关键词: 分层网格划分, 地图可视化, K-D树索引

Abstract: In traditional map visualization, point clustering is used in the display of massive map markers, but all kinds of point clustering algorithms are run-time calculations without hierarchical mechanism, and there is no filtering mechanism for map markers stack when a large number of points are scattered and displayed. In response to this problem, a solution of clustering and scattering of massive map markers is proposed based on hierarchical grid division. This method builds a K-D tree index for the center point of the hierarchical grid, and builds a quadtree index for the massive points. Through the index and storage technology, the efficient query of clustering is realized. Add grid filtering to eliminate stacking problems when massive points are scattered. The comparison is carried out on the experimental case data set, and the results show that, compared with the traditional point clustering scheme, the computing performance is significantly improved in the case of a large amount of data, and the filtering algorithm is added to the scattered display of massive markers, which effectively improves the user experience.

Key words: hierarchical grid division, map visualization, K-D tree index