QGIS Spatial Computing Training Course

Participants can select, assign and transform coordinate reference systems accurately.

• Coordinate reference systems, datums, ellipsoids and transformation accuracy principles
• Assign missing CRS metadata without altering stored coordinate values
• Reproject layers using appropriate target CRS and transformation operations
• Select projected CRS for distance, area and directional analysis
• Diagnose layer misalignment caused by incorrect CRS assignment or transformation
• Check axis order and units in OGC web-service layers

Participants can acquire, import and integrate geographic datasets from common sources.

• Acquire authoritative vector data through WFS, WMS, WMTS and APIs
• Import CSV coordinates, Excel tables, GeoJSON, KML and GeoPackage datasets
• Configure joins and relations using stable identifiers and cardinality checks
• Query OpenStreetMap features with QuickOSM and Overpass query parameters
• Assess positional accuracy, completeness, temporal currency and attribute reliability
• Record source citations and redistribution constraints in project metadata

Participants can create and maintain valid vector datasets for reliable analysis.

• Create GeoPackage layers with controlled geometry types and field definitions
• Digitise points, lines and polygons using snapping and tracing settings
• Apply topology rules to prevent gaps, overlaps, duplicates and invalid geometries
• Validate geometries with QGIS checks and GEOS repair tools
• Use domains, default values and constraints for reliable attribute capture
• Track edits through transaction groups, backups and change documentation

Participants can prepare reliable attribute data for spatial and statistical analysis.

• Design attribute schemas using atomic fields, coded values and stable keys
• Filter features with expressions, selection sets and temporal conditions
• Calculate new fields using QGIS expressions, aggregates and conditional logic
• Summarise records through grouping, statistics panels and pivot-style tables
• Normalise category labels and dates before spatial or statistical analysis
• Identify missing values, outliers and duplicate identifiers systematically

Participants can produce readable, accurate and audience-appropriate thematic maps.

• Classify thematic data using equal interval, quantile and Jenks methods
• Build rule-based symbology from attribute expressions and scale ranges
• Apply data-defined overrides for symbols, labels, rotation and transparency
• Design labels with priority, obstacles, callouts and scale-dependent visibility
• Choose colour schemes that preserve hierarchy, contrast and accessibility
• Create print layouts with map frames, legends, grids and dynamic text

Participants can apply vector geoprocessing methods to answer location-based questions.

• Buffer features using fixed, variable-distance and dissolved output options
• Overlay polygons through intersection, union, difference and symmetric difference operations
• Clip, dissolve and multipart-to-singlepart layers for analysis-ready geometries
• Calculate area, length, perimeter and centroid measures in appropriate units
• Use spatial joins to transfer attributes by location or nearest feature
• Compare candidate sites using proximity, containment and exclusion criteria

Participants can prepare, calculate and extract information from raster datasets.

• Interpret raster bands, cell size, extent, nodata and bit depth
• Load GeoTIFF, cloud-optimised GeoTIFF and imagery service raster sources
• Resample and align raster grids using nearest neighbour and bilinear methods
• Calculate raster values with map algebra and Raster Calculator expressions
• Extract values by mask, points, polygons and sampled transects
• Build raster mosaics and virtual rasters with consistent resolution

Participants can derive and assess land-cover information from multispectral imagery.

• Read multispectral band combinations for vegetation, water and built surfaces
• Compute NDVI, NDWI and NDBI using calibrated band-ratio formulas
• Apply reflectance scaling, cloud masks and quality-assurance bands before indices
• Classify land cover using supervised training samples and Random Forest
• Assess classification accuracy with confusion matrices, reference samples and kappa
• Detect change through image differencing, post-classification comparison and thresholds

Participants can derive terrain evidence and build weighted suitability models.

• Derive hillshade, slope, aspect and contours from digital elevation models
• Model drainage direction, flow accumulation and watershed boundaries from DEMs
• Identify flood exposure using elevation thresholds, proximity and terrain derivatives
• Estimate visibility with viewshed analysis and observer-height parameters
• Apply raster reclassification to suitability criteria and constraint surfaces
• Combine weighted criteria using multi-criteria evaluation and raster overlays

Participants can assess routes, catchments and service accessibility using network data.

• Prepare road networks with direction, impedance, turn and access attributes
• Calculate shortest paths using distance, time and custom cost fields
• Generate service areas for facilities under specified travel-time thresholds
• Locate demand clusters using point density, aggregation and catchment analysis
• Compare facility scenarios against population, risk and accessibility measures
• Interpret network results against data quality and modelling assumptions

Participants can query and manage spatial datasets stored in PostGIS.

• Connect QGIS to PostGIS using saved connections and authentication configurations
• Inspect schemas, geometry columns, primary keys and spatial indexes
• Write SQL selections with WHERE, CASE, GROUP BY and JOIN clauses
• Apply PostGIS ST_Intersects, ST_Buffer and ST_DWithin spatial predicates
• Create database views for reusable filtered and derived spatial datasets
• Assess query plans and indexes for large spatial tables

Participants can create repeatable QGIS geoprocessing workflows with documented parameters.

• Use Processing Toolbox providers, parameters, logs and output destinations
• Chain algorithms in Graphical Modeler for repeatable geoprocessing workflows
• Configure batch processing for multiple areas, dates and input layers
• Record algorithm versions, parameters and environment settings for reproducibility
• Handle temporary outputs, naming conventions and overwrite safeguards systematically
• Troubleshoot failed tools through logs, geometry checks and input validation

Participants can use introductory PyQGIS scripts to automate recurring QGIS tasks.

• Read and modify layers through PyQGIS project, vector and feature APIs
• Automate attribute updates with Python loops, expressions and field mappings
• Run native processing algorithms from scripts with explicit parameter dictionaries
• Build reusable scripts for imports, checks, exports and map production
• Manage paths, exceptions and messages for dependable script execution
• Package scripts with comments, inputs and documented dependencies

Participants can establish controls for spatial data quality, provenance and responsible use.

• Define acceptance criteria for geometry, attributes, CRS and completeness
• Run topology, duplicate, null and range checks before publication
• Maintain metadata using ISO 19115 elements and QGIS layer descriptions
• Apply versioning, naming conventions and retention rules to spatial assets
• Assess disclosure risks when mapping personal, health or sensitive locations
• Prepare reproducibility records covering sources, transformations and analytical decisions

Participants can publish maps and spatial outputs in suitable formats for stakeholders.

• Publish styled layers through QGIS Server, WMS and WFS services
• Create web maps using QGIS2Web exports and Leaflet-compatible outputs
• Export GeoPackage, GeoJSON, CSV, GeoTIFF and PDF deliverables appropriately
• Build Atlas reports driven by feature selections and layout expressions
• Set scale ranges, simplification and caching for responsive published maps
• Review outputs against audience needs, confidentiality and publication standards

Participants can scope a spatial analysis project around a defensible decision question.

• Frame analytical questions as decisions, criteria, evidence and spatial outputs
• Specify study boundaries, units of analysis, assumptions and success measures
• Select fit-for-purpose datasets, CRS, methods and validation evidence
• Plan workflow stages from acquisition through analysis, review and publication
• Estimate processing effort, dependencies, risks and review checkpoints
• Present findings with claims, uncertainty statements and decision recommendations

Participants can build, review and present a complete QGIS spatial analysis project for a defined decision question.

• Define a decision question and scope a complete QGIS analysis project
• Acquire, document and quality-check vector, raster and tabular project data
• Construct reproducible processing workflow combining spatial, raster or network methods
• Produce an analytical map, supporting outputs and documented methodological assumptions
• Conduct peer review using accuracy, reproducibility, cartographic and decision criteria
• Present findings, limitations and recommended actions to a stakeholder audience