1 / 22

Comprehensive Strategies for Information Visualization: From Design Principles to User Engagement

This overview discusses various strategies for creating effective information visualizations by leveraging different visual representations, such as hierarchies, networks, and multi-dimensional data. The focus is on design principles that enhance user experience, including employing techniques like zooming, bifocal views, and semantic zooming to address challenges like the Keyhole Problem. Empirical evaluations of these approaches demonstrate improved user performance and satisfaction. This resource serves as a guide for developing visual overviews that facilitate exploration and understanding across diverse data sets.

mackenzie-gaines
Télécharger la présentation

Comprehensive Strategies for Information Visualization: From Design Principles to User Engagement

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related