通过距离权重描述点的重要程度,采用改进的k-means算法得到点群的聚类中心,进而以聚类中心为基础,构建了层次加权Voronoi图与Voronoi层次树结构.以点群的分布范围、排列方式与密度为度量,给出了基于Voronoi层次树结构的点群综合方法,确保了点群综合前后在空间形态分布上的一致性.结合地理统计学计算,对综合方法作了进一步的量化评估与优化.经验证,本文方法是可行、有效的.
The importance of the point is described by distance weight and the clustering center point of a point group is obtained by modified k-means algorithm. Furthermore, the clustering center is taken as base to construct hierarchical weighted Voronoi diagram and hierarchical tree structure. Distribution scope, arrangement, and density of the group is taken as the measurement to construct the point generalization method based on hierarchical Voronoi diagram tree structure, thus ensuring the consistency in spatial morphology before and after. Combination with geological statistics calculation, this generalization method is estimated and optimized. Finally, the practicability and availability of this method is confirmed through concrete experiment.
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