Temporal Map Labeling: A New Unified Framework with Experiments

TL;DR

This paper introduces a unified, flexible framework for dynamic map labeling that adapts to map operations over time, supporting various applications and demonstrating near-optimal solutions with simple algorithms.

Contribution

It generalizes previous models into a versatile framework that abstracts map operations and applies to fixed and moving features, validated through extensive experiments.

Findings

Simple algorithms achieve near-optimal solutions

The model supports both fixed and moving map features

Extensive experiments validate the framework's effectiveness

Abstract

The increased availability of interactive maps on the Internet and on personal mobile devices has created new challenges in computational cartography and, in particular, for label placement in maps. Operations like rotation, zoom, and translation dynamically change the map over time and make a consistent adaptation of the map labeling necessary. In this paper, we consider map labeling for the case that a map undergoes a sequence of operations over a specified time span. We unify and generalize several preceding models for dynamic map labeling into one versatile and flexible model. In contrast to previous research, we completely abstract from the particular operations (e.g., zoom, rotation, etc.) and express the labeling problem as a set of time intervals representing the labels' presences, activities, and conflicts. The model's strength is manifested in its simplicity and broad range of applications. In particular, it supports label selection both for map features with fixed position as well as for moving entities (e.g., for tracking vehicles in logistics or air traffic control). Through extensive experiments on OpenStreetMap data, we evaluate our model using algorithms of varying complexity as a case study for navigation systems. Our experiments show that even simple (and thus, fast) algorithms achieve near-optimal solutions in our model with respect to an intuitive objective function.