Dual Axis (Layered) Maps in Tableau
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  • Bernard K

Dual Axis (Layered) Maps in Tableau

Updated: Nov 8, 2022


Dual axis (layered) maps in Tableau

Dual axis map is a map with two sets of geographical data overlaid on top of one another. For example, a filled map of different countries with data points for each country’s capital city layered on top.

A good example of a dual axis map is in the example below showing “Number of fatalities as a result of conflict around Africa” – In this map, we’ve shown both fatalities aggregated at country level as well as exact locations where these fatalities occurred.

Example of a dual layered axis map in Tableau

You can access this map here

So, how can I create a dual axis map in Tableau?

To demonstrate this, I will be using Superstores data set to create a dual axis map showing a filled map of different states in US with data point for each city layered on top.

Step by step guide

Lets begin by building a filled map showing States by Sales

  • Drag State to details shelf

  • Drag Sales to color shelf

  • Chose Map under marks card

Simple map in Tableau

Duplicate the Longitude (generated) field as shown below

Duplicating map views
  • On the second Map, remove all dimensions and measures present.

  • Drag city to the detail shelf

  • Choose circle under marks card

Make the map dual axis

Dual axis maps

Executing we’ve

Adding Sales to the Size shelf of the second chart (circle) to resize the cities according to the Sales generated.

Final dual axis map in Tableau

With this dual axis layered map, users can easily tell which State generated more Sales while at the same time tell the leading Cities within those high performing States or in other States.

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About Me

More About the Author

Bernard K

Analytics Consultant | 3X Tableau Certified

Bernard is a data analytics consultant helping businesses reveal the true power of their data and bring clarity to their reporting dashboards. He loves building things and sharing knowledge on how to build dashboards that drive better outcomes.

Let’s discuss your data challenges! Let’s work together!

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