CPSC 583 Visualization 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 Creating a Visualization

End point • Image – animation – interaction • A clear explanation of what the image means in terms of data Creating a Visualization

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 Parallel Rendering Coordinates [Hauser et al., Vis 2000] [Fua et al., Vis Isosurfaces 1999] Glyphs Scatter Plots Line Integral [http://www.axon.com Convolution / gn_Acuity.html] [Cabral & Leedom, Node-link SIGGRAPH 1993] Streamlines Diagrams

[Lamping et al., CHI 1995] [Verma et al., Vis 2000] SciVis InfoVis 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

Raw Data Selection Representation Presentation

Interaction

Adapted from [Spence, 2000]

Slides by Petra Isenberg Visual Information Seeking

Mantra [Shneiderman, 1996] Describes the order of interaction operations

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

Mantra [Shneiderman, 1996] Describes the order of interaction operations

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

Mantra [Shneiderman, 1996] Describes the order of interaction operations

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

Mantra [Shneiderman, 1996] Describes the order of interaction operations

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

Mantra [Shneiderman, 1996] Describes the order of interaction operations

• Overview first • Zoom & filter • Details on demand

Æ useful for many (but not all) infovis applications

Slides by Petra Isenberg Knowledge Crystallization Cycle

Focuses on process of knowledge extraction

[Card et al., 1999] Slides by Petra Isenberg The Analytic Reasoning Process

[Illuminating the Path] Slides by Petra Isenberg Sense-Making Loop

For some types of intelligence analysts [Illuminating the Pa Slides by Petra Isenberg Formalizing Multiple Relations Visualizations

Dataset Relation Visualization

DA

Formalism for Multiple Relationship Visualization Comparison Formalizing Multiple Relations Visualizations

Dataset Relation Visualization

DA DR AA )(

Formalism for Multiple Relationship Visualization Comparison Formalizing Multiple Relations Visualizations

Dataset Relation Visualization

DA DR AA )( → DRVis AAA )(

Formalism for Multiple Relationship Visualization Comparison Formalizing Multiple Relations Visualizations

Relation

DR AA )(

Dataset Visualization

DA → DRVis AAA )(

Formalism for Multiple Relationship Visualization Comparison Formalizing Multiple Relations Visualizations

Relation

DR AA )(

Dataset Visualization

Relation

DR )( → DRVis )( DA AB AAA

Formalism for Multiple Relationship Visualization Comparison Formalizing Multiple Relations Visualizations

Visualization

Relation A → DRVis AA )(

DR AA )(

Dataset Visualization

B → DRVis AA )( Relation

DR )( DA AB

Formalism for Multiple Relationship Visualization Comparison Formalizing Multiple Relations Visualizations

Visualization

Relation A → DRVis AA )(

RA (DA )

Dataset Visualization

B → DRVis AA )( Relation

DR )( DA AB

Visualization

C → DRVis AB )( Formalism for Multiple Relationship Visualization Comparison Multiple Relation Visualizations

Individual Visualizations Coordinated Views Compound Graphs Semantic Substrates

Formalism for Multiple Relationship Visualization Comparison Individual Visualizations

• Any datasets, relations, and visualizations • Manually compare • e.g. different charts in Excel

Formalism for Multiple Relationship Visualization Comparison Coordinated Views

Formalism for Multiple Relationship Visualization Comparison Coordinated Views

→ DRVis AAA )(

Formalism for Multiple Relationship Visualization Comparison Coordinated Views

→ DRVis AAA )( → DRVis ABB )(

Formalism for Multiple Relationship Visualization Comparison Coordinated Views

→ DRVis AAA )( → DRVis ABA )(

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

Formalism for Multiple Relationship Visualization Comparison Compound Graphs

Formalism for Multiple Relationship Visualization Comparison Compound Graphs

→ DRVis AAA )(

Formalism for Multiple Relationship Visualization Comparison Compound Graphs

→ + DRDRVis ABAAA )()(

Formalism for Multiple Relationship Visualization Comparison Compound Graphs

→ DRRVis ABAA )(, • 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)

Formalism for Multiple Relationship Visualization Comparison 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.