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IAT 814 Introduction to Visual Analytics

This introduction to visual analytics explores the intersection of symbols and perceptual science. It emphasizes the importance of grounding visualizations in human perceptual mechanisms rather than arbitrary symbols. Key concepts covered include semiotics in graphics, the strengths and weaknesses of sensory vs. arbitrary representations, and the principles of effective visualization design. The focus is on creating visual languages that utilize our innate perceptual capabilities, making complex data more accessible and understandable across cultures, thereby enhancing decision-making and insight generation.

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IAT 814 Introduction to Visual Analytics

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  1. IAT 814 Introduction to Visual Analytics Symbols vs Perceptual Science IAT 814

  2. Visualization based on science • Visualization based on science – • not recognition of arbitrary symbols • Semiotics of graphics: Bertin, Saussure • The craft of designing visual languages? • The perceptual system has built-in capabilities • Understanding of perceptual mechanisms is fundamental to a science of visualization • Experimental semiotics (Ware) IAT 814

  3. Sensory vs arbitrary symbols • Sensory: • You can see and understand without training. • Match the way our brains are wired • Object shape, color, texture • Arbitrary: • Must be learned • Having no perceptual basis • The word “dog” IAT 814

  4. Arbitrary representations • Strengths • Formally powerful • Capable of rapid change • May already be learned • Visually concise • Weaknesses • Can be hard to learn • Can be easy to forget • Same symbol, different meaning • Different symbol, same meaning IAT 814

  5. Sensory representations • Strengths • Can be understood without training • Resistant to instructional bias • Processed very quickly, and in parallel • Valid across cultures • Weaknesses • Poor mappings can be misunderstood, quickly and without effort, even with instruction and training. • Can’t be unlearned IAT 814

  6. Sensory symbols • “Symbols and aspects of visualizations that derive their expressive power from their ability to use the perceptual power of the brain without learning”. • Empirically testable (ha!) IAT 814

  7. Building a Visualization: Steps • Collect the data (lab work, simulation, archives, ……) • Transform the data into • a format readable and manipulable by the visualization software • the form most likely to reveal information • Visualization algorithms and computational treatments run on graphics hardware or software renderers • Human views and interacts with the visualization • Changes parameters, techniques, view options • User studies to evaluate effectiveness • ideally! IAT 814

  8. What’s a good visualization? • Make a model that captures the essence of a information system • Model = abstraction with • The important things in • The unimportant things out • Different visualizations provide different levels of detail, • Show and hide different things • Support different abstractions • Useful to aid understanding, not just realistic representations (what color is a carbon atom?) • Map the important part of the tasks onto techniques that show the relevant characteristics best Acts of rhetoric! IAT 814

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