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.

1. 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)

7. How - Perception

8. How - Perception

9. How - Perception

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

18. Example - Histogram

19. Example - Stacked Histogram

20. Example - Stacked Line plot

21. Example - Scatter plot matrix

22. Example - Tree

23. Example - Tree

24. Example - Tree

25. Example - Network

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