integrated visual analysis of global terrorism
DESCRIPTION
Integrated Visual Analysis of Global Terrorism. Remco Chang Charlotte Visualization Center UNC Charlotte. Integrated Terrorism Analysis. Multimedia. Real Time. Known Events. Visual GTD. Video Analysis Goals. to describe trends in news content over time - PowerPoint PPT PresentationTRANSCRIPT
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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