- Mach Nife wrote:

>Hi,

Some. I did have a look at your data, and at the

>

>I'm hunting for a software (freeware/openSource if

>possible), that would help estimating the best

>possible semivariogram curve in a non-interactive way.

>As an example, ArcGis Geostatistical Analyst does a

>pretty good job at this when we accept the defaults.

>It does some automatic calculations for the parameters

>of the selected model. I've tried Gstat "Fit" method

>(in the command-line version), but the results aren't

>what I expected. What I need is a command line

>software or one that can be controlled by programming.

>

>Any ideas?

>

>

ArcGIS fit window you sent me. Clearly, we do not

fully agree on what is to be considered a "good" job.

ArcGIS calculates semivariances up to the largest

distances present in your data set; afaik the general

recommendation is not to look further than half the

longest distance (compare acf computation in time

series); the gstat default is one third the diagonal

of the area spanned. Have you tried modifying any

of these defaults? Interval widths?

When looking at the fit, it seems that ArcGIS shows

a couple (4?) directional variograms in a single

plot, but apart from that, the sample variogram suggests

a linear model. It is obvious that fitting three parameters

(exponential model with nugget) to something that

tends to be linear will lead to problems -- an infinite

set of solutions, for instance. When you insist on

having an exponential model, you could for

instance force the range to a certain (large) value.

I suspect ArcGIS stops adjusting the range of the

exponential model when it exceeds the data extent

(Constantin, are you with us?), but should that be

considered good practice?

My experience with automatic, general-purpose

automatic variogram fitting are not very positive;

if it were, gstat would probably have such a function.

Are there other ai-geostats readers who have positive or

negative experiences with, or who routinely trust,

automatically fitted variograms? Which software?

Looking forward to a heated debate,

--

Edzer

>machnife

>

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> - Hi AllIt is difficult to have an automatic best fit semi-variogram until you define what you mean by "best fit". Noel Cressie's goodness of fit statistic goes a long way towards the ideal, but is very insensitive to changes in nugget effect and pretty insensitive to fairly large changes in the ranges. Optimal Cressie fits aren't always optimal visually, either.None of the automated methods I've heard of will choose the type of semi-variogram model and/or the number of nested components. Or anisotropy for the most part.As Ed says, if we knew the criteria we'd all write software for it (and retire!).I also look forward to varied opinions. Semi-variogram fitting is one of the most subjective stages of a geostatistical analysis.Isobel

http://www.kriging.com - Hello,

I have been using a modified version of VARFIT that is available on the

Computer and Geosciences website.

Pardo-Iguzquiza E: VARFIT: a Fortran-77 program for fitting variogram models by weighted least squares. Computers and Geosciences 1999, 25:251-261.

I agree that choosing a set of weighting factors for the fit can be frustrating

and it's why in the program I provided with my latest IJHG paper, I allowed

the user to choose among 5 options for the weights. For the weights N(h)/gamma(h),

I used the experimental semivariogram values, which eliminates the problem of

weights that change during the iterative fitting procedure and also attenuates

the impact of unreliable semivariogram values computed for the first lags

(i.e. impact of preferential sampling of high values as in my WRR paper on

groundwater arsenic concentrations).

I have increasingly used automatic fitting procedures, followed by a visual

assessment of the fit (and yes the fitting of variogram clouds in Arcview is

one of the many ESRI blunders). It has proven convenient in several situations:

1. implementation of Poisson kriging to be used by epidemiologists, who

have no idea what a semivariogram is, for removing noise from rate data,

2. use of indicator kriging for automatic mapping procedures (SIC 2004 paper

that is available on my webpage).

3. testing of new algorithms using hundreds of simulations. The common use

of only one jackknife or simulation to compare the prediction performances of

various algorithms is very hazardous since the ranking can drastically change

would another subset of data be used for validation.

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/

________________________________

From: Mach Nife [mailto:machnife@...]

Sent: Tue 2/28/2006 10:16 AM

To: ai-geostats

Subject: [ai-geostats] Software for Automatic Semivariogram Estimation

Hi,

I'm hunting for a software (freeware/openSource if

possible), that would help estimating the best

possible semivariogram curve in a non-interactive way.

As an example, ArcGis Geostatistical Analyst does a

pretty good job at this when we accept the defaults.

It does some automatic calculations for the parameters

of the selected model. I've tried Gstat "Fit" method

(in the command-line version), but the results aren't

what I expected. What I need is a command line

software or one that can be controlled by programming.

Any ideas?

machnife

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