CS 7450 Information Visualization

1
Introduction

• CS 7450 - Information Visualization
• Jan. 10, 2002
• John Stasko

2
Exercise

• Picnic directions

3
Data Explosion

• Society is more complex
• There simply is more stuff
• Computers, internet and web give people access to
  an incredible amount of data
• news, sports, financial, purchases, etc...

4
How much data?

• Between 1 and 2 exabytes of unique info produced
  per year
• 1000000000000000000 (1018) bytes
• 250 meg for every man, woman and child
• Printed documents only .003 of total

Peter Lyman and Hal Varian, 2000 Cal-Berkeley,
Info Mgmt Systems www.sims.berkeley.edu/how-much
-info
5
Data Overload

• Problem How to make use of the data
• How do we make sense of the data?
• How do we harness this data in decision-making
  processes?
• How do we avoid being overwhelmed?

6
The Challenge

• Transform the data into information
  (understanding, insight) thus making it useful to
  people

7
One Approach

• Provide tools that present data in a way to help
  people understand and gain insight from it
• Cliches
• Seeing is believing
• A picture is worth a thousand words

8
Exercise Redux

• What did you put on your paper?
• Text?
• Pictures?
• People work differently

9
Visualization

• Often thought of as process of making a graphic
  or an image
• Really is a cognitive process
• Form a mental image of something
• Internalize an understanding
• The purpose of visualization is insight, not
  pictures
• Insight discovery, decision making, explanation

10
Main Idea

• Visuals help us think
• Provide a frame of reference, a temporary storage
  area
• External cognition
• Role of external world in thinking and reason
• Examples?

11
Information Visualization

• What is information?
• Items, entities, things which do not have a
  direct physical correspondence
• Notion of abstractness of the entities is
  important too
• Examples baseball statistics, stock trends,
  connections between criminals, car attributes...

12
Information Visualization

• What is visualization?
• The use of computer-supported, interactive visual
  representations of data to amplify cognition.
• From Card, Mackinlay Shneiderman 98

13
What Its Not

• Scientific Visualization
• Primarily relates to and represents something
  physical or geometric
• Examples
• Air flow over a wing
• Stresses on a girder
• Weather over Pennsylvania

14
Information Visualization

• Components
• Taking items without a direct physical
  correspondence and mapping them to a 2-D or 3-D
  physical space.
• Giving information a visual representation that
  is useful for analysis and decision-making

15
Two Key Attributes

• Scale
• Challenge often arises when data sets become very
  large
• Interactivity
• Want to show multiple different perspectives on
  the data

16
Domains for Info Vis

• Text
• Statistics
• Financial/business data
• Internet information
• Software
• ...

17
Examples

• Images
• Minivan comparison
• Bad Excel

18
Atlanta Flight Traffic
Atlanta Journal April 30, 2000
19
2000 Election Ballot
20
Electoral College
Atlanta Journal November 5, 2000
21
Power Costs
Average cost per month to use
Wall Street Journal August 16, 2001
22
London Subway
www.londontransport.co.uk/tube
23
Napoleans March
From E. Tufte The Visual Display of Quantitative
Information
size of army direction
latitude longitude
temperature date
Minard graphic
24
Example
NYC weather
2220 numbers
Tufte, Vol. 1
25
Examples

• Software

26
Map of the Market
www.smartmoney.com/marketmap
Demo
27
StarTree
Hyperbolic tree
www.inxight.com
28
SunBurst
www.cc.gatech.edu/gvu/ii/sunburst
File browser
29
HomeFinder
HCIL Univ. Maryland
30
Tasks in Info Vis

• Search
• Finding a specific piece of information
• How many games did the Braves win in 1995?
• What novels did Ian Fleming author?
• Browsing
• Look over or inspect something in a more casual
  manner, seek interesting information
• Learn about crystallography
• What has Jane been up to lately?

31
Tasks in Info Vis

• Analysis
• Comparison-Difference
• Outliers, Extremes
• Patterns
• Assimilation
• Monitoring
• Awareness

32
Knowledge Crystallization

• Information foraging
• Search for schema (representation)
• Instantiate schema
• Problem solve to trade off features
• Search for a new schema that reduces problem to a
  simple trade-off
• Package the patterns found in some output product

From CMS 98
33
How Vis Amplifies Cognition

• Increasing memory and processing resources
  available
• Reducing search for information
• Enhancing the recognition of patterns
• Enabling perceptual inference operations
• Using perceptual attention mecahnisms for
  monitoring
• Encoding info in a manipulable medium

34
Process
task
Raw data
Data tables
Visual Structures
Views
Data transformations
Visual mappings
View transformations
35
Sources Used

• CMS book
• Spence book

36
Upcoming

• Multivariate data sets
• User (cognitive) tasks