Hi Tom, 

For the krigging I used variogram and modelling through arcGIS. the predictions 
are bad in the sense that If predictions are plotted against real data (where 
you would get a 1:1 line if it was a perfect model) I get strait horizontal 
line. 

(what is 'a Gaußian model and doing block kriging', and how does it work/differ 
from the Arcgis approach (which was called ordinary krigging)? I think the 
problem with using the krigging is that it assumes that things that are closer 
are more alike, but with the volume this does not necessarily seem to hold 
true...
no idea how to get around this.

Thank you for your response
sadz

  



________________________________
From: Tom Gottfried <tom.gottfried@wzw.tum.de>
To: r-sig-geo@r-project.org
Sent: Mon, 28 February, 2011 9:41:25
Subject: Re: [R-sig-Geo] 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 Gaußian 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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