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Inspired by cartographic generalization principles, we present ageneralization technique for rendering line charts at different sizes,preserving the important semantics of the data at that display size. Thealgorithm automatically determines the generalization operators to be appliedat that size based on spatial density, distance, and the semantic importance ofthe various visualization elements in the line chart. A qualitative evaluationof the prototype that implemented the algorithm indicates that the generalizedline charts pre-served the general data shape, while minimizing visual clutter.We identify future opportunities where generalization can be extended andapplied to other chart types and visual analysis authoring tools.

Semantic Resizing of Charts Through Generalization:A Case Study with Line Charts