A1 Journal article (refereed)
Visual Parameter Selection for Spatial Blind Source Separation (2022)

Piccolotto, N., Bögl, M., Muehlmann, C., Nordhausen, K., Filzmoser, P., & Miksch, S. (2022). Visual Parameter Selection for Spatial Blind Source Separation. Computer Graphics Forum, 41(3), 157-168. https://doi.org/10.1111/cgf.14530

JYU authors or editors

Publication details

All authors or editors: Piccolotto, N.; Bögl, M.; Muehlmann, C.; Nordhausen, Klaus; Filzmoser, P.; Miksch, S.

Journal or series: Computer Graphics Forum

ISSN: 0167-7055

eISSN: 1467-8659

Publication year: 2022

Volume: 41

Issue number: 3

Pages range: 157-168

Publisher: Wiley

Publication country: Netherlands

Publication language: English

DOI: https://doi.org/10.1111/cgf.14530

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/82831

Abstract

Analysis of spatial multivariate data, i.e., measurements at irregularly-spaced locations, is a challenging topic in visualization and statistics alike. Such data are inteGral to many domains, e.g., indicators of valuable minerals are measured for mine prospecting. Popular analysis methods, like PCA, often by design do not account for the spatial nature of the data. Thus they, together with their spatial variants, must be employed very carefully. Clearly, it is preferable to use methods that were specifically designed for such data, like spatial blind source separation (SBSS). However, SBSS requires two tuning parameters, which are themselves complex spatial objects. Setting these parameters involves navigating two large and interdependent parameter spaces, while also taking into account prior knowledge of the physical reality represented by the data. To support analysts in this process, we developed a visual analytics prototype. We evaluated it with experts in visualization, SBSS, and geochemistry. Our evaluations show that our interactive prototype allows to define complex and realistic parameter settings efficiently, which was so far impractical. Settings identified by a non-expert led to remarkable and surprising insights for a domain expert. Therefore, this paper presents important first steps to enable the use of a promising analysis method for spatial multivariate data.

Keywords: visualisation; data; variables; complexity; methods of analysis; geostatistics

Free keywords: human-centered computing; visualization techniques; geographic visualization

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2022

JUFO rating: 1