GAMBIT (Geospatial Analysis of Motion-Based Intelligence and Tracking)

Movement data can be combined with geospatial information and transformed into probabilistic graphical models that represent both social and temporal relationships between objects in the observed area. We then apply machine-learning techniques to cluster patterns in these graphical models to assist human users in performing strategic level analysis such as behavior prediction and anomaly detection.

Project members

Project Publications

An Interactive Web Based Spatio-Temporal Visualization System
Ramakrishna, A., Chang, Y., Maheswaran, R.
2013 . In Advances in Visual Computing
Projects: GAMBIT (Geospatial Analysis of Motion-Based Intelligence and Tracking)
[ BibTeX ]
Gang Networks, Neighborhoods and Holidays: Spatiotemporal Patterns in Social Media
Following Human Mobility Using Tweets
Azmandian, M., Singh, K., Gelsey, B., Chang, Y., Maheswaran, R.
2012 . In Workshop on Human-Agent Interaction Design and Models (HAIDM)
Projects: GAMBIT (Geospatial Analysis of Motion-Based Intelligence and Tracking)
[ BibTeX ]
Following Human Mobility Using Tweets
Azmandian, M., Singh, K., Gelsey, B., Chang, Y., Maheswaran, R., , .
2012 . In The Eighth International Workshop on Agents and Data Mining Interaction (ADMI)
Projects: GAMBIT (Geospatial Analysis of Motion-Based Intelligence and Tracking)
[ BibTeX ]