Lecture 3: Fundamentalstmm/courses/533-09/slides/models.pdf · 2009-09-16 · Lecture 3:...
Transcript of Lecture 3: Fundamentalstmm/courses/533-09/slides/models.pdf · 2009-09-16 · Lecture 3:...
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Lecture 3: FundamentalsInformation Visualization
CPSC 533C, Fall 2009
Tamara Munzner
UBC Computer Science
Wed, 16 September 2009
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Papers Covered
Chapter 1, Readings in Information Visualization: Using Vision to Think.Stuart Card, Jock Mackinlay, and Ben Shneiderman, Morgan Kaufmann1999.
Polaris: A System for Query, Analysis and Visualization ofMulti-dimensional Relational Databases. Chris Stolte, Diane Tang andPat Hanrahan, IEEE TVCG 8(1), January 2002.[graphics.stanford.edu/papers/polaris]
Low-Level Components of Analytic Activity in Information Visualization.Robert Amar, James Eagan, and John Stasko. Proc. InfoVis 05.[www.cc.gatech.edu/ john.stasko/papers/infovis05.pdf]
A Nested Model for Visualization Design and Validation. TamaraMunzner. IEEE TVCG 15(6) (Proc. InfoVis 2009), to appear.[www.cs.ubc.ca/labs/imager/tr/2009/NestedModel]
MatrixExplorer: a Dual-Representation System to Explore SocialNetworks. Nathalie Henry and Jean-Daniel Fekete. IEEE Trans.Visualization and Computer Graphics (Proc InfoVis 2006) 12(5), pages677-684, 2006. [www.aviz.fr/ nhenry/docs/Henry-InfoVis2006.pdf]
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Further Readings
The Structure of the Information Visualization Design Space. StuartCard and Jock Mackinlay, Proc. InfoVis 97.[citeseer.ist.psu.edu/card96structure.html]
Automating the Design of Graphical Presentations of RelationalInformation. Jock Mackinlay, ACM Transaction on Graphics, vol. 5, no.2, April 1986, pp. 110-141.
Semiology of Graphics. Jacques Bertin, Gauthier-Villars 1967, EHESS1998
The Grammar of Graphics. Leland Wilkinson, Springer-Verlag 1999
Rethinking Visualization: A High-Level Taxonomy. Melanie Tory andTorsten Moller, Proc. InfoVis 2004, pp. 151-158.
The Eyes Have It: A Task by Data Type Taxonomy for InformationVisualizations. Ben Shneiderman, Proc. 1996 IEEE Visual Languages,also Maryland HCIL TR 96-13.[citeseer.ist.psu.edu/shneiderman96eyes.html]
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Visualization Big Picture
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Mapping
input
data semanticsuse domain knowledge
outputvisual encoding
visual/graphical/perceptual/retinalchannels/attributes/dimensions/variables
use human perception
processing
algorithmshandle computational constraints
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Bertin: Semiology of Graphics
geometric primitives: markspoints, lines, areas, volumes
attributes: visual/retinal variablesparameters control mark appearanceseparable channels flowing from retina to brain
x,y
position
z
sizegreyscalecolortextureorientationshape
[Bertin, Semiology of Graphics, 1967 Gauthier-Villars, 1998 EHESS]6 / 44
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Design Space = Visual Metaphors
[Bertin, Semiology of Graphics, 1967 Gauthier-Villars, 1998 EHESS]
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Data Types
continuous (quantitative)
10 inches, 17 inches, 23 inches
ordered (ordinal)
small, medium, largedays: Sun, Mon, Tue, ...
categorical (nominal)
apples, oranges, bananas
[graphics.stanford.edu/papers/polaris]
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Data Types
continuous (quantitative)
10 inches, 17 inches, 23 inches
ordered (ordinal)
small, medium, largedays: Sun, Mon, Tue, ...
categorical (nominal)
apples, oranges, bananas
[graphics.stanford.edu/papers/polaris]
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Data Types
continuous (quantitative)
10 inches, 17 inches, 23 inches
ordered (ordinal)
small, medium, largedays: Sun, Mon, Tue, ...
categorical (nominal)
apples, oranges, bananas
[graphics.stanford.edu/papers/polaris]
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More Data Types: Stevens
subdivide quantitative further:
interval: 0 location arbitrary
time: seconds, minutes
ratio: 0 fixed
physical measurements: Kelvin temp
[S.S. Stevens, On the theory of scales of measurements, Science 103(2684):677-680, 1946]
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Channel Ranking Varies by Data Type
spatial position best for all types
Position
TextureConnectionContainmentLightness
ShapeLengthAngleSlopeAreaVolume
PositionLengthAngleSlopeAreaVolumeLightness
Texture
ContainmentShape
Connection
Saturation
PositionLightness
TextureConnectionContainmentLengthAngleSlopeAreaVolumeShape
Saturation
Saturation
Hue
Hue
Hue
Quantitative Ordered Categorical
[Mackinlay, Automating the Design of Graphical Presentations of RelationalInformation, ACM TOG 5:2, 1986]
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Mackinlay, Card
data variables1D, 2D, 3D, 4D, 5D, ...
data typesnominal, ordered, quantitative
markspoint, line, area, surface, volumegeometric primitives
retinal propertiessize, brightness, color, texture, orientation, shape...parameters that control the appearance of geometricprimitivesseparable channels of information flowing from retina tobrain
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Combinatorics of Encodings
challenge
pick the best encoding from exponential number ofpossibilities (n + 1)8
Principle of Consistency
properties of the image should match properties of data
Principle of Importance Ordering
encode most important information in most effective way
[Hanrahan, graphics.stanford.edu/courses/cs448b-04-winter/lectures/encoding]
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Mackinlay’s Criteria
Expressiveness
Set of facts expressible in visual language if sentences(visualizations) in language express all facts in data, andonly facts in data.
consider the failure cases...
[Hanrahan, graphics.stanford.edu/courses/cs448b-04-winter/lectures/encoding]
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Cannot Express the Facts
A 1 ⇔ N relation cannot be expressed in a singlehorizontal dot plot because multiple tuples are mapped tothe same position
[Hanrahan, graphics.stanford.edu/courses/cs448b-04-winter/lectures/encoding]
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Expresses Facts Not in the Data
length interpreted as quantitative value
thus length says something untrue about nominal data
[Mackinlay, APT], [Hanrahan,graphics.stanford.edu/courses/cs448b-04-winter/lectures/encoding]
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Mackinlay’s Criteria
Expressiveness
set of facts expressible in visual language if sentences(visualizations) in language express all facts in data, andonly facts in data.
Effectiveness
a visualization is more effective than anothervisualization if information conveyed by one visualizationis more readily perceived than information in other.
subject of the next lecture
[Hanrahan,graphics.stanford.edu/courses/cs448b-04-winter/lectures/encoding]
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Design: Designer vs. Automatic vs. User
designer: studies last time
automatic: select visualization automatically given dataMackinlay, APT
limited set of encodings:scatterplots, bar charts...
Roth et al, Sage/Visageholy grail: entire space of infovis visual encoding
nowhere near goal, esp. with relational/graph data
human-guided: allow user to change encodings
Polaris: user drag and drop exporation
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Polaris
infovis spreadsheet
table cell
not just numbers: graphical elementswide range of retinal variables and marks
table algebra ⇔ interactive interface
formal language
influenced by Wilkinson’s Grammar of Graphics
Grammar of Graphics, Springer-Verlag 1999
commercialized as Tableau Software
[Polaris: A System for Query, Analysis and Visualization of Multi-dimensionalRelational Databases. Chris Stolte, Diane Tang and Pat Hanrahan, IEEE TVCG, 8(1)Jan 2002]
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Polaris: Circles, State/Product:Month
[Polaris: A System for Query, Analysis and Visualization of Multi-dimensionalRelational Databases. Chris Stolte, Diane Tang and Pat Hanrahan, IEEE TVCG, 8(1)Jan 2002]
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Polaris: Gantt Bar, Country/Time
[Polaris: A System for Query, Analysis and Visualization of Multi-dimensionalRelational Databases. Chris Stolte, Diane Tang and Pat Hanrahan, IEEE TVCG, 8(1)Jan 2002]
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Polaris: Circles, Lat/Long
[Polaris: A System for Query, Analysis and Visualization of Multi-dimensionalRelational Databases. Chris Stolte, Diane Tang and Pat Hanrahan, IEEE TVCG, 8(1)Jan 2002]
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Polaris: Circles, Profit/State:Months
[Polaris: A System for Query, Analysis and Visualization of Multi-dimensionalRelational Databases. Chris Stolte, Diane Tang and Pat Hanrahan, IEEE TVCG, 8(1)Jan 2002]
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Fields Create Tables and Graphs
Ordinal fields: interpret field as sequence that partitionstable into rows and columns:
Quarter = (Qtr1),(Qtr2),(Qtr3),(Qtr4) ⇔
Quantitative fields: treat field as single element sequenceand encode as axes:
Profit = (Profit) ⇔
[Hanrahan,graphics.stanford.edu/courses/cs448b-04-winter/lectures/encoding]
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Beyond Data Alone
bigger picture than just visual encoding decisions
Shneiderman’s data+task taxonomydata
1D, 2D, 3D, temporal, nD, trees, networkstext and documents (Hanrahan)
tasks
overview, zoom, filter, details-on-demand,relate, history, extract
data alone not enough
what do you need to do?
mantra: overview first, zoom and filter, details ondemand
[Shneiderman, The Eyes Have It: A Task by Data Type Taxonomy forInformation Visualizations. Proc. 1996 IEEE Visual Languages]
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Tasks, Amar/Eagan/Stasko Taxonomy
low-level tasks
retrieve value, filter, compute derived value,find extremum, sort, determine range,characterize distribution, find anomalies,cluster, correlate
standardized set for better comparison between papers
bottom-up grouping with affinity diagrammingabstraction from domain task down to low-level task
[Amar, Eagan, and John Stasko. Low-Level Components of AnalyticActivity in Information Visualization. Proc. InfoVis 05]
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Control Room Example
Which location has the highest power surge for the given time period?(extreme y-dimension)
A fault occurred at the beginning of this recording, and resulted in atemporary power surge. Which location is affected the earliest? (extremex-dimension)
Which location has the most number of power surges? (extreme count)
[Overview Use in Multiple Visual Information Resolution Interfaces. Lam, Munzner,and Kincaid. Proc. InfoVis 2007]
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Data Models vs. Conceptual Models
data model: mathematical abstraction
set with operationse.g. integers or floats with ∗,+
conceptual model: mental construction
includes semantics, support datae.g. navigating through city using landmarks
[Hanrahan, graphics.stanford.edu/courses/cs448b-04-winter/lectures/encoding/walk005.html]
[Rethinking Visualization: A High-Level Taxonomy. Melanie Tory andTorsten Moller, Proc. InfoVis 2004, pp. 151-158.]
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Models Example
data model
17, 25, -4, 28.6(floats)
conceptual model
temperature
depending on task, transform to data typemaking toast
burned vs. not burned (N)
classifying showers
hot, warm, cold (O)
finding anamolies in local weather patterns
continuous to 4 sig figures (Q)
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Models Example
data model
17, 25, -4, 28.6(floats)
conceptual model
temperature
depending on task, transform to data typemaking toast
burned vs. not burned (N)
classifying showers
hot, warm, cold (O)
finding anamolies in local weather patterns
continuous to 4 sig figures (Q)
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Models Example
data model
17, 25, -4, 28.6(floats)
conceptual model
temperature
depending on task, transform to data typemaking toast
burned vs. not burned (N)
classifying showers
hot, warm, cold (O)
finding anamolies in local weather patterns
continuous to 4 sig figures (Q)
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Time
2D+T vs. 3Dsame or different? depends on POV
input side vs. output side
same
input: time as just one kind of abstract input dimension
different
input: semantics (time steps of dynamically changingdata)output: visual encoding channel of temporal change verydifferent than spatial position change
processing might be different
e.g. interpolate differently across timesteps than acrossspatial position
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Nested Model
separating design into levels
not just the visual encoding level!
1
Separating Design Into Levels
! multiple levels
domain problem characterization
data/operation abstraction design
encoding/interaction technique design
algorithm design
! three separate design problems
! not just the encoding level
! each level has unique threats to validity
! evocative language from security via software engineering
! dependencies between levels
! outputs from level above are inputs to level below
! downstream levels required for validating some upstream threats
cascading dependencies: outputs from level above areinputs to level below
[Munzner. A Nested Model for Visualization Design and Validation. IEEE TVCG 15(6)(Proc. InfoVis 2009), to appear. www.cs.ubc.ca/labs/imager/tr/2009/NestedModel]
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Nested Levels
characterizing problems
understanding domain concepts, current workflowfind gaps where conjecture that vis would helpMatrixExplorer case study example
abstracting into operations on data types
Amar/Stasko tasks: abstract operation exampleMizBee: abstraction on data example
designing encoding and interaction
Bertin, Mackinlay/Card: encodinglater in term: interaction design
creating efficient algorithms
classic CS problem: create algorithm given clearspecification
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Threats To Validity: What Can Go Wrong?
1
Separating Design Into Levels
! multiple levels
domain problem characterization
data/operation abstraction design
encoding/interaction technique design
algorithm design
! three separate design problems
! not just the encoding level
! each level has unique threats to validity
! evocative language from security via software engineering
! dependencies between levels
! outputs from level above are inputs to level below
! downstream levels required for validating some upstream threats
wrong problem
they don’t do that
wrong abstraction
you’re showing them the wrong thing
wrong encoding/interaction
the way you show it doesn’t work
wrong algorithm
your code is too slow
[Munzner. A Nested Model for Visualization Design and Validation. IEEE TVCG 15(6)(Proc. InfoVis 2009), to appear. www.cs.ubc.ca/labs/imager/tr/2009/NestedModel]
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Upstream and Downstream Validation
humans in the loop for outer three levels
threat: wrong problem
validate: observe and interview target users
threat: bad data/operation abstraction
threat: ineffective encoding/interaction technique
validate: justify encoding/interaction design
threat: slow algorithm
validate: analyze computational complexity
implement system
validate: measure system time/memory
validate: qualitative/quantitative result image analysis
[test on any users, informal usability study]
validate: lab study, measure human time/errors for operation
validate: test on target users, collect anecdotal evidence of utility
validate: field study, document human usage of deployed system
validate: observe adoption rates
[Munzner. A Nested Model for Visualization Design and Validation. IEEE TVCG 15(6)(Proc. InfoVis 2009), to appear. www.cs.ubc.ca/labs/imager/tr/2009/NestedModel]
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MatrixExplorer
domain: social network analysis
validation
early: participatory design to generate requirementslater: qualitative observations of tool use by target users
techniquesinteractively map attributes to visual variables
user can change visual encoding on the fly (like Polaris)
filteringselectionsorting by attribute
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Requirements
use multiple representations
handle multiple connected components
provide overviews
display general dataset info
use attributes to create multiple views
display basic and derived attributes
minimize parameter tuning
allow manual finetuning of automatic layout
provide visible reminders of filtered-out data
support multiple clusterings, including manual
support outlier discovery
find where consensus between different clusterings
aggregate, but provide full detail on demand39 / 44
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Techniques: Dual Views
show both matrix and node-link representations
[Fig 3. Henry and Fekete. MatrixExplorer: a Dual-Representation System to ExploreSocial Networks. IEEE TVCG 12(5):677-684 (Proc InfoVis 2006)www.aviz.fr/ nhenry/docs/Henry-InfoVis2006.pdf]
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MatrixExplorer Views
overviews: matrix, node-link, connected components
details: matrix, node-link
controls
[Fig 1. Henry and Fekete. MatrixExplorer: a Dual-Representation System to ExploreSocial Networks. IEEE TVCG 12(5):677-684 (Proc InfoVis 2006)www.aviz.fr/ nhenry/docs/Henry-InfoVis2006.pdf]
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Automatic Clustering/Reordering
automatic clustering as good starting point
then manually refine
[Fig 6. Henry and Fekete. MatrixExplorer: a Dual-Representation System to ExploreSocial Networks. IEEE TVCG 12(5):677-684 (Proc InfoVis 2006)]
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Comparing Clusters
relayout, check if clusters conserved
encode clusters with different visual variables
colorcode common elements between clusters[Fig 11. Henry and Fekete. MatrixExplorer: a Dual-Representation System to ExploreSocial Networks. IEEE TVCG 12(5):677-684 (Proc InfoVis 2006)]
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Credits
Pat Hanrahan
graphics.stanford.edu/courses/cs448b-04-winter/lectures/encoding
Torsten Moller, Melanie Tory
discussions on conceptual models
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