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《Journal of Nanjing University(Natural Sciences)》 2009-06
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Spatial autocorrelation of urban land price:A case study of Nanjing

Zhang Hong-Hui1,2,Zeng Yong-Nian1,Jin Xiao-Bin3(1.School of Info-Physics and Geomatics Engineering,Central South University,Changsha,410083,China;2.Changsha Planning Information Service Center,Changsha,410013,China;3.School of Geographic and Oceanographic Sciences,Nanjing University,Nanjing,210093,China)  
Land price drivers that best describe land price spatial distribution quantitatively are often selected through regression analysis.A problem using conventional statistical methods in spatial land price analysis is that these methods assume the data to be statistically independent while land price data have the tendency to be dependent spatially,known as spatial autocorrelation.By mining these concealed spatial autocorrelation information,we can explore the feasibility of applying urban land price model in a microscopic view.As a result,spatial statistical method was applied in this study to derive the spatial distribution of land price.In this paper,Nanjing city was selected as the study area.Moran's I and local Moran's I are used to describe spatial autocorrelation of land price.What's more,standard linear regression model and spatial autoregressive model of land price are constructed.Results show that residential land price,industrial land price,and commercial land price all have spatial autocorrelation characteristics.Compared with that of industrial land price and commercial land price,spatial aggregation characteristics of residential land price are higher,and the trend of spatial aggregation becomes more and more obvious.The spatial autoregressive model is statistically sound in the presence of spatially dependent data in contrast with the standard linear model,and it has a better goodness-of-fit.At the same time,spatial autocorrelation in the residuals of spatial autoregressive land price model has disappeared.By using spatial models a part of the variance is explained by neighboring values.The estimated regression coefficients of the variables become smaller and the significance of the parameters also decreases in the spatial autoregressive model.
【Fund】: 国家自然科学基金(40771198)
【CateGory Index】: F293.2
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