Visualization
PV251
Autumn 2024
Lectures:
Bára Kozlíková
kozlikova@fi.muni.cz
Seminars:
Katka Furmanová
furmanova@mail.muni.cz
code.enthought.com
www.cehwiedel.com
Course organization
• Lectures every Thursday at 8 AM (D1)
• Seminars bi-weekly (mandatory)
– Who didn’t enroll to a seminar group yet and they
are full, please contact Katka immediately
• Grading
– Semestral project
– Written final exam
– Details can be found in the Interactive syllabus in
IS
1. Introduction to Visualization
visservices.sdsc.edu
www.jackyhackett.com
www.flometrics.com informationandvisualization.de
qutemol.sourceforge.net
Interactive data visualization (Foundations, techniques, and
applications) – Matthew Ward, Georges Grinstein, Daniel Keim
Visualization lectures – Eduard Gröller, Helwig Hauser
Visualization Analysis and Design – Tamara Munzner
What is visualization?
• Conveying the information using a graphical
representation
eagereyes.org
What is visualization?
• „Transformation of symbolic into geometric“
[McCormick et al., 1987]
• „… finding the artificial memory that best
supports our natural means of perception.“
[Bertin, 1967]
• „The use of computer-generated, interactive,
visual representations of data to amplify
cognition.“
[Card, Mackinlay, Shneiderman, 1999]
What is visualization?
• „The purpose of computing is insight, not
numbers“ [R. Hamming, 1962]
• „…to form a mental vision, image, or picture
of something not visible or present to the
sight, or of an abstraction; to make visible to
the mind of imagination“ [Oxford Engl. Dict., 1989]
What is visualization?
• Tool to enable a User insight into Data
• Computer Graphics, but not photorealistic
rendering
Content of the course
• Visualization and interaction techniques
• Data types and their representation
• Comparison of visualization techniques
• Human cognition and processing of information
• Design of efficient visualizations
• And many other topics…
uncommonchick.com
Why creating visualizations?
• Decision making
• View onto data in a context
• Support for computations
• Presenting an idea
• Inspiration
• …
Three main functions of visualization
• Data storage
– Photos, …
• Analysis of information
– Data processing, evaluation,
interaction
• Conveying the information
– Data sharing, cooperation, highlighting important
aspects of data
jkgonzalez1989.blogspot.com
Why is visualization so important?
• Sight is one of the main senses
• We are surrounded by visualization
(newspapers, maps, weather forecast, stock
market, statistics, posters, advertisement, …)
• Improving the decision process,
better understanding
of context of the data
under30ceo.com
Why is visualization so important?
• Anscombe's quartet
What is the best way
to present this data?
The Value of Visualization, Jeffrey Heer, Stanford University
Why is visualization so important?
The Value of Visualization, Jeffrey Heer, Stanford University
“Never trust summary statistics
alone; always visualize your data"
Alberto Cairo
Why is visualization so important?
• Complex structures can be expressed in a
simple and intuitive way
Interactive Data Visualization - Fondations, Techniques and Applications. Matthew Ward
Why is visualization so important?
• In 2002 there were 5 exabytes of new
information
• In 2006 it was 161 exabytes
• In 2020 it was 44 zettabytes
• Need to process
such amount of
data
visualinsights.wordpress.com
https://explodingtopics.com/blog/data-generated-per-day
Research goals in visualization
• Understand how visualization conveys the
information
– What is perceived by the humans?
– How visualization corresponds to the human mindset?
• Design and create principles and techniques
for efficient visualization
– Improve the cognition process
– Strengthen the relationship between visualization and mindset
Consequences of wrong visualization
Interactive Data Visualization - Foundations, Techniques and Applications. Matthew Ward
History
• Visualization is an old discipline
• First visualizations based on intuition – first
graphical illustrations
• Visualization as a research discipline emerged
more than 30 years ago
• First research vis conferences in 1990
History
• Image-based communication appeared much
earlier than written one
Lascaux, France, 15 000 - 13 000 B.C.
Interactive Data Visualization - Foundations, Techniques and Applications. Matthew Ward
History
• Images were transferred to first systems of
writing – Mesopotamia, Egypt, …
Kish limestone tablet – the oldest written
document (3500 B.C.)
Hieroglyphs (3000 B.C.)
Interactive Data Visualization - Foundations, Techniques and Applications. Matthew Ward
History
• Visualizations were created mostly because of
necessity – business routes, religion,
communication
• Mostly maps
Peutinger map of Roman empire
Interactive Data Visualization - Foundations, Techniques and Applications. Matthew Ward
History
• Moon phases (1030)
• Movement of planets
Interactive Data Visualization - Foundations, Techniques and Applications. Matthew Ward
History
• China, 1137
• First geographic map
using Cartesian
coordinates
• Lattice with lines
representing latitude
and longitude
History – cartography
Isolines showing Visualization of
the deviations of compass winds
History
• In 1663 in London, during the cholera
epidemic, visualization helped to reveal the
source of infection
• http://www.imdb.com/title/tt2061801/
• John Snow - On the Mode of
Communication of Cholera
• http://en.wikipedia.org/wiki/The_Ghost_Map
Interactive Data Visualization - Foundations, Techniques and Applications. Matthew Ward
History
• Napoleon’s invasion of Moscow – highlighting
the losses
Interactive Data Visualization - Foundations, Techniques and Applications. Matthew Ward
Meteorology
Visualization of
air pressure
Front visualization
Maps for pilots
Business visualization
• Using two axes
National debt of England
(William Playfair)
Business development between England
and Norway and Denmark (1786)
Interactive Data Visualization - Foundations, Techniques and Applications. Matthew Ward
Population development
• Population size in Sweden 1750 – 1785
• Axes represent year and age category
History
• Graph shows the mortality in army between
04/1854 and 05/1855
(Florence Nightingale)
• Blue – sickness
• Red – injury
• Black - other
Interactive Data Visualization - Foundations, Techniques and Applications. Matthew Ward
Visualization „today“
• Chernoff faces, 1973
– Data properties encoded into geometric facial
features
Visualization today
• Visualization enables different views onto data
– from the qualitative and quantitative point
of view
• Example – metro map vs. street map
Interactive Data Visualization - Foundations, Techniques and Applications. Matthew Ward
Visualization today
• Data can be visualized precisely
• Fast identification of problem
Visualization today
• Various input datasets and objects
• High interactivity for the user
Interactive Data Visualization - Foundations, Techniques and Applications. Matthew Ward
Visualization today
• Medical data (MedVis, VolVis)
• Flow data (FlowVis)
• Abstract data (InfoVis)
• GIS data
• Historical data (archeology)
• Microscopic data (molecular physics)
• Macroscopic data (astronomy)
• Big data
…
Medical visualization
Flow visualization
Abstracted visualization
Visualization vs. computer graphics
• Is visualization a subset of CG or is CG
a subset of visualization?
• CG – goal is the realism, art, entertainment
• Visualization – goal is an efficient conveying of
the information
Pipeline
• Input data analysis
• Input requirements analysis
• Mapping data onto screen
• Enabling interactive manipulation
CG Pipeline
• Modeling
• Viewing
• Clipping
• Removing invisible parts
• Projection
• Rendering
Visualization pipeline
• Data acquisition
• Selection and processing of data
• Mapping of data
• Scene parameters settings
• Rendering
Data acquisition
• Measurement (CT/NMR)
• Simulation (flow simulation)
• Modeling
…
Data selection and processing
• Filtering – e.g., smoothing (noise removal)
• Resampling – e.g., to a lattice of different
resolution)
• Deriving data – e.g., obtaining the gradient,
curvature
• Data interpolation – e.g., linear, cubic
…
Data mapping
• Data are mapped to the representation
suitable for rendering (e.g., geometry)
– Computation of isosurfaces
– Mapping to glyphs, icons
– Computation of the distribution of data in a graph
– Determining the attributes of voxel data (color,
transparency, …)
…
Generating images
• Using computer graphics principles
– Visibility computation
– Lighting
– Alpha blending
– Animation
…
Human cognition and processing
of information
www.yorksir.estranky.cz
cat.rulez.cz
www.quertime.com
appsychtextbk.wikispaces.com
Human cognition
• Process of understanding, collecting, storing
and interpreting the information (based on
previous experience)
• Uses all human senses, sight and hearing are
the most “important”
ones
www.gearfuse.com
Human cognition
• Process of interpretation of the surroundings
and forming its inner representation
• Desinterpretation – cognition error or
targeted
www.ranker.com
Targeted desinterpretation – optical
illusions
www.bobatkins.com
www.roumazeilles.net
library.thinkquest.org
Optical illusions
opticalillusionpictures.net
listverse.com
3d-pictures.feedio.net
Human cognition
• Sight is very limited
www.brainist.com
thinkoutsidetheboxtoday.com
Human cognition
• Users are interacting with visualization
according to their interpretation of visible
information
• 8% of men problems with
color perception
www.healthtap.com
www.neitzvision.com
Perception in the context of visualization
• Color
• Texture
• Movement
www.streetartutopia.com
cz.123rf.com blog.experimentsinmotion.com
Color
• Color balance – uniform distribution of color values in the whole range
• Distinguishability – in a given discrete palette each color has to be similarly
distinguishable from the others
• Flexibility – colors can be selected from any place of the color space used
Healey a Enns – historical record of climate in eastern part of USA. Color = temperature, brightness =
wind speed, orientation = rainfall, size = cloudiness, density = frequency of freeze
Texture
• Healey and Enns – pexels (perceptual texture
elements)
• Size and density are well perceivable,
variations in regularity are perceived worse
www.csc.ncsu.edu
Texture
• Pexel can have 3 discrete values (height, density,
randomness)
• Visualization of areas with large land cultivation
(height = degree of cultivation, density = type of soil,
randomness = crop type)
Movement
• Animation of particle systems, color changes, …
• In general, changes in the image are attracting
attention and improve the cognition process
flicker
http://www.csc.ncsu.edu/faculty/healey/PP/
Movement
• The position of the animated object in the scene
is crucial
– Such an object in the focus area is perceived
differently than an object in the peripheral areas
• Additional movements in the scene are disturbing
the perception process
– The least disturbing is blinking, then oscillation
movement, object transfers
– The most disturbing is the movement of object in
large distances
– http://openaccess.city.ac.uk/3752/1/Perceptually%20
Uniform%20Motion%20Space.pdf