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2

This usually works: from arcpy import env env.overwriteOutput = True env.workspace = outFolder raster_obj=FocalStatistics(raster, neighborhood, 'MAXIMUM', "DATA") raster_obj.save("outZ") del raster_obj ExtractValuesToPoints(in_point_features, outFolder+os.sep+"outZ", out_point_features)


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Ok, so after many trials and tribulations, I have wrangled arcpy into submitting to multiprocessing for running the Solar Analyst tool. Behold: import multiprocessing import time import arcpy import os import shutil def final(x): # Check out any necessary licenses arcpy.CheckOutExtension("Spatial") arcpy.env.overwriteOutput = True ...


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Just use the ArcGIS raster calculator with a CON statement. CON(("h2005" - "h2015") >= 30, 1, 0) This will result in a binary raster where [1] represents differences of >= 30m and [0] no change at this threshold. And yes, set your analysis environment for extent and snap raster.


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A couple of ideas/notes, though not a solution probably. I haven't done anything with python multiprocessing, but have done a decent amount with regular old fork/exec, so read these with that in mind 1) outputfile=open("outputfile.txt","a") It looks like each process is going to try to open and append to the same output file at the same tile. I expect that ...


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I found an ESRI supplemental tool called Zonal Statistics As Table 2, which allows me to calculate overlapping polygons. http://blogs.esri.com/esri/arcgis/2013/11/26/new-spatial-analyst-supplemental-tools-v1-3/


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In case someone is interesting to accomplish this task, without needing it to be in ArcGIS for Desktop. In R, there is the automap package, function autoKrige. The new_data argument, which indicates where the interpolation will happen, accepts points (besides grid and polygons). The manual says: new_data: a sp object containing the prediction ...


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I ended up creating a temporary unique id and doing a tabular join (Join Field) between the table I created via zonal statistics and the original polygon layer.


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There are number of tools from GRASS and SAGA which allows for cost analysis which you can access from the Processing Toolbox:


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If you want to compare vector-paths the "Road Graph Plugin" is working fine. If this is interessting for you dont forget to use the "Topology Checker" before analysing the data.


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In the screen shot you have given I think this can be easily done with a spatial join. You just need to set the match option to HAVE_THEIR_CENTER_IN. Be mindful that if you have tortuous shapes with holes in then this may fail for those scenarios.


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It looks like you need to do a Viewshed analysis. This can be accomplished using ArcGIS Viewshed (3D): ArcGIS Viewshed


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I have some spatial cluster detection code in java that should be able to get you started on how to handle this. Basically you have a "population at risk" (buildings?) and an incidence rate (customers?), this code which was all developed to detect cancers then scans the map looking for abnormally high (or low) rates and draws a circle there.


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The projection chosen for any spatial data analysis is very important. Your chosen (so far) projection, NWT Lambert, is a conformal projection. Conformal projections are very common in topographic mapping and are ideal when doing land surveying calculations, where measured and calculated surveyed angles are preserved exactly after being projected from the ...


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You should save the output raster to a raster format, not to a geodatabase, e.g.: outReclass1.save(r"C:\output\reclass.tif") EDIT: And there is a missing bracket in the Reclassify line (RemapRange is enclosed but not Reclassify). I've updated my code as well. You can also simplify the remap parameter a little bit by using the missing_values parameter for ...


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You could rasterize (Polyline to Raster) the lines and use the Euclidean Distance tool as suggested. If the lines are in the same feature class, note that ED cell values will be distance from a line, so values will increase away from the two lines until the halfway point. If you want the distance from one line to another, you'd need to run ED on only one ...



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