[R-sig-Geo] Geographically weighted regression on categorical variable

[Guy Bayegnak]

Hi All,

I am trying to perform geographically weighted regression on categorical variables.  The majority of answers I found on the web suggest that this is not doable or not recommended.  I found only one post from Roger Bivan (https://stat.ethz.ch/pipermail/r-help/2007-September/141586.html ) that indicated that it was possible, and that the R-sig-geo list is more focused on this kind of question. I have therefore registered, but I am not sure if the mailing list is "searchable".

I have point data collected over a geographical area A.  My data are groundwater quality type. And I have 3 types. When I plot it of a map it looks like 2 of the types are clustered and occur next to each other.  My suspicion is that these two clustered type may be influenced by their proximity to the some potential sources.  I used the spatstats package to explore the data,  using L-cross function.  The result of the analysis show that the 3 types appear to be influenced by the source, although the two groundwater that are clustered appear to deviate much more from the theoretical L-cross.  Now I am trying to explore the relationship between the water types and the potential source using geographically weighted regression on categorical variables.  Most of the material a read deals with continuous variables, and tend to focus on areal (polygons) features rather than point features.

Is there any way to perform geographically weighted regression on points categorical variables using R?

Thanks,
GAB




This email and any files transmitted with it are confide...{{dropped:7}}