I am currently about to run a spatial regression (OLS, Lag, Error etc) for my dataset. My dataset contains 1000 randomly generated points throughout the utilisation distribution for my animals (so, there are more or less high and low values attached to whether an animal goes to this area frequently). Since the species that I study has a very specific core range (where most of its time is spent), there will be hotspots (used often) and cold spots (used infrequently). I hope to determine whether their utilisation distribution is primarily in response to predation risk, the location of "competitive" individuals (i.e. other animals) as well as food availability.
In so far, I ran an OLS, Lag Model (there is spatial auto-correlation in my response) and an Error model in the package spdep in R. The Error model usually has the lowest AIC. In addition, I made spatial weights based off of euclidean distance in Geoda.
One thing that has really been bugging me since I have yet to find a guide on this, and I am rather new with spatial analytics involves well...choosing the right weights (i.e. K neighbour selection, queen, rook, distance). It should be noted that since I am dealing with 1000 random points for this analysis...and, they are throughout the landscape (for the animals home range) there are obvious "hot spots" of clusters for high and low values. Attached is a photo from geoda that show where these values are.
Anyways, I apologise for the daft question. However, given the fact that I have the following:
- animal home range data (0.1-99) that were then made into 1000 points (my response)
- these 1000 points show clear clusters (primarily at feeding and sleeping sites)
- predictors (attached to these points through arcgis) including food availability and predation.
I am truly concerned about the appropriate spatial weights to use given this. Digging into the literature hasn't really helped and to be honest, I am beginning to become more confused than informed!