Guidelines
Suggest edit

Use two closed shapes for trend judgments in mixed scatterplots

For trend judgments in single-view scatterplots, prefer closed shape encodings on heterogeneous point displays to improve fidelity and mitigate slower or less accurate reading in mixed-symbol views for readers making rapid visual analytic judgments.

  • purpose:refine
  • basis:empirical
  • task:trend
  • chart:scatter
  • quality:fidelity
  • lever:encoding
  • channel:shape:use
  • density:dense

advice

Closed shape pairs

Use two closed shapes when one scatterplot must support trend judgments across two shape-coded groups. For example, replace an open-open pair such as asterisk plus cross with a closed-closed pair such as triangle plus square when both groups occupy the same mixed point cloud.

reason

Why closed pairs work for trend reading

Closed shapes are separated more efficiently than open line-based shapes when both groups share one dense field of points. That makes it easier to decide which group forms the clearer linear pattern.

Mechanism: Closed shapes reduce interference during mixed-symbol trend reading, so readers can identify the more linear group faster and with fewer mistakes.

Evidence: In the collated results, the closed-closed triangle-square pair ranked first for both accuracy and time on the correlate task, while the open-open asterisk-cross pair ranked last; the reported significant pairwise difference was closed-closed over open-open. The source paper also reports faster and more accurate processing for closed than open shapes and a closed-target advantage in single-plot linear-relationship judgments (Zeng & Battle, 2023; Burlinson et al., 2018).

Notes: The advantage depends on task and on whether both groups are mixed into one plot.

context

Use when all are true

  • User Goal: Decide which of two groups shows the stronger linear relationship.
  • Task: Trend judgment across two shape-coded groups.
  • Data: Two quantitative axes with one categorical grouping mapped to shape.
  • Chart Setting: One scatterplot contains both groups at once, and the point field is visually dense or cluttered.
  • Audience: Readers making quick visual analytic judgments.
  • Success Criterion: Higher trend-reading accuracy with shorter response time.

exceptions

Do not use when any are true

Break it when: The groups are shown in separate homogeneous plots, or the primary task is average-value judgment rather than trend judgment. Why: The study did not find a reliable open/closed advantage in side-by-side homogeneous displays, and the average-value task showed no significant shape-category effect.

costs

What this costs

Sacrifice: The rule is task-specific rather than a universal ranking for every scatterplot task.
Risk: The benefit can weaken or change with difficulty and with the distractor category paired against the target.
Mitigation: Validate the closed-closed mapping on the actual mixed-plot trend task instead of assuming it will transfer to other tasks.

mistakes

Common failure mode

Mistake: Keep two open shapes in the same mixed scatterplot when the chart’s job is trend judgment. Why it fails: The open-open pair was the worst trend condition, and open shapes were slower and less accurate to process than closed shapes.

check

How to test it

Failure Sign: Reviewers hesitate or disagree about which group forms the clearer line.
Quick Check: If both legend symbols are open line-segment shapes in one mixed scatterplot, flag the chart for revision.
Stronger Test: Compare the current mapping against a triangle-square version on the same data with a short timed trend-judgment check.

fix

What to change

  • Replace the two open symbols with two closed symbols.
  • Start with a triangle and square if you need a direct source-tested pair.
  • If your chart already uses separate homogeneous plots, do not spend revision time on open-versus-closed pairing for this task.

References

Burlinson, D., Subramanian, K., & Goolkasian, P. (2018). Open vs. Closed Shapes: New Perceptual Categories? IEEE Transactions on Visualization and Computer Graphics, 24(1), 574–583. https://doi.org/10.1109/TVCG.2017.2745086
Zeng, Z., & Battle, L. (2023). A Review and Collation of Graphical Perception Knowledge for Visualization Recommendation. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3544548.3581349