Spatially Enabled Data Frames for Spatial Analysis

NathanHeickLACSD — Thu, 12 Sep 2019 01:21:09 GMT

I am interested in the possibility of using SEDFs for doing unions and intersections of point geometries and selecting point geometries completely within line geometries. I can do this with normal means, but I was hoping I could do it with SEDFs faster and more concisely. I am still evaluating that. I just wanted to know if you can do these operations with SEDFs. Do the spatial columns contain normal geometry objects? Can you have multiple shape columns on a single data frame? If not, I will go back to my original plan of using geoprocessing tools, cursors, and sets.

Thanks,

Nathan

Re: Spatially Enabled Data Frames for Spatial Analysis

DanPatterson_Retired — Thu, 12 Sep 2019 02:07:03 GMT

If you examine the code, arcpy or shapely or python or all. See line 123 on

C:\YourArcGISProInstallPath\bin\Python\envs\arcgispro-py3\Lib\site-packages\arcgis\geometry\_types.py

If you have Pro installed, it will use what is available (arcpy, shapely, pure python... depends). Lots of json stuff, but the code is pretty easy to follow. Some are pure python implementations (eg convex hull). Worth a look through the code base, but if you are looking something miraculously different then you might be disappointed.

It isn't so much what is offered, but where it can be used and jupyter notebook and jupyter lab do have some interesting stuff. I do find pandas a bit of a bloat when a lot of the functionality can be more readily implemented in numpy, but it definitely offers a good introduction to integrating commercial and open source analysis. It is generally a well-supported package geared to particular uses.

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Spatially Enabled Data Frames for Spatial Analysis

Re: Spatially Enabled Data Frames for Spatial Analysis