I have been given a large spreadsheet of sample data where each point coordinate represents the end of the sampling extent. I am tasked to represent these points as a sampling extent of 100' x 8' with the point being the end. The spreadsheet has from and to directions (EW - from East to West) so that I have the direction that the polygon should be oriented.

I have over a thousand records to create polygons for so manual digitizing is not really an option for the timeline given.

Anyone know of any tools that can create a polygon based on a point location and extent indicators? Or string of tools? Am I out of luck for automation?

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    Do you know a little about Python? You could define boundary points based on source point and then create polygon, something like in this qestion, no matter this is lines: gis.stackexchange.com/questions/34647/… – david_p Oct 21 '15 at 16:33

was able to figure this one out with the help of a coworker, so in case anyone is interested here was the workflow:
1. Select a set of points that have the same orientation (eg EW)
2. Copy / Paste points and then Move 100feet in the direction of the orientation (ex direction of EW would mean I move the point E or -100feet)
3. Repeat steps 1 and 2 for remaining orientations
4. Use point to line tool and use a unique field ID (in this case I had a tagID that identified each point set) in the Line_field.
5. Once the lines are created for each set of points use the Buffer tool to create a 8' buffer with flat ends.
This will give the 100' x 8' rectangle. My model includes a few other steps for generating additional data, but those are the basic steps for creating a polygon with only one point input.


Not sure about the solution in ModelBuilder but there are many ways to do that in Python.

First, you need to read the records using one of the excel libraries or export it to text and loop over the text file. Then create geometries - quadrangles based on your requirements and write them into a feature class

You can also look at Shapely library for creating polygons and ogr/gdal for persisting the data.

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