d ata v isualisation cricos provider 00111d christopher fluke ausgo/aao observational techniques...
TRANSCRIPT
Data Visualisation
CRICOS provider 00111D
Christopher Fluke
AusGO/AAO Observational Techniques Workshop2014
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.
Data Science
DisplayTechnology
Interaction
Software
Visualisation-enabled Knowledge Discovery
Publication
A Data Life Cycle
4
Collect DataCollect Data
Filter/Modify Data
Filter/Modify Data
Characterise Data
Characterise Data
Display DataDisplay Data
Interpret DataInterpret Data
Publish/Present Data
Publish/Present Data
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
http://img.dailymail.co.uk/
What do you see?
Qualitative
Visual inspection
Comparative
Side-by-side comparison
Data overlays
Quantitative
Selection
Statistics
IntuitiveInteraction
Increasing complexity
Increasing scientific value?
Visualisation Taxonomy
Hypothesis Testing
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
Table 3. Hassan & Fluke (2011), PASA
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
• 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”
NGC 628 in HI
Data: THINGS survey http://www.mpia-hd.mpg.de/THINGS/Data.html
Vis: S2PLOT, Volume Render, 256x256x72 voxels
What is the [median|average|maximum|…] flux in this 3D region?
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)
Credit: Wikimedia Commons
“They wonder at the star”
(Halley’s Comet)
Uranometria: Bayer (1601)Li
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First accurate grid for star positions
CfA2 Redshift Survey (1986)
Three-dimensional structure of the Universe
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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
Scientific Visualisation
PhysicalGeometric
Information Visualisation
AbstractMulti-dimensional
PresentationGraphicsPublications
Education & Public Outreach
AstronomyVisualisation
Types of visualisations
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
Vector Data– Vector Plots– Directed glyphs– Length, colour,
thickness
Meshes/Volume data– Isosurfaces
• Value• Colour
– Volume rendering• Data range• Transfer function
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2D Contour Lines
Vector Field
25
Volume visualisations
Points Splats
Isosurface
VolumeRender
Colour
Used correctly, colour enhances comprehensionUsed 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=R3ozwTRepqM27
Colour Maps
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?
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Colour Maps: N = 1000 steps
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Tints, Shades, Tones
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Add whiteAdd black
Add grey
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