1. Information Visualization
Chris North
cs3724: HCI

2. Presentations
terrence witt
Vote: UI Hall of Fame/Shame?

3. Homework1 results Avg: ~76 User and Task analysis?
HCI metrics? principles?
“Learning time is good”
“Learning time is good because users have knowledge X and we know that because of Z”

4. Quiz Some principles for layout design: fitts law: distance, size
Start top left, left to right, top to bottom
Avoid scroll bars at all cost!!! …

5. discussion
“What about my mom…?”

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

7. The Big Problem How? 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

8. 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!

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

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

11. College Degree %
Per Capita Income

12. 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?

13. The Big Problem
Human
Data
Data Transfer
How?

14. The Bigger Problem
Data
Human
Data Transfer
How?

15. Interactive Graphics
Homefinder

16. 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

17. Visualization can do this!
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!

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

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

20. Class Motto
Show me the data!

21. What’s the Big Deal?

22. Presentation is everything!

23. 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

24. Homework #2: Info. Vis. Tools
Get some data:
Tabular, >=5 attributes (columns), >=500 items (rows)
Use 2 visualization tools + Excel:
Spotfire, TableLens, Parallel Coordinates
Mcbryde 104c
2 page report:
Discoveries in data
Comparison of tools
Due:
Feb 19: A-K
Feb 21: L-Z

25. Project 2: Java 3 students per team Ambitious project
0: form team (feb 14)
1: design (feb 28)
2: initial implementation (mid march)
3: final implementation (end march)

26. February Feb 14: Project 2, java: teams due
Feb 19,21: Homework #2, info vis
Feb 26: midterm
Feb 28: Project 2, java: design due
2 tracks:
Infovis, design, eval
Java

27. Next Project 1 due now Presentations: proj1 design
Next Tues: jerome holman, john gibson
Next Thurs: john randal, tom shultz