Learning Objectives

After completing this lesson, you’ll be able to:

• Use conditional attribute values in the Attribute Manager.
• Use conditional attribute values to map data according to complex conditions.

Resources

• Starting workspace
  • For Safe Software-hosted training courses, you can find this on your virtual machine here: C:\FMEData\Workspaces\TransformAttributes\flood-risk-project.fmw
• Complete workspace
  • C:\FMEData\Workspaces\TransformAttributes\flood-risk-project-complete.fmw
• DEM-Clipped.dem
  • C:\FMEData\Data\ElevationModel\DEM-Clipped.dem
• Addresses.gdb.zip
  • C:\FMEData\Data\Addresses\Addresses.gdb.zip
• CoastalZones.zip
  • C:\FMEData\Data\Boundaries\CoastalZones.zip

Exercise

Jennifer is working on a workspace to calculate the tsunami flood risk for all addresses in the city. The flood risk score combines closeness to the shoreline and elevation above sea level. It is on a scale from one to five (1-5) and is calculated using this table:

She has built the workspace so that each address has an elevation and distance from the shoreline. Now, she wants to use conditional values to calculate the flood risk score.

1) Open Starting Workspace

• Start FME Workbench (2026.1 or later).
• Open the starting workspace (C:\FMEData\Workspaces\TransformAttributes\exercise-flood-risk-project.fmw).
• Ensure Caching is enabled and run the workspace.
  • You will get an Unexpected Input dialog, but you can ignore it. FME displays this dialog when it reads features from the source dataset that lack a feature type defined on the canvas.
• To observe the data, inspect all three CoastalZones [GML] reader feature type caches and the AttributeRenamer's Output cache (Ctrl or ⌘-click to inspect multiple caches).

• This colored screenshot shows how the addresses fall within (and are assigned) a zone denoting their distance from the shoreline. They also possess an elevation value.
  • We must now use this information to assign a flood risk to each address.

2) Finish Configuring AttributeManager

• We already have an AttributeManager in the starting workspace after the AttributeRenamer.
• Double-click the AttributeManager to open its parameters.
• Ignore the existing attributes and scroll to the bottom of the dialog to find the FloodRisk attribute.
  • The starting workspace already has a partly-configured conditional value for FloodRisk, but we need to finish it.
• In the Attribute Value field for the FloodRisk attribute, click the drop-down arrow and choose Conditional Value.
  • A new dialog similar to a Tester/TestFilter transformer opens. It has fields for Test Condition and Output Value.

• The dialog already maps the conditions for where FloodRisk = 1 (the highest) through FloodRisk = 4 (the second-lowest). According to the table of calculations, FloodRisk = 1 can occur only where Zone = 100 AND Elevation <= 10. The Test uses the Boolean operator AND to ensure both conditions are true.
  • We need to finish configuring the dialog with the case for FloodRisk = 5.

• Double-click the cell in the Test column to the right of the gray Else If text to add a new Test.
  • Clicking opens a Tester-like dialog.

• Configure the Conditional Value Definition dialog as follows.
  • Remember to specify the output Value (5) below the Test Clauses area.

• Click OK to close this part of the dialog.
• The main Conditional Definition dialog is complete. It should look like the image below.
  • It is essential to keep these tests in the correct order; otherwise, a feature may pass the tests in the wrong order and receive a lower risk than expected.

• Click OK to accept the changes.
• The main AttributeManager dialog should now look like the image below.

3) Add Inspector

We would like to separate the data into layers for easier inspection. We can accomplish this with an Inspector transformer with a Group By setting.

• Add a single Inspector transformer connected to the AttributeManager's Output port.
• Open the Inspector parameters dialog.
• Check Group Processing and set Group By to the newly created FloodRisk attribute.
• Save and run the workspace.
• You should see each address colored to match its flood risk.
  • You can adjust the feature symbology in Display Control to produce a better result, like the image below.
  • You can also turn off each zone by clicking the checkboxes in Display Control to see which addresses are most/least at risk.

Congratulations. You have successfully used conditional values to filter and map features with a single transformer.

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