GIS Decision Support Systems Training Course

Participants can select appropriate spatial data models and coordinate reference systems for analysis.

• Vector, raster, network and tabular data roles within decision workflows
• Feature geometry matched to assets, boundaries, events and service catchments
• Coordinate reference systems selected for distance, area and display accuracy
• Projected and geographic coordinate systems distinguished through measurement consequences
• Datum transformations checked before combining datasets from different custodians
• Scale and resolution assessed against the intended decision granularity

Participants can assess whether GIS data is suitable for a stated decision purpose.

• Accuracy, completeness, consistency, currency and lineage assessed against decision needs
• ISO 19115 metadata fields reviewed for source, scope and maintenance status
• Attribute domains and coding schemes checked before analytical use
• Positional uncertainty documented for boundaries, addresses and field observations
• Missing values profiled using frequency tables, null counts and spatial patterns
• Fitness-for-purpose statements written for each critical input dataset

Participants can prepare spatial datasets through documented, repeatable GIS processing steps.

• Geodatabases structured with naming conventions, layer groups and version control
• Field types, aliases and coded domains configured for analytical reliability
• Geocoding results reviewed through match rates and exception records
• Duplicate features identified using spatial joins and attribute matching
• Topology rules applied to gaps, overlaps, dangles and invalid geometry
• Processing histories recorded through model logs and data transformation notes

Participants can identify spatial patterns, outliers and relationships before modelling decisions.

• Attribute summaries compared across administrative areas, asset classes and time periods
• Choropleth classification selected using natural breaks, quantiles and equal intervals
• Hot spot patterns inspected using density surfaces and clustered observations
• Spatial joins used to associate events with zones, facilities and populations
• Outliers investigated through map inspection, summary statistics and source checks
• Descriptive maps separated from causal claims and decision recommendations

Participants can isolate decision-relevant features using attribute and location-based queries.

• SQL attribute queries constructed with Boolean operators, wildcards and null handling
• Definition queries used to maintain reusable operational feature subsets
• Spatial selections applied through containment, intersection, touching and proximity relationships
• Compound criteria combined using selection sets and logical query sequences
• Threshold rules documented for eligibility, exclusion and escalation decisions
• Query outputs validated against sample records and mapped locations

Participants can measure proximity and access to support service, asset and risk decisions.

• Buffer distances set using policy standards, operational ranges and uncertainty allowances
• Multiple-ring buffers compared for graduated service and risk zones
• Nearest-feature analysis identifies closest facilities, hazards and response resources
• Euclidean distance rasters generated for continuous proximity scoring
• Service coverage gaps identified from population, facility and distance layers
• Proximity assumptions tested against barriers, routes and actual travel conditions

Participants can use network methods to assess routes, catchments and service accessibility.

• Network datasets prepared with road hierarchy, turn restrictions and travel costs
• Shortest-path routing compares response routes under defined impedance rules
• Service areas generated from travel time, distance and capacity thresholds
• Origin-destination cost matrices quantify access between demand and supply locations
• Location-allocation models test candidate facility placement against demand distribution
• Network assumptions documented for road closures, speed profiles and mode choice

Participants can prepare and analyse raster surfaces for location-based decision criteria.

• Raster cell size selected according to source resolution and decision scale
• Raster alignment controlled through extent, snap raster and coordinate settings
• Reclassification converts continuous values into defined decision suitability scores
• Map algebra combines raster criteria through arithmetic and conditional operators
• Focal statistics identify local terrain, density and neighbourhood conditions
• NoData values managed to prevent unintended gaps within output surfaces

Participants can derive terrain and drainage indicators for hazard and site assessment.

• Digital elevation models checked for voids, sinks and vertical units
• Slope and aspect derived for construction, access and erosion assessments
• Hillshade used to inspect terrain interpretation and elevation artefacts
• Flow direction and accumulation model drainage pathways and runoff concentration
• Watershed boundaries delineated from outlets and conditioned elevation surfaces
• Terrain-derived constraints incorporated into site exclusion and suitability rules

Participants can incorporate remotely sensed imagery and derived indicators into GIS models.

• Satellite image resolution compared across spatial, spectral, temporal and radiometric dimensions
• Band combinations selected for vegetation, water, built surfaces and bare soil
• NDVI calculated to indicate vegetation condition and change patterns
• Supervised classification outputs assessed using training samples and confusion matrices
• Change detection identifies altered land cover between comparable image dates
• Remote-sensing indicators validated against field observations and reference imagery

Participants can structure a transparent multi-criteria GIS decision model.

• Decision hierarchies separate objectives, criteria, constraints and candidate alternatives
• Boolean overlays apply mandatory exclusion and eligibility rules
• Weighted linear combination aggregates standardised criteria into suitability scores
• Analytic Hierarchy Process supports pairwise comparison of decision criteria
• Criterion weights recorded with rationale, stakeholder source and approval status
• Constraints distinguished from preferences before generating ranked alternatives

Participants can convert diverse GIS criteria into comparable suitability scores.

• Continuous criteria standardised using linear, fuzzy and threshold membership functions
• Cost and benefit criteria transformed into consistent suitability directions
• Ordinal scores defined with explicit classes, thresholds and evidence sources
• Weighted overlay models configured with aligned raster scales and extents
• Suitability classes labelled using decision-ready categories and confidence notes
• Model outputs checked for unintended dominance by highly variable criteria

Participants can compare spatial decision scenarios and test model sensitivity.

• Baseline scenarios established from approved criteria, weights and operational constraints
• Alternative scenarios reflect policy priorities, budget limits and stakeholder preferences
• One-way sensitivity analysis tests changes to individual criterion weights
• Threshold sensitivity tests exclusion zones, capacity limits and service targets
• Rank reversals identified when candidate sites change under revised assumptions
• Scenario comparison tables record consequences, uncertainties and recommended options

Participants can assess distributional impacts and ethical risks within GIS decision models.

• Risk matrices connect likelihood, consequence, exposure and mitigation measures
• Vulnerability indicators mapped using transparent proxy variables and source limitations
• Equity checks compare service access across demographic and geographic groups
• Environmental justice concerns identified through proximity to burdens and benefits
• Privacy risks assessed before mapping sensitive people, households or incidents
• Decision rules reviewed for bias, exclusion and disproportionate impacts

Participants can produce clear maps that support decisions without overstating evidence.

• Map purpose determines scale, projection, extent, hierarchy and annotation choices
• Visual variables assigned to show priority, uncertainty, magnitude and category
• Classification methods selected to avoid misleading spatial comparisons
• Labels, legends and callouts designed for rapid executive interpretation
• Uncertainty communicated through transparency, symbols, notes and confidence classes
• Print and screen layouts checked for accessibility, legibility and reproduction quality

Participants can communicate GIS findings through focused dashboards and decision briefings.

• Key performance indicators selected from decision questions and accountable actions
• Dashboard filters connect geography, time, asset type and priority categories
• Interactive maps paired with charts, tables and narrative decision prompts
• Briefing structures separate evidence, options, recommendation, risk and required approval
• Executive summaries state material findings without technical detail overload
• Data refresh responsibilities assigned for operational dashboard sustainability

Participants can establish governance controls for reliable GIS decision-support outputs.

• Data governance roles assigned for ownership, stewardship, custody and approval
• OGC web services assessed for discoverability, interoperability and access control
• Quality assurance checklists cover inputs, processing, outputs and publication
• Model documentation records parameters, dependencies, exceptions and known limitations
• Audit trails retain source versions, processing dates and reviewer decisions
• Release criteria define sign-off requirements before decision-product distribution

Participants can plan the adoption of GIS decision-support workflows within operational teams.

• Process maps identify decision triggers, handoffs, approvals and information dependencies
• Use cases prioritised by decision value, feasibility and data readiness
• Minimum viable models defined for early operational deployment
• Roles and training needs mapped across analysts, managers and field teams
• Maintenance plans specify refresh cycles, monitoring indicators and change control
• Benefits measures linked to response time, coverage, cost and risk reduction

Participants can build, review and present a complete GIS decision-support model with documented recommendations and limitations.

• Decision brief defines objective, users, alternatives, criteria and decision deadline
• Input datasets prepared with metadata, quality checks and reproducible processing records
• Spatial analysis combines constraints, suitability criteria and scenario-specific assumptions
• Sensitivity results identify stable recommendations and material model uncertainties
• Decision map and briefing communicate options, recommendation, risks and required actions
• Peer review tests traceability, logic, visual clarity and governance readiness