GIS Mapping and Spatial Analysis with QGIS and R Training Course

Participants can configure maintainable QGIS projects and import common spatial data sources.

• QGIS interface panels, browsers and project files configured deliberately
• Organise layer groups, styles and naming conventions for maintainability
• GeoPackage datasets created with portable paths and documented contents
• Load CSV, GeoJSON, shapefile and WMS sources with validation
• Set project CRS, display units and transformation preferences correctly
• Use QGIS processing history to record repeatable operations

Participants can clean, structure and join vector datasets for reliable analysis.

• Inspect feature counts, null values, duplicates and invalid geometries
• Attribute tables filtered, sorted and edited using field calculator expressions
• Create derived fields using conditional logic, dates and string functions
• Join census, customer or asset tables using controlled identifiers
• Relate one-to-many records without duplicating geographic features unnecessarily
• Repair geometry errors with QGIS validity checks and fix tools

Participants can choose, transform and verify coordinate reference systems for analysis and mapping.

• Geographic and projected coordinate systems compared for analytical suitability
• Select equal-area, conformal and equidistant projections by measurement purpose
• Reproject vector layers while preserving authoritative source data
• Datum transformations checked against area, accuracy and source metadata
• Georeference scanned plans using ground control points and residual errors
• Diagnose layer misalignment caused by incorrect CRS assignment

Participants can produce clear thematic maps suited to professional decision-making.

• Visual hierarchy established through symbol size, colour, contrast and labelling
• Choose choropleth classifications using quantiles, natural breaks and equal intervals
• Normalise count data using rates, ratios and population denominators
• Apply proportional symbols, graduated symbols and density surfaces appropriately
• Map layouts include scale bars, legends, sources, dates and disclaimers
• Colour palettes assessed for accessibility, print reproduction and interpretation

Participants can assess spatial data fitness and specify quality controls for field-collected data.

• Positional, attribute, temporal and completeness errors assessed systematically
• Acceptance criteria defined for source authority, recency and accuracy
• Design point, line and polygon capture forms with mandatory fields
• Domain lists, range checks and unique identifiers prevent entry errors
• Sampling plans distinguish convenience, random, systematic and stratified approaches
• Document lineage from collection, cleaning, transformation and publication stages

Participants can build repeatable geoprocessing workflows for common spatial questions.

• Buffers calculated using appropriate distance units and geographic projections
• Clip, dissolve, merge and multipart conversion applied to vector datasets
• Select locations by attribute, location and expression-based conditions
• Processing models chain tools into documented repeatable workflows
• Batch processing applies standard operations across multiple geographic units
• Output datasets named, stored and checked against defined specifications

Participants can combine layers to quantify exposure, coverage and geographic relationships.

• Intersect and union operations calculate overlapping land, assets and populations
• Spatial joins transfer attributes using containment, intersection and nearest features
• Area-weighted calculations estimate populations within administrative or hazard zones
• Service catchments compared with demand locations and demographic indicators
• Overlay results checked for sliver polygons and double-counted features
• Summaries grouped by district, facility, land-use class and risk category

Participants can measure access and identify suitable locations using distance and network methods.

• Euclidean distance surfaces distinguish straight-line proximity from travel access
• Network datasets prepared with connected roads, directionality and impedance fields
• Shortest-path analysis calculates routes using distance, time or cost
• Service areas model reachable populations within defined travel-time thresholds
• Location-allocation concepts compare candidate sites against demand points
• Accessibility findings tested against barriers, transport modes and data gaps

Participants can process raster datasets and derive terrain indicators for spatial decisions.

• Raster cells, bands, resolution and NoData values interpreted correctly
• Digital elevation models prepared for slope, aspect and hillshade generation
• Raster calculator expressions derive thresholds, indices and suitability scores
• Reclassify continuous surfaces into defined operational or risk categories
• Zonal statistics summarise raster values by administrative and asset polygons
• Resampling methods compared for continuous, categorical and satellite datasets

Participants can prepare satellite imagery and interpret basic land-cover classification outputs.

• Multispectral bands interpreted for vegetation, water, soil and built surfaces
• Band combinations selected for visual interpretation and feature discrimination
• NDVI calculated and interpreted with seasonal and atmospheric limitations
• Supervised classification uses training samples, classes and validation data
• Confusion matrices report producer accuracy, user accuracy and overall accuracy
• Change detection compares classified imagery using consistent spatial alignment

Participants can organise larger spatial datasets using relational database and PostGIS principles.

• Relational tables structured with primary keys, foreign keys and constraints
• PostGIS geometry types, SRIDs and spatial indexes explained practically
• SQL SELECT, WHERE, GROUP BY and JOIN queries retrieve spatial records
• Spatial predicates identify intersects, contains, within and nearest relationships
• Database views package recurring queries for controlled reporting outputs
• QGIS connections access PostGIS layers with read and edit permissions

Participants can create reproducible R workflows for importing, inspecting and exporting spatial data.

• RStudio projects organise scripts, inputs, outputs and package dependencies
• sf objects store vector geometries, attributes and coordinate reference systems
• Read GeoPackage, GeoJSON, CSV and shapefile data into R
• Tidy data transformations use dplyr filtering, grouping and mutation
• Script parameters separate reusable logic from project-specific file paths
• Export processed spatial layers with documented formats and coordinate systems

Participants can perform and document vector-based spatial analysis using R packages.

• sf geometry operations create buffers, intersections, unions and centroids
• Spatial joins assign area, facility and boundary attributes to features
• Distance matrices calculate nearest sites and pairwise geographic separation
• Aggregate point observations into grids, districts and service catchments
• Map vector results with ggplot2 scales, labels and annotation layers
• Validate R outputs against QGIS results and source-data totals

Participants can process raster data in R for environmental and suitability analysis.

• terra raster objects manage single-band and multiband spatial datasets
• Crop, mask and resample rasters using defined alignment rules
• Cell calculations derive indices, thresholds and weighted suitability surfaces
• Extract raster values at points, lines and polygon zones
• Stack time-series rasters for seasonal comparison and change measurement
• Write GeoTIFF outputs with NoData values and descriptive metadata

Participants can identify clustering, outliers and spatial dependence before modelling.

• Spatial weights matrices define contiguity, distance bands and nearest neighbours
• Global Moran's I tests overall spatial autocorrelation in mapped indicators
• Local Moran's I identifies clusters and spatial outlier locations
• Getis-Ord Gi* maps statistically significant hot and cold spots
• Rate smoothing stabilises unreliable indicators from small population denominators
• Interpret significance alongside multiple testing, scale and data limitations

Participants can estimate continuous geographic surfaces and assess interpolation uncertainty.

• Point measurements assessed for sampling density, distribution and measurement error
• Inverse distance weighting configured with distance power and neighbourhood settings
• Kriging concepts introduce variograms, range, sill and nugget effects
• Cross-validation compares predicted values against observed measurement locations
• Interpolation uncertainty mapped alongside estimated values for responsible interpretation
• Select interpolation methods according to process knowledge and decision use

Participants can assess geographic relationships while checking spatial model assumptions.

• Ordinary least squares models relate geographic outcomes to explanatory variables
• Residual maps reveal geographic structure missed by non-spatial models
• Multicollinearity assessed using correlation matrices and variance inflation factors
• Spatial lag and spatial error models introduced for dependent observations
• Model diagnostics inspect residual autocorrelation, heteroscedasticity and influential observations
• Communicate association, uncertainty and causality limits in model findings

Participants can package spatial work for review, reuse and controlled publication.

• Folder structures separate raw data, processed data, scripts and outputs
• Version control principles record changes to scripts, models and documentation
• Methods notes specify sources, parameters, transformations and analytical limitations
• Quality assurance checklists test geometry, attributes, CRS and map outputs
• Sensitive locations protected through aggregation, masking and access controls
• Publish maps using appropriate formats, attribution and licensing statements

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

• Define a decision question, geographic scope, indicators and stakeholder audience
• Prepare source datasets with documented quality checks and coordinate systems
• Apply appropriate vector, raster, network or statistical analytical methods
• Build decision-ready maps, tables and reproducible R or QGIS outputs
• Review findings against assumptions, uncertainty, ethics and data limitations
• Present methods, evidence, recommendations and next analytical steps clearly