spatial regression with large data sets?

[deleted_user]

Hi,
I've tried various methods to model spatially autocorrelated residuals from a logistic regression using lattice (grid cell) data. My data set is too large (>50,000) for GLIMMIX to handle. My machine has 4 gb ram, but I assume this is not enough as I believe a NxN matrix must be populated. Is there a technique that accounts for spatial correlation that can handle larger data sets? I see that HPMIXED has sparse matrix technology but it does not seem to allow for residual correlation as specified by a spherical model, for instance. I've heard that generalized estimating equations (GEE) may work, but I do not see a way to do this in SAS aside from specifying many clusters. The problem is that any one observation could be in multiple 'clusters' in a sense because of spatial distances. The correlation in residuals from a basic logistic regression ends at 700 m distance, so a zero could be in the correlation matrix at this distance, hence my thinking of using sparse matrices if possible. Any advice is very appreciated.
Seth