Spherical similarity explorer for comparative case analysis

Zhang, Leishi, Rooney, Chris, Nachmanson, Lev, Wong, B. L. William, Kwon, Bum Chul, Stoffel, Florian, Hund, Michael and Qazi, Nadeem (2016) Spherical similarity explorer for comparative case analysis. In: IS&T Electronic Imaging 2016 Conference on Visualization and Data Analysis 2016, 16-18 Feb 2016, San Francisco, CA, USA.

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Comparative Case Analysis (CCA) is an important tool for criminal investigation and crime theory extraction. It analyzes the commonalities and differences between a collection of crime reports in order to understand crime patterns and identify abnormal cases. A big challenge of CCA is the data processing and exploration. Traditional manual approach can no longer cope with the increasing volume and complexity of the data. In this paper we introduce a novel visual analytics system, Spherical Similarity Explorer (SSE) that automates the data processing process and provides interactive visualizations to support the data exploration. We illustrate the use of the system with uses cases that involve real world application data and evaluate the system with criminal intelligence analysts.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Zhang, L. et al., 2016. Spherical Similarity Explorer for Comparative Case Analysis. Electronic Imaging, 2016(1), pp.1–10. Available at: http://dx.doi.org/10.2352/issn.2470-1173.2016.1.vda-496.
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 20767
Notes on copyright: Leishi Zhang, Chris Rooney, Lev Nachmanson, William Wong, Bum Chul Kwon, Florian Stoffel, Michael Hund, Nadeem Qazi, Uchit Singh, and Daniel Kelm, “Spherical similarity explorer for comparative case analysis,” IS&T Electronic Imaging, Visualization and Data Analysis 2016, pg. VDA-496.1-VDA-496.10 (2016). Reprinted with permission of IS&T: The Society for Imaging Science and Technology sole copyright owners of Electronic Imaging, Visualization and Data Analysis 2016.
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Depositing User: Leishi Zhang
Date Deposited: 24 Oct 2016 13:48
Last Modified: 05 Apr 2019 05:45
URI: https://eprints.mdx.ac.uk/id/eprint/20767

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