Hi everyone,

This is a bit long-winded, but I respect everyone's mind on this list and would 
like any criticism and suggestion, if your time allows.

I would like to include a spatially-lagged variable in logistic regression in 
order to decrease some autocorrelation problems in a land-use change model.  
The model is for the probability of a cell not developed in 1985 becoming 
developed by year 2006.  I would like to use Moran's I to estimate my 
spatially-varying weights.

The problem is this: I am modeling the probability of land becoming developed 
from 1985 to 2006 (to capture contemporary dynamics) but developed land 
pre-1985 likely has an influence, and, so, there is a mismatch between the 
areas having an influence and the response.  For background, the response is 
binary (0=undeveloped, 1=developed).

I could just calculate Moran's I for all areas, but this would also include the 
autocorrelation of cells developed pre-1985 with other cells developed pre-1985 
 (which is not of interest for several reasons including that the areas 
developed long ago were influenced by 'accidents' of history which are wholly 
unobservable, plus the decision-making process for land development changes 
over time).  I could just calculate Moran's I on the increment of growth from 
1985 to 2006 (masking out pre-1985 developed areas) but this would neglect a 
source of contagious effect.

My possible solution is this.  Allow, cross products in the numerator in the 
usual manner between observations in the recent growth increment (1985 to 2006) 
but add a restriction that cell values for pre-1985 growth (minus the mean) are 
only multipliled with the cell values of the current increment (0=not developed 
in 1985 or 2006, 1=not developed in 1985 but developed in 2006).  So, I am 
trying to get at how autocorrelated the new increment is with itself and the 
previous growth (but not previous growth with itself).  The mean for use in 
subtraction would possibly be the mean over all cells in the lattice.  This 
seems in a sense to be the cross-correlation between: 1) all developed cells 
and 2) cells potentially developed from 1985 to 2006 (the response).   When 
stated this way, it seems that possibly two means should be used (all 
development and current increment), ala' the usual covariance and cross 
correlation formulation.

Seth Myers
PhD Candidate
SUNY ESF

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