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1 - ROSAEC Whorkshop Information Visualization A Brief Introduction Jinwook Seo, Ph.D. Visual Processing Lab School of Computer Science and Engineering Seoul National University We research and develop "software MRI's". Software MRI’s

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Page 1: InfoVis intro rosaec

1 - ROSAEC Whorkshop

Information VisualizationA Brief Introduction

Jinwook Seo, Ph.D.

Visual Processing Lab

School of Computer Science and Engineering

Seoul National University

�We research and develop "software MRI's".

Software MRI’s

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Information visualization

� Data presentation

� Emphasize the important aspects

� Tone down irreverent aspects

� Avoid distortions

� Discovery

� Understand trends

� Figure out underlying principles

� Avoid distortions

� Iterative process

Data Overload

� 5 exabytes of new information in 2002

� 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?

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The Challenge

� Transform the data into information

(understanding, insight) thus making it useful to

people.

� Support specific tasks

� Improve performance as compared to existing

mechanisms

Knowledge Crystallization – Sensemaking

Knowledge representation

organize/codify information

Identify relevant dimensions of data

Collect information of interest

Analyze data

Refine schema

Record/communicate

Make a decision, act

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Definitions

� The use of computer-supported, interactive, visual

representations of abstract data to amplify cognition

-- Stuart Card, Jock Mackinlay, Ben Shneiderman, 1999

� Finding the artificial memory that best supports our

natural means of perception

-- Bertin, 1983

� Provide tools that present data in a way to help

people understand and gain insight from it

Visual Aids for Thinking

�We build tools to amplify cognition.

� Provide a frame of reference, a temporary storage

area

�Example: multiplication (Card, Moran, & Shneiderman)

� In your head, multiply 35 x 95

� Now do it on paper

� People are 5 times faster with the visual aid

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So Why Vision?

�Why visualization?

� Sonification

� Touchification

� Smellification

� Tastification

� Bandwidth, bandwidth, bandwidth

� Vision: 100 MB/s

� Ears: <100 b/s

� Telepathy

� Haptic/tactile

� Smell

InfoVis is Interdisciplinary

� Graphics: drawing in real time (<100 ms)

� Cognitive psychology: appropriate representation

� HCI: using users and tasks to guide design and

evaluation

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Exports and Imports of England to and from Denmark & Norway (1700~1780)

The Commercial and Political Atlas, William Playfair, 1786

from The Visual Display of Quantitative Information

Exports and Imports of England to and from North America (1770~1782)

The Commercial and Political Atlas, William Playfair, 1786

from The Visual Display of Quantitative Information

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from Microsoft Excel 2007

Wheat Prices and Wages

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Advance of Napoleon's Grande Armée into Russia in 1812

Charles Joseph Minard, 1861 Size of army

Position

Direction of movement

Temperature

Time

from The Visual Display of Quantitative Information

1864 Exports of French Wine

E. Tufte “Visual Display of Quantitative Information” p 25,Charles Minard

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Map of British Coal Exports

The 1854 London Cholera Epidemic

James Snow

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Anything interesting?

Periodic Table Data

Anything interesting?Scatter PlotScatter PlotScatter PlotScatter Plot

Ionization Energy

50 75 100 125 150 175 200 225 250

0

10

20

30

40

50

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Correlation…What else?Scatter PlotScatter PlotScatter PlotScatter Plot

Ionization Energy

50 75 100 125 150 175 200 225 250

0

10

20

30

40

50

OutliersScatter PlotScatter PlotScatter PlotScatter Plot

Ionization Energy

50 75 100 125 150 175 200 225 250

0

10

20

30

40

50

He

Rn

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RawData

DataTables

VisualStructures

Views

DataTransformations

VisualMappings

ViewTransformations

User/Task

InfoVis Reference Model

User Interaction

Visual Encoding

Accuracy of

Quantitative

Perceptual

Tasks

Cleveland & McGill

1984

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Visual Encoding

Jock Mackinlay, 1987

Data Types

� 1-D Linear Document Lens, SeeSoft

� 2-D Map GIS, ArcView, Google Map

� 3-D World CAD, Medical

� Multi-Dim Parallel Coordinates, Spotfire, XGobi, Visage, Influence Explorer, TableLens

� Temporal Perspective Wall, LifeLines

� Tree Cone/Cam/Hyperbolic, Treemap

� Network Netmap, netViz, SeeNet, Butterfly, Multi-trees

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Tasks

� Overview Gain an overview of the entire collection

� Zoom Zoom in on items of interest

� Filter Filter out uninteresting items

� Details-on-demand

Select an item or group and

get details when needed

� Relate View relationships among items

� History Keep a history of actions to support undo, replay, and progressive refinement

� Extract Allow extraction of sub-collections and of the query parameters

Design Guidelines/Principles

� Visual presentation of query components

� Visual presentation of results

� Rapid, incremental and reversible actions

� Immediate and continuous feedback

� Selection by pointing (not typing)

� Reduces errors

� Encourages exploration

Ben Shneiderman

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Tufte’s Design Principles

� Tell the truth

� Graphical integrity

� Do it effectively with clarity, precision…

� Design principles/aesthetics

E. Tufte, The Visual Display of Quantitative Information (1983)E. Tufte, Envisioning Information (1990)E. Tufte, Visual Explanations (1997)E. Tufte, Beautiful Evidence(2006)

Graphical Integrity

20022001200019991998

500

475

450

Stock market crash?

� Your graphic should tell the truth about your data

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Show entire scale

20022001200019991998

500

250

0

Show in context

20001990198019701960

500

250

0

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Graphics Reveal the Data

Anscombe’s Quarte

E. Tufte “Visual Display of Quantitative Information” p 25,tables and images from Wikipedia

Measuring Misrepresentation

� Visual attribute value should be

directly proportional to data

attribute value

� Height/width vs. area vs. volume

Size of effect shown in graphicSize of effect in data

Lie factor =

“Lie factor” = 2.8

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Design Principles

�Maximize data-ink ratio

Data ink ratio =Data ink

Total ink used in graphic

= proportion of graphic’s ink devotedto the non-redundant display ofdata-information

Design Principles

� Avoid chart junk

� Extraneous visual

elements that detract

from message

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Chart junk

58

59

60

61

62

63

64

65

66

Ford GM Pontiac Toyota

Maintenance cost / year

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

Ford GM Pontiac Toyota

Maintenance cost / year

Ford

GM

Pontiac

Toyota

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

Ford GM Pontiac Toyota

Maintenance cost / year

Design Principles

� Utilize multifunctioning graphical elements

� Graphical elements that convey data information and a design function

� “to clarify, add detail”

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Design Principles

�Use small multiples

� Repeat visually similar

graphical elements nearby

rather than spreading far

apart

� The same graphical design

structure is repeated

� Learn once and compare

→ invite comparisons

Design Principles

� Show mechanism, process, dynamics, and causality

� Cause and effect are key

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Design Principles

� Utilize narratives of space and time

� Tell a story of position and chronology through visual

elements

SeeSoft

� A Tool for Visualizing Line Oriented

Software Statistics

� AT&T Bell Laboratories

� to aid in the management

and development systems

� early 1990’s

� 52 files comprising 15,255

lines of code

� color for the age of the

code (blue to red)

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London Subway

London Subway www.londontransport.co.uk/tube

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Electoral College

Atlanta JournalNovember 5, 2000

Perspective Wall

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Parallel Coordinates

� n-dimensional space with n vertical parallel lines

Maurice d’Ocagne 1885

Al Inselberg 1959

Graph Visualization - Edge Bundle

http:/ / www.win.tue.nl/~ dholten/papers/bundles_infovis.pdf

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GOTreePlus

Spotfire by TIBCO

http:/ / spotfire.tibco.com/ tour/

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Tableau

http:/ /www.tableausoftware.com/products/ tour

Treemaps – Map of the Market

http:/ / www.smartmoney.com/ map- of- the- market/

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Questions or Comments?