  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.
