[GRASS-user] mantel correlogram

[Thomas Adams]

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 <tech_dev at wildintellect.com>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 <tech_dev at wildintellect.com>
> 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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