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Visual Knowledge Discovery and Criminal Justice. Insights from a Computational Crime Analysis Research

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Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1126))

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Abstract

Extracting actionable knowledge from heterogeneous information enclosed in criminal proceedings is one of the main challenges in the fast-growing field of computational crime analysis. Public prosecutors, on the other hand, can usually rely only on databases (containing complaints, criminal records, or police reports) accessible via traditional textual interfaces that lack advanced and visually-intuitive information extraction functionalities. This work presents the results of an ongoing research project that turns to visual knowledge discovery to help public prosecutors analyze structural and functional features of criminal networks under investigation. In this vein, the paper dwells on an experimental platform for computational crime analysis where visualization has three different goals: (a) highlighting the structural and qualitative features of both criminal organizations and their components; (b) depicting the progress and transformation of criminal networks over time; (c) enhancing the interaction between the legal scholar/expert and computational heuristics in the knowledge formation process. The outline of the solutions devised within the project—which has seen the involvement of domain experts and the use of real data—becomes an occasion to sketch a few insights about the potential role of visualization in criminal justice and, more in general, in the analysis of social phenomena.

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Notes

  1. 1.

    https://bit.ly/2QbZEKE.

  2. 2.

    The platform handles data about social relations that are represented as a graph \(G = (V,E)\), where \(V =\) persons included in the case files, and \(E =\) relation, such as telephone or environmental tappings.

  3. 3.

    https://www.highcharts.com.

  4. 4.

    https://doc.linkurio.us/ogma/latest/.

  5. 5.

    https://d3js.org.

  6. 6.

    The expression refers to immediately executive measures of coercion resulting in limitations of personal freedom or the availability of goods. Such measures against the accused aim: (a) to prevent inappropriate behaviors during the course of the criminal proceeding (e.g., attempts to conceal evidence or to commit other crimes); (b) to ensure the enforcement of the judgment.

  7. 7.

    It can only be assessed with the contribution of specific categories of domain experts like psychiatrists or psychologists.

  8. 8.

    https://www.selenium.dev/documentation/webdriver/.

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Author information

Authors and Affiliations

  1. National Institute for Public Policy Analysis, 00198, Rome, Italy

    Nicola Lettieri

  2. Department of Law, Economics, Management and Quantitative Methods, University of Sannio, Piazza Arechi II, 82100, Benevento, BN, Italy

    Alfonso Guarino

  3. Department of Computer Science, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, SA, Italy

    Delfina Malandrino & Rocco Zaccagnino

Authors
  1. Nicola Lettieri
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  2. Alfonso Guarino
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  3. Delfina Malandrino
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  4. Rocco Zaccagnino
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Corresponding author

Correspondence to Alfonso Guarino .

Editor information

Editors and Affiliations

  1. Dept. of Computer Science, Central Washington University, Ellensburg, WA, USA

    Boris Kovalerchuk

  2. Darmstadt University of Applied Sciences, Darmstadt, Germany

    Kawa Nazemi

  3. Dept. of Computer Science, Central Washington University, Ellensburg, WA, USA

    Răzvan Andonie

  4. ISEL, Polytechnic Institute of Lisbon, Lisboa, Portugal

    Nuno Datia

  5. Department of Informatics, London South Bank University, London, UK

    Ebad Bannissi

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Lettieri, N., Guarino, A., Malandrino, D., Zaccagnino, R. (2024). Visual Knowledge Discovery and Criminal Justice. Insights from a Computational Crime Analysis Research. In: Kovalerchuk, B., Nazemi, K., Andonie, R., Datia, N., Bannissi, E. (eds) Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery. Studies in Computational Intelligence, vol 1126. Springer, Cham. https://doi.org/10.1007/978-3-031-46549-9_13

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