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
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
analy
sts 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.
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