MODELS AND FRAMEWORKS. Information Visualization Fall 2009 Jinwook Seo SNU CSE

Transcription

1 MODELS AND FRAMEWORKS Information Visualization Fall 2009 Jinwook Seo SNU CSE

2 Wednesday Prof. Hee-Joon Bae, Seoul National University Bundang Hostpital blood pressure and END (early neurologic deterioration)

3 Readings Automating the Design of Graphical Presentations of Relational Information Jock Mackinlay, ACM TOG, vol. 5, no. 2, April 1986, pp Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational Databases, Chris Stolte, Diane Tang and Pat Hanrahan, IEEE TVCG, Vol. 8, No. 1, January Semiology of Graphics, Jacques Bertin, Gauthier- Villars 1967, U of Wisconsin Press, 1983

4 Mapping input data semantics use domain knowledge output visual encoding visual/graphical/perceptual/retinal channels/attributes/dimensions/variables use human perception processing algorithms handle computational constraints

5 Visual Variables Size Value (Density) Texture Color Orientation Shape 3D Animation/Time Bertin, Semiology of Graphics, 1967 Gauthier-Villars, 1983 UW Press

6 Visual Variables Plane XY position 3 classes of representations point : location line : length area : size Retinal Variables Z elevation (n+1) 8 possibilities Bertin, Semiology of Graphics, 1967 Gauthier-Villars, 1983 UW Press

7 Mackinlay & Card data variables 1D, 2D, 3D, 4D, 5D,... 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 owing from retina to brain Automating the Design of Graphical Presentations of Relational Information,Jock Mackinlay, ACM Transaction on Graphics

8 Mapping Visual Variables Bertin, Semiology of Graphics, 1967 Gauthier-Villars, 1998 EHESS

9 Mapping Visual Variables Bertin, Semiology of Graphics, 1967 Gauthier-Villars, 1983 UW Press

10 Data Types Level Can define Relation or Operation Mathematical structure Example nominal mode equality (=) set gender, color ordinal median order (<) totally ordered set ranking, social class interval mean standard deviation subtraction (-) weighted average relative zero affine line date, temperature (C, F) ratio geometric mean coefficient of variation addition (+) multiplication absolute zero field age, length, temperature (K) from wikipedia

11 Visual Bertin, Semiology of Graphics, 1967 Gauthier-Villars, 1983 UW Press

12 Visual Variables Bertin, Semiology of Graphics, 1967 Gauthier-Villars, 1998 EHESS

13 Property of Visual Variables Selection immediately isolation of one category Association immediate grouping Order Quantity Bertin, Semiology of Graphics, 1967 Gauthier-Villars, 1983 UW Press

14 Visual Encoding Accuracy of Quantitative Perceptual Tasks Automating the Design of Graphical Presentations of Relational Information,Jock Mackinlay, ACM Transaction on Graphics, 1986 Cleveland & McGill, Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods 1984

15 Visual Encoding Principles Channel Ranking Varies by Data Type Automating the Design of Graphical Presentations of Relational Information,Jock Mackinlay, ACM Transaction on Graphics, 1986

16 Polaris infovis spreadsheet table cell not just numbers: graphical elements wide range of retinal variables and marks table algebra interactive interface formal language influenced by Wilkinson's Grammar of Graphics Grammar of Graphics, Springer-Verlag 1999 commercialized as Tableau Software Tamara Munzner

17 Polaris

18 Polaris

19 Tableau

20 Expressiveness of Visualizations Set of facts expressible in visual language if sentences (visualizations) in language express all facts in data, and only facts in data. Automating the Design of Graphical Presentations of Relational Information,Jock Mackinlay, ACM Transaction on Graphics, 1986

21 Incorrect Use of Visualizations Automating the Design of Graphical Presentations of Relational Information,Jock Mackinlay, ACM Transaction on Graphics, 1986

22 Cannot express facts A 1 N relation cannot be expressed in a single horizontal dot plot because multiple tuples are mapped to the same position Hanrahan, graphics.stanford.edu/courses/cs448b-04-winter/lectures/encoding

23 Effectiveness of Visualizations A visualization is more effective than another visualization if information conveyed by one visualization is more readily perceived than information in other. Automating the Design of Graphical Presentations of Relational Information,Jock Mackinlay, ACM Transaction on Graphics

24 Efficiency If, in order to obtain a correct and complete answer to a given question, all other things being equal, one construction requires a shorter observation time than another construction, we can say that it is more efficient for this question. - Bertin, Semiology of Graphics, 1967 Mental cost applied to visual perception

25 Encoding Rules Principle of Consistency properties of the image should match properties of data Principle of Importance Ordering encode most important information in most effective way

26 Who s in charge? designer: design studies automatic: select visualization automatically given data Mackinlay, APT limited set of encodings: scatterplots, bar charts... Roth et al, Sage/Visage holy grail: entire space of infovis visual encoding nowhere near goal, esp. with relational/graph data user-guided: allow user to change encodings Polaris: user drag and drop exploration Jinwook Seo/Tamara Munzner

27 Beyond data alone data alone not enough what do you need to do? bigger picture than just visual encoding decisions Shneiderman's data+task taxonomy data 1D, 2D, 3D, multi-d, temporal, trees, networks tasks overview, zoom, filter, details-on-demand, relate, history, extract mantra: overview first, zoom and filter, details-on on-demand Jinwook Seo/Tamara Munzner

28 Information Visualization: Data Types InfoViz SciViz. 1-D Linear Document Lens, SeeSoft, Info Mural, Value Bars 2-D Map GIS, ArcView, PageMaker, Medical imagery 3-D World CAD, Medical, Molecules, Architecture Multi-Var Temporal Tree Network Parallel Coordinates, Spotfire, XGobi, Visage, Influence Explorer, TableLens, DEVise Perspective Wall, LifeLines, Lifestreams, Project Managers, DataSpiral Cone/Cam/Hyperbolic, TreeBrowser, Treemap Netmap, netviz, SeeNet, Butterfly, Multi-trees (Online Library of Information Visualization Environments) otal.umd.edu/olive

29 Information Visualization: Mantra Overview, zoom & filter, details-on on-demand Overview, zoom & filter, details-on on-demand Overview, zoom & filter, details-on on-demand Overview, zoom & filter, details-on on-demand Overview, zoom & filter, details-on on-demand Overview, zoom & filter, details-on on-demand Overview, zoom & filter, details-on on-demand Overview, zoom & filter, details-on on-demand Overview, zoom & filter, details-on on-demand Overview, zoom & filter, details-on on-demand Ben Shneiderman

30 Spotfire: Retinol s role in embryos & vision

31 Spotfire: DC natality data

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33 Credits Ben Shneiderman, UMD wiki.cs.umd.edu/cmsc734_08/index.php?title=main_page Tamara Munzner, UBC people.cs.ubc.ca/~tmm/courses/533-09/ Pat Hanrahan, Stanford graphics.stanford.edu/courses/cs448b-04- winter/lectures/encoding