exploratory visual analysis in large high-resolution displays
DESCRIPTION
TRANSCRIPT
Exploratory Visual Analysis in Large High-Resolution Display Environments
Khairi Reda
• 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
!2background
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
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
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
• 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
biologybackground[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??
!5
information space
visual exploration
!5
information space
visual exploration
!5
information space
visual exploration
!5
information space
+ + =?
visual exploration
!6visual exploration
temporal separation
!6visual exploration
temporal separation spatial separation (i.e., juxtaposition)
!6visual exploration
temporal separation spatial separation (i.e., juxtaposition)
?Aha!
!6visual 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!
related work
!8
Low-level visualization tasks (e.g., find the state with the highest population growth between 1960 and 1990)
Task completion
time
Data sizeStr
ictly
linea
r sca
ling
User performance on a
big display
[Yost and North 06, Yost et al. 07, Ball et al. 07]related work
!9related work [Andrews et al. 10, Andrews and North 13]
!9related work [Andrews et al. 10, Andrews and North 13]
theory !10
theory !10
View1
display
View2
View3
View4
theory !10
View1
display
View2
View3
View4time
sequentialaccess to
information
theory !10
View1
display
View2
View3
View4
View1 View2
View3 View4
display
time
sequentialaccess to
information
theory !10
View1
display
View2
View3
View4
View1 View2
View3 View4
display
time
sequentialaccess to
information
embodied, non-sequential
access to information
theory !10
View1
display
View2
View3
View4
View1 View2
View3 View4
display
time
sequentialaccess to
information
embodied, non-sequential
access to information
Cost (Temporal-separation) Cost (Spatial-separation)
theory !10
View1
display
View2
View3
View4
View1 View2
View3 View4
display
time
sequentialaccess to
information
embodied, non-sequential
access to information
Cost (Temporal-separation) Cost (Spatial-separation)=>
<
!11[Norman 02]
seven stages of
action
Goal
System
Intention
Action
Execution Perception
Interpretation
Evaluation
Cost ~ Usability: the ease and learnability of a human-made object
theory
!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
!12[Lam 08]
Cost (temporal-separation)
theory
framework of interaction
costs in visualizations
Evaluate interpretation
Interpret perception
!12[Lam 08]
Cost (temporal-separation)
theory
framework of interaction
costs in visualizations
Evaluate interpretation
Interpret perceptionTemporal-view association
!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 ]
}
!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
!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
!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
!13[Lam 08]
Perceive stateExecute physical sequence
Visualization tool
Cost (spatial-separation)
theory
!13[Lam 08]
Perceive stateExecute physical sequence
Visualization tool
Cost (spatial-separation)
theory
Potentially strenuous physical navigation
(head turns, walking, leaning)
!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)
!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
!15
to nestto food
off-trail navigation?
Understanding the navigational strategies of Seed harvester ants
case study [Offord et al. 13]
!15
to nestto food
off-trail navigation?
Understanding the navigational strategies of Seed harvester ants
case study [Offord et al. 13]
!16case study
!16case study
!16case study
On-trail ants West side ants East North south
!16case study
On-trail ants West side ants East North south
design pattern: query-by-example brush
!17
Findings: user strategy
case study
• Think-aloud protocol
• Verbal protocol analysis
• Coding scheme: Observations, Hypotheses, and Decisions
• Interactions: Query by example, Workspace management
Workspacemanagement
Query by example
Hypothesis formulation
Decision making
Observing outliers
layout-preserving interactions
temporal separation of views
!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
experiment !19
Visualization interface
[Cockburn et al. 08]
201220092006
TheftBurglaryNarcotics
overview map
experiment !19
Visualization interface
[Cockburn et al. 08]
design pattern: “seed and grow”
201220092006
TheftBurglaryNarcotics
overview map
experiment !19
Visualization interface
[Cockburn et al. 08]
design pattern: “seed and grow”
201220092006
TheftBurglaryNarcotics
overview map
!20
participantexperimenter
4 m
eter
s
wide-angle video camera
keyboard & mouse
papernotepad
CAVE2
experiment [Reda et al. 13b]
!21
Experimental conditions
small !
3 x 4 panels 12 Megapixels 40 degree FOV
large !
13 x 4 panels 54 Megapixels
190 degree FOV
experiment
!21
Experimental conditions
small !
3 x 4 panels 12 Megapixels 40 degree FOV
large !
13 x 4 panels 54 Megapixels
190 degree FOV
experiment
!22experiment
• Participants
• 10 unpaid volunteers (4 female) recruited from EVL
• Distributed evenly under the two experimental conditions
• Procedure
• 15-minutes training
• 2.5 hours of open-ended exploration
• Think-aloud protocol
• Semi-structured debriefing interview
• Verbal protocol analysis
• Coding scheme: Observation, hypothesis, question, goal, comment.
• 5-points quality score: lower-score refer to isolated insights. Higher scores imply broader, more integrative insights
!23
Results: exploration time
experiment
!23
Results: exploration time
experiment
0!20!40!60!80!
100!120!140!
large! small!
min
utes!
Average length of exploratory activity!
!23
Results: exploration time
experiment
0!20!40!60!80!
100!120!140!
large! small!
min
utes!
Average length of exploratory activity!
t(8) = 33.5, p < .01
!24
Results: observations
experiment
!24
Results: observations
experiment
0!
40!
80!
120!
160!
200!
O1! O2! O3! O4! O5! All!
Average number of observations!
large!small!
* * * *
!24
Results: observations
experiment
0!
40!
80!
120!
160!
200!
O1! O2! O3! O4! O5! All!
Average number of observations!
large!small!
* * * *
𝝌2(4, N=1327) = 263.3, p < .001
!24
Results: observations
experiment
0!
40!
80!
120!
160!
200!
O1! O2! O3! O4! O5! All!
Average number of observations!
large!small!
* * * *0!
0.2!
0.4!
0.6!
0.8!
1!
1.2!
1.4!
1.6!
O1! O2! O3! O4! O5! All!
obse
rvat
ion
/ min
ute
of a
nalys
is!
Observation rate!
large!small!
* * *
𝝌2(4, N=1327) = 263.3, p < .001
!25
Results: hypotheses
experiment
!25
Results: hypotheses
experiment
0!
5!
10!
15!
20!
25!
H1! H2! H3! H4! H5! All!
Average number of hypotheses!
large!small!
!25
Results: hypotheses
experiment
0!
5!
10!
15!
20!
25!
H1! H2! H3! H4! H5! All!
Average number of hypotheses!
large!small!
𝝌2(4, N=145) = 67.3, p < .001
!25
Results: hypotheses
experiment
0!
5!
10!
15!
20!
25!
H1! H2! H3! H4! H5! All!
Average number of hypotheses!
large!small!
0!
0.05!
0.1!
0.15!
0.2!
0.25!
H1! H2! H3! H4! H5! All!
hypo
thes
is /
min
ute
of a
naly
sis!
Hypothesis formulation rate!
large!small!
𝝌2(4, N=145) = 67.3, p < .001
!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
!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
com
mut
ativ
e in
sigh
ts
!27experiment
Results: quality of insights
!27experiment
Results: quality of insights
!27experiment
insight “breadth”
prob
abilit
y
• The large display is more likely to elicit broader, more integrative insights.
Results: quality of insights
!28
Results: user behavior
experiment
!28
Results: user behavior
experiment
participant S1 (small)
!28
Results: user behavior
experiment
participant S1 (small)
participant L5 (large)
!29experiment
p(large) - p(small) Results: user behavior
!29experiment
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
!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)
!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)
!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
!32
Scientific reasoning through dual search
outlook [Klahr and Dunbar 88]
!33
Scientific reasoning through dual search: experimenters
outlook
Hypothesis space
experiment / Information
space
[Klahr and Dunbar 88]
!33
Scientific reasoning through dual search: experimenters
outlook
Hypothesis space
experiment / Information
space
[Klahr and Dunbar 88]
!33
Scientific reasoning through dual search: experimenters
outlook
Hypothesis space
experiment / Information
space
[Klahr and Dunbar 88]
!33
Scientific reasoning through dual search: experimenters
outlook
Hypothesis space
experiment / Information
space
[Klahr and Dunbar 88]
!33
Scientific reasoning through dual search: experimenters
outlook
Hypothesis space
experiment / Information
space
[Klahr and Dunbar 88]
!33
Scientific reasoning through dual search: experimenters
outlook
Hypothesis space
experiment / Information
space
[Klahr and Dunbar 88]
!33
Scientific reasoning through dual search: experimenters
outlook
Hypothesis space
experiment / Information
space
[Klahr and Dunbar 88]
!33
Scientific reasoning through dual search: experimenters
outlook
Hypothesis space
experiment / Information
space
[Klahr and Dunbar 88]
!33
Scientific reasoning through dual search: experimenters
outlook
Hypothesis space
experiment / Information
space
[Klahr and Dunbar 88]
!33
Scientific reasoning through dual search: experimenters
outlook
Hypothesis space
experiment / Information
space
[Klahr and Dunbar 88]
observations
!33
Scientific reasoning through dual search: experimenters
outlook
Hypothesis space
experiment / Information
space
[Klahr and Dunbar 88]
observations
!33
Scientific reasoning through dual search: experimenters
outlook
Hypothesis space
experiment / Information
space
[Klahr and Dunbar 88]
generalize
!34
Scientific reasoning through dual search: theorists
outlook
Hypothesis space
[Klahr and Dunbar 88]
experiment / Information
space
!34
Scientific reasoning through dual search: theorists
outlook
Hypothesis space
[Klahr and Dunbar 88]
experiment / Information
space
!34
Scientific reasoning through dual search: theorists
outlook
Hypothesis space
[Klahr and Dunbar 88]
predict
experiment / Information
space
!34
Scientific reasoning through dual search: theorists
outlook
Hypothesis space
[Klahr and Dunbar 88]
predict
collect evidence
experiment / Information
space
!34
Scientific reasoning through dual search: theorists
outlook
Hypothesis space
[Klahr and Dunbar 88]
predict
collect evidence
experiment / Information
space
!34
Scientific reasoning through dual search: theorists
outlook
Hypothesis space
[Klahr and Dunbar 88]
predict
collect evidence
experiment / Information
space
!34
Scientific reasoning through dual search: theorists
outlook
Hypothesis space
[Klahr and Dunbar 88]
predict
collect evidence
experiment / Information
space
!34
Scientific reasoning through dual search: theorists
outlook
Hypothesis space
[Klahr and Dunbar 88]
predict
collect evidence
experiment / Information
space
!34
Scientific reasoning through dual search: theorists
outlook
Hypothesis space
[Klahr and Dunbar 88]
predict
collect evidence
experiment / Information
space
!34
Scientific reasoning through dual search: theorists
outlook
Hypothesis space
[Klahr and Dunbar 88]
predict
collect evidence
experiment / Information
space
!34
Scientific reasoning through dual search: theorists
outlook
Hypothesis space
[Klahr and Dunbar 88]
predict
collect evidence
experiment / Information
space
!34
Scientific reasoning through dual search: theorists
outlook
Hypothesis space
[Klahr and Dunbar 88]
predict
collect evidence
experiment / Information
space
!34
Scientific reasoning through dual search: theorists
outlook
Hypothesis space
[Klahr and Dunbar 88]
predict
collect evidence
experiment / Information
space
evidence
!34
Scientific reasoning through dual search: theorists
outlook
Hypothesis space
[Klahr and Dunbar 88]
predict
collect evidence
experiment / Information
space
evidence
!34
Scientific reasoning through dual search: theorists
outlook
Hypothesis space
[Klahr and Dunbar 88]
predict
collect evidence
evaluate evidence
experiment / Information
space
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
!35contributions
!35contributions
theory[c2]
!35contributions
theory[c2]
!35contributions
theory[c2]
design patterns
[c3]
!35contributions
apply
theory[c2]
design patterns
[c3]
!35contributions
apply
theory[c2]
design patterns
[c3]
case study + experiment
[c4-5]
!35contributions
apply instantiate
theory[c2]
design patterns
[c3]
case study + experiment
[c4-5]
!35contributions
apply instantiate
validate
theory[c2]
design patterns
[c3]
case study + experiment
[c4-5]
!35contributions
apply instantiate
validate
theory[c2]
design patterns
[c3]
case study + experiment
[c4-5]
!35contributions
apply instantiate
validate
theory[c2]
design patterns
[c3]
case study + experiment
[c4-5]
scientific reasoning
hypothesis
observations
[c6]
!35contributions
apply instantiate
validate reflect
theory[c2]
design patterns
[c3]
case study + experiment
[c4-5]
scientific reasoning
hypothesis
observations
[c6]
!36future 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
thank you!
Extras
!39
background
Why visualize data?
!40
visualization statistics
I II III IVx mean 9 9 9 9y 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]
background
Why visualize data?
!40
visualization statistics
I II III IVx mean 9 9 9 9y 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
!41
intuition, prior hypotheses,
experience
!!
Visualization
spontaneous observations,
new hypotheses
observations: unit of knowledge acquired from the visualization !hypothesis: conjecture that cannot be directly inferred from the visualization
visual exploration
{insight
form exploratory goals
interpret visual patterns
[Liu and Stasko 10, Treisman 86]
!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
!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
!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
!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]
!45
action
perception
visual exploration
!46
action
perception
data mining, filtering,
transformation, re-projection
induction, deduction, analogical thinking
T1
T2
information space insights
visual exploration [Sedig et al. 12]
!46
action
perception
data mining, filtering,
transformation, re-projection
induction, deduction, analogical thinking
T1
T2
information space insights
Temporal separation
visual exploration [Sedig et al. 12]
!47theory
!!
!!
!!
!!
!!
View1 V2 V3 V5V4
[Maxcey-Richard and Hollingworth 13]
unexpected discoveries
Cost (temporal-separation): bottom-up costs
time
!47theory
!!
!!
!!
!!
!!
View1 V2 V3 V5V4
[Maxcey-Richard and Hollingworth 13]
unexpected discoveries
Cost (temporal-separation): bottom-up costs
timeP(View1)
!48theory
• 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
!48theory
• 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
?
!49case study
!49
Screen surface
Time
2D movement
Trajec
tory
case study
experiment !50
Visualization interface
[Cockburn et al. 08]
201220092006
TheftBurglaryNarcotics
overview map
experiment !50
Visualization interface
[Cockburn et al. 08]
design pattern: “seed and grow”
201220092006
TheftBurglaryNarcotics
overview map
experiment !50
Visualization interface
[Cockburn et al. 08]
Density of crime incidence
Yearly, weekly, daily crime trends
design pattern: “seed and grow”
201220092006
TheftBurglaryNarcotics
overview map
experiment !50
Visualization interface
[Cockburn et al. 08]
Density of crime incidence
Yearly, weekly, daily crime trends
design pattern: “seed and grow”
201220092006
TheftBurglaryNarcotics
overview map
!51experiment
structure layoutfreeform layout
large
small
!52experiment
• 3 “mental states”
• Make observation
• Form goal
• Formulate hypothesis
• 2 types of interaction
• Layout-preserving (brush-and-link / pan map)
• Layout-changing (create new view, changing view contents)
Results: user behavior
!53
Results: user behavior / strategy
experiment
average (small)
average (large)
!54
0!
1!
2!
3!
4!
5!
large!
small!
usability utility
usability / utility
experiment
!55experiment
Results: quality of insights
Do higher-level insights occur at a later time in the activity?
!55experiment
Results: quality of insights
minutes into activity
leve
l of i
nsig
ht
Do higher-level insights occur at a later time in the activity?
!56
Form goal!
Visualization tool
Form system operators!
Form physical sequence!
Execute physical sequence Perceive state
Evaluate interpretation
Interpret perception
!56
Form goal!
Visualization tool
Form system operators!
Form physical sequence!
Execute physical sequence Perceive state
Evaluate interpretation
Interpret perception
!56
Form goal!
Visualization tool
Form system operators!
Form physical sequence!
Execute physical sequence Perceive state
Evaluate interpretation
Interpret perception
!56
Form goal!
Visualization tool
Form system operators!
Form physical sequence!
Execute physical sequence Perceive state
Evaluate interpretation
Interpret perception
!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