Dear Raul,
Any changes in the size {<GS>support</GS>}, shape {<GS>anisotropy</GS>} etc of
cells (grids) in cell-based analysis including from spatial analysis (GIS etc)
and geostatistics (grided variogram, kriging etc) need to be considered
carefully while they can provide very different correlation structure and so
result in very different interpretations. Geostatistically speaking, the
support
effect should be considered initially very well. The support can be simply
defined as how your choice (e.g. cell) is being represented with the samples
inside it (e.g., number of samples per cell).
There are some methods you will find them interesting, I think:
For an evaluation of the dispersion of data into cells, consider Morisita
(Morishita) factor (e.g., from Kanevsky);
Also consider declusterting methods (e.g. from Clayton);
...
Best Regards,
Younes
yfa.st...@ymail.com
http://alghalandis.com
________________________________
________________________________
From: Raul Sierra Alcocer <raul.sierra.alco...@gmail.com>
To: ai-geostats@jrc.it
Sent: Tue, 15 March, 2011 3:49:31 AM
Subject: AI-GEOSTATS: choosing grid resolution
Dear list,
I am a PhD student in Computer Science new to spatial analysis, and I
am working in a data mining platform at my institute that will provide
some tools for spatial data mining. It is basically analysis of
spatial features using rectangular grids. My question is if there are
any studies about the effects of grid resolution in the results of
correlation analysis between spatial features. When I say effects, I
mean how the choice of cell size affects the statistical significance
of the results.
Thanks in advance,
Raúl Sierra.
---------------------------------------------------------------
Centro de Ciencias de la Complejidad,
Universidad Nacional Autonoma de México.
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