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

作     者：ZHANG Haitao GUO Long CHEN Jiaying FU Peihong GU Jianli LIAO Guangyu 

作者机构：College of Resources and EnvironmentHuazhong Agricultural University 

出 版 物：《Chinese Geographical Science》 (中国地理科学（英文版）)

年 卷 期：2014年第24卷第2期

页      面：191-204页

核心收录：

学科分类：090101[农学-作物栽培学与耕作学] 09[农学] 0901[农学-作物学] 

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) 

主　　题：spatial lag model spatial error model geographically weighted regression model global spatial autocorrelation local spatial aurocorrelation 

摘      要：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.