Hi
If you have many data (as I guess you have, given the way in which
you collected the data)
you can code the data by means of an indicator approach (as reported by Bob)
and apply a moving windows approach to calculate inside each window
the proportion
(well, it is like a probability of occurrence) of each category. Then
you can chose to attribuite to the center
of the windows (i.e. your grid node) the category with the maximum probability.
Well, this work only if you have many data compared to the resolution you need.
If you have not many data the indicator kriging approach seems a good idea.
Another choice is to use a transition probability methodology.
Bye
Sebastiano
At 23.15 22/05/2007, Beatriz Yannicelli De Olivera Bueno wrote:
Dear list:
I have to map the bottom types of a coastal area with high spatial
heterogeneity. We have geo-referred ecosound tracks with data on
bottom/community types: rocky, sandy, rocky with macroalgae, etc.
Does anyone know which is the best interpolation algorithm for this type
of data which is not continuous but categorical in a continuous space?
And software that can perform this type of 'interpolation'?
My goal is to map and to estimate the area covered by each type of
bottom/community.
Thanks in advance
Beatriz
Beatriz Yannicelli
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