Toward Predictive Crime Analysis via Social Media, Big ...€¦ · •Predictive Crime Analysis via...
Transcript of Toward Predictive Crime Analysis via Social Media, Big ...€¦ · •Predictive Crime Analysis via...
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Toward Predictive Crime Analysis via
Social Media, Big Data, and GIS
Anthony J. Corso
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Agenda
• Introduction- Background- Objectives- Problem
• Data
• Artifact Outcomes
• Current and Future Research
• Conclusion
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Introduction
• Objectives- Realize links between crime and domain specific data
- Investigate Spatial and Temporal Analysis of Crime, Nearest Neighbor Clustering, Kernel Density Estimation, and Risk Terrain Modeling techniques
- Investigate linguistic analysis of social media combined with crime and domain specific data for real-time predictive capabilities
• Problem- Identify predictive real-time ??? via social media and historical
record
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Theory Base for Research
• Abstract
• Concrete
Tweet
Statements
Existence Relational
NominalOrdinalInterval
RatioReynolds, P. D. 2007. A primer in theory construction, Boston.
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Tweet Anatomy
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Tweet Grammar
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Natural Language Processing
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Big Data
• What does it mean???- Millions, billions, trillions of records
- A lot of data, A lot of data, A lot of data, A lot of data
- Not, A lot of data is still small data
• Problem- Data pipe
- JavaScript Object Notation (JSON)- General Architecture for Text Engineering (GATE)- Java- ArcGIS
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Data, Maps, and GIS Overview
Small Scale Large Scale
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Data, Maps, and GIS Integration
Tweet Corpus Crime Data SNAP Locations
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Hot Spot Maps Preliminary
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Retrospective Hot Spot Maps
Hot Spot No Social Media Hot Spot With Social Media
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Regression Analysis
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Contributions and Outcomes• Predictive Crime Analysis via Social Media
- Hypothesis: A GIS hot spot map or risk terrain model increases in precision and accuracy as a social media corpus is operationalization into GIS risk layers.
- Independent Variable: Each different mapping treatment to produce a visualization
- STAC: Spatial and Temporal Analysis of Crime: geographically locates the densest clusters of incidents on the map
- Nnh: Nearest Neighbor Clustering: technique is based on a threshold distance to which the crime incidents are compared to identify clusters, e.g., city block
- KDE: Kernel Density Estimation: continuous smoothed surface with variation of the density of crime over a study area
- RTM: Two factors that have different operational spatial influences but can be spatially joined. The spatial overlap of risk factors creates a more risky environment where crime would be expected to occur in the future.
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Current Research College Crime
Number of Colleges
Seattle
Salt Lake
San Diego Dallas
New Madrid
Chicago Boston
New Orleans
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Future Research Prediction
Tweet
Tweet
Tweet
Tweet Linguistic Decay
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Conclusion and Questions
• Integrating domain specific and crime data
• A predictive social media artifact is possible
• GIS RTM artifact construction is encouraging