d ata v isualisation cricos provider 00111d christopher fluke ausgo/aao observational techniques...

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

23

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