[R-sig-Geo] review on autocorrelation methods

[Marta Rufino]

Hello,

There is recent article about 'methods to account for spatial 
auto-correlation in the analysis of species distribution data", which I 
think it might clarify on some of the points being adressed and it is 
interesting to bring up to this  discussion list.
The authors compare several spatial models, using simulated data, etc. 
And also review the current knowledge on spatial autocorrelation, 
according top their vue (off-course :-)) (which is interesting also).

Dormann et al 2007. Methods to account for spatial auto-correlation in 
the analysis of species distributional data: a review" Ecography.

Hope this helps,
Best wishes,
Marta



> Date: Tue, 29 Jan 2008 20:17:42 +0100
> From: "G. Allegri" <giohappy at gmail.com>
> Subject: Re: [R-sig-Geo] Regression kriging
> To: "Edzer Pebesma" <edzer.pebesma at uni-muenster.de>
> Cc: r-sig-geo at stat.math.ethz.ch, Jose Funes <jefunes at gmail.com>
> Message-ID:
> 	<e12429640801291117j4f31de22iae9d03bbeb6d4218 at mail.gmail.com>
> Content-Type: text/plain; charset=ISO-8859-1
>
> Dear Edzer,
> I've "medidated" on the answer you gave to Jose. Two considerations have raise:
>
>  1 - when you say that the approach of GLM is a way to consider
> spatial dependence. I'm not sure about this. GLM are a way to account
> for link functions between the dependent variables and covariates (ex.
> Poisson family for count datas), but they don't take account,
> implicitly, of sptial correlation. Am I wrong?
> Rather (generalized) mixed models are a counterpart to geostatical methods are.
>
> 2 - A task of my research is to find the "best" relations between a
> set of covariates, to make a simple multicriteria analysis,
> overlapping different map layers thorugh map algebra. In this case,
> the common geostatistical methods don't help me much. I'm considering
> to use multivariate regression, but keeping in count of spatial
> correlation. What's the best approach? I've thought to Mixed Models,
> but another way could be using GLS estimation, based on the residauls
> covariance. What's your suggestion?
>
> Giovanni
>
> PS I think it could be an answer to Jose too...
>
>
>
>
> 2008/1/27, Edzer Pebesma <edzer.pebesma at uni-muenster.de>:
>