Bayesian Modelling of Alluvial Diagram Complexity
This work addresses visualization design challenges for data analysts, but it is incremental as it builds on existing research in diagram complexity.
The study investigated how visual features affect the readability and interpretation of alluvial diagrams by conducting crowdsourced user studies with varying complexity levels, finding that feature importance differs between task-based and perceived complexity.
Alluvial diagrams are a popular technique for visualizing flow and relational data. However, successfully reading and interpreting the data shown in an alluvial diagram is likely influenced by factors such as data volume, complexity, and chart layout. To understand how alluvial diagram consumption is impacted by its visual features, we conduct two crowdsourced user studies with a set of alluvial diagrams of varying complexity, and examine (i) participant performance on analysis tasks, and (ii) the perceived complexity of the charts. Using the study results, we employ Bayesian modelling to predict participant classification of diagram complexity. We find that, while multiple visual features are important in contributing to alluvial diagram complexity, interestingly the importance of features seems to depend on the type of complexity being modeled, i.e. task complexity vs. perceived complexity.