- When comparing kriging versus regression, I meant

using linear regression between sparse and exhaustive

datasets to interpolate the sparse one, since as Digbi

Milligan pointed out in general case regression is not

an estimation method.

--- Gali Sirkis <donq20vek@...> wrote:

> Seumas,

to

>

> see few practical points that you may find useful:

>

> 1. kriging vs regression:

>

> a) kriging honors original data points, while

> regression does not

> b) kriging allows to account for anizotropy

> c) kriging allows to control the influence of the

> data

> points

>

> 2. Kriging versus other interpolation technics

>

> a) Kriging allows to decluster data

> b) kriging allows to estimate uncertainty of

> estimation

> c) kriging allows to use for estimation secondary

> information from another exhaustive dataset

>

> 3. Kriging vs simulations

>

> a) Kriging produces smoother version than real

> distribution, while simulation gives more details

> b) simulations allow to estimate joint uncertainty,

> for example probability that values in several

> adjacent points are above certain level.

> c) simulation allows to estimate risk of various

> scenarios - while kriging only shows the most

> probable

> one.

>

> All the best,

>

> Gali Sirkis.

>

>

> >

> > Hello everyone,

> >

> > I apologize if this question is too elementary for

> > this list;

> > I want to understand the key differences between

> > linear regression,

> > kriging, conditional simulation and other

> > interpolation techniques such as

> > IDW or splines in the analyses of spatial data. I

> > would like to know the

> > assumptions, strengths and weaknesses of each

> > method, and when one method

> > should be preferred to another. I browsed the

> > archives and looked at some

> > of the on-line papers, but they are written at a

> > level beyond my own

> > current understanding. It seems to me that this

> > would be a great topic for

> > the first chapter of an introductory spatial

> > analysis textbook. Can anyone

> > recommend any basic textbooks or references on

> this

> > topic?

> > Any assistance you can offer would be appreciated.

> >

> > Sincerely,

> >

> > Seumas Rogan

> >

> >

> >

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>

>

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http://celebrity.mail.yahoo.com - For ore resource modelling I've used IDW on a highly skewed lognormally

distributed deposit, where no variograms could be produced. With lognormally

distributed data often found in ore resources, having a good variogram is

important, to avoid large errors in kriging hence it may be preferential to

use

IDW and a topcut. However if your data is not so highly skewed even

approximating

a variogram can provide superior results. I used to model topography

surfaces

and Kriging with a 'guessed' variogram produced good results compared to

IDW which produced highly spiked and erroneous results.

Digby

www.users.on.net/~digbym