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


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.


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



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


  1. 1.
    Apache Opennlp. Accessed 13 Sept 2017
  2. 2.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the Eleventh International Conference on Data Engineering, March 6–10, 1995, Taipei, Taiwan, pp. 3–14 (1995)Google Scholar
  3. 3.
    Baker, C.F., Fillmore, C.J., Lowe, J.B.: The Berkeley framenet project. In: 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, COLING-ACL ’98, August 10–14, 1998, Université de Montréal, Montréal, Quebec, Canada. Proceedings of the Conference, pp. 86–90 (1998)Google Scholar
  4. 4.
    Bennell, C., Canter, D.V.: Linking commercial burglaries by modus operandi: tests using regression and ROC analysis. Sci. Justice 42(3), 153–164 (2002)CrossRefGoogle Scholar
  5. 5.
    Bradel, L., North, C., House, L., Leman, S.: Multi-model semantic interaction for text analytics. In: IEEE Conference on Visual Analytics in Science and Technology (VAST), pp. 163–172 (2014)Google Scholar
  6. 6.
    Brandes, U., Pich, C.: Eigensolver methods for progressive multidimensional scaling of large data. In: Kaufmann M., Wagner D. (eds) Graph Drawing, 14th International Symposium, GD 2006, Karlsruhe, Germany, September 18–20, 2006. Revised Papers, Volume 4372 of Lecture Notes in Computer Science, pp. 42–53. Springer (2006)Google Scholar
  7. 7.
    Canter, D.V., Alison, L.J., Alison, E., Wentink, N.: The organized/disorganized typology of serial murder: myth or model? Psychol. Public Policy Law 10(3), 293–320 (2004)CrossRefGoogle Scholar
  8. 8.
    Collier, D.: The comparative method. Polit. Sci. State Discipl. II, 105–118 (1993)Google Scholar
  9. 9.
    Cope, N.: Intelligence led policing or policing led intelligence?: Integrating volume crime analysis into policing. Br. J. Criminol. 44, 188–203 (2004)CrossRefGoogle Scholar
  10. 10.
    Demiralp, Ç: Clustrophile: a tool for visual clustering analysis. CoRR (2017).
  11. 11.
    Endert, A.: Semantic interaction for visual analytics: inferring analytical reasoning for model steering. Synth. Lect. Vis. 4(2), 1–99 (2016)CrossRefGoogle Scholar
  12. 12.
    Endert, A., Fiaux, P., North, C.: Semantic interaction for sensemaking: inferring analytical reasoning for model steering. IEEE Trans. Vis. Comput. Gr. 18(12), 2879–2888 (2012)CrossRefGoogle Scholar
  13. 13.
    Endert, A., Fiaux, P., North, C.: Semantic interaction for visual text analytics. In: ACM SIGCHI Conference Human Factors in Computing Systems (CHI), pp. 473–482 (2012)Google Scholar
  14. 14.
    Frank, E., Hall, M.A., Witten, I.H.: The WEKA workbench. In: Kaufmann, M. (ed.) Data Mining: Practical Machine Learning Tools and Techniques. Springer, Berlin (2016)Google Scholar
  15. 15.
    Gleicher, M.: Explainers: expert explorations with crafted projections. IEEE Trans. Vis. Comput. Gr. 19(12), 2042–2051 (2013)CrossRefGoogle Scholar
  16. 16.
    Gomariz, A., Campos, M., Marín, R., Goethals, B.: Clasp: an efficient algorithm for mining frequent closed sequences. In: Advances in Knowledge Discovery and Data Mining, 17th Pacific-Asia Conference, PAKDD 2013, Gold Coast, Australia, April 14–17, 2013, Proceedings, Part I, pp. 50–61 (2013)Google Scholar
  17. 17.
    IBM. Ibm i2 intelligence analysis platformGoogle Scholar
  18. 18.
    Jäckle, D., Stoffel, F., Mittelstädt, S., Keim, D.A., Reiterer, H.: Interpretation of dimensionally-reduced crime data: a study with untrained domain experts. In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), Vol. 3: IVAPP, Porto, Portugal, February 27–March 1, 2017, pp. 164–175 (2017)Google Scholar
  19. 19.
    Jentner, W., Ellis, G., Stoffel, F., Sacha, D., Keim, D.A.: A visual analytics approach for crime signature generation and exploration. In: The Event Event: Temporal and Sequential Event Analysis, IEEE VIS 2016 Workshop (2016)Google Scholar
  20. 20.
    Johansson, J., Opach, T., Glaas, E., Neset, T.S., Navarra, C., Linnér, B., Rød, J.K.: Visadapt: a visualization tool to support climate change adaptation. IEEE Comput. Gr. Appl. 37(2), 54–65 (2017)CrossRefGoogle Scholar
  21. 21.
    Jurafsky, D., Martin, J.H.: Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, Prentice Hall Series in Artificial Intelligence, 2nd edn. Prentice Hall, Pearson Education International, Upper Saddle River (2009)Google Scholar
  22. 22.
    Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)CrossRefzbMATHGoogle Scholar
  23. 23.
    Manning, C.D., Surdeanu, M., Bauer, J., Finkel, J.R., Bethard, S., McClosky, D.: The stanford corenlp natural language processing toolkit. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014, June 22–27, 2014, Baltimore, MD, USA, System Demonstrations, pp 55–60 (2014)Google Scholar
  24. 24.
    Miller, G.A.: Wordnet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  25. 25.
    NPIA: National policing improvement agency: Professional practice on analysis (2008)Google Scholar
  26. 26.
    Prowse, J., Bennett, E.: Working Manual of Criminal Law. Carswell Legal Pubns, Toronto (2000)Google Scholar
  27. 27.
    Ruppert, T., Staab, M., Bannach, A., Lücke-Tieke, H., Bernard, J., Kuijper, A., Kohlhammer, J.: Visual interactive creation and validation of text clustering workflows to explore document collections. Electron. Imaging 2017(1), 46–57 (2017)CrossRefGoogle Scholar
  28. 28.
    Sacha, D., Jentner, W., Zhang, L., Stoffel, F., Ellis, G.: Visual comparative case analytics. In: Sedlmair, M., Tominski, C. (eds.) EuroVis Workshop on Visual Analytics (EuroVA). The Eurographics Association, Lyon (2017)Google Scholar
  29. 29.
    Sacha, D., Sedlmair, M., Zhang, L., Lee, J.A., Peltonen, J., Weiskopf, D., North, S.C., Keim, D.A.: What you see is what you can change: human-centered machine learning by interactive visualization. Neurocomputing 268, 164–175 (2017)CrossRefGoogle Scholar
  30. 30.
    Sacha, D., Zhang, L., Sedlmair, M., Lee, J .A., Peltonen, J., Weiskopf, D., North, S .C., Keim, D .A.: Visual interaction with dimensionality reduction: a structured literature analysis. IEEE Trans. Vis. Comput. Gr. 23(1), 241–250 (2017)CrossRefGoogle Scholar
  31. 31.
    Saneifar, H., Bringay, S., Laurent, A., Teisseire, M.: S2MP: similarity measure for sequential patterns. In: Data Mining and Analytics 2008, Proceedings of the Seventh Australasian Data Mining Conference (AusDM 2008). Glenelg/Adelaide, SA, Australia, 27–28 November 2008, Proceedings, pp. 95–104 (2008)Google Scholar
  32. 32.
    Sedlmair, M., Meyer, M.D., Munzner, T.: Design study methodology: reflections from the trenches and the stacks. IEEE Trans. Vis. Comput. Gr. 18(12), 2431–2440 (2012)CrossRefGoogle Scholar
  33. 33.
    Stasko, J.T., Görg, C., Liu, Z.: Jigsaw: supporting investigative analysis through interactive visualization. Inf. Vis. 7(2), 118–132 (2008)CrossRefGoogle Scholar
  34. 34.
    T-SNE-Java. Accessed 04 Dec 2017
  35. 35.
    T-SNE-Javascript. Accessed 04 Dec 2017
  36. 36.
    Turkay, C., Filzmoser, P., Hauser, H.: Brushing dimensions: a dual visual analysis model for high-dimensional data. IEEE Trans. Vis. Comput. Gr. 17(12), 2591–2599 (2011)CrossRefGoogle Scholar
  37. 37.
    Visual analytics for sense-making and criminal intelligence analysis. 14 Sept 2017
  38. 38.
    van der Corput, P., van Wijk, J.J.: Exploring items and features with i\({}^{\text{ f }}\), f\({}^{\text{ i }}\)-tables. Comput. Gr. Forum 35(3), 31–40 (2016)CrossRefGoogle Scholar
  39. 39.
    Wenskovitch, J., Crandell, I., Ramakrishnan, N., House, L., North, C.: Towards a systematic combination of dimension reduction and clustering in visual analytics. IEEE Trans. Vis. Comput. Gr. 24, 131–141 (2017)CrossRefGoogle Scholar
  40. 40.
    Wise, J.A.: The ecological approach to text visualization. JASIS 50(13), 1224–1233 (1999)CrossRefGoogle Scholar
  41. 41.
    Wong, B.L.W., Rooney, C., Kodagoda, N.: White paper: analyst user interface: thinking landscape as design concept. Technical report, Middlesex University London, January (2017)Google Scholar
  42. 42.
    Xu, K., Attfield, S., Jankun-Kelly, T.J., Wheat, A., Nguyen, P.H., Selvaraj, N.: Analytic provenance for sensemaking: a research agenda. IEEE Comput. Gr. Appl. 35(3), 56–64 (2015)CrossRefGoogle Scholar
  43. 43.
    Yan, X., Han, J., Afshar, R.: Clospan: Mining closed sequential patterns in large datasets. In: Proceedings of the Third SIAM International Conference on Data Mining, San Francisco, CA, USA, May 1–3, 2003, pp. 166–177 (2003)Google Scholar
  44. 44.
    Yuan, X., Ren, D., Wang, Z., Guo, C.: Dimension projection matrix/tree: interactive subspace visual exploration and analysis of high dimensional data. IEEE Trans. Vis. Comput. Gr. 19(12), 2625–2633 (2013)CrossRefGoogle Scholar
  45. 45.
    Zhang, L., Rooney, C., Nachmanson, L., Wong, B.L.W., Kwon, B.C., Stoffel, F., Hund, M., Qazi, N., Singh, U., Keim, D.A.: Spherical similarity explorer for comparative case analysis. In: Visualization and Data Analysis 2016, San Francisco, California, USA, February 14–18, 2016, pp. 1–10 (2016)Google Scholar

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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