Point group geographic entity selection method under the restriction of correlated elements

Abstract

The invention discloses a point group geographic entity selection method under the restriction of correlated elements. The method comprises the following steps of: 1) maintaining the contour form of a point-like geographic element group; 2) maintaining the density distribution characteristics of the point group, clustering the point elements according to the density, and sorting and selecting; and 3) restricting correlated elements. By the method, a map needed by a user can be obtained rapidly and effectively. The aesthetic quality of the map is ensured and the prominence is given to the key points for easy query analysis while strengthening the essence, abandoning the refuse and maintaining the logical relation and solitary relation between the map objects by analyzing the requirements of the user.

Description

technical field [0001] The present invention relates to a method for selecting a point cloud geographic entity, more specifically, the present invention relates to a method for selecting a point cloud geographic entity limited by associated elements. Background technique [0002] The automatic target selection of point geographic elements is an important part of map synthesis, and it is also an unavoidable problem in the multi-scale expression of massive POIs. It is possible to keep important point-like geographic elements, discard minor point elements to make the map clear and eye-catching, and maintain the level of detail of the map content corresponding to the scale. Therefore, the selection of point group elements mainly needs to solve two problems, one is how many point elements to select, and the other is which points to select or which points to delete. For the determination of the selected number of point-like elements, there are many relatively mature models (such ...

AI Technical Summary

Problems solved by technology

[0009] In addition, many domestic scholars have also proposed many better algorithm models for point group target selection. For example, Wu Hehai (1997) proposed to use the convex hull principle to select point group targets. contour features, but the effect is not obvious in reflecting the density contrast of each region; Ai Tinghua (2000) used Delaunay triangulation to describe the distribution characteristics of point groups; Deng Hongyan (2003) proposed a point group target selection model based on genetic algorithm, the method It can better maintain the density distribution characteristics, but the calculation speed is slow; Guo Qingsheng (2007) proposed a progressive synthesis method for the spatial target group, which focuses on how to maintain the spatial distribution characteristics of the original point set

Images