北京市景观可达性与住宅价格空间关联
Spatial correlation analysis of landscape accessibility and residential housing price in Beijing
查看参考文献34篇
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文摘
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随着城市居民对住宅环境要求的不断提高,城市景观对城市住宅价格分异影响日趋显著。分析景观对住宅价格分异格局的影响,可为城市住宅空间结构的规划提供依据,为规划与管理部门提供决策参考。以北京城区二手房小区为样本,基于ArcGIS空间分析方法和特征价格模型,探讨景观因素对北京市住宅价格空间分异格局的影响。以主流房地产交易网站二手商品房报价资料为基础数据,共采集到2012年1月北京市城六区有效住宅小区样本3174个,对住宅样点进行空间化处理,并建立住宅空间信息数据库。运用密度分析、空间插值等方法,分析北京市住宅空间分布特征与价格空间分异格局。核密度分布图显示:北京市住宅空间分布呈现显著的向心化与离心化并存现象,总体上以天安门为中心向周边呈衰减趋势,在地铁转换站点形成了多个集聚次中心。在此基础上,从住宅属性、交通因素、区位特征等方面选择主要解释变量,构建地理加权回归模型,对住宅价格影响因素进行分析,重点探讨景观可达性(如绿地、水景、山景等)与住宅价格的关联。结果表明:次中心与住宅价格关联最为显著,绿地、水景、山景与住宅价格存在一定程度关联。其中,山景和高绿化率对住宅价格增效明显;由于水质较差,北京城六区内河流与住宅价格存在负相关;污水处理以及丧葬场所等污染源与住宅价格也存在显著负相关。远离污染源、靠近宜人景观、低容积率、高绿化率是居民选择住宅的需求。 |
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其他语种文摘
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Location of urban housing directly affects housing price. Choice of housing involves considerations of various public service facilities such as schools, job accessibilities, among many others, which have been widely discussed in existing literature. In this paper, we explore the spatial correlation of landscape accessibility with housing price in Beijing. Based on ArcGIS spatial analysis method and geographically weighted regression model, this paper examines the spatial heterogeneity and the main determinants of the second-hand housing prices in the urban area of Beijing. Through major real estate dealer websites, we collected the second-hand housing data on prices in January 2012 for downtown Beijing, with a total number of 3174 samples. After establishing the housing spatial database, spatial interpolation and kernel density estimation are applied to explore the spatial distribution and heterogeneity of housing price. The kernel density map shows that the residential space in downtown Beijing has evident agglomeration characteristics in general, that is, density decreases gradually from Tian'anmen Square to the periphery. High density also occurs at sub-centers formed near the subway transfer stations, and the sub-centers in Shijingshan and Tongzhou have begun to take shape. With the help of spatial interpolation analysis in ArcGIS, we mapped the spatial pattern of housing price in Beijing. It can be clearly seen from the result that housing price also decreases from city center to the periphery, which is similar to the spatial pattern of housing density. Housing price reaches the peak within the Second Ring Road, with some high price sub-centers emerge between the 3rd and the 4th Ring Road or at the outer suburban districts along the subway lines. Finally, by using geographically weighted regression model, we analyzed the influencing factors of housing price, including traffic factors, locational features, maintenance cost and landscape accessibility (green space coverage, distance to the nearest lake or river, distance to the nearest mountain) and so on. The results show that the distance to sub-centers has the most significant impact on housing price, and there is a certain degree of correlation between landscape accessibility and housing price. Specifically, houses with high greening rate and those located near a mountain is much more expensive; due to the poor water quality, waterscape has a negative impact on housing price; sewage treatment plants, burial grounds and other sources of pollution also exert negative impact on housing price. People prefer houses far from sources of pollution and near pleasant landscape features; low plot ratio and high green space coverage are also favored. The spatial correlation analysis of landscape accessibility and residential housing prices provides a foundation for the planning of urban residential space and references for the planning and management departments of the city government. |
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来源
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地理科学进展
,2014,33(4):488-498 【核心库】
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关键词
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地理加权回归
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景观可达性
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住宅价格
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北京
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地址
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中国科学院大学资源与环境学院, 北京, 100049
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1007-6301 |
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学科
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自然地理学 |
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基金
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国家自然科学基金项目
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中国科学院大学2012年度校长基金项目。
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文献收藏号
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CSCD:5131390
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