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regression kriging with non-euclidean distances
3 messages · Alisa Coffin, Michael Sumner, Facundo Muñoz
The soap package that accompanies mgcv may be of interest: http://www.maths.bath.ac.uk/~sw283/simon/software.html Cheers, Mike.
Alisa Coffin wrote:
Don & Facundo, This is something that I'm interested in as well for coastal marine applications. The only solution I've found so far is a cumbersome "barrier" function available in the kriging operation of ArcGIS. I would be interested in seeing an (better) implementation of the idea in R. I was able to find a few references on the subject that I pass along to you and anyone else that might find it useful: Krioruchko, K. and A. Gribov. 2002. Geostatistical interpolation and simulation in the presence of barriers. X. Sanchez-Vila et al. (eds.), *geoENV IV -- Geostatistics for Environmental Applications,* pp. 331-342. Kluwer Academic Publishers: the Netherlands. Curriero, F.C. 1997. The use of non-Euclidean distances in geostatistics, PhD thesis. Little, LS, D Edwards and DE Porter. 1997. Kriging in estuaries: as the crow flies or as the fish swims? Journal of experimental marine biology and ecology. 213:1-11. Rathbun, S. 1998. Spatial modeling in irregularly shaped regions: kriging estuaries. Envirometrics. 9:109-129. Jensen, OP, MC Christman and TJ Mller. 2006. Landscape-based geostatistics: a case study of the distribution of blue crab in Chesapeake Bay. Envirometrics. 17:605-621. Regards, Alisa Coffin On Thu, Mar 20, 2008 at 2:42 PM, Don MacQueen <macq at llnl.gov> wrote:
Facundo,
I am interested in essentially the same thing,
though my application is inside buildings. I am
wondering what technique(s), software, etc. you
would be using to calculate your distances.
I asked about this about a month ago, here on
r-sig-geo, and received some helpful replies. The
subject line at that time was
[R-sig-Geo] Shortest path around obstacles (OT)
-Don
At 7:05 PM +0100 3/20/08, Facundo Mu?oz wrote:
Hello All,
I'm performing regression kriging (using both gstat and geoR) in an
urban environment. Thus, i have discontinuities (buildings) in the
prediction space which make distances to be non-euclidean.
I need to estimate variogram and make predictions using distances
calculated by myself and passed to the algorithms (say, in a distance
matrix), instead of letting them calculate distances based on
coordinates.
?Has anyone faced this issue before?
Thanks in advance,
Facundo Mu?oz
--
Departament d'Estad?stica i Investigaci? Operativa Universitat de
Val?ncia (Estudi General)
Facultat de Matem?tiques, Dr. Moliner 50, 46100 Burjassot, Spain.
(+34) 96 354 3987, fax: (+34) 96 354 3238
e-mail: Facundo.Munoz at uv.es, web: http://www.uv.es/~famarmu/<http://www.uv.es/%7Efamarmu/>
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-- -------------------------------------- Don MacQueen Environmental Protection Department Lawrence Livermore National Laboratory Livermore, CA, USA 925-423-1062
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