> 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.robin...@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-boun...@stat.math.ethz.ch > [mailto:r-sig-geo-boun...@stat.math.ethz.ch] On Behalf Of > Tomislav Hengl > Sent: 13 January 2009 09:35 > To: 'Katona Lajos'; r-sig-geo@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-boun...@stat.math.ethz.ch > > [mailto:r-sig-geo-boun...@stat.math.ethz.ch] On Behalf Of > Katona Lajos > > Sent: Thursday, January 08, 2009 10:30 PM > > To: r-sig-geo@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 > > > > _______________________________________________ > > R-sig-Geo mailing list > > R-sig-Geo@stat.math.ethz.ch > > https://stat.ethz.ch/mailman/listinfo/r-sig-geo > > _______________________________________________ > R-sig-Geo mailing list > R-sig-Geo@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/r-sig-geo > _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo