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
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.
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
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.
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.
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.
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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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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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
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.
Labeling
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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