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Data visualizations present a massive number of potential messages to anobserver. One might notice that one group's average is larger than another's,or that a difference in values is smaller than a difference between two others,or any of a combinatorial explosion of other possibilities. The message that aviewer tends to notice--the message that a visualization 'affords'--is stronglyaffected by how values are arranged in a chart, e.g., how the values arecolored or positioned. Although understanding the mapping between a chart'sarrangement and what viewers tend to notice is critical for creating guidelinesand recommendation systems, current empirical work is insufficient to lay outclear rules. We present a set of empirical evaluations of how differentmessages--including ranking, grouping, and part-to-whole relationships--areafforded by variations in ordering, partitioning, spacing, and coloring ofvalues, within the ubiquitous case study of bar graphs. In doing so, weintroduce a quantitative method that is easily scalable, reviewable, andreplicable, laying groundwork for further investigation of the effects ofarrangement on message affordances across other visualizations and tasks.Pre-registration and all supplemental materials are available athttps://osf.io/np3q7 and https://osf.io/bvy95 .

The Arrangement of Marks Impacts Afforded Messages: Ordering, Partitioning, Spacing, and Coloring in Bar Charts