Map Layering in Tableau: Visualizing High Schools and Postsecondary Institution Recruitment Competition
Geospatial analytics functionality continues to expand in Tableau. The ability to create multiple map layers in spatial visualizations was released in 2020.1. Map layers allow a visual to include data points or geometries of different types (point and polygon, for example) and allow for a user to show or hide or reorder certain layers. The limitations of the dual axis plot are no more.
As an example scenario, perhaps I’m a recruitment and enrollment consultant for associates-level institutions in the state of Illinois. The declining postsecondary pipeline of traditionally aged high school graduates has resulted in institutions becoming increasingly competitive with one another. This is particularly the case for institutions in close proximity to others of a similar profile and as students elect to continue their education closer to home.
To identify the institutions who could benefit most from my services, I’d like to map the location of all public high schools in relation to 2 year institutions in Illinois. Furthermore, I’d like to identify how many associates-level institutions each high school has within a 10 mile radius; this allows me to classify the competition intensity for graduates at each high school.
To accomplish this, I’ll use Tableau’s map layers and spatial function capabilities. Here’s a link to the completed Tableau Public dashboard.
Summary of Steps
First, I’ll map the locations of all public high schools in Illinois.
Next, I’ll add a second map layer to indicate where all associates-level institutions are located that have a public high school within 10 miles in Illinois.
Third, I’ll display the competition for graduates that each high school has by sizing and coloring each mark based on the number of associates-level institutions each has within 10 miles.
Lastly, I want to be able to display the subset of high schools that are within a specified distance of a specific institution. I want to be able to filter while still maintaining the high school competitive gauge in my final results.
With this visualization, I can assist a postsecondary institution in strategically prioritizing where to dedicate their recruitment resources to high schools within a certain distance — for example, should I focus recruitment efforts at the 5 high schools south of Fox College where there are 7 or fewer associates institutions within 10 miles of the high school? Or, should I dedicate all of my resources to a few, larger high schools to the northeast, knowing that there are 16 other equidistant institutions expending their own recruitment resources there to attract students?
More of the Details:
To create this visualization, I’ve used three map layers: high school locations, postsecondary locations, and the circular area buffer around each of the postsecondary institutions. For the data model, I’ve used the intersect join with a buffer calculation in the underlying data of two shape files — one for high schools and the other for secondary institutions.
This physical table is related to a csv extract containing demographic information (level, control, etc.) to allow needed record filtering.
Leave a comment below if you’re interested in more specifics on how I created this visualization.