[GRASS-user] mantel correlogram

[Alex Mandel]

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

>