intro to data visualization
DESCRIPTION
Slides used in capita selecta HCI course H05N2ATRANSCRIPT
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Data Visualization - An introduction
Prof Jan AertsBiodata Visualization and AnalysisESAT/SCDUniversity of LeuvenBelgium
twitter: @jandotGoogle+: +Jan [email protected]://biovizanlab.wordpress.comhttp://saaientist.blogspot.com
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1. What is data visualization?
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“A good sketch is better than a long speech” (Napoleon)
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“A good sketch is better than a long speech” (Napoleon)
shows: size of the army, geographical coordinates, direction that the army was traveling, location of the army with respect to certain dates, temperature along the path of the retreat
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John Snow - cholera map
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Shape of Songs: “Like a Prayer” (Madonna)Martin Wattenberg
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http://multimedia.mcb.harvard.edu/anim_innerlife.html
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What I use as a definition:
“computer-based visualization systems providing visual representations of datasets intended to help people carry out some task more effectively.” (T Munzner)
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cognition <=> perceptioncognitive task => perceptive task
“eyes beat memory”
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• record information
• blueprints, photographs,seismographs, ...
• analyze data to support reasoning
• develop & assess hypotheses
• discover errors in data
• expand memory
• find patterns (see Snow’s cholera map)
• communicate information
• share & persuade
• collaborate & revise
Why do we visualize data?
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pictorial superiority effect
“information”
“informa” “i”65% 1%
72hr
exploration explanation
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2. Exploration <-> explanation
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exploration explanation
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exploration explanation
visual analytics infographics
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exploration explanation
visual analytics infographics
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exploration explanation
visual analytics infographics
hypothesis generation
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exploration explanation
“visual analytics”
=> identify unexpected patterns
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J van Wijk
exploration explanation
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Anscombe’s quartet
• uX = 9.0
• uY = 7.5
• sigma X = 3.317
• sigma Y = 2.03
• Y = 3 + 0.5X
• R2 = 0.67
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A concrete example: hive plots
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Martin Krzewinsky
same network
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Martin Krzewinsky
different networks!
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3D, anyone?
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3D, anyone?
occlusioninteraction complexityperspective distortion
text legibility
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Gene interaction data: “gene A regulates gene B”
Functions in linux operation system: “function A calls function B”
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regulator
manager
workhorse
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3. Why specifically learn about dataviz?
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Isn’t it all just about using common sense?
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• huge space of design alternatives => many tradeoffs
• many possibilities known to be ineffective
• avoid random walk through parameter space
• avoid some of our past mistakes
• extensive experimentation has already been done
• guidelines continue to evolve
• we reflect on lessons learned in design studies
• iterative refinement usually wise
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4. Stages of data visualization
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How do we get from data to visualization? We need to understand:
• properties of the data
• properties of the image
• the rules mapping data to image
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4.1. Properties of the data
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S Stevens “On the theory of scales and measurements” (1946)
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4.2. Properties of the image - perception
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Semiology of graphics
• Jacques Bertin, Gauthier-Villars 1967, EHESS 1998
• semiology = study of signs and sign processes, likeness, analogy, metaphor, symbolism, signification, and communication (Wikipedia)
• visual encoding:
• what - points, lines, areas (, patterns, trees/networks, grids)
• where - positional: XY (1D, 2D, 3D)
• how - retinal: Z (size, lightness, texture, colour, orientation, shape)
• when - temporal: animation
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“marks” - geometric primitives
“channels” - control appearance of marks
H
V
S
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Gestalt laws - interplay between parts and the whole (Kurt Koffka)
series of principles
Election results Florida:
• black = Bush
• white = Gore
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Gestalt - Principle of Simplicity
Every pattern we see is seen such that we see a structure that is as simple as possible.
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Gestalt - Principle of Proximity
Things that are close to each other are seen as belonging together (=> clusters)
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Gestalt - Principle of Similarity
Things that are similar in some way are perceived as belonging together.
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Gestalt - Principle of Closure
You will try to complete a pattern.
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Gestalt - Principle of Connectedness
Things that are connected are perceived as belonging together. This encoding is stronger than similarity, shape, colour, and size.
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Gestalt - Principle of Good Continuation
Objects that are arranged in a straight or smooth line tend to be seen as a unit.
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Gestalt - Principle of Common Fate
Objects that move in the same direction tend to be seen as a unit.
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Gestalt - Principle of Familiarity
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Gestalt - Principle of Symmetry
Symmetrical areas tend to be seen as figures against asymmetrical backgrounds.
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Context affects perceptual tasks
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Pre-attentive vision
= ability of low-level human visual system to rapidly identify certain basic visual properties
• some features “pop out”
• used for:
• target detection
• boundary detection
• counting/estimation
• ...
• visual system takes over => all cognitive power available for interpreting the figure, rather than needing part of it for processing the figure
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Really fast; see http://www.csc.ncsu.edu/faculty/healey/PP/
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1. Combining pre-attentive features does not always work => would need to resort to “serial search” (most channel pairs; all channel triplets)e.g. is there a red square in this picture
Limitations of preattentive vision
2. Speed depends on which channel (use one that is good for categorical; see further (“accuracy”))
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4.3. Mapping data to image: visual encoding
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Language of graphics
• graphics = sign system:
• each mark (point, line, area) represents a data element
• choose visual variables to encode relationships between data elements
• difference, similarity, order, proportion
• only position supports all relationships (see later)
• huge range of alternatives for data with many attributes
• find images that express & effectively convey the information
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Which encoding should I use?
• From huge list of possibilities, you have to choose the best one.
• Principle of Consistency
• properties of the representation should match properties of the data (e.g. pie chart: area vs radius)
• Principle of Importance Ordering
• encode the most important piece of information in the most “effective” way (i.e. spatial position)
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Steven’s psychophysical law
= proposed relationship between the magnitude of a physical stimulus and its perceived intensity or strength
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Accuracy of quantitative perceptual tasks
McKinlay
what/where (qualitative)how much (quantitative)
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Accuracy of quantitative perceptual tasks
McKinlay
what/where (qualitative)how much (quantitative)
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Accuracy of quantitative perceptual tasks
McKinlay“power of the plane”
what/where (qualitative)how much (quantitative)
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Accuracy of quantitative perceptual tasks
McKinlay
what/where (qualitative)how much (quantitative)
grouping: see Gestalt laws
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COLOUR
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COLOUR ... is tricky, and often used wrong
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Colour space
• = mathematical model to talk about colour
• RGB (red-green-blue)
• most common, but less useful
• HSV (hue-saturation-value)
• more useful
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colorbrewer2.org
in R: please use RColorBrewer!
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Context affects colour perception
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Context affects colour perception
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Dangers of Depth (3D)
• We do NOT see in 3D; we see in 2.05D.
• occlusion
• interaction complexity
• perspective distortion
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3D example
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Lie factor
size of effect shown in graphic“lie factor” =
size of effect in data
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3D scatter plots are better as series of 2D projections
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Dynamic data
• animation is good sometimes, but often not:
• we can only follow 3-4 visual cues simultaneously
• change in “mental map”
• change blindness (e.g. http://nivea.psycho.univ-paris5.fr/CBMovies/BarnTrackFlickerMovie.gif)
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5. Interaction
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Overview, zoom and filter, details on demand(Schneiderman’s Information Seeking Mantra)
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• sorting
• filtering
• browsing/exploring
• comparison
• characterizing trends & distributions
• finding anomalies & outliers
• ...
Operations on the data
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Techniques to support these operations
• re-orderable matrices
• brushing
• linked views
• overview & detail
• focus & context
• ...
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6. Validation
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Evaluate the right thing
Munzner, 2009
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Slide/picture acknowledgments
• Jeffrey Heer
• Tamara Munzner
• Jessie Kennedy
• Nils Gehlenborg
• Miriah Meyer
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“I think this presentation went quite well...”