A Framework for Multiclass Contour Visualization

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Sihang Li, Jiacheng Yu, Mingxuan Li, Le Liu, Xiaolong (Luke) Zhang, Xiaoru Yuan

 View presentation:2022-10-19T16:45:00ZGMT-0600Change your timezone on the schedule page
2022-10-19T16:45:00Z

Exemplar figure, described by caption below — Variations of three design parameters under our proposed multiclass contour visualization framework. The contours are drawn using our declarative DSL. Through a user study, it can be learned that different choices of these three parameters affect the user's ability to complete different tasks.

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Abstract

Multiclass contour visualization is often used to interpret complex data attributes in such fields as weather forecasting, computational fluid dynamics, and artificial intelligence. However, effective and accurate representations of underlying data patterns and correlations can be challenging in multiclass contour visualization, primarily due to the inevitable visual cluttering and occlusions when the number of classes is significant. To address this issue, visualization design must carefully choose design parameters to make visualization more comprehensible. With this goal in mind, we proposed a framework for multiclass contour visualization. The framework has two components: a set of four visualization design parameters, which are developed based on an extensive review of literature on contour visualization, and a declarative domain-specific language (DSL) for creating multiclass contour rendering, which enables a fast exploration of those design parameters. A task-oriented user study was conducted to assess how those design parameters affect users' interpretations of real-world data. The study results offered some suggestions on the value choices of design parameters in multiclass contour visualization.