predict a map from point data
Hi Sadz, Am 28.02.2011 01:54, schrieb Sadz A:
Hi, I'm trying to predict the distribution of timber over an area, I have point location data- so it would make sense to use a krigging to interpolate the data over the whole map. Unfortunately the krigging predictions are pretty bad.
What does "pretty bad" mean? What did you actually do? There are many "flavours" of doing kriging including variogram estimation and modelling. E.g. you seem to interpolate volume data, so I suppose your "point location data" actually represent a volume too. Sounds like fitting a Gau?ian model and doing block kriging could be appropriate.
I have now got environmental data for my study site and have used the r package 'quantreg' to make a model that I can predict the volume from R code: fit <- rqss(sum_vol~qss(env.factor1,lambda=1)+ qss(env.factor2,lambda=1), tau = 0.9) predi<-predict(fit, new, interval = "none", level = 0.9)
Do you mean you have any covariates? Maybe multivariable geostatistics (the gstat package) are your friend.
unfortunately this is not working either (a problem with the predict function). I have also tried linear modelling, Gams and inverse interpolations.
Any errors, traceback()? regards, Tom
Does anyone have any ideas on how I could get a spatial map of volume
distribution from the point data?
Any help is appreciated,
thank you
sadz
ps- if anything is unclear I would be happy to clarify
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