Visual Parameter Selection for Spatial Blind Source Separation

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...

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Vydáno v:Computer graphics forum Ročník 41; číslo 3; s. 157 - 168
Hlavní autoři: Piccolotto, N., Bögl, M., Muehlmann, C., Nordhausen, K., Filzmoser, P., Miksch, S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Blackwell Publishing Ltd 01.06.2022
Témata:
CCS Concepts
Domains
Evaluation
Geochemistry
GeoGraphic visualization
Human‐centered computing → Visualization techniques
Multivariate analysis
Parameter identification
Prototypes
Separation
Signal processing
Spatial data
Visualization
GeoGraphic visualization
CCS Concepts
Human‐centered computing → Visualization techniques
ISSN:0167-7055, 1467-8659
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Shrnutí: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.
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.14530