120 lines
3.3 KiB
Markdown
120 lines
3.3 KiB
Markdown
# Data Visualization for Public Policy
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---
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## Recap: Why Do We Create Visualizations?
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- To **better understand** large, complex datasets.
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- To **influence others** through compelling, evidence-based storytelling.
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---
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## Influence: The Power of Visual Communication
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Effective data visualizations can:
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- Draw attention to critical problems or potential solutions.
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- Argue for specific policy interventions.
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- Connect an audience with large and potentially abstract data concepts.
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---
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## Key Ideas Exercise
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What are *your* golden rules of data visualization?
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---
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## (Some) Key Rules for Effective Data Visualization
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### 1. Audience-Centered Design
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- Take time to consider and understand your audience's background, expertise, and information needs.
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- The "best" data visualization is one that the audience understands & remembers.
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### 2. **Prioritize Truthful Representation**
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- Correct chart types & encodings.
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- Never sacrifice **data integrity** in the name of a "better" chart.
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- Avoid misleading choices: truncated axes, dual axes, etc.
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- Consider the role of **uncertainity** in representing your data.
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---
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<!-- Mackinlay's Effectiveness Hierarchy-->
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---
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### 3. **Maximize Clarity and Comprehension**
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- Simplify complex information where possible. It is OK to refer a user to a table or other source for deeper analysis.
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- Remove unnecessary visual elements -- "chart junk"
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- Guide the viewer's attention to key insights with **labeling**.
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---
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## Tufte's Key Ideas Revisited
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- Graphical Integrity: Above all else, show the data.
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- Maximize the data-ink ratio.
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- Minimize chart junk.
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- Aim for high chart density, consider *small multiples*.
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- Revision & Editing are essential.
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---
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### 4. **Optimize for Accessibility**
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- Use color-blind friendly palettes.
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- Ensure readability for viewers with different visual capabilities. (Contrast,font size, etc.)
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- Provide *alternative text descriptions* in web presentations.
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- `<img src="..." alt="A graphic representing the length of rivers..." />`
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- Accessibility tools: contrast/color/WCAG checkers.
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---
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### 5. **Build a Compelling Narrative**
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- Create a clear, coherent story and use graphics to support it.
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- Each chart should have a clear "why" -- don't make users wonder why you're showing them something.
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- Use visual elements & conventions to guide the viewer through key arguments and order.
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- Connect data to broader context and implications.
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---
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### 6. **Embrace Iterative Improvement**
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- Seek feedback from diverse perspectives, especially those represented in your audience.
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- Be willing to revise and refine, if someone had an issue others will too.
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---
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### 7. **Consider Ethical Implications**
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- Represent marginalized groups respectfully: color choices, language.
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- Remember that pixels often represent people, dismissing outliers/etc. should not be done without consideration.
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- Be transparent about data sources and limitations.
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- Use visualization as a tool for **understanding and persuasion, not manipulation**.
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---
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## Conclusion
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Effective data visualization is both an art and a science.
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Understand your data and what you people to understand.
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Center your audience.
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Prioritize clarity & truth.
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Be creative & have fun!
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