Guidelines
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Avoid filled area encoding when viewers must estimate bivariate trends

For by-eye trend estimation in bivariate charts, avoid filled area encoding on quantitative plots with ordered x-values to improve fidelity and mitigate less accurate relationship judgments for viewers inferring trends directly from the plotted data.

  • purpose:refine
  • basis:empirical
  • task:trend
  • data:quantitative
  • quality:fidelity:use
  • lever:encoding
  • channel:area:avoid

advice

Avoid area fill for trend judgment

Avoid filled area encoding when the chart’s job is to let viewers estimate a bivariate trend by eye. For example, replace an area chart with a line graph or a scatter plot when viewers need to judge the relationship between two quantitative variables.

reason

Why area fill hurts trend estimation

Filled area makes one side of the trend visually heavier than the other. That asymmetry pulls by-eye judgments away from the underlying relationship, so viewers estimate the trend less accurately than when they read only points or a line.

Mechanism: Filled area changes the perceived balance of the plotted relationship. This makes the overall trend harder to judge accurately from the visual marks alone.

Evidence: In the collated extraction for the correlate task, the line and point designs were tied and both significantly outperformed the area design in accuracy, and the source study explains the area-chart deficit as a within-the-area bias during regression by eye (Zeng & Battle, 2023; Correll & Heer, 2017).

context

Use when readers must infer the relationship directly

  • User Goal: Estimate the overall relationship or trend by eye.
  • Task: Judge correlation or trend from the plotted data rather than from a supplied fitted model.
  • Data: Two quantitative variables shown as a bivariate plot with ordered x-values.
  • Chart Setting: A static chart where the visual marks themselves carry the trend judgment.
  • Success Criterion: More accurate by-eye estimation of the relationship.

exceptions

Do not use when trend estimation is not the chart's job

Break it when: The chart is not intended to support by-eye estimation of the relationship between the variables. Why: The evidence only establishes the drawback of filled area for regression-by-eye accuracy.

costs

Tradeoffs of removing area fill

Sacrifice: You give up the filled-region form as the main bivariate display. Risk: This rule does not tell you whether to switch to a line graph or a scatter plot; the study found both beat the area chart and did not separate them. Mitigation: Make the first edit the same in either case: remove the filled area before asking viewers to judge the relationship.

mistakes

Common failure mode with area fill

Mistake: Keep the filled area and assume viewers will read the relationship as accurately as they would from a line or points. Why it fails: The filled-area design was less accurate for the correlation-style task than both the line and point designs.

check

Check whether area fill is hurting the chart

Failure Sign: Readers must infer trend or relationship from a chart whose main mark is a filled area under a boundary line. Quick Check: If the chart asks for by-eye trend judgment and uses area fill, flag it for revision. Stronger Test: Render the same data as a line graph or scatter plot and compare the versions in review; prefer the non-area version when accurate trend judgment is the goal.

fix

Fix the filled-area problem

  • Remove the fill and keep the same boundary as a line graph.
  • Replace the area chart with a scatter plot if the plotted points are the primary evidence for the relationship.
  • Re-run the trend-reading review on the non-area version after the edit.

References

Correll, M., & Heer, J. (2017). Regression by Eye: Estimating Trends in Bivariate Visualizations. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 1387–1396. https://doi.org/10.1145/3025453.3025922
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