Modeling of spatial distributions of farmland density and its temporal change using geographically weighted regression model
Modeling of spatial distributions of farmland density and its temporal change using geographically weighted regression model
复制标题
使用地理加权回归模型对农田密度的空间分布及其时间变化进行建模
DOI：
10.1007/s11769-013-0631-8
复制
发表时间：
2014-01
影响因子：
3.4
通讯作者：
Liao Guangyu
中科院分区：
文献类型：
--
作者：
Zhang Haitao;Guo Long;Chen Jiaying;Fu Peihong;Gu Jianli;Liao Guangyu
采用空间自回归（SAR）模型和地理加权回归（GWR）模型，对湖北省谷城县1999年和2009年耕地密度的空间格局及其时间变化进行了模拟，并从空间异质性和非平稳性两个方面探讨了整体和局部空间自相关的差异。结果表明，耕地密度的空间分布、时间变化与驱动因子之间存在较强的空间正相关性，空间自相关系数随空间滞后距离的增大而减小。SAR模型揭示了因变量和自变量之间的全局空间关系，而GWR模型则显示了驱动因子和耕地指数的空间变化拟合程度和局部权重系数（即，耕地密度和时间变化）。GWR模型在构建耕地空间模型时具有平滑性。GWR模型的系数能准确反映不同驱动因子对不同地理位置农田的影响程度。GWR模型的性能指标表明，GWR模型在不同时刻的模拟结果均优于其他模型，精度提高明显。研究中采用的全球和局部耕地模型在不同尺度下的耕地指数空间分布特征不同，为不同驱动因子影响下的耕地保护提供了理论依据。
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.
登录
查看更多内容
影响因子：
7
作者：
Chen You-qi
通讯作者：
Chen You-qi
影响因子：
4.9
作者：
L. Xiaoyan;Z. Shuwen;Wang Zongming;Zhan Huilin
通讯作者：
L. Xiaoyan;Z. Shuwen;Wang Zongming;Zhan Huilin
影响因子：
2.6
作者：
Harris, P.;Fotheringham, A. S.;Charlton, M.
通讯作者：
Charlton, M.
影响因子：
9.1
作者：
Yuan-Fong Su;G. Foody;K. Cheng
通讯作者：
Yuan-Fong Su;G. Foody;K. Cheng
影响因子：
7.1
作者：
Lichtenberg, Erik;Ding, Chengri
通讯作者：
Ding, Chengri