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4

You could use the raster Calculator something like (raster1>raster2>raster3)*1 OR (raster1>raster3>raster2)*1 OR (raster2>raster3>raster1)*2 OR (raster2>raster1>raster3)*2 OR (raster3>raster2>raster1)*3 OR (raster3>raster1>raster2)*3


2

You don't need to be updating row[1] for every feature, so cursor.updateRow(row) should be under the final else statement. Also, I'd suggest using with statements as closing is better supported: fc = r"C:\Points\Test.gdb\Points_3d" with arcpy.da.UpdateCursor (fc, ["Elevation","Slope"]) as cursor: firstRun = True for row in cursor: if ...


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Based especially on your later description of how coverage generally works, you might be interested in computing the minimum bounding geometry for your point data. This will generate a fairly conservative estimate of your AIS coverage (in that it will probably be an understatement of the actual coverage) than your cell based aproach, but it will guarantee ...


2

Analysis I would like to share the process of discovering and fixing the problem (whose solution is documented in comments to the question) because it works in many similar circumstances. Look at the error message. Really read it. This one says Parameters are not valid. Make some guesses about what that gobbledygook means. (Experience helps ...


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If you do not need to merge topology, but just add new polygons, you can simply use: ab <- rbind(a,b) If you get a "non-unique Polygons ID slot values" error it means that the rownames of the objects are the same. To fix this you can use spChFIDs to change the rownames and associated slot relationships. Since the slots in the object use the rownames to ...


1

The two area calculation sums are within 0.0638% of each other, which is pretty close. It looks like you are calculating the area of a geodesic polygons, which is a complicated problem, with several different algorithms, each with a slightly different result. I can point out the algorithms used for different versions of PostGIS, but the algorithm for Oracle ...


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See the documentation for the Fuzzy Membership tool. It fully explains how to use the tool and gives some examples of calling the tool in Python.


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Use the Lookup tool/function from the Spatial Analyst->Reclass toolset in your Raster Calculator expression. i.e using the CellStatistics tool/function to sum: CellStatistics([Lookup("rastera", "floatfld"), Lookup("rasterb", "floatfld"), Lookup("rasterc", "floatfld")], "SUM") i.e adding them up manually: Lookup("rastera", "floatfld") + ...


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One possible solution I can think of would be to use the reclass by table tool in the spatial analyst extension to reclassify your rasters to show concentrations instead of crop data. Eg. (using python) ReclassByTable(in_raster, attribute_table, crop_type_field, crop_type_field, concentration_field) You could then use simple map algebra or raster ...


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Spatial autocorrelation does look at whether two phenomena have similar spatial distribution, however I believe what you want goes a bit beyond that. You're looking at a regression analysis, which explores correlation between several independent or explanatory variables and an occurrence (dependent variable). In your case, why is this disease outbreak ...


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At first glance this seems quite puzzling. However if you check the help page on zonal statistics the second comment states the following; "When the zone and value inputs are both rasters of the same resolution, they will be used directly.If the resolutions are different, an internal resampling is applied to make them match before the zonal operation is ...



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