Semantic Resizing of Charts Through Generalization:A Case Study with Line Charts
This work addresses the challenge of semantic resizing in data visualization for users needing adaptable charts, but it is incremental as it focuses on line charts with limited quantitative validation.
The paper tackled the problem of rendering line charts at different sizes while preserving data semantics, using a generalization technique inspired by cartographic principles. The result was a prototype that minimized visual clutter and maintained general data shape, as indicated by qualitative evaluation.
Inspired by cartographic generalization principles, we present a generalization technique for rendering line charts at different sizes, preserving the important semantics of the data at that display size. The algorithm automatically determines the generalization operators to be applied at that size based on spatial density, distance, and the semantic importance of the various visualization elements in the line chart. A qualitative evaluation of the prototype that implemented the algorithm indicates that the generalized line charts pre-served the general data shape, while minimizing visual clutter. We identify future opportunities where generalization can be extended and applied to other chart types and visual analysis authoring tools.