colorvisualization

Color visualization is the practice of representing data through color to reveal structure and trends. It is used in scientific visualization, geosciences, biology, medicine, and engineering to highlight gradients, clusters, and anomalies.

Common techniques involve color maps or palettes that map data values to colors. For scalar fields, sequential palettes run from light to dark; diverging palettes emphasize a critical midpoint; qualitative palettes distinguish categories. A key design goal is perceptual uniformity: equal data steps should produce roughly equal visual changes, and luminance should be monotonic with data values. Rainbow palettes, while popular, can mislead by nonuniform luminance and hue biases, so many practitioners prefer perceptually uniform maps.

Color mapping can use various color spaces. While many displays rely on RGB, perceptual color spaces such as CIELAB or CAM02-UCS support more uniform color differences and facilitate palette design. Considerations include ensuring sufficient contrast, maintaining consistent lightness, and accounting for nonlinearity of display devices through gamma correction. For vector fields, color can encode direction or magnitude with additional encodings like texture or glyphs.

Accessibility and best practices: account for color vision deficiencies by testing palettes with simulated protanopia/deuteranopia; provide alternatives or redundant encodings; ensure readability in grayscale; document rationale and provide legends. When possible, validate palettes with representative data and avoid implying magnitude when not present.

Applications and tools: Color visualization spans meteorology, earth observation, neuroscience, medicine, and engineering. Standard workflows include selecting an appropriate palette, validating perceptual properties, and integrating color maps into plotting libraries. Repositories and standards such as perceptually uniform palettes and color map guides inform practice.