GIS Data Quality and Validation Training Course

Participants can write measurable data specifications and acceptance criteria for a spatial dataset.

• Feature catalogue structure: classes, attributes, domains, relationships and identifiers
• Minimum data specification sections for internal and supplier deliveries
• Quality requirements expressed as measurable thresholds and tolerances
• Acceptance criteria separating critical defects from tolerable minor defects
• Business rules translated into testable spatial and attribute constraints
• Traceability matrix connecting requirements, validation tests and evidence

Participants can identify coordinate-reference risks and assess their effect on positional quality.

• Geographic versus projected coordinate reference systems and measurement consequences
• Datum transformations, grid shifts and positional displacement sources
• Scale, resolution and capture method effects on positional accuracy
• Horizontal and vertical accuracy statements using stated confidence levels
• Root mean square error calculation from independent check points
• Detecting coordinate anomalies through extent, units and overlay inspection

Participants can profile a GIS dataset and prioritise defects for investigation.

• Dataset inventory capturing source, owner, currency, format and intended use
• Attribute profiling with null counts, frequencies, ranges and outliers
• Geometry profiling for feature counts, types, extents and coordinate ranges
• Duplicate detection using identifiers, spatial coincidence and attribute matching
• Missing-value patterns distinguished from valid unknown or not-applicable values
• Defect prioritisation using severity, prevalence, impact and remediation effort

Participants can measure omission and commission errors against a defined reference source.

• Completeness measures: excess items, missing items and required attributes
• Reference-frame selection for roads, assets, addresses and land parcels
• Omission and commission rates calculated from matched feature inventories
• Coverage gaps identified through grids, administrative units and service areas
• Temporal completeness assessed against update cycles and event dates
• Completeness exceptions documented with ownership and correction deadlines

Participants can configure and interpret topology tests for common vector data models.

• Topology rules for overlaps, gaps, dangles, self-intersections and duplicates
• Polygon coverage validation for parcel, zoning and land-use datasets
• Network connectivity checks for roads, pipes, cables and drainage
• Referential integrity between spatial features, tables and related records
• Coded-value domains, subtypes and contingent values for attribute validity
• False-positive review and exception handling for legitimate topology conditions

Participants can design validation controls for attribute completeness, validity and consistency.

• Mandatory-field checks based on feature type and operational status
• Range, format and pattern validation using regular expressions
• Cross-field rules comparing dates, classifications, quantities and statuses
• Controlled vocabularies, lookup tables and code-list governance
• Duplicate record resolution using deterministic and probabilistic matching methods
• Attribute correction logs preserving original values, reasons and approvers

Participants can plan and report a positional accuracy assessment using independent reference data.

• Independent reference data requirements for positional accuracy testing
• Check-point selection across terrain, density and feature categories
• Displacement measurement using perpendicular distance and coordinate residuals
• RMSE, mean error and percentile statistics for positional results
• NSSDA reporting concepts and confidence-based accuracy statements
• Positional accuracy limitations for imagery-derived and digitised features

Participants can select and justify sampling methods for GIS quality inspections.

• Census inspection versus sample inspection based on risk and cost
• Simple random, systematic, stratified and cluster sampling approaches
• Stratification by geography, feature class, source and capture period
• Sample-size considerations using confidence level, precision and expected error
• Random-point generation and sample-frame construction in GIS
• Bias controls for convenience samples, accessible areas and known defects

Participants can calculate and interpret quality measures for sampled spatial datasets.

• Proportions, rates and confidence intervals for sampled defect estimates
• Producer's accuracy, user's accuracy and overall classification accuracy
• Confusion matrices built from classified maps and reference observations
• Kappa limitations and alternative interpretation of agreement results
• Acceptance sampling decisions using stated defect thresholds and confidence
• Uncertainty communication without overstating statistical precision

Participants can validate a thematic classification using reference samples and accuracy evidence.

• Reference-sample independence from training data for unbiased accuracy estimates
• Class definitions aligned with image resolution and spectral separability
• Ground truth, field observations and high-resolution imagery as references
• Confusion-matrix interpretation for omission and commission classification errors
• Area-adjusted accuracy estimation for unequal class sampling designs
• Change-detection validation using temporal alignment and stable reference areas

Participants can assess quality risks in raster, elevation and gridded datasets.

• Raster resolution, resampling and pixel alignment effects on analysis
• NoData values, masks and edge artifacts in gridded products
• Digital elevation model checks for sinks, spikes and striping
• Surface accuracy assessment using surveyed elevations and residual statistics
• Radiometric consistency checks across mosaics, scenes and acquisition dates
• Raster metadata recording resolution, processing history and known limitations

Participants can document dataset lineage, quality evidence and use limitations in metadata.

• ISO 19115 metadata elements supporting discovery, evaluation and reuse
• Lineage statements recording sources, transformations, dates and responsible parties
• Process-step documentation for geocoding, editing, conflation and generalisation
• Quality-report structure linking measures, results, scope and evaluation method
• Usage constraints, disclaimers and distribution statements based on evidence
• Metadata maintenance triggers following updates, corrections and source replacement

Participants can configure repeatable validation checks using QGIS processing and data-management tools.

• QGIS geometry checker rules for invalid geometries, overlaps and gaps
• Field calculator expressions for null, range and format checks
• Processing Modeler workflows combining selections, joins and output reports
• Virtual layers and SQL queries for cross-table consistency tests
• GeoPackage constraint options for domains, uniqueness and required values
• Validation outputs organised into defect layers, summaries and review queues

Participants can configure repeatable validation checks using ArcGIS Pro quality-control tools.

• ArcGIS Pro Data Reviewer checks for geometry and attribute conditions
• Geodatabase domains, subtypes and attribute rules for prevention controls
• Topology datasets configured with feature-class ranks and rule exceptions
• ModelBuilder sequences for repeatable import, validation and reporting
• Arcade expressions for calculation, constraint and validation rules
• Reviewer results managed through status, correction and verification fields

Participants can plan controlled remediation while preserving data lineage and auditability.

• Correction priorities set by criticality, downstream dependency and safety impact
• Standard operating procedures for edit review, approval and quality rechecks
• Conflation methods for reconciling authoritative and operational feature records
• Geocoding remediation for unmatched, low-score and ambiguous address records
• Versioning and change logs protecting approved baseline datasets
• Root-cause analysis using five whys and cause-and-effect diagrams

Participants can establish roles, controls and supplier requirements for sustained GIS data quality.

• Data owner, steward, custodian and producer responsibilities defined clearly
• Quality control versus quality assurance in spatial data production
• Supplier statements of work with deliverables, tests and acceptance gates
• Service-level measures for timeliness, correction turnaround and defect recurrence
• Issue registers, escalation routes and approval authority for release decisions
• Periodic quality monitoring using dashboards, scorecards and trend analysis

Participants can prepare a decision-ready report that states quality findings and release recommendations.

• Data quality report structure for technical reviewers and decision makers
• Quality indicators displayed through scorecards, maps, tables and defect summaries
• Release recommendations: accept, accept with conditions, remediate or reject
• Risk statements connecting quality limitations to intended analytical uses
• Corrective-action plans with owners, dates, priorities and verification criteria
• Stakeholder briefings that distinguish evidence, assumptions and unresolved issues

Participants can build, review and present a complete GIS data quality validation pack for a defined operational dataset.

• Capstone dataset brief interpreted into scope, users and quality requirements
• Data specification completed with rules, thresholds and acceptance criteria
• Profiling, topology and attribute checks run against supplied spatial data
• Sampling plan and accuracy evidence documented with stated limitations
• Quality report prepared with defect register and corrective-action recommendations
• Peer review and release-decision presentation defended using validation evidence