Why Visualization? - Information Engineering Group

Why Visualization? - Information Engineering Group

Information Visualization &
Visual Analytics
Wolfgang Aigner, Technische Universität Wien, aigner@ifs.tuwien.ac.at
13. Juni 2012
Outline
About me
Motivation & Introduction
Visualization Design
The Good - The Bad – The Ugly
Examples
Visual Analytics
Demo
Resources
2 About me
3 MOTIVATION & INTRODUCTION
Information overload
[Howson, 2008]
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Why Visualization?
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8
Goal
9 Method
10 Human Vision
high bandwidth
fast, parallel
pattern recognition
pre-attentive
increases cognitive resources
expand human working memory
“The eye... the window of the soul,
is the principal means
by which the central sense
can most completely and abundantly
appreciate the infinite works of nature.”
Leonardo da Vinci (1452 – 1519)
[Few, 2006]
11 Example
12 Example
13 INTERACTIVITY
Car Example - Interactivity
16
VISUALIZATION DESIGN
Three central questions
data
representations
&
interaction
goal/task
appropriateness
user/audience
Who are the users of the systems? (Users)
What kind of data are they working with? (Data)
What are the general tasks of the users? (Tasks)
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Purpose
Exploration / Explorative Analysis
undirected search
no a priori hypotheses
get insight into the data
begin extracting relevant information
come up with hypotheses
interactivity
Confirmation / Confirmative Analysis
directed search
verify or reject hypotheses
Presentation
communicate and disseminate analysis results
19 InfoVis Reference Model
[Card et al., 1999]
20
Visual Variables – Mackinlay
[Mackinlay, 1987]
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Visual Mapping: Example
year
length
popularity
subject
award?
[garysaid.com]
22
Visual Mapping: Example
[Spotfire]
23
THE GOOD
Florence Nightingale – Rose chart (1855)
[Nightingale, 1858]
25 Guttenberg Plagiarism
Gregor Aisch, Plagiatszeilen in der
Guttenberg-Dissertation, Created at:
March 1, 2011, Retrieved at: August
31, 2011, http://vis4.net/blog/de/posts/
guttenberg-plagiarism/
26 Weather chart
Aigner, Miksch, Tominski, Schumann.
Visualization of Time-Oriented Data,
Springer, 2011.
27 »Diagrams can lead to great insight, but also to the lack of it.«
Tufte, 1997
THE BAD
29
The Challenger Disaster
January 27, 1986:
US-Space Shuttle Challenger explodes
72 seconds after launch
Reasons:
Sealing-rings in the right booster
were damaged due to weather
conditions
Reliability-problems of
the so-called O-rings were
known
30
Challenger Disaster
The manufacturer of
the boosters warned
NASA before launch
that the expected
cold temperatures
might be an extra
risk.
NASA did not see
any correlation
between the failing
of O-Rings and the
temperatures.
This was wrong!
31
Challenger Disaster:
Tufte‘s Re-Visualization
Edward R. Tufte showed that the risk would have been obvious to
NASA engineers if a better visualization would have been used
32
Visualization Design
data
representations
&
interaction
goal/task
appropriateness
user/audience
Expressiveness
A visualization is considered to be
expressive if the relevant information of a
dataset (and only this) is expressed by the
visualization. The term "relevant" implies
that expressiveness of a visualization can
only be assessed regarding a particular
user working with the visual representation
to achieve certain goals.
„A visualization is said to be expressive if
and only if it encodes all the data
relations intended and no other data
relations.“ [Card, 2008, p. 523]
[Mackinlay, 1986]
Effectiveness
A visualization is effective if it addresses
the capabilities of the human visual
system. Since perception, and hence the
mental image of a visual representation,
varies among users, effectiveness is
user-dependent. Nonetheless, some
general rules for effective visualization
have been established in the visualization
community.
„Effectiveness criteria identify which of
these graphical languages [that are
expressive], in a given situation, is the
most effective at exploiting the capabilities
of the output medium and the human
visual system.“ [Mackinlay, 1986]
THE UGLY
Tell the truth about the data
[Tufte, 1983]
Lie factor = Size of effect shown in graphic / Size of effect in data
Fuel Economy Standard Redesign
Lie Factor
Lie Factor: 141
Beer Sales Redesign
Christian Resei, AK-NÖ, treffpunkt 04/10, Magazin der NÖ Arbeiterkammer, S. 6
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Example
Tufte Design Principles
1. 
Above all else show the data.
2. 
Maximize the data-ink ratio.
3. 
Erase non-data-ink.
4. 
Erase redundant data-ink.
5. 
Revise and edit.
[Tufte, 1983]
VISUALIZATION TECHNIQUE
EXAMPLES
Newsmap / Treemap
Marcos Weskamp, Newsmap, Retrieved at: Oct 14, 2011, http://newsmap.jp
45 Example: File Structure to Tree
File System:
3 Folders
Root
Dir 1
File 1
6 Files
1) Root -> whole Screen
Root
File 2
Dir 2
File 3
Dir 3
1 MB
2 MB
2 MB
File 4
3 MB
File 5
1 MB
File 6
1 MB
Example: File Structure to Tree
File System:
3 Folders
6 Files
2) Cutting - according to the size
(30% and 70% of the space)
Dir 1 Root
Dir 2
Root
Dir 1
File 1
1 MB
File 2 2 MB
Dir 2
File 3 2 MB
Dir 2-1
File 4
3 MB
File 5
1 MB
File 6
1 MB
Example: File Structure to Tree
File System:
Root
Dir 1
File 1
3 Folders
6 Files
File 2 2 MB
Dir 2
File 3 2 MB
Dir 2-1
3) Iteration: folder and subfolder
File 1
File 2
File 1
Root
Dir 2
1 MB
File 3
Root
File 2 Dir 2-1
File 4
3 MB
File 5
1 MB
File 6
1 MB
Example: File Structure to Tree
File System:
Root
Dir 1
File 1
3 Folders
6 Files
File 2 2 MB
Dir 2
File 3 2 MB
Dir 2-1
File 3
Root
File 2 File 4
File 6
File 1
File 5
One Solution
1 MB
File 4
3 MB
File 5
1 MB
File 6
1 MB
Horizon Graph
[Reijner, 2005]
visualization technique for comparing a large number of time-dependent variables
based on the two-tone pseudo coloring
Cycle Plot
[Cleveland, 1994]
technique to make seasonal and trend components visually discernable
showing individual trends as line plots embedded within a plot that shows the seasonal
pattern
mean value for each weekday as grey line
VISUAL ANALYTICS
Analytical Methods
Screen Resolution:
1024 * 768 = 786.432
Yearly Measurements of Water Level in Low.Austria:1
5.256.000
Number of Cellular Phones in Austria (2005):2
8.160.000
Transmitted Emails Every Hours (World-Wide):3
Whole Data often not Presentable
1. 
Applying Analytical Methods
(Data Reduction)
2. 
Visualization of Most Important Data
and Information
35.388.000
today: peta (1015)
tomorrow: exa (1018) &
zeta (1021)
Analytical Methods
Statistics, Machine Learning & Data Mining
1 ... Amt der NÖ Landesregierung, Abt. WA5 - Hydrologie, http://www.noel.gv.at/SERVICE/WA/WA5/htm/wnd.htm
2 ... CIA Factbook, https://www.cia.gov/cia/publications/factbook/
3 ... How Much Information?, UC Berkeley, http://www2.sims.berkeley.edu/research/projects/how-much-info-2003/
Visual Analytics – What is it?
James Thomas & Kristin A. Cook
NVAC (National Visualization and Analytics Center), Seattle, USA
“Visual Analytics
is the science of
analytical reasoning
facilitated by
interactive
visual interfaces”
DEMO
TimeRider
[Rind, et al., 2011-2012]
BOOKS & RESOURCES
Eduard Tufte
1983 / 2001
1990
1997
2006
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Stephen Few
Show Me the
Numbers: Designing
Tables and Graphs to
Enlighten, Analytics
Press, 2004
Information
Dashboard Design:
The Effective Visual
Communication of
Data, O'Reilly Media,
2006
Now You See It:
Simple Visualization
Techniques for
Quantitative Analysis,
Analytics Press, 2009
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Web resources
InfoVis:Wiki (http://www.infovis-wiki.net)
Visual Analytics Digital Library (http://vadl.cc.gatech.edu/)
… etc. …
Infosthetics Blog (http://infosthetics.com/)
EagerEyes.org (http://eagereyes.org/)
… etc. …
see http://www.infovis-wiki.net/index.php?title=Category:Web_resources
for more
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Commercial Software
Tableau
Spotfire
MagnaView
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Free tools and libraries
No programming required
Tableau Public - Free service that lets you create
and share data visualizations on the web.
http://www.tableausoftware.com/products/public
Many Eyes - Free visualization site from IBM
Research.
http://manyeyes.alphaworks.ibm.com/manyeyes/
Google Chart Tools - Rich gallery of interactive
charts and data tools
http://code.google.com/apis/chart/
Gapminder World - Flash based Visualization that
shows the world development indicators with a
Scatterplot, Map and Animation (for Time).
http://tools.google.com/gapminder/
Google Fusion Tables - Collaborative online
visualization with community features similar to
Manyeyes.
http://tables.googlelabs.com/
Programming required
Processing - Java-based open source
programming language and environment
http://processing.org/
Protovis - JavaScript library that composes
custom views of data with simple marks such as
bars and dots.
http://www.protovis.org/
d3.js - Small, free JavaScript library for
manipulating documents based on data.
http://mbostock.github.com/d3/
prefuse - visualization framework for Java
http://prefuse.org/
flare - ActionScript library for visualizations that
run in the Adobe Flash Player.
http://flare.prefuse.org/
JFreeChart - Java class library for generating
charts.
http://www.jfree.org/jfreechart/index.html
65
Summary: InfoVis...
...
is a very complex task
...
can help to get insight into data more quickly
...
requires preparation and sensible handling of the information
...
should make use of the properties of human visual perception
...
requires sensible handling, relative to the task
...
is a big challenge, if you want to do it good
66
See & Understand
Detect the Expected - Discover the Unexpected
Kontakt
Dipl.-Ing. Dr.
Wolfgang Aigner
Technische Universität Wien
Institut für Softwaretechnik & Interaktive Systeme
Favoritenstr. 9-11/188
1040 Wien
T +43 (1) 58801-18833
E aigner@ifs.tuwien.ac.at
Thanks to
www.cvast.tuwien.ac.at
Thanks to
Alessio Bertone
Thomas Turic
(Danube Universty Krems)
(Danube Universty Krems)
Heidrun Schumann
Christian Tominski
(University of Rostock)
(University of Rostock)
Silvia Miksch
Bilal Alsallakh
Paolo Federico
Theresia Gschwandtner
Klaus Hinum
Katharina Kaiser
Tim Lammarsch
Alexander Rind
Andreas Seyfang
(CVAST, Vienna University of Technology)
(CVAST, Vienna University of Technology)
(CVAST, Vienna University of Technology)
(CVAST, Vienna University of Technology)
(in2vis, Vienna University of Technology)
(CVAST, Vienna University of Technology)
(HypoVis, Vienna University of Technology)
(HypoVis, Vienna University of Technology)
(Brigid, Vienna University of Technology)
Margit Pohl
Markus Rester
(CVAST, Vienna University of Technology)
(Vienna University of Technology)
NEW BOOK
Wolfgang Aigner • Silvia Miksch
Heidrun Schumann • Christian Tominski
Visualization of
Time-Oriented Data
with a foreword by Ben Shneiderman
Springer
1st Edition, 2011, XVIII, 286 p. 221 illus., 198 in color.
Hardcover, ISBN 978-0-85729-078-6.
Table of Contents
Introduction • Historical Background •
Time & Time-Oriented Data • Visualization Aspects •
Interaction Support • Analytical Support •
Survey of Visualization Techniques • Conclusion
www.timeviz.net
survey.timeviz.net
www.infovis-wiki.net
Contribute & Benefit!