What do heat maps represent in data visualization?

Heat maps are a powerful tool in data visualization, particularly useful for displaying complex data in a way that is easy to interpret at a glance. They represent individual values in a dataset as colors in a two-dimensional space, allowing viewers to quickly identify patterns, variations, and correlations within the data.

The color gradient typically used in heat maps enables the differentiation of values, where different colors or intensities indicate various data ranges. This makes heat maps particularly effective for visualizing large datasets where traditional charts might fall short in clarity or comprehensibility.

For instance, in fields like finance, heat maps can help quickly identify performance metrics across different sectors or market trends, while in genomics, they can illustrate gene expression levels across different conditions. The focus on color-coding individual data points allows for an immediate understanding of complex interrelations and insights that would require deeper analysis if presented numerically or through less visual means.