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Commentary

Trouble rather the tiger in his lair than the sage amongst his books,

For to you Kingdoms and their armies are things mighty and enduring,

To him they are but toys of the moment,

to be overturned by the flicking of a finger.

Attributed to “Anonymous,” in

Tactics of Mistake, Gordon Dickson

Introduction toInformation Visualization for HCI

Shaun P. Morrissey

10 March 2007

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Shaun P. Morrissey

B.S. Physics, Rensselaer Polytechnic Inst. M.S., Experimental Particle Physics, Carnegie-Mellon Univ. M.S., Computer Science, UMass/Lowell D.Sc. Student, UML,

– Visualization Applied to Firewall Security

– Prof. Georges Grinstein, IVPR Emergency Medical Technician (MA, NH, NREMT)

– Deputy Chief, Amherst EMS, Amherst, NH Technical Systems Analyst

– Vulnerability

– Information Warfare

– Air Command & Control

– Acquisition Planning

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Outline

Why? Visualization

– Data Attributes– Scientific vs Information

Perception– Eye structure– Luminance/Brightness - contrast illusions– Color– Change-Blindness– Pre-attentive processing

Dimensionality– 1, 2, 3-D and projections– Lossless representations

Examples

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Randu Example

Jump to Data Desk file– Show visual impact of weaknesses in early IBM 360 linear

congruential pseudo-random number generator Successive triplets of calls are strongly correlated

– Point out that verbal description doesn’t mean much, but even your manager will understand [picture]

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Visualization Issues

Type– Scientific

– Information Data/Attribute Characteristics

– Nominal/Categorical

– Ordinal

– Interval

– Ratio/Affine

Tam, R. C., Healey, C. G., Flak, B., and Cahoon, P. "Volume Rendering of Abdominal Aortic Aneurysms." In Proceedings IEEE Visualization '97 (Phoenix, Arizona, 1997), pp. 43-50

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Perception: Eye Structure

Lens focuses light on macula lutea and fovea centralis

– Macula lutea: small yellow spot– Fovea centralis: area of greatest

visual acuity; photoreceptor cells tightly packed

Optic disc: blind spot. Area through which blood vessels enter eye, where nerve processes from sensory retina meet and exit from eye

100k cones inside 2 degrees (100 points on head of a pin)

At 10 degrees, down by 100 in density

At edge of field, fist sized objects Saccadic motion

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Rods/Cones

QuickTime™ and aTIFF (Uncompressed) decompressor

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Perception: Luminance/Brightness &Contrast Illusions

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Perception: Color in Light

Why can an RGB monitor show us Yellow?

Na-light 589 nm

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Rods/Cones

Bipolar receptor cells. Responsible for color vision and visual acuity.

– Numerous in fovea and macula lutea; fewer over rest of retina.

– As light intensity decreases so does our ability to see color.

– Visual pigment is iodopsin: three types that respond to blue, red and green light

– Overlap in response to light, thus interpretations of gradation of color possible: several millions

Cones

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Three Channels

L

M

SL+M+S = Brightness

L - M = Red-Green

(L+M) - S = Yellow-Blue

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Color components per frequency

Healey, C. G. "Effective Visualization of Large, Multidimensional Datasets." PhD Thesis (1996), Department of Computer Science, University of British Columbia.

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Brown is what?

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Berlin & Kay, 1981 studied 100 languages

Post & Greene, 1986, consistent color naming

Suggests color coding only good for about six-eight categories

Colors and Coding

Red*, Pink, Purple, Blue, Aqua, Green**, Yellow, Orange, White

*Perceptual red was not pure, required some blue

** Two pure greens (514 nm (2/3) and 525 nm (1/3) )

White

BlackRed

Green, Yellow

Yellow, GreenBlue Brown

Pink

Purple

Orange

Gray

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Change Blindness

Airplane Dinner Tourists

Samples mentioned above found at:

http://www.csc.ncsu.edu/faculty/healey/PP/index.html

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Pre-attentive Processing

http://www.csc.ncsu.edu/faculty/healey/PP/index.html

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Pre-Attentive Processing (images)

http://www.intelligententerprise.com/showArticle.jhtml;jsessionid=1ZEZHJBWGTV0OQSNDLPCKHSCJUNN2JVN?articleID=31400009&pgno=2

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Pre-attentive images

QuickTime™ and aTIFF (Uncompressed) decompressor

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Visual Currying

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Pre-Computer Use of Preattentive Processing

Titan Missile Status Panel Hatch Dive Status

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Dials galore

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Dials galore

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Visualizations

Networks– http://www.visualcomplexity.com/vc/

– 561 network visualizations Vizit Text Visualization

– http://www.neoformix.com/2007/ATextExplorer.html

– http://www.marumushi.com/apps/newsmap/index.cfm Monte Carlo techniques and Scatterplot Matrix

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TreeMaps:Space Filling

QuickTime™ and aTIFF (Uncompressed) decompressor

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QuickTime™ and aTIFF (Uncompressed) decompressor

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Space Filling with fixed partitioning:Quadtree with zooming

[Teoh 2002] Figure 2: Data from January 2, 2000 to March 3, 2000. Colored pixels in main window show involved prefixes resolved to first 18 bits. Zoom windows resolve prefixes completely.

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Quadtree detail

Figure 25. [Teoh 2002] Figure 1: Quadtree coding of IP prefixes. Left: Top levels of the tree, and the most significant bits of the IP prefixes represented by each sub-tree (sub-square). 4 lines representing AS numbers surround the square

representing the IP prefix space. Right: Actual data. A line is drawn for every IP-AS pair in an OASC.

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Themescape

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3D Scatter: Projection Loss

QuickTime™ and aTIFF (Uncompressed) decompressor

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Multidimensional Visualization Technique Viewer

http://filer.case.edu/~dbh10/eecs466/report.html

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Worlds within Worlds

QuickTime™ and aTIFF (Uncompressed) decompressor

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http://www1.cs.columbia.edu/graphics/projects/AutoVisual/AutoVisual.html#figure_optcompare

http://www1.cs.columbia.edu/graphics/projects/AutoVisual/AutoVisual.html#figure_optcompare

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Lossless Representations:Parallel Coordinates

QuickTime™ and aTIFF (Uncompressed) decompressor

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Multidimensional Visualization Technique Viewer

http://filer.case.edu/~dbh10/eecs466/report.htmlGoogle: Parallel Axes Inselberg

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Chernoff Faces

QuickTime™ and aTIFF (Uncompressed) decompressor

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Stars (variant on glyphs)

QuickTime™ and aTIFF (Uncompressed) decompressor

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Piles/Columns of Glyphs

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Focus + Context: Distortion

Leung, Y. K. and Apperley, M. D. 1994. A review and taxonomy of distortion-oriented presentation techniques. ACM Trans. Comput.-Hum. Interact. 1, 2 (Jun. 1994), 126-160. DOI= http://doi.acm.org/10.1145/180171.180173

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Example: The Perspective Wall

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Example: Fisheye Magnification Functions

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Example: Fisheye view transformations

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Research Agenda: Visual Analytics

Document:

http://nvac.pnl.gov/docs/RD_Agenda_VisualAnalytics.pdf

Website:

http://nvac.pnl.gov/agenda.stm