[R-sig-Geo] Special issue: Spatial and spatio-temporal models for interpolating climatic and meteorological data

[Tim Appelhans]

Dear R-sig-geo,

is anyone interested in working together on a submission for the below 
mentioned special issue of SPASTA?

The situation is as follows:
We have about 3.5 yrs worth of temperature and humidity data from Mt. 
Kilimanjaro at hourly resolution. In addition we have numerous landscape 
data derived from a high-resolution DEM (30m) plus NDVI images at the 
same spatial resolution.

So far, we have conducted a (though not in the strict sense) 
spatio-temporal interpolation study on a monthly basis for the above 
mentioned data focussing on machine learning / data mining algorithms. 
As a reference we also used ordinary kriging (through the 'automap' 
package). The results of this exercise look quite promising (see link 
below for a figure showing the RMSE of predictions - observations for 
250 repeated random subsampling runs). There are 6 machine learning 
algorithms that perform significantly better than our reference kriging 
runs.

https://www.dropbox.com/s/rlvvxxbr355hi44/ML_results_temperature_monthly.pdf

Here's the caveat:
The kriging done for this exercise can hardly be considered optimal. We 
simply used the autoKrige function.

Therfore, I would like to find one (or more) person(s) with profound 
knowledge of 'classical' spatio-temporal interpolation methods to 
provide an exhaustive comparison of the most promising machine learning 
algorithms and optimised classical approaches (e.g. various kriging 
flavours, IDW, GWR etc).

So, if anyone from this list might be interested, please write me and 
I'll be happy to provide more detailed information on both the results 
of the presented figure as well as the intended comparison study for the 
special issue (or another journal - this merely seems a very appropriate 
opportunity to come forward).

Regards,
Tim



On 07/28/2014 01:07 PM, Tomislav Hengl wrote:
>
> Dear R-sig-geo,
>
> This is to inform you that the submissions for the special issue on 
> "Spatial and spatio-temporal models for interpolating climatic and 
> meteorological data" (based on the http://dailymeteo.org/2014 
> conference) are now open.
>
> Spatial Statistics (SPASTA): 
> http://www.journals.elsevier.com/spatial-statistics/
> Special issue title: Spatial and spatio-temporal models for 
> interpolating climatic and meteorological data
> Guest editor(s): Dr. Tomislav Hengl, Dr. Edzer Pebesma, Dr. Robert J 
> Hijmans
>
> Submissions open: 1st of July 2014
> Submissions close: 15th of October 2014
> Acceptance deadline (closing of the special issue): 1st of March 2015
>
> If you plan to submit a paper for this special issue (and have not 
> participated in the conference), please reply to this e-mail with a 
> working title / 300 words abstract, and why you think this paper 
> should be included in the special issue.
>
> Submission guidelines:
>
> 1. Download the Elsevier article template (e.g. LaTeX template from 
> http://www.elsevier.com/author-schemas/latex-instructions);
> 2. Study the themes of interest 
> (http://dailymeteo.org/2014#toc-themes-9HSRW43W);
> 3. This special issue is about methods for interpolating climatic and 
> meteo data, but also about using Open Source software to achieve this. 
> Consider using a combination of R and/or Python and LaTeX code to 
> produce papers that include both formulas and code snippets.
> 4. Once you have managed to compile a PDF of your article for 
> peer-review, visit the SPASTA editorial system at 
> http://ees.elsevier.com/spasta/default.asp, register a new author 
> account (if required) and then login and upload your article.
> 5. During the article submission, you need to select the right article 
> type "SI:Dailymeteo.org/2014" when you reach the "article type" step 
> in the submission process, so that their papers will be routed 
> together for the special issue into the right channel, not get mixed 
> with other SI papers, or regular papers in the system.
>
> cheers,
>
> Tomislav Hengl (ISRIC — World Soil Information)
>
> _______________________________________________
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-- 
#####################################
Tim Appelhans
Department of Geography
Environmental Informatics
Philipps Universität Marburg
Deutschhausstraße 12
35032 Marburg (Paketpost: 35037 Marburg)
Germany

Tel +49 (0) 6421 28-25957

http://environmentalinformatics-marburg.de/