Domain 4: Professional Practice, Ethics, and Data Governance
Domain 4 tests professional practice: ethics, data licensing, privacy, quality assurance, metadata standards, and reproducible workflows. Questions ask what a GIS professional should do when data are sensitive, licenses conflict, or accuracy claims exceed evidence.
Ethics and privacy
Professionals owe accuracy, transparency, and avoidance of foreseeable harm. Sensitive locations-endangered species, critical infrastructure, crime victims-may require aggregation, buffering, or suppression before publication. Address-level data can enable re-identification; aggregation units must be large enough to protect individuals. Document any generalization and its effect on interpretation.
Licensing and standards
Licenses govern use, redistribution, derivatives, and attribution. Public availability does not equal unrestricted use; check share-alike, no-derivatives, and commercial terms. Metadata standards such as ISO 19115 and the FGDC Content Standard for Digital Geospatial Metadata define fields for identification, data quality, lineage, spatial reference, distribution, and constraints.
QA/QC and accuracy
Quality control checks geometry, attributes, completeness, consistency, and positional accuracy. Report accuracy with its method and confidence level-for example, horizontal RMSE from independent checkpoints, or 95% circular error. Do not report coordinates to more precision than the source supports. Version control and documented parameters make analyses reproducible.
Worked example
A requested map shows nest locations of a protected species. The record is public, but precise coordinates could invite disturbance. The analyst publishes a 10 km grid showing presence/absence, retains precise coordinates in a restricted internal layer, and documents the generalization in metadata and the map note. If a reviewer asks for exact points, the analyst explains the harm rationale and offers a controlled review under a data-use agreement.
Traps
- Assuming open data is license-free.
- Publishing precise locations of vulnerable people or resources.
- Reporting false precision beyond positional accuracy.
- Omitting datum transformations or tool parameters from metadata.
- Treating a visually clean map as evidence of data quality.