A linear space algorithm for computing maximal common subsequences
Communications of the ACM
GREC '01 Selected Papers from the Fourth International Workshop on Graphics Recognition Algorithms and Applications
Algorithm Design
Ranking spaces for predicting human movement in an urban environment
International Journal of Geographical Information Science
Optimizing active ranges for consistent dynamic map labeling
Computational Geometry: Theory and Applications
Journal of Computer and System Sciences
Situated local and global orientation in mobile you-are-here maps
Proceedings of the 12th international conference on Human computer interaction with mobile devices and services
Automatic generation of destination maps
ACM SIGGRAPH Asia 2010 papers
Consistent labeling of rotating maps
WADS'11 Proceedings of the 12th international conference on Algorithms and data structures
A probabilistic model for road selection in mobile maps
W2GIS'13 Proceedings of the 12th international conference on Web and Wireless Geographical Information Systems
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In order to visualize a road network without producing visual clutter, a subset of all road segments needs to be selected. Many algorithms for road segment selection are based on a relevance score for edges in a network (for example betweenness centrality) and proceed by taking a greedy selection based on these weights. This can give dissatisfactory results. In order to improve readability, we introduce a stroke-based constraint and provide an efficient dynamic program that makes an optimal selection given this constraint. Next, we consider the computation of animated road selections from changing edge weights (for example a focus area that follows a moving user). Handling each time step of the animation individually can lead to distracting flickering effects. Here we introduce an optimization objective to achieve a more stable selection and provide a polynomial-time algorithm for solving it. While separately solvable in polynomial time, we show that the combination of the stroke constraints and stability optimization is NP-hard.