1 / 14 Integrated Visual Analysis of Global Terrorism Remco Chang Charlotte Visualization Center UNC Charlotte.

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Presentation transcript:

1 / 14 Integrated Visual Analysis of Global Terrorism Remco Chang Charlotte Visualization Center UNC Charlotte

2 / 14 Integrated Terrorism Analysis Multimedia Visual GTD Real Time Known Events

3 / 14 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

4 / 14 Multimedia Analysis

5 / 14 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

6 / 14 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!!

7 / 14 Integrating Terrorism Data Analysis and News Analysis Terrorism Databases Terrorism Visual Analysis News Story Databases News Visual Analysis Jigsaw Terrorism VA Broadcast VA Stab/ TIBOR Reasoning Environment Framing, Affective Analysis NVAC Next: full, Web-based multimedia content

8 / 14 Visual GTD Flow Chart Entity Relationships (Geo-temporal Vis) Dimensional Relationships (ParallelSets) Entity Analysis (Search By Example)

9 / 14 Five Flexible Entry Components

10 / 14

11 / 14 Parallel Sets View Parallel Sets – Displays relationships among categorical dimensions – Shows intersections and distributions of categories

12 / 14 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

13 / 14 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

14 / 14 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