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Visual Overview Strategies

Visual Overview Strategies. cs5984: Information Visualization Chris North. Multi-D 1D 2D 3D Hierarchies/Trees Networks/Graphs Document collections. Design Principles Empirical Evaluation Java Development Visual Overviews Multiple Views. Where are we?. Quiz.

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Visual Overview Strategies

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  1. Visual Overview Strategies cs5984: Information Visualization Chris North

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

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

  4. Why Overviews? Data Screen a a data data a data data

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

  8. Cartography

  9. Overview Strategies for Large Scale • Screen: Reduce visual representation size • Pack more on the screen • 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 summaps n attributes to 2 (x,y) • Multi-dimensional scaling:statistical technique to map n-D to 1,2,3-D usingdistance 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

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

  17. Summary • Reduce visual representation (Hammer) • Reduce data scale (Chainsaw) • Eliminate • Aggregate

  18. DataWear • Umer Farooq • IEEE InfoVis 2001

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

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

  21. 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

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