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Modeling of spatial Distributions of Farmland Density and Its Temporal Change Using Geographically Weighted Regression Model

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Chinese Geographical Science 2014年第2期24卷 191-204页

作者： ZHANG Haitao GUO Long CHEN Jiaying FU Peihong GU Jianli LIAO Guangyu College of Resources and Environment Huazhong Agricultural University

This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and *** showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance *** models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial *** coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical *** performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was *** global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.

关键词： spatial lag model spatial error model geographically weighted regression model global spatial autocorrelation local spatial aurocorrelation

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