I agree, there -IS- "No need to pass csv around" because using the R spgrass6 package, one can read/write GRASS vector and raster files directly from R, so there are no intermediate files. I do this "all the time" -- incredibly powerful using GRASS & R together.
Tom On Tue, Mar 11, 2014 at 1:08 PM, Alex Mandel <[email protected]>wrote: > You are right, I didn't read it that closely 1st time around. My point > was that all of it can be done in R, and there are geospatial specific > packages that have all the tests one might want. The bare minimum > interaction is via rgdal or spgrass to pull data over from existing > GRASS data sets. If the data isn't already in GRASS then rgdal one can > read the original files directly. No need to pass csv around. Of course > if it is in GRASS then you should have it all in the same projection > already anyways if you put it all into the same mapset/location. > > The other book likely to have exactly what you want (field sampling > design) is Ch 5. > > http://www.amazon.com/Spatial-Analysis-Ecology-Agriculture-Using/dp/1439819130/ref=la_B001K6MGR8_1_1?s=books&ie=UTF8&qid=1394557436&sr=1-1 > > Enjoy, > Alex > > On 03/11/2014 09:58 AM, Thomas Adams wrote: > > Alex, > > > > I believe Tyler does plan on using R for the statistical analyses, but > > using GRASS GIS in combination with R is the easiest path, I think. > > > > Tom > > > > On Tuesday, March 11, 2014, Alex Mandel <[email protected]> > wrote: > > > >> Use R. It includes Moran's I and Geary's C tests for > >> spatial-autocorrelation. Look like it has mantel too. > >> > >> You'll probably need the sp, spdep and rgdal packages. You might also > >> want to use the Raster package to extract the sampling data, or you can > >> use spGRASS to tie the R and Grass together. > >> > >> See chapter 9 (1st ed) of Applied Spatial Data Analysis with R. > >> http://www.asdar-book.org/ > >> > >> Enjoy, > >> Alex > >> > >> On 03/11/2014 09:18 AM, Tyler Smith wrote: > >>> Hello, > >>> > >>> We're preparing a field sampling program, and would like to determine > >>> a minimum distance between samples to reduce/eliminate spatial > >>> autocorrelation. I think a good approach would be to calculate a > >>> mantel correlogram, and use the range of the correlogram as our > >>> minimum sampling distance. > >>> > >>> * Questions > >>> > >>> 1) is this a reasonable approach > >>> 2) if so, how best to do this? > >>> > >>> * Details > >>> We have a vector map with the point coordinates of several hundred > >>> potential sampling sites, and ~ 10 raster layers with appropriate data > >>> to test for spatial autocorrelation (WORLDCLIM, soils). I could do > >>> something like the following, but I'm not sure if there's a simpler or > >>> more appropriate approach: > >>> > >>> 1) extract the raster data for each point > >>> 2) save the data to csv; import into R > >>> 3) calculate the spatial distances between points, after projecting > >>> the lat-long data into an appropriate scale (?) > >>> 4) calculate the climate distance using the WORLDCLIM data > >>> 5) use the 'mgram' function in the 'ecodist' package to calculate the > >>> actual correlogram between the spatial distance and climate distance > >>> > >>> Any suggestions on the approach or the methods would be welcome! > >>> > >>> Thanks, > >>> > >>> Tyler > > > > >
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