土地利用的多尺度空间自相关模型比较(英文)

Comparison of spatial autoregressive models on multi-scale land use

该文以广州市花都区为例,选用拉格朗日乘子检验和拟合度检验分析比较了经典线性回归模型、空间滞后模型、空间误差模型对于土地利用与驱动因子在不同尺度上的解释力和适用度。研究表明:1)经典线性回归模型的残差表现出正相关性,但比原始土地利用数据弱,表明经典线性回归模型能部分地解释土地利用空间布局但忽略了土地利用的空间依赖性;2)与经典线性回归模型相比,空间滞后模型、空间误差模型很好地消除了空间自相关性,并且有更好的拟合度;3)研究区不同地类、不同尺度的最优空间自回归模型的具体形式是不同的,表明最优空间自回归模型的具体形式也存在尺度依赖性。

As a case study on the Huadu district of Guangzhou city,this paper compared classical linear regression model,spatial lag model （SLM） and spatial error model （SEM） through the Lagrange Multiplier （LM） and goodness-of-fit （GOF） tests,in terms of their explanatory power and applicability on land use at different scales.The results showed： 1） The residuals of the classical linear regression models were proved its positive autocorrelation,which were weaker than those of the original land use data,this indicated that classical linear regression model could partially explain the spatial layout but could not capture all spatial dependency in the land use data;2） With higher GOF,the SLMs and SEMs could better diminish the spatial autocorrelation than the linear regression model;3） Land use at different scales required different optimal autoregressive models,i.e.the specific model had the character of scale dependency.