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• ## [ai-geostats] spatial relationships

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• Mark I could not agree more with Gregoire (with one proviso, see below). Both geostatistics and any weighted average estimators are based on the same
Message 1 of 3 , Sep 1, 2004
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Mark

I could not agree more with Gregoire (with one
proviso, see below).

Both geostatistics and any weighted average estimators
are based on the same assumptions -- that relationship
between values at two locations depends on the
distance between them and (possibly) their relative
orientation. If you cannot get a decent semi-variogram
after trying every type of graph [normal, robust,
relative] and every transformation and/or
interpretation of your data [logarithm, indicator,
rank transforms, Normal scores, mixed populations],
you do not have a distance-based relationship. This
conclusion also rules out: inverse distance weighting
of any kind; Delaunay triangles; Thiessen polygons and
so on.

My proviso: there are other forms of spatial
relationship than pure distance/direction types. The
simplest example of this is data with a trend, where
the value at a specified point will depend on its
absolute position. There may be an added component for
the 'residuals' which turns out to be
distance/direction based. There are also many examples
where, for example, flow characteristics, connectivity
and so on play a large part in the structure of your
variable.

In short: no decent semi-variogram does NOT mean no
spatial relationship. It means no simple second-order
stationary geostatistical type spatial relationship.

Isobel
http://geoecosse.bizland.com/whatsnew.htm

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• 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
Message 1 of 3 , Sep 2, 2004
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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

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• 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
Message 1 of 3 , Sep 3, 2004
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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:
Chaosheng.Zhang@...
Web 1: www.nuigalway.ie/geography/zhang.html
Web 2: www.nuigalway.ie/geography/gis/index.htm
----------------------------------------------------------------------------

----- Original Message -----
From: "Isobel Clark" <drisobelclark@...>
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
> 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
>
href="http://geoecosse.bizland.com/softwares">http://geoecosse.bizland.com/softwares
>
>
>
>
>
>
___________________________________________________________ALL-NEW Yahoo! Messenger - all new features - even more fun!
http://uk.messenger.yahoo.com
>
>

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