An Introduction to Data and

Information Visualization

Beatriz Sousa Santos, University of Aveiro, 2016

Universidade de Aveiro

Departamento de Electrónica,

Telecomunicações e Informática

Outline of the lectures:

Data and Information Visualization: Introduction

- Data Characteristics

Computer graphics:

- Interactive graphics systems; Primitives and attributes

- Geometric transformations and projections

- Visualization pipeline, visibility, illumination and surface rendering

- Visual Perception

Information Visualization:

- Main issues

- Representations

- Presentation

- Interaction

- Evaluation

2

3

Definition

Objectives

History

Applications

Model

How to obtain and evaluate a Visualization?

6

• Visualization aims at exploring data as to gain a better comprehension of phenomena through the capacities of the Human Visual System

• Which agrees with the purpose of computing in general:

“The purpose of computing is insight not numbers”

(Hamming, 1962)

• We can say:

“The purpose of Visualization is insight not graphics”

Objectives

7

• Intelligence Amplification as opposed to Artificial Intelligence

(Fred Brooks, 1999)

• Visualization may have a significant role in the amplification of human capacities

• Several scientific disciplines contribute to Visualization:

– Computer Graphics

– Human-Computer Interaction

– Software Engineering

– Image Processing

– Signal Processing

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• Visualization is different from Computer Graphics and Image

Processing since:

1- it deals mainly with multi-dimensional data

( >= 3D, time varying)

2- data transformation is fundamental

(may be changed to increase insight)

3- it is essentially interactive,

(including the user in the process of data creation,

transformation and visualization)

• However, there is some overlap:

– The output of a visualization process is an image

– In general uses much CG

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• The usefulness of graphical representations of large amounts of data has been recognized long ago:

XVIII e XIX centuries- use of graphics in statistics and science:

W. Playfair, C. J. Minard

XX century- J. Bertin, E. Tufte

• The use of the computer made Visualization a more practicable discipline:

1987 - Identification of Visualization as an autonomous discipline

Visualization in Scientific Computing

(McCormick, de Fanti and Brown – 1987)

Brief history

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One of the oldest known Visualizations

Inclination of orbits along the time - Xth century (Tufte,1983)

13

One of the first Visualizations used in “business”

Import/export during the period from1770 to1782

by William Playfair (Tufte, 1983)

One of the first visualizations

using contours (isolines)

Magnetic declination 1701

Edmund Halley (Tufte,1983)

14

Multidimensional Visualization

6 dimensions: place (2), n. of men and direction of the army, date, temperature

Russia campaign of Napoleon 1861 by Charles Minard (Tufte,1983)

15

Visualization in scientific discovery

Discovering the cause of the London cholera out brake, 1853-54

(Wikipedia)

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Definition

Objectives

History

Applications

Model

How to obtain a Visualization?

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Applications

• Data Visualization is currently used in many scientific areas:

– Medicine

– Meteorology, climatology, oceanography

– Fluid dynamics

– Cosmology

– etc., etc.

• Let us see some examples …

• Can you think of an area where data visualization cannot be applied?

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Medicine (e.g. surgery training)

• VOXELman, University of Hamburg

http://www.voxel-man.de/simulator/temposurg/video.html

• Temporal bone surgery

• Movement of the drill is

controlled with a force

feedback device

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Stereoscopic displays

- dextroscope

- screen

Interaction devices:

- phanton (force feedback)

- touch

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http://tidesandcurrents.noaa.gov

/sltrends/sltrends.shtml

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• In general:

Data (scientific) Visualization (DV) - Data having an inherent spatial structure

(e.g., CAT, MR, geophysical, meteorological, fluid dynamics data)

Information Visualization (IV) – Data not having an inherent spatial structure

(e.g., stock exchange , S/W, Web usage patterns, text)

• These designations may be misleading; both DV and IV start with (raw) data and allow to extract information

• Borders between these areas are not well defined, neither it is clear if there is any advantage in separating them (Rhyne, 2003)

Data and Information Visualization

Ground

Penetrating Radar (1999) Tomography

(2004)

Data (Scientific) Visualization (examples “made in UA”)

Tomography

(2008)

Tomography and SPECT

(1996)

Electrical and mechanical

ground resistivity (2010)

Tomography (2011)

Laser scanner (2015)

Web site usage

(UA, 2004)

Information Visualization (examples “made in UA”)

Pedigree trees

(UA, 2011)

Human Migrations

(UA, 2015)

Ranking Visualization

(UA, 2015)

www.portugal-migration.info

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Botanical Visualization of Huge Hierarchies

Unix Home-directory

Ipod Echosystem

2001

2004

Road traffic between Swedish counties

http://www.Visualcomplexity.com

(http://www.oculusinfo.com/demos.html#)

Stock market

Information Visualization (examples)

competitors

content providers/distributors

technology providers

accessory makers

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Data

acquisition Data

Computing

Results

User

Hypothesis

Understanding

Framework

(Brodlie et al., 1992)

• Visualization includes not only image production from the data, but also

their transformation and manipulation (if possible their acquisition)

• It is a “human-in-the-loop” problem

• Visualization may be used with different purposes:

- personal exploration - explorative analysis

- discussion with colleagues - confirmative analysis

- presentation to other people

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Classical examples for:

a) exploration

b) presentation a)

b)

for

• Most often to promote insights and support users in work scenarios

• But also in the so called Casual Visualization, to depict personally meaningful

information in visual ways that support everyday users also in non-work

situations

https://vimeo.com/91325884

Pousman, Z., Stasko, J.T. and Mateas, M., 2007. Casual Information Visualization: Depictions of Data in Everyday Life. IEEE Transactions on Visualization and Computer Graphics, 13(6), pp. 1145-1152.

Presentation example: World health

34 https://www.youtube.com/watch?v=jbkSRLYSojo

• “The use of computer-supported, interactive, visual representations of data

to amplify cognition” (Card et al., 1999)

• However, it can be used with different meanings:

– the field within Computing

– the representations obtained using methods and systems

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Visualization: meaning

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Data Visualization reference model

Measured data:

CAT, MR, ultra-sound

laser Digitizers,

Satellites, …..

Data

Transform Map Display

Visualization technique

Simulated data:

Finite Element

Analysis, Numeric

methods, ……

(adapted from Schroeder et al., 2006)

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• Then a visualization technique is applied, involving:

- data transformation through several methods

(e.g. noise filtering, outlier elimination, changing resolution, scale

transformation, etc.)

- mapping to an adequate form to representation (e.g. graphic primitives and attributes, color)

- producing an image or sequence of images (rendering)

• This process is repeated as needed to provide insight

• Data can be:

- simulated

(e.g. stress of a mechanical part,

phantom of the human body, etc.)

- measured from real phenomena Visualization technique

map transform display

Data

Simulated

Data

Measured

Data

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• The choice of the right mapping is

fundamental, and is particularly difficult in InfoVis

• It’s generally easier in Data Visualization, since the data are inherently spatial

• Consider terrain altitude data or values of a function:

- different mappings or abstract visualization objects can be used,

e.g.

- contours (iso-lines)

- pseudo-color

- three-dimensional surface

Patent landscape (Cheng, 2003)

http://www.ipo.gov.uk/informatic-

recycleseparate.pdf

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Information Visualization Reference Model

Visualization can be described as the mapping of data to visual form

supporting human interaction for visual sense making

(Card et al., 1999)

Raw

data

Data

tables

Visual

structures Views

task

Data

Transformation

Visual

Mappings

View

Transformation

Human interaction

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(Chi et al., 1998)

General template for visualization applications

(http://www.infovis-wiki.net)

Information visualization application development requires balancing:

- data management

- visual mappings

- computer graphics

- interaction

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• Visualization is evolving as a scientific discipline:

– It started as a "craft“ - solutions where obtained ad hoc using a few

heuristics

– Then a more scientific phase - researchers started to build

foundations and theories

– It is in an engineering phase - engineers refine theories and try to

establish guidelines

Finally, is becoming common

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But after all:

– How can we produce a Visualization?

– And evaluate it?

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• There are no “recipes” to chose adequate Visualization techniques

• There are principles derived form human perception and cognition

• A correct definition of goal is fundamental to efficacy

How can we produce a Visualization?

Reveal shape Analyze structure

(Simulation of an astrophysical phenomenon) (Keller & Keller, 1993)

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What future?

• Visualization seems to become:

– More 3D

– More interactive

– More beyond desktop

– More accurate

– More intelligent

– More multimodal

– More distributed

– More collaborative

– More mobile

– More remotely controllable

– More web based

– …

– Integrating more other technologies

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• This course will address:

– Data characteristics

– Representations, presentation and interaction

– The Human Visual System and perception

– Evaluation

InfoVis Books

• Spence, R., Information Visualization, Design for Interaction, 2nd ed., Prentice Hall, 2007

• Munzner, T., Visualization Analysis and Design, A K Peters/CRC Press, 2014

• Mazza, R., Introduction to Information Visualization, Springer, 2009

• Ware, C., Information Visualization, Perception to Design, 2nd ed.,Morgan Kaufmann, 2004

• Card, S., J. Mackinlay, B. Shneiderman, Readings in Information Visualization: Using Vision to Think, Academic Press, 1999

• Bederson, B. , B. Shneiderman, The Craft of Information Visualization: Readings and Reflections, Morgan Kaufmann, 2003

• Tufte, E., The Visual Display of Quantitative Information, Graphics Press, 1983

• Tufte, E., Envisioning Information, Graphics Press, 1990

• Friendly, M., "Milestones in the history of thematic cartography, statistical graphics, and data visualization“, 2008

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Data Vis Books and reports

• Brodlie, K., L. Carpenter, R. Earnshaw, J. Gallop, R. Hubbold, A. Mumford, C.

Osland, P. Quarendon, Scientific Visualization, Techniques and Applications,

Springer Verlag, 1992

• Hansen, C., C. Jonhson (eds.), The Visualization Handbook, Elsevier, 2005

• Jonhson, C., R. Moorhaed, T. Munzner, H. Pfister, P. Rheingans, T. Yoo,

Visualization Research Challenges, NHI/NSF, January, 2006

• Keller, P., M. Keller, Visual Cues, IEEE Computer Society Press, 1993

• Rosenblum, L., R. Earnshaw, J. Encarnação, H. Hagen, A. Kaufman, S.

Klimenko, G. Nielson, F. Post, D. Thalmannn (eds.), Scientific Visualization,

Advances and Challenges, IEEE Computer Society Press, Academic Press,

1994

• Schroeder, W., K. Martin, B. Lorensen, The Visulization Toolkit- An Object

Oriented Approach to 3D Graphics, 4th ed., Prentice Hall, 2006

• Ward, M. G. Grinstein and D. Keim, Interactive Data Visualization: Foundations,

Techniques, and Applications, A K Peters/CRC Press , 2010

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Bibliography-papers

• Rhyne, T. M., "Does the Difference between Information and Scientific Visualization Really Matter?“, IEEE Computer Graphics and Applications, May/June, 2003, pp. 6-8

• Rhyne, T. M., “Scientific Visualization in the Next Millennium”, IEEE Computer Graphics and Applications, Jan./Feb., 2002, pp. 20-21

• Hibbard, B., “ Top Ten, Visualization Problems”, SIGGRAPH Computer Graphics Newsletter, VisFiles, May 1999, Vol. 33, N.2

• Johnson, C., “Top Scientific Visualization Research Problems”, IEEE Computer Graphics and Applications: Visualization Viewpoints, July/August, 2004, pp. 13-17

• Eick, S., "Information Visualization at 10," IEEE Computer Graphics and Applications, vol. 25, no. 1, Jan/Feb, 2005, pp. 12-14

• Keefe, D., “Integrating Visualization and Interaction Research to Improve Scientific Workflows”, IEEE Computer Graphics and Applications, vol. 30, no. 2, Mar/April, 2010, pp. 8-13

• Globus, A., E. Raible, “Fourteen Ways to Say Nothing With Scientific Visualization”,

Computer, 27, 7, July 1994, pp. 86-88

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Interesting links:

• http://www.infovis-wiki.net/

• http://www.visualcomplexity.com/vc/

• http://selection.datavisualization.ch/

Images of the 1rst slide:

S. Silva, B. Sousa Santos, J. Madeira (2012). Exploring different parameters to assess left ventricle global and regional functional analysis from coronary CT angiography. Computer Graphics Forum, vol. 31, n. 1, 146–159.

V. Gonçalves, P. Dias, M. J. Fontoura, R. Moura, and B. Sousa Santos, “Investigating landfill contamination by visualizing geophysical data.,” IEEE Comput. Graph. Appl., vol. 34, no. 1, pp. 16–21, 2014.

P. Dias, L. Neves, D. Santos, C. Coelho, M. T. Ferreira, S. Silv, B. Sousa Santos (2015) “CraMs: Craniometric Analysis Application Using 3D Skull Models,” IEEE Comput. Graph. Appl., vol. 35, no. 6, pp. 11–17.

R. Mazza, Introduction to Information Visualization, 2004 (cap.1)