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The Visual Computer

, Volume 34, Issue 9, pp 1225–1241 | Cite as

Making machine intelligence less scary for criminal analysts: reflections on designing a visual comparative case analysis tool

  • Wolfgang Jentner
  • Dominik Sacha
  • Florian Stoffel
  • Geoffrey Ellis
  • Leishi Zhang
  • Daniel A. Keim
Original Article
  • 192 Downloads

Abstract

A fundamental task in criminal intelligence analysis is to analyze the similarity of crime cases, called comparative case analysis (CCA), to identify common crime patterns and to reason about unsolved crimes. Typically, the data are complex and high dimensional and the use of complex analytical processes would be appropriate. State-of-the-art CCA tools lack flexibility in interactive data exploration and fall short of computational transparency in terms of revealing alternative methods and results. In this paper, we report on the design of the Concept Explorer, a flexible, transparent and interactive CCA system. During this design process, we observed that most criminal analysts are not able to understand the underlying complex technical processes, which decrease the users’ trust in the results and hence a reluctance to use the tool. Our CCA solution implements a computational pipeline together with a visual platform that allows the analysts to interact with each stage of the analysis process and to validate the result. The proposed visual analytics workflow iteratively supports the interpretation of the results of clustering with the respective feature relations, the development of alternative models, as well as cluster verification. The visualizations offer an understandable and usable way for the analyst to provide feedback to the system and to observe the impact of their interactions. Expert feedback confirmed that our user-centered design decisions made this computational complexity less scary to criminal analysts.

Keywords

Crime intelligence analysis Visual analytics Clustering System design Human–computer interaction Sequential pattern mining Text analysis Dimensionality reduction 

Notes

Acknowledgements

This work was supported by the EU project VALCRI under grant number FP7-SEC-2013-608142.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Middlesex UniversityLondonUK
  2. 2.Universität KonstanzKonstanzGermany

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