GIS Fundamentals and Applications Training Course

Participants can distinguish GIS data models and choose appropriate structures for spatial questions.

• Vector geometry types: points, lines, polygons, multipart features, and vertices
• Raster cells, bands, resolution, extent, and NoData value handling
• Attribute tables, unique identifiers, coded domains, and relational table joins
• Topology rules for gaps, overlaps, dangles, duplicates, and containment
• Represent discrete objects and continuous phenomena using suitable spatial models
• Define spatial relationships through adjacency, containment, intersection, and proximity

Participants can assess geospatial data sources and document their suitability for intended use.

• Assess authoritative datasets, open data portals, imagery archives, and field collections
• ISO 19115 metadata elements for lineage, extent, quality, constraints, and contacts
• Positional accuracy, attribute accuracy, completeness, currency, and logical consistency
• Compare spatial resolution, temporal frequency, licensing conditions, and collection methods
• Create a data inventory linked to project questions and acceptance criteria
• Record source limitations before combining datasets in analytical workflows

Participants can establish a structured GIS workspace and prepare data for analysis.

• Configure QGIS projects, layer groups, relative paths, and project properties
• Organise geodatabases, geopackages, raster folders, outputs, and documentation files
• Load shapefiles, GeoJSON, CSV files, GeoTIFFs, and web-service layers
• Set display order, symbology defaults, labels, scale ranges, and layer visibility
• Inspect geometry, attributes, CRS definitions, extent, and missing-value patterns
• Apply consistent naming conventions for datasets, fields, outputs, and map layouts

Participants can create accurate spatial layers and maintain usable attribute data.

• Create feature classes with geometry types, fields, aliases, and coded values
• Digitise assets, boundaries, routes, and land parcels from reference imagery
• Use snapping, tracing, vertex editing, and topology checks during capture
• Calculate fields using expressions, conditional logic, dates, and string functions
• Join external tables using stable identifiers and documented key fields
• Validate mandatory attributes, duplicate records, invalid geometry, and domain values

Participants can query, join and summarise spatial datasets using SQL-based methods.

• Relational database concepts: tables, primary keys, foreign keys, and indexes
• Write SQL selection queries using WHERE, LIKE, IN, BETWEEN, and NULL
• Filter records by date, category, value ranges, and data-quality conditions
• Join one-to-one and one-to-many tables without duplicating analytical records
• Summarise features by administrative area, service zone, or asset category
• Use spatial indexes to improve performance on large vector datasets

Participants can design maps that communicate spatial evidence clearly and accurately.

• Apply visual hierarchy through symbol size, colour, contrast, labels, and whitespace
• Choose nominal, ordinal, interval, and ratio symbols for attribute types
• Classify thematic data using equal interval, quantile, natural breaks, and standard deviation
• Design legends, titles, scale bars, north arrows, sources, and disclaimers
• Use map projections and scales appropriate to the intended communication purpose
• Export print-ready and digital maps with accessible colours and readable labels

Participants can integrate web services and field-collected data into GIS workflows.

• Connect WMS, WFS, WMTS, and XYZ services through OGC endpoints
• Assess web-service authority, update frequency, scale limits, and licensing terms
• Configure mobile field forms with required fields, domains, and location capture
• Synchronise field observations, photographs, timestamps, and attachments with GIS layers
• Geocode addresses using reference data, match scores, and exception review
• Integrate operational spreadsheets with spatial features through consistent identifiers

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

• Buffer features using distances, dissolved zones, variable widths, and exclusions
• Overlay layers using intersect, union, clip, erase, and identity operations
• Select features by location using containment, intersection, proximity, and overlap
• Calculate areas, lengths, counts, densities, and percentages from spatial outputs
• Dissolve features by category while preserving relevant summary statistics
• Interpret overlay results against data precision, scale, and boundary uncertainty

Participants can analyse gridded data and derive terrain variables for planning decisions.

• Raster algebra using local, focal, zonal, and global operations
• Derive slope, aspect, hillshade, curvature, and viewshed from elevation models
• Reclassify continuous raster values into transparent decision categories
• Calculate zonal statistics for administrative units, catchments, and service zones
• Resample raster datasets using nearest neighbour, bilinear, and cubic methods
• Evaluate cell size, interpolation assumptions, edge effects, and terrain artefacts

Participants can prepare satellite imagery and interpret its spectral characteristics.

• Electromagnetic spectrum, spectral signatures, reflectance, and atmospheric interference
• Compare Sentinel-2, Landsat, aerial imagery, and commercial satellite products
• Interpret spatial, spectral, radiometric, and temporal resolution trade-offs
• Prepare imagery through band stacking, clipping, mosaicking, and cloud masking
• Create false-colour composites for vegetation, water, built areas, and bare soil
• Inspect histograms, band statistics, contrast stretches, and image artefacts

Participants can classify imagery and assess land-cover change using defensible procedures.

• Define land-cover classes, training samples, reference data, and classification objectives
• Compare supervised classification, unsupervised clustering, and object-based image analysis
• Calculate NDVI, NDWI, NDBI, and other spectral indices from multispectral imagery
• Perform post-classification comparison for land-cover change detection
• Assess classification accuracy using confusion matrices, producer accuracy, and user accuracy
• Report uncertainty, seasonal effects, cloud contamination, and mixed-pixel limitations

Participants can analyse travel networks and evaluate access to facilities or services.

• Build routable networks from road segments, turn restrictions, and travel costs
• Calculate shortest paths using distance, travel time, and impedance attributes
• Generate service areas for hospitals, branches, warehouses, and emergency facilities
• Identify nearest facilities for demand points using network-based accessibility measures
• Compare catchment coverage against population, demand, risk, and capacity data
• State assumptions about road quality, congestion, barriers, and travel behaviour

Participants can build transparent suitability models for site-selection and prioritisation decisions.

• Frame site-selection questions with criteria, constraints, thresholds, and decision owners
• Standardise criteria using reclassification, scoring scales, and direction of preference
• Apply weighted overlay using documented weights and consistent raster scales
• Use the Analytic Hierarchy Process for pairwise comparison of criteria
• Test model sensitivity by changing weights, thresholds, and exclusion rules
• Map suitable areas with assumptions, uncertainty statements, and review criteria

Participants can specify repeatable GIS workflows with quality controls and governance requirements.

• Build repeatable processing models using graphical tools and documented parameters
• Batch-process datasets through naming rules, input validation, and output folders
• Apply quality-control checks for geometry, attributes, CRS, metadata, and completeness
• Use peer review checklists before publishing maps, datasets, and analytical results
• Define data stewardship roles, access controls, retention periods, and update cycles
• Record analytical decisions to support reproducibility, auditability, and handover

Participants can build, review and present a complete GIS analysis addressing a defined operational decision.

• Define a decision question, users, spatial scope, assumptions, and success criteria
• Assemble documented datasets from vector, raster, imagery, tabular, and web sources
• Prepare data through CRS alignment, cleaning, attribute validation, and metadata review
• Apply appropriate spatial analysis methods and test alternative analytical assumptions
• Produce decision maps, summary tables, methods notes, and limitation statements
• Present findings, recommendations, evidence quality, and implementation actions to reviewers