基于空间自相关的城市住宅地价空间分异规律研究——以石家庄市城区为例
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摘要
针对住宅地价在城市空间中的分异规律问题,该文以2007~2016年石家庄市城区住宅项目土地出让数据为基础,运用空间自相关法及地统计学半变异函数分析了石家庄市城区住宅地价的空间自相关性和变异性,在此基础上结合趋势面分析和克里金插值法探索城区住宅地价的空间分布特征。结果表明:1)石家庄市城区住宅地价总体上呈现显著的空间自相关性,空间距离尺度不同,城市住宅地价具有不同的空间关联度,随着相关距离的增大,自相关性逐渐减弱;住宅地价变异函数表现出各向异性和各向同性,不同方向有不同结构特征,空间自相关尺度为7300m;2)石家庄市城区住宅地价空间分异规律显著,既有连续性,也存在差异性和变异性,高地价区凸向裕华区和新华区。
Aiming at the distribution law of residential land prices in urban space,based on the data of urban residential land transaction in Shijiazhuang City from 2007 to 2016,the spatial autocorrelation and geostatistics variograms were used to analyze the spatial autocorrelation and variability of residential land prices in Shijiazhuang,and the spatial distribution characteristics of residential land price in Shijiazhuang city were explored by using the trend surface analysis and Kriging interpolation method. The results indicate that:(1) The residential land price in Shijiazhuang city exhibited significant positive spatial autocorrelation generally. For different space distance scales,the urban residential land price had different spatial correlation degrees. As the correlation distance increased,the autocorrelation gradually decreased. The residential land price semivariogram showed the anisotropy and isotropy,and there were different structural features in different directions; global spatial autocorrelation scale was 7300 m.(2) The spatial differentiation law of residential land price in Shijiazhuang city was obvious,it was not only continuity,but also difference and variability. High price area appeared in the District of Yuhua and Xinhua.
Aiming at the distribution law of residential land prices in urban space,based on the data of urban residential land transaction in Shijiazhuang City from 2007 to 2016,the spatial autocorrelation and geostatistics variograms were used to analyze the spatial autocorrelation and variability of residential land prices in Shijiazhuang,and the spatial distribution characteristics of residential land price in Shijiazhuang city were explored by using the trend surface analysis and Kriging interpolation method. The results indicate that:(1) The residential land price in Shijiazhuang city exhibited significant positive spatial autocorrelation generally. For different space distance scales,the urban residential land price had different spatial correlation degrees. As the correlation distance increased,the autocorrelation gradually decreased. The residential land price semivariogram showed the anisotropy and isotropy,and there were different structural features in different directions; global spatial autocorrelation scale was 7300 m.(2) The spatial differentiation law of residential land price in Shijiazhuang city was obvious,it was not only continuity,but also difference and variability. High price area appeared in the District of Yuhua and Xinhua.
引文
[1]公云龙,张绍良,章兰兰.城市地价空间自相关分析-以宿州市为例[J].经济地理,2011,31(11):1906-1911.
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[13]宋佳楠,金晓斌,唐健,张志宏,丁宁,赵婕,周寅康.中国城市地价水平及变化影响因素分析[J].地理学报,2011,66(8):1045-1054.
[14]华文,范黎,吴群,彭补拙.城市地价水平影响因素的相关分析-以江苏省为例[J].经济地理,2005,25(2):203-205,218.
[15]张鸿辉,曾永年,金晓斌.南京市城市地价空间自相关分析[J].南京大学学报(自然科学版),2009,45(6):821-830.
[16]李洁茹,张军海,李仁杰,傅学庆.数字地价模型的石家庄城区地价空间分布规律探讨[J].测绘科学,2017,42(8):60-68,101.
[17]杨会霞,葛京凤.不同集约梯度下地价与城市土地集约利用关系研究[J].干旱区资源与环境,2015,29(8):33-38.
[18]Getis A,Ord J K.The analysis of spatial association by use of distance statistics[J].Geographic Analysis,1992,24:189-206.
[19]Anselin L The local indicators of spatial association-LISA[J].Geographical Analysis,1995,27:93-115.
[20]张松林,张昆.空间自相关局部指标Moran指数和G系数研究[J].大地测量与地球动力学,2007(3):31-34.
[21]Ord J K,Getis A.Local spatial autocorrelation atatistics:Distributional issues and an application[J].Geographical Analysis,1995,27(4):286-306.
[22]楼立明.城市地价信息的空间分析及其应用研究[D].杭州:浙江大学,2004:66-76.
[23]朱求安,张万昌,余钧辉.基于GIS的空间插值方法研究[J].江西师范大学学报(自然版),2004,28(2):183-188.
[24]张松林,张昆.全局空间自相关Moran指数和G系数对比研究[J].中山大学学报(自然科学版),2007,46(4):93-97.
[25]吴文佳,张晓平,李媛芳.北京市景观可达性与住宅价格空间关联[J].地理科学进展,2014,33(4):488-498.
[2]李灿,刘超,葛京凤,梁爽,刘静鹏.基于城镇等别的地价合理性研究-以河北省十至十五等城镇为例[J].干旱区资源与环境,2017,31(6):51-58.
[3]刘超,梁彦庆,李博翀,葛京凤,赵莉.城市住宅地价变化影响因素的通径分析-以石家庄为例[J].干旱区资源与环境,2017,31(10):38
[4]杜能的经济理论简介[A].中华外国经济学说研究会.中华外国经济学说研究会第十四次学术讨论会论文摘要文集[C].北京:中华外国经济学说研究会,2006:11.
[5]William A.Location and Land Use[M].Cambridge:Harvard University Press,1964:20-50.
[6]Luo J.Modeling Urban Land Values in a GIS Environment[EB/OL].Dalam http://www.uwm.edu/Dept/GIS/flyers/junluocompetition04.pdf,2004.
[7]张静,张丽芳,濮励杰,管驰明.基于GWR模型的城市住宅地价的时空演变研究-以江苏省为例[J].地理科学,2012,32(7):828-834.
[8]邹利林,杨俊,胡学东.中国城市住宅价格时空演变研究进展与展望[J].地理科学进展,2013,32(10):1479-1489.
[9]王洋,王德利,王少剑.中国城市住宅价格的空间分异格局及影响因素[J].地理科学,2013,33(10):1157-1165.
[10]蒋芳,朱道林.基于GIS的地价空间分布规律研究-以北京市住宅地价为例[J].经济地理,2005,25(2):199-202.
[11]丁洪建.基于面板数据的省会城市居住地价的差异及成因研究[J].中国土地科学,2009,23(6):14-19.
[12]王真,郭怀成,何成杰,李娜,郁亚娟,刘慧,冯长春.基于统计学的北京城市居住用地价格驱动力分析[J].地理学报,2009,64(10):1214-1220.
[13]宋佳楠,金晓斌,唐健,张志宏,丁宁,赵婕,周寅康.中国城市地价水平及变化影响因素分析[J].地理学报,2011,66(8):1045-1054.
[14]华文,范黎,吴群,彭补拙.城市地价水平影响因素的相关分析-以江苏省为例[J].经济地理,2005,25(2):203-205,218.
[15]张鸿辉,曾永年,金晓斌.南京市城市地价空间自相关分析[J].南京大学学报(自然科学版),2009,45(6):821-830.
[16]李洁茹,张军海,李仁杰,傅学庆.数字地价模型的石家庄城区地价空间分布规律探讨[J].测绘科学,2017,42(8):60-68,101.
[17]杨会霞,葛京凤.不同集约梯度下地价与城市土地集约利用关系研究[J].干旱区资源与环境,2015,29(8):33-38.
[18]Getis A,Ord J K.The analysis of spatial association by use of distance statistics[J].Geographic Analysis,1992,24:189-206.
[19]Anselin L The local indicators of spatial association-LISA[J].Geographical Analysis,1995,27:93-115.
[20]张松林,张昆.空间自相关局部指标Moran指数和G系数研究[J].大地测量与地球动力学,2007(3):31-34.
[21]Ord J K,Getis A.Local spatial autocorrelation atatistics:Distributional issues and an application[J].Geographical Analysis,1995,27(4):286-306.
[22]楼立明.城市地价信息的空间分析及其应用研究[D].杭州:浙江大学,2004:66-76.
[23]朱求安,张万昌,余钧辉.基于GIS的空间插值方法研究[J].江西师范大学学报(自然版),2004,28(2):183-188.
[24]张松林,张昆.全局空间自相关Moran指数和G系数对比研究[J].中山大学学报(自然科学版),2007,46(4):93-97.
[25]吴文佳,张晓平,李媛芳.北京市景观可达性与住宅价格空间关联[J].地理科学进展,2014,33(4):488-498.