Visualization in law enforcement

Authors:
Hsinchun Chen;Homa Atabakhsh;Chunju Tseng;Byron Marshall;Siddharth Kaza;Shauna Eggers;Hemanth Gowda;Ankit Shah;Tim Petersen;Chuck Violette
Affiliations:
University of Arizona, Tucson, AZ;University of Arizona, Tucson, AZ;University of Arizona, Tucson, AZ;University of Arizona, Tucson, AZ;University of Arizona, Tucson, AZ;University of Arizona, Tucson, AZ;University of Arizona, Tucson, AZ;University of Arizona, Tucson, AZ;Tucson, AZ;Tucson, AZ
Venue:
dg.o '05 Proceedings of the 2005 national conference on Digital government research
Year:
2005

Citing 1
Cited 1

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Visualization

Abstract

Visualization techniques have proven to be invaluable in crime analysis. By interviewing and observing Criminal Intelligence Officers (CIO) and civilian crime analysts at the Tucson Police Department (TPD), we identified two crime analysis tasks that can especially benefit from visualization: crime pattern recognition and criminal association discovery. As part of an extension to the COPLINK project [1], we have developed two systems to provide automatic visual assistance for these tasks. The Spatial-Temporal Visualization (STV) system assists in identifying crime patterns by integrating a synchronized view of three visualization techniques: a GIS view, a timeline view and a periodic pattern view. The Criminal Activities Network (CAN) system extracts, visualizes and analyzes criminal relationships using spring-embedded and blockmodeling algorithms. We present the functionalities of the STV and CAN systems in the demonstration section.