Title: RE: AI-GEOSTATS: Moran scatterplot
-Original Message-
From: Pat Bellamy
Sent: 28 November 2003 13:57
To: 'Monica Palaseanu-Lovejoy'
Subject: RE: AI-GEOSTATS: Moran scatterplot
Dear Monica
I think it would be worth looking at the following papers as it should give a way of estimating the spatial correlation robustly without having to ignore some of the outliers.
Lark, R.M. 2000. A comparison of some robust estimators of the variogram for use in soil survey. European Journal of Soil Science, 51, 137--157.
Lark, R.M. 2002. Modelling complex soil properties as contaminated regionalized variables. Geoderma. 106, 171--188.
Murray Lark and I have been using these robust estimators on soil contamination data.
Yours
Pat
Mrs Pat Bellamy B.Sc. M.Sc.
Statistician/Computer Analyst
National Soil Resources Institute (NSRI),
Cranfield University at Silsoe,
Silsoe,Bedfordshire,
MK45 4DT,
UK
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-Original Message-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]]On
Behalf Of Monica Palaseanu-Lovejoy
Sent: 28 November 2003 11:43
To: [EMAIL PROTECTED]
Subject: AI-GEOSTATS: Moran scatterplot
Hi everybody,
I want to ask your opinion on some results from Moran scatterplot.
I am working with soil contamination data, and in my opinion the
dataset is formed by 2 different distributions, one more diffusive
which is the majority of the data, and one generated by a point
source process represented by the outliers in the dataset (about
18% found out with box-plot). The dataset is strongly positively
skewed.
If i use the full dataset and i build the moran scatterplot, i have a
global Moran of about 0.02 - no spatial correlation - even though
one might expect at least some spatial autocorrelation in the soil
contamination data. If i am eliminating all outliers i identified with
the box-plot i get a global Moran of about 0.36 - much much better.
But if i eliminate only part of the outliers, and not all of them, i get a
global moran of 0.49!
I would interpret this as: Some outliers (probably the lowest values
of my upper outliers - no lower outliers detected by box-plot on my
data) belong to the diffusive process, which has a good spatial
autocorrelation, while the rest of the data should belong to the point
source process. That means not all outliers were generated by the
point source pollution, and some are genuinely generated by the
diffusive process. Since i am dealing with contamination, of course
i am interested in what outliers represent, much more if they are
above the environmental pollution threshold.
Do you think is correct my interpretation? How important is this
finding from a statistical point of view? (if it is at all).
Thank you so much for your help,
Monica
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