Progress in studies on automated generalization of spatial point cluster

In the field of computer-assisted cartography, cartographic generalization is one of the basic theories and methods of cartography. It is not only an abstract cognition of objective world, but also an important means of spatial information transformation and an important step of cartography. In digital environment, especially in GIS, all kinds of geographic entities and phenomena on the Earth´s surface are abstracted as point, line, and polygon. 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 whole characteristic of point cluster. Thus, the whole spatial configuration of point cluster must be taken into account in cartographic generalization. In this paper, the former generalization methods of spatial point cluster are discussed in detail, including statistics, settlement-spacing ratio, gravity modeling, distribution-coefficient control, circle growth, convex hull and Delaunay triangulation structure. The advantages and disadvantages of those methods are appraised. For these methods, reduction of spatial points is settled commendably, but there are still insufficiencies of maintaining the boundary and spatial structure of spatial point cluster. In the end, the optimum method to improve on maintaining the structure of spatial point-cluster is set forth and the content of this method is designed in brief.