Introduction to Information Visualization

Slavomir Petrik, Vaclav SkalaCentre of Computer Graphics and VisualizationUniversity of West BohemiaPlzen, Czech Republic2007

Overview of the talk

• History of visualization

• Scientific visualization vs. Information visualization

• Concepts, directions and techniques of InfoVis

• 1D, 2D, nD techniques• Tree and graph-based vis. • Network structure vis.

• Visualization in InfoVis

• Interacting with visualization

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From single sketch to tree maps

World map with Babylon in its centre 2300 BC(British museum)

Growing amount of information within a single image …

14th centuryRoman Britain

1864Civil war

15th centuryLeonardo da

Vinci

Today…Network structure

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Science of visualization

• Visualization of science vs. science of visualization

Large data01010101001000100011110010

01001 00100001111001000100

00100011 111001 1000100110

… .

Direct visualization vs.

visualization of structure

Scientific visualization

Information visualization

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Areas of interest

Scientific visualizationdeals with direct visualization of data that have natural geometricstructure

Information visualizationdeals with more abstract data represented by trees or graphs

Visual Analyticsscientific investigation of the use of visualization in sense-making and reasoning

• Still not defined precisely !

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Information visualization

Dataacquisition

Preprocessingenrichment, transformation

Basic concept

Examples

Ptolemy world map, 150 AD Napoleon march into RussiaCharles Minard, 1861

Data description by structures

Visualization

Highlight selected information

Information visualization

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Information visualization II.

• 1D, 2D techniques• High dimensional data• Tree-based techniques• Network visualization• Documents visualization

Visualization

Importance of colorsFocus + context

Interaction with visualization

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1D techniques

• Linear traverse of data

Table LensRao, 1994 ( Multivariate data )

ScatterplotKlein, 2002 ( Span Space )

FacetMaps ( InfoVis 2006 )

LensBar ( InfoVis 1998 )

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2D techniques

• Fit the 2nd dimension data to the first one, GIS applications

Large datasetsHealey, 1999

Enridged contour mapsvan Wijk, Telea, Vis 2001

World mapperInfoVis 2006

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nD techniques

• 2D restriction of screen• Multiple views and projections

Scatterplot matrixCleveland, 1985

Parallel coordinatesInselberg, 1990

Dimensional stackingLangton et al. 2007

… generalization:Moustafa, Wegman, 2002

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nD techniques II.

• with help of user interaction

HypercellSantos, 2002

World within worldsFeiner, 1990

Interactive scatterplotsKosara, 2004

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nD techniques III.

• multiple views and projections for dimensionality reduction

Perspective wallMackinlay et al. 1991

Prosection viewsFurnas, 1994

SunflowerRose, 1999

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Tree-based techniques

• data organized and explored via tree structure• two different views of a tree

• Side view

• Top view

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Tree-based techniques (side view)

• various forms of side view • combined with user interaction to choose proper view

Cone treeRobertson et al., 1991

... generalized by Jeong & Pang, 1998

Cylindrical treeDachselt, Ebert, 2001

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Tree-based techniques (top view)

• space filling problem

Tree mapShneiderman, 1992

800 files on disk

Recent surveys on Tree maps:http://www.cs.umd.edu/hcil/treemap-history/index.shtmlhttp://www.cse.ohio-state.edu/~kerwin/treemap-survey.html

Cushion tree mapWijk, 1999

Ordered and quantum tree mapBederson, 2002

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Tree-based techniques (top view)

Analysis of state transition graphsPretorius, TVCG 2006

+ Arc diagram

Bar tree

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Visualizing network structure

• intended to visualize a structure of computer network• a lot of items that need to be shown in a meaningful way• closely related to graph drawing problem

H3 Directed graph in 3D hyperbolic spaceMunzer, obertson et al., 1991

( video H3 )

MBoneMunzer, 1996

Radial layoutYee, 2001

Edge bundlesHolten, 2006

Topographic vis. Cortese, 2006

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Document visualization

“So much has already been written about everything that can’t find out anything about it.”

- James Thurber ( 1961 )

• Document visualization is not information retrieval

• Vast document storage: www, digital libraries (structured vs. unstructured

documents)

• Purpose: to gain insight into content of text and text collections

• Emerged at the beginning of ’90 with growing size of electronic text documents

TilebarHearst, 1995

SeesoftEick, 1992

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Document visualization

• growing size of documents vs. multidimensional browsing

(Wise, 1995: Visualizing non-visual)

SpireWise, 1995

In-SpirePacific Northwest National Lab.http://in-spire.pnl.gov2004

( ThemeView )

( Starlight )

( Theme river )• for temporal patterns

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Summary of the first part

1D techniques

Table LensScatterplots

LensBarFacetMaps

2D techniquesMaps with bars

Enridged contour maps

Worldmapper

nD techniques

Scatterplot matrix

Parallel coords.

Dimensional stacking

Tree-based techniques

Side-viewTop-view

Network visualizationH3

Edge bundlesMBone

Documentvisualization

LinearnD techniques

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Focus & context

• highlighted important parts of data• put “important” into the context of the rest of data

Fisheye lens [ Furnas, 1981 ] Depth of field

… also in scientific visualization [ Kruger, 2006 ]

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Visual attention

• Emphasizing important information

( by color, texture, depth of field )

• Cognitive psychology( perception, long term vs. short term memory )

Kosara, S-DOF, 2002, 2003

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Application: Software visualization

Program structureTelea, 2002

Dynamic memory allocationMoreta, 2006

• visualizing structure of software modules

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Application: Material properties

• visualizing mechanical properties of materials (ZCU Plzen)

• attempt to visualize many information within a single picture

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Summary & conclusion

• Overview of the former and current state of Information visualization was presented

• 5 main areas of research (and many derived and combined)

• 1D techniques

• 2D techniques

• nD techniques

• Tree and graph-based visualization

• Network structure visualization

• Focus & context paradigm

• Real-life application: software visualization

Two future directions:

• Perception and cognition studies

• Large and dynamic data visualization

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Thank you

Actual papers and references used in this presentation can be found

in the supplementary material distributed with this presentation.

This work has been supported by the project 3DTV NoE FP6 No: 511568

and Ministry of Education, Youth and Sports of the Czech Republic

project VIRTUAL No: 2C06002.

Slavomir Petrik, Vaclav SkalaCenter of Computer Graphics and Visualizationhttp://herakles.zcu.cz

University of West BohemiaPlzen, Czech Republic, 2007