[ai-geostats] Comparison of sample areas

[C.J.Banks]

Dear All

I have sampled three rectangular fields within a larger area and measured a 
variable of interest at a lot of points in each of these fields. Values were 
taken from all over each of the sampled fields but are not necessarily over a 
systematic grid. I am interested in testing whether the three sampled fields 
come from the same population. However, an ANOVA (or other similar tests) 
assumes that there is no correlation between values within each of the sampled 
fields, which isn't true because of the spatial nature. I'm sure that similar 
studies have been done and would appreciate any pointers to useful sources or 
appropriate statistical tests. 

For those who are interested the data are depths of snow on different floes in 
the same area of the Antarctic.

Many thanks

Chris

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RE: [ai-geostats] Comparison of sample areas

[Colin Badenhorst]

Chris,

How about using a T-test to test for similar sample means, or an
F-test to test for similar sample variance?

Regards,
Colin  

-Original Message-
From: C.J.Banks [mailto:[EMAIL PROTECTED] 
Sent: 28 February 2006 14:26
To: ai-geostats
Subject: [ai-geostats] Comparison of sample areas

Dear All

I have sampled three rectangular fields within a larger area and
measured a variable of interest at a lot of points in each of these
fields. Values were taken from all over each of the sampled fields but
are not necessarily over a systematic grid. I am interested in testing
whether the three sampled fields come from the same population. However,
an ANOVA (or other similar tests) assumes that there is no correlation
between values within each of the sampled fields, which isn't true
because of the spatial nature. I'm sure that similar studies have been
done and would appreciate any pointers to useful sources or appropriate
statistical tests. 

For those who are interested the data are depths of snow on different
floes in the same area of the Antarctic.

Many thanks

Chris





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[ai-geostats] Software for Automatic Semivariogram Estimation

[Mach Nife]

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

[Edzer J. Pebesma]

Mach Nife wrote:


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?
 



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.
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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[ai-geostats] Re: Software for Automatic Semivariogram Estimation

[Isobel Clark]

Hi AllIt is difficult to have an automaticbest 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.Isobelhttp://www.kriging.com* By using the ai-geostats mailing list you agree to follow its rules 
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Re: [ai-geostats] Re: Software for Automatic Semivariogram Estimation

[Edzer J. Pebesma]

Isobel Clark wrote:


Behrang
 
What weighting do you use in the weighted least squares?
 
Isobel


I have found choosing suitable weights always a frustrating
event. Cressie's weights, let's say N_h/[(gamma(h))^2], has
attractive properties, both intuitively and statistically. Here,
gamma(h) is the model value, not the sample variogram
value (because that might be zero; think of binary data). N_h
is the number of point pairs used to estimate semivariance
at lag (interval) h.

It's downside is that while fitting the variogram, gamma(h)
changes, and so the weights change. This has consequences:
while fitting, the criterion you try to minimize may actually
increase while the fit gets better. This is hard to deal with.
If you calculate e.g. a weighted R^2, and look at the trace,
it will go up, down, and then up, down, etc. The context changes.

If you fix gamma(h), say to it's starting values, then the final
fit may very much depend on which starting values you used.

Isobel, how do you deal with this?

As an alternative, (and the default value in gstat under R or
S-Plus), I now tend to use N_h/h^2 [*], which is equivalent to
Cressie's weights for a linear variogram with no nugget. It
works often, but will give rediculusly large weight to a
semivariance value with h very close to zero (think duplicate
measurements). Besides these two, gstat has the options
of weights N_h, and of no (=constant) weights.
--
Edzer

[*] If I'm correct, this was first suggested in a paper by
Zhang and ... in Computers  Geosciences, early nineties.
I strongly disliked it then, but consider it acquired taste.

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

[Isobel Clark]

EdI use the Cressie statistic to four significant figures as a guide in the interactive fitting, but generally end up using a visual judgement. It tracks as you drag the model around, so you can watch it change.I think the 'real' visual objective function is probably the perpendicular (to tangent) distance to the model line, which is effectively thecombination of both gamma and h. One should then be able to alter the relative weighting between distance and height. Haven't tried this yet.Isobel  http://www.kriging.com* By using the ai-geostats mailing list you agree to follow its rules 
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RE: [ai-geostats] Re: Software for Automatic Semivariogram Estimation

[Pierre Goovaerts]

Hi Susan,
 
I would recommend the Stanford Geostatistical Modeling Software (S-GeMS)
that is public domain and that I use in all my short courses (some of your
colleagues have actually be trained by me). The software can be downloaded from
http://pangea.stanford.edu/~nremy/GEMS/
 
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: Hohner, Susan [mailto:[EMAIL PROTECTED]
Sent: Tue 2/28/2006 1:28 PM
To: AI Geostats mailing list
Subject: RE: [ai-geostats] Re: Software for Automatic Semivariogram Estimation



Yikes!

 

I was working through the tutorial for the Geostatistical Analyst Extension 
when this email discussion popped up.  Any recommendations for a traditional 
geostatistics software package?

 

Thanks,

Susan

 

Susan Hohner, Senior Geographer

Everglades Division, Mail Stop 4440

South Florida Water Management District

3301 Gun Club Road, West Palm Beach, FL 33406

(561) 682-6801 phone

(561) 682-0100 fax

[EMAIL PROTECTED]

http://www.sfwmd.gov

 



From: Chaosheng Zhang [mailto:[EMAIL PROTECTED] 
Sent: Tuesday, February 28, 2006 12:25 PM
To: AI Geostats mailing list
Subject: Re: [ai-geostats] Re: Software for Automatic Semivariogram Estimation

 

Dear all,

 

I have the same concerns with ArcGIS Geostatistical Analyst Extension (v.9.1). 
I would use a traditional geostatistics software package to fit the variogram 
models in a very traditional way, and input the parameters to ArcGIS for 
kriging. It seems that ArcGIS has its own reasons to show variograms in a 
non-traditional way, but I find it almost impossible to fit the variograms 
mannually. You can change the parameters, but it is very hard to see how well 
they fit. By the way, you can change the lag distance or interval in ArcGIS (it 
is called lag size there).

 

Cheers,

 

Chaosheng

--
Dr. Chaosheng Zhang
Lecturer in GIS
Department of Geography
National University of Ireland, Galway
IRELAND
Tel: +353-91-492375
Fax: +353-91-495505
E-mail: [EMAIL PROTECTED]
Web1: www.nuigalway.ie/geography/zhang.html
Web2: www.nuigalway.ie/geography/gis

 

 


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

[Behrang Kushavand]

Dear Prof. Clark
Here is thepaper:
http://www.ansinet.org/fulltext/jas/jas581405-1407.pdf
Formula (4) is the weight factor.
King regards.
Behrang.

- Original Message - 

  From: 
  Isobel Clark 
  To: Behrang Kushavand 
  Cc: AI Geostats mailing list 
  Sent: Tuesday, February 28, 2006 9:53 
  PM
  Subject: [ai-geostats] Re: Software for 
  Automatic Semivariogram Estimation
  
  Behrang
  
  What weighting do you use in the weighted least squares?
  
  Isobel
  http://www.kriging.comBehrang 
  Kushavand [EMAIL PROTECTED] 
  wrote:
  hi,I 
have a software for Variogram AUTO Modeling (winvam) that works with 
gslib(GAMV.exe).First you must calculate experimental variogram 
(omni or directional) withgamv.exe and then by using winvam, you can fit 
the best model by leastsquare and weights least square criteria for 
given model(s),You can find it 
at:http://www.ai-geostats.org/software/Geostats_software/WinVAM.htmKing 
regards.Behrang.- Original Message -From: "Edzer 
J. Pebesma" <[EMAIL PROTECTED]>To: "Mach Nife" 
<[EMAIL PROTECTED]>Cc: "ai-geostats" Sent: 
Tuesday, February 28, 2006 7:59 PMSubject: Re: [ai-geostats] Software 
for Automatic Semivariogram Estimation Mach Nife 
wrote: 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?   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. 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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