Estimating catchment area with contour line

[MarkGuagliardo]

I want to estimate the "catchment" area or service area footprint for a series of hospitals (about 125 U.S. hospitals). I have the geocoded residential addresses of patients for each hospital for a period of time, say 1 year.

I don't think bounding containers such as convex or concave hulls are appropriate because every hospital has a small percentage of patients whose addresses are very far away. For example, a Nebraska tourist may be hospitalized in Boston while on vacation. The Boston container would reach to Nebraska. Not very useful.

My thought is to create density rasters from patient locations. One raster for each hospital. Then for each raster estimate the contour lines that would capture 95% of the volume of patient density. I might have to adjust the cut-point higher or lower than 95%, but as long as it is consistent across hospitals that would be okay.

Unfortunately I don't see an easy way to do this for 125 hospitals. For a single hospital I might be able to determine the 5th percentile cut point of raster intensity and use that in the "contour list" tool. But:

1. I am not sure that is the same as estimating 95% of patient density, and
2. It is not practical to do this manually for 125 rasters.

I realize that Geospatial Modeling Environment might have what I need, but it is difficult in our IT environment to get that installed on our server. (Not impossible, but it is an option.)

Suggestions?

[MarkGuagliardo]

Bill,

As always, thanks for the very thorough and thoughtful reply. This will be invaluable guidance when we get around to addressing issues that we probably should be working on.

But for the moment we have a simpler goal - to objectively define the "footprint" of each hospital. Two important points of clarification:

1. Our hospitals are spaced far enough apart that they do not compete with each other for the most part, and

2. We are not concerned (for the moment) about competition from hospitals owned by other systems.

I know this is an unusual situation. We are addressing a narrow and unusual question that I can't elaborate on here. The question requires us to discover the footprint of the hospital. I have called it the catchment area. That might not be the best term for it.

Thanks,

Mark G.

[MarkGuagliardo]

I never got around to solving this problem. I forgot that I had posted here. I did a search and found...my own question already posted by me. And I can no longer see Bill's response, which impressed me at the time.

My recent thinking has identified a problem with my first approach, then a second approach, also with a significant flaw, then a third approach, which is an elaboration of the first approach, but which I don't know how to execute.

Bear with me. Let's keep it simple. Consider a scenario of two hospitals with non-overlapping service areas, and we want to discover the de facto catchment area for 90% of patient cases for each hospital. (Let's also not worry that some persons have multiple encounters.)

Approach #1: Create a density raster. Draw isolines. Choose the one that encloses 90% of patients. There's your catchment area.

Problem: The isoline creation process is not guided by percentage or number of total cases. It is guided by a Z value assigned to each raster cell that represents density of people in the cell's immediate surroundings. So, we can estimate the isoline for 20 patients per square mile but that does not tell us how many patients are within that isoline.

Approach #2: Create drive time service areas at closely spaced time intervals around each clinic location using Network Analyst. For example, one service area polygon for every additional 2 minutes. Next perform some kind of spatial join to capture the number of patient points associated with polygon. Polygon covering 90% of patents is your catchment area.

Problem: The service area polygon represents potential service, not actual patient origination points. A hospital located in the center of town might draw most of it's patients from communities lying to its north. However, the service area polygons are anchored around the hospital location. Thus to get to 90% might require a very large drive time service area that reaches far north while also covering large irrelevant areas to the south, east and west. This method is objective but not necessarily reflective of actual catchment area.

Approach #3: Go back to Approach #1. Draw closely spaced isolines. Export them to a polygon feature class. For each polygon, capture the number of patients it overlays, compute the percentage and find the polygon that covers 90%.

Does anyone see a problem with Approach #3? It seems tedious and could be computationally intensive if applied to 150 hospitals and 5 million patients across the country. Even it is sound logic I don't know how to do it efficiently.