Bivariate maps allow you to display two variables on the same map, revealing deeper insights into data patterns. While these maps can be powerful, we recommend reviewing our best practices for bivariate maps before using one, as they may not always be the best option for storytelling.
Watch this video or scroll down for more detail.
Step 1: Create a Map
To create a bivariate map, you must first start with a basic map. Follow these steps:
Add a map component: In your Dashboard or Report, navigate to where you want the map to appear and add a new map component.
Add a data layer: Once in the map creation screen, select “Add mySidewalk Layer” to select data from the mySidewalk library or "Add My Layer" to select a user layer from your Layers library.
Adjust geography: Set the geography and sub-geography for your map.
Skip this if you're using a user layer from your Layers library.
Style by data: Under the "Fill" property, select "Style by data" and choose your first dataset.
Step 2: Toggle on Bivariate Mode
After selecting your first dataset, you will see the option to toggle on "Bivariate Mode."
Enable Bivariate mode: Toggle the Bivariate option to "On."
Select a second dataset: Choose the second dataset you'd like to visualize alongside the first.
Note: If your first dataset is a georeferenced user layer, you can select either mySidewalk data or data from that user layer for your second dataset. Watch a video of how to do this.
Step 3: Choose a Color Scheme
You can select from two pre-defined color schemes:
The Yellow/Blue palette is more accessible for individuals with visual impairments.
You can swap the color representation by clicking the swap button next to the color options, if needed.
Step 4: Fine-Tuning Your Map (Optional)
To make your map easier to interpret, you may need to make some adjustments:
Adjust outlines: To reduce visual clutter, adjust the line width for your bivariate layer to 1px and reduce opacity to 20%.
Normalize your data: Normalizing data makes it easier to compare different datasets. Learn more about normalization here.
Additional customization: Refer to this article for several options for changing how your map looks and reads. Note that not all of these options are available for bivariate maps due to limitations that help ensure the map is clear and concise; however, many of them are. For example, you might want to change the break methodology or add other layers to draw attention to areas of interest.
Step 5: Interpreting Your Bivariate Map
Once your map is complete, start interpreting the data patterns:
Single color dominance (ex. map is mostly blue): Shows high values in one variable but not the other.
Mixed colors: Shows agreement between the two variables—both are high (black or indigo) or both are low (gray).
You’ve now created and customized a bivariate map! With these steps, you can gain deeper insights into your data by visualizing the relationship between two variables. Remember, if your findings aren't clear, a monochromatic map might be a better option.