## Re: [ai-geostats] Spatial analysis

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• ... A figure would be nice to interpretate the situation. Put it on some website, this makes it easier for people to access it if they want to and links are
Message 1 of 5 , Aug 6 8:05 AM
>Is there a way I can characterise the variability at each spatial scale that would allow me to compare both between scales for each plot and between the plots?
>
>
A figure would be nice to interpretate the situation. Put it on some
website, this makes it easier for people to access it if they want to
and links are accepted by the maillinglist server.

I had a similar problem and I used a Moran's I index, as far as I know
the most simple test you can do and it can me automated. You could
calculate the index for every scale and compare scales in and between
plots and you can calculate them for every plot to compare plots.

This is the quick and easy approach to the problem I would use... but
who am I...

Koen.
• Hi, It is true that the general consensus is that you need at least 50 to 100 points for a stable semi-variogram, but myself i bent this rule a little and i
Message 2 of 5 , Aug 6 9:22 AM
Hi,

It is true that the general consensus is that you need at least 50 to
100 points for a stable semi-variogram, but myself i bent this rule a
little and i worked with 35 points ....

But you can use LISA (Local Indicators of Spatial Association -
LISA, 1995, by Luc Anselin, geographical Analysis 27 (2), pp 93 -
115). LISA is implemented in GeoDA freely available from
http://sal.agecon.uiuc.edu/default.php

GeoDa is very friendly and easy to use, but you need to be at least
a little familiar with ESRI products (ArcView or ArcGIS) and
especially shape files.

You can also use sdep package from R (http://cran.r-project.org/) -
which is a free statistical software - very powerful but with a very
steep curve of learning.

The idea is to use local indicators such as local Moran's I and local
Gearry c for investigating local spatial instabilities (homogeneity
versus heterogeneity).

I hope this helps,

Monica

Monica Palaseanu-Lovejoy
University of Manchester
School of Geography
Mansfield Cooper Bld. 3.21
Manchester M13 9PL
England, UK
Tel: +44 (0) 275 8689
Email: monica.palaseanu-lovejoy@...
• Hello! I would suggest using spatial autocorrelation such as the Moran s I or Geary s Ratio. Both methods would allow you to measure both the proximity of
Message 3 of 5 , Aug 6 12:37 PM
Hello!

I would suggest using spatial autocorrelation such as the Moran's I or Geary's Ratio. Both methods would allow you to measure both the proximity of locations and the similarities of the characteristics at these locations.

Regards,

Allan C. Harris - QA/QC & Environmental Engineer
Operations Assurance Services (OAS)
US Department of Energy - Fernald Closure Project
P.O. Box 538705 MS45
Cincinnati, Ohio 45253-8705
work 513.648.3184; fax 513.648.3076
Email Allan.Harris@...

"The Ultimate Inspiration Is The Deadline!"

-----Original Message-----
From: Monica Palaseanu-Lovejoy
[mailto:monica.palaseanu-lovejoy@...]
Sent: Friday, August 06, 2004 12:23 PM
To: jd92@...; ai-geostats@...
Subject: Re: [ai-geostats] Spatial analysis

Hi,

It is true that the general consensus is that you need at least 50 to
100 points for a stable semi-variogram, but myself i bent this rule a
little and i worked with 35 points ....

But you can use LISA (Local Indicators of Spatial Association -
LISA, 1995, by Luc Anselin, geographical Analysis 27 (2), pp 93 -
115). LISA is implemented in GeoDA freely available from
http://sal.agecon.uiuc.edu/default.php

GeoDa is very friendly and easy to use, but you need to be at least
a little familiar with ESRI products (ArcView or ArcGIS) and
especially shape files.

You can also use sdep package from R (http://cran.r-project.org/) -
which is a free statistical software - very powerful but with a very
steep curve of learning.

The idea is to use local indicators such as local Moran's I and local
Gearry c for investigating local spatial instabilities (homogeneity
versus heterogeneity).

I hope this helps,

Monica

Monica Palaseanu-Lovejoy
University of Manchester
School of Geography
Mansfield Cooper Bld. 3.21
Manchester M13 9PL
England, UK
Tel: +44 (0) 275 8689
Email: monica.palaseanu-lovejoy@...

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• Good day everyone ! It is true that the general consensus is that you need at least 50 to 100 points for a stable semi-variogram, but myself i bent this rule
Message 4 of 5 , Aug 7 4:32 AM
Good day everyone !

"It is true that the general consensus is that you need at least 50 to
100 points for a stable semi-variogram, but myself i bent this rule a
little and i worked with 35 points .... "

.... and I even worked with 9 or 10 points recently (how shocking !).

It is true that one of the favourite comments made by reviewers (those
unexperienced in geostatistics) is "how many points do you need/did you
use" and the favourite reply of the authors is almost systematically
minimum" followed by a reference to Cressie's book. Such a reply is a
convenient way to get around the discussion. I must admit, I also used
the same reference to avoid wasting time and space with discussions that
would distract the reader from the main argument treated.

Practically, the number of points you need to describe a spatial
correlation is very much depending on your experience with the
phenomenon you study. If you expect a strong correlation (because of
previous case studies, litterature) and see such a correlation, even if
calculated on very few observations, why not trust your results? If you
don't see anything, than you could consider increasing your sample size.
Few observations will obviously not lead you to results that are
statistically robust but they may be enough to confirm your assumptions.
I believe everyone who has a bit of experience in geostatistics has been
impressed by the large amount of silly semi-variograms published in peer
reviewed papers: even if these semi-variograms were calculated on
thousands of points, many are showing pure noise. Nevertheless, some
dare fitting a model on these semi-variograms and derive conclusions
and/or generate maps.

My 2 cents contribution for the week ...

Cheers,

Gregoire
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