1 / 14 Integrated Visual Analysis of Global Terrorism Remco Chang Charlotte Visualization Center UNC Charlotte
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Integrated Visual Analysis of Global Terrorism
Remco ChangCharlotte Visualization Center
UNC Charlotte
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Integrated Terrorism AnalysisMultimedia
Visual GTD
Real Time
Known Events
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Video Analysis Goals
• to describe trends in news content over time
• to discover breaking news and hot topics over time
• to trace conceptual development of news
• to retrieve news of interests effectively
• to collect evidences and test hypotheses for intelligent analysis
• to compare group (such as different channels) differences in content
• to associate news content with social events
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Multimedia Analysis
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Video Analysis Example
CNN Fox News MSNBC• News contains view points and opinions• Find local, regional, national, and international reports of the
same event to get a complete picture
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NVAC Collaborations
• PNNL – A. Sanfilippo (Content Analysis and Information Extraction of closed caption)
• PNNL – W. Pike (Emotional state extraction from closed caption)
• Penn State – A. MacEachren (Geographical analysis)• Georgia Tech – J. Stasko (Jigsaw, entity relationships)
• Visual Analytics is the point of integration!!
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Integrating Terrorism Data Analysisand News Analysis
Terrorism Databases
Terrorism Visual
Analysis
News Story Databases
News Visual
Analysis
Jigsaw
TerrorismVA
BroadcastVA
Stab/TIBORReasoningEnvironment
Framing,Affective Analysis
NVAC
Next: full, Web-based multimedia content
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Visual GTD Flow Chart
Entity Relationships(Geo-temporal Vis)
Dimensional Relationships(ParallelSets)
Entity Analysis(Search By Example)
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Five Flexible Entry Components
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Parallel Sets View
• Parallel Sets– Displays
relationships among categorical dimensions
– Shows intersections and distributions of categories
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Parallel Sets View
• Dynamic filtering on continuous dimensions can show more information
• Here we see the large proportion of facility attacks and bombings in Latin America during the early 1980s
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Analysis using Longest Common Sequence (LCS)• Two strings of data (each representing a series of events)
– GATCCAGT– GTACACTGAG
• Basic algorithm returns length of longest common subsequence: 6
• Can return trace of subsequence if desired:– GTCCAG
• GATCCAGT• GTACACTGAG
• Additional variations can take into account event gap penalties, time gap penalties, and exploration of shorter, or alternate, common subsequences
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Grouping using MDS in 2D
• Each o represents a terrorist group
• Groups form cluster according to naturally occurring trend sizes
• Sharp divide between large clusters in right hemisphere
• Left hemisphere contains many smaller clusters
MDS Analysis by TargetType