Hi Craig,

> -----Original Message-----
> From: Craig von Hagen [mailto:craigvonhagen@...]
> Hi All,
>
> I have an interesting problem to solve, I hope
> someone could help me.
>
> We are working on flood early warning in Somalia
> and we have the following situation.
>
> We have an existing network of manual rain gauges
> that we receive on a monthly basis with daily
> readings taken manually by a person in the field.
> These however can be unreliable.
>
> We have an option to install automatic rain gauges
> that would give us an accurate measurement of
> rainfall per day. We would like to use
> geo-statistics to then give a prediction and error
> surface and then use these surfaces to evaluate how
> accurate and reliable our existing manual network
> is.
>
> Is there a way to calculate the optimal network
> (number and location) for the automatic stations so
> that we get a reliable prediction surface which we
> can then use to evaluate our manual network?
> [...]
> Thanks and regards
> Craig

I am quite sure that there are methods to estimate an optimal station-setup
(probably colleagues with more experience will answer), but I want to point out
an other thing:
rainfall is mostly not uniform distributed. The topography has a major impact,
also differences of the land cover can result in differences of the (micro -
meso scale) climate. There are probably also coastal effects.

I think, that it would be necessary to take this also into account. Which makes
the calculation of number and place of the new stations more difficult.

Hope this helps a little bit, cheers Wolfram

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In the definition of the cross variogram you can see that it is not
adimentional (depend of units >> Km, %, ppm, etc.), you can avoid  this
effect using standardize Ordinary Co-Kriging.

Adrian

-----Original Message-----
From: "Behrang Kushavand" <Kushavand@...>
To: <ai-geostats@...>
Date: Wed, 4 Jan 2006 19:55:01 +0330
Subject: [ai-geostats] Traditional OCK or Standardize OCK?

> Dear All,
>
>
>
> Is it true that estimation variance of standardize Ordinary Co-Kriging
> (SOCK) is always equal or smaller than Traditional Ordinary Co-Kriging
> (TOCK)?
>
> What is the advantage of TOCK to SOCK (I think it is about negative
> weights) and are there any criteria to choice TOCK or SOCK?
>
>
>
> Thanks
>
> Behrang
>
>


________________________________________________________________________________\
____________
Participe en el V Congreso Internacional de Educación Superior
"Universidad 2006". La Habana, Cuba, del 13 al 17 de Febrero del 2006
http://www.universidad2006.cu
_____________________________________
Instituto Superior Minero Metalúrgico de Moa
Dr. Antonio Núñez Jiménez
http://www.ismm.edu.cu

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Signoff ai-geostats

The downside of SOCK (often not mentioned) is that as a minimum requirement one
must know the difference(s) between the population means (i.e., the means of the
random functions) of the primary and secondary variables. In practice, one
rarely knows these and uses the differences between the sample means instead,
which is incorrect, unless one takes the associated estimation errors into
account. However, when the BLUE of the differences between population means is
used and the associated estimation errors are taken into account, then I suspect
that SOCK boils down to something very close or identical to TOCK. Along similar
lines, recall that substituting the BLUE of the population mean in the simple
kriging equations yields a predictor that is identical to the ordinary kriging
predictor (I think it is in Cressie's book, but in fact it is not that difficult
to establish this result).

The main (only?) purpose of using ordinary kriging instead of simple kriging is
that one often does not know the population mean and cannot simply assume that
it is equal to the sample mean or some other combination of the sample data.
That is why ordinary kriging is used much more often than simple kriging. It
puzzles me why so many geostatisticians so easily replace TOCK by SOCK and
ignore the problem above. It is not the right method to avoid large and many
negative weights, there are much better ways for that (see discussion of one
month ago).

Gerard

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

tel +31 317 474628 / 482420
email gerard.heuvelink@...
http://www.sil.wur.nl/UK/


-----Original Message-----
From: Pierre Goovaerts [mailto:Goovaerts@...]
Sent: donderdag 5 januari 2006 0:20
To: Adrián Martínez Vargas; Behrang Kushavand; ai-geostats@...
Subject: RE: [ai-geostats] Traditional OCK or Standardize OCK?



Hi,

The main difference between SOCK and TOCK is that, in the standardized
form, only one unbiasedness constraint is imposed, i.e. the sum of all
primary and secondary data weights is one, while in the traditional
version a separate constraint is applied for each variable, i.e.
sum of primary data weights is one and the sum of secondary data
weights is zero for each secondary variable. The traditional
constraints lead to larger and more frequent negative weights
for the secondary variables. The difference between SOCK and
TOCK estimates is expected to increase as differences between
the variance of primary and secondary variables increases.
The different types of cokriging are described and compared in the
following paper:
Goovaerts, P. 1998. Ordinary cokriging revisited.
Mathematical Geology, 30(1): 21-42.

Cheers,

Pierre

Pierre Goovaerts
Chief Scientist at BioMedware
516 North State Street
Ann Arbor, MI 48104
Voice: (734) 913-1098 (ext. 8)
Fax: (734) 913-2201
http://home.comcast.net/~goovaerts/



-----Original Message-----
From: Adrián Martínez Vargas [mailto:amvargas@...]
Sent: Wed 1/4/2006 12:53 PM
To: Behrang Kushavand; ai-geostats@...
Cc:
Subject: Re: [ai-geostats] Traditional OCK or Standardize OCK?
In the definition of the cross variogram you can see that it is not
adimentional (depend of units >> Km, %, ppm, etc.), you can avoid  this
effect using standardize Ordinary Co-Kriging.

Adrian

-----Original Message-----
From: "Behrang Kushavand" <Kushavand@...>
To: <ai-geostats@...>
Date: Wed, 4 Jan 2006 19:55:01 +0330
Subject: [ai-geostats] Traditional OCK or Standardize OCK?

> Dear All,
>
>
>
> Is it true that estimation variance of standardize Ordinary Co-Kriging
> (SOCK) is always equal or smaller than Traditional Ordinary Co-Kriging
> (TOCK)?
>
> What is the advantage of TOCK to SOCK (I think it is about negative
> weights) and are there any criteria to choice TOCK or SOCK?
>
>
>
> Thanks
>
> Behrang
>
>


________________________________________________________________________________\
____________
Participe en el V Congreso Internacional de Educación Superior
"Universidad 2006". La Habana, Cuba, del 13 al 17 de Febrero del 2006
http://www.universidad2006.cu
_____________________________________
Instituto Superior Minero Metalúrgico de Moa
Dr. Antonio Núñez Jiménez
http://www.ismm.edu.cu

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Signoff ai-geostats

Dear All,

Is it true that estimation variance of standardize Ordinary Co-Kriging (SOCK) is always equal or smaller than Traditional Ordinary Co-Kriging (TOCK)?

What is the advantage of TOCK to SOCK (I think it is about negative weights) and are there any criteria to choice TOCK or SOCK?

Thanks

Behrang

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Signoff ai-geostats

Hi list,

First I would like to thank to the people answering my question about which
interpolation method / model is better when I am using only deterministic
methods and I have a lack of data. Obviously it is not a clear answer ....
so i will try to get more data or .... figure something else ;-)))

So .... figuring something else made me to think about interpolating along
lines / canals.

I have a network of canals and few water stations along them. Knowing that
the flow gradient along canals is very small and water levels are varying
very smoothly, how can I interpolate along canals at set distances, for
example? I guess i need a very simple algorithm like at mid distance in
between 2 stations i will have the average values of those 2 stations, and
so on. I wonder if you know about any software to do this automatically.

Thank you for your assistance once more,

Monica

Monica Palaseanu-Lovejoy
University of Florida

_________________________________________________________________
Is your PC infected? Get a FREE online computer virus scan from McAfee®
Security. http://clinic.mcafee.com/clinic/ibuy/campaign.asp?cid=3963

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Dear list,

Just a simple question...

I'm trying to produce a contour map from a structural surface. This
surface is highly non-stationary (actuaally dips towards NW)
I wolud like to know which could be the best solution for getting a
reliabel map:

1) Fit a plyonomial surface to the sample values, which is used as a
drift. Substract the drift from the observations to get the residuals.
Estimate the variogram of the residuals (now the residual variable is
stationary). Perform OK on the residuals. Add the polynomial surface to
the residuals to get the interpolated surface.

2) Compute variograms from the observations. Use universal kriging to
get the interpolated surface.

Thank you

Oriol
--



______________________________________

Oriol Falivene
ofaliven@...
http://www.ub.es/ggac

tel. (+34) 93 4021373
fax (+34) 93 4021340

Fac. de Geologia,
Univ. de Barcelona

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* To unsubscribe to ai-geostats, send the following in the subject or in the
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Hi,

The main difference between SOCK and TOCK is that, in the standardized
form, only one unbiasedness constraint is imposed, i.e. the sum of all
primary and secondary data weights is one, while in the traditional
version a separate constraint is applied for each variable, i.e.
sum of primary data weights is one and the sum of secondary data
weights is zero for each secondary variable. The traditional
constraints lead to larger and more frequent negative weights
for the secondary variables. The difference between SOCK and
TOCK estimates is expected to increase as differences between
the variance of primary and secondary variables increases.
The different types of cokriging are described and compared in the
following paper:
Goovaerts, P. 1998. Ordinary cokriging revisited.
Mathematical Geology, 30(1): 21-42.

Cheers,

Pierre

Pierre Goovaerts
Chief Scientist at BioMedware
516 North State Street
Ann Arbor, MI 48104
Voice: (734) 913-1098 (ext. 8)
Fax: (734) 913-2201
http://home.comcast.net/~goovaerts/



-----Original Message-----
From: Adrián Martínez Vargas [mailto:amvargas@...]
Sent: Wed 1/4/2006 12:53 PM
To: Behrang Kushavand; ai-geostats@...
Cc:
Subject: Re: [ai-geostats] Traditional OCK or Standardize OCK?
In the definition of the cross variogram you can see that it is not
adimentional (depend of units >> Km, %, ppm, etc.), you can avoid  this
effect using standardize Ordinary Co-Kriging.

Adrian

-----Original Message-----
From: "Behrang Kushavand" <Kushavand@...>
To: <ai-geostats@...>
Date: Wed, 4 Jan 2006 19:55:01 +0330
Subject: [ai-geostats] Traditional OCK or Standardize OCK?

> Dear All,
>
>
>
> Is it true that estimation variance of standardize Ordinary Co-Kriging
> (SOCK) is always equal or smaller than Traditional Ordinary Co-Kriging
> (TOCK)?
>
> What is the advantage of TOCK to SOCK (I think it is about negative
> weights) and are there any criteria to choice TOCK or SOCK?
>
>
>
> Thanks
>
> Behrang
>
>


________________________________________________________________________________\
____________
Participe en el V Congreso Internacional de Educación Superior
"Universidad 2006". La Habana, Cuba, del 13 al 17 de Febrero del 2006
http://www.universidad2006.cu
_____________________________________
Instituto Superior Minero Metalúrgico de Moa
Dr. Antonio Núñez Jiménez
http://www.ismm.edu.cu

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Signoff ai-geostats

Dear Oriol Falivene,

If you could assume a linear (or polynomial trend) for the dip, the situation
you are talking about, is the classical situation  for the use of intrinsic
random functions, IRKk-kriging and generalized covariances or generalized
variograms. The theory is quite good explained  in the thick standard books
on Geostatistics such as that of Cressie or of Chiles and Delfiner. In
pratice it is quite difficult to actually estimate the generalized variograms
and to find good software for that.

Can anyone hint me and Oriol to good software for IRK-k kriging, especially
regarding the estimation and modelling of the generalised variograms? The
IRKk/Universal kriging is present everywhere, but the modelling...?

In case of nonpolynomial trend, the estimation of the variogram gets even more
tricky.
> I wolud like to know which could be the best solution for getting a
> reliabel map:
> 1) Fit a plyonomial surface to the sample values, which is used as a
> ...
1) is wrong, since removing the trend removes the assumption of stationarity,
which often also results in strange behavior of the variogram (e.g. dropping
for long distances again)

2) is not strictly wrong, however whenever a trend is really present, you can
not fit a ordinary variogram model, due to a quadratic increase in the
variogramm. You will need a generalized variogram model and than we are back
at IRFk-kriging.

Best regards,
Gerald v.d. Boogaart



Am Donnerstag, 5. Januar 2006 10:57 schrieb Oriol Falivene:
> Dear list,
>
> Just a simple question...
>
> I'm trying to produce a contour map from a structural surface. This
> surface is highly non-stationary (actuaally dips towards NW)
> I wolud like to know which could be the best solution for getting a
> reliabel map:
>
> 1) Fit a plyonomial surface to the sample values, which is used as a
> drift. Substract the drift from the observations to get the residuals.
> Estimate the variogram of the residuals (now the residual variable is
> stationary). Perform OK on the residuals. Add the polynomial surface to
> the residuals to get the interpolated surface.
>
> 2) Compute variograms from the observations. Use universal kriging to
> get the interpolated surface.
>
> Thank you
>
> Oriol
> --
>
>
>
> ______________________________________
>
> Oriol Falivene
> ofaliven@...
> http://www.ub.es/ggac
>
> tel. (+34) 93 4021373
> fax (+34) 93 4021340
>
> Fac. de Geologia,
> Univ. de Barcelona

--
-------------------------------------------------
Prof. Dr. K. Gerald v.d. Boogaart
Professor als Juniorprofessor fuer Statistik
http://www.math-inf.uni-greifswald.de/statistik/

office: Franz-Mehring-Str. 48, 1.Etage rechts
e-mail: Gerald.Boogaart@...
phone:  00+49 (0)3834/86-4621
fax:    00+49 (0)89-1488-293932 (Faxmail)
fax:    00+49 (0)3834/86-4615   (Institut)

paper-mail:
Ernst-Moritz-Arndt-Universitaet Greifswald
Institut für Mathematik und Informatik
Jahnstr. 15a
17487 Greifswald
Germany
--------------------------------------------------

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Dear Craig von Hagen,

If you install a network of automated gauges, which is dense enough to make a
map to check the manual gauges, sure enough it makes the manual network
superfluous.

And such network would not really help evaluating the operators:
* Anyway if the measurement of a manual gauge is underestimated systematically
by the interpolated surface I would first suspect a difference in
microclimate to be the origin of that and not a bad operator.

* If the variation of the measurement is underestimated by the kriging error I
would suspect an additional measurment error in the manual measurements or a
ill specified variogramm before blameing the operator.

If you would like to check a manual network you might consider two options:

1) Install mobile automatic gauges next to manual ones and check by simple
comparison (however you need to consider wether the operator should be
allowed to know that he is controlled currently). Than move the mobile gauge
to the next station.

2) To check single operators you can run cross-validation with the existing
manual network: estimate a prediction and a kriging error from all stations
but the one to be checked and compare. You need to add the nugget effect of
the semivariogram to the kriging error to get the variance of
Prediction-Measurement.

Best regards,
Gerald v.d. Boogaart




Am Dienstag, 3. Januar 2006 14:53 schrieb Craig von Hagen:
> Hi All,
>
> I have an interesting problem to solve, I hope
> someone could help me.
>
> We are working on flood early warning in Somalia
> and we have the following situation.
>
> We have an existing network of manual rain gauges
> that we receive on a monthly basis with daily
> readings taken manually by a person in the field.
> These however can be unreliable.
>
> We have an option to install automatic rain gauges
> that would give us an accurate measurement of
> rainfall per day. We would like to use
> geo-statistics to then give a prediction and error
> surface and then use these surfaces to evaluate how
> accurate and reliable our existing manual network
> is.
>
> Is there a way to calculate the optimal network
> (number and location) for the automatic stations so
> that we get a reliable prediction surface which we
> can then use to evaluate our manual network?
>
> I am the most familiar with the ArcGIS GeoStatistical
> Analyst.
>
> Thanks and regards
> Craig
>
>
> Craig von Hagen
> FAO - GLCN/Africover/SWALIM Projects
> PO Box 30470-00100
> Nairobi, Kenya
>
> Tel: +254 20 444 3331
> Fax: +254 20 444 1993
>
> www.africover.org
> www.glcn.org; www.glcn-lccs.org
> www.faoswalim.org
>
>
>
> ___________________________________________________________
> To help you stay safe and secure online, we've developed the all new Yahoo!
> Security Centre. http://uk.security.yahoo.com

--
-------------------------------------------------
Prof. Dr. K. Gerald v.d. Boogaart
Professor als Juniorprofessor fuer Statistik
http://www.math-inf.uni-greifswald.de/statistik/

office: Franz-Mehring-Str. 48, 1.Etage rechts
e-mail: Gerald.Boogaart@...
phone:  00+49 (0)3834/86-4621
fax:    00+49 (0)89-1488-293932 (Faxmail)
fax:    00+49 (0)3834/86-4615   (Institut)

paper-mail:
Ernst-Moritz-Arndt-Universitaet Greifswald
Institut für Mathematik und Informatik
Jahnstr. 15a
17487 Greifswald
Germany
--------------------------------------------------

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Dear all
can anyone explain to me the definition of multigaussian random function?
Thanks
Simone
-----------------------------
Dr. Simone Sammartino
PhD student
- Geostatistical analyst
- G.I.S. mapping
I.A.M.C. - C.N.R.
Geomare-Sud section
Port of Naples - Naples
marenostrum@...
-----------------------------

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Signoff ai-geostats

Dear list,

Just a simple question...

I'm trying to produce a contour map from a structural surface. This
surface is highly non-stationary (actuaally dips towards NW)
I wolud like to know which could be the best solution for getting a
reliabel map:

1) Fit a plyonomial surface to the sample values, which is used as a
drift. Substract the drift from the observations to get the residuals.
Estimate the variogram of the residuals (now the residual variable is
stationary). Perform OK on the residuals. Add the polynomial surface to
the residuals to get the interpolated surface.

2) Compute variograms from the observations. Use universal kriging to
get the interpolated surface.

Thank you

Oriol
--



______________________________________

Oriol Falivene
ofaliven@...
http://www.ub.es/ggac

tel. (+34) 93 4021373
fax (+34) 93 4021340

Fac. de Geologia,
Univ. de Barcelona



* By using the ai-geostats mailing list you agree to follow its rules
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Hello,

It is indeed correct that as for simple cokriging, the standardized OCK
requires knowledge of the population means for both primary and
secondary variables, and as I mentioned in my book p. 232 "Provided the
data are representative of the study area, these means can be estimated
from the sample means". Of course, we could also account for the uncertainty
attached to those samples means.. but the same can be said regarding the
uncertainty attached to the parameters of the semivariogram model...

The main reason ordinary kriging is used instead of simple kriging is
its ability to accommodate changes in the mean across the study area
(what I called global trend in my book) through the use of local
search windows. The interesting fact for standardized OCK is that,
even if a global mean is used in the standardization, local means
are still re-estimated within each search window thanks to the
unbiasedness constraint. The main assumption however is that after
rescaling by their global means both primary and secondary variables
have the same local mean, see Goovaerts (1997, 1998). For me, this
might be the main weakness/limitation of the approach. As always,
cross-validation is a good way to compare the prediction performances
of the different estimators.

Pierre

Pierre Goovaerts
Chief Scientist at BioMedware
516 North State Street
Ann Arbor, MI 48104
Voice: (734) 913-1098 (ext. 8)
Fax: (734) 913-2201
http://home.comcast.net/~goovaerts/

-----Original Message-----
From: Heuvelink, Gerard [mailto:Gerard.Heuvelink@...]
Sent: Thu 1/5/2006 4:31 AM
To: Pierre Goovaerts; Adrián Martínez Vargas; Behrang Kushavand;
ai-geostats@...
Cc:
Subject: RE: [ai-geostats] Traditional OCK or Standardize OCK?
The downside of SOCK (often not mentioned) is that as a minimum requirement one
must know the difference(s) between the population means (i.e., the means of the
random functions) of the primary and secondary variables. In practice, one
rarely knows these and uses the differences between the sample means instead,
which is incorrect, unless one takes the associated estimation errors into
account. However, when the BLUE of the differences between population means is
used and the associated estimation errors are taken into account, then I suspect
that SOCK boils down to something very close or identical to TOCK. Along similar
lines, recall that substituting the BLUE of the population mean in the simple
kriging equations yields a predictor that is identical to the ordinary kriging
predictor (I think it is in Cressie's book, but in fact it is not that difficult
to establish this result).

The main (only?) purpose of using ordinary kriging instead of simple kriging is
that one often does not know the population mean and cannot simply assume that
it is equal to the sample mean or some other combination of the sample data.
That is why ordinary kriging is used much more often than simple kriging. It
puzzles me why so many geostatisticians so easily replace TOCK by SOCK and
ignore the problem above. It is not the right method to avoid large and many
negative weights, there are much better ways for that (see discussion of one
month ago).

Gerard

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

tel +31 317 474628 / 482420
email gerard.heuvelink@...
http://www.sil.wur.nl/UK/


-----Original Message-----
From: Pierre Goovaerts [mailto:Goovaerts@...]
Sent: donderdag 5 januari 2006 0:20
To: Adrián Martínez Vargas; Behrang Kushavand; ai-geostats@...
Subject: RE: [ai-geostats] Traditional OCK or Standardize OCK?



Hi,

The main difference between SOCK and TOCK is that, in the standardized
form, only one unbiasedness constraint is imposed, i.e. the sum of all
primary and secondary data weights is one, while in the traditional
version a separate constraint is applied for each variable, i.e.
sum of primary data weights is one and the sum of secondary data
weights is zero for each secondary variable. The traditional
constraints lead to larger and more frequent negative weights
for the secondary variables. The difference between SOCK and
TOCK estimates is expected to increase as differences between
the variance of primary and secondary variables increases.
The different types of cokriging are described and compared in the
following paper:
Goovaerts, P. 1998. Ordinary cokriging revisited.
Mathematical Geology, 30(1): 21-42.

Cheers,

Pierre

Pierre Goovaerts
Chief Scientist at BioMedware
516 North State Street
Ann Arbor, MI 48104
Voice: (734) 913-1098 (ext. 8)
Fax: (734) 913-2201
http://home.comcast.net/~goovaerts/



-----Original Message-----
From: Adrián Martínez Vargas [mailto:amvargas@...]
Sent: Wed 1/4/2006 12:53 PM
To: Behrang Kushavand; ai-geostats@...
Cc:
Subject: Re: [ai-geostats] Traditional OCK or Standardize OCK?
In the definition of the cross variogram you can see that it is not
adimentional (depend of units >> Km, %, ppm, etc.), you can avoid  this
effect using standardize Ordinary Co-Kriging.

Adrian

-----Original Message-----
From: "Behrang Kushavand" <Kushavand@...>
To: <ai-geostats@...>
Date: Wed, 4 Jan 2006 19:55:01 +0330
Subject: [ai-geostats] Traditional OCK or Standardize OCK?

> Dear All,
>
>
>
> Is it true that estimation variance of standardize Ordinary Co-Kriging
> (SOCK) is always equal or smaller than Traditional Ordinary Co-Kriging
> (TOCK)?
>
> What is the advantage of TOCK to SOCK (I think it is about negative
> weights) and are there any criteria to choice TOCK or SOCK?
>
>
>
> Thanks
>
> Behrang
>
>


________________________________________________________________________________\
____________
Participe en el V Congreso Internacional de Educación Superior
"Universidad 2006". La Habana, Cuba, del 13 al 17 de Febrero del 2006
http://www.universidad2006.cu
_____________________________________
Instituto Superior Minero Metalúrgico de Moa
Dr. Antonio Núñez Jiménez
http://www.ismm.edu.cu

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Hi Monica,

> -----Original Message-----
> From: Monica Palaseanu-Lovejoy [mailto:mpal_lovejoy@...]
> Hi list,
> [...]
> I have a network of canals and few water stations along them.
> Knowing that
> the flow gradient along canals is very small and water levels
> are varying
> very smoothly, how can I interpolate along canals at set
> distances, for
> example? I guess i need a very simple algorithm like at mid
> distance in
> between 2 stations i will have the average values of those 2
> stations, and
> so on. I wonder if you know about any software to do this
> automatically.
>
> Thank you for your assistance once more,
>
> Monica
> [...]

What about a spline interpolation?

Look for instance at
http://www.csit.fsu.edu/~burkardt/cpp_src/spline/spline.html

There is also a matlab version.

However, splines are sometimes hard to use for extrapolation purposes.

A simple inverse distance weighting method could also be used.

I think the most difficult part is the fusion and segregation within a network
of canals?

Isn't there specialised software available (do you know
http://www.dhisoftware.com).

HTH, cheers Wolfram

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Craig,

You may wish to take a look at what FEWS-NET has been doing in Southern
Africa.  It look very similar to what you have in mind.  See
http://www.sadc-hazards.net/.  Particularly,
http://gisdata.usgs.net/sa_floods/aspmap/.  I know there are several
technical papers that provide additional detail.  One such paper is at
http://www.isprs.org/commission1/proceedings02/paper/00030.pdf.  You might
want to get in touch with the authors to see if there are any others.

With best regards,
Tom Parris
------------------------------------------------------------
Thomas M. Parris
Director of Sustainability Programs
ISciences, LLC
61 Main Street, Suite 200
Burlington, VT  05401  USA

Tel:   +802-864-2999              http://www.isciences.com/
Fax:   +617-344-2580              http://www.terraviva.net/
Email: parris@...
-----------------------------------------------------------

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-----Original Message-----
From: Gregoire Dubois [mailto:gregoire.dubois@...]
Sent: 12 January 2006 15:01
To: ai-geostats@...
Subject: [ai-geostats] Optimization of monitoring networks

Dear list,

I am looking for references (and possibly software) on network optimization. The variable monitored has no importance and I am looking for references and topological algorithms.

A question I have is the following: given an area A with a particular shape (e.g. defined by country borders) and a number of stations N (e.g. for mobile phone emitters), how do I define the optimal locations for these stations?

Thanks for any hints.

Gregoire

__________________________________________
Gregoire Dubois (Ph.D.)

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

TP 441, Via Fermi 1
21020 Ispra (VA)
ITALY
 
Tel. +39 (0)332 78 6360
Fax. +39 (0)332 78 5466
Email: gregoire.dubois@...

WWW: http://www.ai-geostats.org
WWW: http://rem.jrc.cec.eu.int
 
"The views expressed are purely those of the writer and may not in any circumstances be regarded as stating an official position of the European Commission."

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Hi Gerald,

Thanks very much. However maybe it was not clear in my
first email. We are not just interested in checking
the gauge readers but also in:

1.      Connecting Somalia to the Global Observing
System
2.      Feeding into the GTS to allow for validation
of RFE data (and corrections used in the RFE 2.0
algorithm).
3.      Using the data collected to provide data on
storm duration and intensity (at present we only get
24 hour readings)
4.      Real time flood forecasting and drought
monitoring.

The use of the rain gauges for checking/validating
manual readings is an added benefit.

I am also looking into the WMO methods for
establishing a gauge network.

So far responses received from the list for optimal
gauge location inlcude: stratification by elevation -
assumption being rain increases with height (although
not really the case here as we have rainfall on the
coast).
  - wieghting locations in terms of their contribution
to flooding
  - adding gauges at locations where their is most
uncertainty
  - taking into account topogrpahy, slope and coastal
effects.

Further inputs are welcome...

Regards
Craig


--- Gerald van den Boogaart
<boogaart@...> wrote:

> Dear Craig von Hagen,
>
> If you install a network of automated gauges, which
> is dense enough to make a
> map to check the manual gauges, sure enough it makes
> the manual network
> superfluous.
>
> And such network would not really help evaluating
> the operators:
> * Anyway if the measurement of a manual gauge is
> underestimated systematically
> by the interpolated surface I would first suspect a
> difference in
> microclimate to be the origin of that and not a bad
> operator.
>
> * If the variation of the measurement is
> underestimated by the kriging error I
> would suspect an additional measurment error in the
> manual measurements or a
> ill specified variogramm before blameing the
> operator.
>
> If you would like to check a manual network you
> might consider two options:
>
> 1) Install mobile automatic gauges next to manual
> ones and check by simple
> comparison (however you need to consider wether the
> operator should be
> allowed to know that he is controlled currently).
> Than move the mobile gauge
> to the next station.
>
> 2) To check single operators you can run
> cross-validation with the existing
> manual network: estimate a prediction and a kriging
> error from all stations
> but the one to be checked and compare. You need to
> add the nugget effect of
> the semivariogram to the kriging error to get the
> variance of
> Prediction-Measurement.
>
> Best regards,
> Gerald v.d. Boogaart
>
>
>
>
> Am Dienstag, 3. Januar 2006 14:53 schrieb Craig von
> Hagen:
> > Hi All,
> >
> > I have an interesting problem to solve, I hope
> > someone could help me.
> >
> > We are working on flood early warning in Somalia
> > and we have the following situation.
> >
> > We have an existing network of manual rain gauges
> > that we receive on a monthly basis with daily
> > readings taken manually by a person in the field.
> > These however can be unreliable.
> >
> > We have an option to install automatic rain gauges
> > that would give us an accurate measurement of
> > rainfall per day. We would like to use
> > geo-statistics to then give a prediction and error
> > surface and then use these surfaces to evaluate
> how
> > accurate and reliable our existing manual network
> > is.
> >
> > Is there a way to calculate the optimal network
> > (number and location) for the automatic stations
> so
> > that we get a reliable prediction surface which we
> > can then use to evaluate our manual network?
> >
> > I am the most familiar with the ArcGIS
> GeoStatistical
> > Analyst.
> >
> > Thanks and regards
> > Craig
> >
> >
> > Craig von Hagen
> > FAO - GLCN/Africover/SWALIM Projects
> > PO Box 30470-00100
> > Nairobi, Kenya
> >
> > Tel: +254 20 444 3331
> > Fax: +254 20 444 1993
> >
> > www.africover.org
> > www.glcn.org; www.glcn-lccs.org
> > www.faoswalim.org
> >
> >
> >
> >
>
___________________________________________________________
> > To help you stay safe and secure online, we've
> developed the all new Yahoo!
> > Security Centre. http://uk.security.yahoo.com
>
> --
> -------------------------------------------------
> Prof. Dr. K. Gerald v.d. Boogaart
> Professor als Juniorprofessor fuer Statistik
> http://www.math-inf.uni-greifswald.de/statistik/
>
> office: Franz-Mehring-Str. 48, 1.Etage rechts
> e-mail: Gerald.Boogaart@...
> phone:  00+49 (0)3834/86-4621
> fax:    00+49 (0)89-1488-293932 (Faxmail)
> fax:    00+49 (0)3834/86-4615   (Institut)
>
> paper-mail:
> Ernst-Moritz-Arndt-Universitaet Greifswald
> Institut für Mathematik und Informatik
> Jahnstr. 15a
> 17487 Greifswald
> Germany
> --------------------------------------------------
>
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> follow its rules
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>
> Signoff ai-geostats






___________________________________________________________
Yahoo! Messenger - NEW crystal clear PC to PC calling worldwide with voicemail
http://uk.messenger.yahoo.com

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__________________________________________
Gregoire Dubois (Ph.D.)

European Commission (EC)
Joint Research Centre (JRC)
WWW: http://www.ai-geostats.org

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

-----Original Message-----
From: Michel BOBBIA [mailto:michel.bobbia@...]
Sent: 12 January 2006 17:23
To: ai-geostats@...
Subject: Re: [ai-geostats] Optimization of monitoring networks

Hello

I have buy a book that seems to deal with this subject :

 

Werner G. Müller

Collecting Spatial Date

Optimum Design of Experiments for Random Fields

second edition

Physica-Verlag (Springer)

 

Unfortunatly, my knowledge in design of experiments is not enough to understand the book, but I am reading it carefully...

However, I am interested in any solution/information to this problem !

 

Regards

 

Michel BOBBIA

Air Normand

 

----- Original Message -----

From: Gregoire Dubois

To: ai-geostats@...

Sent: Thursday, January 12, 2006 3:00 PM

Subject: [ai-geostats] Optimization of monitoring networks

Dear list,

I am looking for references (and possibly software) on network optimization. The variable monitored has no importance and I am looking for references and topological algorithms.

A question I have is the following: given an area A with a particular shape (e.g. defined by country borders) and a number of stations N (e.g. for mobile phone emitters), how do I define the optimal locations for these stations?

Thanks for any hints.

Gregoire

__________________________________________
Gregoire Dubois (Ph.D.)

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

TP 441, Via Fermi 1
21020 Ispra (VA)
ITALY
 
Tel. +39 (0)332 78 6360
Fax. +39 (0)332 78 5466
Email: gregoire.dubois@...

WWW: http://www.ai-geostats.org
WWW: http://rem.jrc.cec.eu.int
 
"The views expressed are purely those of the writer and may not in any circumstances be regarded as stating an official position of the European Commission."

---

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----- Original Message -----

From: Gregoire Dubois

To: ai-geostats@...

Sent: Thursday, January 12, 2006 3:00 PM

Subject: [ai-geostats] Optimization of monitoring networks

Dear list,

I am looking for references (and possibly software) on network optimization. The variable monitored has no importance and I am looking for references and topological algorithms.

A question I have is the following: given an area A with a particular shape (e.g. defined by country borders) and a number of stations N (e.g. for mobile phone emitters), how do I define the optimal locations for these stations?

Thanks for any hints.

Gregoire

__________________________________________
Gregoire Dubois (Ph.D.)

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

TP 441, Via Fermi 1
21020 Ispra (VA)
ITALY
 
Tel. +39 (0)332 78 6360
Fax. +39 (0)332 78 5466
Email: gregoire.dubois@...

WWW: http://www.ai-geostats.org
WWW: http://rem.jrc.cec.eu.int
 
"The views expressed are purely those of the writer and may not in any circumstances be regarded as stating an official position of the European Commission."

---

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Dear list,

one solution for the network optimization problem is to use
Voronoi diagrams. Optimal locations can be defined as
locations minimizing the average nearest neighbour distance.

Think of the locations of public mailboxes in a town. The
mailboxes are on an optimal location if the average distance
of the people in the town to their nearest public mailbox is minimized.

This problem is described in detail in the chapter on Locational
Optimization in

@Book{OBSS,
   author =  "Atsuyuki Okabe and Barry Boots and Kokichi Sugihara and Sung
Nok Chiu",
   title =  "Spatial tessellations: Concepts and applications of Voronoi
diagrams",
   publisher =    "Wiley",
   year =         "2000",
   series =       "Probability and Statistics",
   address =      "NYC",
   edition =      "2nd"
}

kind regards,
                                                             Roderik
Lindenbergh



>From: "Gregoire Dubois" <gregoire.dubois@...>
>Reply-To: <gregoire.dubois@...>
>To: <ai-geostats@...>
>Subject: [ai-geostats] Optimization of monitoring networks
>Date: Thu, 12 Jan 2006 16:00:33 +0100
>
>Dear list,
>
>I am looking for references (and possibly software) on network
>optimization. The variable monitored has no importance and I am looking
>for references and topological algorithms.
>A question I have is the following: given an area A with a particular
>shape (e.g. defined by country borders) and a number of stations N (e.g.
>for mobile phone emitters), how do I define the optimal locations for
>these stations?
>
>Thanks for any hints.
>
>Gregoire
>
>
>
>__________________________________________
>Gregoire Dubois (Ph.D.)
>
>European Commission (EC)
>Joint Research Centre (JRC)
>Institute for Environment and Sustainability (IES)
>
>TP 441, Via Fermi 1
>21020 Ispra (VA)
>ITALY
>
>Tel. +39 (0)332 78 6360
>Fax. +39 (0)332 78 5466
>Email: gregoire.dubois@...
>
>WWW: http://www.ai-geostats.org
>WWW: http://rem.jrc.cec.eu.int
>
>"The views expressed are purely those of the writer and may not in any
>circumstances be regarded as stating an official position of the
>European Commission."
>


--
                         Dr. R.C. Lindenbergh

                         Delft Institute of Earth Observation
                           and Space Systems, section MGP
                         Delft University of Technology

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Karen,

You may want to look at the work performed by the global rundoff data centre
and the University of new Hampshire on this topic.  See
http://grdc.bafg.de/servlet/is/Entry.987.Display/ and
http://www.grdc.sr.unh.edu/.  In particular, look at the technical document
that can be downloaded from http://www.grdc.sr.unh.edu/html/paper/index.html

With best regards,
Tom Parris
------------------------------------------------------------
Thomas M. Parris
Director of Sustainability Programs
ISciences, LLC
61 Main Street, Suite 200
Burlington, VT  05401  USA

Tel:   +802-864-2999              http://www.isciences.com/
Fax:   +617-344-2580              http://www.terraviva.net/
Email: parris@...
-----------------------------------------------------------

________________________________

From: Liu, Karen (MNR) [mailto:karen.liu@...]
Sent: Monday, January 09, 2006 9:45 AM
To: ai-geostats@...
Subject: [ai-geostats] Runoff and evapotransporation



Dear all:



Does anyone have the experiences or know something about creating Annual
Mean Runoff and Evapotransporation using Annual Mean temperature and
precipitation and other necessary raster data sets (like soil or land
cover)?



Many thinks,



Karen Liu

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Dear all
I'm trying to import in Rockworks a 3D solid model created with ISATIS, but,
even following the instruction of Rockware software guide, I'm getting an
error...
Does anyone of you have a example ascii XYZG file ready to be imported and
directly transformed into Rockworks solid model file that surely works?...
Thanks
Simone
-----------------------------
Dr. Simone Sammartino
PhD student
- Geostatistical analyst
- G.I.S. mapping
I.A.M.C. - C.N.R.
Geomare-Sud section
Port of Naples - Naples
marenostrum@...
-----------------------------

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Dear all
does anyone of you know about applications of geostatistics to side scan sonar
data?
Any kind of indication would be appreciated.
Thank you.
Simone
-----------------------------
Dr. Simone Sammartino
PhD student
- Geostatistical analyst
- G.I.S. mapping
I.A.M.C. - C.N.R.
Geomare-Sud section
Port of Naples - Naples
marenostrum@...
-----------------------------

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Dear list,

I am looking for references (and possibly software) on network optimization. The variable monitored has no importance and I am looking for references and topological algorithms.

A question I have is the following: given an area A with a particular shape (e.g. defined by country borders) and a number of stations N (e.g. for mobile phone emitters), how do I define the optimal locations for these stations?

Thanks for any hints.

Gregoire

__________________________________________
Gregoire Dubois (Ph.D.)

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

TP 441, Via Fermi 1
21020 Ispra (VA)
ITALY
 
Tel. +39 (0)332 78 6360
Fax. +39 (0)332 78 5466
Email: gregoire.dubois@...

WWW: http://www.ai-geostats.org
WWW: http://rem.jrc.cec.eu.int
 
"The views expressed are purely those of the writer and may not in any circumstances be regarded as stating an official position of the European Commission."

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Gregoire,

In order to answer your question, you should first define a criterion (when is a
network 'optimal'?).

If the criterion is to minimise the spatially averaged kriging variance (or
something similar) then you might conisder the work done by Jan Willem van
Groenigen in the 90s. He used a numerical optimisation approach (i.e., simulated
annealing), which takes computer time but is very flexible and can handle
irregulary shaped areas as well as situations in which there are given, fixed,
prior locations.

If the criterion is to minimise the maximum distance from any point in the area
to the nearest sampling point, then you can use a technique described by Dick J
Brus, which is very fast. Dick adapted the k-means cluster algorithm for this
purpose.

Gerard

	 -----Original Message-----
	 From: Gregoire Dubois [mailto:gregoire.dubois@...]
	 Sent: Thu 12/01/2006 18:17
	 To: 'Michel BOBBIA'
	 Cc: ai-geostats@...
	 Subject: RE: [ai-geostats] Optimization of monitoring networks


	 Dear Michel,

	 Good idea !

	 I remember some of Werner Müller's papers and presentations but, as far as I
remember, he was not considering the impact of complex border effects coming
from the shape of the borders of the monitored area and was mainly talking about
optimizing sensor locations considering the spatial correlation of the monitored
phenomenon. Does his book discuss optimization regardless of the monitored
phenomenon?
	 I guess answers to my question can be found in the field of mathematical
morphology but had no chance so far to find anything useful to me. I guess
people installing emitters/antennas for mobile phones have answers to my
question..

	 Thanks,

	 Gregoire

	 __________________________________________
	 Gregoire Dubois (Ph.D.)

	 European Commission (EC)
	 Joint Research Centre (JRC)
	 WWW: http://www.ai-geostats.org <http://www.ai-geostats.org/>

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

		 -----Original Message-----
		 From: Michel BOBBIA [mailto:michel.bobbia@...]
		 Sent: 12 January 2006 17:23
		 To: ai-geostats@...
		 Subject: Re: [ai-geostats] Optimization of monitoring networks


		 Hello
		 I have buy a book that seems to deal with this subject :

		 Werner G. Müller
		 Collecting Spatial Date
		 Optimum Design of Experiments for Random Fields
		 second edition
		 Physica-Verlag (Springer)

		 Unfortunatly, my knowledge in design of experiments is not enough to
understand the book, but I am reading it carefully...
		 However, I am interested in any solution/information to this problem !

		 Regards

		 Michel BOBBIA
		 Air Normand


			 ----- Original Message -----
			 From: Gregoire Dubois <mailto:gregoire.dubois@...>
			 To: ai-geostats@...
			 Sent: Thursday, January 12, 2006 3:00 PM
			 Subject: [ai-geostats] Optimization of monitoring networks


			 Dear list,

			 I am looking for references (and possibly software) on network optimization.
The variable monitored has no importance and I am looking for references and
topological algorithms.

			 A question I have is the following: given an area A with a particular shape
(e.g. defined by country borders) and a number of stations N (e.g. for mobile
phone emitters), how do I define the optimal locations for these stations?

			 Thanks for any hints.

			 Gregoire



			 __________________________________________
			 Gregoire Dubois (Ph.D.)

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

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

			 Tel. +39 (0)332 78 6360
			 Fax. +39 (0)332 78 5466
			 Email: gregoire.dubois@...

			 WWW: http://www.ai-geostats.org <http://www.ai-geostats.org>
			 WWW: http://rem.jrc.cec.eu.int <http://rem.jrc.cec.eu.int>

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


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Dear list,

Thanks a lot for the many replies to my query on how to distribute
points in a complex area in an optimal way.

A few of you gave me a list of papers on network optimization that do
not reply to my question as these take into account the spatial
correlation of the monitored phenomenon. My question was about the
location of points independently of any variable and was thus somehow
geometrical question.

To reuse Gerards' words, I want to minimise the maximum distance from
any point in the area to the nearest sampling point, not minimize the
kriging variance. An impressive review on the last topic (sampling
design + matlab codes for network optimization) can be found in Gunter
Spoeck's PhD, see  http://www.math.uni-klu.ac.at/~guspoeck/book.pdf ,
(22 MB pdf).

Chapter 9 in Okabe et al.  is a good reference and discusses as a case
study the optimal distribution of mailboxes in Tokyo. However, the
borders of the monitored area are defined as a rectangle, which
simplifies greatly the problem.

Another reference (see end of mail) given to me seems to be more
appropriate for what I am looking for. Still have to read it properly
but it is very much related to what I am looking for (here, its is about
the distrubution of siren locations). Thanks Morton !


SOFTWARE:

From Jeffrey W. Lively, I received the following suggestions:

- Visual Sample Plan (VSP).  It is maintained by Battelle for the DOE's
Pacific Northwest National Laboratory in Washington State.
It has an algorithm that they call adaptive fill which is designed to
suggest to you the optimal spatial location to place additional samples.

- SADA.  It has comparable adaptive fill algorithms and can suggest
sample placement based on reducing the amount of uncertainty in addition
to simple geometric considerations.  It is developed and maintained by
the University of Tennessee and is available for download from the web.

Very useful (thanks to jeolson@... for that!) is the
following resource: http://www.faqs.org/faqs/graphics/algorithms-faq/


PAPERS

In addition to Roderick's reference "Spatial tessellations: Concepts and
applications of Voronoi diagrams", by Atsuyuki Okabe and Barry Boots and
Kokichi Sugihara and Sung Nok Chiu. Published by Wiley, NYC (2000),
worth to mention are:

Papers in Regional Science
Volume 83 Page 565 - July 2004
doi:10.1111/j.1435-5597.2004.tb01925.x
Volume 83 Issue 3

A lattice covering model for evaluating existing service facilities
Morton E. O'Kelly, Alan T. Murray

Abstract. This article presents the following location problem: align a
regularly spaced grid of new facilities as well as possible with a set
of existing centres. The problem has some similarity to a problem in
classical central place theory, namely the spatial arrangement of
services with a particular range of coverage. The article poses the
problem, gives a non-linear formulation, and details solution
approaches. A robust heuristic, based on geometric insights, is also
devised: if the basis for the new grid is centred on at least one fixed
centre, an enumeration of various rotation angles will be effective for
finding local minima (and maxima). As a practical application of this
problem, a region may wish to supplement an existing system of fixed
siren locations with additional facilities in such a way as to fill in,
or complete, the partial coverage pattern. An evaluation of the siren
system in Dublin, OH, USA, is utilised to demonstrate the effectiveness
of the technique.

2 other references provided by Dan Bebber are:

Button J et al. 1996. Mobile network design and optimisation. British
Telecom Technology Journal 14:29-46
G. W. Wiskin, R. G. Manton and J. H. Causebrook 1992. Masts, Antennas
and Service Planning, Focal Press.

I did not list other references I received on monitoring network
optimization for groundwater, rainfall, etc. modelling as these are not
directly linked to my question.

Thanks to all for the replies and discussions !

Gregoire

__________________________________________
Gregoire Dubois (Ph.D.)

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

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

Tel. +39 (0)332 78 6360
Fax. +39 (0)332 78 5466
Email: gregoire.dubois@...

WWW: http://www.ai-geostats.org
WWW: http://rem.jrc.cec.eu.int

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

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To my knowledge, many work has been done by applying geostatistics to the 2D mapping of coal properties (thickness, sulfur content..). I don't know about many publications implementing 3D modelling. Does anyone know about publications dealing with this topic?

Following is a list of papers dealing with 2D mapping of coal properties (most have been published in Mathematical Geology or the International Journal of coal Geology):
 

Starks, T. H., N. A. Behrens, and J. H. Fang, 1982, The combination of sampling and kriging in the regional estimation of coal resources: Mathematical Geology, v. 14, p. 87-106.

Davis, B. M., and K. A. Greenes, 1983, Estimation using spatially distributed multivariate data: An example with coal quality: Mathematical Geology, v. 15, p. 287-300.

Bancroft, B. A., and G. R. Hobbs, 1986, Distribution of kriging error and stationarity of the variogram in a coal property: Mathematical Geology, v. 18, p. 635-652.

Journel, A. G., and M. Rossi, 1989, When do we need a trend in kriging?: Mathematical Geology, v. 21, p. 715-739.

Schuenemeyer, J. H., and H. Power, 2000, Uncertainty estimation for resource assessment - an application to coal: Mathematical Geology, v. 32, p. 521-541.

Watson, W. D., L. F. Ruppert, L. J. Bragg, and S. J. Tewalt, 2001, A geostatistical approach to predicting sulfur content in the Pittsburgh coal bed: International Journal of Coal Geology, v. 48, p. 1-22.

Hohn, M. E., and R. R. McDowell, 2001, Uncertainty in Coal Property Valuation in Wes Virginia: a case study: Mathematical Geology, v. 33, p. 191-217.

Tercan, A. E., and A. I. Karayigit, 2001, Estimation of lignite reserve in the Kalburcayiri field, Kangal basin, Sivas, Turkey: International Journal of Coal Geology, v. 47, p. 91-100.

Turner, B. R., and D. Richardson, 2004, Geological controls on the sulphur content of coal seams in the Northumberland Coalfield, Northeast England: International Journal of Coal Geology, v. 60, p. 169-196.
 

Best regards
 

Oriol Falivene
 
 
 

"Collier, Perry (TS)" wrote:

 Hi allI have a minerals background and as such I'm unfamiliar with geostatistical applications to the coal mining industry.  I would appreciate any feedback on applications of geostats (modelling, simulation etc) to coal - from basic to advanced/cutting edge.  Personal experiences and/or papers would be great.  I need help to move to the "dark side"!Cheers 

<?xml:namespace prefix = st1 ns = "urn:schemas-microsoft-com:office:smarttags" />Perry Collier

Senior Geologist

<?XML:NAMESPACE PREFIX = U1 />Rio Tinto Technical Services

Phone: +61 7 3327 7676 

Mobile: 0408 015 837

Fax:     +61 7 3327 7640 

PO Box 2207MiltonQld 4064

Perry.Collier@... 

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--
 
 

______________________________________

Oriol Falivene
oriolfalivene@...
http://www.ub.es/ggac

tel. (+34) 93 4021373
fax (+34) 93 4021340

Fac. de Geologia,
Univ. de Barcelona
 

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hello gregoire

Happy new year

For network characterization and optimization, you have at disposal, dealing
with the localization of samples, and not with measurements:

- voronoi polygons, with statistics of area of polygons, distances between
points
- delaunay triangulation
- Morishita diagram
- entropy diagram
- fractal dimension of monitoring network
- declustering.

Tehy are described, with their programs, in Chapter 2  "monitoring networks" of
our book,  "Analysis and Modelling of sptaial environmental and pollution data"
(M. Kanevski, M. Maignan) and the software for it.
This contributes to the analysis and optimal locations of measuring locations,
wihtout considerations of the variable measured.


In case your problem would be a classification problem, for instance the optimal
locations of samples for separating two classes, then the SVM approach seems
more adequate. Refer to our IAMG 2006 (in Toronto)  publication, where the SVM
Support Vector Machine shows the area for optimal additional sampling, based on
conditional standard deviation of SISIM models (Re Chapter 9 Support Vector
Machines for environmental spatial data). The SD at the border between the 2
regions separated by Support vectors is
used for identification of the next optimal sampling locations, and this is
different from the usual kriging estimation variance.



best regards, Michel

********************************************************************************\
************************




De: "Gregoire Dubois" <gregoire.dubois@...>
>>A: <ai-geostats@...>
>>Date: Thu, 12 Jan 2006 16:00:33 +0100
>>Sujet: [ai-geostats] Optimization of monitoring networks
>>
>>Dear list,
>>
>>I am looking for references (and possibly software) on network
>>optimization. The variable monitored has no importance and I am
>>looking for references and topological algorithms. A question I have
>>is the following: given an area A with a particular shape (e.g.
>>defined by country borders) and a number of stations N (e.g. for
>>mobile phone emitters), how do I define the optimal locations for
>>these stations?
>>

Michel Maignan
Prof. Uni. Lausanne
Dir. Gestion des Risques, BC Genève
00 41 79 679 80 13

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