I am trying to decide on the correct technique for a multivariate regression with spatial data. I would like to run a regression where the dependent variable is the current snow depth and the independent variables include physiographic parameters (slope, aspect, elevation, etc.) and snow depth for the same site in past years using daily data. The goal is to produce a statistical model with which I can interpolate snow depth across a whole basin based on the physiographic parameters.
Initially I was going to use a standard MVR but came across geographically weighted regression (GWR), which I think is more appropriate since snow depth is very spatially correlated. The third step, after establishing a model and interpolating, would be to distribute the residuals that I'll have at points where I know the snow depth; a common approach in the literature is elevation detrended inverse distance weighting.
- Would it be incorrect to use MVR instead of GWR?
- If I use GWR, would it still makes sense to distribute the residuals? From what I read, GWR already includes some correction for the inevitable residuals unlike MVR.
Please correct me if I'm wrong or seem to have misunderstand anything. I'm quite new to spatial statistics. Most of my GWR knowledge comes from Geographically Weighted Regression.