Once I have fitted a spatial model (point-referenced data), I need to make a prediction map.
- A natural approach is to make prediction over a fine grid over the region. However, the required resolution is very high, resulting in >10,000,000 points where I need to make a prediction.
- Alternatively, I could make prediction on a coarser grid, so that only, say 10,000 predictions are required, and then interpolate it using some 'quick and easy' method (linear interpolation, spline etc.)
The first approach is theoretically optimal, assuming the model gives some sort of BLUP. The second approach might not be as theoretically sound but is computationally feasible (since there is typically no need to deal with large matrices).
- Am I losing a lot by adopting approach 2 instead of 1?
- At what point is it ok to switch from approach 1 to approach 2?
- In approach 2, how would you determine the resolution of the coarse grid? (apart from whatever computational time you can afford)
(I understand all these depend on context, but general guidelines help)
p.s. I use R, so I wouldn't mind a bit of R in your answer, if you need to.