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.
1. Create a map:
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:
Navigate to the top of the editing panel and add a layer. You can add a MySidewalk Layer or one of your own.
Adjust the desired geography and choose a sub-geography
Select Style by Data within the Fill Color style property
Select your first dataset
2. Toggle Bivariate within a map:
Once you have selected one dataset, you will now see the option to toggle on Bivariate.
Toggle on Bivariate
Select your second dataset
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.)
3. Select color scheme
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.
Pro Tip: Outline Adjustment
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.
Add a new layer that is the same geography as your bivariate layer
Adjust the line width for your bivariate layer to 1 px and the line opacity to 50%
You will likely need to normalize your data in order to make the variables occur in the same universe.
Optional: Change the way my map looks
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.
4. Interpreting your Bivariate Map
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:
Mostly single color (either variable 1 or 2): Data reflects high measurements in one or the other variable, without having high measurement of the other
Mixed colors: “Agreement” between the two variables, i.e. high measurements of each (dark) or low measurements of each (light)