HeatMaps

A heatmap is a data visualization that uses color to represent values in a two-dimensional field. By mapping numerical data to colors, heatmaps provide a compact overview that makes patterns, clusters, and outliers quickly visible.

In a matrix heatmap, data are arranged in rows and columns, and each cell's color reflects its magnitude. Geographic heatmaps map values to locations on a map, using color intensity to indicate density or intensity across regions. Variants include density heatmaps, which show the concentration of events, and calendar or time heatmaps, which display values across time along a grid.

Construction considerations include data alignment on a grid, normalization or scaling, and the choice of a color scale. Perceptually uniform palettes (for example from light to dark along a single hue) help avoid misinterpretation. Accessibility concerns call for colorblind-safe schemes and clear legends.

Applications span genomics (gene expression matrices), finance (correlation or risk matrices), web analytics (click or attention heatmaps on pages), user experience research, climatology, and epidemiology.

Limitations include sensitivity to the chosen scale and color map, potential misinterpretation of values without appropriate legends, and overplotting for large datasets. When used, heatmaps should be complemented with explanatory labels and, if possible, alternative visualizations.

Tools commonly used to create heatmaps include statistical and graphics software such as Python's seaborn and matplotlib, R packages, MATLAB, and spreadsheet programs.