[GRASS-user] mantel correlogram

[Tyler Smith]

Thanks for your suggestions. It looks like the R Borg is continuing to assimilate procedures that once required specialty software. Time to learn some new packages!

Tyler

On March 11, 2014 1:08:10 PM EDT, 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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