## AI-GEOSTATS: How reliable are your kriging variances?

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• G day all, I reckon we need to quantify the reliability of the kriging variance map. Because sometimes its going to be an accurate map, and other times its
Message 1 of 5 , Feb 18, 2003
G'day all,

I reckon we need to quantify the reliability of the kriging variance
map. Because sometimes its going to be an accurate map, and other times
its going to be way off the mark.

Imagine the situation when there are two maps with similar kriging
variances. However when we look at the semivariagram fit one of them
closely follow the line of fit while the other has a much larger
scatter. This means that one of the maps is actually much more accurate
then the other.

But as maps are currently presented we would never know!!

Could this be a big problem? I think it could. Particularly when the
estimation is quite bad, meaning that the variances have been
underestimated and should likely be much larger.

One solution could be to make the kriging variances proportional to the
model fit. Maybe the error between the kriging variance (as estimated
using the semivariagram) and the estimation variance (using real data
points) could be used to do this?

Does anyone know if this has been discussed before? Has it ever been
considered. Or am I totally off the trail and should activate my GIS
beacon?

For those that are interested I'll explain how I got to the above
conclusion:

Kriging can be summarised by the following:
Var(est) = f(weights and semivariance between all points that have a
positive weight), and we obtain the Var(krig) by minimising Var(est)
with respect to the weights. This is how we get the weights.

But in order to do this we need to know what the semivariance between
the points is. However if we're estimating a point we don't have then we
can't calculate the semi-variance, so we can't find the appropriate
weights. However, if we have a model for the semi-variance then we can
predict what the semi-variance should be using this model and we can
then calculate the appropriate weights. Which is why we require a
semivariagram model. So the semivariagram fit is vital in generating not
only the estimates, but their reliability also.

What this all boils down to is that the most important thing when
kriging is the ASSUMPTION that the points used to generate the
semi-variagram are capable of representing the semivariance for all
points. As well as the ASSUMPTION that the correct model has been fit,
and that its a good fit.

If either of these assumption fails then the kriging variance is
incorrect.

More to the point if the model is a poor fit then the kriging variance
is less likely to be accurate.

This brought me to my question. Should we have some statitistic that
quantifies how reliable our kriging variances are?

Christopher G Howden
Statistical Ecologist
Department of Land and Water Conservation
(Work) 02 9895 7130
(Fax) 02 9895 7867
(Mob) 0410 689 945

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• Dear Chris Bayesian kriging is what you should use, if you want to include estimation uncertainty into the kriging variances. Some useful references are: Le,
Message 2 of 5 , Feb 19, 2003
Dear Chris

Bayesian kriging is what you should use, if you want to include estimation
uncertainty into the kriging variances. Some useful references are:

Le, N.D. and Zidek, J.V. (1992).
Interpolation with uncertain covariances: a Bayesian alternative to
Kriging.
Journal of Multivariate Analysis, 43, p. 351-74.

Handcock, M.S. and Stein, M.L. (1993).
A Bayesian analysis of kriging.
Technometrics, 35, p. 403-10.

Kitanidis, P.K. (1986).
Parameter uncertainty in estimation of spatial functions: Bayesian
analysis.
Water Resources Research, 22, p. 499-507.

Best regards / Venlig hilsen

Søren Lophaven
******************************************************************************
Master of Science in Engineering | Ph.D. student
Informatics and Mathematical Modelling | Building 321, Room 011
Technical University of Denmark | 2800 kgs. Lyngby, Denmark
E-mail: snl@... | http://www.imm.dtu.dk/~snl
Telephone: +45 45253419 |
******************************************************************************

On Wed, 19 Feb 2003, Chris Howden wrote:

> G'day all,
>
> I reckon we need to quantify the reliability of the kriging variance
> map. Because sometimes its going to be an accurate map, and other times
> its going to be way off the mark.
>
> Imagine the situation when there are two maps with similar kriging
> variances. However when we look at the semivariagram fit one of them
> closely follow the line of fit while the other has a much larger
> scatter. This means that one of the maps is actually much more accurate
> then the other.
>
> But as maps are currently presented we would never know!!
>
> Could this be a big problem? I think it could. Particularly when the
> estimation is quite bad, meaning that the variances have been
> underestimated and should likely be much larger.
>
> One solution could be to make the kriging variances proportional to the
> model fit. Maybe the error between the kriging variance (as estimated
> using the semivariagram) and the estimation variance (using real data
> points) could be used to do this?
>
> Does anyone know if this has been discussed before? Has it ever been
> considered. Or am I totally off the trail and should activate my GIS
> beacon?
>
>
>
>
> For those that are interested I'll explain how I got to the above
> conclusion:
>
> Kriging can be summarised by the following:
> Var(est) = f(weights and semivariance between all points that have a
> positive weight), and we obtain the Var(krig) by minimising Var(est)
> with respect to the weights. This is how we get the weights.
>
> But in order to do this we need to know what the semivariance between
> the points is. However if we're estimating a point we don't have then we
> can't calculate the semi-variance, so we can't find the appropriate
> weights. However, if we have a model for the semi-variance then we can
> predict what the semi-variance should be using this model and we can
> then calculate the appropriate weights. Which is why we require a
> semivariagram model. So the semivariagram fit is vital in generating not
> only the estimates, but their reliability also.
>
> What this all boils down to is that the most important thing when
> kriging is the ASSUMPTION that the points used to generate the
> semi-variagram are capable of representing the semivariance for all
> points. As well as the ASSUMPTION that the correct model has been fit,
> and that its a good fit.
>
> If either of these assumption fails then the kriging variance is
> incorrect.
>
> More to the point if the model is a poor fit then the kriging variance
> is less likely to be accurate.
>
> This brought me to my question. Should we have some statitistic that
> quantifies how reliable our kriging variances are?
>
>
>
>
>
> Christopher G Howden
> Statistical Ecologist
> Department of Land and Water Conservation
> (Work) 02 9895 7130
> (Fax) 02 9895 7867
> (Mob) 0410 689 945
>
>
> --
> * To post a message to the list, send it to ai-geostats@...
> * As a general service to the users, please remember to post a summary of any useful responses to your questions.
> * To unsubscribe, send an email to majordomo@... with no subject and "unsubscribe ai-geostats" followed by "end" on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list
> * Support to the list is provided at http://www.ai-geostats.org
>

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• Hi Christopher, I believe you forgot a key assumption, homoscedasticity. In most situations this assumption is not realistic and we would like the kriging
Message 3 of 5 , Feb 19, 2003
Hi Christopher,

I believe you forgot a key assumption, homoscedasticity.
In most situations this assumption is not realistic
and we would like the kriging variance to somehow
depend on the local variability of data.
Rescaling globally the kriging variance to
account for uncertainty about variogram model
won't solve this problem.
Your map might be "globally" more accurate
but locally it will still fail to indicate
where prediction errors might be larger.

Regarding statistics to account for reliability of kriging variance,
the key question is what do you want to do with that variance.
If it's used to derive local probability distributions
under the multiGaussian model, you can assess
precision and accuracy of uncertainty models
using cross-validation. I addressed this issue
in the following paper:
Goovaerts, P. 2001.
Geostatistical modelling of uncertainty in soil science.
Geoderma, 103: 3-26.
and would be glad to send you a PDF copy of the paper if needed.

Regards,

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, 19 Feb 2003, Chris Howden wrote:

> G'day all,
>
> I reckon we need to quantify the reliability of the kriging variance
> map. Because sometimes its going to be an accurate map, and other times
> its going to be way off the mark.
>
> Imagine the situation when there are two maps with similar kriging
> variances. However when we look at the semivariagram fit one of them
> closely follow the line of fit while the other has a much larger
> scatter. This means that one of the maps is actually much more accurate
> then the other.
>
> But as maps are currently presented we would never know!!
>
> Could this be a big problem? I think it could. Particularly when the
> estimation is quite bad, meaning that the variances have been
> underestimated and should likely be much larger.
>
> One solution could be to make the kriging variances proportional to the
> model fit. Maybe the error between the kriging variance (as estimated
> using the semivariagram) and the estimation variance (using real data
> points) could be used to do this?
>
> Does anyone know if this has been discussed before? Has it ever been
> considered. Or am I totally off the trail and should activate my GIS
> beacon?
>
>
>
>
> For those that are interested I'll explain how I got to the above
> conclusion:
>
> Kriging can be summarised by the following:
> Var(est) = f(weights and semivariance between all points that have a
> positive weight), and we obtain the Var(krig) by minimising Var(est)
> with respect to the weights. This is how we get the weights.
>
> But in order to do this we need to know what the semivariance between
> the points is. However if we're estimating a point we don't have then we
> can't calculate the semi-variance, so we can't find the appropriate
> weights. However, if we have a model for the semi-variance then we can
> predict what the semi-variance should be using this model and we can
> then calculate the appropriate weights. Which is why we require a
> semivariagram model. So the semivariagram fit is vital in generating not
> only the estimates, but their reliability also.
>
> What this all boils down to is that the most important thing when
> kriging is the ASSUMPTION that the points used to generate the
> semi-variagram are capable of representing the semivariance for all
> points. As well as the ASSUMPTION that the correct model has been fit,
> and that its a good fit.
>
> If either of these assumption fails then the kriging variance is
> incorrect.
>
> More to the point if the model is a poor fit then the kriging variance
> is less likely to be accurate.
>
> This brought me to my question. Should we have some statitistic that
> quantifies how reliable our kriging variances are?
>
>
>
>
>
> Christopher G Howden
> Statistical Ecologist
> Department of Land and Water Conservation
> (Work) 02 9895 7130
> (Fax) 02 9895 7867
> (Mob) 0410 689 945
>
>
> --
> * To post a message to the list, send it to ai-geostats@...
> * As a general service to the users, please remember to post a summary of any useful responses to your questions.
> * To unsubscribe, send an email to majordomo@... with no subject and "unsubscribe ai-geostats" followed by "end" on the next line in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list
> * Support to the list is provided at http://www.ai-geostats.org
>

--
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* As a general service to the users, please remember to post a summary of any useful responses to your questions.
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• ... Because sometimes its going to be an accurate map, and other times its going to be way off the mark. ... variances. However when we look at the
Message 4 of 5 , Feb 19, 2003
>G'day all,
>
>I reckon we need to quantify the reliability of the kriging variance map.
Because sometimes its going to be an accurate map, and other times its
going to be way off the mark.
>
>Imagine the situation when there are two maps with similar kriging
variances. However when we look at the semivariagram fit one of them
closely follow the line of fit while the other has a much larger scatter.
This means that one of the maps is actually much more accurate then the
other.

Statistically, your question is related to the fact that the covariance
matrix from the fit of the variogram model is usually disregarded in
variogram parameters covariance matrix and geostatisticians replied that
this source of uncertainty was usually less important than other problems
and should not be of much importance.
Rubén
http://webmail.udec.cl

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• ... they are completely and perfectly reliable IOW, the kriging variances are the objective functions to be optimized not the parameters to be estimated (the
Message 5 of 5 , Feb 20, 2003
>
>A couple of observations on your problem
>
>1. The kriging variances are computed, not estimated, hence in that sense
they are completely and perfectly "reliable"

IOW, the kriging variances are the objective functions to be optimized not
the parameters to be estimated (the latter ones are the values of the
spatial variable at non observed locations). It seems to me they are
perfectly "reliable" (or perhaps 'exact') in linear kriging but not so in
non linear kriging, in which they should be approximate.

>2. However, the kriging variances are computed using (1) the variogram or
covariance model, (2) the coordinates of the data locations used to
generate the kriged value, (2) the coordinates of the location being
kriged, (3) the degree of the polynomial used to represent the drift (if
neighborhood. Therein lies the problem, while it may be that the
coordinates are known (nearly perfectly), the variogram or covariance must
be estimated from the data and that is not a "perfect" process.

Under standard geostatistical procedures the variogram or covariance is
taken as known during kriging. I once reviewed a ms. sent to a statistical
journal in which the authors assessed by simulation the degree to which
this assumption affects OK (IIRW). They found that for fairly low sample
sizes, the effect of ignoring the covariance matrix of parameters in the
model variogram was very minor.

>While changing the search neighborhood may not change the estimates much,
it can sometimes have a big effect on the kriging variances, an easy way to
see this is to do a little experimenting in cross-validation.
>
>3. Note that if you multiply the variogram by a positive number (either
bigger or smaller than one) you will NOT change the kriging weights and
hence not change the kriged values but you will change the kriging
variance.

That is why the use of standardized experimental variograms to fit the
model variogram do not change the kriging weights as compared with the
standard experimental variogram.

Your other points (snipped) are very well taken.
Note that i did not posit the original question as may appear by the way
Donald's message showed up on the list.

Rubén
http://webmail.udec.cl

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