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!
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.
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!
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.
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
is this a reasonable approach
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:
extract the raster data for each point
save the data to csv; import into R
calculate the spatial distances between points, after projecting
the lat-long data into an appropriate scale (?)
calculate the climate distance using the WORLDCLIM data
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!
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.
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@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.
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 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@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.
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!
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.
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.
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.
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
is this a reasonable approach
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:
extract the raster data for each point
save the data to csv; import into R
calculate the spatial distances between points, after projecting
the lat-long data into an appropriate scale (?)
calculate the climate distance using the WORLDCLIM data
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!
On Tue, Mar 11, 2014 at 7:43 PM, Tyler Smith <tyler@plantarum.ca> wrote:
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!
Please consider to eventually add some short workflow here:
I can do that – how do I make example datasets of mine available (which I would prefer, instead of using SPEARFISH or one of the other common datastes)?
Tom
···
On Sat, Mar 15, 2014 at 9:48 AM, Markus Neteler <neteler@osgeo.org> wrote:
On Tue, Mar 11, 2014 at 7:43 PM, Tyler Smith <tyler@plantarum.ca> wrote:
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!
Please consider to eventually add some short workflow here:
On Sat, Mar 15, 2014 at 3:10 PM, Thomas Adams <tea3rd@gmail.com> wrote:
Markus,
I can do that -- how do I make example datasets of mine available (which I
would prefer, instead of using SPEARFISH or one of the other common
datastes)?
if not too big and if having an open data license we could host them
on an OSGeo server (incl. the GRASS server).