Geospatial Information and Knowledge Discovery (GIKD) of the co-PIs and their students: With large, diverse geospatial datasets and an integrated, user-centric interface to analyze user-defined algorithms, the Instrument will give researchers the opportunity to experiment with spatially-aware information and knowledge discovery algorithms.

This will allow geospatial pattern mining and discovery of interesting geospatial patterns on spatial objects in rare event detection, spatio-temporal change and trend detection, and correlation mining. For example, in rare event detection, unpredictable events are extremely difficult to detect because they don't occur often or they occur at a time/location where they are not expected (e.g., detecting traffic congesting during a disaster situation).

The Instrument will provide researchers with historical data that is used to establish a baseline for dynamic event behavior models, and scenarios when deviations from the normal model are identified. Algorithms can then be built to better enable prediction models. These can be input into the Instrument for testing and future rare event detection and prediction.

The Instrument will also aid in the investigation of bootstrapping techniques [CBO+02, JDC87] and cost-sensitive learning approaches [Elk01,ZE01] for rare event detection in spatial data with semantic awareness.

References Cited

[CBO+02] N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. "SMOTE: Synthetic Minority Over-sampling TEchnique." Journal of Artificial Intelligence Research, 16:321-357, 2002.

[JDC87] A. K. Jain, R. C. Dubes, and C.-C. Chen. "Bootstrap techniques for error estimation." IEEE Transactions on Pattern Analysis and Machine Intelligence, 9:628-633, 1987.

[Elk01] C. Elkan. "The foundations of cost-sensitive learning." In IJCAI, pages 973-978, 2001.

[ZE01] B. Zadrozny and C. Elkan. "Learning and making decisions when costs and probabilities are both unknown. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pages 204-213, 2001.