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

30

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

31

Community-based links