Interpretation of Dimensionally-reduced Crime Data : A Study with Untrained Domain Experts

Jäckle, Dominik; Stoffel, Florian; Mittelstädt, Sebastian; Keim, Daniel A.; Reiterer, Harald

Interpretation of Dimensionally-reduced Crime Data : A Study with Untrained Domain Experts

Dateien

Jaeckle_0-419085.pdfGröße: 2.48 MBDownloads: 354

Datum

2017

Projekt

SFB TRR 161 TP C 01 Quantitative Messung von Interaktion

Open Access-Veröffentlichung

Open Access Green

Sammlungen

Informatik und Informationswissenschaft

Publikationstyp

Beitrag zu einem Konferenzband

Publikationsstatus

Published

Erschienen in

Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Setúbal, Portugal: SCITEPRESS, 2017, pp. 164-175. ISBN 978-989-758-228-8. Available under: doi: 10.5220/0006265101640175

Zusammenfassung

Dimensionality reduction (DR) techniques aim to reduce the amount of considered dimensions, yet preserving as much information as possible. According to many visualization researchers, DR results lack interpretability, in particular for domain experts not familiar with machine learning or advanced statistics. Thus, interactive visual methods have been extensively researched for their ability to improve transparency and ease the interpretation of results. However, these methods have primarily been evaluated using case studies and interviews with experts trained in DR. In this paper, we describe a phenomenological analysis investigating if researchers with no or only limited training in machine learning or advanced statistics can interpret the depiction of a data projection and what their incentives are during interaction. We, therefore, developed an interactive system for DR, which unifies mixed data types as they appear in real-world data. Based on this system, we provided data analys ts of a Law Enforcement Agency (LEA) with dimensionally-reduced crime data and let them explore and analyze domain-relevant tasks without providing further conceptual information. Results of our study reveal that these untrained experts encounter few difficulties in interpreting the results and drawing conclusions given a domain relevant use case and their experience. We further discuss the results based on collected informal feedback and observations.

Fachgebiet (DDC)

004 Informatik

Schlagwörter

Dimensionality Reduction, Multivariate Data, Crime Data, Qualitative Study

Konferenz

12th International Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017), 27. Feb. 2017 - 1. März 2017, Porto, Portugal

Zitieren

ISO 690

JÄCKLE, Dominik, Florian STOFFEL, Sebastian MITTELSTÄDT, Daniel A. KEIM, Harald REITERER, 2017. Interpretation of Dimensionally-reduced Crime Data : A Study with Untrained Domain Experts. 12th International Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017). Porto, Portugal, 27. Feb. 2017 - 1. März 2017. In: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Setúbal, Portugal: SCITEPRESS, 2017, pp. 164-175. ISBN 978-989-758-228-8. Available under: doi: 10.5220/0006265101640175

BibTex

@inproceedings{Jackle2017-02-27Inter-39720,
  year={2017},
  doi={10.5220/0006265101640175},
  title={Interpretation of Dimensionally-reduced Crime Data : A Study with Untrained Domain Experts},
  isbn={978-989-758-228-8},
  publisher={SCITEPRESS},
  address={Setúbal, Portugal},
  booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications},
  pages={164--175},
  author={Jäckle, Dominik and Stoffel, Florian and Mittelstädt, Sebastian and Keim, Daniel A. and Reiterer, Harald}
}

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Universitätsbibliographie

Ja