Visual Parameter Selection for Spatial Blind Source Separation

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dc.contributor.authorPiccolotto, Nikolausen_US
dc.contributor.authorBögl, Markusen_US
dc.contributor.authorMuehlmann, Christophen_US
dc.contributor.authorNordhausen, Klausen_US
dc.contributor.authorFilzmoser, Peteren_US
dc.contributor.authorMiksch, Silviaen_US
dc.contributor.editorBorgo, Ritaen_US
dc.contributor.editorMarai, G. Elisabetaen_US
dc.contributor.editorSchreck, Tobiasen_US
dc.date.accessioned2022-06-03T06:05:54Z
dc.date.available2022-06-03T06:05:54Z
dc.date.issued2022
dc.description.abstractAnalysis 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.en_US
dc.description.number3
dc.description.sectionheadersWorkflows and Parameters
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume41
dc.identifier.doi10.1111/cgf.14530
dc.identifier.issn1467-8659
dc.identifier.pages157-168
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.14530
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14530
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing --> Visualization techniques; Geographic visualization
dc.subjectHuman
dc.subjectcentered computing ?? Visualization techniques
dc.subjectGeographic visualization
dc.titleVisual Parameter Selection for Spatial Blind Source Separationen_US
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