Visual Analytics and Imaging Laboratory (VAI Lab)
Computer Science Department, Stony Brook University, NY
Abstract: Small multiples is a popular visualization technique for dealing with overdraw in multiclass
data. Small multiples are great at showing pieces of data individually, however,
they do not explain how the different pieces fit together. They can also be difficult to
understand for unacquainted users. We propose an interactive technique which uses the
paradigm of exploded views to make small multiples visualizations more intelligible for
unacquainted users. An exploded view is a drawing in which the different components
of the object are separated by distance in such a way that the relationship between these
components becomes apparent and hidden components of the data are revealed. We use
the exploded view paradigm to create various animation designs for multi-class data.
The designs are then compared using the Elo ranking scheme. We hypothesize that
the exploded view animations increase the ability of users to appreciate the relations
among data clusters (in the compound view) and at the same time get a clearer idea
about the features of the individual data clusters (in the exploded view). We conduct a
user study to compare this interactive approach with a compound view and an animated
small multiples visualization.
Teaser: Below are differenet types of explosions for the San Francisco crime dataset:
Column (a) through (f) are subsequent snapshots of the explosion. For example, the Firework design in row 1 has three phases. The first phase is an implosion, where the points gather at the center of the component to form a small ball,(c). This view tells us which components have centers that are close to each other. In the second phase the small ball then moves to the final position, (d) and in the third phase, the components explode like a firework, (f).
Video: Watch it to get a real feel for the benefits of the explosive view paradigm (over just small multiples):
Paper: S. Mahmood, K. Mueller, “An Exploded View Paradigm to Disambiguate Scatterplots” Computers & Graphics, 73, 37-46, 2018. pdf