1. Visual Overview Strategies cs5984: Information Visualization Chris North

2. Where are we? Multi-D 1D 2D 3D Hierarchies/Trees Networks/Graphs Document collections Design Principles Empirical Evaluation Java Development Visual Overviews Multiple Views

3. Quiz 4 focus+context strategies: bifocal Perspective Wide-angle lens bubble

4. Why Overviews? Data Screen d a ta a a a

5. Advantages of Overviews Helps solve the Keyhole Problem: Map, organization (spatial layout of concepts) What information is (not) available? Adds context info, relationships Enables direct access Encourages exploration HCI metrics: Improves user performance, learning time, error rates, retention, satisfaction –Studies, e.g. Beard&Walker, Leung, Plaisant, Chimera, North, etc.

6. Visual Overview Design Goals Visual: take advantage of human visual processing Information Rich: show as much as you can! (while maintaining a clean design) Interaction Affordances: enable quick access to details E.g. Zooming, Overview+Detail, Focus+Context

7. Data Scale Small scale data = easy Just show everything But, there’s always more data… How much can you show? (3,2) (5,7) (9,9) Attribute 1 Attribute 2

8. Cartography

9. Overview Strategies for Large Scale 1.Screen: Reduce visual representation size Pack more on the screen 2.Data: Reduce data scale Use less data to fit screen Data Screen

10. 1. Reduce Visual Representation “Hammer” Data Screen

11. Reduce Visual Representation Stasko, “Information Mural” Ben, Ahmed

12. 2. Reduce Data Scale “Chainsaw” Data Screen

13. Data Scale Reduce data scale to fit screen Reduce # attributes Reduce # items Reduce value “size” 2 Approaches: Eliminate Aggregate

14. Reduce # Attributes Eliminate attributes Scatterplot: selects 2 attributes, ignores rest Aggregate attributes Column math: grade = (hw1 + hw2) / 2 Star Coordinates: vector sum maps n attributes to 2 (x,y) Multi-dimensional scaling: statistical technique to map n-D to 1,2,3-D using distance between points

15. Reduce # Items Eliminate items VIDA (Visual Info Density Adjuster): show high priority items (video) Human-Eye View: focused info density Aggregate items Group many items into one –SQL “group by” –Snap-Together Visualization: drill down (1:M) –Aggregate Towers Semantic zooming, Abstraction –Pad++, Jazz

17. Aggregation with Zooming Rayson, “Aggregate Towers” Anil, Supriya

18. Summary 1.Reduce visual representation (Hammer) 2.Reduce data scale (Chainsaw) Eliminate Aggregate

19. DataWear Umer Farooq IEEE InfoVis 2001

20. Assignment Thurs: Multiple View Strategies Chi, “Visualization Spreadsheet” » mudita, abhi North, “Snap-Together Visualization” » varun, kumar

21. Next Week Tues: Trees Rao, “Hyperbolic Trees” » david, harsha Robertson, “Cone Trees” » anuj, atul Thurs: Trees Johnson, “Treemaps” » vishal, jeevak Beaudoin, “Cheops” » jon, mudita

22. Homework #3 See website for important details Due Tues Oct 23 Zoomable visualization design Use Jazz HiNote to create an information space Topic ideas: hobby, life story, event, academic field Goal: help someone learn about topic 1 page report: analysis of zooming concept, your design Be creative, have fun! http://vtopus.cs.vt.edu/~north/infoviz/hinoteapplet.html