InfoVis intro rosaec
Transcript of InfoVis intro rosaec
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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
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Correlation…What else?Scatter PlotScatter PlotScatter PlotScatter Plot
Ionization Energy
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OutliersScatter PlotScatter PlotScatter PlotScatter Plot
Ionization Energy
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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
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Stock market crash?
� Your graphic should tell the truth about your data
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Show entire scale
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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
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Ford GM Pontiac Toyota
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Ford GM Pontiac Toyota
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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?