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 199...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 non-stationarity.Results 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 increased.SAR 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 model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The 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 obvious.The 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.展开更多
简要介绍了空间滞后模型(Spatial Lag Model,SLM)和空间误差模型(Spatial Error Model,SEM)及其估计与检验方法,应用南京市住宅小区均价数据运行特征价格模型,并根据拉格朗日乘数检验以及空间回归模型选择原则,选择空间滞后模型进行了...简要介绍了空间滞后模型(Spatial Lag Model,SLM)和空间误差模型(Spatial Error Model,SEM)及其估计与检验方法,应用南京市住宅小区均价数据运行特征价格模型,并根据拉格朗日乘数检验以及空间回归模型选择原则,选择空间滞后模型进行了回归分析,结果表明,在分析住宅小区均价的影响因素时,空间滞后模型比特征价格模型具有更强的解释能力.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.40601073,41101192,41201571)Fundamental Research Funds for the Central Universities(No.2011PY112,2011QC041,2011QC091)Huazhong Agricultural University Scientific&Technological Self-innovation Foundation(No.2011SC21)
文摘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 non-stationarity.Results 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 increased.SAR 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 model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The 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 obvious.The 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.
文摘简要介绍了空间滞后模型(Spatial Lag Model,SLM)和空间误差模型(Spatial Error Model,SEM)及其估计与检验方法,应用南京市住宅小区均价数据运行特征价格模型,并根据拉格朗日乘数检验以及空间回归模型选择原则,选择空间滞后模型进行了回归分析,结果表明,在分析住宅小区均价的影响因素时,空间滞后模型比特征价格模型具有更强的解释能力.
