[R] spatial statistics vs. spatial econometrics
Prof Brian Ripley
ripley at stats.ox.ac.uk
Mon Aug 4 10:30:13 CEST 2003
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
Brian D. Ripley, ripley at stats.ox.ac.uk
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
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