A point-feature label placement algorithm considering spatial distribution and label correlation
Wen Cao, Feilin Peng, TONG Xiaochong, Haoran Dai, ZHANG Yong · DOAJ (DOAJ: Directory of Open Access Journals) · 2022
The problem of Point-Feature Label Placement is one of the difficulties in data visualization. Large correlation and overlap among the labels of dense point features lead to the low efficiency of labeling and unreasonable labeling results. Fully mining the local spatial distribution characteristics and label correlation of dense point features, this paper proposes an automatic point feature label placement algorithm considering spatial distribution and label correlation of point features. Firstly, we build a label association model by mining the spatial distribution characteristics of point features and the label correlation; Secondly, the spatial clustering algorithm based on label association model is used to describe and analyze its global spatial distribution characteristics, which divides a single dataset into several independent sub-datasets to eliminate the interference and ambiguity among independent datasets in the overall solution; Finally, the labeling order rules based on the ascending order of label association model are constructed by using the local spatial distribution characteristics of point feature and the label correlation, which is used to guide the solution of the approximate optimal solution of label placement in the multi-hierarchy metaheuristic algorithm. The experimental results show that: when the label density range from 5% to 40%, the efficiency of the new algorithm is improved by 10.41%~28.92%, and the label quality evaluation value has dropped by 5.5~35.9, which effectively improves efficiency and quality of label placement.