Commentary
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Transcript of Commentary
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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
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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
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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
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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
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Multidimensional Visualization Technique Viewer
http://filer.case.edu/~dbh10/eecs466/report.html
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Worlds within Worlds
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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
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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
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Stars (variant on glyphs)
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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