reviewing data visualization: an analytical taxonomical study
DESCRIPTION
Visualization taxonomyTRANSCRIPT
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José F. Rodrigues Jr., Agma J. M. Traina,
Maria C. Ferreira de Oliveira, Caetano Traina Jr.
University of São Paulo
Computer Science Department
ICMC-USP
Brazil
Reviewing Data Visualization: an Analytical Taxonomical Study
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OutlineIntroductionComponents of VisualizationSpatialization and position stimulusShape and color stimuliExamplesHybridism and Subspace VisualizationsInteraction techniquesConclusions
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Overview• Analytical review of visualization techniques
• Taxonomical approach
• An introductory point of view
• A functional rather than descriptive taxonomy
• Goal: discretization of visualization techniques
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Previous taxonomiesAuthor Characteristics
Keim´s1D, 2D, multiD, text/web,
hierarchies/graphs and algorithm/software
standard 2D/3D, geometrical, iconic, dense
pixel and stacked
standard, projection, filtering, zoom, distortion and link & brush
Schneiderman’s Taskby Data Type
one, two, three, multi-dimensional, tree and
network
overview, zoom, filter, details-on-demand, relate, history and
extract
Grinstein and Ward’s
Taxonomy Draft
geometric, symbolic, 2D/3D and static/dynamic
browsing, sampling, indirect, associative and system
oriented
Ed Chi’s Data StateReference Model
data, abstraction, transformation and mapping tasks, presentation and interaction
Tory and Möoller’sDiscrete/Continuous
([one, two, three, multi-dimensional] x [scalar, vector, tense, multi-variate])
two, three, multi-dimensional and
graph & tree
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Proposal
• Generalizing taxonomy
• No specific characteristics like:– specific representational patterns (“iconic and
pixel-oriented techniques”)– predisposition of representativeness (“network
and tree techniques”)– axes arrangement (“stacked techniques”)– dimensionality (“2D/3D techniques”)– interaction (“static/dynamic techniques”).
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Proposal
• Generalizing taxonomy
• No specific characteristics like:– specific representational patterns (“iconic and
pixel-oriented techniques”)– predisposition of representativeness (“network
and tree techniques”)– axes arrangement (“stacked techniques”)– dimensionality (“2D/3D techniques”)– interaction (“static/dynamic techniques”).
Rather, we consider characteristics common to every visualization technique:
• the spatialization process
• the set of pre-attentive stimuli
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OutlineIntroductionComponents of VisualizationSpatialization and position stimulusShape and color stimuliExamplesHybridism and Subspace VisualizationsInteraction techniquesConclusions
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Spatialization Component
• “One thing can be viewed if and only if it is in space.” spatial/visual domain
• Spatialization: in order to visualize data, first we must map it to the spatial/visual domain
Data Visualization
Spatialization(data)
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Pre-attentive Stimuli Component
• Pre-attentive stimuli: characteristics that pop up to our eyes maximization of just noticeable differences
How many “4” symbols?
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Taxonomy overview
Visualization
Scene
Spatialization(data)
Position
stimulus
Shape
stimulus
Color
stimulus
Data
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OutlineIntroductionComponents of VisualizationSpatialization and position stimulusShape and color stimuliExamplesHybridism and Subspace VisualizationsInteraction techniquesConclusions
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Spatialization Classes• Visualization techniques can be grouped
according to how they are spatialized
– Structure exposition– Patterned– Projection– Reproduction
• Spatialization drives positional pre-attention
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TreeMap
(x,y)=f(data.level)
Spatialization Class:Structure exposition
while(!condition(x,y)) do(x,y)=iterationi+1(x,y)
Force directed graph layout
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Spatialization Class:Patterned
CircularRecurrent pattern(xi+1, yi+1)=f(xi, yi)
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Spatialization Class:Projection
z=f(x,y)(x,y)=f(d1, d2, d3, d4, d5, d6, d7, d8)
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(x,y) = (x’, y’)
Spatialization Class:Reproduction
(x,y,z) = (x’, y’,z’)
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OutlineIntroductionComponents of VisualizationSpatialization and position stimulusShape and color stimuliExamplesHybridism and Subspace VisualizationsInteraction techniquesConclusions
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Pre-attentive Stimulus - Shape
DifferentiationCorrespondenceRelationshipMeaning
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Pre-attentive Stimulus - Color
CorrespondenceDifferentiation
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OutlineIntroductionComponents of VisualizationSpatialization and position stimulusShape and color stimuliExamplesHybridism and Subspace VisualizationsInteraction techniquesConclusions
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Example – Pie Chart
• Spatialization: pattern - parameter: circular non-ordered attribute x
• Shape: correspondence
- parameter: portion of the circle
• Color: discrete differentiation
- parameter: use pool of colors
label x
A 15
B 20
C 15
D 10
E 08
F 09
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Example – Spiral Pixel Display
• Shape: none
• Spatialization: pattern - parameter: spiral ordered attribute x
• Color: continuous correspondence
- parameter: descending red
label x
A 15
B 20
C 15
D 10
E 08
F 09
…
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Example – Chernoff faces
• Shape: meaning and continuous correspondence
-parameter: meaningful faces
-parameter: attributes-to-face mapping
• Spatialization: projection- parameters: attributes 0 and 1
• Color: none
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Example – Chernoff faces
• Shape: meaning and continuous correspondence
-parameter: meaningful faces
-parameter: attributes-to-face mapping
• Spatialization: projection- parameters: attributes 0 and 1
• Color: none
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Example – Parallel Coordinates
• Spatialization: pattern and projection - parameter: horizontal pattern for non-ordered attribute names - parameter: vertical projection of attributes
• Shape: relationship
- parameter: line between related values
• Color: correspondence
- parameter: map color to attribute Z
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Example – Parallel Coordinates
• Spatialization: pattern and projection - parameter: horizontal pattern for non-ordered attribute names - parameter: vertical projection of attributes
• Shape: relationship
- parameter: line between related values
• Color: correspondence
- parameter: map color to attribute Z
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More examples
Technique SpatializationPosition Shape Color (usual)
Dimensional Stacking Multiple ProjectionReferential - Differentiation
Scatter Plots Multiple ProjectionReferential - Differentiation
Stick FiguresProjectionReferential,Multiple PatternedCorrespondence
Differentiation,Correspondence
Differentiation
Table LensPatternedCorrespondence,Multiple ProjectionReferential
Correspondence Differentiation
Treemaps Structure ExpositionArrangement Correspondence Differentiation
Geographical Maps ReproductionReferential
Differentiation,Correspondence
Differentiation
Direct VolumeRendering
ReproductionReferential MeaningDifferentiation,Correspondence
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OutlineIntroductionComponents of VisualizationSpatialization and position stimulusShape and color stimuliExamplesHybridism and Subspace VisualizationsInteraction techniquesConclusions
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Hybridism and Multiple Spatializations
1st spatialization
2nd spatialization
Star glyph
Stick
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Hybridism and Multiple Spatializations
1st spatialization
2nd spatialization
Star glyph
Stick
• Other examples:• Dimensional Stacking
• Worlds-within-Worlds
• Circle Segments
• Pixel Bar Charts
• Multiple spatialization cycles is a key for diversity in visualization design
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OutlineIntroductionComponents of VisualizationSpatialization and position stimulusShape and color stimuliExamplesHybridism and Subspace VisualizationsInteraction techniquesConclusions
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Interaction Techniques• Interaction techniques and visualization
techniques as disjoint, but related, concepts
• How to identify an interaction technique?
1) It must permit to alter the pre-attentive stimuli;
2) It must be applicable to any visualization technique (efficiently or not).
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Interaction Techniques• Interaction techniques and visualization
techniques as disjoint, but related, concepts
• How to identify an interaction technique?
1) It must permit to alter the pre-attentive stimuli;
2) It must be applicable to any visualization technique (efficiently or not).
Well defined and general
Promote alterations that redefine the visualization
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Interaction Classes• Filtering
– color alteration (brushing)– shape alteration (selection contour added to the presentation)
• View transformation– shape alteration (scale and zoom for bigger items)– position alteration (rotation and translation)
• Distortion:– position alteration (different perspectives)
• Details-on-demand:– shape alteration (text added to the presentation)
• Parametric:– shape, position or color alteration via redefinition of parameters
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Interaction Classes• Filtering
– color alteration (brushing)– shape alteration (selection contour added to the presentation)
• View transformation– shape alteration (scale and zoom for bigger items)– position alteration (rotation and translation)
• Distortion:– position alteration (different perspectives)
• Details-on-demand:– shape alteration (text added to the presentation)
• Parametric:– shape, position or color alteration via redefinition of parameters
Link & Brush is an automation available only when brushing and multiple techniques are present.
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OutlineIntroductionComponents of VisualizationSpatialization and position stimulusShape and color stimuliExamplesHybridism and Subspace VisualizationsInteraction techniquesConclusions
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Conclusions
• A taxonomy for discretization
Customized Visualization
Spatialization(data)
Position
stimulus
Shape
stimulus
Color
stimulus
Data
Parameters
Parameters Parameters
Parameters
Evaluation
Knowledge base
New designs
Modular implementaion
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Conclusions
• Goals– Introduce a new point of view
– Diminish the subjective nature of visualizations– Foment discussions for further thoughts
contributing to start a research for more precise, general and integrated approaches for:• design: new techniques based on the combination
of known spatializations and pre-attentive features
• evaluation: discrete assessment of techniques• implementation: reusable modular projects
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End
Thanks for coming