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Authors: Dominik Jäckle 1 ; Florian Stoffel 1 ; Sebastian Mittelstädt 2 ; Daniel A. Keim 1 and Harald Reiterer 1

Affiliations: 1 University of Konstanz, Germany ; 2 Siemens AG, Germany

ISBN: 978-989-758-228-8

Keyword(s): Dimensionality Reduction, Multivariate Data, Crime Data, Qualitative Study.

Related Ontology Subjects/Areas/Topics: Abstract Data Visualization ; Computer Vision, Visualization and Computer Graphics ; General Data Visualization ; High-Dimensional Data and Dimensionality Reduction ; Visual Data Analysis and Knowledge Discovery ; Visual Representation and Interaction ; Visualization Applications

Abstract: 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. (More)

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Paper citation in several formats:
Jäckle D., Stoffel F., Mittelstädt S., Keim D. and Reiterer H. (2017). 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 - Volume 3: IVAPP, (VISIGRAPP 2017) ISBN 978-989-758-228-8, pages 164-175. DOI: 10.5220/0006265101640175

@conference{ivapp17,
author={Dominik Jäckle and Florian Stoffel and Sebastian Mittelstädt and Daniel A. Keim and Harald Reiterer},
title={Interpretation of Dimensionally-reduced Crime Data: A Study with Untrained Domain Experts},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017)},
year={2017},
pages={164-175},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006265101640175},
isbn={978-989-758-228-8},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017)
TI - Interpretation of Dimensionally-reduced Crime Data: A Study with Untrained Domain Experts
SN - 978-989-758-228-8
AU - Jäckle D.
AU - Stoffel F.
AU - Mittelstädt S.
AU - Keim D.
AU - Reiterer H.
PY - 2017
SP - 164
EP - 175
DO - 10.5220/0006265101640175

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