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

Analysis of TreeMaps on Stock Market 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 Visualization, Chapter 1 Mapping from reality to imagesMapping from reality to images Mapping from images to mental modelsMapping 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:Intuition: –“When you are underground, it doesn’t matter where you are.” User focus of attention can depend on goalsUser focus of attention can depend on goals –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 London From Transport of London

Using Visualization for Analysis Data validationData validation Outlier detectionOutlier detection Suggestion and evaluation of modelsSuggestion and evaluation of models Discovery of relationships among subsets of dataDiscovery of relationships among subsets of data

Case Study: Space Shuttle Disaster 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 Example Number of damaged O-rings Temp (F) at time of launch

Tufte’s Challenger Disaster Example Number of damaged O-rings Temp (F) at time of launch

Information Visualization, 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 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?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?What’s wrong with this? What is he really getting at?

Tufte 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 Principle Maximize the data-ink ratio: data ink data ink Data-ink ratio = total ink used in graphic total ink used in graphic Avoid “chart junk”

Tufte Principles Use multifunctioning graphical elementsUse multifunctioning graphical elements Use small multiplesUse small multiples Show mechanism, process, dynamics, and causalityShow mechanism, process, dynamics, and 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 Integrity Some lapses intentional, some notSome lapses intentional, some not Lie Factor = size of effect in graph size of effect in dataLie Factor = size of effect in graph size 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 course

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