Data Visualization for Public Policy

Recap: Why Do We Create Visualizations?

  • To better understand large, complex datasets.
  • To influence others through compelling, evidence-based storytelling.

Influence: The Power of Visual Communication

Effective data visualizations can:

  • Draw attention to critical problems or potential solutions.
  • Argue for specific policy interventions.
  • Connect an audience with large and potentially abstract data concepts.

Key Ideas Exercise

What are your golden rules of data visualization?

(Some) Key Rules for Effective Data Visualization

1. Audience-Centered Design

  • Take time to consider and understand your audience's background, expertise, and information needs.
  • The "best" data visualization is one that the audience understands & remembers.

2. Prioritize Truthful Representation

  • Correct chart types & encodings.
  • Never sacrifice data integrity in the name of a "better" chart.
  • Avoid misleading choices: truncated axes, dual axes, etc.
  • Consider the role of uncertainity in representing your data.

3. Maximize Clarity and Comprehension

  • Simplify complex information where possible. It is OK to refer a user to a table or other source for deeper analysis.
  • Remove unnecessary visual elements -- "chart junk"
  • Guide the viewer's attention to key insights with labeling.

Tufte's Key Ideas Revisited

  • Graphical Integrity: Above all else, show the data.
  • Maximize the data-ink ratio.
  • Minimize chart junk.
  • Aim for high chart density, consider small multiples.
  • Revision & Editing are essential.

4. Optimize for Accessibility

  • Use color-blind friendly palettes.
  • Ensure readability for viewers with different visual capabilities. (Contrast,font size, etc.)
  • Provide alternative text descriptions in web presentations.
    • <img src="..." alt="A graphic representing the length of rivers..." />
  • Accessibility tools: contrast/color/WCAG checkers.

5. Build a Compelling Narrative

  • Create a clear, coherent story and use graphics to support it.
  • Each chart should have a clear "why" -- don't make users wonder why you're showing them something.
  • Use visual elements & conventions to guide the viewer through key arguments and order.
  • Connect data to broader context and implications.

6. Embrace Iterative Improvement

  • Seek feedback from diverse perspectives, especially those represented in your audience.
  • Be willing to revise and refine, if someone had an issue others will too.

7. Consider Ethical Implications

  • Represent marginalized groups respectfully: color choices, language.
  • Remember that pixels often represent people, dismissing outliers/etc. should not be done without consideration.
  • Be transparent about data sources and limitations.
  • Use visualization as a tool for understanding and persuasion, not manipulation.

Conclusion

Effective data visualization is both an art and a science.

Understand your data and what you people to understand.

Center your audience.

Prioritize clarity & truth.

Be creative & have fun!

Mackinlay's Effectiveness Hierarchy