On the Readability of Abstract Set Visualizations

TL;DR

This paper compares three static set visualization systems—LineSets, EulerView, and MetroSets—through human experiments, finding that MetroSets offers superior performance and scalability for understanding set relationships.

Contribution

It introduces a controlled experiment evaluating the readability of three set visualization methods, highlighting MetroSets' advantages in performance and scalability.

Findings

MetroSets outperforms others in speed and accuracy

Statistically significant differences favor MetroSets

MetroSets scales better with larger data sets

Abstract

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.