1. Introduction to Data Visualization Hao Wang Thoughtworks

2. Data Acquisition

3. 2D Scalar Fields

4. Quantitative v.s Qualitative

5. Redundant Cues

6. Trumbo’s Principles Ordered values should be represented by ordered colors Significantly different levels should be given significantly different colors

7. Height Field

8. Isocontours

9. Marching Triangles

10. Marching Cubes

11. Marching Cubes

12. Speedup Octree Extrema Graph View Dependent Approach

13. Direct Volume Rendering What if we want to see multiple layers of data?

14. Optical Models

15. Optical Models Maximum Intensity Projection Absorption Absorption and Emission

16. Transfer Functions

17. Isosurfacev.s Volume Rendering Isosurface: hard, binary Volume Rendering: soft, noisy

18. Vector Fields

19. Streamlines

20. LIC

21. LIC

22. 3D Vector Fields

23. Critical Points

24. Information Visualization

25. Multivariate Data

26. Hierarchical Data

27. Hierarchical Data

28. Map Data

29. Social Data

30. Acknowledgement Charles Hansen, University of Utah Claudio Silva, University of Utah Carlos Scheidegger, AT&T IBM Manyeyes