Tag Info

Hot answers tagged


Take raster NDVI dataset and use a raster calculator-type function to create a binary surface of 1 and NULL (no data); 1 is where values are between 0.1 and 1; NULL is everything else. For each farm vector feature, clip your binary-classified NDVI dataset. For each clipped NDVI dataset, count the number of cells that are not NULL, and multiply this number ...


I believe you also have to check out the extension. import arcpy import sys if arcpy.CheckExtension("Spatial") == "Available": arcpy.CheckOutExtension("Spatial") from arcpy.sa import Con from arcpy import env fDir=r'd:\scratch\fdir' outFolder=r'd:\aerials\images' env.workspace = outFolder fDir=arcpy.Raster(fDir) ...


This may be the tool you are looking for: Extract values to points.


I don't think there is any limit (mathematically speaking) to the z-score. I've got results up to 100-200 in some occasions. Just google search images of the morans zscore results and you will see a lot of cases with scores greater than 40.


There is no simple implementation of a Kernal Density Estimate using weights in R. Most of the advice for KDE's are limited to spatial locations only. You can write a function to project results from the ks package to a grid, but this is not entirely straight forward. My best advice is to leverage existing implementations from a GIS. The best option I have ...

Only top voted, non community-wiki answers of a minimum length are eligible