Heat map

A heat map (also written as heatmap) is a way to visualize two-dimensional data by using color to show the size of individual values. The color can vary by shade or brightness. In fields like crime analysis or website click-tracking, color may instead show how densely data points are clustered together, rather than a specific value for each point. Although the term "heat map" is fairly new, people have been shading matrices for more than a hundred years.

The technique began with two-dimensional displays of values in a data matrix, where larger values appeared as small dark gray or black squares (pixels) and smaller values as lighter squares. The earliest known example comes from 1873, when Toussaint Loua hand-drew and colored a shaded matrix to show social statistics across Paris districts. In 1899, Flinders Petrie introduced seriation—reordering rows and columns to reveal structure in a data matrix. Louis Guttman developed the Scalogram in 1950, a method for ordering binary matrices to expose a one-dimensional scale. In 1957, Peter Sneath displayed cluster analysis results by permuting rows and columns to group similar values together. Robert Ling implemented this idea in 1973 with a computer program called SHADE, using overstruck printer characters to create shades of gray, one character-width per pixel. Leland Wilkinson created the first computer program for cluster heat maps with high-resolution color graphics in 1994 (SYSTAT). The Eisen et al. display replicates that earlier SYSTAT design. Software designer Cormac Kinney trademarked the term "heat map" in 1991 for software showing real-time financial market data. SS&C Technologies, Inc. acquired the trademark in 1998 but did not renew it, so it was annulled in 2006.

There are two main types of heat maps: spatial and grid. Over ten subtypes exist. A spatial heat map shows the magnitude of a spatial phenomenon as color, usually overlaid on a map. For example, temperature can be displayed across a world map, with colors ranging from blue (cold) to red (hot). A grid heat map shows magnitude as color in a two-dimensional matrix, where each dimension represents a category of trait, and the color shows the measurement for the combination of traits. For instance, one dimension might be year, the other month, and the value temperature—showing how temperature changed over years in each month. Grid heat maps are further divided into clustered and correlogram types. A clustered heat map, like the monthly temperature example, organizes data into clusters. A correlogram is a clustered heat map with the same trait on both axes, showing how traits interact; it appears as a triangle because the combination A-B is the same as B-A. In a grid heat map, cells are fixed in size and shape, and the goal is to detect or suggest clusters. A spatial heat map, often used on maps or satellite imagery, has no cells; colors vary continuously.

Heat maps are widely used because they simplify data and make analysis visually clear. In business analysis, they provide a visual overview of a company's performance, functioning, and areas needing improvement, helping teams and clients see growth and other efforts. On websites, multiple heat maps are often combined to understand user behavior and identify best- and worst-performing page elements. Specific types include mouse tracking (or hover) maps, which show where users move their cursor; eye tracking maps, which measure eye position, fixation volume, duration, and areas of interest; click tracking (or touch) maps, which visualize clicks on both clickable and non-clickable page elements; AI-generated attention maps, which use algorithms to predict where a visitor's attention will go; and scroll tracking maps, which show scrolling behavior and which sections users spend the most time on. Heat maps are also used in exploratory data analysis for both small and large datasets.