I completely fail to recognise your description of V&R ch 14. That is (in part) about *continuous* spatial fields, and does include pictures of correlograms and variograms. That's not the same problem as looking for spatial dependence in areally-sampled data.
My 1981 book (is that the one you haven't read?) treats these different problems in different chapters. So do the other widely-cited references in spatial statistics: geographers tend to ignore all the rest of the spatial sciences, however. On Thu, 31 Jul 2003, Michael Roberts wrote: > 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). Wrong. Kriging is primarily interested in prediction. > 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? They are different problems with different fields of applications. There are quite a few other different problems you would probably mis-identify as `camps', including several approaches to spatial point patterns. -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
