A kernel density routine developed for the "R" language has been popular for use by government agencies to estimate core home range for Bighorn Sheep using telemetry points as input data. Mostly out of curiosity, I developed a routine using the most recent ArcGIS kernel density routine in Spatial Analyst. It produces very similar results using the HREF bandwidth. I asked the developer of the R home range routine what he thought and this was his answer:
This is his reasoning for using only the R routine and kernel density function and not using the ArcGIS python routine that I developed:
"The main issue is that the Home Range Arc tool uses a quartic kernel density function which is only an approximation to the Gaussian density function employed by the R home range estimator. ArcGIS uses it because it's fast and good enough for visualizations for every day users, but it's not a density function that you actually see in the home range literature. It's also not the density function used when we were estimating the foray frequencies and distances, which is in itself a reason not to go with it."
So, my question to you is "do you agree that ArcGIS is not capable of generating a home range polygon from telemetry points?". My reasoning is that it is because coming up with a representative polygon is an art and not an exact science. Sometimes the home range polygon is even hand delineated using expert knowledge. Also, the ArcGIS kernel density function was improved with version 10.3.
When using the HREF bandwidth what I have observed is that the R-script performance is faster on small datasets of telemetry points but the ArcGIS python routine is faster on larger datasets. The resulting polygon is very close.
Your thoughts regarding this will be appreciated. I have the routine working with both desktop and PRO.