Introduction to data visualization
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This document discusses data visualization techniques and best practices. It introduces data visualization and explores why visualization is useful both for declarative and exploratory purposes. It then examines principles of human perception and how to design effective visualizations according to color, size, position and other perceptual factors. Finally, it covers types of visualizations like histograms, scatter plots, trees and networks, as well as anti-patterns to avoid and ways to reduce distortion and provide proper context.
Introduction to data visualization
- 2. Which one is a better visualization?
- 3. Why visualization? Borrowed from COMP7509 lecture notes
- 4. Why visualization - Declarative
- 5. Why visualization - Exploratory
- 6. Which one is a better visualization? (Again)
- 10. How - Perception
- 11. How - Perception
- 12. How - Perception
- 13. How - Perception
- 14. How - Perception - reverse
- 15. How - Perception - Color
- 16. How - Perception - Color
- 17. How - Perception - Color
- 19. Example - Stacked Histogram
- 20. Example - Stacked Line plot
- 21. Example - Scatter plot matrix
- 22. Example - Tree
- 23. Example - Tree
- 24. Example - Tree
- 26. Example - Arc Diagrams
- 27. Bad usage
- 28. Bad usage
- 29. Bad usage
- 30. Bad usage
- 31. Bad usage - Fix
- 32. Lie factor
- 33. Lie factor
- 34. Distortion
- 35. Area vs numeric data
- 36. Context
- 37. End