What Is a Heat Map?

Written by Coursera Staff • Updated on

Explore what heat maps are, how professionals use them across different settings, and what tools to learn to begin building your own.

[Featured Image] A social media specialist looks at a heat map on a laptop to better understand user behavior.

Heat maps are visual representations of data that use color to show values or densities. You can use heat maps in many professional settings, as heat maps excel at showing patterns and trends within large and complex data sets. In this article, we will discuss what heat maps are, examples of where you might see heat maps used, how to know if you should choose a heat map for your data, and tools you can use to build your own.

What is a heat map?

Heat maps visually represent data using color to represent the distribution and density of values. You can think of a heat map as a table that uses color instead of numbers to represent values. You can use heat maps to visualize data, draw attention to patterns and correlations, show data variations, and simplify complex data sets. 

When creating a heat map, you assign each table cell (or matrix) a color depending on the data type. Usually, darker colors will represent higher values, while lighter shades represent lower values. You can also choose color pairings, such as warm colors like reds and yellows for “hot spots” and cool colors like blues and greens for “cold spots.” 

Examples of heat map usage across industries

You can use heat maps in many different industries to illustrate data. Some common uses for heat maps include the following:

Understanding user behavior

When analyzing website traffic, you can display user behavior with heat maps. By highlighting the areas of a webpage where users spend the most time, businesses can gain insights into user engagement to optimize their websites. Heat maps can track user behavior like mouse movements, eye tracking, and clicks.

Illustrating gene expression patterns

In biological research, heat maps help researchers visualize and interpret gene expression patterns across different conditions or samples, aiding in identifying genetic trends. 

Exploring geographic characteristics

Heat maps assist in illustrating key characteristics within geographic regions. They can show climate, terrain, or socioeconomic factors variations, enabling informed decision-making in urban planning, environmental studies, and more.

Showing real estate trends

The real estate sector utilizes heat maps to visualize property values, demand, and supply trends across different neighborhoods or regions. This helps identify market trends, such as foreclosure patterns across a geographic region.

Conducting an occupancy analysis

Heat maps help understand occupancy patterns in various spaces, such as offices, retail stores, or public venues. Over time, this information can inform resource allocation and facility management.

Finding crime patterns

Heat maps show crime patterns across regions, which can aid in police efforts and interventions. Over time, heat maps can also illustrate changes in crime as responses to specific events or initiatives.

Mapping surface temperature

Heat maps are valuable in scientific research for mapping surface temperatures across landscapes or bodies of water. They contribute to climate studies, environmental monitoring, and natural resource management.

Defining environmental and geographic distributions

Environmental scientists and geographers use heat maps to visualize species distribution, climate, and pollutants. This aids in conservation efforts, ecological studies, and pollution control.

Conducting a population density analysis

Demographers and urban planners rely on heat maps to analyze population density variations within cities and regions. These maps help inform infrastructure development and resource allocation.

When to choose a heat map

Heat maps are a great choice when you have data suited for this type of visualization. Heat maps can often represent multivariate data in an easily understandable way. Heat maps are also often used with data across a range. This is because ranged data better lends itself to color gradients, showing how values vary across a scale. The nature of your data can vary, but it is important to be able to show variations through color.

Pros and cons of heat maps

Choosing a heat map will also depend on whether the pros are features that are beneficial to you and whether the cons are deal-breakers. While pros and cons will vary depending on your industry and purpose, some commonly experienced pros and cons of heat maps include the following: 

Pros of heat maps

  • Quick data interpretation: Heat maps make it easy for you and your audience to gain a quick insight into the bigger picture of your data.

  • Effective for large data sets: Heat maps excel at representing large data sets, especially for viewers who may not be accustomed to complex data representations.

  • Effective cluster representation: Heat maps excel at showing clusters of similar data points when they compile clearly in one area.

  • Shows the entire data set at once: In some cases, such as plotting Earth science measurements, heat maps can provide a comprehensive view of the data set. Heat maps can be designed on a coordinate system, which allows them to show distributions across geographic regions.

Cons of heat maps

  • May be misleading: If the heat map does not account for all the contributing variables or clearly explain what is being shown, the figure may mislead audiences.

  • Can be difficult to interpret: If the heat map does not have the appropriate gradient or color scale, the results can be difficult to decipher for common audiences. Colors may also look different depending on neighboring colors, so carefully considering your color scale is important.

  • Doesn’t always show exact values: Because heat map visuals don’t always show exact values, answering specific questions may be difficult with some representations due to the limited information provided in the visual.

Tools to build your own heat map

You can explore heat maps further by learning some common tools to build heat maps with your own data sets (or data sets you find online for practice). Many tools can help you build different types of heat maps. To start, check out the following top tools:

  • Excel: Excel offers built-in features for creating basic heat maps. You can use conditional formatting to color cells based on data values.

  • Tableau: Tableau is a data visualization software that you can use to build heat maps. This is a great tool for those with limited programming knowledge who want increased customization.

  • Python (with seaborn): Python is a common programming language used for data visualization. You can use the Python programming language to build heat maps with seaborn.

  • R (with ggplot2): R is another programming language with similar data visualization functionality. The ggplot2 package in R provides tools for creating customized heat maps.

Learn more with Coursera.

You can continue exploring heat maps and other data visualization tools with exciting courses on the Coursera learning platform. As a beginner, consider introductory courses such as Data Visualizations With Excel and Cognos, which can provide an overview and basic skill exercises without requiring background programming knowledge.

Keep reading

Updated on
Written by:

Editorial Team

Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.