Pierre
If the relationship between your two variables is negative, the "pseudo" cross semi-variogram will start high and drop off, just like the co-located one. Difference is, the former doesn't go negative, the latter starts at zero and is all negative.
One other feature of the "pseudo" version is that it can (if desired) preserve the 'sense' as well as relative direction, providing an 'odd' function rather than the symmetric-around-zero co-located semi-variogram. This can be useful if you are working in space-time or with a phenomenon in which absolute direction is a factor.
Isobel
Pierre Goovaerts <[EMAIL PROTECTED]> wrote:
Pierre Goovaerts <[EMAIL PROTECTED]> wrote:
Hi,
It just represents half the average squared difference between the values of the two variables
measured at the same location.. I don't know why you compute the pseudo cross-variogram
but, personally, I don't like this statistic, mainly because of the lack of interpretation...
for example, it cannot take negative values, hence you can't differentiate
between positive and negative correlations. It is useful mainly when the two variables have
not been measured at the same locations.
Pierre
Pierre Goovaerts
Chief Scientist at BioMedware Inc.
Courtesy Associate Professor, University of Florida
President of PGeostat LLC
Office address:
516 North State Street
Ann Arbor, MI 48104
Voice: (734) 913-1098 (ext. 8)
Fax: (734) 913-2201
http://home.comcast.net/~goovaerts/
________________________________
From: [EMAIL PROTECTED] on behalf of Peijun Li
Sent: Wed 9/20/2006 12:35 PM
To: ai-geostats@jrc.it
Subject: AI-GEOSTATS: pseudo cross variogram: h=0
Dear List,
I recently use the pseudo cross variogram (PCV) for remote sensing applications. However, I don't know what does the PCV reflect when lag h=0? As we know, when lag h=0, the (univariate) variogram reflects the nugget effect. Is there any similar meaning for PCV? Could you give me some references related to PCV?
Thanks in advance for reply.
Peijun Li
Peking University
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