Dear Geostat
I'm a beginner and I had the unliky case of a tridimensional multivariate
geochemical dataset to start my geostatistical adventure...moreover data are
taken from sediment samples of a high polluted port and present a high
variability...
My question is:
histogram of variables doesn't reveal nor a gaussian distribution neither a
lognormal one, that is that variables are not well distributed on any of these
distributions...do I have to force to find some distribution that could well
represent my data? The condition based on the fact that Kriging performs well on
gaussian variables is so constraining? Or maybe the main thing is the shape of
variogram?...Do I have to try to lay variables back to gaussian shape or I can
direct my strengths on studying and modelling variogram?...And if so, if
variogram are unbounded, do I have to consider variables in a non stationary
framework or it's possible that such a bad distribution could false the shape of
the variogram? (For example for log transformed variable, even if histogram do
not get better, some variograms are better shaped and not so unbounded)...
I know I'm confused but I need some practical suggests to come from all this
good and perfect theory to my hugly reality...
Thanks
Simone

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It probably means that you have a spatial trend in your data.
Remove any trend, then try again.

Dan Bebber

Department of Plant Sciences
University of Oxford
South Parks Road
Oxford OX1 3RB
UK
Tel. 01865 275000



> -----Original Message-----
> From: Jose Luis Gomez Dans [mailto:jgomezdans@...]
> Sent: 03 November 2004 16:19
> To: ai-geostats@...
> Subject: [ai-geostats] Understanding variograms
>
>
> Hi all,
> I'm new to the list, so bear with me :)
> I'm trying to carry out some kriging of some (x,y,v)
> data set on to a
> regular grid.  I have read some of the literature, and
> I think that
> this would be a good option (I have also examined the possibility of
> interpolating using a TIN, but I feel a bit more
> "safe" using a
> statistical approach!).
>
> In order to do my kriging, I need to have a model
> semi-variogram, and I
> thought about using gstat for this (from the command
> line, no R
> interfaces, my files are just ASCII (x,y,z) triplets).
> Given that my
> data files are huge (of the order of millions of
> points), I selected a
> region of interest, and tried to produce a sample
> semi-variogram, with
> views to fitting a model.
>
> In gstat, I got something that resembles y=exp(x),
> which looks
> distinctly wrong. The semivariogram value is ever
> increasing, and does
> not reach a sill at all. This is strange, and rather
> unexpected. I have
> tried doing a log transform of the data, but the shape
> of the curve is
> mostly unchanged.
>
> I would be very grateful if anyone could shed any
> light on what this
> variogram means, how I could make progress modelling
> it and so on.
>
> Many thanks!
> José
>
> --
> Jose L Gomez-Dans, Research Assistant
> Bristol Glaciology Centre, Geographical Sciences/CPOM
> University of Bristol, Bristol, UK
>
>
>
>
>
>
> ___________________________________________________________ALL
> -NEW Yahoo! Messenger - all new features - even more fun!
http://uk.messenger.yahoo.com

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Corinne

The whole point (as I understand it) of using an
indicator approach is to remove all question of what
the original distribution was. All your sample values
become 0 or 1, at each 'cutoff'.

Not knowing the algorithms you are using, I cannot
comment on what other needs it might have but SIS
should be 'distribution free'.

I am not clear why you feel your sill needs to be 1.
Is this a requirement of the software package?

Isobel

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Detrending is a pretty basic practice in geostatistics. I think you should
do some more reading before you plunge into analyses.

Dan Bebber

> -----Original Message-----
> From: Jose Luis Gomez Dans [mailto:jgomezdans@...]
> Sent: 03 November 2004 17:01
> To: Dan Bebber
> Subject: Re: [ai-geostats] Understanding variograms
>
>
> Dan and Isobel,
> Many thanks for your prompt reply!
>
> On Wednesday 03 Nov 2004 16:39, you wrote:
> > It probably means that you have a spatial trend in
> your data.
> > Remove any trend, then try again.
>
>  OK, my data are point heights above the geoid over a
> large area. While
> there could well be a trend, how would I go
> de-trending the data? I
> have a digital elevation model of this region, and I
> guess I could
> subtract the grid value for each point considered, and
> that should get
> rid of things like slope effects (in effect, a
> linear-ish trend).
>
> Does this make any sense?
>
> Many thanks
> Jose
>
> --
> Jose L Gomez-Dans, Research Assistant
> Bristol Glaciology Centre, Geographical Sciences/CPOM
> University of Bristol, Bristol, UK
>
>
>
>
>
>
> ___________________________________________________________ALL-NEW
>  Yahoo! Messenger - all new features - even more fun!
http://uk.messenger.yahoo.com

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Dear List:
I am trying to build a porosity control map using a discrete log with 7
facies, using sequential indicator simulation.  The lag size must be set
very large, especially for the vertical, if the sill is to get anywhere
near a value of 1.  The wells are also very far apart, so the data may
be beyond a reasonable correlation range anyway.

I've never done this before, so I have a silly question.  Does SIS
require that the data follow a Normal (Gaussian) distribution?  Should
we have transformed these indicator log values to ensure they are
Normal?

>Corinne
21.022, (713)215-7173;    Cell: (832)465-3311
FAX  713-985-1444
Oxy email: corinne_danielli@...>


6.00 - 6:30 email, phone
6.30 - 11.30 OMED
11.30 - 12.30 out (time approx.)
12.30 - 1.00 email, phone
1.00 - 3.00 NAGS
3.00 - ???  may be here...

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Another explanation for this shape is that Jose is looking at
the variogram for short distances. It is well known that for
very continuous attributes, such as elevation or depth to water table.
the semivariogram is expected to display a parabolic behaviour at
the origin, which can be modeled using a Gaussian or cubic model
for example. If one computes the semivariogram before it reaches
its sill, then one will look only at a power curve and conclude
that a trend is present. As always, everything is a matter of scale,
and what looks like as a trend at a local scale can be modeled as
part of a stationary process at a more regional scale.

Cheer,

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 Wed, 3 Nov 2004, Dan Bebber wrote:

> Detrending is a pretty basic practice in geostatistics. I think you should
> do some more reading before you plunge into analyses.
>
> Dan Bebber
>
> > -----Original Message-----
> > From: Jose Luis Gomez Dans [mailto:jgomezdans@...]
> > Sent: 03 November 2004 17:01
> > To: Dan Bebber
> > Subject: Re: [ai-geostats] Understanding variograms
> >
> >
> > Dan and Isobel,
> > Many thanks for your prompt reply!
> >
> > On Wednesday 03 Nov 2004 16:39, you wrote:
> > > It probably means that you have a spatial trend in
> > your data.
> > > Remove any trend, then try again.
> >
> >  OK, my data are point heights above the geoid over a
> > large area. While
> > there could well be a trend, how would I go
> > de-trending the data? I
> > have a digital elevation model of this region, and I
> > guess I could
> > subtract the grid value for each point considered, and
> > that should get
> > rid of things like slope effects (in effect, a
> > linear-ish trend).
> >
> > Does this make any sense?
> >
> > Many thanks
> > Jose
> >
> > --
> > Jose L Gomez-Dans, Research Assistant
> > Bristol Glaciology Centre, Geographical Sciences/CPOM
> > University of Bristol, Bristol, UK
> >
> >
> >
> >
> >
> >
> > ___________________________________________________________ALL-NEW
> >  Yahoo! Messenger - all new features - even more fun!
> http://uk.messenger.yahoo.com
>
>
>

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The classic example in Isaaks & Srivastava has a lot of data points. More
data gives a better description of the process, but the problem is with
computation: 1000 samples gives you 499,500 pairs, whereas 10,000 samples
gives you 49,995,000 pairs. This requires a lot of memory.

Dan

p.s. If you need some basic geostatistical procedures, there are plenty of
programs out there.
____________________________
Dr. Daniel P. Bebber
Department of Plant Sciences
University of Oxford
South Parks Road
Oxford
OX1 3RB
Tel. 01865 275060

> -----Original Message-----
> From: Mark Coleman [mailto:mark@...]
> Sent: 03 November 2004 20:40
> To: ai-geostats@...
> Subject: [ai-geostats] Typical sample sizes for variogram calculations
>
>
> Greetings,
>
> I am coding some basic geostatistical procedures and was curious about
> the "typical" sorts of sample sizes researchers run into. I know that
> sizes of n=1000 are fairly common. How about sizes of N=10,000 or
> greater? Are variograms computed on samples this large?
>
> Thanks,
>
> -mark
>
>
>

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Greetings,

I am coding some basic geostatistical procedures and was curious about
the "typical" sorts of sample sizes researchers run into. I know that
sizes of n=1000 are fairly common. How about sizes of N=10,000 or
greater? Are variograms computed on samples this large?

Thanks,

-mark

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Mark,

    this really depends on how you want to estimate the parameters of the
covariance / variogram. If you want to use maximum likelihood, then due
to the need to invert a matrix, which is O(n^3), generally sizes above
about 1000 become rather prohibitive on a desktop computer. One possible
alternative that attempts to retain statistical rigour but scale
gracefully with sample size is our Sparse Sequential method:

http://www.ncrg.aston.ac.uk/~csatol/ogp/index.html

Alternatively you could use methods of moment estimators (i.e. the
classic sample variogram) and fit these empirically using some function.
Note that in computing the sample variograms one can work in a
sequential fashion, so that not all pair comparissons need be stored,
but they must be computed .... so it will be slower, scaling as O(n^2)
in the computation of the sample variogram.

cheers

Dan

Mark Coleman wrote:
> Greetings,
>
> I am coding some basic geostatistical procedures and was curious about
> the "typical" sorts of sample sizes researchers run into. I know that
> sizes of n=1000 are fairly common. How about sizes of N=10,000 or
> greater? Are variograms computed on samples this large?
>
> Thanks,
>
> -mark
>
>
>
> ------------------------------------------------------------------------
>
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--

Dr Dan Cornford 		 d.cornford@...
Computer Science
Aston University
Aston Triangle 		 tel +44 (0)121 204 3451
Birmingham B4 7ET 	 fax +44 (0)121 333 6215

http://www.ncrg.aston.ac.uk/~cornfosd/

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Mark

Our free downloadable data sets range from 16 to over
20,000. The biggest set I worked wth was a small
section of a South African gold mine - 450,000.

Isobel
http://geoecosse.bizland.com/softwares

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Hi there,

Hope somebody can answer a newbie's question. Although it may be
ill-conditioned, my question is, how can I compare the result of OLS
model and a model by universal kriging (OLS with residuals fitted by
variogram)? What I mean is, how can I show kriging the residuals does
improve the model? Could the comparison be done in the sense of R^2 of
OLS? Or the only way is to do Chi-square test based on difference of
log likelihood?

Thanks a lot.

Tib

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Dear list,

First I want to thank you all for the help you gave me last year. It
resulted in a Master degree with honours! So, thank you for all the
support, tips and tricks.

So, here I am again with a brainstorm question...

The situation:

-------------------------------------------
I'll give an idea of the analysis.

In short, I tested a sampling design three scale levels. An elementary
sample unit (ESU) level, a Cell level (1x1km) and a site level (3x3km).

The link shows you an illustration of the situation:

http://users.pandora.be/requested/images/sampling.jpg

In every ESU leaf are indexes were measured at the given locations, in
the given patern.

To check for spatial dependence at an ESU level I did a simple Moran's
I/Geary's C analysis. All results were negative, so I concluded that
under current conditions, in this vegetation sampling could as well be
done at random and location didn't matter. This had some implications,
in respect to further field surveys. Not having to deal with complex
site descriptions and measuring problems => costs less time and money.

Because of the tricky things like boundary situations, I skipped the
Cell level (1x1km) and went straight to the site level. Problem was that
the vallues of the whole pool weren't exactly normal and not fixable by
transformation. So for the whole pool of data I did an indicator
(kriging) analysis avoiding the distribution problem. This came out
negative with no spatial relations for any of the cutoff levels.

At all cutoff levels the semivariogram looked like this or close to it:

http://users.pandora.be/requested/images/cutoff2.gif

Just as an extra, the semivariogram of all the datapoint (far from
normal distributed) looks like this.. notice the downward curving of the
tail end of the semivariogram:

http://users.pandora.be/requested/images/semivario.png
(Lag distance in meter)

I also averaged the data on an ESU level, given 38 ESU's this isn't much
to work with but those data showed a normal distribution so I calculated
a semivariogram for this data.

The semivariogram for the averaged data:

http://users.pandora.be/requested/images/variogram.png

This would suggest a sill of around 0.10 and a range of some 1200 m.
Some wobly sinus movements can be seen in the semivariogram, this could
be due to the dune like environment of the site... but this is a rather
bold statement given the veeeery few sampling points (38 average ESU
values).

-----------------------------------------------------

So, If I need to interpolate between measured values for validation of
satellite images (the final goal of all this), I will need a model. And
more important, I can't use the images to validate the model because I
need the model to validate the images. So the model has to be rather good.

Is cross-validation of the semivariogram model a valid option to check
the model or not (given the small amount of data used for the
semivariogram and the model based on it)? Any other tests? Other ways to
optimise the current model?

Any ideas on approaches to test the sampling design itself, and not so
much the model that would be needed for an actual application
(validation of the sat. images)?

Any remarks no my current way of thinking? Mistakes I could have made?

Any ideas to get more out of the data, given the fact that the whole
pool (individual measurements) is faaaaaaaaaaaaaaaar from normal, but
the averaged data per ESU is?

Thank you for reading it all,
Koen.

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Koen,

Some quick thoughts.

Is it possible, you have a mixture of distributions in your data?  It
may be why when you combine many distributions (as in your pooled
data), you tend to see normal distribution.  Maybe you can try to
segregate the data into classes based on magnitude (or some other
criteria), and then model the classes separately.  We had success
doing that with placer gold.  Each class was better modeled using data
from within the class.

Two of the three variograms look like all-nugget.  Based on my
experience, that is not a good sign, i.e. tough times ahead.  Your
best bet seems to be to estimate it at the pooled level (for which you
have a variogram) and throw in a distribution around each ESU rather
than go for individual predictions within the ESU.


Rajive Ganguli, Ph.D., P.E., C.O.I
Associate Professor of Mining Engineering
University of Alaska Fairbanks
================================
Office: 317 Duckering Building
Mailing Add: Box 755800, Fairbanks, AK 99775
ph: 907-474-7631, fax: 907-474-6635
web: http://www.faculty.uaf.edu/ffrg/
-------------------------------------------
"He uses statistics as a drunken man uses lamp-posts... for support rather
than illumination."  - Andrew Lang (1844-1912)




On Fri, 05 Nov 2004 11:56:48 +0100, Koen Hufkens
<koen.hufkens@...> wrote:
> Dear list,
>
> First I want to thank you all for the help you gave me last year. It
> resulted in a Master degree with honours! So, thank you for all the
> support, tips and tricks.
>
> So, here I am again with a brainstorm question...
>
> The situation:
>
> -------------------------------------------
> I'll give an idea of the analysis.
>
> In short, I tested a sampling design three scale levels. An elementary
> sample unit (ESU) level, a Cell level (1x1km) and a site level (3x3km).
>
> The link shows you an illustration of the situation:
>
> http://users.pandora.be/requested/images/sampling.jpg
>
> In every ESU leaf are indexes were measured at the given locations, in
> the given patern.
>
> To check for spatial dependence at an ESU level I did a simple Moran's
> I/Geary's C analysis. All results were negative, so I concluded that
> under current conditions, in this vegetation sampling could as well be
> done at random and location didn't matter. This had some implications,
> in respect to further field surveys. Not having to deal with complex
> site descriptions and measuring problems => costs less time and money.
>
> Because of the tricky things like boundary situations, I skipped the
> Cell level (1x1km) and went straight to the site level. Problem was that
> the vallues of the whole pool weren't exactly normal and not fixable by
> transformation. So for the whole pool of data I did an indicator
> (kriging) analysis avoiding the distribution problem. This came out
> negative with no spatial relations for any of the cutoff levels.
>
> At all cutoff levels the semivariogram looked like this or close to it:
>
> http://users.pandora.be/requested/images/cutoff2.gif
>
> Just as an extra, the semivariogram of all the datapoint (far from
> normal distributed) looks like this.. notice the downward curving of the
> tail end of the semivariogram:
>
> http://users.pandora.be/requested/images/semivario.png
> (Lag distance in meter)
>
> I also averaged the data on an ESU level, given 38 ESU's this isn't much
> to work with but those data showed a normal distribution so I calculated
> a semivariogram for this data.
>
> The semivariogram for the averaged data:
>
> http://users.pandora.be/requested/images/variogram.png
>
> This would suggest a sill of around 0.10 and a range of some 1200 m.
> Some wobly sinus movements can be seen in the semivariogram, this could
> be due to the dune like environment of the site... but this is a rather
> bold statement given the veeeery few sampling points (38 average ESU
> values).
>
> -----------------------------------------------------
>
> So, If I need to interpolate between measured values for validation of
> satellite images (the final goal of all this), I will need a model. And
> more important, I can't use the images to validate the model because I
> need the model to validate the images. So the model has to be rather good.
>
> Is cross-validation of the semivariogram model a valid option to check
> the model or not (given the small amount of data used for the
> semivariogram and the model based on it)? Any other tests? Other ways to
> optimise the current model?
>
> Any ideas on approaches to test the sampling design itself, and not so
> much the model that would be needed for an actual application
> (validation of the sat. images)?
>
> Any remarks no my current way of thinking? Mistakes I could have made?
>
> Any ideas to get more out of the data, given the fact that the whole
> pool (individual measurements) is faaaaaaaaaaaaaaaar from normal, but
> the averaged data per ESU is?
>
> Thank you for reading it all,
> Koen.
>
>
> * By using the ai-geostats mailing list you agree to follow its rules
> ( see http://www.ai-geostats.org/help_ai-geostats.htm )
>
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body (plain text format) of an email message to sympa@...
>
> Signoff ai-geostats
>
>


--
Rajive

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-----Original Message-----
From: Gayle Hanssen [mailto:dms@...]
Sent: 09 November 2004 17:11
To: ai-geostats@...
Subject: [ai-geostats] Very narrow Archean Gold Reef distribution

Dear All

 

I am involved in trying to find a solution for a gold mine in Zimbabwe.  The veins are very thin (from 5cm to 4m) with grades from trace to 2.2% Au. 

 

We are able to physically model the veins (a series of them).  However, I am informed that the grade distribution is a "J-type" distribution and needs to be "normalised" before we can do anything with the data.  A log-normal distribution does not work and it is still hugely skewed.

 

Any suggestions on solutions or software applications that can solve this problem?

 

Regards

Gayle

 

 

Gayle Hanssen
Digital Mining Services
P O Box HG528, Highlands
Harare, Zimbabwe

 

Ph +263 4 730 534
Cell +263 11 601 973
e-mail dms@...

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Hi-

Without knowledge of the geometry of the veins the suggestion may be pretty
silly, but if the veins occur in swarms which are combined for mining I
would suggest diluting to full mineable thickness (if the veins are
intersected at odd angles this may be fairly tricky) and then looking at
mineable grade thickness and mineable thickness.

Regards

Robert (Bob) L. Sandefur PE
Senior Geostatistician / Reserve Analyst
CAM
200 Union Suite G-13
Lakewood, Co
80228

rsandefur@...

303 472-3240 (cell) <-best  choice

303 716-1617 ext 14



________________________________

From: Gayle Hanssen [mailto:dms@...]
Sent: Tuesday, November 09, 2004 10:11
To: ai-geostats@...
Subject: [ai-geostats] Very narrow Archean Gold Reef distribution


Dear All

I am involved in trying to find a solution for a gold mine in Zimbabwe.  The
veins are very thin (from 5cm to 4m) with grades from trace to 2.2% Au.

We are able to physically model the veins (a series of them).  However, I am
informed that the grade distribution is a "J-type" distribution and needs to
be "normalised" before we can do anything with the data.  A log-normal
distribution does not work and it is still hugely skewed.

Any suggestions on solutions or software applications that can solve this
problem?

Regards
Gayle


Gayle Hanssen
Digital Mining Services
P O Box HG528, Highlands
Harare, Zimbabwe

Ph +263 4 730 534
Cell +263 11 601 973
e-mail dms@...

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Greetings,

I have a 1000x1000 evenly spaced grid in matrix form, where each point
represents the estimated intensity of a point pattern.

I'd like to create a corresonding color contour plot to show the
variation of intensity across the given region.

Any suggestions on freeware that would create a nice plot? I think the
Spatial Module for S-plus has this capability, but unfortunately I don't
have the module.

Kind Regards,
Dave



David Afshartous, PhD
University of Miami
Department of Management Science
School of Business
Coral Gables, FL 33124
phone: 305-284-8005

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Dear listers,
I have a set of core drill data that have not the same length. So that for
the variographic studies and the calculation of resources (using kriging), I
have to make a compositing (i.e. producing composites with the same length)
on core drill data. But at the same time I have to lower outliers high grade
data to a certain limit, in the concern to reduce their influence.
My question is: have I to deal with outliers  before the compositing?
Thanks for any advice

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Hi,

You should look into R and its packages .... it is the free version of
S-PLUS. If you are used with the command window in S-PLUS
then R should be quite easy for you. if not - there is a quite steep
learning curve - but it worth while. Besides, R is free.

http://cran.r-project.org/

Good luck,

Monica
Monica Palaseanu-Lovejoy
University of Manchester
School of Geography
Mansfield Cooper Bld. 3.21
Oxford Road
Manchester M13 9PL
England, UK
Tel: +44 (0) 275 8689
Email: monica.palaseanu-lovejoy@...

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Kai

I would suggest you take a look at:

Introduction to Geostatistics: Applications in
Hydrogeology (Stanford-Cambridge Program)
P. K. Kitanidis

which is a great base to work from.

Isobel
http:///geoecosse.bizland.com

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-----Original Message-----
From: Kai.Zosseder@... [mailto:Kai.Zosseder@...]
Sent: Tue 11/23/2004 12:16 PM
To: ai-geostats@...
Cc:
Subject: [ai-geostats] problem of spatial continuity of groundwater head

Hello list,

A few days ago I had a discussion about spatial continuity of hydraulic heads and the following question: Is it allowed to use a krging technique for this variable?

First Opinion: there are different processes which influence the groundwater heads, e.g.:
        - gravity (hydraulic gradient)
        - grounwater recharge
        - anthropogenic influence (pumping/injection)
So there is a global trend which is overlayed from different additional trends like groundwater recharge and anthropogenic influence (pumping/injection) and you can´t seperate these processes. These are the reasons that you get problems with the ergodicity and it is not possible to use kriging techniques, because you haven´t a spatial continuity which is based on one process.

Second Opinion: There is a basic process for the groundwater heads (the movement based on the Darcy law and that´s it) . There exist a global trend (but you can handle it with Universal kriging) and the differences depend on the other processes maybe give you another trend by large scale problems (and then you have problems with the ergodicity and have to use moving window statistics to divide you working area). So you have a spatial continuity which is not necesseraly influenced by problems with ergodicity and you can use kriging techniques. Maybe the anthropogenic influence could be a process which disturb the spatial continuity.

I know it´s quite a hydrogeology problem but anyway I would like to hear your opinion or experiences on that topic.

Looking forward to the answers,

Kai

Dipl. Kai Zoßeder
Bayerisches Geologisches Landesamt
Heßstr. 128
D-80797 München
Tel.: 089-9214 2655

http://www.bayern.de/gla
http://www.bis.bayern.de

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Hello list,

A few days ago I had a discussion about spatial continuity of hydraulic heads and the following question: Is it allowed to use a krging technique for this variable?

First Opinion: there are different processes which influence the groundwater heads, e.g.:
        - gravity (hydraulic gradient)
        - grounwater recharge
        - anthropogenic influence (pumping/injection)
So there is a global trend which is overlayed from different additional trends like groundwater recharge and anthropogenic influence (pumping/injection) and you can´t seperate these processes. These are the reasons that you get problems with the ergodicity and it is not possible to use kriging techniques, because you haven´t a spatial continuity which is based on one process.

Second Opinion: There is a basic process for the groundwater heads (the movement based on the Darcy law and that´s it) . There exist a global trend (but you can handle it with Universal kriging) and the differences depend on the other processes maybe give you another trend by large scale problems (and then you have problems with the ergodicity and have to use moving window statistics to divide you working area). So you have a spatial continuity which is not necesseraly influenced by problems with ergodicity and you can use kriging techniques. Maybe the anthropogenic influence could be a process which disturb the spatial continuity.

I know it´s quite a hydrogeology problem but anyway I would like to hear your opinion or experiences on that topic.

Looking forward to the answers,

Kai

Dipl. Kai Zoßeder
Bayerisches Geologisches Landesamt
Heßstr. 128
D-80797 München
Tel.: 089-9214 2655

http://www.bayern.de/gla
http://www.bis.bayern.de

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Dear listers,

My question is about comparing two value lists mapped onto the same
point pattern:
We have calculated a kind of economical potential estimate from data
collected for 576 towns from  gravitation modell using linear distances
between towns first, then using the minimum time required to reach one
town from the other via road network. So, we have two values for the
same 576 points which differ from each other with some magnitude and in
spatial distribution, too. We would like to spatially compare two data
to find areas where the two methods gives the most different results.

Our idea was to calculate a (simple) linear regression for the two
values, and then map the residual values, looking for the continuous sub
areas where residuals are autocorrelated. We are not interested in
estimation,  but in finding the anomaleous places, where one modell
locally significally over- or underestimates the other one.

I am interested in your opinion about this approach, I would be thankful
to get ideas about probably better (standard?) methods for comparing two
mapped value sets.

Jozsef Fabian

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Hi Wolfram,

You forgot to mention what you want to do with these data.
If the objective is to perform kriging, then you can use
either kriging with nonsystematic errors or soft
indicator kriging to account for the variable level of
reliability of your data.

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 Mon, 29 Nov 2004, Wolfram Ruehaak wrote:

> Dear all,
>
> I have difficulties to find information's about the following problem.
>
> I have a lot of spatially scattered measurements. These measurements
> have - resulting from different measurement methods - different
> measurement errors, which are known.
>
> For example some have an total error of 5%, some of 10% and a third
> group of 20%.
>
> I want to give these values a quality-weight in the range from 0.0 to
> 1.0. (In this case three different weights.)
>
> How can I do this?
>
> Simple is a weight = 0 which is a value so bad I don't want to use it,
> and a weight = 1 which could be the value for the group with the best
> measurements (in this case error = 5%).
>
> Is there a statistically firmed way to quantify the weights.
>
> Any suggestions will be very welcome.
> Is there any literature that discusses this matter?
>
> Thanks in advance.
>
> Wolfram Ruehaak
>
>
>

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Dear all,

I have difficulties to find information's about the following problem.

I have a lot of spatially scattered measurements. These measurements
have - resulting from different measurement methods - different
measurement errors, which are known.

For example some have an total error of 5%, some of 10% and a third
group of 20%.

I want to give these values a quality-weight in the range from 0.0 to
1.0. (In this case three different weights.)

How can I do this?

Simple is a weight = 0 which is a value so bad I don't want to use it,
and a weight = 1 which could be the value for the group with the best
measurements (in this case error = 5%).

Is there a statistically firmed way to quantify the weights.

Any suggestions will be very welcome.
Is there any literature that discusses this matter?

Thanks in advance.

Wolfram Ruehaak

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