SIMS 247 Information Visualization and Presentation Prof. Marti Hearst August 31, 2000

SIMS 247SIMS 247Information Visualization Information Visualization

and Presentation and Presentation

Prof. Marti HearstProf. Marti Hearst

August 31, 2000August 31, 2000

Last time: Trees and GraphsLast time: Trees and Graphs

(Show PARC Information Visualizer Video)

File Systems as Trees:File Systems as Trees:The Treemap (Shneiderman)The Treemap (Shneiderman)

A Good Use of TreeMaps and InteractivityA Good Use of TreeMaps and Interactivity

www.smartmoney.com/marketmap

Analysis of TreeMaps on Stock Analysis of TreeMaps on Stock Market OverviewsMarket Overviews

• How does this succeed?How does this succeed?• How does it fail?How does it fail?• How can it be improved?How can it be improved?

Information VisualizationInformation Visualization, Chapter 1, Chapter 1

• Mapping from reality to imagesMapping from reality to images• Mapping from images to mental Mapping from images to mental

modelsmodels– Example: London Underground Map,

invented by Henry Beck in 1931– What is interesting about this?

London Underground MapFrom Transport of London

London Underground Map, closeup of central LondonFrom Transport of London

Geographic tourist map of London, including bus linesFrom Transport of London

Idealized Transport MapsIdealized Transport Maps

• Intuition:Intuition:– “When you are underground, it doesn’t matter

where you are.”

• User focus of attention can depend on User focus of attention can depend on goalsgoals– Particular departure and destination stations +

route– Main transfer points

• Opposite of most visualization problemsOpposite of most visualization problems– Going from geographic to abstract!

Map modified with fare zones

Geographic bus map of LondonFrom Transport of London

Using Visualization for AnalysisUsing Visualization for Analysis

• Data validationData validation• Outlier detectionOutlier detection• Suggestion and evaluation of Suggestion and evaluation of

modelsmodels• Discovery of relationships among Discovery of relationships among

subsets of datasubsets of data

Case Study: Space Shuttle DisasterCase Study: Space Shuttle Disaster

by Edward Tufte (Visual Explanations, 1990) by Edward Tufte (Visual Explanations, 1990)

• Visualization for ExplanationVisualization for Explanation• Main point: Main point:

– The data about the problem was available, but

– The data was not presented in a convincing

way

Tufte’s Challenger Disaster ExampleTufte’s Challenger Disaster Example

Number of damaged O-rings

Temp (F) at time of launch

55 65 75

0

4

8

12

Tufte’s Challenger Disaster ExampleTufte’s Challenger Disaster Example

Number of damaged O-rings

Temp (F) at time of launch

25 35 45 55 65 75

0

4

8

12

Information VisualizationInformation Visualization, Chapter 2, Chapter 2

• Rearrangement and InteractionRearrangement 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 EDATukey 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 MaximizationTufte’s Notion of Data Ink Maximization

• What is the main idea?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 What’s wrong with this? What is he really getting at?he really getting at?

TufteTufte

• Principles of Graphical ExcellencePrinciples 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 PrincipleTufte Principle

Maximize the data-ink ratio:Maximize the data-ink ratio:

data inkdata ink

Data-ink ratio = --------------------------Data-ink ratio = --------------------------

total ink used in total ink used in graphicgraphic

Avoid “chart junk”

Tufte PrinciplesTufte Principles

• Use multifunctioning graphical elementsUse multifunctioning graphical elements• Use small multiplesUse small multiples• Show mechanism, process, dynamics, Show mechanism, process, dynamics,

and causalityand causality• High data densityHigh 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 IntegrityTufte’s Graphical Integrity

• Some lapses intentional, some not Some lapses intentional, some not • Lie Factor = size of effect in graph Lie Factor = size of effect in graph

size of effect in datasize of effect in data• Misleading uses of areaMisleading uses of area• Misleading uses of perspectiveMisleading uses of perspective• Leaving out important contextLeaving out important context• Lack of taste and aestheticsLack of taste and aesthetics

From Tim Craven’s LIS 504 courseFrom Tim Craven’s LIS 504 coursehttp://instruct.uwo.ca/fim-lis/504/504gra.htm#data-ink_ratiohttp://instruct.uwo.ca/fim-lis/504/504gra.htm#data-ink_ratio

How to Exaggerate with GraphsHow to Exaggerate with Graphsfrom Tufte ’83from Tufte ’83

“Lie factor” = 2.8

How to Exaggerate with GraphsHow to Exaggerate with Graphsfrom Tufte ’83from Tufte ’83

Error:Shrinking along both dimensions

The Importance of RearrangementThe Importance of Rearrangement

• Examples from IV book, Chapter 2Examples from IV book, Chapter 2– Crops data– Eye/hair color data– Titanic data (also Chapter 3)