I have a large citywide XL table with address and meter information that I broke out spatially by neighborhood association into 29 different feature classes to make usage maps. It was a fairly long process (geocoding, several spatial joins, etc) to get things all broken out.
Now I have an updated version of the original table with updated meter reads, and I'm looking for a way to automate populating the new meter reads into the individual feature classes. This may end up being a monthly job. I have a field I can join by, but I'm stuck as to how I can do this across 29 feature classes without repeating the process 29 times.
Is there a way I can use ModelBuilder or (fairly basic) python to run this process across multiple separate feature classes in one fell swoop?
1 Answer 1
Is there a reason to not merge your 29 different feature classes into one large feature with a field marked as "key" (spanning "01-29" or something rather) so that you can easily differentiate between the original classes? That way you could perform your join (by address or meter information) and use the modelbuilder or field calculator to perform your update in one step, rather than perform it recursively 29 times. You would still be able to selectively view your data by either select by attributes (using the "key" you created), spatially selecting a neighborhood (select by location and using a "boundary" created by dissolving the area of interest or creating a buffer) or using Definition Query limiting the displayed features to the area in question. I hope I've understood your question and this helps.