GIS projects often require information from several sources before meaningful analysis can begin. Parcel, assessment, tenure, transit, infrastructure, and environmental datasets may use different schemas, naming conventions, coordinate systems, and update schedules.

Manually cleaning and combining these datasets can be slow, difficult to repeat, and vulnerable to inconsistent processing. I developed this representative FME workflow to demonstrate how multi-source spatial data can be transformed into standardized, analysis-ready outputs through a controlled and repeatable ETL process.

The workflow is designed so source information can be replaced or updated without rebuilding the entire process manually.

The workflow connects spatial and tabular datasets representing parcels, assessment information, land tenure, transportation access, and environmental context.

Initial processing prepares the data for integration by:

• Removing unnecessary fields

• Renaming and standardizing attributes

• Converting field types where required

• Filtering null, incomplete, or unusable records

• Reviewing coordinate systems and spatial alignment

• Identifying invalid or empty geometry

• Standardizing values used across multiple sources

These early checks prevent inconsistent source information from being carried into the final analytical dataset.

After the source datasets are cleaned, the workflow applies a series of spatial and attribute-based transformations.

The process includes:

• Joining tabular information to spatial features

• Overlaying parcels with area-based environmental or administrative layers

• Transferring contextual attributes through spatial relationships

• Filtering records according to project criteria

• Merging compatible outputs from multiple branches

• Creating calculated fields needed for analysis and reporting

• Routing rejected or incomplete records for review

• Standardizing the final schema before writing the outputs

Transformers are organized into logical sections so the workflow can be reviewed, maintained, and updated efficiently.

The completed workflow produces:

• A repeatable FME spatial ETL workspace

• Cleaned and standardized GIS layers

• Integrated parcel and contextual information

• Analysis-ready geodatabase outputs

• Separate QA/QC or rejected-record outputs

• Consistent fields and classifications

• Data suitable for ArcGIS Pro, web GIS, dashboards, and reporting

• A workflow that can be adapted for scheduled processing in FME Flow

The project demonstrates how spatial ETL can reduce manual processing, improve consistency, and make complex multi-source data easier to maintain and reuse.