Interactive Information Visualization -...
Transcript of 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
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InfoVis
Foreword Introduction Visual perception Visualization Interaction
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.
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InfoVis
Foreword Introduction Visual perception Visualization Interaction
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.
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InfoVis
Foreword Introduction Visual perception Visualization Interaction
General information
Online ressources
Home page for the class:<http://mosig.imag.fr/ClassNotes/UIS-AHCI>
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Foreword Introduction Visual perception Visualization Interaction
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.
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Foreword Introduction Visual perception Visualization Interaction
Overview
"Pre-attentive" perception
Find the blue dot. . .
. . . in constant time, no matter the number of red dots!
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Foreword Introduction Visual perception Visualization Interaction
Overview
"Pre-attentive" perception (cont.)
Find the square dot. . .
. . . in constant time, no matter the number of circles!
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Foreword Introduction Visual perception Visualization Interaction
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
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Foreword Introduction Visual perception Visualization Interaction
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
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Foreword Introduction Visual perception Visualization Interaction
Overview
Anscombe’s Quartet (1973) (cont.)
figure 1 : Anscombe’s quartet plotted.
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Foreword Introduction Visual perception Visualization Interaction
Overview
Visual thinking
"A picture is worth a thousand words"— anonymous, 1911
"Un petit dessin vaut mieux qu’un long discours"— Napoléon Bonaparte, 18xx
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Foreword Introduction Visual perception Visualization Interaction
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.
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Foreword Introduction Visual perception Visualization Interaction
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
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GAPMINDER WORLD 2012Mapping the Wealth and Health of Nations
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figure 3 : Hans Rosling’s <Gapminder world>.
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Foreword Introduction Visual perception Visualization Interaction
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)
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Foreword Introduction Visual perception Visualization Interaction
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
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Foreword Introduction Visual perception Visualization Interaction
Visualization
Challenges
scale: what is a large dataset?
diversity: what is information?
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Foreword Introduction Visual perception Visualization Interaction
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.
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Foreword Introduction Visual perception Visualization Interaction
Human visual system
Pre-attentive processing
Parallel processing by the retina (bottom-up) of:
orientation;
color;
texture;
movement;
etc.
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Foreword Introduction Visual perception Visualization Interaction
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.
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Foreword Introduction Visual perception Visualization Interaction
Human visual system
Goal-directed processing
Sequential processing by upper level in the brain (top-down):
object segmentation;
object identification;
etc.
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Foreword Introduction Visual perception Visualization Interaction
Human visual system
Eye anatomy
figure 4 : Right eye.
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Foreword Introduction Visual perception Visualization Interaction
Human visual system
Acuity
figure 5 : Density of receptors.
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Foreword Introduction Visual perception Visualization Interaction
Human visual system
Visual field
figure 6 : Binocular visual field.
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Foreword Introduction Visual perception Visualization Interaction
Human visual system
Summary
figure 7 : Your central visual field <http://xkcd.com/1080/>.
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Foreword Introduction Visual perception Visualization Interaction
Gestalt psychology
Gestalt psychology (192X)
figure 8 : "My wife and my mother-in-law." (1915)
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Foreword Introduction Visual perception Visualization Interaction
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)
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Foreword Introduction Visual perception Visualization Interaction
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.
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Foreword Introduction Visual perception Visualization Interaction
Gestalt psychology
Emergence
figure 9 : emergence
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Foreword Introduction Visual perception Visualization Interaction
Gestalt psychology
Emergence (cont.)
EmergenceThe global perception can not be explained by the sum of itsparts.
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Foreword Introduction Visual perception Visualization Interaction
Gestalt psychology
Reification
figure 10 : reification
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Foreword Introduction Visual perception Visualization Interaction
Gestalt psychology
Reification (cont.)
ReificationThe perception contains more spatial information that thestimulus on which it is based: part of the perception isgenerated.
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Foreword Introduction Visual perception Visualization Interaction
Gestalt psychology
Multistability
figure 11 : multistability
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Foreword Introduction Visual perception Visualization Interaction
Gestalt psychology
Multistability (cont.)
MultistabilityAmbiguous stimuli can generate different perceptions but theycan not coexist simultaneously.
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Foreword Introduction Visual perception Visualization Interaction
Gestalt psychology
Invariance
figure 12 : invariance
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Foreword Introduction Visual perception Visualization Interaction
Gestalt psychology
Invariance (cont.)
InvarianceObjects are recognized independently of various variations,such as geometrical transformations, lighting, etc.
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Foreword Introduction Visual perception Visualization Interaction
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.
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Foreword Introduction Visual perception Visualization Interaction
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.
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Foreword Introduction Visual perception Visualization Interaction
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)
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Foreword Introduction Visual perception Visualization Interaction
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.
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Foreword Introduction Visual perception Visualization Interaction
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.
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Foreword Introduction Visual perception Visualization Interaction
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.
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Foreword Introduction Visual perception Visualization Interaction
Visual mapping
Graphic variables
figure 15 : Graphic variables [Bertin, 1967].
• Jacques Bertin. Sémiologie graphique. 1967.
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Foreword Introduction Visual perception Visualization Interaction
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.
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Foreword Introduction Visual perception Visualization Interaction
Visual mapping
Guidelines for mapping
figure 16 : Variable properties [Bertin, 1967].
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Foreword Introduction Visual perception Visualization Interaction
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.
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Foreword Introduction Visual perception Visualization Interaction
Visual mapping
Design space for mapping
figure 18 : Characterization of Film finder [Card & Mackinlay, 1997].
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Foreword Introduction Visual perception Visualization Interaction
Visualization zoo
Taxonomy of networks
figure 19 : Taxonomy of networks [Bertin, 1967].
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Foreword Introduction Visual perception Visualization Interaction
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.
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Foreword Introduction Visual perception Visualization Interaction
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.
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Foreword Introduction Visual perception Visualization Interaction
Visualization zoo
A tour through the visualization zoo (cont.)
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..
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Foreword Introduction Visual perception Visualization Interaction
Visualization zoo
A tour through the visualization zoo (cont.)
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..
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Foreword Introduction Visual perception Visualization Interaction
Interaction
The Information Visualization Pipeline (bis)
figure 20 : InfoVis pipeline [Chi & Riedl, 1998].
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Foreword Introduction Visual perception Visualization Interaction
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.
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Foreword Introduction Visual perception Visualization Interaction
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.
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Foreword Introduction Visual perception Visualization Interaction
Interaction
Alternate visualization of graphs
figure 23 : Reorderable matrices [Bertin, 1967].
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Foreword Introduction Visual perception Visualization Interaction
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.
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Foreword Introduction Visual perception Visualization Interaction
Interaction
Hybrid tree/matrix visualizationsPa
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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.
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Foreword Introduction Visual perception Visualization Interaction
Interaction
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