Our client presented us with a challenge of keeping two datasets in sync. One dataset was maintained by the GIS department and they are constantly in the process of updating the parcel boundaries. Sometimes these updates are small changes such as correcting the borders to match the property lines. Other times, these changes are significant. Significant changes can include merging parcels, splitting parcels or large corrections to the parcel shape. The other dataset is maintained by the maintenance department and it denotes which parcels they must go out and do a maintenance task on. This maintenance dataset was originally derived from the GIS dataset, but all synchronizations were being done manually. The main challenge is that if a parcel was previously a maintenance parcel and it has been merged or split, then a decision must be made on whether the new parcels are still considered maintenance parcels. This presentation will show how you can use the HausdorffDistanceCalculator transformer that is on the FME Hub, and some of the properties of the polygons to determine which changes are large enough to need to be reviewed. Anything that does not meet the threshold is automatically passed into the maintenance dataset, and anything that meets the threshold is passed into a “needs review” dataset. This immensely reduces the amount of data that the reviewer must look at and makes our client successful by saving them a ton of time.

1. Using the Hausdorff
distance to identify
significant changes in
polygon shapes
Justin Schweppe

2. Hello!
My name is Justin Schweppe, and I am a Senior Developer at 1898 &
Co. a part of Burns & McDonnell. I have used FME for the past 6 years
to make our clients successful!

3. A ton of time is wasted manually
reviewing GIS data. We can fix that.
This presentation will show how we were able to us the Hausdorff Distance to
save time for our client.

4. Today’s Problem
Our client had the arduous task of
maintaining two data sets. The GIS
department would make updates to the
boundaries of their parcels and the
maintenance department would have to
identify if these changes affected their
dataset. This was a difficult and time
consuming manual process.
Ease Data Fatigue

5. Automating
Change
Detection
How do we do that?
Our client tasked us to automate this
process
We needed to figure out a process of
automating this.
Minor changes could be ignored and
passed into as updates, but how do you
determine what is minor.
Using the difference between the area and
the perimeter was not enough to
accurately determine this. We needed
something that was a little more
sophisticated.

6. The Solution: Hausdorff distance
Felix Hausdorff introduced the Hausdorff distance in his book
Grundzüge der Mengenlehre in 1914. Informally, two sets are close in
the Hausdorff distance if every point of either set is close to some
point of the other set. The Hausdorff distance is the longest distance
you can be forced to travel by an adversary who chooses a point in
one of the two sets, from where you then must travel to the other set.
In other words, it is the greatest of all the distances from a point in one
set to the closest point in the other set.

7. This is perfect for our
application
However, it is a complicated formula and it might
take considerable time to write from scratch.
Fortunately FME has the great resource that is the
FME hub.

8. Putting it into Action
The only problem was our client was using an
older version of FME Server, so I did have to
backport the custom transformer.

9. Automating change detection
Slow difficult process that
impedes other efforts
The solution (often
includes FME Hub )
Fast automated process
that is simple to review

10. FME helps 1898 and co a part of Burns
and McDonnell make our clients
successful!!

11. Always check
the FME Hub
Often it will have a solution to your problem

12. Thank you!
jschweppe@burnsmcd.com