保持空间分布特征的群点化简方法

群点目标隐含的空间结构化信息是空间分布分析、地图综合感兴趣的内容。对群点目标分布的信息内容区分为存在性、度量结构与拓扑结构,在Delaunay三角网及其对偶Voronoi图模型上对工量结构定义4个在量;分布范围、分布密度、分布中心及分布轴线,顾及视觉识别Gestalt邻近原则,运用三角形“剥皮”法,确立了非凸多边形所表达的群点分布范围,运用图像灰度表达群点分布密度并通过图像处理方法提取分布中心。建立了Voronoi图动态重建进行群点化简的方法,该方法通过边界点和内部点的分开处理,较好地保持了4个空间分布特征。

A Method of Point Cluster Simplification with Spatial Distribution Properties Preserved

Point cluster object contains much structured information in spatial distribution, which is interesting for the research of spatial analysis and map generalization. This paper divides the spatial distribution information of point cluster into three categories : existing, metrical structure and topological structure, and focuses on the discussion of metrical structure. Based on the Delaunay triangulation and Voronoi diagram model, the paper defines four characteristic parameters for metrical structure description: distribution range, distribution density, distribution center and distribution axis. With the consideration of Gestalt principles in visual adjacency cognition, the presented method finds the distribution range polygon through progressively stripping the outside triangles. The distribution density is represented by Voronoi cell size and visualized as gray image. Applying image process method, the distribution center can be extracted from gray image. A method of point cluster simplification is provided in the paper on the basis of Voronoi polygon establishment in a dynamic way.