Certificate Course in GIS Mapping and Spatial Analysis

Participants can select and apply suitable coordinate reference systems for a mapping task.

• Geographic coordinates, latitude, longitude and datum definitions
• Projected coordinate systems measured in metres, feet or local units
• UTM zones, national grids and appropriate local projection selection
• Datum transformations between WGS 84 and local reference systems
• Projection distortion assessed for area, distance, direction and shape
• Coordinate reference system metadata inspected before combining datasets

Participants can create a GIS project and load source data with traceable settings.

• Project folders structured for source data, outputs, styles and documentation
• GeoPackage, shapefile, GeoJSON and CSV format selection
• Importing spreadsheets with coordinates, addresses and coded attributes
• Layer properties checked for geometry type, extent and coordinate system
• Base maps added with appropriate licensing and scale constraints
• Initial project map composed from workplace-style operational datasets

Participants can clean attributes and query GIS data using logical selection criteria.

• Attribute table fields typed as text, integer, decimal, date
• Null values, duplicates and inconsistent category codes identified systematically
• Field calculator expressions used for derived measures and classifications
• SQL WHERE clauses built with comparison, wildcard and logical operators
• Records selected using dates, values, categories and text patterns
• Joins and relates connected through stable common identifiers

Participants can geocode address records and assess positional match quality.

• Address components standardised before geocoding operational records
• Geocoding services matched against reference streets and address points
• Match scores, candidate records and unmatched addresses reviewed
• Manual rematching procedures documented for ambiguous address results
• Geocoded points checked against administrative boundaries and known locations
• Positional accuracy limits stated when using approximate address matches

Participants can organise GIS data with metadata, naming conventions and access controls.

• Geodatabase feature classes organised by theme, geography and update cycle
• Naming conventions applied to datasets, fields, layers and outputs
• ISO 19115 metadata elements recorded for source, currency and constraints
• Data lineage documented from acquisition through transformation and publication
• Version control principles applied to shared GIS project files
• Sensitive location data classified using access and disclosure rules

Participants can edit spatial features and apply topology rules to identify geometry errors.

• Topology rules defined for gaps, overlaps, dangles and duplicate features
• Snapping tolerances set before digitising boundaries and linear assets
• Geometry editing performed with trace, reshape and split tools
• Attribute domains constrain permitted values during data entry
• Quality-control logs record detected errors, corrections and outstanding issues
• Sampling checks compare edited features against authoritative source records

Participants can produce readable thematic maps matched to a stated decision purpose.

• Visual hierarchy established through symbol size, colour and contrast
• Choropleth maps classified using natural breaks, quantiles and equal intervals
• Proportional symbols matched to count data and perceptual limits
• Colour schemes selected for sequential, diverging and categorical variables
• Labels placed using scale ranges, priorities and conflict resolution
• Map layouts include title, legend, scale bar, sources and date

Participants can analyse proximity and spatial relationships using standard vector tools.

• Buffers created at fixed, variable and multiple distance thresholds
• Intersect, union and erase operations compare overlapping geographic areas
• Spatial joins transfer attributes based on containment or proximity
• Point-in-polygon analysis summarises records within administrative areas
• Nearest-neighbour tools identify closest assets, services or incidents
• Distance results checked against projection units and operational assumptions

Participants can build transparent multi-criteria suitability models for site decisions.

• Decision criteria converted into measurable spatial layers and thresholds
• Reclassification assigns common suitability scores to different input measures
• Weighted overlay combines criteria using documented stakeholder priorities
• Boolean overlay excludes unsuitable areas through mandatory constraints
• Sensitivity analysis tests how changed weights affect ranked locations
• Suitability outputs documented with assumptions, exclusions and limitations

Participants can evaluate travel access and service coverage using network analysis.

• Network datasets model roads, paths, turn restrictions and travel costs
• Service areas generated from facilities using time or distance thresholds
• Origin-destination cost matrices compare demand points and service locations
• Shortest-path routes calculated with defined travel impedance values
• Location-allocation models test candidate facility placement scenarios
• Accessibility results interpreted against capacity, demand and network assumptions

Participants can identify and communicate spatial patterns without overstating statistical evidence.

• Spatial autocorrelation measured using Global Moran's I statistics
• Hot Spot Analysis identifies statistically significant high and low clusters
• Kernel density estimation converts incident points into intensity surfaces
• Standard deviation ellipses summarise directional distribution and dispersion
• Modifiable areal unit problem considered when aggregating geographic data
• Statistical outputs interpreted with confidence levels and data limitations

Participants can process raster datasets and derive terrain-based analytical surfaces.

• Raster properties inspected for cell size, bands, extent and NoData
• Resampling methods compared using nearest neighbour, bilinear and cubic techniques
• Raster calculator expressions combine classified and continuous grid values
• Digital elevation models derive slope, aspect and hillshade surfaces
• Contour lines generated at intervals suited to terrain and map scale
• Raster outputs clipped, masked and aligned to analysis boundaries

Participants can interpret satellite imagery and select suitable data for monitoring tasks.

• Satellite sensor resolution compared across spatial, spectral and temporal dimensions
• Sentinel-2 and Landsat imagery selected for monitoring requirements
• True-colour and false-colour composites reveal different surface characteristics
• Spectral signatures distinguish vegetation, water, bare soil and built surfaces
• Cloud cover, shadows and seasonal timing evaluated before image selection
• Image interpretation records distinguish observable evidence from inferred conditions

Participants can classify imagery and assess change using documented accuracy procedures.

• Training samples selected for supervised image classification categories
• Maximum likelihood classification assigns pixels using spectral probability
• Normalized Difference Vegetation Index measures relative vegetation greenness
• Post-classification comparison identifies land-cover transitions between dates
• Confusion matrices calculate producer, user and overall accuracy
• Ground-truth reference data separated from classification training samples

Participants can design field data collection workflows that integrate with GIS datasets.

• Field forms designed with required fields, domains and conditional logic
• Global Navigation Satellite System accuracy recorded with each observation
• Photo attachments, timestamps and observer identifiers support field verification
• Offline maps prepared for areas without dependable mobile connectivity
• Field records synchronised, reviewed and reconciled with master datasets
• Collection protocols define safety, consent and data-quality requirements

Participants can document and automate repeatable GIS processing steps.

• ModelBuilder workflows chain geoprocessing tools into repeatable procedures
• Batch processing applies identical parameters across multiple geographic areas
• GDAL commands convert, reproject and clip common geospatial formats
• Processing histories record tool settings, inputs and output paths
• Reusable scripts parameterise boundary, date and threshold variables
• Workflow documentation identifies dependencies, assumptions and expected outputs

Participants can communicate GIS findings with suitable evidence, caveats and map outputs.

• Analysis briefs define audience, decision, scope and acceptance criteria
• Map series organised to progress from context to detailed findings
• Methods statements explain datasets, processing steps and analytical choices
• Uncertainty communicated through notes, classifications and confidence statements
• Peer review checks maps against decision questions and source records
• Recommendations distinguish evidence-based actions from further data requirements

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

• Project brief specifies decision question, users, constraints and success criteria
• Source datasets prepared with documented projections, metadata and quality checks
• Spatial methods selected to answer the approved analytical question
• Maps and analytical outputs reviewed against stated decision requirements
• Findings presented with assumptions, limitations and defensible recommendations
• Peer feedback incorporated into final project documentation and map package