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Thinking with Visualizations: sense making loops

Thinking with Visualizations: sense making loops. Colin Ware Data Visualization Research Lab University of New Hampshire. Visual Thinking Virtual Machine. Capture common interactive processes Analytic tools for designers Based on a virtual machine. Visual Thinking Design Patterns.

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Thinking with Visualizations: sense making loops

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  1. Thinking with Visualizations:sense making loops Colin Ware Data Visualization Research Lab University of New Hampshire

  2. Visual Thinking Virtual Machine • Capture common interactive processes • Analytic tools for designers • Based on a virtual machine

  3. Visual Thinking Design Patterns • Visual Query • Reasoning with a Hybrid of a Visual Display and Mental Imagery • Design sketching • Sensemaking • Visual Monitoring • Cognitive Reconstruction • Drill Down • Drill Down, Close out with hierarchical aggregation • Pathfinding with a map or diagram • Seed then Grow • Find Local Patterns in a Network • Pattern Comparison in a large information space • Cross View Brushing • Dynamic Queries

  4. The visual query • Transforming a problem into a pattern search • E.g. path in a network diagram

  5. More visual queries Vowel formants How far from the kitchen to the Dining room Can I use a simple frequency analysis To identify vowel sounds Ware:Vislab:CCOM

  6. The power of line in creative thinking LOC

  7. Interactive pattern: Design Sketching Combining meaning with external information

  8. Thinking visuallyEmbedded processes • Define problem and steps to solution • Formulate parts of problem as visual questions/hypotheses • Setup search for patterns • Eye movement control loop • IntraSaccadic Scanning Loop (form objects)

  9. Cost of Epistemic Actions • Intra-saccade (0.04 sec) (Query execution) • An eye movement (0.5 sec) < 10 deg : 1 sec> 20 deg. • A hypertext click (1.5 sec but loss of context) • A pan or scroll (3 sec but we don’t get far) • Brushing • Dynamic queries • Tree manipulation, etc. Goal  rapid queries without loss of context

  10. Thinking Brushing • Touching one visual representation object causes other representations of that same objects to be highlighted • E.g. a table and a graph. • A map and a graph.

  11. brushing • Touch one instance of an object. Other instances are highlighted

  12. Parallel Coordinates • Brushing • Touch and all data reps are highlighted

  13. Trees • Cone Tree • Hyperbolic Tree • Standard MS browser

  14. The Cone Tree

  15. Graphs: The topological rangequery Constellation: Hover queries (Munzner) MEGraph Brushing Dynamic Queries

  16. Dynamic queries • The use of interactive sliders to select ranges in multi-dimensional data. • Ahlberg and Shneiderman [Video]

  17. Magic lenses • Lenses that transform what is behind them Video

  18. Pattern Comparison in a large information space Ware:Vislab:CCOM

  19. The process of visual pattern comparisons Execute an epistemic action, navigating to location of first target pattern. Retain subset of first pattern in visual working memory. Execute an epistemic action by navigating to candidate location of a comparison pattern. Compare working memory pattern with part of pattern at candidate location. 4.1  If a suitable match is found terminate search.4.2  If a partial match is found,  navigate back and forth between candidate location and master  pattern location loading additional subsets of candidate pattern into visual working memory and making comparison until a suitable match or a mismatch is found. If a mismatch is found repeat Ware:Vislab:CCOM

  20. Solution 1 : ZoomingSolution 2: Magnifying windows Zooming vs Windows + eye movements Plumlee, M. D., & Ware, C. (2006). Zooming versus multiple window interfaces: Cognitive costs of visual comparisons. ACM Transactions on Computer-Human Interaction, 12(2), 179-209.

  21. Solution 3: Snapshot gallery(with links to original space) Good in case where >20 comparisons must be made Ware:Vislab:CCOM

  22. Drill down with hierarchial aggregation • Click on something and it opens to reveal more

  23. Trees Analysis: time cost, rootedness, text support.

  24. Opening and closing Nested Graphs Intelligent Zoom (Bartram et al., 1995) Manual: Parker et al., 1998 GraphVisualizer3D Poor because of 3D, need to zoom pan Mixed initiative may be needed.

  25. Ware:Vislab:CCOM

  26. Tasks and Data • Who, what, when, where and how? • Entities, relationships and attributes of entities and relationships • When – implies a time line, temporal patterns. Time line interactions • Where – implies map, and zooming, mag windows as needed Ware:Vislab:CCOM

  27. Claim: Only 4+ basic types of data visualization • Maps • Chart (scatter plots, time series, bar, etc) • Node Link diagrams • Tables • + Glyphs • Note: this leaves out custom diagrams – eg assembly diagrams Ware:Vislab:CCOM

  28. Example with twitter data:Monitoring vs. Exploring Monitoring Exploring

  29. Visualization Concept: MemeVis Community-based links

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