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What are the best practices for using heat maps to identify patterns in large datasets?

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Heat maps are a powerful tool for visualizing complex data and finding patterns, trends, and outliers. They use color gradients to represent numerical values in a matrix or grid, making it easy to compare and contrast different variables or categories. However, heat maps can also be misleading or confusing if not designed and interpreted properly. In this article, you will learn some best practices for using heat maps to identify patterns in large datasets, such as how to choose the right data, color scheme, scale, and layout.

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Jayanth MK

Data Scientist | Phd Scholar | Research & Development | ExSiemens | IBM/Google Certified Data Analyst | Freelance…
Darko Medin

Data Scientist and a Biostatistician. Developer of ML/AI models. Researcher in the fields of Biology and Clinical…
Eren Unlu

Co-Founder

1 Choose the right data

Not all data are suitable for heat maps. You should use heat maps when you have a large number of observations or variables that are related or correlated, and you want to see how they vary across different dimensions or groups. For example, heat maps can be useful for showing the distribution of customer satisfaction ratings across different products and regions, or the correlation matrix of different stock prices over time. However, heat maps are not ideal for showing individual values, exact numbers, or small differences, as they can be obscured by the color scale or the resolution of the screen.

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Darko Medin

Data Scientist and a Biostatistician. Developer of ML/AI models. Researcher in the fields of Biology and Clinical Research. Helping companies with Digital products, Artificial intelligence, Machine Learning.
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This is probably the most important aspect of the heatmap visualizations. Choosing the right data. One can present many types of data on the heatmap, categorical, continuous, with different ranges, different distributions. Frequently the goal is to present many different types of the heatmap and it takes a lot of knowledge but also creativity to effectively use the heatmaps. PS choosing the right data type is important for other reasons too such as answering the right research questions or applicability of dendrograms which frequently go with the heatmaps.

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Eren Unlu

Co-Founder
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Generally, in real use cases you would need a preprocessing layer before heatmap visualizations. For instance, a simple log transformation would not work (log1p might be required, negative values might needed to be translated into a positive interval, box-cox like transformation might be more suitef etc etc). In addition, based on the context, you might always need to customize the palette choice.

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Nizamuddin Siddiqui

Founder - AI Center | Influencer Marketer for AI Businesses | AI Content Creator
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I believe the most important thing is to use a single dark color gradient so that it becomes easy to explain the map to stakeholders. For example, creating a heatmap where the dark color represents higher values and the light color represents the smaller values.

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2 Choose the right color scheme

The color scheme of a heat map can have a significant impact on how the data are perceived and interpreted. You should choose a color scheme that matches the type and range of your data, and that is easy to understand and distinguish. For example, you can use a sequential color scheme (such as shades of blue) for data that have a natural order or direction, such as temperature or income. You can use a diverging color scheme (such as red and green) for data that have a meaningful midpoint or reference value, such as deviation from the mean or percentage change. You can use a categorical color scheme (such as rainbow colors) for data that have distinct groups or categories, such as gender or region. However, you should avoid using too many colors, as they can be distracting or confusing, and you should consider the contrast, accessibility, and cultural implications of your color choices.

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Darko Medin

Data Scientist and a Biostatistician. Developer of ML/AI models. Researcher in the fields of Biology and Clinical Research. Helping companies with Digital products, Artificial intelligence, Machine Learning.
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Many color schemes exist. But essentially, you need to set three types of color and then different nuances for them. A lower/decrease color, neutral/tendency to 0 color and higher/increase color. One example is red, grey, green, another example is blue, no color , red. Color can also be set just to show increase or decrease. So from no color to green for example. Finally, the color scheme should be intuitive for the viewer and translate what is happening in the data towards visual intuition on the heatmap.

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Eren Unlu

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I always say this, the popular full spectrum heatmaps are destroying the proper visualization of data, just because they somehow have a catchy look. Actually, you need to prefer monochromatic hue for a singular value. Only, in case there are logical compartments in values you may choose a custom multichromatic palette, one hue shade for each compartment. One example of this is preferring a bipolar palette like "coolwarm" for negative/positive values.

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3 Choose the right scale

The scale of a heat map determines how the numerical values are mapped to the color scheme, and how the data are distributed and normalized. You should choose a scale that reflects the distribution and variability of your data, and that highlights the patterns and outliers that are relevant for your analysis. For example, you can use a linear scale for data that are evenly distributed and have a small range, such as test scores or ratings. You can use a logarithmic scale for data that are skewed or have a large range, such as population or income. You can use a z-score scale for data that are centered around a mean or standard deviation, such as deviation from the mean or percentage change. However, you should be careful not to distort or exaggerate your data by using an inappropriate scale, and you should always label and explain your scale clearly.

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Jayanth MK

Data Scientist | Phd Scholar | Research & Development | ExSiemens | IBM/Google Certified Data Analyst | Freelance Trainer | Instructor | Mentor | Data Science | Machine Learning | AI | NLP/CV |
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choosing the right scale for a heat map is akin to selecting the right lens for a clear view. It's crucial to align the scale with the nuances of your data, ensuring that patterns emerge distinctly without distortion. Through experience, I've learned that a thoughtful choice of scale can accentuate relevant trends and outliers, facilitating a more insightful interpretation. Transparency in labeling and clear explanations become paramount, acting as a guide for users to navigate the heat map terrain effectively. This practice not only enhances the visual appeal but also ensures the accurate portrayal of data dynamics in large datasets.

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Darko Medin

Data Scientist and a Biostatistician. Developer of ML/AI models. Researcher in the fields of Biology and Clinical Research. Helping companies with Digital products, Artificial intelligence, Machine Learning.
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Heatmap is one of the few data visualization tools where data transformation is often needed. Maximum values can inflate and bring closer the rest of the values frequently and scaling is often needed. Log transformation can help frequently, but keep in mind that log transformation can alter the data characteristics. Min-max scaling can be used and is often better option that log transformation. Power transformation can be another good option but only for specific situations where power transformation will not remove the important aspects of the data. Row-wise, column-wise scaling can also be used. Discretization or creating your own scale is my favorite way of scaling the data for heatmaps.

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4 Choose the right layout

The layout of a heat map refers to how the rows and columns of the matrix or grid are ordered and arranged, and how the data are grouped and segmented. You should choose a layout that facilitates the comparison and exploration of your data, and that reveals the patterns and relationships that are meaningful for your analysis. For example, you can use a hierarchical layout for data that have a nested or hierarchical structure, such as product categories or geographic regions. You can use a clustered layout for data that have a similarity or distance measure, such as correlation or dissimilarity. You can use a sorted layout for data that have a ranking or ordering criterion, such as frequency or magnitude. However, you should avoid using a random or arbitrary layout, as it can obscure or mislead your data, and you should always provide labels and legends for your layout.

Heat maps are a versatile and effective way to visualize large and complex data, but they also require careful design and interpretation. By following these best practices, you can create heat maps that are clear, accurate, and insightful, and that help you identify patterns in your data.

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Darko Medin

Data Scientist and a Biostatistician. Developer of ML/AI models. Researcher in the fields of Biology and Clinical Research. Helping companies with Digital products, Artificial intelligence, Machine Learning.
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One of the best ways to present heatmaps is using the layouts which include hierarchical clustering. Different types of clustering algorithms can be applied and based on them the heatmap ordered. Programming languages such as R even have base function which implement this way of presenting the heatmap. The heatmap can also be segmented based on clustering. In addition to clustering advanced heatmaps other segments such as categorical columns which can facilitate interpretation. Many columns can be added on the side of the heatmap or on the top and represent different domain based categorical information. One package to recommend, 'ComplexHeatmap' for R.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the best practices for using heat maps to identify patterns in large datasets?

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3

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5

1 Choose the right data

2 Choose the right color scheme

3 Choose the right scale

4 Choose the right layout

5 Here’s what else to consider

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What are the best practices for using heat maps to identify patterns in large datasets?

1

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5

1 Choose the right data

2 Choose the right color scheme

3 Choose the right scale

4 Choose the right layout

5 Here’s what else to consider

Statistics

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