李吉江;赵荣;王勇;杨毅;罗安;

• 1:山东农业大学
• 2:中国测绘科学研究院

摘要(Abstract)：

针对传统的空间自回归模型拟合精度较低且无法顾及空间异质性的问题,该文提出了改进的地理加权自回归模型。并以北京市住宅小区特征价格数据为例,利用探索式空间数据分析方法分析住宅价格数据的空间自相关性,探讨其时空演变特征;建立了空间自回归模型、地理加权回归模型和地理加权自回归模型,并在模型之间进行精度对比和分析。实验结果表明:北京市住宅价格具有明显的空间相关性与空间集聚特征;由于综合考虑了空间自相关性和空间异质性,地理加权自回归模型不仅能大幅度提高模型的拟合优度和解释能力,还能更好地揭示住宅价格的空间变化规律,为数据的空间探索提供了新的方向。

关键词(KeyWords)： 住宅价格;空间自回归模型;地理加权自回归模型;空间异质性

Abstract:

Keywords:

基金项目(Foundation): 中国测绘科学研究院基本科研项目(7771627);; 公益性行业科研专项(201512021,201512033)

作者(Author): 李吉江;赵荣;王勇;杨毅;罗安;

Email:

DOI: 10.16251/j.cnki.1009-2307.2018.09.016

参考文献(References)：

• [1]MORAN P.The interpretation of statistical maps[J].Journal of the Royal Statistical Society,1948,37:243-251.
• [2]MORAN P.Notes on continuous stochastic phenomena[J].Biometrika,1950,37(1/2):17-23.
• [3]CLIFF A D,ORD J K.Spatial processes:models and applications[J].Economic Geography,1983,59(3):322-325.
• [4]ANSELIN L.Spatial econometrics:methods and models[M].Dordrecht:Springer,1988.
• [5]KELEJIAN H H,PRUCHA I.Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances[J].Journal of Econometrics,2010,157(1):53.
• [6]QU X,LEE L.Estimating a spatial autoregressive model with an endogenous spatial weight matrix[J].Journal of Econometrics,2015,184(2):209-232.
• [7]郭双,魏传华.空间自回归模型的变量选择[J].中央民族大学学报(自然科学版),2015,24(8):92-96.(GUO Shuang,WEI Chuanhua.Variable selection of spatial autoregressive model[J].Journal of MUC(Natural Sciences Edition),2015,24(8):92-96.)
• [8]张可娟.空间自回归模型的研究与应用[D]南宁:广西大学,2012.(ZAHNG Kejuan.Research and application of spatial autoregressive model[D].Nanning:Guangxi University,2012.)
• [9]兰峰,张媛.商品住宅价格上涨的空间自回归模型及其实证[J].统计与决策,2012,13:94-97.(LAN Feng,ZHANG Yuan.Spatial autoregressive model for the rise of commercial housing price[J].Statistics and Decision,2012,13:94-97.)
• [10]曾晖.城市住宅价格时空分布规律研究[D].南京:南京林业大学,2012.(ZENG Hui.Research on and spatial temporal distribution rule of urban residence price[D].Nanjing:Nanjing Forestry University,2012.)
• [11]FOTHERINGHAM A S,CHARLTON M,BRUNSDON C.The geographically of parameter space:an investigation of spatial nonstationarity[J].International Journal of Geographical Information Systems,1996,10(5):605-627.
• [12]ZOU Y.Subprime mortgages and housing price variations in the philadelphia metropolitan area[J].Professional Geographer,2015,67(3):412-426.
• [13]郑晓燕,周鹏.武汉市房价的空间分布格局及其影响因素分析[J].国土与自然资源研究,2016(2):26-31.(ZHENG Xiaoyan,ZHOU Peng.Spatial distribution and influrence factors of housing price:the case of Wuhan[J].Territory and Natural Resources Study,2016(2):26-31.)
• [14]覃文忠,王建梅,刘妙龙.混合地理加权回归模型算法研究[J].武汉大学学报(信息科学版),2007,32(2):115-119.(QIN Wenzhong,WANG Jianmei,LIU Miaolong.Research on hybrid geographic weighted regression model[J].Geomatics and Information Science of Wuhan University,2007,32(2):115-119.)
• [15]王新刚,孔云峰.城市住房价格局部线性地理加权回归分析:以湖北省黄石市为例[J].中国土地科学,2015(3):82-89.(WNAG Xingang,KONG Yunfeng.Local linear geographic weighted regression analysis on the urban housing price:a case study of Huangshi City,Hubei Province[J].China Land Science,2015(3):82-89.)
• [16]孙倩,汤放华.基于空间扩展模型和地理加权回归模型的城市住房价格空间分异比较[J].地理研究,2015(7):1343-1351.(SUN Qian,TANG Fanghua.The comparison of city housing price spatial variances based on spatial expansion and geographical weighted regression models[J].Geographical Research,2015(7):1343-1351.)
• [17]KELEJIAN H,PRUCHA I.A generalized spatial two-stage least squares procedure forestimating a spatial autoregressive model with autoregressive disturbances[J].Journal of Real Estate Finance and Economics,1998,17(1):99-121.
• [18]WU B,LI R,HUANG.B.A geographically and temporally weighted autoregressive model with application to housing prices[J].Taylor&Francis,Inc.,2014,28(5):1186-1204.
• [19]HUANG B,WU B,BARRY T.Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices[J].International Journal of Geographical Information Science,2010,24(3):383-401.
• [20]LIU J,YANG Y.A geographically temporal weighted regression approach with travel distance for house price estimation[J].Entropy,2016,18(8):303.