- ... Some. I did have a look at your data, and at the ArcGIS fit window you sent me. Clearly, we do not fully agree on what is to be considered a good job.Message 1 of 3 , Feb 28, 2006View SourceMach 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.
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,
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- Hi All It 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 goesMessage 2 of 3 , Feb 28, 2006View SourceHi 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
- 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-77Message 3 of 3 , Feb 28, 2006View SourceHello,
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.
Chief Scientist at BioMedware
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From: Mach Nife [mailto:machnife@...]
Sent: Tue 2/28/2006 10:16 AM
Subject: [ai-geostats] Software for Automatic Semivariogram Estimation
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.
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