Hello,

 

I am looking for advice on what kind of models I can use for my data. I
originally posted to the r-sig-gr list but it's a bit quiet there.

 

The dependent (y) variable are count data (poisson - possible overdispersed)
and are located at an irregular pattern. The independent data (x1, x2, x3,
...) are based on regions (closed polygons) (but could be represented at the
centroids of these regions if this helped, but these centroid will not
correspond to the location of the y's). My problem is that I do not have an
exact (other than geographical) data match between the y's and x's. In
particular I have about 8,000 y observation points and 20,000 x regions.

 

What I would like to examine is how the proximity of various regions (via
their x1, x2, x3, ... values) influenced the value of my y variable. I have
looked at some common spatial models (SEM, SAR and GWR) but they all require
the y and x data to be observed at the same locations. 

 

What I have done is to aggregate my y counts to the region geography, so
that I have for each region a y aggregated count and the corresponding x1,
x2, x3, ... , but I have lots of missing values where a region has no y
count, and my concern is that these missing values will mean I lose the
information about these regions in forming my relationship.

 

Thanks.

 


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