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IEEE VR2023顶会

Comparing Scatterplot Variants for Temporal Trends Visualization in Immersive Virtual Environments

Carlos Quijano-Chavez, Luciana P. Nedel, Carla M. D. S. Freitas

2023年份
4被引次数

摘要

Trends are changes in variables or attributes over time, often represented by line plots or scatterplot variants, with time being one of the axes. Interpreting tendencies and estimating trends require observing the lines or points behavior regarding increments, decrements, or both (reversals) in the value of the observed variable. Previous work assessed variants of scatterplots like Animation, Small Multiples, and Overlaid Trails for comparing the effectiveness of trends representation using large and small displays and found differences between them. In this work, we study how best to enable the analyst to explore and perform temporal trend tasks with these same techniques in immersive virtual environments. We designed and conducted a user study based on the approaches followed by previous works regarding visualization and interaction techniques, as well as tasks for comparisons in three-dimensional settings. Results show that Overlaid Trails are the fastest overall, followed by Animation and Small Multiples, while accuracy is task-dependent. We also report results from interaction measures and questionnaires.

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