TY - JOUR
T1 - Visualizing criminal relationships
T2 - Comparison of a hyperbolic tree and a hierarchical list
AU - Xiang, Yang
AU - Chau, Michael
AU - Atabakhsh, Homa
AU - Chen, Hsinchun
N1 - Funding Information:
Michael Chau is a research assistant professor in the School of Business at the University of Hong Kong. He received his PhD degree in Management Information Systems from the University of Arizona and a Bachelor degree in Computer Science (Information Systems) from the University of Hong Kong. When at the University of Arizona, he was an active researcher in the Artificial Intelligence Lab, where he participated in several research projects funded by NSF, NIH, NIJ, and DARPA. His current research interests include information retrieval, natural language processing, Web mining, and multi-agent systems.
Funding Information:
This project has been supported by the following grants:
PY - 2005/11
Y1 - 2005/11
N2 - In crime analysis, law enforcement officials have to process a large amount of criminal data and figure out their relationships. It is important to identify different associations among criminal entities. In this paper, we propose the use of a hyperbolic tree view and a hierarchical list view to visualize criminal relationships. A prototype system called COPLINK Criminal Relationship Visualizer was developed. An experiment was conducted to test the effectiveness and the efficiency of the two views. The results show that the hyperbolic tree view is more effective for an "identify" task and more efficient for an "associate" task. The participants generally thought it was easier to use the hierarchical list, with which they were more familiar. When asked about the usefulness of the two views, about half of the participants thought that the hyperbolic tree was more useful, while the other half thought otherwise. Our results indicate that both views can help in criminal relationship visualization. While the hyperbolic tree view performs better in some tasks, the users' experiences and preferences will impact the decision on choosing the visualization technique.
AB - In crime analysis, law enforcement officials have to process a large amount of criminal data and figure out their relationships. It is important to identify different associations among criminal entities. In this paper, we propose the use of a hyperbolic tree view and a hierarchical list view to visualize criminal relationships. A prototype system called COPLINK Criminal Relationship Visualizer was developed. An experiment was conducted to test the effectiveness and the efficiency of the two views. The results show that the hyperbolic tree view is more effective for an "identify" task and more efficient for an "associate" task. The participants generally thought it was easier to use the hierarchical list, with which they were more familiar. When asked about the usefulness of the two views, about half of the participants thought that the hyperbolic tree was more useful, while the other half thought otherwise. Our results indicate that both views can help in criminal relationship visualization. While the hyperbolic tree view performs better in some tasks, the users' experiences and preferences will impact the decision on choosing the visualization technique.
KW - Criminal data analysis
KW - Hierarchical list
KW - Hyperbolic tree
KW - Information visualization
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U2 - 10.1016/j.dss.2004.02.006
DO - 10.1016/j.dss.2004.02.006
M3 - Article
AN - SCOPUS:25444517581
SN - 0167-9236
VL - 41
SP - 69
EP - 83
JO - Decision Support Systems
JF - Decision Support Systems
IS - 1
ER -