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/
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