空间点集自动概括方法的优化设计

在考虑空间点集整体结构的前提下,从系统的观点和人类视觉的角度出发,把空间点集划分成三个子集:边界点子集、聚集中心子集和内部点子集。对于边界点子集采用并改进Delaunay三角网方法确定边界点以及边界点的取舍;对于聚集中心,采用模糊聚类分析方法确定中心点子集的组成。最后在确定边界点子集和聚集中心子集的前提下,设计内部点子集自动化简的最优化方法。

The Optimum Method for Automated Generalization of Spatial Point Clusters

In the field of computer-assisted cartography, cartographic generalization is one of the basic theories and methods of mapping. It is not only an abstract cognition of objective world, but also an important means of spatial information transformation and an important step of mapping. In digital environment, especially in GIS, all kinds of geographic entities and phenomena on the earth's surface are abstracted as points, lines, and polygons. Point cluster is one of the most important objects in the spatial analysis. In GIS, size, direction and shape of spatial points are not vital, however the holistic spatial configuration of spatial point cluster can represent the overall characteristics of point cluster. Thus, the whole spatial configuration of point cluster must be taken into account in automatic cartographic generalization. For the former methods, the reduction of spatial points is settled commendably, but there are still insufficiencies of maintaining the boundary and spatial structure of spatial points. In this paper, the boundary and the clustering center of point clusters are defined in detail. For the point clusters, the delaunay triangle is built and the points on the boundary are located. Then a method to extract the key points on the boundary is set forth. For the center points, the cluster method is quoted to locate them. Finally, the optimum method to improve the maintenance of the structure of spatial point clusters is identified and the content of this method is designed at length.