Hi,
In fact, as long as the weights are all positive and sum up to one, your
interpolated probability
will always be between 0 and 1; so you should be all right..
The approach proposed by Sebastiano is similar to median indicator kriging in
the sense
that the weights assigned to the observations will be the same across all
indicators (here instead of
a single indicator semivariogram used to compute the kriging weights, the same
weighting set
will be applied to all indicators since the data configuration, hence the size
of the Thiessen polygons,
doesn't change among indicators). Because all the weights are positive and
remain the same
for the different indicators, this approach should eliminate all order relation
deviations
(all estimated probabilities will be between 0 and 1, and at each location
their sum will be one).
Pierre
-----Original Message-----
From: Gregoire Dubois [mailto:[EMAIL PROTECTED]
Sent: Mon 9/5/2005 7:00 AM
To: 'seba'; [email protected]
Cc:
Subject: RE: [ai-geostats] natural neighbor applied to indicator
transforms
Ciao Sebastiano,
I realized nobody replied to your question (sorry for have added
confusion here).
I don't see any objection in applying any interpolator to probability
values.
However, you should better use exact interpolators to avoid getting
probabilities of occurences > 1 (or smaller than 0)
Cheers
Gregoire
-----Original Message-----
From: seba [mailto:[EMAIL PROTECTED]
Sent: 02 September 2005 10:07
To: [email protected]
Cc: [email protected]; 'Nicolas Gilardi'
Subject: RE: [ai-geostats] natural neighbor applied to
indicator transforms
I try to reformulate my question.....
When performing direct (i.e. without crossvariogram) indicator
kriging, practically we interpolate probability values by means of ordinary
kriging. These probability values could represent the probability of occurrence
of some category or the probability to overcome some threshold.
My question is: is there anything wrong to interpolate these
probability values with other interpolating algorithm like, for example natural
neighbor (or triangulation)?
In my opinion is all ok ..... considering also that we have no
problem of order relation violations.
Again, this technique is applied only for a preliminary data
analysis
Then a short consideration directed about the importance of
boundaries:
Quoting Nicolas Gilardi
"My personnal feeling about the distinction between using a
classification algorithm or a regression one is the importance you put on the
boundaries.If you look for smooth boundaries, with uncertainty estimations,
etc., then a regression algorithm (like indicator kriging) is certainly a good
approach."
Well, if you use fuzzy classification the boundaries become
continuos...fuzzy.
Bye
S. Trevisani
* 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
