Hi all,
I'm doing a project for my course, and I got stuck understanding the
requirements of the project.
The project ask me to use 4 ways to do the spatial prediction:
regression-based predictors (no spatial component), ordinary kriging (no
regression component), universal kriging (regression trend and spatially
correlated residuals), and nearest neighbours or inverse distance( no
modeling of spatial correlation). I was totally confused about the with or
without spatial component part, especially for the first and last method.
It made me very depressed because I check a lot of materials and still
don't get it, and I think maybe I just didn't understand the whole point of
spatial statistics.
I'm really appreciated if anybody can explain the difference of these
different prediction methods!

Bingjie

	[[alternative HTML version deleted]]

