How to Create a Density Map in Tableau

A density map is one of the best ways to see geographic hotspots in your data, transforming thousands of overlapping dots into a clear, intuitive heatmap. Tableau makes creating them surprisingly easy, turning raw location data into a powerful visualization that shows where the action is. This article will walk you through exactly how to build, customize, and interpret a density map from scratch.

What is a Density Map (and Why Should You Use One)?

A density map, often called a heat map, visualizes where a large number of data points are concentrated. Instead of displaying each point individually, the map uses color to indicate concentration. Areas with many closely placed points appear "hot" (often shown in red and yellow), while areas with fewer points appear "cool" (in blue and green).

So what's the advantage? Imagine you have a dataset with 10,000 customer addresses in New York City. Plotting each one as a separate dot would create an unreadable mess, especially in dense areas like Manhattan. Individual points would completely overlap, hiding the underlying patterns. A density map elegantly solves this problem by showing you concentration at a glance, indicating:

• Which neighborhoods have the most customers.
• The precise hotspots for ride-sharing pickups during rush hour.
• Areas within a city that experience the highest number of reported crimes.
• Geographic clusters of social media mentions about your brand.

Density maps excel at revealing the overall distribution and concentration patterns when the exact location of single data points is less important than understanding the bigger picture.

Getting Your Data Ready

The foundation of any good map in Tableau is properly formatted geographic data. For a density map, a symbol map, or any visualization where you plot discrete points, you have one primary requirement: latitude and longitude coordinates.

Your dataset must have two separate columns, one for Latitude and one for Longitude, for each and every point you want to plot. While Tableau can generate coordinates from place names like cities, states, or zip codes, this is meant for filled maps (choropleths). For the precision required in a density map, you need to provide the exact coordinates yourself.

A simple dataset might look something like this:

If your source data only contains street addresses but not coordinates, you’ll need to do a process called geocoding. You can use online converters, API services like Google Maps, or specialized software to convert your physical addresses into the necessary latitude and longitude pairs.

How to Create a Density Map in Tableau: A Step-by-Step Guide

Once your data is correctly formatted with latitude and longitude columns, building the density map itself takes less than a minute. Let’s walk through the steps.

Step 1: Connect to Your Data

Open Tableau Desktop or Tableau Public and connect to your data source. This might be a Microsoft Excel file, a CSV text file, or a direct connection to a server like Google Sheets.

Step 2: Assign the Correct Geographic Roles

Tableau is smart, but it's a good idea to confirm that it has correctly identified your location fields. In the Data Pane on the left side of the screen, you should see your fields listed.

• Your Latitude field should have a small globe icon next to it with "N/S" (North/South).
• Your Longitude field should have a globe icon with "E/W" (East/West).

If they don't have these icons (e.g., they just show "Abc" or "#"), you'll need to assign the role manually. Right-click the field, hover over Geographic Role, and select the appropriate role (Latitude or Longitude).

Step 3: Build the Basic Map

This is where Tableau starts to work its magic. To create a map view, you simply place your geographic fields on the correct shelves.

• Drag the Longitude pill from your Data Pane and drop it onto the Columns shelf.
• Drag the Latitude pill and drop it onto the Rows shelf.

As soon as you do this, Tableau will generate a map of the world. Initially, you'll see a single point on the map, as Tableau attempts to plot the average of all your coordinates. This is perfectly normal and exactly what we expect to see at this stage.

Step 4: Plot Your Individual Data Points

Your map needs to be instructed to plot each individual row from your dataset, rather than an aggregate. To do this, you'll need to disaggregate the view using a unique identifier.

Find a field in your Data Pane that uniquely identifies each row, such as "Store ID," "Transaction ID," or "Customer Name". Drag that field and drop it onto the Detail card within the Marks card.

Now your map will display a point or circle for each row in your dataset. Immediately, your map might be filled with so many points that it's difficult to discern on sight, especially in densely populated areas. This is precisely where creating a density map helps solve the issue.

Step 5: Switch to the Density Mark Type

This final step is exactly what turns a chaotic cluster of points into a clear density map. It's surprisingly simple.

On the Marks card, you’ll see a dropdown menu that is likely set to Automatic or Circle. Click this dropdown menu. In the list that appears, select Density.

Instantly, Tableau redraws your map. The individual circles vanish, replaced by the colorful, cloudy concentration display of a density map. Tableau automatically handles the calculations to determine concentrations and applies a default color scheme.

Customizing and Refining Your Density Map

The default map is a great start, but custom touches will make your analysis much clearer and more presentable. Adjusting the color and intensity are the most important edits you can make.

Adjusting Colors

The default blue density map is functional, but you may want something that feels more like a traditional "heat" map or a color scheme that matches your brand.

To change it, click the Color card within the Marks card. From the menu, select Edit Colors.... This opens up a dialog where you can choose from dozens of pre-built color palettes. Palettes like "Temperature Diverging" or "Red-Blue Diverging" work very well for showing hot and cold spots.

Tuning the Intensity

Sometimes the default hotspots can be too subtle or too overpowering. You can adjust the sensitivity of the density calculation using the Intensity slider.

Click on the Color card. Below the color palette options, you’ll see an Intensity slider. Dragging this slider to the right (e.g., to 75% or 90%) will increase the "heat" of your hotspots, making them appear brighter and more focused. Dragging it to the left makes the map less sensitive, requiring more points to create a bright yellow or red area.

Working with Size

The Size card on the Marks card lets you control the radius of a hotspot. Tying this with intensity, you can fine-tune the map's appearance. A larger size creates a smoother, more generalized representation, while a smaller size provides a more detailed, pixelated view.

Adding Map Layers for Context

Lastly, a density map is more useful with some context. What city are we looking at? Which neighborhoods are hotspots? You can add these details by navigating to the Map menu at the top of the Tableau window and clicking Map Layers.... From here, you can toggle on features like county borders, street names, coastlines, and more to make your map more understandable to your audience.

Final Thoughts

Creating a density map in Tableau boils down to a simple, powerful process: use latitude and longitude coordinates, place them on the right shelves, and switch your mark type to "Density." It’s an incredibly quick way to transform a dense dataset of location points into a beautiful visualization that instantly reveals patterns and geographic trends.

Learning tools like Tableau is a fantastic skill, but building visualizations and keeping them updated still takes time away from acting on insights. My colleagues and I created Graphed because we wanted to turn hours of data analysis into a 30-second conversation. We help you connect your data sources - like Shopify or Google Analytics - in one click. Then you can use plain English to get answers, asking things like, "Create a density map of customers from our last marketing campaign," and get an interactive, real-time dashboard built for you instantly.