DSMA Notes

Visualization Asthetic

2 min read

Visualization Aesthetic

2024-12-24

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Theory

Standard Visualization Aesthetics

Expanded Visualization Aesthetics

  1. Maximize Signal-to-Noise Ratio

    • Emphasize the most critical data or trends while minimizing visual distractions.
    • Remove unnecessary gridlines, excessive colors, or redundant elements.

  2. Use Appropriate Chart Types

    • Match the chart type to the data and message:
      • Use bar charts for comparisons.
      • Line charts for trends.
      • Pie charts only for proportions (and sparingly).
      • Scatterplots for relationships between variables.

  3. Consistency in Design

    • Use consistent scales, colors, fonts, and symbol meanings across charts to avoid confusion.
    • Ensure alignment of axes when comparing similar charts.

  4. Leverage Pre-Attentive Attributes

    • Use visual attributes (like color, size, position, shape) to guide the viewer’s attention to critical data points.
    • Highlight significant changes or patterns without overwhelming the viewer.

  5. Design for Accessibility

    • Use colorblind-friendly palettes.
    • Avoid over-reliance on color alone to encode data (e.g., use patterns or labels alongside colors).
    • Ensure readability with appropriate font sizes and contrast.

  6. Facilitate Comparisons

    • Use aligned scales and consistent units to make comparisons intuitive.
    • Avoid visual distortions like truncated axes or exaggerated proportions.

  7. Focus on Data Integrity

    • Represent data truthfully without manipulation or misleading visual effects.
    • Clearly state data sources and assumptions.
    • Show uncertainty or variability (e.g., confidence intervals, error bars) when relevant.

  8. Simplify Without Oversimplifying

    • Present data in a way that avoids overwhelming the viewer but retains critical details.
    • Avoid excessive aggregation that obscures meaningful patterns.

  9. Support Interaction (for Digital Visualizations)

    • Allow users to drill down into data, filter categories, or hover over points for more information.
    • Ensure interactivity enhances, rather than complicates, the data story.

  10. Focus on Storytelling

    • Frame the visualization to tell a clear and compelling story.
    • Use annotations, titles, and subtitles to provide context and guide the viewer’s interpretation.

  11. Respect Viewer’s Cognitive Load

    • Avoid overloading charts with too many data series or complex interactions.
    • Use small multiples (repeated small charts) to break down complex information into digestible pieces.

  12. Consider Cultural Context

    • Be aware of cultural conventions in interpreting visual elements (e.g., red may signify danger in some contexts and celebration in others).

Examples

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Implementation

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Q&A

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PTR

Graph View

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