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Prospective spatio-temporal data analysis for security informatics
Spatio-temporal data analysis plays a central role in many security-related applications including those relevant to transportation infrastructure and border security. In this paper, we investigate prospective spatio-temporal analysis methods that aim to identify "unusual" clusters of events, or hotspots, in both spatial and temporal dimensions. We propose a support vector machine-based approach and compare it with a well-known prospective method based on space-time scan statistic using three problem scenarios. The first two scenarios are based on simulated data with known hotspots. The third scenario uses a real-world crime analysis data set involving vehicles.
Prospective spatio-temporal data analysis for security informatics
Spatio-temporal data analysis plays a central role in many security-related applications including those relevant to transportation infrastructure and border security. In this paper, we investigate prospective spatio-temporal analysis methods that aim to identify "unusual" clusters of events, or hotspots, in both spatial and temporal dimensions. We propose a support vector machine-based approach and compare it with a well-known prospective method based on space-time scan statistic using three problem scenarios. The first two scenarios are based on simulated data with known hotspots. The third scenario uses a real-world crime analysis data set involving vehicles.
Prospective spatio-temporal data analysis for security informatics
Wei Chang, (author) / Daniel Zeng, (author) / Hsinchun Chen, (author)
2005-01-01
308752 byte
Conference paper
Electronic Resource
English
Prospective Spatio-Temporal Data Analysis for Security Informatics (I)
| British Library Conference Proceedings | 2005
Advances in Spatio-Temporal Analysis
| Online Contents | 2009