Evaluation of Parallel Coordinates: Overview, Categorization and Guidelines for Future Research
Overview
Authors
Affiliations
The parallel coordinates technique is widely used for the analysis of multivariate data. During recent decades significant research efforts have been devoted to exploring the applicability of the technique and to expand upon it, resulting in a variety of extensions. Of these many research activities, a surprisingly small number concerns user-centred evaluations investigating actual use and usability issues for different tasks, data and domains. The result is a clear lack of convincing evidence to support and guide uptake by users as well as future research directions. To address these issues this paper contributes a thorough literature survey of what has been done in the area of user-centred evaluation of parallel coordinates. These evaluations are divided into four categories based on characterization of use, derived from the survey. Based on the data from the survey and the categorization combined with the authors' experience of working with parallel coordinates, a set of guidelines for future research directions is proposed.
Visual Parameter Space Exploration in Time and Space.
Piccolotto N, Bogl M, Miksch S Comput Graph Forum. 2024; 42(6):e14785.
PMID: 38505647 PMC: 10947302. DOI: 10.1111/cgf.14785.
Towards a unified terminology for sonification and visualization.
Enge K, Rind A, Iber M, Holdrich R, Aigner W Pers Ubiquitous Comput. 2023; 27(5):1949-1963.
PMID: 37869040 PMC: 10589160. DOI: 10.1007/s00779-023-01720-5.
High-dimensional spatiotemporal visual analysis of the air quality in China.
Liu J, Wan G, Liu W, Li C, Peng S, Xie Z Sci Rep. 2023; 13(1):5462.
PMID: 37015990 PMC: 10073083. DOI: 10.1038/s41598-023-31645-1.
Visual Parameter Selection for Spatial Blind Source Separation.
Piccolotto N, Bogl M, Muehlmann C, Nordhausen K, Filzmoser P, Miksch S Comput Graph Forum. 2022; 41(3):157-168.
PMID: 36248193 PMC: 9543588. DOI: 10.1111/cgf.14530.
Optimal spindle detection parameters for predicting cognitive performance.
Adra N, Sun H, Ganglberger W, Ye E, Dummer L, Tesh R Sleep. 2022; 45(4).
PMID: 34984446 PMC: 8996023. DOI: 10.1093/sleep/zsac001.