DATA VISUALISATION
Christopher Fluke
AusGO/AAO Observational Techniques Workshop
2014
CRICOS provider 00111D
Question 1.
Talk to the person next to you, and discuss
what visualisation means to you.
2-3 minutes
What is Visualisation?
The process of creating [computer-generated] images
in order to gain new understanding or insight
into data.
Display
Technology
Data
Software
Science
Interaction
Visualisation-enabled
Knowledge Discovery
Publication
A Data Life Cycle
Publish/Pres
ent Data
Collect Data
Characterise
Data
Filter/Modify
Data
Display Data
Interpret
Data
4
Why Visualise?
How do you write an algorithm to find
something that you don’t know is there?
The Ultimate Visualisation System
www.eyedesignbook.com
Never Forget…
• There are many things we do not know about the
way the human visualisation system works
• Not everyone sees the world in quite the same way:
– Colour blindness
– Stereo blindness
• Our visual system is good at identifying shapes
– Face recognition
– Nephelococcygia
What do you see?
http://img.dailymail.co.uk/
Visualisation Taxonomy
Quantitative
Comparative
Selection
Statistics
Qualitative
Side-by-side
comparison
Visual inspection
Data overlays
Intuitive
Interaction
Hypothesis Testing
Increasing complexity
Increasing scientific value?
Three-dimensional Visualisation
• Qualitative – easy
•
Look at data
NGC 628 in HI
Data: THINGS survey
http://www.mpia-hd.mpg.de/THINGS/Data.html
Vis: S2PLOT, Volume Render,
256x256x72 voxels
Three-dimensional Visualisation
• Qualitative – easy
•
Look at data
NGC 628 in HI
Data: THINGS survey
http://www.mpia-hd.mpg.de/THINGS/Data.html
Table 3. Hassan & Fluke (2011), PASA
Vis: S2PLOT, Volume Render,
256x256x72 voxels
Three-dimensional Visualisation
• Qualitative – easy
•
Look at data
• Comparative –
harder
•
Model + data
Duchamp source-finder catalogue
overlaid on volume rendering.
Data: Ursa Major galaxy cluster at 21cm (V.Kilborn)
Image: Hassan, Fluke, Barnes, 2011, ADASS XX
Three-dimensional Visualisation
• Qualitative – easy
•
Look at data
• Comparative –
harder
•
Model + data
• Quantitative –
hardest
•
•
•
Dynamic selection
Statistics
“Operators”
What is the
[median|average|maxim
um|…] flux in this 3D
region?
NGC 628 in HI
Data: THINGS survey
http://www.mpia-hd.mpg.de/THINGS/Data.html
Vis: S2PLOT, Volume Render,
256x256x72 voxels
The Development of
Astronomy Visualisation
• Making sense of the sky
• Recording to remember
• Exploration and discovery
Zodiac of Dendera
(Ptolemaic Period?
300 BCE-30 BCE)
Bayeux Tapestry (c.1070s)
“They wonder at
the star”
(Halley’s Comet)
Credit: Wikimedia Commons
Linda Hall Library of Science, Engineering and Technology
Uranometria: Bayer (1601)
First accurate grid for star positions
CfA2 Redshift Survey (1986)
Three-dimensional structure of the Universe
Toomre & Toomre, 1972, ApJ, 178, 623
© American Astronomical Society
Visualisation is
very important for
numerical data
Types of Astronomical Data
Brunner et al. (2001):
•
•
•
•
•
Imaging data: 2D, narrow , fixed epoch
Catalogs: secondary parameters determined from
processing (coordinates, fluxes, sizes, etc).
Spectroscopic data and products (e.g. redshifts, chemical
composition, etc).
Studies in the time domain - moving objects, variable and
transient sources (synoptic surveys)
Numerical simulations from theory
They each pose their own problems for effective
visualisation
Types of visualisations
Information
Visualisation
Astronomy
Visualisation
Abstract
Multi-dimensional
Scientific
Visualisation
Physical
Geometric
Presentation
Education &
Graphics
Public Outreach
Publications
Visual Elements
Points
– Point size
– Point colour
Symbols/glyphs/markers
– Symbol size
– Symbol colour
Lines/contours
– Line thickness
– Line style
– Line colour
Polygons/surfaces
– Colour
– Texture
23
Vector Data
– Vector Plots
– Directed glyphs
– Length, colour, thickness
Meshes/Volume data
– Isosurfaces
• Value
• Colour
– Volume rendering
• Data range
• Transfer function
2D Contour Lines
Vector Field
25
Volume visualisations
Points
Isosurfac
e
Splats
Volume
Render
Colour
Used correctly, colour enhances comprehension
Used incorrectly, colour reduces comprehension
“Optical Nervous System”
– Or “How the inside of your head feels”
– From a lecture by Alan Watts (1915-1973)
– Interpreted by David McConville (Elumenati)
– http://www.youtube.com/watch?v=R3ozwTRepqM
27
Colour Maps
Credit: Wikipedia Commons
We can use colour to represent value by providing a colour map
Need to know minimum and maximum data value
– Out of range values?
– Number of steps?
28
Hue
based
Saturation
based
Colour Maps: N = 1000 steps
29
Add grey
Add white
Add black
http://www.color-wheel-artist.com/hue.html
Tints, Shades, Tones
Think about the visualisation software/tools that you
have used.
Now choose one of these packages.
b) What is this software’s best/most useful feature to you?
c) “If I could change one thing about this package it would
be…”
Discuss your answer with your neighbours, and find out
whether the software they use might help you.
5 minutes
Reading List
• Brunner, R.J., Djorgovski, S.G., Prince, T.A., Szalay, A.S., 2001, Massive
Datasets in Astronomy, arXiv:astro-ph/0106481
• Farmer, R.S., 1934, Celestial Cartography, PASP, 50, 34
• Fluke, C.J., Bourke, P.D., O’Donovan, D., 2006, Future Directions in Astronomy
Visualization, PASA, 23, 12
• Globus, A., Raible, E., 1994, Fourteen Ways to Say Nothing with Scientific
Visualization, Computer, 27, 86
• Hassan, A.H., Fluke, C.J., 2011, Scientific Visualization in Astronomy: Towards
the Petascale Astronomy Era, PASA, 28, 150
• Norris, R.P., 1994, The Challenge of Astronomical Visualisation, ADASS III, ASP
Conference Series, 61, eds. D.R.Crabtree, R.J.Hanisch, J.Barnes, p.51