Scale-Adaptive Placement of Hierarchical Map Labels

Abstract

Nowadays, digital map services provide a large amount of spatial data and thus facilitate users to dynamically navigate map contents across multiple scales on small mobile devices. In this context, consistently placing map labels in interactive navigation is important but still technically challenging, especially when the labels are associated with multiple layers, which are inherent in map contents. In this paper, we introduce a genetic-based approach to optimize the placement of annotation labels with different ranges of map scales by maximizing label visibility of the existing scale while avoiding unwanted mutual overlaps and sudden popping effects. This is accomplished by grouping the label IDs into multiple chromosomes according to their importance and then forming composite chromosomes, each of which is reordered to optimize the overall visibility of the labels. Our formulation also allows the individual labels to move across the scale adaptively in order to further improve label placement on the respective scales. We show several experimental results to present the effectiveness of the proposed approach.