[R-sig-Geo] analyse geo-time data
Rob Robinson
rob.robinson at bto.org
Tue Jan 13 10:51:21 CET 2009
> Pebesma, E.J., Duin, R.N.M., Burrough, P.A., 2005. Mapping
> sea bird densities over the North Sea:
> spatially aggregated estimates and temporal changes.
> Environmetrics 16(6), 573--587.
> http://dx.doi.org/10.1002/env.723
> (The authors claim to have put the R script on-line but I
> could not locate them anymore)
>
Coincidentally, I was looking at this the other day try...
library(gstat)
demo(fulmar)
Cheers
Rob
*** Want to know about Britain's birds? Try www.bto.org/birdfacts ***
Dr Rob Robinson, Senior Population Biologist
British Trust for Ornithology, The Nunnery, Thetford, Norfolk, IP24 2PU
Ph: +44 (0)1842 750050 E: rob.robinson at bto.org
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==== "How can anyone be enlightened, when truth is so poorly lit" =====
> -----Original Message-----
> From: r-sig-geo-bounces at stat.math.ethz.ch
> [mailto:r-sig-geo-bounces at stat.math.ethz.ch] On Behalf Of
> Tomislav Hengl
> Sent: 13 January 2009 09:35
> To: 'Katona Lajos'; r-sig-geo at stat.math.ethz.ch
> Subject: Re: [R-sig-Geo] analyse geo-time data
>
>
> Dear Katona,
>
> R (i.e. its packages) are definitively suited for analysis of
> spatio-temporal data. Try searching the packages in the
> [http://cran.r-project.org/web/views/Environmetrics.html]
> views; in fact, there is a section dedicated to time-series
> [http://cran.r-project.org/web/views/TimeSeries.html].
>
> There are several good papers on spatio-temporal interpolation e.g.:
>
> Pebesma, E.J., Duin, R.N.M., Burrough, P.A., 2005. Mapping
> sea bird densities over the North Sea:
> spatially aggregated estimates and temporal changes.
> Environmetrics 16(6), 573--587.
> http://dx.doi.org/10.1002/env.723
> (The authors claim to have put the R script on-line but I
> could not locate them anymore)
>
> If you are interested in the analysis of time-series data,
> take a look at this book:
>
> Chatfield, C., 2003. The Analysis of Time Series: An
> Introduction (6th edition). CRC Press, pp. 352.
> http://people.bath.ac.uk/mascc/TS
>
> Dynamic modeling of spatial phenomena is more difficult (e.g.
> dynamic simulation of flu spreading).
> Maybe you should consider using some diffusion algorithm from
> ecology? E.g.: diffusion function implemented in the
> "simecol" package:
>
> http://bm2.genes.nig.ac.jp/RGM2/pkg.php?p=simecol
>
> Or maybe consider using some hydrological flow models as
> implemented in e.g. SAGA GIS.
>
>
> Few remaining questions:
> 1. What kind of variables are your talking about? Give some examples.
> 2. Does your data has a point support or is it areal (polygons)?
>
>
> HTH,
>
> Tom Hengl
> http://spatial-analyst.net
>
>
>
>
> > -----Original Message-----
> > From: r-sig-geo-bounces at stat.math.ethz.ch
> > [mailto:r-sig-geo-bounces at stat.math.ethz.ch] On Behalf Of
> Katona Lajos
> > Sent: Thursday, January 08, 2009 10:30 PM
> > To: r-sig-geo at stat.math.ethz.ch
> > Subject: [R-sig-Geo] analyse geo-time data
> >
> > Dear all,
> >
> > can you suggest/advise statistical methode in R to analyse my time
> > series and regional/spatial data?
> > I have 174 region and daily (365) data for every region (geo-time
> > data). (There is 174*365=63510
> > data/observation)
> >
> > How can I building a model what is founded on parameters of
> spatial and time series.
> >
> > I'd like to simulate how to expand a contagious disease
> (flu). Find typical patterns and paths.
> >
> > What do you think what is the best way to discover and
> analyse my data?
> >
> > Thank you in anticipation,
> > Lajos Katona
> >
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>
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