This document provides an introduction to data visualization techniques including 2D scalar fields, quantitative vs qualitative representations, isocontours, marching cubes, direct volume rendering, transfer functions, vector fields visualization using streamlines and line integral convolution, and visualizing hierarchical, map, and social data. It discusses principles for representing ordered data with colors and representing different levels with different colors. It also covers speeding up techniques like octrees and extrema graphs and handling multiple data layers.

1. Introduction to Data VisualizationHao WangThoughtworks

2. Data Acquisition

3. 2D Scalar Fields

4. Quantitative v.s Qualitative

5. Redundant Cues

6. Trumbo’s PrinciplesOrdered values should be represented by ordered colorsSignificantly different levels should be given significantly different colors

7. Height Field

8. Isocontours

9. Marching Triangles

10. Marching Cubes

11. Marching Cubes

12. SpeedupOctreeExtrema GraphView Dependent Approach

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

14. Optical Models

15. Optical ModelsMaximum Intensity ProjectionAbsorptionAbsorption and Emission

16. Transfer Functions

17. Isosurfacev.s Volume RenderingIsosurface: hard, binaryVolume 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. AcknowledgementCharles Hansen, University of UtahClaudio Silva, University of UtahCarlos Scheidegger, AT&TIBM Manyeyes