Reviewing Data Visualization: an Analytical Taxonomical Study

www.icmc.usp.br/pessoas/junio

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


OutlineIntroductionComponents of VisualizationSpatialization and position stimulusShape and color stimuliExamplesHybridism and Subspace VisualizationsInteraction techniquesConclusions


Overview• Analytical review of visualization techniques

• Taxonomical approach

• An introductory point of view

• A functional rather than descriptive taxonomy

• Goal: discretization of visualization techniques


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


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”).


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




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)


Pre-attentive Stimuli Component

• Pre-attentive stimuli: characteristics that pop up to our eyes maximization of just noticeable differences

How many “4” symbols?


Taxonomy overview

Visualization

Scene


Position

stimulus

Shape

stimulus

Color

stimulus

Data




Spatialization Classes• Visualization techniques can be grouped

according to how they are spatialized

– Structure exposition– Patterned– Projection– Reproduction

• Spatialization drives positional pre-attention


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


Spatialization Class:Patterned

CircularRecurrent pattern(xi+1, yi+1)=f(xi, yi)


Spatialization Class:Projection

z=f(x,y)(x,y)=f(d1, d2, d3, d4, d5, d6, d7, d8)


(x,y) = (x’, y’)

Spatialization Class:Reproduction

(x,y,z) = (x’, y’,z’)




Pre-attentive Stimulus - Shape

DifferentiationCorrespondenceRelationshipMeaning


Pre-attentive Stimulus - Color

CorrespondenceDifferentiation




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


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

…


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


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


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


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


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




Hybridism and Multiple Spatializations

1st spatialization

2nd spatialization

Star glyph

Stick


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




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).


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


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


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.




Conclusions

• A taxonomy for discretization

Customized Visualization


Position

stimulus

Shape

stimulus

Color

stimulus

Data

Parameters

Parameters Parameters

Parameters

Evaluation

Knowledge base

New designs

Modular implementaion


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


End

Thanks for coming

Data & Analytics

Reviewing Data Visualization: an Analytical Taxonomical Study