Postal address clean-upTwo unusual FME Workbench applications

Andrew Zolnai for

Mouvement Démocrate Europe du Nord

The premise

• Help French 2017 presidential / parliamentary election campaign

• Using nationbuilder.com for a London UK based campaign

• Liste Electorale Consulaire is structured, or is it?

• Ergo normalise 4 address columns to upload…

Standards? What standards?

Nothing new here…

Regex on steroids

• 1Spatial hosted Safe FME World Tour 2015 leg in London• scrape 4 years worth of playlists and tracks off the StrayFM website • and categorise and rank the most played artists and tracks

• Unusual use of FME Workbench for non-spatial data

• Similar exercise with help from Safe• StringSearcher – search address components• AttributeSplitter – split them into similar parts• AttributeManager – re-order into one schema

So what’s going on?

• Get the first matches of address strings in the 4 address fields

• If string is empty then assign the next address string to it

• Country name is constant last string

• Build normalised string sets backward from it

But << drum roll >>

• FeatureMerger works on non-spatial data too!

1. Initial load:• find rejected addresses

• repeat the procedure if possible

2. Ongoing updates:• find the new entries as updated lists are received

• repeat the procedure on the “delta” only

And what did we get?

• Metadata… metadata… metadata… ……..

• 15Mb CSV list with verbose 30Mb PDF

• Resulted in 10Mb cleaned up CSV list

• Uploaded clean address base into nationbuilder.com