A heat map (or heatmap) is a graphical representation of data where the individual values contained in a matrix are represented as colors. "Heat map" is a newer term but shading matrices have existed for over a century.
Heat maps originated in 2D displays of the values in a data matrix. Larger values were represented by small dark gray or black squares (pixels) and smaller values by lighter squares. Loua (1873) used a shading matrix to visualize social statistics across the districts of Paris. Sneath (1957) displayed the results of a cluster analysis by permuting the rows and the columns of a matrix to place similar values near each other according to the clustering. Jacques Bertin used a similar representation to display data that conformed to a Guttman scale. The idea for joining cluster trees to the rows and columns of the data matrix originated with Robert Ling in 1973. Ling used overstruck printer characters to represent different shades of gray, one character-width per pixel. Leland Wilkinson developed the first computer program in 1994 (SYSTAT) to produce cluster heat maps with high-resolution color graphics. The Eisen et al. display shown in the figure is a replication of the earlier SYSTAT design.
Software designer Cormac Kinney trademarked the term 'heat map' in 1991 to describe a 2D display depicting financial market information. The company that acquired Kinney's invention in 2003 unintentionally allowed the trademark to lapse.
There are different kinds of heat maps:
- Biology heat maps are typically used in molecular biology to represent the level of expression of many genes across a number of comparable samples (e.g. cells in different states, samples from different patients) as they are obtained from DNA microarrays.
- The tree map is a 2D hierarchical partitioning of data that visually resembles a heat map.
- A mosaic plot is a tiled heat map for representing a two-way or higher-way table of data. As with treemaps, the rectangular regions in a mosaic plot are hierarchically organized. The means that the regions are rectangles instead of squares. Friendly (1994) surveys the history and usage of this graph.
- A density function visualization is a heat map for representing the density of dots in a map. It enables one to perceive density of points independently of the zoom factor. Perrot et al (2015) proposed a way to use density function to visualize billions and billions of dots using big data infrastructure with Spark and Hadoop.
There are many different color schemes that can be used to illustrate the heat map, with perceptual advantages and disadvantages for each. Rainbow color maps are often used, as humans can perceive more shades of color than they can of gray, and this would purportedly increase the amount of detail perceivable in the image. However, this is discouraged by many in the scientific community, for the following reasons:
- The colors lack the natural perceptual ordering found in grayscale or blackbody spectrum colormaps.
- Common colormaps (like the "jet" colormap used as the default in many visualization software packages) have uncontrolled changes in luminance that prevent meaningful conversion to grayscale for display or printing. This also distracts from the actual data, arbitrarily making yellow and cyan regions appear more prominent than the regions of the data that are actually most important.
- The changes between colors also lead to perception of gradients that aren't actually present, making actual gradients less prominent, meaning that rainbow colormaps can actually obscure detail in many cases rather than enhancing it.
Choropleth maps vis-à-vis heat maps
Choropleth maps are sometimes incorrectly referred to as heat maps. A choropleth map features different shading or patterns within geographic boundaries to show the proportion of a variable of interest, whereas the coloration a heat map (in a map context) does not correspond to geographic boundaries.
Several heat map software implementations are freely available:
- R, a free software environment for statistical computing and graphics, contains several functions to trace heat maps, including interactive cluster heat maps  (via the heatmaply R package).
- Gnuplot, a universal and free command-line plotting program, can trace 2D and 3D heat maps
- Google Fusion Tables can generate a heat map from a Google Sheets spreadsheet limited to 1000 points of geographic data.
- Dave Green's 'cubehelix' colour scheme provides resources for a colour scheme that prints as a monotonically increasing greyscale on black and white postscript devices
- Openlayers3 can render a heat map layer of a selected property of all geographic features in a vector layer.
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Lake effect snow – weather radar information is usually shown using a heat map.
Example showing the relationships between a heat map, surface plot, and contour lines of the same data
Combination of surface plot and heat map, where the surface height represents the amplitude of the function, and the color represents the phase angle.
Score of each contiguous region of a dartboard (not to scale)
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- How NOT to Lie with Visualization – Bernice E. Rogowitz and Lloyd A. Treinish – IBM Thomas J. Watson Research Center, Yorktown Heights, NY
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- Perrot, A.; Bourqui, R.; Hanusse, N.; Lalanne, F.; Auber, D. (2015). Large interactive visualization of density functions on big data infrastructure. IEEE 5th Symposium on Large Data Analysis and Visualization (LDAV), 2015. pp. 99–106. doi:10.1109/LDAV.2015.7348077. ISBN 978-1-4673-8517-6.
- The History of the Cluster Heat Map. Leland Wilkinson and Michael Friendly.
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