  Set systems are used to model data that naturally arises in many contexts:
social networks have communities, musicians have genres, and patients have
symptoms. Visualizations that accurately reflect the information in the
underlying set system make it possible to identify the set elements, the sets
themselves, and the relationships between the sets. In static contexts, such as
print media or infographics, it is necessary to capture this information
without the help of interactions. With this in mind, we consider three
different systems for medium-sized set data, LineSets, EulerView, and
MetroSets, and report the results of a controlled human-subjects experiment
comparing their effectiveness. Specifically, we evaluate the performance, in
terms of time and error, on tasks that cover the spectrum of static set-based
tasks. We also collect and analyze qualitative data about the three different
visualization systems. Our results include statistically significant
differences, suggesting that MetroSets performs and scales better.

Human-Computer Interaction