1. Visualization Design Principles cs5984: Information Visualization Chris North

2. Quiz What is the purpose of visualization?

3. Basic Visualization Model Data Visualization Visual Mapping Interaction

4. Data Table Attributes (aka: dimensions, variables, columns, …) Items (aka: cases, tuples, data points, rows, …) Data Values Data Types: Quantitative Ordinal Categorical/Nominal

5. Visual Mapping 1.Map: data items  visual marks Visual marks: Points Lines Areas Volumes

6. Visual Mapping 1.Map: data items  visual marks 2.Map: data item attributes  visual mark attributes Visual mark attributes: Position, x, y Size, length, area, volume Orientation, angle, slope Color, gray scale, texture Shape

7. Example Hard drives for sale: price ($), capacity (MB), quality rating (1-5) P C Color = rating

8. Example: Spotfire Film database Year  X Length  Y Popularity  size Subject  color Award?  shape

9. Ranking Visual Attributes 1.Position 2.Length 3.Angle, Slope 4.Size 5.Color Increased accuracy for quantitative data -W.S. Cleveland

10. Basic Visualization Model Data Visualization Visual Mapping Interaction So far: simple static charts Most of semester: more complex mappings interaction strategies

11. Primary Inputs: Data User Task Compare, known item search, patterns, outliers,… Scale # items # attributes User characteristics Standards/guidelines System resources Visualization Design Process Design Visualization Bag of tricks: View types Interaction strategies

12. Issue: Scale # of attributes (dimensionality) # of items # of possible values (e.g. bits/value)

13. Design Principles 5 HCI Metrics: –User performance *** Time, success rate, recovery, clicks, actions –Learning time –Error rate –Retention time –User satisfaction

14. Cost of Knowledge Frequently accessed info should be quick Infrequently accessed info can be slow

15. Increase Data Density Calculate data/pixel “A pixel is a terrible thing to waste.”

16. Eliminate “Chart Junk” How much “ink” is used for non-data? Reclaim empty space (% screen empty) Attempt simplicity (e.g. am I using 3d just for coolness?)

17. Interactivity Interaction to handle increased scale Direct Manipulation –Visual representation –Rapid, incremental, reversible actions –Pointing instead of typing –Immediate, continuous feedback Encourage exploration

18. Information Visualization Mantra Overview first, zoom and filter, then details on demand E.g. Spotfire

19. The “Insight” Factor Avoid the temptation to design 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

20. Open your mind Think bigger, broader Does the design help me explore, learn, understand? Reveals the data

21. Class Motto Show me the data!

22. Information Types Multi-dimensional: databases,… 1D: timelines,… 2D: maps,… 3D: volumes,… Hierarchies/Trees: directories,… Networks/Graphs: web, communications,… Document collections: digital libraries,…

23. Assignment + Presentations Book: Ch. 1, 3, 4 Read for Tues: –Inselberg “Multidimensional detective” (parallel coordinates) – ganesh, christa –Kandogan “Star Coordinates” – rohit, david Read for Thurs: –Rao “Table Lens” – harsha, vishal –Keim “VisDB” – ming, binli

24. Homework #1 Due Sept 6 (1 week) Data: Eye-tracking data Web logs Multi-D engineering data Bio-informatics? User study data GTA Purvi: Demos in 104c: Friday 4pm Available in 104c: F,M-W 4-7pm

25. Projects Proposal due: Sept 13

26. Presentations Mapping Show pictures / demo / video Strengths, weaknesses Hci metrics, insight factor