  Authors often transform a large screen visualization for smaller displays
through rescaling, aggregation and other techniques when creating
visualizations for both desktop and mobile devices (i.e., responsive
visualization). However, transformations can alter relationships or patterns
implied by the large screen view, requiring authors to reason carefully about
what information to preserve while adjusting their design for the smaller
display. We propose an automated approach to approximating the loss of support
for task-oriented visualization insights (identification, comparison, and
trend) in responsive transformation of a source visualization. We
operationalize identification, comparison, and trend loss as objective
functions calculated by comparing properties of the rendered source
visualization to each realized target (small screen) visualization. To evaluate
the utility of our approach, we train machine learning models on human ranked
small screen alternative visualizations across a set of source visualizations.
We find that our approach achieves an accuracy of 84% (random forest model) in
ranking visualizations. We demonstrate this approach in a prototype responsive
visualization recommender that enumerates responsive transformations using
Answer Set Programming and evaluates the preservation of task-oriented insights
using our loss measures. We discuss implications of our approach for the
development of automated and semi-automated responsive visualization
recommendation.
