Thinking with Visualizations:sense making loops
Colin WareData 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
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
The visual query
Transforming a problem into a pattern search
E.g. path in a network diagram
More visual queries
Ware:Vislab:CCOM
Vowel formantsCan I use a simple frequency analysisTo identify vowel sounds
How far from the kitchen to the Dining room
The power of line in creative thinking LOC
Interactive pattern: Design Sketching
Combining meaning with external information
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)
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
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.
brushing
Touch one instance of an object. Other instances are highlighted
Parallel Coordinates
Brushing Touch and all data
reps are highlighted
Trees
Cone Tree Hyperbolic Tree Standard MS browser
The Cone Tree
Graphs: The topological rangequery
Constellation: Hover queries (Munzner)
MEGraph
BrushingDynamic Queries
Dynamic queries
The use of interactive sliders to select ranges in multi-dimensional data.
Ahlberg and Shneiderman
[Video]
Magic lenses
Lenses that transform what is behind them
Video
Pattern Comparison in a large information space
Ware:Vislab:CCOM
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
Solution 1 : ZoomingSolution 2: Magnifying windows
Zooming vsWindows + 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.
Solution 3: Snapshot gallery(with links to original space)
Ware:Vislab:CCOM
Good in case where >20 comparisons must be made
Drill down with hierarchial aggregation
Click on something and it opens to reveal more
Trees
Analysis: time cost, rootedness, text support.
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
Ware:Vislab:CCOM
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
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
Example with twitter data:Monitoring vs. Exploring
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Analytic Probe Task Description Data DimensionsWhat 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 aboveMon
itor
ing
Analytic Probe Task Description Data DimensionsWhat 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 memes Find relations by topic, by communities. Topical, StructuralWhat 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
Exp
lori
ng
Visualization Concept: MemeVis
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Community-based links