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

Dominik Jäckle, Florian Stoffel, Sebastian Mittelstädt, Daniel A. Keim, Harald Reiterer
2017 Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications  
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
more » ... n 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 analysts 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. Konstanzer Online-Publikations-System (KOPS) URL: Erschienen in:
doi:10.5220/0006265101640175 dblp:conf/grapp/JackleSMKR17 fatcat:qil3i4o5mne6lbu2b6ug4xwjq4