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

6. The power of line in creative thinking LOC

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

8. Thinking visually Embedded 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) 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.

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

13. Parallel Coordinates Brushing Touch and all data reps are highlighted

14. Trees Cone Tree Hyperbolic Tree Standard MS browser

15. The Cone Tree

17. Graphs: The topological range query Constellation: Hover queries (Munzner) MEGraph Brushing Dynamic Queries

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

19. Magic lenses Lenses that transform what is behind them Video

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

21. The process of visual pattern comparisons Ware:Vislab:CCOM 1.Execute an epistemic action, navigating to location of first target pattern. 2.Retain subset of first pattern in visual working memory. 3.Execute an epistemic action by navigating to candidate location of a comparison pattern. 4.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. 5.If a mismatch is found repeat

22. Solution 1 : Zooming Solution 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.

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

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

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

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

27. Ware:Vislab:CCOM

28. 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

29. Claim: Only 4+ basic types of data visualization 1. Maps 2. Chart (scatter plots, time series, bar, etc) 3. Node Link diagrams 4. Tables 5. + Glyphs Note: this leaves out custom diagrams – eg assembly diagrams Ware:Vislab:CCOM

30. Example with twitter data: Monitoring vs. Exploring 30 Analytic ProbeTask DescriptionData Dimensions What are the latest emergent memes? Identify memes of interest that are gathering momentum before they go viral. Topical (Textual, Linguistic) How did these memes originate and spread? Identify the communit(ies) of interest in which the memes first appeared Communities, Temporal What is the geographic footprint of the meme? Identify the meme’s original location(s) and the “hottest” regions where it spread. Geospatial, Temporal What are the active memes in a particular [place, topic, community]? Issue a query specifying region, topic, community, and/or time range of interest. Explore the details of memes of interest. All of the above Monitoring Analytic ProbeTask DescriptionData Dimensions What are the key memes associated with a subject Identify trends in a particular subject area. E.g. an international trade summit Topical (Textual, Linguistic) What are related memesFind relations by topic, by communities.Topical, Structural What are key attributes?Find links, hashtags, URLs, etc.Record structure. How did these memes originate and spread? Identify time course of meme propagation across communities. Communities, Temporal What is the geographic footprint of the meme? Identify course of geographic propagation of meme from its start location over time. Geospatial, Temporal Who are the key players?Find the key individuals most influential in the origination and spread of each meme. Graph Structure Exploring

31. Visualization Concept: MemeVis 31 Community-based links