[R-sig-Geo] analyse geo-time data

[Rob Robinson]

> 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
Fx: +44 (0)1842 750030        W: www.bto.org/cv/rob_robinson.htm

==== "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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> > R-sig-Geo at stat.math.ethz.ch
> > https://stat.ethz.ch/mailman/listinfo/r-sig-geo
> 
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