Visualization of Clandestine Labs from Seizure Reports: Thematic Mapping and Data Mining Research Directions
This work addresses the challenge of analyzing and visualizing clandestine lab seizure data for law enforcement or geoinformatics applications, but it appears incremental as it builds on existing topic modeling and data mining techniques.
The paper tackles the problem of visualizing spatiotemporal events from reports, focusing on methamphetamine lab seizures, by developing an end-to-end information retrieval system that integrates event extraction, topic modeling, and thematic mapping, reporting preliminary results on tracking these events across time and space.
The problem of spatiotemporal event visualization based on reports entails subtasks ranging from named entity recognition to relationship extraction and mapping of events. We present an approach to event extraction that is driven by data mining and visualization goals, particularly thematic mapping and trend analysis. This paper focuses on bridging the information extraction and visualization tasks and investigates topic modeling approaches. We develop a static, finite topic model and examine the potential benefits and feasibility of extending this to dynamic topic modeling with a large number of topics and continuous time. We describe an experimental test bed for event mapping that uses this end-to-end information retrieval system, and report preliminary results on a geoinformatics problem: tracking of methamphetamine lab seizure events across time and space.