A heatmap shows patterns, trends, and correlations in data. It does this by using color as a direct representation of the values. By adding a date or a time scale on the x-axis it shows how the values evolve over time.

A heatmap can serve as a perfect starting point to get the lay of the land. It lets you explore the data and it gives hints on where to look for other outliers, other viewpoints, or specific angles. Combining a heatmap with other charts might be a good idea if your goal is to tell a more detailed story.

A heatmap can have the shape of a table or a matrix or it can function as a color layer over a geographical map. If shaped as a matrix, a heatmap is a perfect way to reveal correlations. A heatmap as an actual map shows the density of a value at a certain place or area. 

Heatmaps can also be seen as a layer over other chart types. Coloring those charts numerically uses the same visualization logic and has the same impact as a ‘regular’ heatmap.


Variations on a heatmap

The charts below are variations on the heatmap. To learn how to make them with Datylon, check out the heatmap user documentation in the Datylon Help Center.
One dimensional heatmap
One dimensional heatmap

One dimensional heatmap

If you want to zoom in on one category and focus on the evolution of that variable, you can use heatmaps in only one dimension. These charts are very popular in climate communication and often visualize temperatures.

Geographical heatmap
Geographical heatmap

Geographical heatmap

If there is a geographical dimension to your data, you can add a color layer to a map and show the density of a value at a certain place or area. 

A choropleth map is a variation on the heatmap
A choropleth map is a variation on the heatmap

Choropleth map

Although a choropleth map is visually similar to a geographical heatmap, it does show data in a different way. In choropleth maps, colored regions correlate with geographic or artificial boundaries. The color shows a proportional value, such as an average, for one of those delineated regions

Alternatives for a heatmap

Substitute your heatmap with any of the charts below when you want a visual alternative, that still allows you to explore the data.
Replace your heatmap with parallel coordinates
Replace your heatmap with parallel coordinates

Parallel coordinates

When both dimensions in the data are categorical, we can replace a heatmap with parallel coordinates. Instead of using color to represent value, the parallel coordinate uses the location of the categories on the axis to denote the values.

Replace the heatmap with a bumpchart or another multi-series line chart
Replace the heatmap with a bumpchart or another multi-series line chart

Bump chart or other multi-series line charts

A heatmap data with an evolution dimension is easily transferable to a multi-series line chart. The correlation factor will be lost a bit but instead, you will be able to compare the values between the series much better.

Replace your heatmap with a scatter plot
Replace your heatmap with a scatter plot

Scatter plots with size and/or color binding

If you want to keep showing correlations in the data, and you want to plot the numerical values more specifically on an axis instead of in bins, you can choose a scatter plot. Binding the marks to the color or the size shows the density of a value.

Pro tips for designing a heatmap

Learn how to improve the readability and visual appeal of your map.
The different types of scales for a heatmap

Coloring - 3 types of scales

Choosing the type of scale depends on the type of data that gives color to your heatmap and on the level of detail you want your reader to have. We can choose between a categorical, a numerical sequential or a numerical diverging scale.

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Selecting the right scale for your heatmap

Coloring - numerical or categorical scale

If your data has an order to it, meaning that it is somehow sortable, a numerical scale is the one to go with. If the data is nominal, you should choose a categorical one.

When the data only varies in one direction, a sequential scale is the best choice. When your numerical data has a logical breakpoint and the data varies in two directions, a diverging scale is the way to go.

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Choosing a stepped or a continuous scale for your heatmap

Coloring - stepped or continuous scale

Finally, there is a difference between stepped or continuous scales. With data that is not continuous, but ordinal, you should always go for a stepped scale.

But with continuous data, we can choose what scale we want. Choosing a stepped scale for continuous data helps you make your point more clear and lets your readers derive values more easily. A continuous scale gives a more nuanced view and allows more interpretation up to the reader.

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Sorting your heatmap


Sorting the columns in a tabular heatmap is not always possible.
If your x-axis is numerical or temporal, you cannot sort it at all.
If it is categorical, and there is no order to be followed, sorting it ascending or descending might improve readability. This also goes for the categorical Y-axis.

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Adding data labels to every cell of your heatmap


If you do want to add an extra layer of detail to your heatmap, you can add data labels to every ‘cell’ in the matrix. This also works the other way around. If you have a flat table or data sheet, adding a color layer to the values can instantly help the readability and comprehension of the data.

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Are you ready to make your own heatmap with Datylon? 

Create a Datylon account and get started for free online or download
a Datylon for Illustrator plug-in with a 14-day trial.
Connect with a Datylon expert for a free demo session. 

Heatmap inspiration

Scroll and click on the images below to find inspiration samples of heatmaps. With your Datylon account, you can use these designs, customize them and update them with new data.