2. • Society is generating data at ever
escalating rates
• The rise of the supercomputer
• Advances in data collection
instruments and techniques (e.g.,
high-throughput genome
sequencing)
• We can now afford to capture and
store larger quantities of information,
in the hope that we can learn
something unexpected from it
!2
background
3. background !3[Tukey and Wilk 66, Tukey 77, Tukey 80]
• Confirm / disconfirm a
particular hypothesis
!
• Typically involves statistical
tests and computation
modeling
!
• Perfect for computers
!
!
!
Confirmatory
analysis
• Look for unexpected patterns
and outliers
!
• Ask new questions!
!
• Not a fit task for computers
!
!
!
!
Exploratory
analysis
Hypothesis-driven
inquiry
Data-driven
inquiry
4. background !3[Tukey and Wilk 66, Tukey 77, Tukey 80]
• Confirm / disconfirm a
particular hypothesis
!
• Typically involves statistical
tests and computation
modeling
!
• Perfect for computers
!
!
!
Confirmatory
analysis
• Look for unexpected patterns
and outliers
!
• Ask new questions!
!
• Not a fit task for computers
!
!
!
!
Exploratory
analysis
Hypothesis-driven
inquiry
Data-driven
inquiry
5. background !3[Tukey and Wilk 66, Tukey 77, Tukey 80]
• Confirm / disconfirm a
particular hypothesis
!
• Typically involves statistical
tests and computation
modeling
!
• Perfect for computers
!
!
!
Confirmatory
analysis
• Look for unexpected patterns
and outliers
!
• Ask new questions!
!
• Not a fit task for computers
!
!
!
!
Exploratory
analysis
Hypothesis-driven
inquiry
Data-driven
inquiry
New perceptual aids are needed to facilitate the
exploration of “big data” sources
6. • Cost of digital displays is decreasing rapidly
• Large high-resolution displays are
becoming more affordable
• Deployed in real-world scientific settings
• Collaboration between co-located teams
• Visual analysis and exploration of large-
scale datasets
Nanoscale materials science
Earth sciences
Ecology and
behavioral
biology
background [Reda et al. 13b, Febretti et al. 13, Reda et al 13c] !4
Could these display improve the rate
and quality of insight during exploratory
visual analysis? And how/why?
?
14. !6
visual exploration
temporal separation spatial separation (i.e., juxtaposition)
1. What is the effect of increasing the physical size and resolution of the
display on user behavior, during visual exploration?
2. And on insight acquisition?
3. Are there new design patterns for exploratory multi-view
visualizations on large high-resolution displays?
?
Aha!
16. !8
Low-level visualization tasks
(e.g., find the state with the highest population growth between 1960 and 1990)
Task
completion
time
Data size
Strictlylinearscaling
User performance on a
big display
[Yost and North 06, Yost et al. 07, Ball et al. 07]related work
27. !12[Lam 08]
Cost (temporal-separation)
theory
Form goal!
Visualization tool
Form physical sequence!
Execute physical sequence
Form system operators!
Perceive state
framework of
interaction
costs in
visualizations
Evaluate interpretation
Interpret perception
30. !12[Lam 08]
Cost (temporal-separation)
theory
framework of
interaction
costs in
visualizations
Evaluate interpretation
Interpret perceptionTemporal-view association
Mental map (re)-building
[Purchase et al. 07]
Cognitive integration
[Ratwani et al. 08, Plumlee & Ware 06 ]
}
31. !12[Lam 08]
Cost (temporal-separation)
theory
Form goal!
Visualization tool
Form physical sequence!
Execute physical sequence
Form system operators!
Perceive state
framework of
interaction
costs in
visualizations
Evaluate interpretation
Interpret perception
32. !12[Lam 08]
Cost (temporal-separation)
theory “cognitive resistance”
Form goal!
Visualization tool
Form physical sequence!
Execute physical sequence
Form system operators!
Perceive state
framework of
interaction
costs in
visualizations
Evaluate interpretation
Interpret perception
33. !13[Lam 08]
Perceive stateExecute physical sequence
Visualization tool
Interpret perception
Form goal!
Form system operators!
Form physical sequence!
Evaluate interpretation
Cost (spatial-separation)
theory
36. !13[Lam 08]
Perceive stateExecute physical sequence
Visualization tool
Cost (spatial-separation)
theory
Managing attention in a
more complex environment
Visually resolve a
more cluttered visualization
Potentially strenuous
physical navigation
(head turns, walking, leaning)
37. !14
• Study I: exploratory case study
• Comparative visual analysis of ensemble datasets on large
displays
• Real-world application + domain expert (behavioral ecology)
!
• Study II: comparative study
• Investigate the effects of increasing the size/resolution of the
display on user behavior and insight acquisition
• Simulated analysis task on a real-world dataset
38. !15
to nestto food
off-trail navigation?
Understanding the navigational strategies of Seed harvester ants
case study [Offord et al. 13]
39. !15
to nestto food
off-trail navigation?
Understanding the navigational strategies of Seed harvester ants
case study [Offord et al. 13]
44. !17
Findings: user strategy
case study
• Think-aloud protocol
• Verbal protocol analysis
• Coding scheme: Observations,
Hypotheses, and Decisions
• Interactions: Query by example,
Workspace management
Workspace
management
Query by
example
Hypothesis
formulation
Decision
making
Observing
outliers
layout-preserving
interactions
temporal separation of views
45. !18
Study II: Effects of increasing the display size and resolution on
user analytic behavior and insight acquisition
• Goals
• Measure effects of increasing the display size and resolution on quantity and quality of
discoveries made during visual exploration
• Understand variations in user behavior and analytic strategy induced as a result of using a
larger display with more pixels
• Study design
• Two display conditions (Small, Large)
• Between-subject design
• Think-aloud protocol
• Scenario
• Open-ended visual exploration task: analysis of Chicago crime patterns between 2006 -
2012 (approximately 2.8 million crime incidents)
experiment
64. !26
Results: quantity and rate of insights
experiment
• Comparable rate of insight between the two display conditions
• Participants chose to spend 35 minutes extra time on average exploring the
dataset with the large display
• This may have caused participants to make more observations with the
large display
65. !26
Results: quantity and rate of insights
experiment
• Comparable rate of insight between the two display conditions
• Participants chose to spend 35 minutes extra time on average exploring the
dataset with the large display
• This may have caused participants to make more observations with the
large display
minutes into activity
commutativeinsights
73. !29
experiment
p(large) - p(small)
• Significant tendency to “integrate information / continue to make observations” with
the large display
• More frequent transitions to “form goal” (36% increase, non-significant)
• Suggests the formulation and pursuit of more exploratory goals with the large
display
Results: user behavior
74. !30
Discussion: human limits
experiment
• Some complaints suggest information overload situations with the large display
• “It’s hard to look over so much! It’s so hard to compare so many things.”
• Difficulty in integrating information across spatially disparate views
• “By the time I finished turning my head to the other side I would forget [the contents of
the previous view]”
• Participants on the large display seem to limit themselves to 6-7 columns (87-102
degrees of visual angle)
75. !30
Discussion: human limits
experiment
• Some complaints suggest information overload situations with the large display
• “It’s hard to look over so much! It’s so hard to compare so many things.”
• Difficulty in integrating information across spatially disparate views
• “By the time I finished turning my head to the other side I would forget [the contents of
the previous view]”
• Participants on the large display seem to limit themselves to 6-7 columns (87-102
degrees of visual angle)
76. !31
Discussion: summary
RQ1 - What is the effect of increasing the display size/resolution on user behavior?
• Significant increase in the length of the exploratory activity.
• Suggests a tendency to pursue more ambitious exploratory goals with the large display
• Small display participants seem to resist exploration when they did not have a priori intuition: “I only bothered to look
at the years when I knew something about an area– like Cabrini Green and the Taylor area”
!
• Significant tendency to integrate observations / continue to derive new observations!
RQ2 - What is the effect of increasing the display size/resolution on insight acquisition?
• Significant increase in the number of observations reported
• Slight increase in the number of hypotheses formulated (not significant)
• Comparable rates of insight (for observations and hypotheses)
• Significant tendency to generate higher-level, more integrative insights.
!
RQ3 - Are there new design patterns for multi-view based visualization on large displays?
• “Seed and grow” design patter
• Loose view coordination model to support multiple exploratory threads
• Lens metaphor to facilitate the exploration of disparate parts of the information space
90. !34
Scientific reasoning through dual search: theorists
outlook
Hypothesis
space
[Klahr and Dunbar 88]
experiment /
Information
space
91. !34
Scientific reasoning through dual search: theorists
outlook
Hypothesis
space
[Klahr and Dunbar 88]
experiment /
Information
space
92. !34
Scientific reasoning through dual search: theorists
outlook
Hypothesis
space
[Klahr and Dunbar 88]
predict
experiment /
Information
space
93. !34
Scientific reasoning through dual search: theorists
outlook
Hypothesis
space
[Klahr and Dunbar 88]
predict
collect
evidence
experiment /
Information
space
94. !34
Scientific reasoning through dual search: theorists
outlook
Hypothesis
space
[Klahr and Dunbar 88]
predict
collect
evidence
experiment /
Information
space
95. !34
Scientific reasoning through dual search: theorists
outlook
Hypothesis
space
[Klahr and Dunbar 88]
predict
collect
evidence
experiment /
Information
space
96. !34
Scientific reasoning through dual search: theorists
outlook
Hypothesis
space
[Klahr and Dunbar 88]
predict
collect
evidence
experiment /
Information
space
97. !34
Scientific reasoning through dual search: theorists
outlook
Hypothesis
space
[Klahr and Dunbar 88]
predict
collect
evidence
experiment /
Information
space
98. !34
Scientific reasoning through dual search: theorists
outlook
Hypothesis
space
[Klahr and Dunbar 88]
predict
collect
evidence
experiment /
Information
space
99. !34
Scientific reasoning through dual search: theorists
outlook
Hypothesis
space
[Klahr and Dunbar 88]
predict
collect
evidence
experiment /
Information
space
100. !34
Scientific reasoning through dual search: theorists
outlook
Hypothesis
space
[Klahr and Dunbar 88]
predict
collect
evidence
experiment /
Information
space
101. !34
Scientific reasoning through dual search: theorists
outlook
Hypothesis
space
[Klahr and Dunbar 88]
predict
collect
evidence
experiment /
Information
space
102. !34
Scientific reasoning through dual search: theorists
outlook
Hypothesis
space
[Klahr and Dunbar 88]
predict
collect
evidence
experiment /
Information
space
103. evidence
!34
Scientific reasoning through dual search: theorists
outlook
Hypothesis
space
[Klahr and Dunbar 88]
predict
collect
evidence
experiment /
Information
space
104. evidence
!34
Scientific reasoning through dual search: theorists
outlook
Hypothesis
space
[Klahr and Dunbar 88]
predict
collect
evidence
evaluate
evidence
experiment /
Information
space
105. evidence
!34
Scientific reasoning through dual search: theorists
outlook
Hypothesis
space
[Klahr and Dunbar 88]
predict
collect
evidence
evaluate
evidence
re-frame
experiment /
Information
space
117. !36
future work
• New design patterns?
• Desktop has “overview, zoom, detail-on-demand” [Shneiderman, 96]
• “Spread, eye-ball, coalesce”?
• Design space for multi-view-based visualizations on large displays
• Collaborative visual analytics
• This is where the cost investment makes more sense.
• Applications and real-world case studies
122. background
Why visualize data?
!40
visualization statistics
I II III IV
x mean 9 9 9 9
y mean 7.50 7.50 7.50 7.50
x variance 10 10 10 10
y variance 3.75 3.75 3.75 3.75
x/y correlation 0.81 0.81 0.81 0.81
[Anscombe’s quartet]
123. background
Why visualize data?
!40
visualization statistics
I II III IV
x mean 9 9 9 9
y mean 7.50 7.50 7.50 7.50
x variance 10 10 10 10
y variance 3.75 3.75 3.75 3.75
x/y correlation 0.81 0.81 0.81 0.81
[Anscombe’s quartet]
Contained within the data of any investigation is information that can yield conclusions to
questions not even originally asked. That is, there can be surprises in the data… To regularly
miss surprises by failing to probe thoroughly with visualization tools is terribly inefficient
because the cost of intensive data analysis is typically very small compared with the cost of
data collection.
-William Cleveland, the Elements of Graphing Data
125. !42
Alternate projections
of the same information
Multiple visualization
states (i.e., exploratory
threads)
Different subsets of
information
(aka, small-multiples)
visual exploration
http://flowingdata.com/tag/small-multiples/
[Wang Baldonado et al. 2000]
Vis!
workspace 1
Vis!
workspace 2
Vis!
workspace 3
126. !43
Visualization
• User performance kept pace with increasing data and screen size [Yost
et al. 07]
• Increased physical navigation over virtual navigation [Ball and North 05,
Ball et al. 07]
• Mixed results in map-related tasks; some interfaces are poorly-suited to
large displays (e.g. context + focus lenses) [Jakobsen and Hornbæk 11]
Intelligence
analysis
(text)
• Memory externalization, semantic spatial arrangement (users attach
meaning to space), Schematization [Andrews et al. 10]
• Automatic inferring of semantics from interactions [Endert et al. 12b]
Office
environment
• Improved spatial cognition + embodied memory [Tan 04]
• Reduced gender performance disparities in virtual navigation [Tan 03b]
• Reduced window switching, and reduced user frustration [Ball and
North 05]
• Increased productivity in cognitively demanding office work [Czerwinski
et al. 03, Bi et al. 09]
related work
127. !44
High-res / reduced FOV
[Ball and North 08]related work
Low-res / wide FOV
• High-resolution + physical navigation is more important to
improving user performance
128. !44
High-res / reduced FOV
[Ball and North 08]related work
Low-res / wide FOV
• High-resolution + physical navigation is more important to
improving user performance
context + focus displays
[Baudisch et al. 02]
134. !48
theory
• Top-down costs
• Discourage the formation of new exploratory goals
• `Tunnel vision’ phenomenon where exploration is focused on isolated subsets
in the information space
!
• Bottom-up cost
• Overuse of visual working memory to retain visual patterns of interest
• Reduction in the probability of making spontaneous inferences relating to
patterns deposited in temporally-separated views
Cost (temporal-separation): summary
135. !48
theory
• Top-down costs
• Discourage the formation of new exploratory goals
• `Tunnel vision’ phenomenon where exploration is focused on isolated subsets
in the information space
!
• Bottom-up cost
• Overuse of visual working memory to retain visual patterns of interest
• Reduction in the probability of making spontaneous inferences relating to
patterns deposited in temporally-separated views
Cost (temporal-separation): summary
?
147. !55
experiment
Results: quality of insights
minutes into activity
levelofinsight
Do higher-level insights occur at a later time in the activity?
148. !56
Form goal!
Visualization tool
Form system operators!
Form physical sequence!
Execute physical sequence Perceive state
Evaluate interpretation
Interpret perception
149. !56
Form goal!
Visualization tool
Form system operators!
Form physical sequence!
Execute physical sequence Perceive state
Evaluate interpretation
Interpret perception
150. !56
Form goal!
Visualization tool
Form system operators!
Form physical sequence!
Execute physical sequence Perceive state
Evaluate interpretation
Interpret perception
151. !56
Form goal!
Visualization tool
Form system operators!
Form physical sequence!
Execute physical sequence Perceive state
Evaluate interpretation
Interpret perception
152. !57contributions
1. Theory of how interaction costs affect user behavior in exploratory visual analysis
• `Tunnel vision’ phenomenon in situations involving large amounts of data
• Costs are elevated due to excessive temporal-separation of views
2. Effects of increasing the size and resolution of the visualization interface on user
behavior and insights acquisition
• Increased user investment / exploration
• More observations
• Formation of higher-level, more integrative insights
3. Design patterns for large high-resolution displays
• Query-by-example brush for the analysis of ensemble data
• “Seed and grow” pattern: loose view coordination model with lens metaphor
