A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Data Type Agnostic Visual Sensitivity Analysis (2024)
Piccolotto, N., Bögl, M., Muehlmann, C., Nordhausen, K., Filzmoser, P., Schmidt, J., & Miksch, S. (2024). Data Type Agnostic Visual Sensitivity Analysis. IEEE Transactions on Visualization and Computer Graphics, 30(1), 1106-1116. https://doi.org/10.1109/tvcg.2023.3327203
JYU-tekijät tai -toimittajat
Julkaisun tiedot
Julkaisun kaikki tekijät tai toimittajat: Piccolotto, Nikolaus; Bögl, Markus; Muehlmann, Christoph; Nordhausen, Klaus; Filzmoser, Peter; Schmidt, Johanna; Miksch, Silvia
Lehti tai sarja: IEEE Transactions on Visualization and Computer Graphics
ISSN: 1077-2626
eISSN: 2160-9306
Julkaisuvuosi: 2024
Volyymi: 30
Lehden numero: 1
Artikkelin sivunumerot: 1106-1116
Kustantaja: Institute of Electrical and Electronics Engineers (IEEE)
Julkaisumaa: Yhdysvallat (USA)
Julkaisun kieli: englanti
DOI: https://doi.org/10.1109/tvcg.2023.3327203
Julkaisun avoin saatavuus: Avoimesti saatavilla
Julkaisukanavan avoin saatavuus: Osittain avoin julkaisukanava
Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/91804
Tiivistelmä
Modern science and industry rely on computational models for simulation, prediction, and data analysis. Spatial blind source separation (SBSS) is a model used to analyze spatial data. Designed explicitly for spatial data analysis, it is superior to popular non-spatial methods, like PCA. However, a challenge to its practical use is setting two complex tuning parameters, which requires parameter space analysis. In this paper, we focus on sensitivity analysis (SA). SBSS parameters and outputs are spatial data, which makes SA difficult as few SA approaches in the literature assume such complex data on both sides of the model. Based on the requirements in our design study with statistics experts, we developed a visual analytics prototype for data type agnostic visual sensitivity analysis that fits SBSS and other contexts. The main advantage of our approach is that it requires only dissimilarity measures for parameter settings and outputs (Fig. 1). We evaluated the prototype heuristically with visualization experts and through interviews with two SBSS experts. In addition, we show the transferability of our approach by applying it to microclimate simulations. Study participants could confirm suspected and known parameter-output relations, find surprising associations, and identify parameter subspaces to examine in the future. During our design study and evaluation, we identified challenging future research opportunities.
YSO-asiasanat: paikkatiedot; klusterianalyysi; signaalinkäsittely; visualisointi
Vapaat asiasanat: data visualization; task analysis; spatial databases; sensitivity analysis; analytical models; data models; predictive models
Liittyvät organisaatiot
OKM-raportointi: Kyllä
VIRTA-lähetysvuosi: 2023
JUFO-taso: 3
