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3

I am afraid that you will need to create 79 distance rasters with the distance for each point. This can be done in model builder (with iterate features) or with python (loop on the ID with make feature layers). Once you have your 79 rasters, you can use "extract multi values to points" that will yield a origin-destination cost matrix in your attribute ...


2

You can try enabling it via the Python window (under Geoprocessing > Python). Open a Python window and enter: arcpy.CheckExtension("Spatial") That will tell you if a Spatial Analyst license is available. If it is, enter the the command: arcpy.CheckOutExtension("Spatial") That will check out the license and you'll be able to use the Spatial Analyst tools. ...


0

I am sure there is a better 'raster-only' solution. But you could (if you raster data is not big)- convert your raster in to a a point feature (1 point per pixel). And classify the raster based on the vector you generated.


2

You could have a look at the RasterStats plugin. It can compute histograms for zones (I don't know in what format, though), which could help you finding how much of each class is located in each zone. There are other zonal statistics plugins (ZonalStats, Zonal statistics), but I don't think they can give accurate enough statistics for what you're trying to ...


2

After displaying your points as X,Y data you need to save the layer to a spatial format that is useable in the ArcMap geoprocessing tools. You can right-click on the points in your table of contents and choose to export them to a shapefile (or other format). Once this has completed, choose the option to add the new layer to the map. Use this new layer as ...


2

You can easily calculate root mean square error (RMSE) for any Spatial Analyst interpolation method. Here's how I would do it: Add two extra fields to your point layer, and call them something like: interpolation and SqDeviation Run the interpolator of your choice to create the new surface (IDW, Kriging, Nearest Neighbor, etc.). Run the tool "Extract ...


1

The result, however it happens to be stored or presented to you, evidently will be the equivalent of one raster for each class: a raster showing the counts of type "A", another showing the counts of type "B", and so on. Compute these count grids by taking focal statistics of the indicator grids. Recall that the indicator for any class is a grid having ones ...


3

Depends on the machine specs and dataset size and number of points. One way to speed things up is to set the environment processing extent of the snap pour point tool. So if you are snapping a few points in a sub-catchment onto a flow accumulation for the whole of America then may be thats why... Try setting the extent to the extent of your study region. ...


3

In the examples posted for Cell Statistics the import statement used is different to yours. There they use: import arcpy from arcpy import env from arcpy.sa import * which means CellStatistics() will be recognized as a function. You have used simply: import arcpy so you will need to fully describe where in ArcPy to find that function: result = ...


1

For CellStatistics you need Saptial analyst license, I guess you don't have it checked out, so the tool is not available to you. You should check it out first like: arcpy.CheckOutExtension("Spatial") and after that put your: result = CellStatistics([ras1,ras2], "RANGE", "NODATA")... You can also chcek it by desktop client and it should work as well.


1

The raster interpolation, reclassification, math, and surface analysis tools in 3D Analyst that have the same name as their Spatial Analyst counterparts are using the same underyling functions.


1

Here is how I would set this up using raster classification and raster calculator: 1: Reclassify each raster dataset to the following: Slope between 20-40 degrees = 1 , all else 0 Rasters facing West (what ever your degree range is) = 10, all else 0 Cells in an un forested area = 100, all else 0 2: Raster calculator and add all 3 rasters ...


2

Here is how I would write this to reduce the number of tests : Con("Slope" <= 1 , 0.2 , Con("Slope" <= 3 , 0.3 , Con("Slope" <= 5, 0.4 , 0.5))) Note that you need to write several Con() because "|" is only used to test booleans.


1

Why don't you just use a reclassify? Other option it would be to read the raster like array and then perform the changes in the sense you want.



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