基于Voronoi图的点群目标普适综合算法

A Generic Algorithm for Point Cluster Generalization Based on Voronoi Diagrams

点要素综合算法的目的是在点数减少的情况下尽量正确地传输包含在点群中的信息,但是目前提出的两种算法均不能达到此要求,如为居民地选取的增长算法不能很好处理拓扑信息,而基于Voronoi的算法又没有考虑点的重要性程度(即点包含的专题信息)。为克服这些缺点,提出了一个新的算法。该算法采用以下两种方法确保不同信息的正确传输:(1)根据基本选取法则确保点数的正确;(2)反复构造剩余点的Voronoi图,并根据一个点与其周围点重要性程度的比较来确定其删除与否,从而使拓扑、专题和几何信息能正确传输。该算法的缺点是没有考虑点的符号化,由此可能导致地图上符号的压盖和重叠。

The main purpose of the algorithms for point feature generalization is to correctly transmit information contained by point clusters with the reduction of point number. However two groups of existing algorithms are not satisfactory: the incremental algorithms for settlement selection can’t deal with topological information well, while the Voronoi-based algorithm doesn’t take into account the importance values(thematic information) of points. To overcome their shortcomings, an integrated algorithm is given. The new algorithm employs two methods to ensure different kinds of information transmitted correctly: (1) determine the point number on resulting maps by the radical law, so that statistical(positional) information is transmitted correctly; (2) recursively construct Voronoi diagrams of the retained points, and delete points by comparing the significance value of a point with those of its Voronoi neighbors, so that topological, thematic and geometrical information are transmitted correctly. The drawback of this algorithm is that the symbolization of point features is not considered, which may cause overlap and congestion of map symbols on resulting maps.