Papers Covered Further Readings Frameworks Chapter 1, Readings in Information Visualization: Using Vision to The Structure of the Information Visualization Design Space Stuart Lecture 4: Frameworks/Models Think. Stuart Card, Jock Mackinlay, and Ben Shneiderman, Morgan Card and Jock Mackinlay, Proc. InfoVis 97 ◮ Mackinlay/Card/(Bertin) Kaufmann 1999. [citeseer.ist.psu.edu/card96structure.html] ◮ Data Types, Marks, Retinal Attributes (incl Information Visualization The Eyes Have It: A Task by Data Type Taxonomy for Information Automating the Design of Graphical Presentations of Relational Position) Visualizations Ben Shneiderman, Proc. 1996 IEEE Visual Information. Jock Mackinlay, ACM Transaction on Graphics, vol. 5, CPSC 533C, Fall 2007 ◮ Shneiderman, Amar/Eagan/Stasko Languages, also Maryland HCIL TR 96-13 no. 2, April 1986, pp. 110-141. [citeseer.ist.psu.edu/shneiderman96eyes.html] ◮ Data, Tasks Semiology of Graphics. Jacques Bertin, Gauthier-Villars 1967, Polaris: A System for Query, Analysis and Visualization of ◮ Tory/Moeller, Hanrahan EHESS 1998 Tamara Munzner Multi-dimensional Relational Databases. Chris Stolte, Diane Tang ◮ Data/Conceptual Models The Grammar of Graphics. Leland Wilkinson, Springer-Verlag 1999 and Pat Hanrahan, IEEE TVCG 8(1), January 2002. ◮ Stolte/Tang/Hanrahan, (Wilkinson) UBC Computer Science [graphics.stanford.edu/papers/polaris] Rethinking Visualization: A High-Level Taxonomy. Melanie Tory and Torsten M¨ oller, Proc. InfoVis 2004, pp. 151-158. ◮ Table Algebra ⇔ Visual Interface The Value of Visualization. Jarke van Wijk. Visualization 2005 19 September 2007 ◮ van Wijk [www.win.tue.nl/ vanwijk/vov.pdf] A Function-Based Data Model for Visualization. Lloyd Treinish, Visualization 1999 Late Breaking Hot Topics ◮ Value Low-Level Components of Analytic Activity in Information Visualization. Robert Amar, James Eagan, and John Stasko. Proc. Multiscale Visualization Using Data Cubes. Chris Stolte, Diane Tang InfoVis 05 [www.cc.gatech.edu/ john.stasko/papers/infovis05.pdf] and Pat Hanrahan, Proc. InfoVis 2002 Visualization Big Picture Mapping Bertin: Semiology of Graphics Design Space = Visual Metaphors ◮ geometric primitives: marks ◮ points, lines, areas, volumes ◮ input ◮ attributes: visual/retinal variables ◮ data semantics ◮ parameters control mark appearance ◮ use domain knowledge ◮ separable channels flowing from retina to brain ◮ output ◮ visual encoding ◮ x,y ◮ visual/graphical/perceptual/retinal ◮ position ◮ channels/attributes/dimensions/variables ◮ z ◮ use human perception ◮ size ◮ processing ◮ greyscale ◮ algorithms ◮ color ◮ handle computational constraints ◮ texture ◮ orientation ◮ shape [Bertin, Semiology of Graphics, 1967 Gauthier-Villars, 1998 EHESS] [Bertin, Semiology of Graphics, 1967 Gauthier-Villars, 1998 EHESS] Data Types Data Types Data Types ◮ continuous (quantitative) ◮ continuous (quantitative) ◮ continuous (quantitative) ◮ 10 inches, 17 inches, 23 ◮ 10 inches, 17 inches, 23 ◮ 10 inches, 17 inches, 23 inches inches inches ◮ ordered (ordinal) ◮ ordered (ordinal) ◮ small, medium, large ◮ small, medium, large ◮ days: Sun, Mon, Tue, ... ◮ days: Sun, Mon, Tue, ... ◮ categorical (nominal) ◮ apples, oranges, bananas [Hanrahan, graphics.stanford.edu/courses/ [graphics.stanford.edu/papers/polaris] cs448b-04-winter/lectures/encoding/walk025.html] More Data Types: Stevens Channel Ranking Varies by Data Type Mackinlay, Card Shneiderman’s Data+Tasks Taxonomy ◮ data variables ◮ spatial position best for all types ◮ 1D, 2D, 3D, 4D, 5D, etc ◮ data types ◮ data ◮ subdivide quantitative further: Quantitative Ordinal Nominal ◮ nominal, ordered, quantitative ◮ 1D, 2D, 3D, temporal, nD, trees, networks ◮ marks Position Position Position ◮ text and documents (Hanrahan) ◮ interval: 0 location arbitrary Length Density Hue ◮ point, line, area, surface, volume ◮ tasks ◮ time: seconds, minutes Angle Saturation Texture ◮ geometric primitives Hue Connection ◮ overview, zoom, filter, details-on-demand, Slope ◮ retinal properties Area Texture Containment ◮ relate, history, extract ◮ ratio: 0 fixed Volume Connection Density ◮ size, brightness, color, texture, orientation, Density Containment Saturation ◮ data alone not enough ◮ physical measurements: Kelvin temp shape... Saturation Length Shape ◮ what do you need to do? ◮ parameters that control the appearance of Hue Angle Length Texture Slope Angle geometric primitives ◮ mantra: overview first, zoom and filter, [S.S. Stevens, On the theory of scales of measurements, Science 103(2684):677-680, 1946] Connection Area Slope ◮ separable channels of information flowing from details on demand Containment Volume Area Shape Shape Volume retina to brain [Shneiderman, The Eyes Have It: A Task by Data Type Taxonomy for [Mackinlay, Automating the Design of Graphical Presentations of Relational ◮ closest thing to central dogma we’ve got Information Visualizations] Information, ACM TOG 5:2, 1986]
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