Advanced Spatial Analysis with QGIS Training Course

Participants can select, transform and verify coordinate reference systems for analytical tasks.

• Geographic and projected coordinate reference systems compared for analytical use
• EPSG codes, datums and coordinate transformations interpreted within QGIS
• Distance and area distortion assessed before proximity and overlay calculations
• Project CRS selection based on study extent and required measurements
• On-the-fly reprojection distinguished from permanent vector and raster reprojection
• Coordinate accuracy checks using control points, metadata and visual comparison

Participants can prepare spatial datasets and document fitness for analytical use.

• Geometry validity checks identify self-intersections, duplicates and empty features
• Attribute profiling finds nulls, inconsistent categories and implausible values
• Topological rules define permitted overlaps, gaps and boundary relationships
• Repair tools correct invalid geometries and multipart feature structures
• Field Calculator standardises coded values, dates and derived indicators
• Metadata records capture source, currency, accuracy, lineage and usage restrictions

Participants can combine operational and spatial data using appropriate join and aggregation methods.

• Attribute joins matched through stable identifiers and documented key fields
• Spatial joins transfer counts, summaries and nearest-feature attributes
• Dissolve operations aggregate features using policy, administrative or service categories
• Group statistics calculate totals, rates, means and percentile measures
• Expressions build conditional indicators from operational and geographic attributes
• Aggregation bias examined through changing zones, scales and denominator choices

Participants can apply vector geoprocessing methods to identify spatial relationships and constraints.

• Fixed and variable buffers represent access thresholds and exclusion zones
• Intersect, union and difference operations identify shared and excluded areas
• Clip and extract tools prepare study-area datasets without altering sources
• Distance-to-nearest analysis measures exposure, access and service proximity
• Multi-ring buffers compare graduated influence zones around facilities
• Overlay outputs checked for sliver polygons, duplicates and boundary artefacts

Participants can calculate routes, catchments and accessibility measures using network datasets.

• Network topology requirements for connected routes, turns and travel costs
• Shortest-path analysis compares distance, travel time and impedance values
• Service areas delineate reachable populations within defined travel thresholds
• Origin-destination matrices quantify access between demand and supply locations
• Network constraints model one-way streets, closures and restricted links
• Accessibility indicators combine travel cost with population and facility capacity

Participants can assess point distributions, sampling approaches and clustering evidence.

• Random, systematic and stratified sampling designs matched to survey objectives
• Kernel density estimation visualises concentration patterns from point observations
• Nearest-neighbour analysis tests clustering against complete spatial randomness
• Quadrat counts compare observed event distributions across equal-area cells
• Point intensity interpreted against population, exposure and reporting bias
• Sampling frames documented with inclusion rules, coverage and positional accuracy

Participants can identify spatial autocorrelation and statistically significant local patterns.

• Spatial weights matrices define neighbour relationships and distance decay
• Global Moran's I measures overall spatial autocorrelation in mapped indicators
• Local Moran's I identifies high-high, low-low and spatial outlier areas
• Getis-Ord Gi star detects statistically significant hotspot and coldspot clusters
• Rate standardisation controls population denominators before area comparison
• Multiple testing and modifiable areal unit limitations reported clearly

Participants can prepare raster surfaces and perform cell-based spatial analysis.

• Raster resolution, extent, alignment and NoData values assessed before analysis
• Resampling methods compared for categorical, continuous and imagery datasets
• Raster calculator expressions combine cells using conditional and arithmetic logic
• Reclassification converts continuous surfaces into decision-relevant categories
• Cell statistics summarise multiple rasters through minimum, mean and range
• Raster alignment ensures compatible grids before map algebra operations

Participants can derive terrain variables and interpret their operational implications.

• Digital elevation model sources assessed for resolution, datum and artefacts
• Slope and aspect derived for construction, erosion and access assessment
• Hillshade parameters selected to reveal terrain form without misleading relief
• Curvature measures identify ridges, valleys and drainage-relevant terrain features
• Flow direction and accumulation model surface-water movement across terrain
• Watershed delineation defines upstream contributing areas from outlet points

Participants can construct transparent suitability models from multiple spatial criteria.

• Suitability criteria derived from policy requirements, constraints and stakeholder priorities
• Boolean overlay distinguishes mandatory exclusions from eligible candidate areas
• Weighted linear combination combines standardised criteria into suitability scores
• Analytic Hierarchy Process compares criterion priorities through pairwise judgments
• Fuzzy membership functions represent gradual suitability and uncertain thresholds
• Sensitivity analysis tests how weight changes alter priority locations

Participants can prepare satellite imagery and derive indices for land-surface analysis.

• Sentinel-2 and Landsat band structures selected for analytical objectives
• Cloud masks and scene quality flags remove unsuitable image pixels
• Band composites combine visible, near-infrared and shortwave infrared information
• NDVI calculation estimates vegetation greenness from red and near-infrared bands
• NDBI and NDWI distinguish built surfaces and open-water features
• Reflectance, radiance and atmospheric correction concepts interpreted for image comparison

Participants can classify imagery and report the reliability of land-cover outputs.

• Training samples selected across representative land-cover spectral signatures
• Supervised classification assigns pixels using labelled reference observations
• Random forest classification parameters tuned for stable thematic outputs
• Post-classification filtering removes isolated pixels and mapping noise
• Confusion matrices calculate producer, user and overall classification accuracy
• Independent validation samples separate model training from performance assessment

Participants can compare spatial conditions over time and report meaningful change.

• Time-enabled layers organise observations using standardised date and time fields
• Temporal controller settings animate changing events, assets and environmental observations
• Before-and-after raster comparison identifies land-cover and surface-condition change
• Change detection thresholds distinguish meaningful variation from measurement noise
• Time-series summaries reveal seasonal patterns, trends and anomalous periods
• Consistent extents, classifications and denominators support valid temporal comparison

Participants can interpolate point measurements and evaluate prediction uncertainty.

• Interpolation suitability assessed through sampling density, distribution and spatial dependence
• Inverse distance weighting estimates surfaces from nearby measured observations
• Ordinary kriging models spatial autocorrelation through fitted variograms
• Variogram range, sill and nugget interpreted for interpolation decisions
• Cross-validation compares predicted values against withheld measurement locations
• Prediction uncertainty mapped alongside interpolated estimates for responsible interpretation

Participants can use spatial SQL to query, summarise and prepare database-held GIS data.

• PostGIS geometry types, SRIDs and spatial indexes support efficient queries
• ST_Intersects and ST_Within filter features using spatial relationships
• ST_Buffer and ST_Distance calculate proximity measures within SQL queries
• ST_Union and ST_Area support polygon aggregation and area summaries
• Spatial indexes examined through query plans and selective filtering
• QGIS database connections manage PostGIS layers, queries and saved views

Participants can automate repeatable QGIS geoprocessing workflows using graphical models.

• QGIS Model Designer chains inputs, algorithms and outputs into repeatable workflows
• Model parameters expose study areas, thresholds and source layers safely
• Conditional branches apply alternative processing paths based on input values
• Batch processing runs standard tools across multiple datasets and periods
• Output naming rules preserve lineage, dates and analytical version control
• Model documentation records purpose, dependencies, parameters and expected outputs

Participants can adapt Python processing scripts for controlled GIS automation.

• PyQGIS console access inspects layers, fields and project properties
• Processing algorithm calls automate buffering, clipping and raster calculations
• Python variables and loops process repeated files, regions and scenarios
• Error handling records failed operations and missing input data
• Script parameters separate reusable logic from project-specific values
• Version-controlled scripts preserve tested procedures and change history

Participants can communicate spatial findings, uncertainty and recommendations for decision makers.

• Map purpose determines projection, extent, scale, symbology and annotation choices
• Classification methods compare equal interval, quantile and natural breaks outputs
• Colour schemes distinguish sequential, diverging and categorical data relationships
• Uncertainty annotations identify incomplete coverage, confidence ranges and data limitations
• Layout templates standardise titles, legends, sources, dates and coordinate information
• Analytical narratives connect evidence, interpretation, limitations and recommended actions

Participants can build, review and present a complete documented spatial-analysis project addressing a defined decision question.

• Capstone brief defines decision question, users, constraints and acceptance criteria
• Source datasets prepared through documented quality checks and coordinate verification
• Analytical workflow combines vector, raster, network or statistical methods appropriately
• Processing model records repeatable steps, parameters and intermediate outputs
• Validation evidence tests results against reference data and stated assumptions
• Decision map and technical report communicate findings, uncertainty and recommendations