An Adaptive Plotting Method for Large-Scale Symbols

In view of current shortcomings in solving problem of overlapping when plotting large-scale symbols, this paper aims to explore an adaptive plotting method for large-scale symbols and data visualization. We utilize improved SA algorithm to achieve an automatic avoidance of similar classes, and employ SP-DBSCAN algorithm to achieve automatic clustering of different classes. We construct a qualitive evaluation model based on the two algorithms mentioned above and optimize key procedures of each algorithm. Eventually, through practical experiments, we compare the visualization effect and algorithm performance of different schemes, whose results verify that our technical scheme is more effective and superiority. Then we demonstrate the new solution in large scale symbols plotting, which might be effectively applied to practical application.