Dear list members
I would like to have some comments, suggestions or critics about the
following topic:
building a (preliminary) local uncertainty model of the spatial
distribution of discrete (categorical) variables by means of natural
neighbor interpolation method applied to indicator
a debate on
regression versus classification... I presume there may there
some material as well regarding the issue discussed below.
Best regards,
Gregoire
-Original Message-
From: seba
[
mailto:[EMAIL PROTECTED]]
Sent: 30 August 2005 18:17
To: ai-geostats@unil.ch
Subject: [ai-geostats
I try to reformulate my question.
When performing direct (i.e. without crossvariogram) indicator kriging,
practically we interpolate probability values by means of ordinary
kriging. These probability values could represent the probability of
occurrence of some category or the probability to
will be one).
Pierre
-Original Message-
From: Gregoire Dubois [mailto:[EMAIL PROTECTED]
Sent: Mon 9/5/2005 7:00 AM
To: 'seba'; ai-geostats@unil.ch
Cc:
Subject: RE: [ai-geostats] natural neighbor applied to indicator
transforms
Ciao
Dear list members
Despite free opensource software (for example Gstat) could perform full
indicator Kriging (i.e. using cross variogram) I have not
found free software for fitting 3D cross variograms. Surely, commercial
software can do that, but it could
be a little bit expensive from the
There is, also vis5d that from what I have seen it could works.
Then also in grass gis software there is something for 3D visualization.
In general I have seen that most software for 3D visualization work fine
for continuos attributes while for discrete one you
have to play a little with color
not in any circumstances be regarded as stating an
official position of the European Commission.
-Original Message-
From: seba
[
mailto:[EMAIL PROTECTED]]
Sent: 28 March 2006 10:00
To: [EMAIL PROTECTED]
Subject: Re: [ai-geostats] Comparing autocorrelation
(semivariogram) surfaces
Hi
Hi
You could also express the variogram in this other
form
2gamma(h) = Var{Z(u)-Z(u+h)} = E{[Z(u)-Z(u
+ h)]^2}-E{ Z(u)-Z(u+h)}^2
the second element of the right equation goes away when the phenomenon is
stationary.
Sebastiano
At 21.15 09/04/2006, Isobel Clark wrote:
Hi. At last one I can
dear Reza Nazarian
I think that there are many things to say about this topic.(for
example the idea to use a gaussian distribution for porosity should be
checked). But I think that in the good book Geostatistical
reservoir modeling: Clayton V. Deutsch, Oxford University press you
will find
Hi
Well, in general the simulations are useful if you need to evaluate
spatial uncertainty, for
example if you have to apply some transfer function to your data or the need
to evaluate the probability that some neighbor cells overcome
jointly a given threshold.
Then kriging variance is
Hi
Maybe you can ask to the authors of the various papers about
transition probabilities.
From what I remember in groundwater vista (or maybe GSM) t-prog is
implemented for modeling 3d geological architecture.
Bye
Sebastiano T
At 17.21 25/01/2008, you wrote:
Dear list,
A would appreciate
Hi Gregoire
If I have understood the problem, the weird thing is that if there is
a strong correlation between data and residuals and your data show
continuity, the residuals should not present a pure nugget effect.
Sebastiano T.
At 12.59 30/01/2008, Gregoire Dubois wrote:
Dear list,
Dear list members
I would like to share a short consideration.
Some day ago I was doing some prediction exercises by means of
ordinary kriging with linear variograms.
And doing this exercise I realized (something that probably I should
have realized many years ago!) that
things change when
Hi
Well, some time you have the impression that kriging is not an exact
interpolator because of
you have a high nugget effect and the interpolation grid nodes have
not the same location
of available data. The variability represented by the nugget effect
is filtered every time
an
Hi Isobel
I didn't know the existence of the two schools of thought! So thanks
for the clarification.
The point is the I interpret smoothing (filtering) properties of
kriging by means of the dual representation of kriging
interpolator given for example in Goovaerts's book Geostatistics for
Dear list members
I'm trying to find a software which permits to optimize the
sampling network (number and combinations of samples) for problems
of bivariate or multiple regression.
This is a typical problem in monitoring.
We start with N monitoring stations and we try to build a regression
of their informative content. In some way, kriging,
which gives less weight to clustered samples, goes already in this direction.
Sebastiano
At 16.50 06/03/2008, Melles, Stephanie wrote:
Hi Seba,
I noticed that there were no responses to your posting from a few weeks
back and I'm cleaning out my mailbox today
Hi
Well, with indicator a trend means (from my point of view) that there
is some trend in the probability of occurrence
of a given category. If you use a ordinary kriging with a small
neighbor search in some way you already
take into account the trend (i.e. with ok we assume that the mean
There was something wrong with the last message (the message for
some reason appear cutted!?)
...I send it again...
Dear list members
Another time, I have some questions about the decomposition between
the trend and residuals.
I have some philosophical as well as technical questions for you.
://wiki.aston.ac.uk/DanCornford/
tel: +44 (0)121 204 3451
mob: 07766344953
---
-Original Message-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf
Of seba
Sent: 16 May 2008 08:22
To: ai-geostats@jrc.it
Subject: AI-GEOSTATS: The trend
Hi Patricia
I think that an indicator (or a transition probability) approach
could be easier to interpret
(i.e. each indicator variogram
tell you something about the spatial pattern of each single facies).
Then, in regard to the sill of your variogram it depends if your
variogram is calculated
Date: Wed, 08 Oct 2008 11:24:46 +0200
To: enayat khojasteh [EMAIL PROTECTED]
From: seba [EMAIL PROTECTED]
Subject: Re: AI-GEOSTATS: Coordinate transformation
Hi
Well, in my opinion your transformation is meaningful if effectively
the spatial variability along
the vertical direction (i.e
Hi Cristiano
I think that you can try to use moving windows statistics to check if
there are strong differences in variability and mean values
among the eastern and southern part of the spatial domain (obviously
trying to understand
the processes behind these differences).
Then, I guess that
Dear List members
I received a lot of references from you and I was also lucky because
last week I had the possibility to be at a small workshop of B. D. Malamud (who
worked a lot on fractals and time series),
who suggested me some other references about the issue.
Now I'm in hurry...but I hope
variable that is distributed NOT on a transect,
but rather at discontinuous (i.e. random) locations in a 2D study area?
Thank you,
Gustavo Vasques
--- Em qua, 11/3/09, seba sebastiano.trevis...@libero.it escreveu:
De: seba sebastiano.trevis...@libero.it
Assunto: AI-GEOSTATS: variogram
Hi
I'm not sure of your question..did you mean that you see a periodic
behavior on variogram?
On which physical-chemical attribute did you calculate it?
Well, having to do with geology I guess that your periodicity is
related to layering.
Bye
Sebastiano
At 00.09 04/06/2009, Reza
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
Hi
In general I agree with the comments reported in the preceding replies.
Then I would add that the problem, if any one exists, doesn't relies
on the lack of a good gui but maybe on the lack
of a kind of standard and internationally accepted set of programming
routines directed to
Dear list members
I'm always fighting with the decomposition of trend and residuals or
more generally I need to decompose
my signal in high and low frequency variability. Without coming to
wavelets techniques a simple
way is to use moving window averages to obtain a smoothed signal and
Maybe I'm getting old...there was something wrong also in the past mail!
Please consider this one.
Sorry for the wrong mails.
Dear list members
I'm always fighting with the decomposition of trend and residuals or
more generally I need to decompose
my signal in high and low frequency
Hi José
Thank you for your reply.
Effectively I'm trying to figure out the theoretical reasons for their use.
Bye
Sebas
At 12.30 01/02/2010, José M. Blanco Moreno wrote:
Dear Seba,
As far as I know, the shape of the kernel is not
that important. Any kernel will yield
approximately the same
Statistical Association, 85: 749-759.
En/na seba ha escrit:
Hi José
Thank you for your reply.
Effectively I'm trying to figure out the theoretical reasons for their use.
Bye
Sebas
---
*From:* owner-ai-geost...@jrc.ec.europa.eu
[mailto:owner-ai-geost...@jrc.ec.europa.eu] *On Behalf Of *seba
*Sent:* 02 February 2010 08:39
*To:* Pierre Goovaerts
*Cc:* ai-geostats@jrc.it
*Subject:* Re: AI-GEOSTATS: moving averages and trend
Hi Pierre
I think that for my task factorial
Hi
I think this could be useful:
Modern Spatiotemporal Geostatistics
by
http://www.amazon.com/Spatiotemporal-Geostatistics-Studies-Mathematical-Geology/dp//s/ref=rdr_ext_aut?_encoding=UTF8index=booksfield-author=George%20ChristakosGeorge
Christakos
Then I remeber that in Cressie's book there
Hi Adrian
Really interesting, I'll give a look!
Sebas
At 08.27 15/04/2010, you wrote:
I'm working in a new geostatistical GPL library
with no limit in the number of: dimensions,
variables, external drifts, geographical drift,
etc... It is callable from python. The
preliminaries results are
Hi Marco
Considering that you are dealing with 5 years
average data, likely there should be some spatial continuity in your data...so,
in a first instance, you should follow a
geostatistical approach and see what data are telling you.
But from a perspective of an hydrogeologist,
first, I'll
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