Dear R users, I am putting together reading and resources lists for spatial statistics and spatial econometrics and am looking for some pointers from more experienced practitioners.
In particular, I find two "camps" in spatial modelling, and am wondering which approach is better suitied to which situation. The first camp is along the lines of Venables and Ripley's Chapter 14 (and presumably Ripley's book, but I don't have that yet)--spatial trends and kriging (e.g., the geoR package); the second along the lines of Anselin's book--spatial lag and spatial-autocorrelation models (e.g., the spdep package). As far as I can tell, these amount to the same thing (in princple). The first camp likes to use row-standardized "weight matricies" in building covariance structures (to ensure there isn't too much dependence?). I find this very unappealing to many models. This camp doesn't seem to look at variograms or correlegrams as often--they just fit the model, which I also find unappealing. The covariance structures also tend to be very simple. It looks like there is more flexibility in the second camp. Mixed model procedures also seem to have spatial covariance structures. Is there a reason why there appears to be so few cross references between these camps? What makes each approach best for different kinds of problems? I'd greatly appreciate your insights. Many thanks, Michael J. Roberts Resource Economics Division Production, Management, and Technology USDA-ERS (202) 694-5557 (phone) (202) 694-5775 (fax) ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
