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MAT 259 Visualizing Information

MAT 259 Visualizing Information. Winter 2006, e-studio, Art 2220 Tues 10:00-12:00, Lecture Thurs 10:00-12:00, Lab George Legrady, legrady@arts.ucsb.edu TA Angus Forbes, angus.forbes@gmail.com Course Web Site : http://www.mat.ucsb.edu/~g.legrady/ (click on “courses”, click on “MAT 259”).

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MAT 259 Visualizing Information

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  1. MAT 259 Visualizing Information • Winter 2006, e-studio, Art 2220 • Tues 10:00-12:00, Lecture • Thurs 10:00-12:00, Lab • George Legrady, legrady@arts.ucsb.edu • TA Angus Forbes, angus.forbes@gmail.com • Course Web Site : http://www.mat.ucsb.edu/~g.legrady/(click on “courses”, click on “MAT 259”) George Legrady

  2. Course Goals • An introduction to information visualization • An overview of varied methodologies • Comparison between uses in diverse disciplines • Introduction to self-organizing algorithms • Project driven course with focus on theory and practice • Working with cultural data, • Exploration of methodologies, • Visualization output to reflect aesthetic consideration George Legrady

  3. Workload • Attendance at weekly lectures • Active participation • Online reports on readings • Attendance & reports on visiting lectures • Completion of warm-up and final projects George Legrady

  4. Visualization & Cross-Disciplinary Fertilization • Domain visualization, an emerging field • Multi-disciplinary: Difficult to get the overview of the field • Researchers bring their own discipline’s perspective • Examination of other disciplines: export and import of methods, ideas, models, or empirical results • Creative imagination required to foresee how outside info fits the problem at hand George Legrady

  5. Discipline Driven Methodologies • Each discipline has a particular implementation goal • LSIS: citation indexing, bibliographic indexing, etc. • Scientific Visualization: Map physical phenomena in 2D, or 3D • Information Visualization: Analyzing and transforming nonspatial data into visual form • Geographic Information Systems (GIS): Cartographic framework, a familiar way to map data • Art: Aesthetics, complexity, culturally meaningful results George Legrady

  6. Goal Driven Methodologies • Information Visualization: visually map abstract, nonspatial info • Information retrieval research in vast data sets • Depicting the overall semantic structure of a set of documents • Identifying patterns through visualization (DNA) George Legrady

  7. User Meta Model • Data Extraction • Definition of Units of Analysis • Selection of Measures • Calculation of similarity between units • Ordination: assignments of coordinates to each unit • Analysis and Interpretation of output visualization George Legrady

  8. Classification Methods • Alphabetical: arbitrary learned system • Numeric: arbitrary learned system • Scalar: (hotel star system) implies value scale • Sequential (time): based on units • Spatial: “sense of place” • Categories: similar things grouped together • Associative: (If a to b, then c to d) • Metaphoric: A way to establish context • Random: Creates complexity (game beginnings) George Legrady

  9. Visualization Process • Multivariate data to be presented in 2D in print or computer screen • by applying mathematical dimensionality algorithms to map the data • Clustering techniques to group similar data • Spatial proximity matrix: similar data/close, difference/distance • Large amounts of data presented in limited space: • Panning, zooming, filtering to access data George Legrady

  10. What is Visualization? • Design of the visual appearance of data objects and their relationships • Ability to comprehend large amounts of data • Reduction in search time through visualization • Provides a better understanding of complex data sets • Reveal relationships and properties through visual perception • Multiple simultaneous perspectives • Effective communication George Legrady

  11. Formal & Aesthetic Functions • Visualization Design: years of expertise and diverse skills • Visual communication: a language system (function of form, colors, etc) • Complex data relationship benefit from storytelling • Narrative methods enhance communication George Legrady

  12. Interaction Design • Search and browse through data • Zoom, filtering, panning, etc. • 1) Overview, 2) Zoom-in (filter), 3) Details-on-demand • “Browsing explores both the organization or structure of the information space, and its content” (Chen, 1998) • Information architects design layered info spaces based on classification systems • 3 Navigational Paradigms: 1) spatial, 2) semantic, 3) social (using behavior of like-minded people) (Dourish) George Legrady

  13. Visualization Outcomes • Effective exploitation of perceptual principles • Helps communication with non-specialists • Discover hidden (semantic) patterns, structures • Contribute to knowledge development in all disciplines George Legrady

  14. References (Selected) • “Visualizing Knowledge Domains”, Borner, Chen, Boyak • Journal of Information Visualization • Kohonen Self-Organizing Algorithm • Visual Complexity • Information Aesthetics • Edward Tufte George Legrady

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