Advanced QGIS Mapping and Data Analysis Training Course

Participants can acquire, inspect and connect diverse spatial data sources while preserving usable data structures.

• Vector, raster, database, and web service sources assessed before ingestion
• GDAL/OGR import options preserve geometry types, encodings, and field names
• Delimited text, GeoPackage, FileGDB, and GeoJSON loading workflows configured
• OGC WMS, WMTS, WFS, and WCS services connected with authentication
• STAC catalog searches filter imagery by footprint, date, and cloud cover
• Data provenance recorded through layer metadata, source URLs, and licences

Participants can design spatial data structures and apply validation controls before analysis or editing.

• Attribute field types, constraints, defaults, and aliases designed for editing
• Domain values enforced with value maps, relations, and form widgets
• Normalized lookup tables linked through primary keys and foreign keys
• Geometry validation identifies duplicate vertices, self-intersections, and invalid rings
• Topology rules prevent gaps, overlaps, dangles, and unintended multipart features
• GeoPackage versus PostGIS selection based on concurrency, scale, and governance

Participants can manage precise multi-layer edits using controlled forms, constraints and review workflows.

• Snapping tolerances, tracing, and topological editing configured for precise capture
• Advanced digitising tools apply perpendicular, tangent, and distance constraints
• Transaction groups coordinate multi-layer edits within shared database connections
• Default values populate audit fields using expressions and project variables
• Relation reference widgets support parent-child record maintenance during editing
• Versioned editing workflows define review, approval, and rollback responsibilities

Participants can derive, query and aggregate spatial information using QGIS expressions and PostGIS SQL.

• QGIS expressions calculate conditional fields, arrays, dates, and text transformations
• Virtual fields and field calculator logic documented for repeatable derivations
• Spatial joins transfer attributes using contains, intersects, and nearest predicates
• Aggregate functions summarise grouped records through relation and overlay contexts
• Selection expressions isolate records using NULL handling, regular expressions, and variables
• PostGIS SQL queries combine CTEs, window functions, and spatial predicates

Participants can select and evaluate vector geoprocessing methods for defined decision questions.

• Buffer, dissolve, clip, intersect, and difference operations matched to decisions
• Overlay outputs checked for sliver polygons, multipart features, and attribute loss
• Distance analysis distinguishes planar, ellipsoidal, network, and cost-distance measures
• Voronoi polygons, convex hulls, and minimum bounding geometries characterise coverage
• Spatial indexes and processing parameters improve performance on large datasets
• Algorithm history records inputs, parameters, software versions, and output locations

Participants can build reusable QGIS models that standardise multi-step spatial processing.

• Processing Modeler chains inputs, algorithms, and outputs into reusable workflows
• Model parameters expose distances, fields, and thresholds for controlled reuse
• Conditional branches manage empty selections, optional layers, and alternative outputs
• Batch processing applies standardised geoprocessing across multiple areas and dates
• Graphical model documentation explains dependencies, assumptions, and expected outputs
• Model outputs use deterministic names, folders, and overwrite controls

Participants can assess spatial patterns and communicate the limits of statistical interpretation.

• Point pattern analysis distinguishes clustering, dispersion, and random spatial distributions
• Kernel density estimation selects bandwidths, cell sizes, and output extents
• Global Moran's I tests spatial autocorrelation and interpretation limits
• Local indicators identify hotspots while controlling interpretation of multiple tests
• Standard deviation ellipses describe directional distribution and geographic dispersion
• MAUP, ecological fallacy, and boundary effects qualify analytical conclusions

Participants can model travel networks and assess access to services, facilities and assets.

• Network graphs model nodes, edges, impedance, restrictions, and turn costs
• Service areas estimate reachable populations within defined travel-time thresholds
• Shortest-path routing compares distance, time, and constrained route scenarios
• Origin-destination matrices quantify accessibility between facilities and demand points
• Location-allocation criteria rank candidate sites against coverage and demand objectives
• OpenStreetMap network data assessed for completeness, directionality, and restrictions

Participants can prepare raster data and build transparent raster-based suitability models.

• Raster data models distinguish continuous surfaces, categorical classes, and bit depth
• GDAL warping aligns pixel grids, extents, resolutions, and coordinate systems
• Resampling methods compare nearest neighbour, bilinear, cubic, and average techniques
• Raster Calculator expressions derive suitability surfaces from weighted criteria
• Reclassification schemes document thresholds, class meanings, and NoData treatment
• Zonal statistics summarise raster values within administrative and operational boundaries

Participants can analyse multispectral imagery using indices, classification and accuracy assessment methods.

• Multispectral band combinations reveal vegetation, water, built surfaces, and burn scars
• NDVI, NDWI, and NBR indices calculated with sensor-specific band assignments
• Cloud masks, scale factors, and reflectance units checked before analysis
• Supervised classification uses training samples, feature bands, and validation data
• Confusion matrices report producer accuracy, user accuracy, and overall accuracy
• Change detection compares co-registered imagery while separating seasonal variation

Participants can derive terrain and hydrological indicators from elevation data for operational analysis.

• Digital elevation models support slope, aspect, hillshade, and curvature derivation
• Hydrological conditioning fills sinks and resolves drainage path discontinuities
• Flow direction and accumulation identify catchments, streams, and drainage divides
• Viewshed analysis defines observer height, target height, and surface assumptions
• Terrain-derived hazard indicators combine slope thresholds, land cover, and proximity
• Vertical datums, resolution, and artefacts qualified before engineering interpretation

Participants can structure, analyse and visualise time-enabled geographic information.

• Temporal controller animates dated layers using start, end, and duration fields
• Time-enabled data structures preserve event timestamps, intervals, and time zones
• Rolling summaries calculate monthly changes, cumulative values, and seasonal baselines
• Temporal joins align observations with boundary versions and reference periods
• Space-time visualisation distinguishes event occurrence from reporting-date artefacts
• Reproducible date filters use project variables, expressions, and parameter inputs

Participants can produce clear, purpose-driven map products and automated map series.

• Visual hierarchy assigns symbol weight according to map purpose and audience
• Data classification compares equal interval, quantile, Jenks, and manual breaks
• Colour schemes address ordered data, categorical distinction, and colour-vision deficiency
• Rule-based symbology applies scale ranges, expressions, and priority ordering
• Label placement uses buffers, callouts, obstacles, and data-defined properties
• Atlas-driven layouts generate map series from indexed features and templates

Participants can prepare print, digital and web-service map outputs for controlled distribution.

• Print layouts control grids, legends, scale bars, north arrows, and credits
• Data-defined layout elements update titles, dates, statistics, and identifiers
• Export settings balance PDF, SVG, PNG, DPI, fonts, and file size
• QGIS Server project configuration publishes WMS, WFS, and WMTS endpoints
• Web map styling considers scale dependency, caching, and client rendering limits
• OGC service capabilities documented with layer names, CRS, and access constraints

Participants can manage spatial data in PostGIS and improve query performance for larger datasets.

• PostGIS schemas separate source data, staging tables, outputs, and permissions
• Geometry types, SRIDs, constraints, and GiST indexes support reliable spatial tables
• Spatial SQL performs buffers, overlays, proximity searches, and aggregations server-side
• Query plans reveal sequential scans, index use, and expensive spatial joins
• Database views publish controlled analytical outputs without duplicating source records
• Role-based permissions protect sensitive locations, personal data, and edit rights

Participants can automate common QGIS processing, validation and output tasks with PyQGIS.

• PyQGIS scripts access layers, features, geometries, fields, and project settings
• Python functions automate attribute updates, exports, validations, and map production
• Processing toolbox algorithms run from scripts with named parameter dictionaries
• Exception handling, logging, and assertions make automated workflows auditable
• Reusable script structure separates configuration, processing logic, and output reporting
• Virtual environments and package dependencies documented for repeatable deployment

Participants can document quality, uncertainty, lineage and governance requirements for GIS outputs.

• ISO 19115 metadata elements record lineage, quality, extent, and distribution
• ISO 19157 quality measures define completeness, positional accuracy, and consistency
• Validation checks compare expected counts, ranges, domains, and topology rules
• Sampling plans distinguish acceptance testing, stratified samples, and targeted inspection
• Uncertainty statements communicate scale limits, temporal currency, and analytical sensitivity
• Data protection assessments identify location privacy, access controls, and retention needs

Participants can plan spatial analysis projects with clear requirements, risks and review criteria.

• Decision questions translated into spatial hypotheses, indicators, and acceptance criteria
• Analysis plans specify inputs, CRS, methods, assumptions, outputs, and reviewers
• Stakeholder requirements prioritised through user stories, map specifications, and constraints
• Risk registers address missing data, bias, processing failures, and misinterpretation
• Reproducibility packs organise data dictionaries, models, scripts, and metadata
• Peer review criteria assess correctness, traceability, cartography, and decision relevance

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

• Project brief defines decision context, users, data sources, and success measures
• Data preparation applies documented CRS, schema, validation, and provenance controls
• Analytical workflow combines spatial SQL, processing models, and appropriate raster methods
• Map products communicate findings through tested symbology, layouts, and uncertainty notes
• Technical documentation records assumptions, parameters, limitations, and reproducibility instructions
• Peer review tests outputs against acceptance criteria before stakeholder presentation