Hi list,

can anybody recommend  software (windows; free if possible !) for
calculating multifractal spectra (f(alpha)-curves) ? I did not find
anything in the geostats archive. (This might be slightly off-topic
however.)

Thanks for any hint,
p

=================================================================

Dr. Peter Bossew
Institute of Physics and Biophysics, University of Salzburg, Austria

home: A-1090 Vienna, Austria, Georg Sigl-Gasse 13/11, ph: +43-1-3177627
peter.bossew@...
peter.bossew@...



--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

Hi everyone, I am new user of the list, so I apologize in advance if the
subject I want to ask has already discussed.
I want to know the procedure for put a graph postscript created
gslib,in a document
LATEX. I did the following
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{figure}
\begin{center} \centerline{\hbox{ \epsfxsize=15cm \epsfysize=8cm
\epsffile{locmap.ps}}} \caption{text.} \end{center}   \end{figure}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

using \usepackage{epsfig}

Thanks

Bernardo Lagos
Depto. Estadistica,
Universidad de Concepcion.
blagos@...


--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

In GSLIB web page exist a link to download an postscript it work good and
ease, look for gsv26550.exe in Google.
>
>
> ----- Original Message -----
> From: "Bernardo Lagos" <blagos@...>
> To: <ai-geostats@...>
> Sent: Friday, July 25, 2003 9:47 AM
> Subject: AI-GEOSTATS: gslib
>
>
> > Hi everyone, I am new user of the list, so I apologize in advance if the
> > subject I want to ask has already discussed.
> > I want to know the procedure for put a graph postscript created
> > gslib,in a document
> > LATEX. I did the following
> > %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
> > \begin{figure}
> > \begin{center} \centerline{\hbox{ \epsfxsize=15cm \epsfysize=8cm
> > \epsffile{locmap.ps}}} \caption{text.} \end{center}   \end{figure}
> > %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
> >
> > using \usepackage{epsfig}
> >
> > Thanks
> >
> > Bernardo Lagos
> > Depto. Estadistica,
> > Universidad de Concepcion.
> > blagos@...
> >
> >
> > --
> > * To post a message to the list, send it to ai-geostats@...
> > * As a general service to the users, please remember to post a summary
of
> any useful responses to your questions.
> > * To unsubscribe, send an email to majordomo@... with no subject and
> "unsubscribe ai-geostats" followed by "end" on the next line in the
message
> body. DO NOT SEND Subscribe/Unsubscribe requests to the list
> > * Support to the list is provided at http://www.ai-geostats.org
> >
>




--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

Good day to the Southern hemisphere !

I presume many are on holiday in the northern hemisphere, hence I hope to get
more feedback from the South :)

In a paper published in Nature (Nature, 1981, Vol. 294, pp. 240-242: Fractal
dimensions of landscapes and other environmental data), Peter Burrough
investigates the fractal dimension of various environmental data by mean of
the slope of the log-log plot of the semivariogram. As a result, Burrough gets
for each variable investigated a fractal dimension that is fluctuating between
1 & 2, as it is the case for 1 dimension. The author suggests the use of D to
as guide for further mapping and interpolation.

Burrough "estimated D assuming that the real data are but a series of
regularly spaced samples of the Weierstrass-Mandlebrot function over
one-dimensional space or time"

My questions are the following ones: what are the pratical consequences in
making the above cited main assumption, that is using spatial data distributed
in 2 dimensions and to consider the variogram as if the data were sampled in
one dimension, like in a transect?

Can one reasonably extrapolate (that is adding +1 to the fractal dimension
obtained above from the log-log plot of the semivariogram) the fractal
dimension in a "pseudo" 1-dimension to a 2-dimensional problem if the
investigated phenomenon does not show any anistropy ?

Thanks for any feedback.

I will summarise useful replies & references.

Gregoire





--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

Dear Gregoire,

Although I am still living in the Northern hemisphere
and today is my wedding anniversary, I will answer
your question..:) In fact I have recently reviewed a
paper dealing with 2-D fractal analysis and I found
the following reference to be of great interest:
Butler et al. (2001) Characterization of the structure
of river-bed gravels using two-dimensional fractal analysis.
Mathematical Geology, 33(3): 301-330.

If the variability is isotropic, you can indeed derive the 2D fractal
dimension by adding 1 to the dimension estimated from the omnidirectional
semivariogram.

In presence of anisotropy, the authors present 2 different approaches:
1. Estimate the fractal dimensions from directional semivariograms,
and by analogy with the rose diagram of ranges they built rose
diagrams of fractal dimensions.
2. Construct variogram maps/surfaces  and estimate fractal dimensions
from semivariance profiles sampled along specific directions.

Again this paper is nicely written and discusses methodological issues
related to 2-dimensional fractal analysis.

Cheers,

Pierre
<><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

Dr. Pierre Goovaerts
President of PGeostat, LLC
Chief Scientist with Biomedware Inc.
710 Ridgemont Lane
Ann Arbor, Michigan, 48103-1535, U.S.A.

E-mail:  goovaert@...
Phone:   (734) 668-9900
Fax:     (734) 668-7788
http://alumni.engin.umich.edu/~goovaert/

<><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

On Sun, 27 Jul 2003, Gregoire Dubois wrote:

> Good day to the Southern hemisphere !
>
> I presume many are on holiday in the northern hemisphere, hence I hope to get
> more feedback from the South :)
>
> In a paper published in Nature (Nature, 1981, Vol. 294, pp. 240-242: Fractal
> dimensions of landscapes and other environmental data), Peter Burrough
> investigates the fractal dimension of various environmental data by mean of
> the slope of the log-log plot of the semivariogram. As a result, Burrough gets
> for each variable investigated a fractal dimension that is fluctuating between
> 1 & 2, as it is the case for 1 dimension. The author suggests the use of D to
> as guide for further mapping and interpolation.
>
> Burrough "estimated D assuming that the real data are but a series of
> regularly spaced samples of the Weierstrass-Mandlebrot function over
> one-dimensional space or time"
>
> My questions are the following ones: what are the pratical consequences in
> making the above cited main assumption, that is using spatial data distributed
> in 2 dimensions and to consider the variogram as if the data were sampled in
> one dimension, like in a transect?
>
> Can one reasonably extrapolate (that is adding +1 to the fractal dimension
> obtained above from the log-log plot of the semivariogram) the fractal
> dimension in a "pseudo" 1-dimension to a 2-dimensional problem if the
> investigated phenomenon does not show any anistropy ?
>
> Thanks for any feedback.
>
> I will summarise useful replies & references.
>
> Gregoire
>
>
>
>
>
> --
> * To post a message to the list, send it to ai-geostats@...
> * As a general service to the users, please remember to post a summary of any
useful responses to your questions.
> * To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
> * Support to the list is provided at http://www.ai-geostats.org
>
>


--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

In the book "Geostatistics for Environmental Scientists"
By Richard Webster & Margaret A. Oliver, Wiley (2000), one will find in page
180 a brief discussion on the back-transformation of the kriging estimates.

In ordinary kriging, when the natural logarithm (ln) is used, the
back-transformation will involve the Lagrange parameter (see equation 8.36).
No problem so far.

But... the authors write in equation 8.38 that if one is using common
logarithms (log10) instead, the unbiased back-transformation of the ordinary
kriging estimates does not involve the Lagrange multiplier anymore.
Is this correct ? In the affirmative, can someone point me to a paper
discussing "natural" log normal kriging versus "common" log normal kriging ?

Thanks again for any help.

Regards,

Gregoire





--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

Gregoire

Thank you for pointing out the lognormal section in
Webster & Oliver. I must confess I hadn't got round to
looking at it in detail.

Their simplification of the lognormal variance is
based on the assumptions (see p.179) that:

(a) the lagrangian multiplier would be close to zero
if the mean is well known
(b) the simple kriging weights would sum close to one
if the data is dense enough

The assumption (a) is one which has also been asserted
by Peter Dowd in some of his publications.

From practical experience (over 30 years) we find that
the lagrangian multiplier is seldom close to zero and,
in fact, where data is dense will tend to be large and
negative.

We have also done some fairly intensive practical
studies of simple kriging and found that, where data
is dense, the kriging weights will tend to be very
much greater than 1 so that the wieght applied to the
"known" mean will be large and negative. Where data is
sparse, weights sum to very much less than 1 so that
poorly sampled areas are allocated the 'global' mean.

Equations 8.35 and 8.39 rely on these assumptions and
the implicit one that the only difference between the
variance of the real values and that of the estimates
is due to the simple kriging variance (i.e. no
condiitonal bias). It has been asserted by several
authors that simple kriging corrects for conditional
bias. Would that that was true!!

Equation 8.36 for ordinary kriging is correct, but we
prefer to use Sichel's proper lognormal confidence
intervals rather than back-transform the variance as
shown in equation 8.37. To use this form you would
have to assume that your errors were Normal even
though your data was lognormal.

I think there is a typo in equation 8.38 and the
subscript 'Y' should be 'SK' to bring it into line
with the other formulae.

The definitive math on the lognormal backtransform can
be found in Noel Cressie's book in equation 3.2.40
(for both types of kriging). Simpler explanations of
the same form can be found in some of my papers at
http://uk.geocities.com/drisobelclark/resume/Publications.html
(note the capital P and look for papers in the second
half of the 1990s).

Isobel Clark
http://geoecosse.bizland.com/whatsnew.htm

________________________________________________________________________
Want to chat instantly with your online friends?  Get the FREE Yahoo!
Messenger http://uk.messenger.yahoo.com/

--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

Dear list members in both hemispheres;

For my PhD thesis, I am studying the spatial variability of penetration
resistance (a proxy for strength) in snow layers in an Alpine snow
cover. Of specific interest are weak layers that are responsible for
snow avalanche release. Are such weak layers less spatially variable
than layers that are not critical for snow stability? To answer this, I
want to compare the range, sill and nugget from model semivariograms
calculated for all layers investigated. I also calculate mean and CV for
each layer.

Measurements:
At 113 locations on each small slope (20m x 20m), measurements of
penetration resistance were made in a nested grid with a spacing of 0.5m
to 2m. The penetration resistance for each layer within the grid was
recorded at all locations. I have data from approximately 100 layers.
Weak layers were identified with separate tests within each grid.

Analyses:
The penetration resistance for each layer was log10 transformed to
approach normality. Grid-scale trends for each layer were investigated
with a (robust) linear regression on the x-y coordinates. In most
layers, this trend was statistically significant, but often in different
directions even in adjacent layers. The trend was removed to do a
geostatistical analysis on the normally distributed residuals. A robust
experimental semivariogram was calculated for the residuals for each
layer. Now I want to fit a spherical model semivariogram to the
experimental semivariograms. The spherical model fits the data from most
layers better than other models.

Questions:
- Is it possible to compare directly the range, the sill and the nugget
of the spherical model semivariograms fitted to the residuals of the
linearly detrended data for each layer?
- Are there any pit-falls that I should be aware of? (Should I test
different semivariogram models for each layer? Should I leave the linear
trend in the data for the structural analyses? ...)

All comments, suggestions and references are welcome and will be much
appreciated. I will be happy to provide more info if needed.

Best regards,

Kalle

PS: No one was hurt during the measurements ;-)




--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

Gregoire,

I do not have the book with me right now, but what (you and) I do know
is that

ln(x)=ln(10)*log10(x)

The difference is only a multiplication by a constant, so it cannot be
true that one case does involve the Lagrange multiplier and the other
does not.

Gerard


Gerard B.M. Heuvelink
Wageningen University and Research Centre
P.O. Box 47
6700 AA Wageningen
The Netherlands

tel +31 317 474628 / 482420
email gerard.heuvelink@...


-----Original Message-----
From: Gregoire Dubois [mailto:gregoire.dubois@...]
Sent: maandag 28 juli 2003 11:39
To: ai-geostats@...
Subject: AI-GEOSTATS: Log-normal back transform in Webster & Oliver

In the book "Geostatistics for Environmental Scientists"
By Richard Webster & Margaret A. Oliver, Wiley (2000), one will find in
page
180 a brief discussion on the back-transformation of the kriging
estimates.

In ordinary kriging, when the natural logarithm (ln) is used, the
back-transformation will involve the Lagrange parameter (see equation
8.36).
No problem so far.

But... the authors write in equation 8.38 that if one is using common
logarithms (log10) instead, the unbiased back-transformation of the
ordinary
kriging estimates does not involve the Lagrange multiplier anymore.
Is this correct ? In the affirmative, can someone point me to a paper
discussing "natural" log normal kriging versus "common" log normal
kriging ?

Thanks again for any help.

Regards,

Gregoire





--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary
of any useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the
message body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

Hello again,

Murray's reply to my question says it all. Thanks again a lot to Isobel Clark
(special thanks for the nice explanation on the behaviour of the Lagrange
parameter !), Gerard Heuvelink and Murray Lark.

Gregoire




------ Original Message ------
Received: Mon, 28 Jul 2003 06:22:12 PM CEST
From: "murray lark (SRI)" <murray.lark@...>
To: 'Gregoire Dubois' <gregoire.dubois@...>
Subject: RE: AI-GEOSTATS: Log-normal back transform in Webster & Oliver

Gregoire,

Just to follow up my previous email, I have had a word with Dick Webster who
confirms that the missing Lagrange multiplier in Equation 8.38 was a printing
error, he thanks you for pointing it out.  Also note that the Lagrange
multiplier should have an argument, bold x_0, since it is specific to the
kriging target site.

Murray Lark

-----Original Message-----
From: Gregoire Dubois [mailto:gregoire.dubois@...]
Sent: 28 July 2003 10:39
To: ai-geostats@...
Subject: AI-GEOSTATS: Log-normal back transform in Webster & Oliver


In the book "Geostatistics for Environmental Scientists"
By Richard Webster & Margaret A. Oliver, Wiley (2000), one will find in page
180 a brief discussion on the back-transformation of the kriging estimates.

In ordinary kriging, when the natural logarithm (ln) is used, the
back-transformation will involve the Lagrange parameter (see equation 8.36).
No problem so far.

But... the authors write in equation 8.38 that if one is using common
logarithms (log10) instead, the unbiased back-transformation of the ordinary
kriging estimates does not involve the Lagrange multiplier anymore.
Is this correct ? In the affirmative, can someone point me to a paper
discussing "natural" log normal kriging versus "common" log normal kriging ?

Thanks again for any help.

Regards,

Gregoire





--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

---------------------------- Mensaje Original ----------------------------
Asunto: Re: AI-GEOSTATS: Large sample size and normal distribution De:
"Ruben Roa Ureta" <rroa@...>
Fecha:  Sat, 2 de Agosto de 2003, 5:22 pm
Para:   "Chaosheng Zhang" <Chaosheng.Zhang@...>
Cc:     ai-geostat@...
--------------------------------------------------------------------------

> Dear list,
>
> I'm wondering if anyone out there has the experience of dealing with the
probability distribution of data sets of a large sample size, e.g.,
n>10,000. I am studying the probability feature of chemical element
concentrations in a USGS sediment database with the sample number of
around 50,000, and have found that it is virtually impossible for any
real data set to pass tests for normality as the tests become too
powerful with the increase of sample size. It is widely oberved that
geochemical data do not follow a normal or even a lognormal
> distribution. However, I feel that the large sample size is also making
trouble.

I pressume your null hypothesis is that the data comes from the given
distribution as is usual in goodness of fit tests. If such is the case
your sample size will almost surely lead to rejection. The well-known
logical inconsistencies of the standard test of hypothesis based on the
p-value are magnified under large n.
You have these options at least:
1) Find some authority that says that for large sample sizes the p-value
is less informative; e.g. Lindley and Scott. 1984. New Cambridge
Elementary Statistical Tables. Cambridge Univ Press; and then you can
throw away your goodness-of-fit test. But be warned that equally important
authorities have said exactly the contrary thing, that the force of the
p-value is stronger for large sample sizes (Peto et al. 1976. British
Medical Journal 34:585-612). To make matters even worse, certainly other
equally important authorities have said that the sample size doesn't
matter (Cornfield 1966, American Statistician 29:18-23).
2) Do a more reasonable analysis than the standard goodness-of-fit test. I
suggest you plot the likelihood function under normal and lognormal models
and derive the probabilistic features of your data by direct inspection of
the function. Also you can test for different location or scale parameters
using the likelihood ratio (its direct valu, not its derived asymptotic
distribution in the sample space) for any two well defined hypotheses.
Ruben



--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

hello list,
I have question regarding stratified (ordinary, simple, simple updating, etc.)
kriging.
when dividing my entire data set in different strata based on additional
information like geographical  classification of natural landscapes, soil maps,
aquifer or watersheds (or whatever is suitable and justifed) and each subset is
modelled and interpolated seperately, how do I have to handle the effect that
estimation points close to the boundary of each polygon have most of their
supporting values defined by the search strategy in the adjacent polygon?
applying a buffer around each dividing polygon to get the observations close to
the boundaries and clip the interpolation results? would this be an option when
using additional information  that rarely has sharp boundaries in reality like
soils?
many many thanks in advance.
oliver
______________________________________________________________________________
Spam-Filter fuer alle - bester Spam-Schutz laut ComputerBild 15-03
WEB.DE FreeMail - Deutschlands beste E-Mail - http://s.web.de/?mc=021120


--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

Dear list,

I'm wondering if anyone out there has the experience of dealing with the
probability distribution of data sets of a large sample size, e.g.,
n>10,000. I am studying the probability feature of chemical element
concentrations in a USGS sediment database with the sample number of around
50,000, and have found that it is virtually impossible for any real data set
to pass tests for normality as the tests become too powerful with the
increase of sample size. It is widely oberved that geochemical data do not
follow a normal or even a lognormal distribution. However, I feel that the
large sample size is also making trouble.

I am looking for references on this topic. Any references or comments are
welcome.

Cheers,

Chaosheng
--------------------------------------------------------------------------
Dr. Chaosheng Zhang
Lecturer in GIS
Department of Geography
National University of Ireland, Galway
IRELAND
Tel: +353-91-524411 x 2375
Fax: +353-91-525700
E-mail: Chaosheng.Zhang@...
Web 1: www.nuigalway.ie/geography/zhang.html
Web 2: www.nuigalway.ie/geography/gis/index.htm
----------------------------------------------------------------------------


--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

Oliver

We have had some success with 'modelling' stuff like
soil types using indicator variables. This gives you a
'probability' map as to whether or not you are in a
particular soil type (or whatever) which you could
then use to modify the inclusion (or perhaps the
weighting?) of your samples when kriging.

Isobel
http://ecosse.ontheweb.com



________________________________________________________________________
Want to chat instantly with your online friends?  Get the FREE Yahoo!
Messenger http://uk.messenger.yahoo.com/

--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

Hi Oliver,

You are right that such stratified kriging will create
discontinuities close to the boundaries and if it does not
make sense you can always perform the kriging on the residuals
and add back the stratified means.
An example is given in the paper you can download from
http://www.terraseer.com/services/courses/geostats/geoderma.pdf

Cheers,

Pierre Goovaerts

<><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

Dr. Pierre Goovaerts
President of PGeostat, LLC
Chief Scientist with Biomedware Inc.
710 Ridgemont Lane
Ann Arbor, Michigan, 48103-1535, U.S.A.

E-mail:  goovaert@...
Phone:   (734) 668-9900
Fax:     (734) 668-7788
http://alumni.engin.umich.edu/~goovaert/

<><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

On Mon, 4 Aug 2003, Oliver Sonnentag wrote:

> hello list,
> I have question regarding stratified (ordinary, simple, simple updating, etc.)
kriging.
> when dividing my entire data set in different strata based on additional
information like geographical  classification of natural landscapes, soil maps,
aquifer or watersheds (or whatever is suitable and justifed) and each subset is
modelled and interpolated seperately, how do I have to handle the effect that
estimation points close to the boundary of each polygon have most of their
supporting values defined by the search strategy in the adjacent polygon?
applying a buffer around each dividing polygon to get the observations close to
the boundaries and clip the interpolation results? would this be an option when
using additional information  that rarely has sharp boundaries in reality like
soils?
> many many thanks in advance.
> oliver
> ______________________________________________________________________________
> Spam-Filter fuer alle - bester Spam-Schutz laut ComputerBild 15-03
> WEB.DE FreeMail - Deutschlands beste E-Mail - http://s.web.de/?mc=021120
>
>
> --
> * To post a message to the list, send it to ai-geostats@...
> * As a general service to the users, please remember to post a summary of any
useful responses to your questions.
> * To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
> * Support to the list is provided at http://www.ai-geostats.org
>


--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

Hello,

I am currently using sequential Gaussian simulation (SGS) to generate
microtopographic soil surfaces from sparse data. There are nearly 16000
observations,  so I'm using a small search neighbourhood (e.g., 16
observations). The mean of the variable I'm concerned with (heights)
increases systematically from the top to the bottom of the data set and
R squared for a fitted first order polynomial is 0.885. An obvious
choice is to detrend the data, use SGS based on simple kriging and then
add the trend back. An alternative might be to use the fitted trend to
define the locally-varying mean and apply SGS based on simple kriging
with locally-varying means (rather than taking the mean of the residuals
as the constant mean and applying standard simple kriging). I suspect
that ordinary kriging (using a power model fitted to the raw variogram)
would result in predictions as accurate as those obtained through
detrending in some way, but given the trend is so obvious I don't want
to ignore it. I am aware that there is a lot of relevant work in the
literature about the application of these approaches in the context of
kriging. However, I would be interested in details of any case studies
that have dealt with large scale trends in a simulation context. I would
also be interested in the views of list members about the approaches
I've mentioned or any others that may be appropriate.

Many thanks in advance,

Chris Lloyd



[Non-text portions of this message have been removed]

Chris

Could I be incredibly obvious and suggest that, if you
use Universal Kriging, the trend is fitted and
simulated automatically with SGS. This is one of the
major advantages of SGS over approaches like Turning
Bands or Monte-Carlo -- if you can krige it, you can
simulate it.

There is a lot of evidence in the literature, dating
back to the early '80s that kriging residuals and
adding back the trend gives you pretty much the same
estimated surface as Universal Kriging. However, what
it doesn't do is give you the right standard error
since it doesn't allow for the trend fitting error. So
I would hazard a guess that simulations done this way
would underestimate the 'true' variability.

Isobel {Clark}
http://drisobelclark.ontheweb.com

PS: could I take this opportunity to remind anyone
interested that the IAMG 2003 is rapidly approaching.
If you haven't registered yet, sort yourself out at
http://www.iamg2003.com or follow the links from our
page at http://ecosse.ontheweb.com/whatsnew.htm

________________________________________________________________________
Want to chat instantly with your online friends?  Get the FREE Yahoo!
Messenger http://uk.messenger.yahoo.com/

--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

Isobel,

I've used universal kriging a lot in the past, but in this case I want
to use a global trend and a small search neighbourhood (since I have
nearly 16000 observations), so I would be estimating the variogram from
one set of residuals and kriging with another. That's why I thought of
using SGS based on simple kriging with locally-varying means defined by
the fitted polynomial, as it seems consistent (but there's still a
problem with not accounting for variation in the polynomial on which the
locally varying mean is based). However, in practice I suspect that UK
would do the trick if this minor(?) inconsistency is overlooked... I
could estimate the variogram of residuals from locally detrended data,
but that could become complicated. I suppose IRF-k kriging is yet
another option. Perhaps comparison of several approaches is the most
sensible route, but I think after your advice I'll start with UK-based
SGS and go from there.

Chris


-----Original Message-----
From: ai-geostats-list@... [mailto:ai-geostats-list@...] On
Behalf Of Isobel Clark
Sent: 06 August 2003 21:17
To: Chris Lloyd
Cc: ai-geostats@...
Subject: Re: AI-GEOSTATS: Simulation and trends

Chris

Could I be incredibly obvious and suggest that, if you
use Universal Kriging, the trend is fitted and
simulated automatically with SGS. This is one of the
major advantages of SGS over approaches like Turning
Bands or Monte-Carlo -- if you can krige it, you can
simulate it.

There is a lot of evidence in the literature, dating
back to the early '80s that kriging residuals and
adding back the trend gives you pretty much the same
estimated surface as Universal Kriging. However, what
it doesn't do is give you the right standard error
since it doesn't allow for the trend fitting error. So
I would hazard a guess that simulations done this way
would underestimate the 'true' variability.

Isobel {Clark}
http://drisobelclark.ontheweb.com

PS: could I take this opportunity to remind anyone
interested that the IAMG 2003 is rapidly approaching.
If you haven't registered yet, sort yourself out at
http://www.iamg2003.com or follow the links from our
page at http://ecosse.ontheweb.com/whatsnew.htm

________________________________________________________________________
Want to chat instantly with your online friends?  Get the FREE Yahoo!
Messenger http://uk.messenger.yahoo.com/

--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary
of any useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the
message body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org


--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

Hola Mail List
Were can I fine information and freeware of plurigaussian simulations?

King Regards

Adrian Martínez Vargas

ISMM, las Coloradas s/n

Moa, Holguín, Cuba

CP 83329

http://www.geocities.com/adriangeologo/adrian.html




[Non-text portions of this message have been removed]

Chris



As have told you Isobel you can loos de main information that gave de SGS, that
is the local variability. You most be careful of how mush spaced is your data,
the SGS can be in occasions les accurate than the turning band (with large
quantity of random bands) and other kriging methods as consequence of increasing
of error associated to the sequential approach. For test the simulations you can
simulate with few variants of the inner kriging methods of the SGS in know
points extracted randomly from the data, if your software don't have implemented
the option of Jackknife you can migrate de points to the closer nodes of a dense
grid, and compare it mean with the true value. In any way the most accurate
methods must be SGS with kriging with local mean and ordinary kriging (remember
the advise of Isobel about the universal Kriging)



I hop it help you



King Regards

Adrian Martínez Vargas

ISMM, las Coloradas s/n

Moa, Holguín, Cuba

CP 83329

http://www.geocities.com/adriangeologo/adrian.html
   ----- Original Message -----
   From: Chris Lloyd
   To: ai-geostats
   Sent: Wednesday, August 06, 2003 12:27 PM
   Subject: AI-GEOSTATS: Simulation and trends


   Hello,



   I am currently using sequential Gaussian simulation (SGS) to generate
microtopographic soil surfaces from sparse data. There are nearly 16000
observations,  so I'm using a small search neighbourhood (e.g., 16
observations). The mean of the variable I'm concerned with (heights) increases
systematically from the top to the bottom of the data set and R squared for a
fitted first order polynomial is 0.885. An obvious choice is to detrend the
data, use SGS based on simple kriging and then add the trend back. An
alternative might be to use the fitted trend to define the locally-varying mean
and apply SGS based on simple kriging with locally-varying means (rather than
taking the mean of the residuals as the constant mean and applying standard
simple kriging). I suspect that ordinary kriging (using a power model fitted to
the raw variogram) would result in predictions as accurate as those obtained
through detrending in some way, but given the trend is so obvious I don't want
to ignore it. I am aware that there is a lot of relevant work in the literature
about the application of these approaches in the context of kriging. However, I
would be interested in details of any case studies that have dealt with large
scale trends in a simulation context. I would also be interested in the views of
list members about the approaches I've mentioned or any others that may be
appropriate.



   Many thanks in advance,



   Chris Lloyd





[Non-text portions of this message have been removed]

Adrian

Thank you for the reminder of one of the strengths of
Turning Bands. Certainly I have no argument with your
points. However Chris' question was about how to
include trend in SGS and that is what my answer is
about.

Isobel
http://ecosse.ontheweb.com

________________________________________________________________________
Want to chat instantly with your online friends?  Get the FREE Yahoo!
Messenger http://uk.messenger.yahoo.com/

--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

Chris

We have always found that estimating the
semi-variogram from the reiduals of a global trend was
sufficient, provided care is taken to use the cross
validation to avoid 'over fitting'. I guess this is
also true for genuine first-stage UK as well!

One of the free tutorials which is downloadable from
the web, on the Wolfcamp data, illustrates this.

Isobel
http://ecosse.ontheweb.com/softwares

________________________________________________________________________
Want to chat instantly with your online friends?  Get the FREE Yahoo!
Messenger http://uk.messenger.yahoo.com/

--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

Dear List-members,

How can I program applications for ArcView GIS?

The book "Statistical Analysis with ArcView GIS", by Jay Lee and David Wong
comes with some example scripts, which have file extension .apr.
Unfortunately these files only work with 3.x version of ArcView. I am using
version 8 and for my PHD I would like to learn how to program applications
for ArcView GIS, especially the Moran I Index.

In which language are .apr files constructed? Are there any good books
concerning this issue? Where do I start.

Greets,

Ellen


[Non-text portions of this message have been removed]

Ellen:
AV8 use Visual Basic for Applications. There are really no good books
out yet on programming in AV8, but plenty of good books on VBA.
The geostats module for AV8 is actually quite good. I met one of the
lead developers at the ESRI user conference and it seems like ESRI
actually got their act together on this one. Not perfect mind you, but
still better than I expected from them.
Hope this helps,
Steve

Ellen De Beuckeleer wrote:

> Dear List-members,
>
> How can I program applications for ArcView GIS?
>
> The book "Statistical Analysis with ArcView GIS", by Jay Lee and David
> Wong comes with some example scripts, which have file extension .apr.
> Unfortunately these files only work with 3.x version of ArcView. I am
> using version 8 and for my PHD I would like to learn how to program
> applications for ArcView GIS, especially the Moran I Index.
>
> In which language are .apr files constructed? Are there any good books
> concerning this issue? Where do I start.
>
> Greets,
>
> Ellen



--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

Hi Ellen

every program written for ArcView use the Avenue language (there is a lot of
books from esri regarding avenue programming). However, ArcGis 8.x uses
ArcObject (practically Visual Basic). the same esri has published a book for
translating avenue scripts into arcobject scripts. you should also consider that
many .apr scripts have a protection (for copiright questions) and thus you
cannot read directly the code.

Luigi Maiorano
PhD Student
College of Natural Resources
University of Idaho, Moscow
83843, Idaho (USA)
maio1323@...

----- Original Message -----
From: Ellen De Beuckeleer <ellen.debeuckeleer@...>
Date: Thursday, August 7, 2003 2:57 pm
Subject: AI-GEOSTATS: programming ArcView GIS

> Dear List-members,
>
> How can I program applications for ArcView GIS?
>
> The book "Statistical Analysis with ArcView GIS", by Jay Lee and
> David Wong
> comes with some example scripts, which have file extension .apr.
> Unfortunately these files only work with 3.x version of ArcView. I
> am using
> version 8 and for my PHD I would like to learn how to program
> applicationsfor ArcView GIS, especially the Moran I Index.
>
> In which language are .apr files constructed? Are there any good books
> concerning this issue? Where do I start.
>
> Greets,
>
> Ellen
>


--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

Ellen,

ArcView 3.x uses ESRI's "Avenue" programming lanquage, there are several good
books and online courses provided by/from ESRI.

Allan C. Harris - Environmental Engineer
Operations Assurance Services (OAS)
US Department of Energy - Fernald Closure Project
P.O. Box 538704  MS45
Cincinnati, Ohio 45253-8704
work 513.648.3184; fax 513.648.3077
Email Allan.Harris@...


-----Original Message-----
From: Steven Citron-Pousty [mailto:Steven.Citron-Pousty@...]
Sent: Thursday, August 07, 2003 9:47 AM
To: Ellen De Beuckeleer
Cc: ai-geostats@...
Subject: Re: AI-GEOSTATS: programming ArcView GIS


Ellen:
AV8 use Visual Basic for Applications. There are really no good books
out yet on programming in AV8, but plenty of good books on VBA.
The geostats module for AV8 is actually quite good. I met one of the
lead developers at the ESRI user conference and it seems like ESRI
actually got their act together on this one. Not perfect mind you, but
still better than I expected from them.
Hope this helps,
Steve

Ellen De Beuckeleer wrote:

> Dear List-members,
>
> How can I program applications for ArcView GIS?
>
> The book "Statistical Analysis with ArcView GIS", by Jay Lee and David
> Wong comes with some example scripts, which have file extension .apr.
> Unfortunately these files only work with 3.x version of ArcView. I am
> using version 8 and for my PHD I would like to learn how to program
> applications for ArcView GIS, especially the Moran I Index.
>
> In which language are .apr files constructed? Are there any good books
> concerning this issue? Where do I start.
>
> Greets,
>
> Ellen



--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org


******************** DISCLAIMER ********************
The information transmitted is intended only for the person
or entity to which it is addressed and may contain confidential
and/or privileged material. If you are not the intended recipient
of this message you are hereby notified that any use, review,
retransmission, dissemination, distribution, reproduction or any
action taken in reliance upon this message is prohibited. If you
received this in error, please contact the sender and delete the
material from any computer.  Any views expressed in this message
are those of the individual sender and may not necessarily reflect
the views of the company.
***************************************************




--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

I have a point data set (~150k records) that contains a ratio variable
(percentages, both positive and negative) that I would like to run a
local cluster analysis on.  Global Moran's I and Geary's C indicate
clustering as a whole, I just need to get a statistical measure of
where.  If I understand correctly (a big "if" at this point), a local
moran's must be aggregated by region or represent continuous data, which
for various reasons I would like to stay away from.  If I can correctly
define a lag distance, would a Local G statistic be better?  What other
tests may appropriate for local cluster analysis of discrete point data
without having to use predefined regions or aggregate the data?





Wes Highfield

Graduate Research Assistant

Department of Landscape Architecture and Urban Planning

Texas A&M University

College Station, TX 77843-3137





[Non-text portions of this message have been removed]

Ellen,

As other list members have already told you,
(1) ArcView 3.x is programmed in ESRI's own "Avenue" language, but
(2) ArcView 8.x (and the other ArcGIS 8.x programs, like ArcInfo 8.x) is
built atop the "ArcObjects" library, which is a completely different code
base and does not work with Avenue.

Visual Basic for Applications ("VBA") is probably the simplest approach
to developing ArcView 8.x scripts that are akin to the ArcView 3.x scripts
that were written in Avenue, but you may prefer to work at a lower level in
the software stack by programming ArcObjects directly.  ArcObjects is a
library of Microsoft Component Object Model ("COM") components, so
ArcObjects can be programmed using either one of Microsoft's two languages
that work within the COM platform [**not** the new .NET platform that
replaces COM, note], namely Visual Basic 6.0 and Visual C++ 6.0, or in
Borland's Delphi, which also works with COM.  See
http://www.esri.com/library/brochures/pdfs/arcob81bro.pdf .

Although this post was 95% redundant with what you have already been
told, I did want to point out explicitly that Visual C++ and Delphi are
alternatives to Visual Basic for ArcGIS 8.x development, since many
programmers won't touch Visual Basic with a barge pole.  :)

Wilmer Rivers

-----Original Message-----
From: Ellen De Beuckeleer [mailto:ellen.debeuckeleer@...]
Sent: Thursday, August 07, 2003 8:57 AM
To: ai-geostats@...
Subject: AI-GEOSTATS: programming ArcView GIS


Dear List-members,

How can I program applications for ArcView GIS?

The book "Statistical Analysis with ArcView GIS", by Jay Lee and David Wong
comes with some example scripts, which have file extension .apr. Unfortunately
these files only work with 3.x version of ArcView. I am using version 8 and for
my PHD I would like to learn how to program applications for ArcView GIS,
especially the Moran I Index.

In which language are .apr files constructed? Are there any good books
concerning this issue? Where do I start.

Greets,

Ellen



[Non-text portions of this message have been removed]

Hi, Adrian -

see text book by

M. Armstrong, Galli et. al: Plurigaussian simulation in geosciences.-
Springer Verlag 2003, including CD with demo-software.
or
Lantuejoul, C. Geostatistical simulation - models and algorithms
Springer, 2002

Heinz Burger
--
*****************************************************
Dr. Heinz Burger
Freie Universitaet Berlin
- Geoinformatik -
Malteserstr. 74-100
12249 BERLIN, Germany
Tel. (49) 30-838-70561 Fax: (49) 30-838-70723
mailto: hburger@...
Web-Seite: http://userpage.fu-berlin.de/~hburger/hb
****************************************************

--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org

> Dear All,
>
> One week ago I posted a question about large n and normal distritbuion,
and have got several good replies from Isobel Clark, Ned Levine, Ruben Roa
Ureta, Thies Dose, Chris Hlavka, Donald Myers and Jeffrey Blume. Jeffrey
is perhaps not in the list, but I assume he has no objections if I copy
his message to the list.

> Generally speaking, when n is too large, e.g., n>1,000 which is very
common in geochemistry nowadays, statistical (goodness-of-fit) tests
become too powerful, and the p-values are less informative. Therefore,
users need to be very careful in using these tests with a large n.
Suggestions to solve this problem include: (1) To use graphical methods;
(2) To develop methods which are suitable for large n; (3) To use methods
which are not sensitive to n.

Chaosheng: if you refer to Jeffrey's and my suggestion to use the
likelihood function as a 'graphical method', well you are right but
probably you are underestimating the power of such approach. You can
obtain point and interval estimates and perform tests of hypothesis using
a pure likelihood approach. In this approach, as the sample size
increases, the pre-experimental probability of misleading or weak evidence
goes to zero. It is free of the logical inconsistencies of Fisherian and
Neyman-Pearson approaches and it is also free of subjective prior
probabilities as in Bayesian analysis.
Useful references are:
Edwards AWF. 1972. Likelihood. Cambridge.
Royall RM. 1997. Statistical evidence. A likelihood paradigm. Chapman.
Royall RM. 2000. J. Amer. Stat. Assoc. 95:760-768.

Ruben

--
* To post a message to the list, send it to ai-geostats@...
* As a general service to the users, please remember to post a summary of any
useful responses to your questions.
* To unsubscribe, send an email to majordomo@... with no subject and
"unsubscribe ai-geostats" followed by "end" on the next line in the message
body. DO NOT SEND Subscribe/Unsubscribe requests to the list
* Support to the list is provided at http://www.ai-geostats.org