-----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
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
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
Thank you in anticipation,
Lajos Katona