SPATIAL MODEL ERROR ANALYSIS USING AUTOCORRELATION INDEXES
SPATIAL MODEL ERROR ANALYSIS USING AUTOCORRELATION INDEXES
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DOI：
10.1016/0304-3800(94)00074-r
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发表时间：
1995-09-01
影响因子：
3.1
通讯作者：
HENEBRY, GM
中科院分区：
文献类型：
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作者：
HENEBRY, GM
目前还没有标准技术来评估模拟空间显式过程的模型的预测性能。空间模拟模型生成的自相关模式是模型预测的一个重要方面。这些模式可以量化使用空间（莫兰的我）和时空（格里菲斯的STI）自相关指数。空间模拟模型的误差分析的方法表明，使用自相关指数来区分随机空间模拟的种子扩散的参数不确定性的影响。由此产生的状态变量的自相关模式显示了一系列非线性，违反直觉的影响。与空间模型误差分析（传染，空间可预测性，邻接，多分辨率程序，分形维数，接口）提出的技术相比，自相关指数可用于区间尺度数据，并具有定义良好的抽样分布，使显着性检验。
No standard techniques yet exist for assessing the predictive performance of models that simulate spatially-explicit processes. Spatial simulation models generate autocorrelation patterns that are a critical aspect of model prediction. These patterns can be quantified using spatial (Moran's I) and spatio-temporal (Griffith's STI) autocorrelation indices. A method of error analysis for spatial simulation models is demonstrated using autocorrelation indices to distinguish among the effects of parameter uncertainty on a stochastic spatial simulation of seed dispersal. The resulting autocorrelation patterns of state variables display a range of nonlinear, counterintuitive effects. In contrast to techniques proposed for spatial model error analysis (contagion, spatial predictability, adjacency, multiple resolution procedure, fractal dimension, interface), autocorrelation indices can be used with interval-scaled data and have well-defined sampling distributions that enable significance testing.