Transcript Thinking Interactively with Visualizations

Intro
Urban Vis
Analytics
Provenance
Wrap-up
Thinking Interactively with Visualizations
Remco Chang
UNC Charlotte
Charlotte Visualization Center
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Role of Interaction
• Most people in the visualization community
believe that interactivity is essential for
visualization and visual analytics:
– “A [visual] analysis session is more of a dialog
between the analyst and the data… the manifestation
of this dialog is the analyst’s interactions with the
data representation” [Thomas & Cook 2005]
– “Without interaction, [a visualization] technique or
system becomes a static image or autonomously
animated images” [Yi et al. 2007]
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Role of Interaction
• However, there has been limited research in
visualization specific to interaction and techniques [Yi
et al. 2007]
– “Interaction is often relegated to a secondary role in these
articles. Interaction rarely is the main focus of research
efforts in the field, essentially making it the “little brother”
of Infovis”
• The goal of this talk is to consider the role of
interaction in computer graphics, information
visualization, and visual analytics.
• First, we think about what interactivity is and how to
make a visualization interactive.
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Offline Rendering
Provenance
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Offline Rendering
• Master’s Thesis -– Modeling the dynamic motion based on kinematic motion
• Jiggling of muscles
– “Skinnable Mesh”
• Volumetric deformation
– Mass-spring models
• 2nd order constraint
• Approximate finite element method (FEM) with implicit integration
• Took all night to render a 500-frame (10 second) sequence
• NOT at all interactive…
– Key differences between each run had to be remembered
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Thinking about Interactivity in Graphics
• Interactivity =
– 12 frames per second appears “smooth” to most
people
– Or, render a frame under 0.08 second
• For complex scenes with lots of polygons
(information)
– Simplify the scene
– Levels of Detail (LOD)
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Urban Simplification
• (left) Original model, 285k polygons
• (center) e=100, 129k polygons (45% of original)
• (right) e=1000, 53k polygons (18% of original)
R. Chang et al., Legible simplification of textured urban models. IEEE Computer Graphics and Applications, 28(3):27–36, 2008.
R. Chang et al., Hierarchical simplification of city models to maintain urban legibility. ACM SIGGRAPH 2006 Sketches, page 130 , 2006.
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Urban Simplification
• Which polygons to remove?
Original Model
Our Textured Model
Simplified Model
using QSlim
Our Model
Visually different, but quantitatively similar!
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Urban Simplification
• The goal is to retain the “Image of the City”
• Based on Kevin Lynch’s concept of “Urban
Legibility” [1960]
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Paths: highways, railroads
Edges: shorelines, boundaries
Districts: industrial, historic
Nodes: Time Square in NYC
Landmarks: Empire State building
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Urban Visualization with Semantics
• How do people think about a city?
– Describe New York…
• Response 1: “New York is large, compact, and crowded.”
• Response 2: “The area where I live there has a strong mix of
ethnicities.”
Geometric, Information, View Dependent (Cognitive)
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Urban Visualization
• Geometric
– Create a hierarchy of shapes based on the rules of legibility
• Information
– Matrix view and Parallel Coordinates show relationships between clusters and
dimensions
• View Dependence (Cognitive)
– Uses interaction to alter the position of focus
R. Chang et al., Legible cities: Focus-dependent multi-resolution visualization of urban relationships. IEEE Transactions on Visualization
and Graphics , 13(6):1169–1175, 2007
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Urban Visualization
• Scenario 1: Comparing cities…
• Charlotte
• Davidson
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Urban Visualization
• Scenario 2:
– Looking for high Hispanic
populations around downtown
Charlotte.
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The Role of Interaction in Visualization
• We can use interactions to… [Yi et al. 2007]
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Select: mark something as interesting
Explore: show me something else
Reconfigure: show me a different arrangement
Encode: show me a different representation
Abstract/Elaborate: show me more or less detail
Filter: show me something conditionally
Connect: show me related items
• In other words, we can use interactions to think.
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(1) WireVis: Financial Fraud Analysis
• In collaboration with Bank of America
– Looks for suspicious wire transactions
– Currently beta-deployed at WireWatch
– Visualizes 15 million transactions over 1 year
• Uses interaction to coordinate four perspectives:
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Keywords to Accounts
Keywords to Keywords
Keywords/Accounts over Time
Account similarities (search by example)
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(1) WireVis: Financial Fraud Analysis
Heatmap View
(Accounts to Keywords
Relationship)
Search by Example
(Find Similar
Accounts)
Keyword Network
(Keyword
Relationships)
Strings and Beads
(Relationships over Time)
R. Chang et al., Scalable and interactive visual analysis of financial wire transactions for fraud detection. Information Visualization,2008.
R. Chang et al., Wirevis: Visualization of categorical, time-varying data from financial transactions. IEEE VAST, 2007.
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(2) Investigative GTD
• Collaboration with U. Maryland’s DHS Center of
Excellence START (Study of Terrorism And Response to
Terrorism)
– Global Terrorism Database (GTD)
– International terrorism activities from 1970-1997
– 60,000 incidents recorded over 120 dimensions
• Visualization is designed to be “investigative” in that it
is modeled after the 5 W’s:
– Who, what, where, when, and [why]
– Interaction allows the user to adjust one or more of the
W’s and see how that affects the other W’s
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(2) Investigative GTD
Who
Where
What
Evidence
Box
Original
Data
R. Chang et al., Investigative Visual Analysis of Global Terrorism, Journal of Computer Graphics Forum (Eurovis), 2008.
When
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(2) Investigative GTD:
Revealing Global Strategy
This group’s attacks
are not bounded by
geo-locations but
instead, religious
beliefs.
Its attack patterns
changed with its
developments.
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(2) Investigative GTD:
Discovering Unexpected Temporal Pattern
A geographicallybounded entity in the
Philippines.
The ThemeRiver shows
its rise and fall as an
entity and its modus
operandi.
Domestic Group
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(3) Analysis of Biomechanical Motion
• Biomechanical motion
sequences (animation) are
difficult to analyze.
• Watching the movie repeatedly
does not easily lead to insight.
• Collaboration with Brown University and Univ. of Minnesota
to examine the mechanics of a pig chewing different types
and amounts of food (nuts, pig chow, etc.)
• The data is typically organized by the rigid bodies in the
model, where each rigid body contains 6 variables per frame
-- 3 for translation, and 3 for rotation.
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(3) Analysis of Biomechanical Motion
R. Chang et al., Interactive Coordinated Multiple-View Visualization of Biomechanical Motion Data, IEEE Vis (TVCG) 2009. To Appear.
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(3) Analysis of Biomechanical Motion
• Our emphasis is on “interactive comparison.”
Following the work by Robertson [InfoVis
2008], comparisons can be performed using:
– Small Multiples
– Side by side comparison
– Overlap
• Between two datasets
• Different cycles in the same data
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(4) iPCA: Interactive PCA
• Quick Refresher of PCA
– Find most dominant eigenvectors as principle components
– Data points are re-projected into the new coordinate system
• For reducing dimensionality
• For finding clusters
• For many (especially novices), PCA is easy to understand
mathematically, but difficult to understand “semantically”.
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(4) iPCA: Interactive PCA
R. Chang et al., iPCA: An Interactive System for PCA-based Visual Analytics. Computer Graphics Forum (Eurovis), 2009.
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(4) Evaluation – iPCA vs. SAS/INSIGHT
• Results
– A bit more accurate
– People don’t “give up”
– Not faster
• Overall preference
– Using letter grades (A
through F) with “A”
representing excellent
and F a failing grade.
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If (Interactions == Thinking)…
• What is in a user’s interactions?
• If (interactions == thinking), what can we learn
from the user’s interactions?
• Is it possible to extract “thinking” from
“interactions”?
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What is in a User’s Interactions?
Keyboard, Mouse, etc
Input
Visualization
Human
Output
Images (monitor)
• Types of Human-Visualization Interactions
– Text editing (input heavy, little output)
– Browsing, watching a movie (output heavy, little input)
– Visual Analysis (closer to 50-50)
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What is in a User’s Interactions?
• Goal: determine if there really is “thinking” in a
user’s interactions.
Grad
Students
(Coders)
Compare!
(manually)
Analysts
Strategies
Methods
Findings
Guesses of
Analysts’
thinking
Logged
(semantic)
Interactions
WireVis
Interaction-Log Vis
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What’s in a User’s Interactions
• From this experiment, we find that interactions contains at least:
– 60% of the (high level) strategies
– 60% of the (mid level) methods
– 79% of the (low level) findings
R. Chang et al., Recovering Reasoning Process From User Interactions. IEEE Computer Graphics and Applications, 2009.
R. Chang et al., Evaluating the Relationship Between User Interaction and Financial Visual Analysis. IEEE Symposium on VAST, 2009.
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What’s in a User’s Interactions
• Why are these so much
lower than others?
– (recovering “methods” at
about 15%)
• Only capturing a user’s
interaction in this case is
insufficient.
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Lessons Learned
• We have proven that a great deal of an analyst’s “thinking” in using
a visualization is capturable and extractable.
• Using semantic interaction capturing, we might be able to collect all
the thinking of expert analysts and create a knowledge database
that is useful for
– Training: many domain specific analytics tasks are difficult to teach
– Guidance: use existing knowledge to guide future analyses
– Verification, and validation: go back and check to see if everything was
done right.
• But not all visualizations are interactive, and not all thinking is
reflected in the interactions.
– A model of how and what to capture in a visualization for extracting an
analyst’s thinking process is necessary.
– Work is currently in preparation for publication.
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Conclusion
• Interactions are important for visualization and visual
analysis
– In considering interactions, one must be aware of the
necessary speed and frame rate of the displays.
• Simplification, LOD, and approximation methods can be used, but
they must retain the salient features in the original data.
– Interactions have been proven to help the understanding
of complex problems.
• Relevant interactions have been integrated in multiple
visualizations for different domains and demonstrated significant
impact.
– Capturing and storing analysts’ interactions have great
potential
• They can be aggregated to become a “knowledge database” that
has traditionally been difficult to create manually.
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Discussion
• What interactivity is not good for:
– Presentation
– YMMV = “your mileage may vary”
• Reproducibility: Users behave differently each time.
• Evaluation is difficult due to opportunistic discoveries..
– Often sacrifices accuracy
• iPCA – SVD takes time on large datasets, use iterative
approximation algorithms such as onlineSVD.
• WireVis – Clustering of large datasets is slow. Either
pre-compute or use more trivial “binning” methods.
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Discussion
• Interestingly,
– It doesn’t save you time…
– And it doesn’t make a user more
accurate in performing a task.
• However, there are empirical
evidence that using interactivity:
– Users are more engaged (don’t
give up)
– Users prefer these systems over
static (query-based) systems
– Users have a faster learning curve
• We would like to find
measurements to determine the
“benefits of interactivity”
Wrap-up
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Future Work
• Interactive Urban Visualization
– Visualizing a Semantic City. An ideal goal would be to have a
“semantic” Google Map that shows more than street layouts, but
describes neighborhood characteristics.
– Further studies on what a “cognitive map” is and how a person gains
and maintains spatial awareness.
• Applying Interaction in visualizations
– Funded NSF proposal applies visualization to studying science policies
– Another funded NSF proposal applies visualization to discovering the
causes and effects of civil strife
– Funded DHS proposal to evaluate “the benefits of interactions”…
• Interaction Capturing (Provenance)
– Semi-automatic method for analyzing what’s in the interaction logs.
– Look to collaborate with PNNL on developing generalizable structures
for recording provenance.
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Thank you!
[email protected]
http://www.viscenter.uncc.edu/~rchang
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Backup Slides
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Journal Publications (11)
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Visualization
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Urban Visualization
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Visualization and Visual Analytics
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Dong Hyun Jeong, Caroline Ziemkiewicz, Brian Fisher, William Ribarsky, and Remco Chang. iPCA: An interactive system for PCAbased visual analytics. Computer Graphics Forum, 2009. to appear.
Remco Chang, Caroline Ziemkiewicz, Tera Marie Green, and William Ribarsky. Defining insight for visual analytics. IEEE Computer
Graphics and Applications, 29(2):14–17, 2009.
Remco Chang, Alvin Lee, Mohammad Ghoniem, Robert Kosara, William Ribarsky, Jing Yang, Evan Suma, Caroline Ziemkiewicz,
Daniel Kern, and Agus Sudjianto. Scalable and interactive visual analysis of financial wire transactions for fraud detection.
Information Visualization, 7:63–76(14), 2008.
Xiaoyu Wang, Erin Miller, Kathleen Smarick, William Ribarsky, and Remco Chang. Investigative visual analysis of global terrorism
database. Computer Graphics Forum, 27(3):919–926, 2008.
Provenance
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Remco Chang, Thomas Butkiewicz, Caroline Ziemkiewicz, Zachary Wartell, Nancy Pollard, and William Ribarsky. Legible
simplification of textured urban models. IEEE Computer Graphics and Applications, 28(3):27–36, 2008.
Thomas Butkiewicz, Remco Chang, Zachary Wartell, and William Ribarsky. Visual analysis of urban change. Computer Graaphics
Forum, 27(3):903–910, 2008.
Thomas Butkiewicz, Remco Chang, William Ribarsky, and Zachary Wartell. Understanding Dynamics of Geographic Domains,
chapter Visual Analysis of Urban Terrain Dynamics, pages 151– 169. CRC Press/Taylor and Francis, 2007.
Remco Chang, Ginette Wessel, Robert Kosara, Eric Sauda, and William Ribarsky. Legible cities: Focus-dependent multi-resolution
visualization of urban relationships. Visualization and Computer Graaphics, IEEE Transactions on, 13(6):1169–1175, Nov.-Dec.
2007.
Wenwen Dou, Dong Hyun Jeong, Felesia Stukes, William Ribarsky, Heather Richter Lipford, and Remco Chang. Recovering
reasoning process from user interactions. IEEE Computer Graphics and Applications, 2009. to appear
Graphics, Virtual Reality, and Interface Designs
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Thomas Butkiewicz, Wenwen Dou, Zachary Wartell, William Ribarsky, and Remco Chang. Multi-focused geospatial analysis using
probes. Visualization and Computer Graphics, IEEE Transactions on, 14(6):1165–1172, Nov.-Dec. 2008.
Dong Hyun Jeong, Chang Song, Remco Chang, and Larry Hodges. User experimentation: An evaluation of velocity control
techniques in immersive virtual environments. Springer-Verlag Virtual Reality, 13(1):41–50, Mar. 2009.
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Conference/Workshop (19)
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Wenwen Dou, Dong Hyun Jeong, Felesia Stukes, William Ribarsky, Heather Richter Lipford, and Remco Chang. Comparing usage patterns of domain
experts and novices in visual analytical tasks. In ACM SIGCHI Sensemaking Workshop 2009, 2009.
Xiaoyu Wang, Wenwen Dou, Rashna Vatcha, Wanqiu Liu, Shen-En Chen, Seok-Won Lee, Remco Chang, and William Ribarsky. Knowledge integrated
visual analysis of bridge safety and maintenance. In Defense, Security and Sensing 2009, 2009. to appear.
Xiaoyu Wang, Wenwen Dou, William Ribarsky, and Remco Chang. Integration of heterogeneous processes through visual analytics. In Defense,
Security and Sensing 2009, 2009. to appear.
Michael Butkiewicz, Thomas Butkiewicz, William Ribarsky, and Remco Chang. Integrating timeseries visualizations within parallel coordinates for
exploratory analysis of incident databases. In Defense, Security and Sensing 2009, 2009. to appear.
Thomas Butkiewicz, Dong Hyun Jeong, William Ribarsky, and Remco Chang. Hierarchical multitouch selection techniques for collaborative geospatial
analysis. In Defense, Security and Sensing 2009, 2009. to appear.
Dong Hyun Jeong, Remco Chang, and William Ribarsky. An alternative definition and model for knowledge visualization. In IEEE Visualization
Workshop on Knowledge Assisted Visualization, 2008.
Xiaoyu Wang, Wenwen Dou, Seok won Lee, William Ribarsky, and Remco Chang. Integrating visual analysis with ontological knowledge structure. In
IEEE Visualization Workshop on Knowledge Assisted Visualization, 2008.
Dong Hyun Jeong, Wenwen Dou, Felesia Stukes, William Ribarsky, Heather Richter Lipford, and Remco Chang. Evaluating the relationship between
user interaction and financial visual analysis. In Visual Analytics Science and Technology, 2008. VAST 2008. IEEE Symposium on, 2008.
Ginette Wessel, Remco Chang, and Eric Sauda. Towards a new (mapping of the) city: Interactive, data rich modes of urban legibility. In Association for
Computer Aided Design in Architecture, 2008.
Ginette Wessel, Remco Chang, and Eric Sauda. Visualizing gis: Urban form and data structure. In Dietmar Froehlick and Michaele Pride, editors,
Seeking the City: Visionaries on the Margins, 96th Annual Conference of Association of Collegiate Schools of Architecture (ACSA), pages 378–384.
Association of Collegiate Schools of Architecture, 2008.
Ginette Wessel, Eric Sauda, and Remco Chang. Urban visualization: Urban design and computer visualization. In CAADRIA 2008 Proceedings of the
13th International Conference on Computer Aided Architectural Design Research in Asia, pages 409–416, Chiang Mai, Thailand, April 9-12, 2008.
Thomas Butkiewicz, Remco Chang, Zachary Wartell, and William Ribarsky. Visual analysis for live lidar battlefield change detection. volume 6983,
page 69830B. SPIE, 2008.
Josh Jones, Remco Chang, Thomas Butkiewicz, and William Ribarsky. Visualizing uncertainty for geographical information in the global terrorism
database. volume 6983, page 69830E. SPIE, 2008.
Alex Godwin, Remco Chang, Robert Kosara, and William Ribarsky. Visual analysis of entity relationships in the global terrorism database. volume
6983, page 69830G. SPIE, 2008.
Thomas Butkiewicz, Remco Chang, Zachary Wartell, and William Ribarsky. Analyzing sampled terrain volumetrically with regard to error and geologic
variation. volume 6495, page 64950O. SPIE, 2007.
Remco Chang, Mohammad Ghoniem, Robert Kosara, William Ribarsky, Jing Yang, Evan Suma, Caroline Ziemkiewicz, Daniel Kern, and Agus Sudjianto.
Wirevis: Visualization of categorical, time-varying data from financial transactions. In Visual Analytics Science and Technology, 2007. VAST 2007. IEEE
Symposium on, pages 155–162, 30 2007-Nov. 1 2007.
Remco Chang, Thomas Butkiewicz, Caroline Ziemkiewicz, Zachary Wartell, Nancy Pollard, and William Ribarsky. Hierarchical simplification of city
models to maintain urban legibility. In SIGGRAPH ’06: ACM SIGGRAPH 2006 Sketches, page 130, New York, NY, USA, 2006. ACM.
Remco Chang, Robert Kosara, Alex Godwin, and William Ribarsky. Towards a role of visualization in social modeling. AAAI 2009 Spring Symposium on
Technosocial Predictive Analytics, 2009. to appear.
Ginette Wessel, Eric Sauda, and Remco Chang. Mapping understanding:Transforming topographic maps into cognitive maps. GeoVis Hamburg
Workshop, 2009.
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Grants Awarded (3)
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NSF SciSIP:
– A Visual Analytics Approach to Science and Innovation Policy.
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PI: William Ribarsky, Co-PIs: Jim Thomas, Remco Chang, Jing Yang.
$746,567. 2009-2012 (3 years).
NSF BCS:
– Collaborative Project: Terror, Conflict Processes, Organizations, & Ideologies: Completing the
Picture.
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PI: Remco Chang
$100,000. 2009-2010 (2 years).
DHS International Program:
– Deriving and Applying Cognitive Principles for Human/Computer Approaches to Complex
Analytical Problems.
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PI: William Ribarsky, Co-PIs: Brian Fisher, Remco Chang, John Dill.
$200,000. 2009-2010 (1 year).
In Submission / Preparation
– 1 other NSF proposal is pending reviews
– 1 NSF and 1 NIH proposals are currently under preparation
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Professional Activities
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Committee / Panelists
– Committee Member: AAAI Spring-09 Symposium on Technosocial Predictive Analytics
– Panelist: 3rd Annual DHS University Summit. Panel: Research to Reality
– 3rd Annual DHS University Summit. Panel: Visual Analytics and Discrete Science Integration
into the DHS Center of Excellence Program
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Invited Talks
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Dec 13, 2006 Google Inc. Simplification of Urban Models based on Urban Legibility
July 6, 2007 Naval Research Lab. Urban Visualization
Oct 4, 2007 Charlotte Viscenter. Urban Visualization
Oct 17, 2007 Charlotte Metropolitan GIS Users Group. GIS and Urban Visualization
Nov 19, 2007 START Center at University of Maryland. Integrated Visual Analysis of the Global
Terrorism Database
Nov 29, 2007 Charlotte Viscenter. Integrated Visual Analysis of the Global Terrorism Database
Jan 25, 2008 DoD/DHS Social Science Modeling and Information Visualization Symposium. Social
Science and Information Visualization on Terrorism and Multimedia
May 14, 2008 Charlotte Metropolitan GIS User Group. Multi-Focused Geospatial Analysis Using
Probes
Aug 27, 2008 DoD/DHS Symposium for Overcoming the Information Challenge in Federated
Analysis: From Concept to Practice. Roadmap of Visualization
Mar 19, 2009 DHS University Summit. Panel: Research to Reality
Mar 19, 2009 DHS University Summit. Panel: Visual Analytics and Discrete Science Integration
into the DHS Center of Excellence Program
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Introduction
• The common thread between all the journals
and conference papers, talks, grants, etc. is
the use of interactivity in graphics,
visualization, and visual analytics.
• This presentation will focus on select projects and
publications due to time constraint. For more
information, see my website:
www.viscenter.uncc.edu/~rchang
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What is in a User’s Interactions?
• Approach:
– Using WireVis, we captured 10 financial analysts’ interactions in
performing fraud detection.
• Financial analysts are from Bank of America, (previous) Wachovia, etc.
• Manufactured test dataset that has embedded known threat scenarios
• The sessions are captured using screen-capturing, video camcorder, and
semantic interaction logging.
– The sessions were then converted (manually) to a text document:
• High level: Strategies
• Mid Level: Methods of implementation
• Low Level: Findings
– We then hired 4 graduate students to “interpret” the financial
analysts’ interactions
• Using proprietary visualizations to examine the interaction logs
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In a Tribute to Randy Pausch
• Here are my “head fakes”
– Visualizations are very useful for analysis of
complex problems with large datasets.
– The Charlotte Visualization Center is a leader in
the visual analytics community.