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 _______________________________________________ grass-user mailing list [email protected] http://lists.osgeo.org/mailman/listinfo/grass-user
