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

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About me

3  

MOTIVATION & INTRODUCTION

Information overload

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[Howson, 2008]

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Why Visualization?

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Goal

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Method

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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)

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[Few, 2006]

Example

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Example

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INTERACTIVITY

Car Example - Interactivity

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VISUALIZATION DESIGN

Three central questions

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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data

goal/task

representations &

interaction

user/audience appropriateness

Purpose

Exploration / Explorative Analysis

undirected search

no a priori hypotheses

get insight into the data

begin extracting relevant information

come up with hypotheses

Confirmation / Confirmative Analysis

directed search

verify or reject hypotheses

Presentation

communicate and disseminate analysis results

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interactivity

InfoVis Reference Model

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[Card et al., 1999]

Visual Variables – Mackinlay

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[Mackinlay, 1987]

Visual Mapping: Example

year

length

popularity

subject

award?

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[garysaid.com]

Visual Mapping: Example

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[Spotfire]

THE GOOD

Florence Nightingale – Rose chart (1855)

25  [Nightingale, 1858]

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/

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Weather chart

27  

Aigner, Miksch, Tominski, Schumann. Visualization of Time-Oriented Data, Springer, 2011.

THE BAD

»Diagrams can lead to great insight, but also to the lack of it.«

Tufte, 1997

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The Challenger Disaster

January 27, 1986: US-Space Shuttle Challenger explodes 72 seconds after launch

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Reasons: Sealing-rings in the right booster were damaged due to weather conditions

Reliability-problems of the so-called O-rings were known

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!

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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

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Visualization Design

data

goal/task

representations &

interaction user/audience appropriateness

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

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Christian Resei, AK-NÖ, treffpunkt 04/10, Magazin der NÖ Arbeiterkammer, S. 6

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

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Marcos Weskamp, Newsmap, Retrieved at: Oct 14, 2011, http://newsmap.jp

Example: File Structure to Tree

File System:

3 Folders

6 Files

1) Root -> whole Screen

Root Root

Root Dir 1

File 1 1 MB

File 2 2 MB Dir 2

File 3 2 MB

File 4 3 MB

File 5 1 MB

File 6 1 MB

Dir 3

Example: File Structure to Tree

File System:

3 Folders

6 Files

2) Cutting - according to the size (30% and 70% of the space)

Root Dir 1

File 1 1 MB

File 2 2 MB Dir 2

File 3 2 MB

File 4 3 MB

File 5 1 MB

File 6 1 MB

Dir 2-1

Root Dir 1 Dir 2

Example: File Structure to Tree

File System:

3 Folders

6 Files

3) Iteration: folder and subfolder

Root Dir 1

File 1 1 MB

File 2 2 MB Dir 2

File 3 2 MB

File 4 3 MB

File 5 1 MB

File 6 1 MB

Dir 2-1

Root

File 1

File 2 Dir 2 Root

File 1

File 2 Dir 2-1

File 3

Example: File Structure to Tree

File System:

3 Folders

6 Files

One Solution

Root Dir 1

File 1 1 MB

File 2 2 MB Dir 2

File 3 2 MB

File 4 3 MB

File 5 1 MB

File 6 1 MB

Dir 2-1

Root

File 1

File 2

File 3

File 4

File

5

File

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Horizon Graph

visualization technique for comparing a large number of time-dependent variables

based on the two-tone pseudo coloring

[Reijner, 2005]

Cycle Plot

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

[Cleveland, 1994]

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 35.388.000

Whole Data often not Presentable 1.  Applying Analytical Methods

(Data Reduction)

2.  Visualization of Most Important Data and Information

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/

today: peta (1015) tomorrow: exa (1018) & zeta (1021)

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

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1990 1997 1983 / 2001 2006

Stephen Few

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Now You See It: Simple Visualization Techniques for Quantitative Analysis, Analytics Press, 2009

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

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

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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

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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 (Danube Universty Krems) Thomas Turic (Danube Universty Krems) Heidrun Schumann (University of Rostock) Christian Tominski (University of Rostock) Silvia Miksch (CVAST, Vienna University of Technology) Bilal Alsallakh (CVAST, Vienna University of Technology) Paolo Federico (CVAST, Vienna University of Technology) Theresia Gschwandtner (CVAST, Vienna University of Technology) Klaus Hinum (in2vis, Vienna University of Technology) Katharina Kaiser (CVAST, Vienna University of Technology) Tim Lammarsch (HypoVis, Vienna University of Technology) Alexander Rind (HypoVis, Vienna University of Technology) Andreas Seyfang (Brigid, Vienna University of Technology) Margit Pohl (CVAST, Vienna University of Technology) Markus Rester (Vienna University of Technology)

www.timeviz.net

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

NEW BOOK

survey.timeviz.net

www.infovis-wiki.net

Contribute & Benefit!