data-visualization-principles

# Data Visualization Principles Skill ## 🔴 AI FIRST Quality Principle > **Apply the AI FIRST principle: never accept first-pass quality. Minimum 2 iterations. Read all output, improve every section. No shortcuts.** ## Purpose Establishes principles for creating effective, accessible, and honest data visualizations for political intelligence data. ## Chart Selection Guide | Data Type | Recommended Chart | |-----------|------------------| | Comparison | Bar chart, grouped bar | | Trend over time | Line chart, area chart | | Part-to-whole | Pie/donut, stacked bar | | Distribution | Histogram, box plot | | Correlation | Scatter plot | | Relationships | Network diagram, force graph | | Geographic | Choropleth map | | Hierarchical | Treemap, sunburst | ## Design Principles 1. **Data-ink ratio** — Maximize data, minimize decoration 2. **Clarity** — Clear labels, legends, and titles 3. **Honesty** — No misleading scales or truncated axes 4. **Accessibility** — WCAG 2.1 AA compliant colors and patterns 5. **Responsiveness** — Adapt to screen sizes 6. **Interactivity** — Tooltips, zoom, filter where appropriate ## Color Guidelines - Use colorblind-safe palettes - Maintain 4.5:1 contrast ratio for text - Use patterns/textures as secondary encoding - Follow cyberpunk theme variables - Limit to 7±2 colors per chart ## Political Data Considerations - Show confidence intervals for predictions - Include data source attribution - Indicate data freshness/staleness - Support party-specific color coding - Handle missing data transparently ## Performance - Canvas for large datasets (>1000 points) - SVG for interactive/accessible charts - Lazy load charts below the fold - Optimize re-renders on data updates ## Related Policies - [Secure Development Policy](https://github.com/Hack23/ISMS-PUBLIC/blob/main/Secure_Development_Policy.md)