Hi,
landcover classification with four classes... though I haven't
experimented with spatial logistic regression, I would expect that there
is more information in the spectral satellite data (feature space) that
you are probably using as predictors than in the adjacency/distance
information. The very nature of a categorical response variable implies
that the response is discontinuous in geographic space, so a
kriging-type approach would only help if you had high sampling density
in your learning sample (several samples within each homogeneous
landcover polygon - not a typical situation).
The feature-space perspective - ignoring geographic space - allows you
to look at many excellent classification techniques that make best use
of your predictor variables; have a look at the book of D.J. Hand:
"Construction and assessment of classification techniques" for an
overview. On the other hand, spatial continuity is often achieved by
applying filters to the resulting prediction image.
Here a recent comparison of different classification techniques in crop
identification:
http://www.zfl.uni-bonn.de/earsel/papers/64-71_brenning.pdf
Multinomial (non-spatial) logistic regression was pretty, support vector
machine performed better. A comparison with spatial log.reg. would of
course be interesting.
Maybe the more important spatial issue in using classification
techniques is the question of error assessment in a spatial context,
e.g. using a spatial cross-validation, or in the above paper
cross-validation at the field level.
By the way, I am organizing a special session on Spatial Classification
at the meeting of the International Federation of Classification
Societies (IFCS) in March 2009 in Dresden, Germany. Theoretical and
applied contributions are welcome, abstract deadline November 3rd. See
http://www.ifcs2009.de/
Cheers
Alex
ivan valencia wrote:
Hi HENK
My data comes from land use classification, a grid with 1km2 resolution.
I have a bynary classification with 4 types of land use, for each land use type
have to run a logistic regression with available covariables also at
the same raster format.
I want to consider spatial logistic regression...is it possible?
{}
ivan
2008/10/9 Henk Sierdsema <[EMAIL PROTECTED]>:
Hi Ivan,
Can you tell me what the purpose is of your modelling? Is it simply producing
spatial predictions based on a logistic model or do you want to incorporate
spatial autocorrelation in the models? Given your last mail it seems you want
to incorporate spatial autocorrelation despite the fact that you deny this in
your second mail. So please extend more on the type of data you have and your
aim. Next to geoRglm, which is only suitable for small datasets, you might also
try regression-kriging.
Is there by the way anyone who has experience with autoregressive models?
Henk
Henk Sierdsema
SOVON Vogelonderzoek Nederland / SOVON Dutch Centre for Field Ornithology
Rijksstraatweg 178
6573 DG Beek-Ubbergen
The Netherlands
tel: +31 (0)24 6848145
fax: +31 (0)24 6848122
-----Oorspronkelijk bericht-----
Van: ivan valencia [mailto:[EMAIL PROTECTED]
Verzonden: woensdag 8 oktober 2008 17:16
Aan: r-sig-geo@stat.math.ethz.ch
Onderwerp: [R-sig-Geo] CODE for spatial logistic regression
Hello
I need some guide about spatial logistic regression, Is it available a
code in R?
{}
LIOV
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Alexander Brenning
[EMAIL PROTECTED] - T +1-519-888-4567 ext 35783
Department of Geography and Environmental Management
University of Waterloo
200 University Ave. W - Waterloo, ON - Canada N2L 3G1
http://www.fes.uwaterloo.ca/geography/faculty/brenning/
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