CPSC 583 Visualization Basics

CPSC 583Visualization Basics

Sheelagh Carpendale

Frameworks

• Shneiderman– Data, Tasks

• Bertin (Mackinlay/Card)– Data Types, Marks, Retinal Attributes (including

Position)• Hanrahan, Tory/Moeller

– Data/Conceptual Models

Slide credits Tamara Munzner (UBC)

Creating a Visualization

Starting point • Data

– Discrete, continuous, variable– Type – int, float, etc.– Range

• Tasks– Why - motivation

• Domain – Meta data, semantics, conceptual model


End point • Image

– animation– interaction

• A clear explanation of what the image means in terms of data


To get there (the middle part)• Mapping

– Use domain knowledge & data semantics– Use human perception– Metaphor (when appropriate)– Data attributes or dimension to visual variables

• Processing – Algorithms– Computational issues (constraints)

Mackinlay, Card (from Bertin)

Data Variables– 1D, 2D, 3D, 4D, 5D, etc

Data Types– nominal, ordered, quantitative

Marks– point, line, area, surface, volume– geometric primitives

Retinal Properties– size, brightness, color, texture, orientation, shape...– parameters that control the appearance of

geometric primitives– separable channels of information flowing from

retina to brain

Shneiderman (data & task taxomony)

Data– 1D, 2D, 3D, nD, trees, networks (text – Hanrahan)

Tasks– Overview, Zoom, Filter, Details-on-demand,– Relate, History, Extract

Data alone is not enough– Task …

Combinatorics of Encodings

Challenge• pick the best encoding from exponential number of

possibilities (n)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]

Difference between SciVis and InfoVis

Direct Volume Rendering

Streamlines

Line Integral Convolution

GlyphsIsosurfaces

SciVis

Scatter Plots

Parallel Coordinates

Node-link Diagrams

InfoVis[Verma et al.,Vis 2000]

[Hauser et al.,Vis 2000]

[Cabral & Leedom,SIGGRAPH 1993]

[Fua et al., Vis 1999]

[http://www.axon.com/gn_Acuity.html]

[Lamping et al., CHI 1995]

Difference between SciVis and InfoVis

• Card, Mackinlay, & Shneiderman:– SciVis: Scientific, physically based– InfoVis: Abstract

• Munzner:– SciVis: Spatial layout given– InfoVis: Spatial layout chosen

• Tory & Möller:– SciVis: Spatial layout given + Continuous– InfoVis: Spatial layout chosen + Discrete– Everything else -- ?

Spence’s Infovis Model

Slides by Petra Isenberg

Raw

Data Selection Representation Presentation

Interaction

Adapted from [Spence, 2000]

Visual Information Seeking MantraDescribes the order of interaction operations

Slides by Petra Isenberg pictures from www.b-eye-network.com

[Shneiderman, 1996]



[Shneiderman, 1996]

pictures from www.b-eye-network.com


• Overview first• Zoom & filter• Details on demand

useful for many (but not all) infovisapplications


[Shneiderman, 1996]

Knowledge Crystallization Cycle

Focuses on process of knowledge extraction


[Card et al., 1999]

The Analytic Reasoning Process


[Illuminating the Path]

Sense-Making Loop


For some types of intelligence analysts

[Illuminating the Pa

Formalizing Multiple Relations Visualizations

Dataset Relation Visualization

AD

Formalism for Multiple Relationship Visualization Comparison



AD )( AA DR




AD )( AA DR )( AAA DRVis →



Dataset Visualization

AD

Relation

)( AA DR

)( AAA DRVis →



Dataset Visualization

AD

Relation

)( AA DR

)( AAA DRVis →Relation

)( AB DR



Dataset

AD

Relation

)( AA DR

Relation

)( AB DR

Visualization

)( AAB DRVis →

Visualization

)( AAA DRVis →



Dataset

AD

Relation

)( AA DR

Relation

)( AB DR

Visualization

)( AAA DRVis →

Visualization

)( AAB DRVis →

Visualization

)( ABC DRVis →


Multiple Relation Visualizations

Individual Visualizations

Coordinated Views

Compound Graphs

Semantic Substrates


Individual Visualizations

• Any datasets, relations, and visualizations

• Manually compare• e.g. different charts in Excel


Coordinated Views


Coordinated Views

)( AAA DRVis →


Coordinated Views

)( AAA DRVis → )( ABB DRVis →


Coordinated Views

• Any datasets, relations, and visualizations• Interactive highlighting• e.g., Snap-Together Visualization (North & Shneiderman, 2000)

)( AAA DRVis → )( ABA DRVis →


Compound Graphs


Compound Graphs

)( AAA DRVis →


Compound Graphs

)()( ABAAA DRDRVis +→


• Secondary relation has no spatial rights• e.g., Overlays on Treemaps (Fekete et al., 2003), ArcTrees

(Neumann et al., 2005), Hierarchical Edge Bundles (Holten, 2006)

Compound Graphs

)(, ABAA DRRVis →


References• Chapter 1, Readings in Information Visualization: Using Vision to

Think. Stuart Card, Jock Mackinlay, and Ben Shneiderman, Morgan Kaufmann 1999.

• The Structure of the Information Visualization Design Space. Stuart Card and Jock Mackinlay, Proc. InfoVis 97 [citeseer.ist.psu.edu/card96structure.html]

• The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. Ben Shneiderman, Proc. 1996 IEEE Visual Languages, also Maryland HCIL TR 96-13 [citeseer.ist.psu.edu/shneiderman96eyes.html]

• Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational Databases. Chris Stolte, Diane Tang and Pat Hanrahan, IEEE TVCG 8(1), January 2002. [graphics.stanford.edu/papers/polaris]

• The Value of Visualization. Jarke van Wijk. Visualization 2005 [www.win.tue.nl/ vanwijk/vov.pdf]

References• Automating the Design of Graphical Presentations of Relational

Information. Jock Mackinlay, ACM Transaction on Graphics, vol. 5, no. 2, April 1986, pp. 110-141.

• Semiology of Graphics, Jacques Bertin, Gauthier-Villars 1967, EHESS 1998

• The Grammar of Graphics, Leland Wilkinson, Springer-Verlag 1999

• Rethinking Visualization: A High-Level Taxonomy Melanie Tory and Torsten Moeller, Proc. InfoVis 2004, pp. 151-158.

CPSC 583 Visualization Basics

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