A quick word of caution before jumping in: in most cases, bivariate maps are not the best option for communicating a story in a map. We recommend reading our best practices to creating a bivariate map to decide if your situation calls for one.

Watch this video or scroll down for more detail.

In order to create a bivariate layer, you must start with a map. Navigate to the place in your Dashboard or Report that you would like your map to go, and add a map component.

Now that you are in the map creation screen:

Once you have selected one dataset, you will now see the option to toggle on Bivariate.

Note: If you have chosen a georeferenced user layer as your first dataset, then you have a choice to choose mySidewalk data or more data from that user layer as your second dataset. (Watch a video of how to do this.)

You may choose from 2 pre-selected color schemes. The yellow/blue palette is the more ADA friendly choice.

You can swap the color representation (which color applies to which variables) by choosing the swap button next to the colors.

There’s one more thing you can do to make your map audience-friendly, and that has to do with the outlines of your sub-geographies (e.g. census tracts, block groups, etc.). Right now, these outlines add visual clutter to our map, which makes the findings we want to communicate more difficult to see at a glance. Part of this clutter is a result of the fact that you are currently visualizing both layers with outlines, but because each layer is using the same sub-geography, this is an unnecessary duplication. These steps will help your data rise to the top of our map.

You will likely need to normalize your data in order to make the variables occur in the same universe.

Because we are asking our audience to understand more information than usual, bivariate map layers limit some options to make sure that our map is clear and concise. However, you might want to change the break methodology or add another layer to outline the area you are referring to. Make sure your settings match on both dataset selections. Refer to How to change the way your map looks for help.

Congratulations, you’ve just made a bivariate map! Like any map, the next step is to answer the question “what is this map telling me?” Because you are blending two individual variables into a bivariate map, this is slightly more challenging than a monochromatic map but you should easily be able to start seeing patterns right away. To interpret those patterns, make use of the below guide to start your analysis and storytelling: