Hi David, A general answer to your question is: yes, R would be useful for such analyses - particularly when interfaced with a GIS. For an introduction to the types of spatial tools available in R see the Task View for spatial data: http://cran.r-project.org/web/views/Spatial.html
Below are a few more specific comments: On Fri, Jan 2, 2009 at 12:12 PM, David Greene <greene...@ntelos.net> wrote: > Dear Sirs: > I am not yet a user of R. My background includes the use of (Turbo) Pascal > for scientific analysis of underwater acoustics problems (e.g. sound ray > tracing and array gain in directional noise fields.) > I have become interested in the following type of problem: > (1) select , say, 1000 random locations within the continental United > States; This could be as simple as using the runif function, but more likely you'll want to look at sp::spsample, or for more advanced tools see the spsurvey and spatstat packages. > (2) characterize (statistically) the probabilities of: > (a) distance to the nearest paved road; > (b) elevation above sea level; > (c) (?) ownership (public or private); etc. R is outstanding for the types of 'statistical characterization' I guess you are interested in. It also has excellent capabilities for importing and manipulating spatial data (e.g. see the "Reading and writing spatial data" section of the Task View). However for doing things like calculating geographic distances using objects of varying types (points, lines, polygons, grids) it's generally easiest to use a GIS (such as GRASS, SAGA, ArcInfo, ...). You can then use the available tools for importing the GIS results into R for statistical analysis, and if you wish, exporting back to the GIS. However if you do not want to put the effort into learning a GIS, it is usually possible to work out a solution using only R. As you run into specific problems the R-sig-geo list is a good place to get helpful answers to well formulated questions. > Would R be useful , perhaps in combination with Google Earth, to carry out As far as I know Google Earth is designed for visualization rather than analysis. R in combination with a GIS is really the way to go. Here is a current book that covers many of the spatial tools available in R http://www.springer.com/public+health/epidemiology/book/978-0-387-78170-9 hope that helps, Kingsford Jones > this kind of study? > Thank you for your consideration. > David C. Greene > greene...@ntelos.net > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.