Spatial weights matrix is used to represent geographical feature similarity.In situ similarity is represented by different kinds of distance measurements based on Euclidean distance.This kind of similarity builds on spatial dependence but neglects spatial nonstationarity.Here geographical feature similarity is defined as attribute and spatial similarity.The concept of accumulated similarity surface and bring in curve evolution and fast marching method calculate the accumulated similarity surfaces of geographical features.Spatial weights matrix is constructed using accumulated similarity surfaces according to both spatial dependence and spatial nonstationarity.Experiments are processed using trend surface and ASTER GDEM as experimental data.The results show that spatial weights matrix based on accumulated similarity surfaces performs better than Euclidean-distance-based spatial weights matrix.%将地理要素相似度定义为属性相似度与空间相似度,提出累积相似度表面的概念,引入曲线演化理论和快速行进方法生成累积相似度表面,构建空间权重矩阵。通过趋势面模拟数据与ASTER GDEM数字高程模型数据的试验分析证明,相比于利用欧氏距离等距离测度方法,通过累积相似度表面构建的空间权重矩阵综合考虑地理要素的属性相似度与空间相似度,体现地理要素的局部空间特征,能够更为准确地反映地理要素空间特征变化趋势。
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