On the Readability of Abstract Set Visualizations
This work addresses the need for effective static visualizations of set data in contexts like print media, though it is incremental as it compares existing methods.
The paper tackled the problem of comparing the readability of three static set visualization systems (LineSets, EulerView, MetroSets) for medium-sized data, finding that MetroSets performed and scaled better with statistically significant differences in time and error.
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.