1. CS 5764 Information Visualization Dr. Chris North

2. Today 1.What is Information Visualization? 2.Who cares? 3.What will I learn? 4.How will I learn it?

3. 1. What is Information Visualization? The use of computer-supported, interactive, visual representations of abstract data to amplify cognition –Card, Mackinlay, Shneiderman

4. The Big Problem Data Human How? Data Transfer

5. The Big Problem Data Human How? Data Transfer Vision: Aural: Smell: Haptics Taste esp

6. Human Vision Highest bandwidth sense Fast, parallel Pattern recognition Pre-attentive Extends memory and cognitive capacity (Multiplication test) People think visually Brain = 8 lbs, vision = 3 lbs Impressive. Lets use it!

7. Find the Red Square:

8. Pre-attentive

9. Which state has highest Income? Avg? Distribution? Relationship between Income and Education? Outliers?

10. Per Capita Income College Degree %

11. %

12. Visual Representation Matters! Text vs. Graphics What if you could only see 1 state’s data at a time? (e.g. Census Bureau’s website) What if I read the data to you? Graphics vs. Graphics depends on user tasks, data, …

13. History: Static Graphics Minard, 1869

14. The Big Problem Data Human visualization Data Transfer

15. The Bigger Problem Data Human interactive visualization Data Transfer

16. Interactive Graphics Homefinder

19. Search Forms Avoid the temptation to design only a form-based search engine More tasks than just “search” How do I know what to “search” for? What if there’s something better that I don’t know to search for? Hides the data Only supports Q&A How can search be integrating with visualization?

20. User Tasks Easy stuff: Min, max, average, % These only involve 1 data item or value Hard stuff: Patterns, trends, distributions, changes over time, outliers, exceptions, relationships, correlations, multi-way, combined min/max, tradeoffs, clusters, groups, comparisons, context, anomalies, data errors, Paths, … Excel can do this Visualization can do this!

21. More than just “data transfer” Glean higher level knowledge from the data Learn = data  insight Reveals data Reveals knowledge that is not necessarily “stored” in the data Insight! Hides data Hampers knowledge Nothing learned No insight

22. Some Philosophy… bigger picture: Insight Vs. statistics, data mining, … Formal vs informal even bigger: Visual Analytics Interaction as central Perception -> cognition Visualization in context

23. Class Motto Show me the data!

24. 2. Who Cares?

25. Presentation is everything

26. My Philosophy: Optimization Visualization = the best of both Impressive computation + impressive cognition Computer Serial Symbolic Static Deterministic Exact Binary, 0/1 Computation Programmed Follow instructions Amoral Human Parallel Visual Dynamic Non-deterministic Fuzzy Gestalt, whole, patterns Understanding Free will Creative Moral

27. 3. What Will I Learn? Design interactive visualizations Critique existing designs and tools Develop visualization software Empirically evaluate designs Understand current state-of-art An HCI focus A visualization = a user interface for data *

28. Topics Information Types: Multi-D 1D, 2D, 3D spatial Hierarchies/Trees Networks/Graphs Document collections Analytics: Analytic theories Analytic methods Strategies: Design Principles Interaction strategies Navigation strategies Visual Overviews Multiple Views Empirical Evaluation Development Theory High-Resolution Displays