frama-c

[ai-review] / critic / data-visualization.md

# Data Visualization Critic Framework (Edward Tufte)

This framework guides the Critic role when evaluating data visualizations, charts, graphs, and information displays from the perspective of Edward Tufte, author of *The Visual Display of Quantitative Information*. This critic focuses on data-ink ratio, chartjunk elimination, clear presentation of complex information, and the principles that make data visualizations both beautiful and informative.

## Data Visualization Evaluation Areas

### 1. Data-Ink Ratio and Chartjunk
**What to Look For:**
- High data-ink ratio (maximum information, minimum ink)
- Elimination of unnecessary decorative elements
- Clean, uncluttered presentation of data
- Focus on the data itself rather than visual embellishments

**Common Problems:**
- Excessive decorative elements (grid lines, borders, backgrounds)
- Unnecessary use of color, patterns, or textures
- Chartjunk that distracts from the data
- Low data-ink ratio with too much non-data ink
- Over-designed elements that obscure information

**Evaluation Questions:**
- Does every visual element serve to present data?
- Are there unnecessary decorative elements that can be removed?
- Is the data-ink ratio maximized?
- Does the visualization eliminate chartjunk?
- Is the focus on the data rather than visual effects?

### 2. Graphical Integrity and Data Representation
**What to Look For:**
- Accurate representation of data relationships
- Proper scaling and proportions
- Honest presentation without distortion
- Clear indication of data sources and context

**Common Problems:**
- Distorted scales that misrepresent data relationships
- Truncated axes that exaggerate differences
- Inappropriate chart types for the data
- Missing context or data sources
- Visual elements that distort quantitative relationships

**Evaluation Questions:**
- Are the data relationships accurately represented?
- Is the scaling appropriate and honest?
- Does the chart type match the data structure?
- Are data sources and context clearly indicated?
- Is there any visual distortion of the data?

### 3. Information Density and Multivariate Data
**What to Look For:**
- Efficient use of space to show multiple variables
- Clear presentation of complex relationships
- Layered information that reveals patterns
- High information density without clutter

**Common Problems:**
- Wasted space that could show more information
- Single-variable focus when multiple variables are relevant
- Inefficient use of visual real estate
- Oversimplification that loses important context
- Low information density with too much white space

**Evaluation Questions:**
- Is the space used efficiently to show maximum information?
- Are multiple relevant variables presented when appropriate?
- Does the visualization reveal complex patterns and relationships?
- Is the information density appropriate for the data complexity?
- Could more information be shown without creating clutter?

### 4. Typography and Text Integration
**What to Look For:**
- Clear, readable typography that supports the data
- Integrated text that provides context and explanation
- Appropriate font sizes and weights
- Text that enhances rather than competes with data

**Common Problems:**
- Poor typography that reduces readability
- Text that competes with or obscures data
- Inconsistent font usage
- Missing or inadequate labels and annotations
- Text that doesn't provide necessary context

**Evaluation Questions:**
- Is the typography clear and readable?
- Does text provide necessary context and explanation?
- Are labels and annotations appropriately sized and positioned?
- Does text enhance rather than compete with the data?
- Is the typography consistent throughout?

### 5. Color and Visual Hierarchy
**What to Look For:**
- Meaningful use of color to represent data
- Clear visual hierarchy that guides the eye
- Appropriate contrast for readability
- Color that enhances rather than distracts

**Common Problems:**
- Arbitrary or decorative use of color
- Poor contrast that reduces readability
- Color that doesn't represent data meaningfully
- Overuse of color that creates visual noise
- Color choices that don't support the data story

**Evaluation Questions:**
- Does color represent data meaningfully?
- Is there sufficient contrast for readability?
- Does the visual hierarchy guide the eye effectively?
- Is color used to enhance rather than distract?
- Are color choices appropriate for the data and audience?

### 6. Narrative and Storytelling
**What to Look For:**
- Clear narrative that guides understanding
- Logical flow from question to data to conclusion
- Appropriate level of detail for the audience
- Story that emerges from the data itself

**Common Problems:**
- Lack of clear narrative or purpose
- Confusing flow that doesn't guide understanding
- Inappropriate level of detail for the audience
- Story that doesn't emerge from the data
- Missing context that prevents understanding

**Evaluation Questions:**
- Is there a clear narrative that guides understanding?
- Does the flow lead logically from question to conclusion?
- Is the level of detail appropriate for the audience?
- Does the story emerge naturally from the data?
- Is there sufficient context for understanding?

## Tufte-Specific Criticism Process

### Step 1: Data-Ink Analysis
1. **Calculate Data-Ink Ratio**: What percentage of ink presents data vs. decoration?
2. **Identify Chartjunk**: What decorative elements can be removed?
3. **Assess Efficiency**: Is every visual element necessary for data presentation?
4. **Evaluate Focus**: Is the focus on data or visual effects?

### Step 2: Integrity Assessment
1. **Check Accuracy**: Are data relationships accurately represented?
2. **Verify Scaling**: Are scales appropriate and honest?
3. **Assess Distortion**: Is there any visual distortion of the data?
4. **Review Context**: Is sufficient context provided?

### Step 3: Information Density Evaluation
1. **Measure Density**: How much information is shown per unit of space?
2. **Check Multivariate**: Are multiple relevant variables presented?
3. **Assess Complexity**: Does the visualization reveal complex relationships?
4. **Evaluate Efficiency**: Could more information be shown without clutter?

### Step 4: Typography and Integration Review
1. **Check Readability**: Is typography clear and readable?
2. **Assess Integration**: Does text enhance rather than compete with data?
3. **Evaluate Context**: Does text provide necessary context?
4. **Review Consistency**: Is typography consistent throughout?

## Tufte-Specific Criticism Guidelines

### Emphasize Data-Ink Ratio
**Good Criticism:**
- "The grid lines add no information and reduce the data-ink ratio - remove them"
- "The decorative background consumes ink without presenting data"
- "This border serves no purpose and should be eliminated"
- "The data-ink ratio is too low - focus on the data, not decoration"

**Poor Criticism:**
- "This looks plain"
- "It needs more visual appeal"
- "Make it more colorful"

### Focus on Graphical Integrity
**Good Criticism:**
- "The truncated y-axis exaggerates the differences - show the full scale"
- "This chart type doesn't match the data structure - use a scatter plot instead"
- "The 3D effect distorts the quantitative relationships"
- "Missing data sources reduce credibility"

**Poor Criticism:**
- "This chart is boring"
- "It needs more visual effects"
- "Make it more exciting"

### Prioritize Information Density
**Good Criticism:**
- "This space could show three additional variables without clutter"
- "The single-variable focus misses important relationships"
- "The low information density wastes valuable space"
- "Add a second y-axis to show related variables"

**Poor Criticism:**
- "This is too complicated"
- "Simplify it"
- "Show less information"

### Consider Narrative and Context
**Good Criticism:**
- "The narrative flow is unclear - guide the reader from question to conclusion"
- "Missing context prevents understanding of the data"
- "The level of detail is inappropriate for this audience"
- "The story doesn't emerge from the data presentation"

**Poor Criticism:**
- "This doesn't tell a story"
- "It's not engaging enough"
- "Make it more interesting"

## Tufte-Specific Problem Categories

### Data-Ink Problems
- **Low Data-Ink Ratio**: Too much ink used for non-data elements
- **Chartjunk**: Decorative elements that distract from data
- **Unnecessary Elements**: Grid lines, borders, backgrounds that add no information
- **Over-Decoration**: Visual effects that obscure the data

### Integrity Problems
- **Distorted Scales**: Inappropriate scaling that misrepresents relationships
- **Truncated Axes**: Missing scale ranges that exaggerate differences
- **Wrong Chart Types**: Inappropriate visualization methods for the data
- **Missing Context**: Insufficient information about data sources or context

### Density Problems
- **Low Information Density**: Wasted space that could show more information
- **Single-Variable Focus**: Missing opportunities to show multiple variables
- **Inefficient Space Use**: Poor use of visual real estate
- **Oversimplification**: Loss of important complexity and context

### Typography Problems
- **Poor Readability**: Typography that reduces comprehension
- **Competing Text**: Text that obscures or competes with data
- **Inconsistent Fonts**: Mixed typography that reduces coherence
- **Missing Labels**: Inadequate annotations and context

### Color Problems
- **Arbitrary Color**: Color that doesn't represent data meaningfully
- **Poor Contrast**: Insufficient contrast that reduces readability
- **Color Noise**: Overuse of color that creates visual distraction
- **Inappropriate Choices**: Color that doesn't support the data story

### Narrative Problems
- **Unclear Purpose**: Missing or unclear narrative direction
- **Poor Flow**: Confusing progression from question to conclusion
- **Wrong Detail Level**: Inappropriate complexity for the audience
- **Missing Story**: Data presentation that doesn't reveal patterns

## Tufte-Specific Criticism Templates

### For Data-Ink Issues
```
Data-Ink Issue: [Specific data-ink problem]
Problem: [What reduces the data-ink ratio]
Impact: [How this affects data presentation and comprehension]
Evidence: [Specific examples of unnecessary elements]
Priority: [High/Medium/Low]
```

### For Integrity Issues
```
Integrity Issue: [Specific integrity problem]
Problem: [How this misrepresents or distorts the data]
Impact: [How this affects understanding and credibility]
Evidence: [Specific examples of distortion or missing context]
Priority: [High/Medium/Low]
```

### For Density Issues
```
Density Issue: [Specific density problem]
Problem: [What reduces information density or efficiency]
Impact: [How this limits the information presented]
Evidence: [Specific examples of wasted space or missed opportunities]
Priority: [High/Medium/Low]
```

## Tufte-Specific Criticism Best Practices

### Do's
- **Maximize Data-Ink**: Ensure every visual element presents data
- **Eliminate Chartjunk**: Remove decorative elements that don't add information
- **Maintain Integrity**: Ensure accurate representation of data relationships
- **Increase Density**: Show maximum information in minimum space
- **Enhance Readability**: Use typography and color to support data presentation

### Don'ts
- **Add Decoration**: Don't include visual elements that don't present data
- **Distort Data**: Don't use scales or effects that misrepresent relationships
- **Waste Space**: Don't use space inefficiently when more information could be shown
- **Compete with Data**: Don't let typography or color obscure the data
- **Oversimplify**: Don't lose important complexity and context

## Tufte-Specific Criticism Checklist

### Data-Ink Assessment
- [ ] Is the data-ink ratio maximized?
- [ ] Are there unnecessary decorative elements?
- [ ] Does every visual element present data?
- [ ] Is chartjunk eliminated?
- [ ] Is the focus on data rather than visual effects?

### Integrity Assessment
- [ ] Are data relationships accurately represented?
- [ ] Is the scaling appropriate and honest?
- [ ] Does the chart type match the data structure?
- [ ] Are data sources and context clearly indicated?
- [ ] Is there any visual distortion of the data?

### Density Assessment
- [ ] Is space used efficiently to show maximum information?
- [ ] Are multiple relevant variables presented when appropriate?
- [ ] Does the visualization reveal complex patterns?
- [ ] Is the information density appropriate for the data complexity?
- [ ] Could more information be shown without creating clutter?

### Typography Assessment
- [ ] Is the typography clear and readable?
- [ ] Does text provide necessary context and explanation?
- [ ] Are labels and annotations appropriately sized and positioned?
- [ ] Does text enhance rather than compete with the data?
- [ ] Is the typography consistent throughout?

### Color Assessment
- [ ] Does color represent data meaningfully?
- [ ] Is there sufficient contrast for readability?
- [ ] Does the visual hierarchy guide the eye effectively?
- [ ] Is color used to enhance rather than distract?
- [ ] Are color choices appropriate for the data and audience?

### Narrative Assessment
- [ ] Is there a clear narrative that guides understanding?
- [ ] Does the flow lead logically from question to conclusion?
- [ ] Is the level of detail appropriate for the audience?
- [ ] Does the story emerge naturally from the data?
- [ ] Is there sufficient context for understanding?

## Tufte-Specific Evaluation Questions

### For Any Data Visualization
1. **Is the data-ink ratio maximized?**
2. **Are data relationships accurately represented?**
3. **Is space used efficiently to show maximum information?**
4. **Is the typography clear and readable?**
5. **Does color represent data meaningfully?**
6. **Is there a clear narrative that guides understanding?**
7. **Are there unnecessary decorative elements?**
8. **Could more information be shown without creating clutter?**
9. **Is sufficient context provided?**
10. **Does the story emerge naturally from the data?**

### For Charts and Graphs
1. **Is the chart type appropriate for the data structure?**
2. **Are scales honest and undistorted?**
3. **Are multiple relevant variables shown when appropriate?**
4. **Are labels and annotations clear and helpful?**
5. **Is the visual hierarchy effective?**

### For Dashboards and Reports
1. **Is information density appropriate for the complexity?**
2. **Are relationships between different data sets clear?**
3. **Is the narrative flow logical and helpful?**
4. **Are data sources and context clearly indicated?**
5. **Does the presentation support decision-making?**

## Tufte's Key Principles Applied

### "Maximize Data-Ink Ratio"
- Ensure every visual element presents data
- Eliminate decorative elements that don't add information
- Focus on the data rather than visual effects
- Remove chartjunk and unnecessary decoration

### "Show Data Variation, Not Design Variation"
- Use visual elements to represent data relationships
- Avoid arbitrary visual effects
- Let the data drive the design choices
- Focus on what the data reveals

### "Graphical Excellence"
- Present complex information clearly and accurately
- Reveal patterns and relationships in the data
- Use appropriate chart types for data structures
- Maintain graphical integrity and honesty

### "Information Density"
- Show maximum information in minimum space
- Use space efficiently to present multiple variables
- Avoid wasted space that could show more information
- Balance density with clarity and readability

### "Narrative and Context"
- Provide clear narrative that guides understanding
- Include sufficient context for interpretation
- Match detail level to audience needs
- Let the story emerge from the data presentation