Nick Hamm | 17 Nov 13:09 2009

Random number seed and unconditional simulation

Dear all

I have a question about the random number seeding in R.

I want to simulate several random fields.  Each RF should have zero
nugget, the same sill but a different range (e.g., 100x100, range: 1
-> 30).  Let's stick with the Gaussian case for now.  I use the
following code

################################
require(geoR)

sigma2 = 10 # Set the sill
s2 = data.frame(phi=1, s2=1, s=1) # phi is the range

for(phi in 1:30)
{

   set.seed(234)
   sim1 = grf(100*100, grid="reg", nx=100, ny=100, xlims=c(1,100),
ylims=c(1,100), cov.model="exponential", cov.pars=c(sigma2, phi),
messages=FALSE)

   image(sim1)
   s2[phi,] = c(phi, var(sim1$data), sd(sim1$data))

}

# Plot the range against  the a priori variance.
plot(s2$phi, s2$s2, ylim=c(0,15))
(Continue reading)

seba | 17 Nov 12:00 2009
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Re: AI-GEOSTATS: Unconditional simulation

Hi Nick
One way is to use simulated annealing (see gslib) putting as objective
function your desired variogram and histogram.
(but I guess that by means of some data transformation you can do that
with a simple sequential gaussian simulation approach)
Bye
Sebas

At 10.06 17/11/2009, Nick Hamm wrote:
>Dear all
>
>I want to simulate a spatially-correlated random field which follows a
>uniform rather than than Gaussian distribution.  Does anybody know a
>straight-forward way to do this?
>
>Nick
>+
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Nick Hamm | 17 Nov 10:06 2009

Unconditional simulation

Dear all

I want to simulate a spatially-correlated random field which follows a
uniform rather than than Gaussian distribution.  Does anybody know a
straight-forward way to do this?

Nick
Enrico Guastaldi | 5 Oct 18:04 2009
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Re: AI-GEOSTATS: Interpolation of measures with measurement errors

Dear Dan and dear lists members,
I'm trying to explain my problem in two steps: the first is the theory, the
second one the practical application on my case study.

1)
Actually my errors are not so gaussian, however I think I could consider
them gaussian like.
It seems I've to set not zero the values of diagonal covariance matrix (used
for the variogram). Maybe I've to put in these matrix the measurements
errors instead zero. However, these are not actual variances, but ranges
deriving from the instrumental error. Is it possible to assume it as
confidence interval and use it such as the diagonal of covariance matrix?
This in theory.

But in practice???

2)
In practice, since I'm not a programmer, I've tried to use the psgp R
package in order to perform the interpolation of my variable with the
associated errors.
I've tried to use the Intamap on line service, but I've got some problem
with the net traffic (maybe) and I did not achieve the result.
So, I installed psgp and intamap in my computer, in order to perform the
calculation locally in my machine.

First ten rows of my dataset are as follows (names of variables:X, Y,
V2_PPM, ERR_V2_PPM:)

715946.900,4826440.340,2.280,0.140
722818.590,4824910.500,2.820,0.140
(Continue reading)

Figen Ramirez | 11 Feb 19:31 2009
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AI-GEOSTATS: theoretical sill

Dear List,

This is a bit long email. Couldn't figure out how to ask shorter.
Please accept my apologies.
I have couple questions about the sample variance and modeling 3D
variograms. In literature I found that some writers strongly recommend
that we have to use sample variance as a theoretical sill and anything
above will indicate a trend. However some papers claim that sample
variance isn't actually representing the total population's variance
so  shouldn't be considered. I am working on 3D modeling. I have
finally had a chance to have a software (GSLIB) that I can check
anisotropy. In GISLIB theoretical sill is optional. Now my questions
are how important do you think theoretical sill? What if you have a
structure that decreases with the distance as it is supposed to be but
the sill is above the theoretical sill?
I have read Isobel Clark's tutorials and followed her and GISLIB guide
and created some variograms for my data:
0, 45, 90 and 135 degrees horizontal,
0, 45, 90, 135, 180, 225, 270 and 315 at a dip of 45 down from horizontal
for 0,45,90 and 135 at dip 90
the vertical variogram clearly shows some trend after 8m which I am
not interested in and also the pair numbers after 8m is very less. The
horizontal variogram is almost flat with a little decrease in gamma at
the beginning and at the end. It is totally above the theoretical
sill. Also it is above the theoretical sill. At this point I am
confused. Could this be the effect of trend in vertical direction? In
45 degree dip down from horizontal the gamma values also increased
above theoretical sill and as the distance increased, the values
decreased??? Without considering the theoretical sill I can say that
vertical variogram shows trend and there is geometric and zonal
(Continue reading)

Picon

AI-GEOSTATS: Illegal downloadable geostats book


Dear all, 

I agree with those of you that have replied to me. The access to the book is
likely to be illegal.

I am subscribed to a keyword alert from Google and forwarded the link
without thinking twice. 

I will inform the authors about the issue. 

I apologize for that...

Gregoire

-----Original Message-----
From: Lena Szymanek [mailto:lena@...] 
Sent: Monday, February 09, 2009 3:18 PM
To: Gregoire DUBOIS (European Commission)
Subject: Re: AI-GEOSTATS: Free downloadable geostats book

Dear Gregoire,

I think this is not a legal way to get this book, unless the authors 
confirm this action.

Best regards

Lena Szymanek

(Continue reading)

Picon

AI-GEOSTATS: Free downloadable geostats book

Dear all,

You may find the following book interesting for you, especially because you
can download it freely from the Internet.

Richard Webster, Margaret A. Oliver. "Geostatistics for Environmental
Scientists"
(Statistics in Practice)
Wiley | 2007-12 | ISBN: 0470028580 | 332 pages | PDF | 5 Mb

http://www.ebooksz.cn/2009/02/geostatistics-for-environmental.html 

Best regards

Gregoire

PS: I hope this message will not arrive 3 times to you  

++++++++++++++++++++
Gregoire Dubois (PhD)

DG Joint Research Centre - European Commission 
Institute for Environment and Sustainability 
Global Environment Monitoring Unit 
Monitoring Of Natural resources for DEvelopment (MONDE)
 
http://gem.jrc.ec.europa.eu
http://www.ai-geostats.org
 
Via Fermi, TP 440,  I-21027 Ispra (VA), ITALY
(Continue reading)

Ashton Shortridge | 23 Jan 17:16 2009
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AI-GEOSTATS: Special Issue on Information Semantics and its Implications for Geographical Analysis

Hello,

Several on this list may be interested in this call. We are especially 
interested in publishing work employing spatial analytical methods, including 
geostatistics, on spatial semantic data. 

---------------------------------------------------------------------------
2nd Call for Papers – Int. Journal of Geographical Information Science
---------------------------------------------------------------------------

Special Issue on
Information Semantics and its Implications for Geographical Analysis
http://www.cogsci.uni-osnabrueck.de/~isga08/SpecialIssue-IJGIS.pdf

Editors:
Ola Ahlqvist, The Ohio State University, U.S.A.
Martin Raubal, University of California, Santa Barbara, U.S.A.
Angela Schwering, University of Osnabrueck, Germany
Ashton Shortridge, Michigan State University, U.S.A.

Ontology and information semantics are central to research on enhanced
interoperability between geographic information systems, services, and data
sets. It is increasingly understood that deeper insights into information
semantics also have consequences for geographic analysis in general.
Development of semantic similarity metrics enables access to a much broader
array of analytical methods for categorical data than those traditionally
employed. Existing examples that demonstrate this potential are fuzzy
accuracy assessment, map similarity estimates, and semantic versions of the
standard variogram. An explicit recognition of the importance of context in
semantic assessments also poses interesting questions for a quantitative
(Continue reading)

Peter Bossew | 14 Jan 09:31 2009
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AI-GEOSTATS: Geostats 2008, Santiago, presentations

Dear list,

some (most) of the presentations of the Geostats 2008 conference, Santiago
1-5 Dec 2008, are now available on-line as pdf's, 
http://www.geostats2008.com/cierre2008/

Peter

-----------------------------------------------------
Peter Bossew 

European Commission (EC) 
Joint Research Centre (JRC) 
Institute for Environment and Sustainability (IES) 

TP 441, Via Fermi 1 
21020 Ispra (VA) 
ITALY 

Tel. +39 0332 78 9109 
Fax. +39 0332 78 5466 
Email: peter.bossew@... 

www:  http://rem.jrc.ec.europa.eu 

"The views expressed are purely those of the writer and may not in any
circumstances be regarded as stating an official position of the European
Commission."

+
(Continue reading)

Frederic HUGUET | 12 Jan 13:53 2009

AI-GEOSTATS: search gam mex sources to use MATLAB Kriging Toolbox on Linux operating system

Dear all,
In order to use MATLAB Kriging Toolbox on Linux operating system, I need gam(s) and ksone mex sources interfacing GSLIB to matlab for compilation.
If anybody can help me to get mex sources, I would be happy.
Thanks in advance
Happy new year

Frédéric HUGUET

Cristiano Ballabio | 9 Jan 11:52 2009
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AI-GEOSTATS: stratifying the spatial domain on the basis of dependent variable

Dear All,

I'm experiencing some doubt about the logic consistency of the procedure I'm following to analyse my data.
The dataset I'm dealing with, comprises 360 sampling points; for each one of this points I have a measure of nitrate concentration in groundwater.
What I'm trying to do is to assess the groundwater vulnerability to nitrate contamination.
The first step has been to interpolate the contamination data from the point observations.
My idea was to approach the problem using regression kriging (given the availabilty of numerous candidate covariates, such as: land use, rainfall, clay content in soils, etc.).

However, after a preliminary look at the data I've found that my data is bimodal. This was not a big concern as the residuals of linear regression are still normal, although strongly heteroskedastic (but this won't affect the estimates of the coefficients, if I recall correctly my statistics courses).

Anyway, after a second look at the data I've tried to separate the two "populations" considering them as a mixture of two normal distributions, the outcome of this clustering shows that each one of these "populations" have a different spatial distribution, as one tends to occupy the northern part of my study area and the other the southern. This difference is not due to anyone of the covariates available, or any factor I can think of...

So the next step I tried was to treat these areas as two different strata fitting a mixed model instead of the simple linear one. This has effectively increased the accuracy of the prediction, because the two groups seem to have different coefficients beside a different intercept.

However what puzzles me is the logic of the procedure. Using the outcome of a process as an input in the estimation of the same outcome sounds like circular logic to me... is this procedure correct?
I know that similar problems are addressed by latent class analysis, but I couldn't find anything about this kind of problems in spatial statistics.
If anybody have dealt with similar problems (or knows of some literature about), I'll gladly hear any suggestion.

Thanks in advance

Cristiano Ballabio


Environmental Sciences Dept. (DISAT)


University of Milano-Bicocca (UNIMIB)


P.za della Scienza, 1


20126 MILANO


Italy


Phone office:      +39 02 64482881


                        +39 02 64482876


Fax:                  +39 02 64482895



Gmane