现出了明显的山地城市多中心结构,其中传统的五大商业圈以及在重庆城市规划中提出的多个组团表现明显,次中心的分布与城市交通设施的分布有着明显的关联.在运用GWR模型进行住房价格影响因子分析得到:住宅小区到城市商圈的通勤时间为影响住宅价格的主要因素;江景资源扩大了住宅价格峰值的分布,推动了城市次中心的发展;城市轨道交通的覆盖让区域范围内的住宅价格升高,随着城市轨道交通的全面覆盖,将快速推动城市次中心发展.Based on 5410 sold residential projects, this paper used Kriging and the Geographically Weighted Regression （GWR） model to reveal the characteristics and determinants of housing prices in the main ur- ban area of Chongqing. The results showed that housing prices of the main urban area of Chongqing formed a clear polycentric pattern： peaking at the major Central Business District （CBD） of Jiefangbei and sub-peaking at several Secondary Business Districts （SBDs）. The pattern of housing prices was found to be influenced by accessibility of urban centers, urban facilities （such as schools, hospitals, metro line）, Yan- gtze and Jialing rivers. Considering spatial non-stationary of housing prices, GWR model was further em- ployed to reduce spatial dependency of data. The results of GWR model showed that the more accessible to the nearest urban center housing are, the higher housing prices are; the nearer to rivers is, the higher housing prices are; the more proximity to metro lines housing are, the higher housing prices are. To sum up, polycentric urban development is regarded as the underlying factors shaping housing prices in moun- tain Chongqing.

关 键 词：山地城市 多中心 住房价格 空间格局 影响因子 

分 类 号：TU984[建筑科学—城市规划与设计]