[R-sig-Geo] Weird experimental 3D spatiotemporal variogram

[Piero Campalani]

Edzer, Ben,
(I was missing the vignette on spatio-temporal analysis with gstat.. !!)

I'm attaching a clearer figure of the experimental variogram: the /origin/
at 0 spatio-temporal lag is not zero actually.

Yes, I am using **variogramST** on a STFDF: the spacetime object looks ok,
then I simply call:
> variogramST(pm~1, mySTFDF, tlags=0:6)

Ben, out of band I can give you my .Rdata cutout with the STFDF if you're
interested.
(It seems from the ST vignette that `variogramST` is now merged into
`variogram` ?)

--------------------------
> R.version.string
[1] "R Under development (unstable) (2012-10-03 r60866)"
--------------------------

Thanks,
Piero

On 15 January 2013 15:15, Benedikt Gräler <ben.graeler at uni-muenster.de>wrote:

> Dear Piero,
>
> how did you compute these variograms, using variogramST in gstat (which
> version?)?
>
> Figures 4-7 in gstat's vignette "Spatio-temporal geostatistics using
> gstat" on CRAN show the missing value for the zero temporal and zero
> spatial lag class. I could not identify this property in your wireframe
> plots.
>
> To me, the temporal effect looks like being "upside-down". I'll be happy
> to take a quick look at your script/data in case your problem still remains.
>
> Best,
>
>  Ben
>
>
>
>
> On 15.01.2013 11:51, Edzer Pebesma wrote:
>
>> Piero,
>>
>> from the orientation of your graph, I could not see very well what
>> happens at zero-time, zero-space lag.
>>
>> Did you compute a pure-time variogram, i.e. with zero space distance?
>> This one should have a missing zero-value, unless you have duplicate
>> measurements.
>>
>> On 01/10/2013 03:20 PM, Piero Campalani wrote:
>>
>>> Dear list,
>>>
>>> I am predicting PM measurements on a spatiotemporal grid with monthly
>>> intervals in time.
>>> At modeling time, I am looking at the experimental 3D variograms
>>> (`wireframes`) but I see that weird decreasing behavior in time (see
>>> wireframes_2008-1.eps for January 2008): there is a peak at 0 time lags,
>>> then correlation in time is much higher over different days.
>>> How can I interpret such variogram?
>>> Would it mean that there is a very high spatial variability for values on
>>> the same day, whereas temporal variability is significantly lower?
>>>
>>> Thanks for any hint,
>>> (I can provide implementation details in case of need)
>>>
>>> Piero
>>>
>>>
>>>
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>>>
>>>
>>
> --
> Benedikt Gräler
>
> ifgi - Institute for Geoinformatics
> University of Muenster
>
> http://ifgi.uni-muenster.de/**graeler<http://ifgi.uni-muenster.de/graeler>
>
> Phone: +49 251 83-33082
> Mail: ben.graeler at uni-muenster.de
>
>
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