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Information Visualization

Information Visualization. Chris North cs3724: HCI. What is Information Visualization?. The use of computer-supported, interactive, visual representations of abstract data to amplify cognition. The Big Problem. contacts, dict/thes, music, news, email, web

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Information Visualization

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  1. Information Visualization Chris North cs3724: HCI

  2. What is Information Visualization? • The use of computer-supported, interactive, visual representations of abstract data to amplify cognition

  3. The Big Problem • contacts, dict/thes, music, news, email, web • Books, papers, scientific data, VB ref Human Data Data Transfer How? • vision: 90Mb/sec • Hear: 10/s, 44k/s • Smell: 1 • Touch: • Taste • Neural link: huge • esp

  4. Human Vision • Highest bandwidth sense • Fast, parallel • Pattern recognition • Pre-attentive • Extends memory and cognitive capacity • (Multiplication test) • People think visually Impressive. Lets use it!

  5. Find the Red Square: The pop-out effect

  6. Which state has highest Income? • Relationship between Income and Education? • Outliers?

  7. College Degree % Per Capita Income

  8. 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?

  9. The Big Problem Human Data Data Transfer How?

  10. Information Integration

  11. The Bigger Problem Data Human Data Transfer How?

  12. Interactive Graphics • Homefinder

  13. forms • 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

  14. User Tasks Excel can do this • 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, … Visualization can do this!

  15. More than just “data transfer” • Glean higher level knowledge from the data • Reveals data • Reveals information about data that is not necessarily “stored” in the data • Learn = data  information • Insight! • Hides data • Hides “information” • Nothing learned • Zero insight

  16. More than just “data transfer” • Glean higher level knowledge from the data The Insight Factor • Reveals data • Reveals information about data that is not necessarily “stored” in the data • Learn = data  information • Insight! • Hides data • Hides “information” • Nothing learned • Zero insight

  17. Class Motto Show me the data!

  18. What’s the Big Deal?

  19. Presentation is everything!

  20. My Philosophy: Optimization • 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 Visualization = the best of both Impressive computation + impressive cognition

  21. VisualizationDesign Principles

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

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

  24. Information Visualization Mantra • Overview first, zoom and filter, then details on demand • Overview first, zoom and filter, then details on demand • Overview first, zoom and filter, then details on demand • Overview first, zoom and filter, then details on demand • Overview first, zoom and filter, then details on demand • Overview first, zoom and filter, then details on demand

  25. InfoVis Design Principles • Increase data density • Eliminate “chart junk” • Mantra: Overview first, zoom&filter, details on demand • Insight factor • Does the design reveal the data? • Does the design help me explore, learn, understand? • Show me the data!

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