An Introduction to Data and

Information Visualization

Beatriz Sousa Santos, University of Aveiro, 2017

Universidade de Aveiro

Departamento de Electrónica,

Telecomunicações e Informática

www.portugal-migration.info

Outline of the lectures:

Computer graphics:

- Interactive graphics systems; Primitives and attributes

- Geometric transformations and projections

- Visualization pipeline, visibility, illumination and surface rendering, geometric transformations and projections

Data and Information Visualization: Introduction

- Data Characteristics

Information Visualization:

- Main issues

- Representations

- Presentation

- Interaction

- Evaluation

2

3

Definition

Objectives

History

Applications

Model

How to obtain and evaluate a Visualization?

5

• Visualization is the process of exploring, transforming and

representing data as images (or other sensorial forms) to gain

insight into phenomena

• There are several expressions used to designate different areas of

Visualization:

– Scientific Visualization

– Data Visualization

– Information Visualization

• The differences among these areas are not completely clear

Definition

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

8

• 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 images

– In general uses much CG

9

• 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

11

“Pre-computer” Visualization:

One of the oldest known Visualizations

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

12

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)

13

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)

14

Visualization in scientific discovery

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

(Wikipedia)

15

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?

16

Medicine (education)

• Human anatomy

• using volume rendering

• VOXELman (University of Hamburg)

• Visible Human project (National Library of

Medicine-USA)

http://www.nlm.nih.gov/research/visible/visible_human.html

https://www.nlm.nih.gov/research/visible/applications.html

http://www.voxel-man.de/3d-navigator/inner_organs/

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• Temporal bone surgery

• Movement of the drill is

controlled with a force

feedback device

https://www.voxel-man.com/simulators/tempo/

https://www.youtube.com/watch?v=CUOm6fxCJqI

VOXELman,

University of Hamburg

Medicine (e.g. surgery training)

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Stereoscopic display + glasses

https://www.voxel-man.com/simulators/dental/

Dentistry (e.g. training)

Interaction devices:

- two phanton (force feedback)

- foot pedal

https://www.youtube.

com/watch?v=CB_v

dW6K42o

19

Medical Imaging

http://www.nlm.nih.gov/research/visible/visible_human.html

https://www.nlm.nih.gov/research/visible/applications.html

https://www.mevislab.de/

Example in Climate research

(by NOAA)

•The Climate Explorer offers graphs,

maps, and data of observed and

projected temperature, precipitation,

and related climate variables for every

county in the contiguous US

• The tool shows projected conditions

for two possible futures: – one in which humans make a

moderate attempt to reduce global

emissions of heat-trapping gases,

– one in which we go on conducting

business as usual.

https://www.climate.gov/maps-data/primer/visualizing-

climate-data

https://toolkit.climate.gov/tools/climate-explorer

22

http://tidesandcurrents.noaa.gov

/sltrends/sltrends.shtml

Example of fluid mechanics visualization

https://www.youtube.com/watch?v=3NsKpHftx7c

25

• 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

• Visualization may be used with different purposes:

- personal exploration - explorative analysis

- discussion with colleagues - confirmative analysis

- presentation to other people

33

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

35 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

37

Whatever the purpose, a visualization:

- Should allow offload internal cognition and memory usage to the

perceptual system, using carefully designed images as a form of

external representations (external memory)

- To support users’ tasks

- Try to find what are the questions they will ask

Why represent data visually?

Vis helps in situations where seeing the dataset structure

in detail is better than seeing only a brief summary of it.

Ascombe quartet: data sets with the

same simple statistical properties

(Munzner, 2014)

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(Tufte, 1983)

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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

• What is Information Visualization?

“The interdisciplinary study of the visual representation of large-scale collection of

non-numerical information, such as files and lines of code in software systems,

library and bibliographic databases, networks of relations on the internet, and so

forth.” (Friendly, 2008)

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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

47

(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?

51

• 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)

55

• This course will address:

– Data characteristics

– Representations, presentation and interaction

– The Human Visual System and perception (if possible)

– Evaluation

– Important skills in the profile of “Data Scientist”

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

59

Data Vis Books and reports

• 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

• 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

• Norman, D., Things that make us smart, Basic Books; Revised ed. Edition,1994

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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)