Automatic Y-axis Rescaling in Dynamic Visualizations

TL;DR

This paper investigates how and when to rescale the y-axis in dynamic visualizations, revealing that optimal rescaling strategies depend on specific tasks and data characteristics, challenging existing fixed or automatic approaches.

Contribution

It provides empirical insights into the factors influencing effective y-axis rescaling in dynamic visualizations, guiding better design choices.

Findings

Rescaling policies are task-dependent.

Rescaling effectiveness varies with data characteristics.

No single rescaling strategy fits all scenarios.

Abstract

Animated and interactive data visualizations dynamically change the data rendered in a visualization (e.g., bar chart). As the data changes, the y-axis may need to be rescaled as the domain of the data changes. Each axis rescaling potentially improves the readability of the current chart, but may also disorient the user. In contrast to static visualizations, where there is considerable literature to help choose the appropriate y-axis scale, there is a lack of guidance about how and when rescaling should be used in dynamic visualizations. Existing visualization systems and libraries adapt a fixed global y-axis, or rescale every time the data changes. Yet, professional visualizations, such as in data journalism, do not adopt either strategy. They instead carefully and manually choose when to rescale based on the analysis task and data. To this end, we conduct a series of Mechanical Turk experiments to study the potential of dynamic axis rescaling and the factors that affect its effectiveness. We find that the appropriate rescaling policy is both task- and data-dependent, and we do not find one clear policy choice for all situations.