One of the most invaluable classes I took in college was an elective in Urban Studies, where I learned about GIS (Geographical Information Systems). The programs back then were archaic but working through the geospatial theory and informatics helped to shape how I thought about markets in general and SNF markets in particular.
Today’s post is a quick rundown of how one can geographically partition SNF markets. I will focus here on my domicile of North Carolina. First things first, let’s plot a map of the state lines and the SNF’s within NC’s borders.
Our first within-state partition will be by county. I would say that this is the most common partition used when people speak about SNF markets. North Carolina has 100 counties. Counties are convenient partitions because they are generally temporally static and tap into the heuristic of these administrative boundaries.
Our second within-state partition will be by zip code. Zip codes are delineated by the United States Postal Service (USPS) and designate delivery points within the United States and its territories. Zip codes are not constrained by state lines and will cross into neighboring states as one can see below. In my personal experience, Zip Codes are difficult to work with because they are nonsensical for marketing purposes, unless they are aggregated into regions that are more cogent and usable.
The next two geographic partitions derive their meaning from their relation to the SNF(s). The first is an approach where we place a SNF in the focus of a circle and define a radius in terms of distance. In this case, we draw a circle around a random SNF with a radius of 45 miles. We can think of this as a single SNF-centric market.
We can then extend this idea to draw similar circles around all NC SNF’s. By adjusting the opacity of each circle, we can capture the density of SNF markets through how dark certain areas are. We can also see that SNF markets cross state lines, and may exert and receive market action on neighboring states.
One limitation of the radial approach is that the distance is as-the-crow-flies. It does not respect how people actually travel, which is on roads and other conveyances that are not linear. Instead, one can use isochrones. Isochrones, like the radial approach, place the SNF in the focus and then draw radii that are time-based. Finally, those radii or edge points are joined to each other to form a closed shape, i.e. an isochrone. In this case, the radii are based on a car-drive time of 60 minutes. Let’s go ahead and plot one SNF and its 60-minute isochrone.
Again, we again extend this idea to draw 60-minute isochrones around all NC SNF’s. By adjusting the opacity of each isochrone, we can capture the density of SNF markets through how dark certain areas are. Again, we can see that SNF isochronic markets cross state lines, and may exert and receive market action on neighboring states. SNF isochronic partitioning is my favorite way to look at SNF markets because it SNF-centric, encodes the friction of travel, and provides a higher resolution of the market. Moreover, it captures the idea of the “roughness” of markets as popularized by the French mathematician, Benoit Mandelbrot.
Thanks for reading along, and I hope that was useful in furthering your understanding of the geospatial aspect of SNF markets.
