Pre UN migration data cleanup workflows

[alexccruz2]

Hello all,

Are there any documented, proven workflows to help check water utility data for errors before the UN migration, such as floating and stacked points, overlapping and dangling lines, etc.?

Thank you.

-Alex C

[RobertKrisher]

Look at the first few videos and articles on this reading list for an explanation of how to approach this: ArcGIS Utility Network migration reading list

If you use the migration wizard to create an initial model, it will show you all the topology errors you need to fix in your source data. Once you've done that, you can then rerun the migration, create a controller for your system, and look for disconnected elements. There are quite a few articles that talk about how to do quality assurance on topology errors in and subnetworks, you can find them in industry specific reading lists.

You can find the collection of reading lists in the Getting started with the utility network page.

[alexccruz2]

Thank you for the response!

Do any workflows exist that can enable an analyst to clean the data before creating an initial model?

Alex

[RobertKrisher]

It should take you less than an hour to set up the wizard to migrate your data to a generic utility network (I've seen it done in as little at 10 minutes). Once you've done this you will get a list of topology errors from the utility network that enforce all its constraints with your data (stacked point features, self-intersecting lines, etc). You can then take these errors, overlay them on your source data, and correct them there.

Sure you could create a workbench or some data reviewer checks to look at your data based off some assumptions about how your data might be migrated, but if there's already a quick and easy tool that can do most of this for you, included in ArcGIS Pro, why not start with that?

Once you've worked through all the initial set of topology errors reported by the tool, then you could consider whether you want to migrate your data into one of the industry standard models to see how well your data matches those standards, or whether you want to build out your own configuration based on your existing model. This is covered in the resources I shared above.