SIMS 247
Information Visualization
and Presentation
Prof. Marti Hearst
August 31, 2000
Last time: Trees and Graphs
(Show PARC Information Visualizer Video)
File Systems as Trees:
The Treemap (Shneiderman)
A Good Use of TreeMaps and Interactivity
www.smartmoney.com/marketmap
Analysis of TreeMaps on Stock
Market Overviews
• How does this succeed?
• How does it fail?
• How can it be improved?
Information Visualization, Chapter 1
• Mapping from reality to images
• Mapping from images to mental models
– Example: London Underground Map,
invented by Henry Beck in 1931
– What is interesting about this?
London Underground Map
From Transport of London
London Underground Map, closeup of central London
From Transport of London
Geographic tourist map of London, including bus lines
From Transport of London
Idealized Transport Maps
• Intuition:
– “When you are underground, it doesn’t matter
where you are.”
• User focus of attention can depend on goals
– Particular departure and destination stations +
route
– Main transfer points
• Opposite of most visualization problems
– Going from geographic to abstract!
Map modified with fare zones
Geographic bus map of London
From Transport of London
Using Visualization for Analysis
•
•
•
•
Data validation
Outlier detection
Suggestion and evaluation of models
Discovery of relationships among
subsets of data
Case Study: Space Shuttle Disaster
by Edward Tufte (Visual Explanations, 1990)
• Visualization for Explanation
• Main point:
– The data about the problem was available, but
– The data was not presented in a convincing way
Tufte’s Challenger Disaster Example
0
4
8
12
Number of damaged O-rings
55
65
75
Temp (F) at time of launch
Tufte’s Challenger Disaster Example
0
4
8
12
Number of damaged O-rings
25
35
45
55
65
Temp (F) at time of launch
75
Information Visualization, Chapter 2
• Rearrangement and Interaction
– “A graphic is never an end in itself: it is a
moment in the process of decision making”
» Bertin 1981
– “Graphing data needs to be iterative
because we often do not know what to
expect of the data.”
» Cleveland 1985
Tukey on EDA
• From “High Interaction Graphics” by Gary wills:
– According to Tukey EDA is about "looking at data to see
what it seems to say" (p. v). "It is detective work numerical detective work - or counting detective work - or
graphical detective work". (p. 1) and "Unless exploratory
data analysis uncovers indications, usually quantitative ones,
there is likely to be nothing for confirmatory data analysis to
consider" (p. 3). For Tukey, the burden of discovering
information in the data falls on EDA, whereas the burden of
proving that the information is not spurious falls on the
traditional data analysis methods.
Tufte’s Notion of Data Ink Maximization
• What is the main idea?
– draw viewers attention to the substance of
the graphic
– the role of redundancy
– principles of editing and redesign
• What’s wrong with this? What is he
really getting at?
Tufte
• Principles of Graphical Excellence
– Graphical excellence is
• the well-designed presentation of interesting data – a
matter of substance, of statistics, and of design
• consists of complex ideas communicated with clarity,
precision and efficiency
• is that which gives to the viewer the greates number of
ideas in the shortest time with the least ink in the
smallest space
• requires telling the truth about the data.
Tufte Principle
Maximize the data-ink ratio:
Data-ink ratio =
data ink
-------------------------total ink used in graphic
Avoid “chart junk”
Tufte Principles
• Use multifunctioning graphical elements
• Use small multiples
• Show mechanism, process, dynamics, and
causality
• High data density
– Number of items/area of graphic
– This is controversial
• White space thought to contribute to good visual design
• Tufte’s book itself has lots of white space
Tufte’s Graphical Integrity
• Some lapses intentional, some not
• Lie Factor = size of effect in graph
size of effect in data
• Misleading uses of area
• Misleading uses of perspective
• Leaving out important context
• Lack of taste and aesthetics
From Tim Craven’s LIS 504 course
http://instruct.uwo.ca/fim-lis/504/504gra.htm#data-ink_ratio
How to Exaggerate with Graphs
from Tufte ’83
“Lie factor” = 2.8
How to Exaggerate with Graphs
from Tufte ’83
Error:
Shrinking
along both
dimensions
The Importance of Rearrangement
• Examples from IV book, Chapter 2
– Crops data
– Eye/hair color data
– Titanic data (also Chapter 3)