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5

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 ...


0

New to ArcGis 10+ is the Raster object... this needs a bit of an idiom shift to get used to it. To turn a file path into a raster use arcpy.Raster("d:\\path\\to\\raster.ext") or just "raster.ext" if it's in your current arcpy.env.workspace. This also means that you need to get rid of these objects using del. Some tools will work with either a path or a ...


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Thank you all for your answers, it was actually "Tabulate area" the option that I needed, because it let me set up specific zones for both the raster and the polygon layer. You were right Chris, I had not exhausted all the zonal options :)


1

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


1

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.


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Respective ArcGIS Help page states that the histogram you see on your screen honours the extent of the raster (if you zoom in or out, the histogram output will change and you will see counts of the current extent) and symbology used defines the number of bins/categories that you will see on your histogram. If you re-symbolise your data by using ...


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The method I use is: if arcpy.CheckExtension("Spatial") == "Available": arcpy.AddMessage("Checking out Spatial") arcpy.CheckOutExtension("Spatial") else: arcpy.AddError("Unable to get spatial analyst extension") arcpy.AddMessage(arcpy.GetMessages(0)) sys.exit(0) and then at the end of your script: arcpy.CheckInExtension("Spatial") ...


3

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) ...


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very good article and i am need to reed more soon , thank you gis design gps surveying


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A thematic/categorical dataset such as landuse would technically be appropriate for a polygon feature class rather than a raster. So, one solution would be to convert your land use raster to a vector layer, and then perform a spatial join that would result in an attribute table with a record for each polygon--land use combination. Another option is to use ...


1

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 ...


0

Take a look at density() function in spatstat package. Official site has a number of manuals and articles about this package (see Documentation). I would recommend to start with Analysing Spatial Point Patterns in R.



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