Color Coding of Large Value Ranges Applied to Meteorological Data
This addresses visualization problems for meteorologists and data analysts working with large-value-range data, representing an incremental improvement over existing color schemes.
The paper tackles the challenge of visualizing meteorological data with large value ranges by developing a novel 'nested' color scheme based on mantissa and exponent representations. In user studies, this scheme significantly outperformed existing color scales like ColorCrafter, Viridis, and Rainbow in interpretation tasks while showing comparable performance in discrimination tasks.
This paper presents a novel color scheme designed to address the challenge of visualizing data series with large value ranges, where scale transformation provides limited support. We focus on meteorological data, where the presence of large value ranges is common. We apply our approach to meteorological scatterplots, as one of the most common plots used in this domain area. Our approach leverages the numerical representation of mantissa and exponent of the values to guide the design of novel "nested" color schemes, able to emphasize differences between magnitudes. Our user study evaluates the new designs, the state of the art color scales and representative color schemes used in the analysis of meteorological data: ColorCrafter, Viridis, and Rainbow. We assess accuracy, time and confidence in the context of discrimination (comparison) and interpretation (reading) tasks. Our proposed color scheme significantly outperforms the others in interpretation tasks, while showing comparable performances in discrimination tasks.