[R-sig-Geo] R-sig-Geo Digest, Vol 161, Issue 6

Wed Jan 18 20:40:03 CET 2017

HI Edzer,
Thanks. 

I tried the gstat unconditional simulation, and seems to produce the desired outputs.

I have a follow up two questions 
1. Is there a literature that describes how the unconditional simulation (data processing, normal score transform, post processing, etc) is handled within the gstat package. I did simulation by generating gstat object. I have looked at the gstat manual. 
2. if I got access to 1, I could figure out if the method is useful for zero inflated datasets, or you may have suggestion to it. I understand normal score transform methods (if implemented) would normalize data but with zeros, I am not sure. 

Thanks

Sanjay

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Today's Topics:

   1. Simulating or bootstrapping of Spatial Data
      (LAMSAL, SANJAY [AG/1005])
   2. Re: Simulating or bootstrapping of Spatial Data (Edzer Pebesma)

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Message: 1
Date: Mon, 9 Jan 2017 19:56:59 +0000
From: "LAMSAL, SANJAY [AG/1005]" <sanjay.lamsal at monsanto.com>
To: "r-sig-geo at r-project.org" <r-sig-geo at r-project.org>
Subject: [R-sig-Geo] Simulating or bootstrapping of Spatial Data
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I have a spatial data of soil bacteria concentration and have its semivariogram model. I wanted to simulate spatial scenarios of bacterial concentration in other fields. I could see this as either bootstrapping the bacteria data on spatial grids placed over other fields such that the generated data is spatially autocorrelated (honors the semivariogram model).

In my case, we want to develop spatial patterns of bacterium across several fields, where spatial pattern differ among fields but all patterns honor the same semivariogram model. Can anyone suggest an approach to accomplish this? I see this as being different from sequential simulation using gstat object (package gstat) where the simulated maps are realizations of original scenario.

Thanks

Sanjay

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Message: 2
Date: Mon, 9 Jan 2017 21:51:20 +0100
From: Edzer Pebesma <edzer.pebesma at uni-muenster.de>
To: r-sig-geo at r-project.org
Subject: Re: [R-sig-Geo] Simulating or bootstrapping of Spatial Data
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Sanjay,

library(gstat)
demo(ugsim)

gives a demo of a sequential Gaussian unconditional simulation, which you seem to want. Several other simulation methods are found in package RandomFields.

On 09/01/17 20:56, LAMSAL, SANJAY [AG/1005] wrote:
> I have a spatial data of soil bacteria concentration and have its semivariogram model. I wanted to simulate spatial scenarios of bacterial concentration in other fields. I could see this as either bootstrapping the bacteria data on spatial grids placed over other fields such that the generated data is spatially autocorrelated (honors the semivariogram model).
> 
> In my case, we want to develop spatial patterns of bacterium across several fields, where spatial pattern differ among fields but all patterns honor the same semivariogram model. Can anyone suggest an approach to accomplish this? I see this as being different from sequential simulation using gstat object (package gstat) where the simulated maps are realizations of original scenario.
> 
> Thanks
> 
> Sanjay
> 
> 
> 
> This email and any attachments were sent from a Monsanto email account and may contain confidential and/or privileged information. If you are not the intended recipient, please contact the sender and delete this email and any attachments immediately. Any unauthorized use, including disclosing, printing, storing, copying or distributing this email, is prohibited. All emails and attachments sent to or from Monsanto email accounts may be subject to monitoring, reading, and archiving by Monsanto, including its affiliates and subsidiaries, as permitted by applicable law. Thank you.
> 
> 	[[alternative HTML version deleted]]
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--
Edzer Pebesma
Institute for Geoinformatics  (ifgi),  University of M?nster Heisenbergstra?e 2, 48149 M?nster, Germany; +49 251 83 33081
Journal of Statistical Software:   http://www.jstatsoft.org/
Computers & Geosciences:   http://elsevier.com/locate/cageo/

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