Hi,

I'm looking for a type of exploratory analysis for spatial data that will
help me to determine the relationship between presence/absence of developed
land use (a complete raster coverage) and minimum distance to lakes or
ponds WHERE the surface area of the lake or pond might exert an influence.

The ultimate goal would be construction of a predictive model taking into
account proximity of various size lakes/ponds, if using the size of
lakes/ponds is warranted.  I'm expecting that larger lakes will have
greater clustering of developed land use for obvious reasons (recreation
and aesthetic properties) and so warrant not being lumped in with smaller
lakes/ponds for use as predictor.

I could construct a map of minimum distance to lakes/ponds that is weighted
by size of lake/pond (larger is weighted more) but the choice of weighting
scheme to use seems arbitrary or computationally intense.  It would be
arbitrary if I do it without any exploratory analysis first (e.g. assume a
weight equal to inverse of area) and computationally intense if I construct
this variable iteratively during the fit of my predictive model (it would
likely be impossible, I'm guessing).  Or, I could break lakes/ponds into
various size classes, construct a spatial variable ( minimum euclidean
distance) from each class, and then enter them all in the model separately.
 However, if I were to do this, I would like my choice of breaks to
classify lakes/ponds by size to be informed by some preliminary analysis.

So sorry this was a bit long in order to express the idea, but can anyone
simply throw out a few types of spatial analyses that might guide my
choices.  I'm not necessarily asking for much detail, just analysis types
to read about more on my own.

Thanks,
Seth

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