Paper Info
Title
Automated criminal link analysis based on domain knowledge
Abstract
Link (association) analysis has been used in the criminal justice domain to search large datasets for associations between crime entities in order to facilitate crime investigations. However, link analysis still faces many challenging problems, such as information overload, high search complexity, and heavy reliance on domain knowledge. To address these challenges, this article proposes several techniques for automated, effective, and efficient link analysis. These techniques include the co-occurrence analysis, the shortest path algorithm, and a heuristic approach to identifying associations and determining their importance. We developed a prototype system called CrimeLink Explorer based on the proposed techniques. Results of a user study with 10 crime investigators from the Tucson Police Department showed that our system could help subjects conduct link analysis more efficiently than traditional single-level link analysis tools. Moreover, subjects believed that association paths found based on the heuristic approach were more accurate than those found based solely on the co-occurrence analysis and that the automated link analysis system would be of great help in crime investigations.
Year
DOI
Venue
2007
10.1002/asi.20552
JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY
Keywords
Field
DocType
information overload,shortest path algorithm,system design,algorithm,association analysis,relation,comparative study,link analysis,criminology,domain knowledge,criminal justice
Data mining,Information overload,Heuristic,Domain knowledge,Computer science,Link analysis,Systems design,Accident prevention,Criminal justice,Dijkstra's algorithm
Journal
Volume
Issue
ISSN
58
6
1532-2882
Citations 
PageRank 
References 
14
0.65
27
Authors
4
Name
Order
Citations
PageRank
Jennifer Schroeder1140.65
Jennifer Jie Xu237530.21
Hsinchun Chen39569813.33
Michael Chau4147197.79