Integrated Visual Analysis of Global Terrorism

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Integrated Visual Analysis of Global Terrorism

Remco ChangCharlotte Visualization Center

UNC Charlotte

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• It’s a large dataset. GTD has 59,603 incidents, with 119 attributes (dimensions) for each incident.

• It covers almost all incidents happened from 1970 to 1997, across the whole world.

• It contains mixed types of attribute --- Categorical and Numerical.

• It also includes uncertain/unknown incidents.

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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

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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