Re: [ai-geostats] spatial relationships

[Chaosheng Zhang]

Hi all,

 

The interesting story given by Tim Glover is a good example of "spatial outliers". The dumptruck loads of polluted soils are too different from the neighbourhood, and thus they should be regarded as spatial outliers. When one cannot get a decent variogram after trying all possible data transformations and robust calculations, another way worth trying is to detect such spatial outliers (e.g., using local Moran's I). 

 

About two years ago, I dealt with a dataset of soil organic carbon in Ireland, and found that exclusion of spatial outliers significantly improves the structure of a variogram. When doing kriging, the excluded values may be put back to preserve information of raw data.

 

This may be an explanation to the conflict between failed variogram and spatial structure. Spatial outliers may destroy spatial structures and thus result in failed variograms.

 

Cheers,

 

Chaosheng

--------------------------------------------------------------------------
Dr. Chaosheng Zhang
Lecturer in GIS
Department of Geography
National University of Ireland, Galway
IRELAND
Tel: +353-91-524411 x 2375
Fax: +353-91-525700
E-mail: [EMAIL PROTECTED]
Web 1: www.nuigalway.ie/geography/zhang.html
Web 2: www.nuigalway.ie/geography/gis/index.htm
----------------------------------------------------------------------------

 

----- Original Message -----

From: "Isobel Clark" <[EMAIL PROTECTED]>

To: <[EMAIL PROTECTED]>

Sent: Thursday, September 02, 2004 3:30 PM

Subject: [ai-geostats] spatial relationships

> Dear oh Dear, I am failing to communicate (again).
>
> As far as I know, I didn't say you could not use
> geostatistics when a trend is present! I regularly use
> Universal Kriging for data with a trend and kriging
> with an external drift when the trend is governed by
> an outside factor (see free tutorial at website).
>
> The question originally posed what how does one decide
> that geostatistics is not appriate. The answer
> Gregoire and myself gave was "when you cannot get a
> semi-variogam graph" after trying all possible
> variations of transforms, interpretation and
> de-trending.
>
> I recently worked with an orange grove in Florida
> (bugs on oranges) which showed no decent
> semi-variogram even though rough inverse distance maps
> looked reasonable. It turned out they had two
> different kinds of tree in the orchard. Separating the
> 'rootstocks' yielded a vastly improved semi-variogram
> and decent geostatistical analysis.
>
> My additional point was that failure to obtain a
> semi-variogram model simply means that there is no
> 'distance related' structure. It does NOT mean there
> is NO spatial structure.
>
> Isobel
> http://geoecosse.bizland.com/softwares
>
>
>
>
>
> ___________________________________________________________ALL-NEW Yahoo! Messenger - all new features - even more fun!  http://uk.messenger.yahoo.com
>
>

---

> * By using the ai-geostats mailing list you agree to follow its rules
> ( see http://www.ai-geostats.org/help_ai-geostats.htm )
>
> * To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to [EMAIL PROTECTED]
>
> Signoff ai-geostats
>

* By using the ai-geostats mailing list you agree to follow its rules 
( see http://www.ai-geostats.org/help_ai-geostats.htm )

* To unsubscribe to ai-geostats, send the following in the subject or in the body 
(plain text format) of an email message to [EMAIL PROTECTED]

Signoff ai-geostats