data exploration, analysis, and representation: integration through visual analytics

ComputingIntro VA Graphics Interaction Wrap-up1/33

Data Exploration, Analysis, and Representation: Integration through Visual Analytics

Remco Chang, PhD

UNC CharlotteCharlotte Visualization Center


Problem Statement

• The growth of data is exceeding our ability to analyze them.

• The amount of digital information generated is growing exponentially…– 2002: 22 EB (exabytes, 1018)– 2006: 161 EB– 2010: 988 EB (almost 1 ZB)

1: Data courtesy of Dr. Joseph Kielman, DHS2: Image courtesy of Dr. Maria Zemankova, NSF


Problem Statement• The data is often complex,

ambiguous, noisy. Analysis of which requires human understanding.– About 2 GB of data is being produced

per person per year– 95% of the Digital Universe’s

information is unstructured

• There isn’t enough man-power to analyze all the data, and the problem is getting worse!

• Solution: help the user– Find patterns– Filter out noise– Focus on the important stuff 1: Data courtesy of Dr. Joseph Kielman, DHS

2: Image courtesy of Dr. Maria Zemankova, NSF


Example: What Does (Wire) Fraud Look Like?• Financial Institutions like Bank of America have legal responsibilities to

report all suspicious wire transaction activities (money laundering, supporting terrorist activities, etc)

• Data size: approximately 200,000 transactions per day (73 million transactions per year)

• Problems:– Automated approach can only detect known patterns– Bad guys are smart: patterns are constantly changing– Data is messy: lack of international standards resulting in ambiguous data

• Current methods:– 10 analysts monitoring and analyzing all transactions– Using SQL queries and spreadsheet-like interfaces– Limited time scale (2 weeks)


WireVis: Financial Fraud Analysis

• In collaboration with Bank of America– Develop a visual analytical tool (WireVis)– Visualizes 7 million transactions over 1 year– Currently beta-deployed at WireWatch

• Uses interaction to coordinate four perspectives:– Keywords to Accounts– Keywords to Keywords– Keywords/Accounts over Time– Account similarities (search by example)

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.


WireVis: A Visual Analytics Approach

Heatmap View(Accounts to Keywords Relationship)

Strings and Beads(Relationships over Time)

Search by Example (Find Similar Accounts)

Keyword Network(Keyword Relationships)


• Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces [Thomas & Cook 2005]

Introducing Visual Analytics

• Since 2004, the field has grown significantly. Aside from tens to hundreds of domestic and international partners, it now has a IEEE conference (IEEE VAST), an NSF program (FODAVA), and a forthcoming IEEE Transactions journal.

Graphics &Visualization

ComputingInteraction

&Reasoning


Visual Analytics, A Graphics Perspective


Visual Analytics, A Graphics Perspective

• Master’s Thesis -- – Simulating dynamic motion

based on kinematic motion• Jiggling of muscles

– Skinnable Mesh • Volumetric deformation

– Compared 3 types of mass-spring systems• Regular (unconstrained) mass-

spring• Reduced degree of freedom• Approximate finite element

method with implicit integration

• Is this applicable beyond graphics and simulation?

R. Chang, Simulation Techniques for Deformable Animated Characters. Master’s Thesis, Brown University, 2000.


From Graphics to Visual Analytics:An Example in 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.


Urban Simplification

• Which polygons to remove?

Original Model Simplified Model using QSlim

Our Textured Model Our Model

Visually different, but quantitatively similar!


Urban Simplification

• The goal is to retain the “Image of the City”

• Based on Kevin Lynch’s concept of “Urban Legibility” [1960]

– Paths: highways, railroads– Edges: shorelines, boundaries– Districts: industrial, historic– Nodes: Time Square in NYC– Landmarks: Empire State building


Algorithm for Preserving Legibility• Paths & Edges

– Hierarchical (single-link) clustering

• Nodes– Merging clusters– Polyline simplification

using convex hulls

• Landmarks– Pixel-based skyline

preservation

• That’s pretty good, right?


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 has a strong mix of

ethnicities.”

Geometric, Information, View Dependent (Cognitive)


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


Urban VisualizationGraphics + Visual Analytics

• Applying graphics approaches– Data transformation (clustering,

LOD, simplification)– Screen-based metrics– Hardware acceleration

• Applying visual analytics principles– Multi-dimensional data

representation– Interactive exploration– Broader applicability


Extending Visual Analytics Principles

• Global Terrorism Database– With University of

Maryland– Application of the

investigative 5 W’s

• Bridge Maintenance – With US DOT– Exploring subjective

inspection reports

• Biomechanical Motion– With U. Minnesota and

Brown– Interactive motion

comparison methods

Where

When

Who

What

Original Data

EvidenceBox

R. Chang et al., Investigative Visual Analysis of Global Terrorism, Journal of Computer Graphics Forum, 2008.



R. Chang et al., An Interactive Visual Analytics System for Bridge Management, Journal of Computer Graphics Forum, 2010. To Appear.





inspection reports



comparison methods



R. Chang et al., Interactive Coordinated Multiple-View Visualization of Biomechanical Motion Data , IEEE Vis (TVCG) 2009.





inspection reports



comparison methods


Human + ComputerA Mixed-Initiative Perspective• Our approach is great and successful! But it’s mostly user-driven…

• Human vs. Artificial IntelligenceGarry Kasparov vs. Deep Blue (1997)– Computer takes a “brute force” approach without analysis– “As for how many moves ahead a grandmaster sees,” Kasparov concludes: “Just one,

the best one”

• Artificial Intelligence vs. Augmented IntelligenceHydra vs. Cyborgs (2005)– Grandmaster + 1 chess program > Hydra (equiv. of Deep Blue)– Amateur + 3 chess programs > Grandmaster + 1 chess program1

• How to systematically repeat the success? – Unsupervised machine learning + User– User’s interactions with the computer

1. http://www.collisiondetection.net/mt/archives/2010/02/why_cyborgs_are.php

Computer Process(Translate) Human


Human + Computer:Dimension Reduction – Lost in Translation• Dimension reduction using principle component analysis (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”.

age

heig

ht

GPA 0.5*GPA + 0.2*age + 0.3*height = ?


Human + Computer:Exploring Dimension Reduction: iPCA

R. Chang et al., iPCA: An Interactive System for PCA-based Visual Analytics. Computer Graphics Forum (Eurovis), 2009.


Human + Computer: Comparing iPCA to SAS/INSIGHT

• Results– Users seem to understand the

intuition behind PCA better– A bit more accurate– Not faster– People don’t “give up”

• Overall preference– Using letter grades (A through

F) with “A” representing excellent and F a failing grade.

• Problem is worse with non-linear dimension reduction• A lot more work needs to be

done…


Human + Computer:User Interactions

• Capture a user’s interactions in a visual analytics system

• Translate the interactions into something that would affect the computation in a meaningful way


• Challenge: • Can we capture and extract a user’s

reasoning and intent through capturing a user’s interactions?


What is in a User’s Interactions?

• Goal: determine if a user’s reasoning and intent are reflected in a user’s interactions.

Analysts

GradStudents(Coders)

Logged(semantic) Interactions

Compare!(manually)

StrategiesMethodsFindings

Guesses ofAnalysts’ thinking

WireVis Interaction-Log Vis


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.


User Interactions, A Computational Approach• Now that we’ve shown that (interaction ~= reasoning )

– Can we automate the process?

• Consider each of a user’s interactions as a fixed-length vector (Design Galleries [Marks et al. Siggraph 97]).

• User interaction in the left application can be represented as a single dimensional vector <P>• User interaction in the right application can be represented as a two dimensional vector <P,

S>



Conclusion• Visual Analytics is a growing new

area that is looking to address some pressing needs– Too much (messy) data, too little time

• By integrating interaction, graphics, and data computation, we have demonstrated that– There are some great benefits– But there are also some difficult

challenges

• With great challenges come great opportunities…– Government agencies– Industrial partners



&Reasoning


Summary of Contributions• Contributions

– Graphics/Visualization• Urban modeling and visualization

– Visualization + Interaction• Role of interactivity in visual thinking• Applying principles to real-world problems

such as financial analytics, terrorism studies, bridge management, biomechanical motion analysis, etc.

– Interaction + Computing• Exploring principle component analysis• Study of user interactions in visual analytics

systems

• In particular, foundations in computer graphics help the development of a human + visual computing research agenda


Future Work (Funded Projects)• NSF SciSIP:

– Title: A Visual Analytics Approach to Science and Innovation Policy. • PI: William Ribarsky, Co-PIs: Jim Thomas, Remco Chang, Jing Yang. • $746,567. 2009-2012 (3 years).

– Abstract: developing metrics and visual tools for identifying patterns in science policies.

• NSF/DOD (Minerva Initiative): – Title: Collaborative Project: Terror, Conflict Processes, Organizations, & Ideologies: Completing the

Picture. • PI: Remco Chang• $100,000. 2009-2010 (2 years).

– Abstract: design and develop visual analytical tools to identifying the causal relationships in government policies and domestic conflicts.

• DHS International Program: – Title: Deriving and Applying Cognitive Principles for Human/Computer Approaches to Complex Analytical

Problems. • PI: William Ribarsky, Co-PIs: Brian Fisher, Remco Chang, John Dill. • $200,000. 2009-2010 (1 year).

– Abstract: identifying new evaluation methods for visual analytical systems, and applying computational methods for analyzing user interactions.

• Quantitative Analysis Division at Bank of America– Exploration and analysis of financial risk


Future Work (On-going Collaborations)• With NSF FODAVA Center at Georgia Tech (Dr. Haesun Park, director)

– Interpreting user interactions to affecting machine learning algorithms– Visual PCA: using perceptual metrics to finding principle components– Applying perceptual constraint to dimension reduction: for animating temporal data in

dimension reduction, find methods to maintain hysteresis

• With University of Kentucky (Drs. Judy Goldsmith, Jinze Liu, Phillip Chang, MD)– Integrating data mining (KDD), POMDP, and visual analytics to prevent sepsis by identifying

biomarkers (Proposal in submission to NSF CDI)

• With geographer and architect at UNC Charlotte (Dr. Jean-Claude Thill and Eric Sauda)– Designing computational methods for identifying neighborhood characteristics (Proposal

in submission to NSF IIS)– Applying the UrbanVis system to analyzing crime (proposal in preparation for DOJ/NIJ)

• With Virginia Tech (Dr. Chris North) and Pacific Northwest National Lab (Dr. Bill Pike and Richard May)– Developing a research agenda for analytic provenance (Workshop proposal in submission

to DHS)


Thank you!



&Reasoning

[email protected]://www.viscenter.uncc.edu/~rchang


Acknowledgement

Bill Ribarsky Zach Wartell

Dong Hyun Jeong, Tom Butkiewicz, Xiaoyu Wang, Wenwen Dou, Tera Green

From the Data Visualization Group (DVG) at UNC Charlotte


Acknowledgement

Eric Sauda Jean-Claude Thill

From the Urban Visualization Group at UNC Charlotte

Ginette Wessel Elizabeth Unruh


Acknowledgement

More Collaborators…

Nancy Pollard, Evan Suma, Heather Lipford, Dan Keefe, Caroline Ziemkiewicz, Robert Kosara, Mohammad Ghoniem Clockwise, starting on the left:


Acknowledgement

• And many many others…

Joseph Kielman, Bill Pike, Theresa O'Connell, Seok-Won Lee, Brian Fisher, Alvin Lee, Jing Yang, Daniel Kern, Agust Sudjianto, Erin Miller, Kathleen Smarick, Felesia Stukes, Marcus Ewert, Larry Hodges, Michael Butkiewicz, Josh Jones, Alex Godwin, Edd Hauser, Shenen Chen, Bill Tolone, Wanqiu Liu, Rashna Vatcha


Journal Publications (16)• Urban Visualization

– R. Chang, T. Butkiewicz, C. Ziemkiewicz, Z. Wartell, N. Pollard, and W. Ribarsky. Legible simplification of textured urban models. IEEE Computer Graphics and Applications, 28(3):27–36, 2008.

– T. Butkiewicz, R. Chang, Z. Wartell, and W. Ribarsky. Visual analysis of urban change. Computer Graphics Forum, 27(3):903–910, 2008.– T. Butkiewicz, R. Chang, W. Ribarsky, and Z. Wartell. Understanding Dynamics of Geographic Domains, chapter Visual Analysis of Urban Terrain Dynamics,

pages 151– 169. CRC Press/Taylor and Francis, 2007.– R. Chang, G. Wessel, R. Kosara, E. Sauda, and W. Ribarsky. Legible cities: Focus-dependent multi-resolution visualization of urban relationships. Visualization

and Computer Graphics, IEEE Transactions on, 13(6):1169–1175, Nov.-Dec. 2007.• Visualization and Visual Analytics

– X. Wang, W. Dou, S.E. Chen, W. Ribarsky, and R. Chang. An interactive visual analytics system for bridge management. Computer Graphics Forum (Eurovis 2010), 2010. Conditional acceptance.

– D. Keefe, M. Ewert, W. Ribarsky, and R. Chang. Interactive coordinated multiple-view visualization of biomechanical motion data. Visualization and Computer Graphics, IEEE Transactions on (IEEE Visualization Conference), 15(6):1383–1390, 2009

– X. Wang, D.H. Jeong, W. Dou, S.W. Lee, W. Ribarsky, and R. Chang. Defining and applying knowledge conversion processes to a visual analytics system. Computers & Graphics, July 2009. [Online] doi:10.1016/j.cag.2009.06.004

– D.H. Jeong, C. Ziemkiewicz, B. Fisher, W. Ribarsky, and R. Chang. iPCA: An interactive system for PCA-based visual analytics. Computer Graphics Forum, 28(3):767–774, 2009.

– R. Chang, C. Ziemkiewicz, T.M. Green, and W. Ribarsky. Defining insight for visual analytics. IEEE Computer Graphics and Applications, 29(2):14–17, 2009.– R. Chang, A. Lee, M. Ghoniem, R. Kosara, W. Ribarsky, J. Yang, E. Suma, C. Ziemkiewicz, D. Kern, and A. Sudjianto. Scalable and interactive visual analysis of

financial wire transactions for fraud detection. Information Visualization, 7:63–76(14), 2008.– X. Wang, E. Miller, K. Smarick, W. Ribarsky, and R. Chang. Investigative visual analysis of global terrorism database. Computer Graphics Forum, 27(3):919–

926, 2008.• Interaction & Provenance

– W. Pike, J. Stasko, R. Chang, and T. O’Connell. Science of interaction. Information Visualization, 8:263–274, 2009.– W. Dou, D.H. Jeong, F. Stukes, W. Ribarsky, H. Lipford, and R. Chang. Recovering reasoning process from user interactions. IEEE Computer Graphics and

Applications, 29(3):52–61, 2009• VR & Interface Designs

– T. Butkiewicz, R. Chang, Z. Wartell, and W. Ribarsky. Alleviating the modifiable areal unit problem with probe-based geospatial analyses. Computer Graphics Forum (Eurovis 2010), 2010. Conditional acceptance

– T. Butkiewicz, W. Dou, Z. Wartell, W. Ribarsky, and R. Chang. Multi-focused geospatial analysis using probes. Visualization and Computer Graphics, IEEE Transactions on, 14(6):1165–1172, Nov.-Dec. 2008.

– D.H. Jeong, C. Song, R. Chang, and L. Hodges. User experimentation: An evaluation of velocity control techniques in immersive virtual environments. Springer-Verlag Virtual Reality, 13(1):41–50, Mar. 2009.


Conference/Workshop (22)• R. Chang, C. Ziemkiewicz, R. Pyzh, J. Kielman, and W. Ribarsky. Learning-based evaluation of visual analytics systems. In ACM SIGCHI BELIV Workshop, 2010. Conditional

acceptance.• D. H. Jeong, T. Green, W. Ribarsky, and R. Chang. Comparative evaluation of two interface tools in performing visual analytics tasks. In ACM SIGCHI BELIV Workshop, 2010.

Conditional acceptance.• G. Wessel, E. Unruh, R. Chang, and E. Sauda. Urban user interface: Urban legibility reconsidered. In Southwest ACSA, 2010.• D. H. Jeong, W. Dou, W. Ribarsky, and R. Chang. Knowledge-oriented refactoring in visualization. In IEEE Visualization Workshop on Refactoring Visualization From Experience,

2009.• D. H. Jeong, W. Ribarsky, and R. Chang. Designing a PCA-based collaborative visual analytics system. In IEEE Visualization Workshop on Collaborative Visualization, 2009.• W. Dou, D. H. Jeong, F. Stukes, W. Ribarsky, H. Lipford, and R. Chang. Comparing usage patterns of domain experts and novices in visual analytical tasks. In ACM SIGCHI

Sensemaking Workshop 2009.• X. Wang, W. Dou, R. Vatcha, W. Liu, S. E. Chen, S. W. Lee, R. Chang, and W. Ribarsky. Knowledge integrated visual analysis of bridge safety and maintenance. In SPIE 2009.• X. Wang, W. Dou, W. Ribarsky, and R. Chang. Integration of heterogeneous processes through visual analytics. In SPIE 2009,.• M. Butkiewicz, T. Butkiewicz, W. Ribarsky, and R. Chang. Integrating timeseries visualizations within parallel coordinates for exploratory analysis of incident databases. SPIE

2009.• T. Butkiewicz, D. H. Jeong, W. Ribarsky, and R. Chang. Hierarchical multitouch selection techniques for collaborative geospatial analysis. In SPIE Defense, Security and Sensing

2009.• D. H. Jeong, R. Chang, and W. Ribarsky. An alternative definition and model for knowledge visualization. In IEEE Visualization Workshop on Knowledge Assisted Visualization,

2008.• X. Wang, W. Dou, S. W. Lee, W. Ribarsky, and R. Chang. Integrating visual analysis with ontological knowledge structure. In IEEE Workshop on Knowledge Assisted Visualization,

2008.• D. H. Jeong, W. Dou, F. Stukes, W. Ribarsky, H. Lipford, and R. Chang. Evaluating the relationship between user interaction and financial visual analysis. In Visual Analytics

Science and Technology. IEEE Symposium on, 2008.• G. Wessel, R. Chang, and E. Sauda. Towards a new (mapping of the) city: Interactive, data rich modes of urban legibility. In Association for Computer Aided Design in

Architecture, 2008.• G. Wessel, R. Chang, and E. Sauda. Visualizing GIS: Urban form and data structure. Seeking the City: Visionaries on the Margins, ACSA, 2008.• G. Wessel, E. Sauda, and R. Chang. Urban visualization: Urban design and computer visualization. In CAADRIA 2008.• T. Butkiewicz, R. Chang, Z. Wartell, and W. Ribarsky. Visual analysis for live lidar battlefield change detection. SPIE, 2008.• J. Jones, R. Chang, T. Butkiewicz, and W. Ribarsky. Visualizing uncertainty for geographical information in the global terrorism database. SPIE, 2008.• A. Godwin, R. Chang, R. Kosara, and W. Ribarsky. Visual analysis of entity relationships in the global terrorism database. SPIE, 2008.• T. Butkiewicz, R. Chang, Z. Wartell, and W. Ribarsky. Analyzing sampled terrain volumetrically with regard to error and geologic variation. SPIE, 2007.• R. Chang, M. Ghoniem, R. Kosara, W. Ribarsky, J. Yang, E. Suma, C. Ziemkiewicz, D. Kern, and A. Sudjianto. Wirevis: Visualization of categorical, time-varying data from financial

transactions. In Visual Analytics Science and Technology, 2007, IEEE Symposium on, 2007.• R. Chang, T. Butkiewicz, C. Ziemkiewicz, Z. Wartell, N. Pollard, and W. Ribarsky. Hierarchical simplification of city models to maintain urban legibility. In SIGGRAPH ’06: ACM

SIGGRAPH 2006 Sketches, 2006.• R. Chang, R. Kosara, A. Godwin, and W. Ribarsky. Towards a role of visualization in social modeling. AAAI 2009 Spring Symposium on Technosocial Predictive Analytics, 2009.• G. Wessel, E. Sauda, and R. Chang. Mapping understanding:Transforming topographic maps into cognitive maps. GeoVis Hamburg Workshop, 2009.


Final Thought…• “The sexy job in the next 10 years will be statisticians,”

said Hal Varian, chief economist at Google. “And I’m not kidding.”

• Yet data is merely the raw material of knowledge. “We’re rapidly entering a world where everything can be monitored and measured,” said Erik Brynjolfsson, an economist and director of the Massachusetts Institute of Technology’s Center for Digital Business. “But the big problem is going to be the ability of humans to use, analyze and make sense of the data.”

• “The key is to let computers do what they are good at, which is trawling these massive data sets for something that is mathematically odd,” said Daniel Gruhl, an I.B.M. researcher whose recent work includes mining medical data to improve treatment. “And that makes it easier for humans to do what they are good at — explain those anomalies.”1

1. New York Times. “For Today’s Graduate, Just One Word: Statistics “, August 5, 2009.



&Reasoning


Backup Slides – Visual Analytics


Individually Not Unique

Analytical Reasoning

and Interaction

Visual Representation

Production, Presentation

Dissemination

Data Representation Transformation

Validation and Evaluation

• Data Mining• Machine

Learning• Databases• Information

Retrieval• etc

• Tech Transfer• Report Generation• etc

• Quality Assurance• User studies (HCI)• etc

• Interaction Design• Cognitive Psychology• Intelligence Analysis• etc.

• InfoVis• SciVis• Graphics• etc


In Combinations of 2 or 3…


and Interaction



Dissemination





Retrieval• etc



In Combinations of 2 or 3…


and Interaction



Dissemination




• Tech Transfer• Report Generation• etc


This Talk Focuses On…


and Interaction



Dissemination







Retrieval• etc


Eureka: Visual Analytics!!

“Saunders, perhaps you’re getting a bit carried away with the visual analytics!”1

1: Slide courtesy of Dr. Maria Zemankova, NSF


Case Study on WireVis


and Interaction

Visual Representa

tion

Production, Presentatio

n Disseminati

on

Data Representat

ion Transformat

ion

Validation and

Evaluation

• User Centric– Designed system based on domain

expertise• Visual Interface

– Multiple coordinated views that link multiple dimensions

• Interactive– Overview, drill-down, reclustering

• Data Clustering– Clustering by accounts, and search

by example• Production

– Connected to a live database and beta-deployed at BofA

• (Validation)– Expert evaluation


Backup Slides – Urban Simplification


Algorithm to Preserve Legibility

• Identify and preserve Paths and Edges• Create logical Districts and Nodes• Simplify model while preserving Paths, Edges, Districts, and

Nodes• Hierarchically apply appropriate amount of texture • Highlight Landmarks and choose models to render


Identifying and Preserving Paths and Edges


• Single-Link Clustering – Iteratively groups the “closest” clusters

together based on Euclidean distance– produces a binary tree (dendrogram)– Penalizes large clusters to create a more

balanced tree

Identifying and PreservingPaths and Edges (1)

a b c d e f

bc de

def

bcdef

abcdef

abc


Identifying and PreservingPaths and Edges (2)


Creating logical Districts and Nodes


Creating logical Districts and Nodes (1)


• Merge two clusters by combining footprints

Creating logical Districts and Nodes (2)

• (c) The resulting “Merged Hull”• (d) The Introduced Error, or “Negative Space”

(a) (b) (c) (d)


Simplification while preserving Paths, Edges, Nodes, and Districts


Simplification while preserving Paths, Edges, Nodes, and Districts (1)

6000 edges 1000 edges

Demo!


Simplification while preserving Paths, Edges, Nodes, and Districts (2)

• After the polylines have been simplified– Create “Cluster Meshes”

– The height of the Cluster Mesh is the median height of all buildings in the cluster


Hierarchical Textures


• Each Cluster Mesh contains 6 textures– 1 Side Texture

– 1 top-down view of the roof texture

– 4 roof textures from 4 angles (south, west, east, north)

Hierarchical Textures (1)

Side texture

Top-down South West East North


• Clusters are divided into “bins” based on their visual importance

• Each bin contains a texture atlas

• Texture atlases from all bins have the same dimension

Hierarchical Textures (2)

n/2 n/4 n/8

….

…


Runtime Levels of Detail


• Starting with the root node of the dendrogram– Approximate the “Negative

Space” as a 3D box – shown as the red box

– Project the visible sides of the box onto screen space

– Reject if the number of pixel is above a user-defined tolerance

Runtime Levels of Detail

a b c d e f

bc de

def

abcdef

abc


Landmark and Skyline Preservation (1)

Original Skyline With LandmarkPreservation

Without LandmarkPreservation


– Project a user-defined pixel tolerance (α) onto the top of each cluster

– If any building within that cluster is taller than the projected tolerance (shown in green), it is drawn separately from the cluster mesh.

Landmark and Skyline Preservation (2)


Results


Probe-based Interface

• Using Probes allows for comparing multiple regions-of-interest simultaneously

R. Chang et al., Multi-focused geospatial analysis using probes. Visualization and Computer Graphics, IEEE Transactions on, 14(6):1165–1172, Nov.-Dec. 2008.


Backup Slides – VA Systems


(2) Investigative GTD

Where

When

Who

What

Original Data

EvidenceBox

R. Chang et al., Investigative Visual Analysis of Global Terrorism, Journal of Computer Graphics Forum (Eurovis), 2008.


WHY?

This group’s attacks are not bounded by geo-locations but instead, religious beliefs.

Its attack patterns changed with its developments.

(2) Investigative GTD: Revealing Global Strategy


Domestic Group

A geographically-bounded entity in the Philippines.

The ThemeRiver shows its rise and fall as an entity and its modus operandi.

(2) Investigative GTD:Discovering Unexpected Temporal Pattern


(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.



R. Chang et al., Interactive Coordinated Multiple-View Visualization of Biomechanical Motion Data , IEEE Vis (TVCG) 2009. To Appear.


• 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



Human + Computer:Dimension Reduction – Lost in Translation• Biomechanical motion analysis revisited…

– 6 degrees of freedom (x, y, z rotation and x, y, z translation)– One single joint

• Applying a non-linear dimension reduction method– Isomap– MDS embedding

• We found:– 3 latent dimensions– 2 of which are ambiguous…


What is in a User’s Interactions?

• Types of Human-Visualization Interactions– Word editing (input heavy, little output)– Browsing, watching a movie (output heavy, little input)– Visual Analysis (closer to 50-50)

Visualization HumanOutput

Input

Keyboard, Mouse, etc

Images (monitor)


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.


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.


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 need better measurements to determine the “benefits of interactivity”


Human + Computer:User Interactions – Lessons Learned• Showing reasoning and intent are capturable.

– Although the study is limited in scope, it establishes a foundation for interaction-capturing related research

• With interaction capturing, we might be able to collect all the thinking of expert analysts and create a knowledge base 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: check to see if everything was done

right.

• Automating the process of extracting thinking is the key.– By formulating user interactions as high dimensional vectors, we

can apply analytical methods


Backup Slides – Professional Activities


Professional Activities• Committee / Panelists

– Program Committee: IEEE Conference on Visual Analytics, 2010– Program Committee: SIG CHI Workshop on BELIV, 2010– Program Committee: AAAI Spring-09 Symposium on Technosocial Predictive Analytics, 2009– Panelist: 3rd Annual DHS University Summit. Panel: Research to Reality, 2009– Panelist: 3rd Annual DHS University Summit. Panel: Visual Analytics and Discrete Science Integration into the DHS Center of Excellence

Program, 2009• Invited Talks

– 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– Apr 27, 2009 University of Kentucky. Thinking Interactively with Visualization– May 29, 2009 University of Victoria. Thinking Interactively with Visualization– Jul 28, 2009 Pacific Northwest National Lab. Thinking Interactively with Visualization– Jul 30, 2009 Microsoft Research. Thinking Interactively with Visualization– Aug 19, 2009 National Visual Analytic Consortium. What Are Your Interactions Doing For Your Visualization?– Sep 30, 2009 University of Kentucky (Grand Rounds at the Department of Surgery). Preventing– Sepsis: Artificial Intelligence, Knowledge Discovery, and Visualization– Jan 21, 2010 Charlotte Viscenter. UrbanVis Research Group: Urban Analytics– Feb 25, 2010 University of Georgia (AI Institute). Thinking Interactively with Visualization

data exploration, analysis, and representation: integration through visual analytics

Documents