A parallel annotation placement method for dense point of interest labels using hexagonal grid
Yingzhe Lei, Tinghua Ai, Xiang Zhang, Jingzhong Li · Cartography and Geographic Information Science · 2020
Point feature label placement (PFLP) has been a fundamental problem in automatic cartography over decades. In this research, labels are approximated by individual characters to offer more freedom of placement on raster-based grids, including allowing labels turning to avoid occlusions. Considering that parallel orientation of label annotation increases the label chances to resolve the dense conflicts, and that hexagonal grids provide more orientations than square grids, this study uses a parallel annotation to conduct point label placement on hexagonal grids, which is efficient to figure out high-quality label placement for points of interest (POI). The approach introduces a detailed representation of point feature labels on hexagonal grids. Then an algorithm of label placement on hexagonal grids is presented, using the methodology of parallel annotation to arrange labels with different combinations of label orientations. The approach is fast and flexible due to the pre-computed grid and maintains good readability due to the parallel annotation.