I have limited experience with spatial analysis, but I am trying
to learn. Perhaps someone with patience for a newbie will field my
questions? If this is not the appropriate forum for such questions
perhaps someone could suggest a more appropriate forum.
I have a small dataset that consists of 128 x and y coordinates
(latitude and longitude, on a small field scale). At each
coordinate there are measurements for various soil properties
along with a biomass yield measurement for each x,y.
Via spdep, using k=4 nearest neighbors for the weights, I have
calculated Moran's I and Geary's C for the univariate
relationships. For most of the measured variables there is
statistically significant spatial autocorrelation.
I have also used Rgeo to plot some quintile scatterplots for each
variable. Just from looking at these quintile plots I can see
that, although the soil variables and yield are spatially
autocorrelated, there does not seem to be a relationship between
the patterns observed in soil variables vs. the patterns observed
in the yield.
In other words, I would like to know if there is a relationship
between yield and phosphorus, taking into account the spatial
dependence. But how can I test this? I have used GeoDa to compute
the multivariate Moran's but I don't really understand what the
program is doing or if this is the correct approach.
Another thing I would like to be able to do is, for each variable,
interpolate between measurements to create 2D maps of the response
surface.
I've searched everywhere I could think of but can't find any clear
(and simple) examples to follow. This site:
http://leg.ufpr.br/geoR/geoRdoc/geoRintro.html was some help but
still kind of beyond me.
Can someone suggest a starting point (a book, article, or
website) that might point me in the right direction? It would be
especially great to have some examples using R to follow.
I would really appreciate any advice on these matters!
Greta