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3

The following code is not polished but should work to create the same output table as the Point Distance tool but requires ArcGIS 10.1 (or later) for Desktop and only a Basic level license: import arcpy,math # Set variables for input point feature classes and output table ptFC1 = "C:/temp/test.gdb/PointFC1" ptFC2 = "C:/temp/test.gdb/PointFC2" outGDB = ...


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Since you are building these in C#, the best way is probably to use the SQLGeography/SQLGeometry builder classes. These can be accessed from the Microsoft.SqlServer.Types library. Some examples of it's use are here. Since you are using GPS points, they are likely to be Lat/Lons. In that case I would use Geography datatype rather than Geometry. If you ...


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This is the exact problem that I am currently working on for my own research. As a result I have been working on a plugin tool for the open-source GIS Whitebox Geospatial Analysis Tools, called 'Isolate Ground Points'. It will take an input LAS file or point-type shapefile and output only the ground points in a multipoint-type shapefile. It turns out that ...


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If you're using a postGIS database, you could use the st_closestpoint function. One argument would be your current location, the other one your inverted circle geometries.


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A wiki article Centroid describes a few methods, including this one, which is probably the one used by most GIS tools: Centroid of polygon The centroid of a non-self-intersecting closed polygon, defined by n vertices (x0,y0), (x1,y1), ..., (xn−1,yn−1), is the point (Cx, Cy), where and where A is the polygon's signed area, ...


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A 32 bit Windows 7 Operating system can use at most 4 GB of RAM[1], combined for all activity on your machine. If your shapefile is very large and you also have many other programs running then you'll probably exceed 4 GB. So, to see which process uses up your memory you can open the Windows Task Manager (using ctrl-alt-delete) and then look at the ...


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This is by far not the best solution so hopefully others will post more efficient answers. Take your buffer layer of your points and run the Polygon-line intersection tool from SAGA (I used this from the Processing Toolbox). Your polygon buffers will now be split into segments depending on the number of lines which intersect it. So visually, you can see ...


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When you create profile lines and use the Profile Graph tool , the tool creates points along the line for you and saves them in the same location as your MXD's default Geodatabase in a folder called ProfileGraph Data.


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Got it! line_center = [] featuresC = Center.getFeatures() for elem in featuresC: xy1 = elem.geometry().asPoint() center = QgsPoint(xy1) line_center.append(center) print line_center line_ends = [] featuresO = Outer.getFeatures() for elem in featuresO: xy2 = elem.geometry().asPoint() ends = ...


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An option to normalize lidar clouds (and keep it as a point cloud) is Fusion. One will need the command line ClipData together with the switches dtm:file, which is the bare-earth model (DTM) + height. ClipData description says: ...When used in conjunction with a bare-earth surface model, this logic allows for sampling a range of heights above ground ...


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I found the answers provided here very helpful and came up with a refined version. So, here's another way to accomplish this task, but without having to alter the original table. SELECT t1.gid AS gid_1, t2.gid AS gid_2, ST_Distance(t1.geom, t2.geom) AS mindist FROM table t1, table t2 WHERE t1.gid != t2.gid AND ST_Distance(t1.geom, t2.geom) != 0 ORDER BY ...


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Answer came from http://forums.arcgis.com/threads/35323-ArcPy-amp-SelectLayerByLocation-Performance?p=119047#post119047 Thanks Jason Scheirer for some more concise code: SelectLayerByLocation(in_layer=arcpy.PointGeometry(arcpy.Point(x, y)), select_features="mylayer") And especially to Chris Snyder for a performance tip: A speedier work around might be ...



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