SSDBM '01 Proceedings of the 13th International Conference on Scientific and Statistical Database Management
The Journal of Machine Learning Research
Discovering Colocation Patterns from Spatial Data Sets: A General Approach
IEEE Transactions on Knowledge and Data Engineering
A spatio-temporal-textual crime search engine
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
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This paper presents an integrated framework for Spatio-Temporal-Textual (STT) information retrieval and knowledge discovery system. The proposed ensemble framework contains an efficient STT search engine with multiple indexing, ranking and scoring schemes, an effective STT pattern miner with Spatio-Temporal (ST) analytics, and novel STT topic modeling. Specifically, we design an effective prediction prototype with a third-order linear regression model, and present an innovative STT topic modeling relevance ranker to score documents based on inherent STT features under topical space. We demonstrate the framework with a crime dataset from the Washington, DC area from 2006 to 2010 and a global terrorism dataset from 2004 to 2010.