interactive information visualization -...

Foreword Introduction Visual perception Visualization Interaction

Interactive Information VisualizationHow to make "a picture [. . . ] worth a thousand words"

Renaud Blanch <[email protected]>

Université Joseph Fourier, Polytech’Grenoble & UFR IM2AG

january 2014

[email protected] UJF-Grenoble 1

InfoVis

mailto:[email protected]


General information

Course objectives

After following this course, you will be able to:

know the scientific foundations of InfoVis;

analyze data sets using visualization techniques; and

build visualization that convey information and ideas.


InfoVis


General information

Planning

Only four lectures (4×1.5h), i.e., a short introduction to thedomain:

Introduction, Human visual perception;

The visualization pipeline, Data types, Seminal works;

Trees and graph visualization; and

Tabular data and time series, InfoVis toolkits.


InfoVis


General information

Online ressources

Home page for the class:<http://mosig.imag.fr/ClassNotes/UIS-AHCI>


InfoVis

http://mosig.imag.fr/ClassNotes/UIS-AHCI


Overview

Problem

Q: What is the best channel to convey information to ahuman?A: Vision because:

highest bandwidth sense (≈ 100MBs−1, then ears< 100bs−1);

extends memory and cognition;

people think visually.


InfoVis


Overview

"Pre-attentive" perception

Find the blue dot. . .

. . . in constant time, no matter the number of red dots!


InfoVis


Overview

"Pre-attentive" perception (cont.)

Find the square dot. . .

. . . in constant time, no matter the number of circles!


InfoVis


Overview

Anscombe’s Quartet (1973)

Four data sets:

I II III IVx y x y x y x y10 8.04 10 9.14 10 7.46 8 6.58

8 6.95 8 8.14 8 6.77 8 5.7613 7.58 13 8.74 13 12.74 8 7.71

9 8.81 9 8.77 9 7.11 8 8.8411 8.33 11 9.26 11 7.81 8 8.4714 9.96 14 8.10 14 8.84 8 7.04

6 7.24 6 6.13 6 6.08 8 5.254 4.26 4 3.10 4 5.39 19 12.50

12 10.84 12 9.13 12 8.15 8 5.567 4.82 7 7.26 7 6.42 8 7.915 5.68 5 4.74 5 5.73 8 6.89


InfoVis


Overview

Anscombe’s Quartet (1973) (cont.)

. . . having the exact same statistical properties:

number of observations (n): 11mean of the x ’s (x): 9.0mean of the y ’s (y ): 7.5equation of regression line: y = 3 + 0.5xsums of squares of x − x : 110.0regression sums of squares: 27.50 (1 d.f.)residual sums of squares of y : 13.75 (9 d.f.)Multiple R2: 0.667


InfoVis


Overview

Anscombe’s Quartet (1973) (cont.)

figure 1 : Anscombe’s quartet plotted.


InfoVis


Overview

Visual thinking

"A picture is worth a thousand words"— anonymous, 1911

"Un petit dessin vaut mieux qu’un long discours"— Napoléon Bonaparte, 18xx


InfoVis


Overview

Visual thinking (cont.)

figure 2 : Charles Minard’s 1869 chart showing the number of men inNapoleon’s 1812 Russian campaign army, their movements, as wellas the temperature they encountered on the return path.


InfoVis


Overview

Communication

Inco

me

per P

erso

n of

the

Wor

ld

Life Expectancy of the World

Liechtenstein

Antigua&Barbuda

Dominica

Palau

NauruTuvalu

Seychelles

St. Kitts& Nevis

AndorraSan Marino

Monaco

Andorra

St.LuciaPanama

Sao Tomeand Principe

Tonga

Samoa

Grenada

Brunei

Comoros

Djibouti

Equatorial Guinea

Gabon

Luxembourg

Namibia

Swaziland

Timor-Leste

Micronesia

Trinidad and Tobago

Albania

Bhutan

Kiribati

Kosovo

Cyprus

Maldives

Slovenia

Suriname

Belize

Mauritius

Bahamas

Malta

Vanuatu

Montenegro

Estonia

Gambia

Guinea-BissauLesotho

Botswana

Mongolia

Oman

Qatar

Iceland

Barbados

BahrainCapeVerde

Latvia

SolomonIslands

Macedonia

Fiji

Guyana

Jamaica

St.Vincentand G.

Armenia

Lithuania

Uruguay

Mauritania

Moldova

Kuwait

Congo, Rep.Liberia

Bosnia and H. Croatia

Lebanon

Israel

Costa Rica

Puerto Rico

New Zealand

Georgia

Central African Rep.

SwedenSingapore

Norway

Ireland

Finland

Austria

Turkmenistan

Slovak Rep.

Kyrgyzstan

Eritrea

DenmarkTaiwan

Papua New Guinea

Hong Kong

United Arab Emirates

South Sudan

Switzerland

Hungary

BelarusAzerbaijan

Dom.R.

Bulgaria

Serbia

Burundi

LibyaNicaragua

Palestine

Sierra Leone

Laos

Benin

Guinea

Somalia

Tajikistan

Togo

El Salvador

Honduras ParaguayJordan

Poland

Bolivia

Haiti

Czech Rep.

Portugal

Tunisia

Rwanda

GreeceBelgiumCuba

Chad

Senegal

Zimbabwe

Zambia

Cambodia

Ecuador

Guatemala

BurkinaFaso

MalawiNiger

Mali

Kazakhstan

Netherlands

Chile

Romania

Cameroon

Sri Lanka

Cote d'Ivoire

Angola

Madagascar

Syria

Australia

Mozambique

Yemen

North Korea

Afghanistan

Ghana

Nepal

Sudan

SaudiArabia

PeruVenezuela

Malaysia

Morocco

Uzbekistan

Italy

Spain

UK Germany

Canada

France

South Korea

Philippines

Vietnam

Ethiopia

Egypt

IranTurkey

Dem. Rep. Congo

Thailand

South Africa

Myanmar

Colombia

Ukraine

Tanzania

Kenya

Argentina

Algeria

Iraq

Uganda

ChinaBangladesh

Indonesia

Pakistan

USA

Russia

Brazil

Nigeria

Japan

Mexico

India

2011 data for all 193 UN Members and for Hong Kong, Kosovo, Palestine, Puerto Rico and Taiwan.

Documentation and!"# version for print at:

3 orless

10100 1000

millions

Colour by region

Size by population

If you want to see more data visit:

www.gapminder.org

Free to copy, share and remix, but attribute to

Gapminder Foundation.

Version 11 September 2012

map

layo

ut b

y Pao

lo Fa

uson

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!"#$%&'().*)!/'*+&,*"'-/+*),'-#'.

GAPMINDER WORLD 2012Mapping the Wealth and Health of Nations

Healthy

Poor Rich

Sick

50

500 1 000 2 000 20 0005 000 10 000 50 000

60

70

80

55

65

75

%&/*$( #() #()-*& in US Dollars (GDP/capita, PPP$ in!ation adjusted, log scale)

,%.(

(0#

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&/2

in ye

ars

figure 3 : Hans Rosling’s <Gapminder world>.


InfoVis

http://www.gapminder.org/world


Visualization

Information Visualization

InfoVis"The use of computer-supported, interactive visualrepresentations of data to amplify cognition"

— Card, Mackinlay & Shneiderman, 1998

Many fields are involved:

graphics (millenniums of history)cognitive psychology (centuries of history)Human-computer interaction (decades of history)


InfoVis


Visualization

Scientific Visualization

SciVizVisualization of data sets captured from real world, having agiven spatialization.

Key differences:

continuous math vs. discrete mathlimited set of application domainssmaller design space


InfoVis


Visualization

Challenges

scale: what is a large dataset?

diversity: what is information?


InfoVis


Human visual system

Simplified model

Visual perception is a two stage process:

a parallel extraction of low-level properties; then

a sequential goal-directed processing.


InfoVis


Human visual system

Pre-attentive processing

Parallel processing by the retina (bottom-up) of:

orientation;

color;

texture;

movement;

etc.


InfoVis


Human visual system

Pre-attentive processing (cont.)

The Feature-Integration Theory of Attention[Treisman & Gelade, 1980] can be seen as a limit caseof Visual Search and Attention: a Signal Detection TheoryApproach [Verghese, 2001].

• A. Treisman and G. Gelade. A Feature-Integration Theory of Attention.In Cog. Psycho., vol. 12, 97–136, 1980.

• Preeti Verghese. Visual Search and Attention: a Signal Detection Theory Approach.In Neuron, vol. 31, 523–535, 2001.


InfoVis


Human visual system

Goal-directed processing

Sequential processing by upper level in the brain (top-down):

object segmentation;

object identification;

etc.


InfoVis


Human visual system

Eye anatomy

figure 4 : Right eye.


InfoVis


Human visual system

Acuity

figure 5 : Density of receptors.


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Human visual system

Visual field

figure 6 : Binocular visual field.


InfoVis


Human visual system

Summary

figure 7 : Your central visual field <http://xkcd.com/1080/>.


InfoVis

http://xkcd.com/1080/


Gestalt psychology

Gestalt psychology (192X)

figure 8 : "My wife and my mother-in-law." (1915)


InfoVis


Gestalt psychology

Gestalt psychology (192X) (cont.)

The Gestalt psychology is a theory of perception that is oftensummed up by:

"The whole is other than the sum of the parts"— Kurt Koffka (1922)


InfoVis


Gestalt psychology

Gestalt psychology (192X) (cont.)

The Gestalt psychology notably describes the perception offorms by the visual system. It relies on four principles:

Emergence;

Reification;

Multistability; and

Invariance.

It also describes our visual perceptions by a set of laws.


InfoVis


Gestalt psychology

Emergence

figure 9 : emergence


InfoVis


Gestalt psychology

Emergence (cont.)

EmergenceThe global perception can not be explained by the sum of itsparts.


InfoVis


Gestalt psychology

Reification

figure 10 : reification


InfoVis


Gestalt psychology

Reification (cont.)

ReificationThe perception contains more spatial information that thestimulus on which it is based: part of the perception isgenerated.


InfoVis


Gestalt psychology

Multistability

figure 11 : multistability


InfoVis


Gestalt psychology

Multistability (cont.)

MultistabilityAmbiguous stimuli can generate different perceptions but theycan not coexist simultaneously.


InfoVis


Gestalt psychology

Invariance

figure 12 : invariance


InfoVis


Gestalt psychology

Invariance (cont.)

InvarianceObjects are recognized independently of various variations,such as geometrical transformations, lighting, etc.


InfoVis


Gestalt psychology

Gestalt laws of grouping

figure 13 : grouping of dots (illustration from <Laws of Organizationin Perceptual Forms (1923)>).

The laws of grouping state how low-level perceptions aregrouped into higher-level objects.


InfoVis

http://psychclassics.yorku.ca/Wertheimer/Forms/forms.htm

http://psychclassics.yorku.ca/Wertheimer/Forms/forms.htm


Gestalt psychology

Gestalt laws of grouping (cont.)

Good Gestalt (Prägnanz)We tend to order our experience in a manner that is regular,orderly, symmetric, and simple.

ProximityObjects that are close tend to be perceived as a group.

SimilarityObjects that are similar (in shape, color, shading, etc.) tend toform a group.

ClosureThe perception fills gaps in stimuli.


InfoVis


Gestalt psychology

Gestalt laws of grouping (cont.)

SymmetryObjects with symmetric disposition tend to be perceived asforming a whole.

Common FateObjects evolving together are perceived as a group.

ContinuityAmbiguous stimuli are perceived preferentially with theinterpretation that is the most continuous.

Past ExperienceWe group things we have learned to group (e.g. letters incursive writing)


InfoVis


Visual mapping

The Visual Information-Seeking Mantra

The Visual Information-Seeking Mantra [Shneiderman, 1996]:

Overview first,

Zoom and filter, then

Details-on-demand.

• Ben Shneiderman. The Eyes Have It: A Task by Data Type Taxonomy forInformation Visualizations. In Proc. Visual Languages, 336–343, 1996.


InfoVis


Visual mapping

The Information Visualization Pipeline

figure 14 : InfoVis pipeline [Chi & Riedl, 1998].

• E. Chi and J. Riedl. An Operator Interaction Framework for Visualization Systems.In proc. InfoVis’98, 63–70, 1998.


InfoVis


Visual mapping

Data

Several taxonomies of data types, e.g., [Card & Mackinlay, 1997]:

Nominal (identity)

Ordered (comparison)

Quantitative (differences, ratios)

• S. Card and J. Mackinlay. The Structure of the Information Visualization DesignSpace. In proc. InfoVis’97, 92–99, 1997.


InfoVis


Visual mapping

Graphic variables

figure 15 : Graphic variables [Bertin, 1967].

• Jacques Bertin. Sémiologie graphique. 1967.


InfoVis


Visual mapping

A note on color

Color is a 3D space, with different parametrizations.The color opponent process model [Ware, 2000] is the most"psychophysical":

black-white (luminance = red + green)

red-greenyellow-blue (luminance−blue)

• C. Ware. Information visualization. 2000.


InfoVis


Visual mapping

Guidelines for mapping

figure 16 : Variable properties [Bertin, 1967].


InfoVis


Visual mapping

Guidelines for mapping (cont.)

figure 17 : Suitability of variables [Mackinlay, 1986].

• J. Mackinlay. Automating the Design of Graphical Presentations of RelationalInformation. ACM Trans. Graph. 5(2): 110–141, 1986.


InfoVis


Visual mapping

Design space for mapping

figure 18 : Characterization of Film finder [Card & Mackinlay, 1997].


InfoVis


Visualization zoo

Taxonomy of networks

figure 19 : Taxonomy of networks [Bertin, 1967].


InfoVis


Visualization zoo

A tour through the visualization zoo

Let’s take a <tour through the Visualization

zoo> [Heer et al., 2010]!• J. Heer, M. Bostock, V. Ogievetsky. A Tour Through the Visualization Zoo.

Communications of the ACM 53(6): 59–67, 2010.


InfoVis

http://hci.stanford.edu/jheer/files/zoo/

http://hci.stanford.edu/jheer/files/zoo/


Visualization zoo

A tour through the visualization zoo (cont.)

Some other zoos:

a <tree visualization reference> [Schulz, 2011];

a <visual survey of visualization techniques for

time-oriented data> [Aigner et al., 2011];

a <survey of text visualization techniques>; and

• H. Schulz. Treevis.net: a Tree Visualization Reference.IEEE Computer Graphics and Applications 31(6): 11–15, 2011.

• W. Aigner, S. Miksch, H. Schumann, C. Tominski.Visualization of Time-Oriented Data. 2011.


InfoVis

http://treevis.net/

http://survey.timeviz.net/

http://survey.timeviz.net/

http://textvis.lnu.se/


Visualization zoo


And some more zoos:

a <survey on multifaceted scientific data

visualisation> [Kehrer & Hauser, 2013];a <review of temporal visualizations based on

space-time cube operations> [Bach et al., 2014];

• J. Kehrer, H. Hauser.Visualization and Visual Analysis of Multifaceted Scientific Data: a Survey.IEEE Transactions on Visualization and Computer Graphics 19(3): 495–513, 2013.

• B. Bach, P. Dragicevic, D. Archambault, C. Hurter, S. Carpendale.A Review of Temporal Data Visualizations Based on Space-Time Cube Operations.In proc. EuroVis’14, State of The Art Reports..


InfoVis

http://multivis.net/

http://multivis.net/

http://spacetimecubevis.com/

http://spacetimecubevis.com/


Visualization zoo


And even more zoos:

a <survey on set visualization> [Alsallakh et al., 2014];a <state of the art in visualizing dynamic graphs>

[Beck et al., 2014].

• B. Alsallakh, L. Micallef, W. Aigner, H. Hauser, S. Miksch, P. Rodgers.Visualizing Sets and Set-typed Data: State-of-the-Art and Future Challenges.In proc. EuroVis’14, State of The Art Reports..

• F. Beck, M. Burch, S. Diehl, D. Weiskopf. The State of the Art in Visualizing DynamicGraphs. In proc. EuroVis’14, State of The Art Reports..


InfoVis

http://www.setviz.net/

http://dynamicgraphs.fbeck.com/


Interaction

The Information Visualization Pipeline (bis)

figure 20 : InfoVis pipeline [Chi & Riedl, 1998].


InfoVis


Interaction

Multi-variate data

figure 21 : <ScatterDice> [avi] [Elmqvist et al., 2008].

• N. Elmqvist, P. Dragicevic, J.-D. Fekete. Rolling the Dice: Multidimensional VisualExploration using Scatterplot Matrix Navigation.In Proc. InfoVis 2008, 1141-1148, 2008.


InfoVis

https://engineering.purdue.edu/~elm/projects/scatterdice.html


Interaction

Zoomable treemaps

figure 22 : <Zoomable Treemaps> [mov] [Blanch & Lecolinet, 2007].

• R. Blanch and É. Lecolinet. Browsing Zoomable Treemaps: Structure-AwareMulti-Scale Navigation Techniques. In Proc. of InfoVis 2007, 1248–1253, 2007.


InfoVis

http://iihm.imag.fr/blanch/projects/ztm/


Interaction

Alternate visualization of graphs

figure 23 : Reorderable matrices [Bertin, 1967].


InfoVis


Interaction

Hybrid visualization of graphs

figure 24 : <NodeTrix> [mov] [Henry et al., 2007].

• N. Henry, J.-D. Fekete, M. J. McGuffin. NodeTrix: A Hybrid Visualization of SocialNetworks. In Proc. of InfoVis 2007, 1302-1309, 2007.


InfoVis

http://www.aviz.fr/Research/Nodetrix


Interaction

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figure 25 : Dendrogramix [Blanch et al., 201X].

• R. Blanch, R. Dautriche and G. Bisson. Dendrogramix: a Hybrid Tree-MatrixVisualization Technique to Support Interactive Exploration of Dendrograms.work in progress.


InfoVis


Interaction

Thank you!

Thank you for your attention!


InfoVis

interactive information visualization -...

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