Heatmap Chart

A heatmap is a data visualization technique that uses color-coding to represent the magnitude of a phenomenon across 2 categories of data.

Heatmaps are table based charts where instead of numbers, you see a spectrum of colors indicating the values.

An example of an embedded heatmap chart

What are Heatmaps Good At Displaying?

Heatmaps excel at revealing patterns, correlations, and variations within large datasets that might be difficult to see from raw numbers alone. They are particularly effective for:

• Identifying high and low values: Quickly spotting areas of peak and trough activity or concentration.
• Showing relationships between two categorical variables: Understanding how different categories interact based on a third quantitative variable.
• Detecting clusters and outliers: Revealing groups of similar data points and identifying unusual values.
• Visualizing matrices and tables: Making large numerical datasets more accessible and interpretable.
• Analyzing spatial data: Representing data geographically or across other defined areas.
• Tracking changes over time (with animation or small multiples): Showing how patterns evolve.

Definition and Description

At its core, a heatmap maps numerical data points to a color scale. Each cell in the grid representing the intersection of two categories is colored based on the value associated with that combination.

• Axes: Typically, the rows and columns of the heatmap represent two different categorical variables.
• Cells: Each cell at the intersection of a row and a column holds a value.
• Color Scale: A crucial element that translates numerical values into colors. This scale usually progresses from one color (representing low values) to another (representing high values), often passing through intermediate colors. The choice of color palette significantly impacts the interpretability of the heatmap.

How to Create an Effective Heatmap

Creating a clear and insightful heatmap requires careful consideration of several factors:

Choosing the Right Data

Heatmaps are best suited for quantitative data associated with two categorical variables.

Selecting an Appropriate Color Palette

This is paramount for effective communication.

• Sequential Palettes: Use a single hue with varying intensity (e.g., light blue to dark blue) to represent data that progresses from low to high. This is ideal for ordered data.
• Diverging Palettes: Use two contrasting colors with a neutral midpoint (e.g., blue to white to red) to highlight deviations from a central value. This is useful for showing positive and negative correlations or values above and below an average.
• Avoid Rainbow Palettes: These can be difficult to interpret as the color order doesn’t intuitively map to numerical order and can introduce visual artifacts.
• Consider Colorblindness: Choose palettes that are accessible to individuals with color vision deficiencies.

Ordering Rows and Columns:

Strategically ordering the categories on the axes can reveal underlying patterns. Consider ordering by:

• Value: Sorting rows or columns based on the sum, average, or specific values can highlight trends.
• Hierarchy: If your categories have a natural hierarchy, maintain that order.
• Similarity: Grouping similar categories together can reveal clusters.

Labeling Clearly

Ensure all axes and the color scale are clearly labeled. Use concise and understandable labels for the categories.

Providing a Clear Legend

The legend is essential for interpreting the colors. It should clearly show the range of values associated with each color.

Handling Missing Data

Decide how to represent missing data. Common approaches include using a distinct color or leaving the cell blank, but always make it clear what this representation signifies.

Considering Cell Annotations (Optional)

For smaller heatmaps, adding the numerical value within each cell can provide additional detail. However, avoid cluttering larger heatmaps.

When to Avoid Heatmaps

While versatile, heatmaps are not always the best choice:

• Too Few Data Points: For very small datasets, a table might be more direct and easier to read.
• Focus on Exact Values: If precise numerical values are critical, a table is generally preferred over a heatmap, which emphasizes patterns.
• Unordered Categorical Data with No Clear Relationship: If the categories on both axes have no logical order and you’re not looking for specific correlations, other chart types might be more suitable.
• Overly Complex Data: Heatmaps with too many categories or a poorly chosen color palette can become cluttered and difficult to interpret.