Visualization of Clandestine Labs from Seizure Reports: Thematic Mapping and Data Mining Research Directions

TL;DR

This paper presents a data mining and visualization approach for mapping clandestine lab seizure events over time and space, integrating topic modeling with event extraction for trend analysis.

Contribution

It introduces a static and explores dynamic topic modeling framework to improve event visualization and trend detection in geospatial seizure data.

Findings

Preliminary results show effective event mapping over time and space.

The static topic model provides insights into seizure event patterns.

Potential for dynamic modeling to enhance trend analysis.

Abstract

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.