Infrastructure Risk Assessment Using GIS Training Course

Participants can design a structured geodatabase for infrastructure risk analysis.

• Geodatabase schemas linking assets, hazards, vulnerabilities and consequence attributes
• Unique asset identifiers maintained across engineering and GIS systems
• Feature classes, domains and subtypes supporting controlled asset data entry
• Relational tables for inspections, maintenance history and condition observations
• Naming conventions and folder structures supporting reproducible risk analysis
• Data dictionaries documenting fields, units, sources and update responsibilities

Participants can validate spatial datasets before using them in risk calculations.

• Coordinate reference systems selected for local distance and area calculations
• Datum transformations checked before combining multi-source infrastructure datasets
• Positional accuracy assessed against asset location and hazard mapping requirements
• Attribute completeness, validity and consistency checks using rule-based queries
• Topology rules identifying gaps, overlaps, dangles and duplicate features
• ISO 19115 metadata elements recorded for critical risk datasets

Participants can prepare asset inventories and assign defensible criticality classes.

• Cleaning asset registers through field standardisation, joins and duplicate detection
• Geocoding facility records and validating ambiguous location matches
• Asset hierarchy models linking components, sites and service systems
• Criticality criteria covering safety, service population, revenue and strategic function
• Consequence matrices converting operational impacts into ranked criticality classes
• Mapping critical assets, redundancy locations and single points of failure

Participants can select hazard datasets and define scenarios appropriate to asset decisions.

• Authoritative hazard datasets evaluated for scale, currency, coverage and uncertainty
• Return periods, exceedance probabilities and scenario likelihoods distinguished correctly
• Flood, seismic, wildfire, heat and landslide hazard data structures
• Historical event inventories used to validate hazard occurrence patterns
• Scenario assumptions documented for current climate and future climate conditions
• Hazard thresholds aligned with asset design standards and operating limits

Participants can interpret remote sensing products for hazard and asset-condition evidence.

• Optical imagery interpreted using tone, texture, pattern and context
• Multispectral indices identifying vegetation stress, burn severity and surface water
• Synthetic aperture radar applications for flood extent and ground deformation
• LiDAR-derived surfaces supporting terrain, vegetation and corridor assessments
• Image dates, cloud cover and spatial resolution matched to decisions
• Ground-truth observations used to confirm remotely sensed classifications

Participants can derive terrain and drainage indicators relevant to infrastructure exposure.

• Digital elevation models checked for voids, sinks and vertical accuracy
• Slope, aspect and curvature derived for terrain-instability screening
• Flow direction and accumulation calculated using hydrological conditioning
• Watershed boundaries delineated for culvert, bridge and drainage assessments
• Topographic wetness index mapped to identify saturated ground conditions
• Elevation profiles created along roads, pipelines and transmission corridors

Participants can quantify flood exposure for linear and point infrastructure assets.

• Flood polygons compared across riverine, pluvial and coastal scenarios
• Buffer analysis identifies assets near flood-prone channels and shorelines
• Spatial intersections calculate exposed asset lengths, counts and replacement values
• Raster zonal statistics summarise inundation depth within asset footprints
• Bridge, culvert and pumping-station exposure assessed using local terrain
• Flood exposure outputs classified by severity, confidence and intervention urgency

Participants can identify infrastructure corridors exposed to terrain and ground-movement hazards.

• Landslide susceptibility factors combined from slope, geology, rainfall and land cover
• Cut slopes and embankments screened using terrain derivatives and imagery
• Erosion-prone reaches identified from drainage concentration and bare-soil indicators
• InSAR displacement trends interpreted for subsidence and slope movement
• Corridor segmentation supports location-specific geotechnical investigation priorities
• Field verification points selected from high-risk terrain model outputs

Participants can calculate exposure measures using appropriate GIS overlay methods.

• Spatial joins transfer hazard attributes to asset features and records
• Intersect and identity operations calculate overlapping hazard exposure areas
• Near analysis measures distances between assets and hazard sources
• Raster reclassification converts continuous hazard values into exposure classes
• Zonal statistics summarise hazard intensity by asset segment or site
• Modifiable areal unit effects considered when aggregating exposure results

Participants can score asset vulnerability using physical condition and design information.

• Vulnerability indicators selected from material, age, condition and design standards
• Fragility curves relate hazard intensity to damage probability estimates
• Condition inspection records converted into consistent vulnerability score classes
• Design elevation, protective works and redundancy reduce vulnerability ratings
• Expert elicitation structured using scoring rubrics and documented assumptions
• Missing vulnerability data handled through confidence scores and conservative rules

Participants can assess operational consequences of asset failure and service interruption.

• Consequence criteria distinguish life safety, environment, service and financial loss
• Population catchments estimate people affected by infrastructure service outages
• Critical facilities mapped against access routes and utility dependencies
• Travel-time analysis estimates detour impacts from road or bridge closure
• Dependency diagrams identify cascading failures between interconnected infrastructure systems
• Consequence scores calibrated against organisational risk tolerance thresholds

Participants can model network disruption and identify critical links and dependencies.

• Network datasets prepared with connectivity, impedance and restriction attributes
• Origin-destination cost matrices quantify access under disruption scenarios
• Shortest-path analysis identifies alternative routes for critical services
• Betweenness centrality highlights network links with disproportionate operational importance
• Utility dependency layers reveal shared corridors and co-located assets
• Failure scenarios mapped to identify isolated communities and service zones

Participants can build a transparent spatial model combining hazard, vulnerability and consequence.

• Weighted linear combination combines standardised hazard, vulnerability and consequence criteria
• Analytic hierarchy process documents pairwise weighting of risk factors
• Criteria normalisation converts mixed measurement scales into comparable scores
• Constraint masks exclude unsuitable areas from priority intervention analysis
• ModelBuilder or graphical workflows document repeatable geoprocessing sequences
• Risk classes defined using natural breaks, quantiles and policy thresholds

Participants can test model reliability and communicate limitations responsibly.

• Sensitivity analysis tests ranking changes under alternative factor weights
• Monte Carlo simulation represents uncertainty in scores and probability estimates
• Historical damage records compared against predicted risk class distributions
• Receiver operating characteristic curves assess binary susceptibility model performance
• Confidence surfaces distinguish strong evidence from sparse or uncertain data
• Assumptions registers record limitations, decisions and required future improvements

Participants can convert spatial risk outputs into prioritised mitigation actions.

• Risk matrices combine likelihood and consequence into treatment priorities
• Asset ranking rules separate immediate action, monitoring and acceptance decisions
• Mitigation options matched to hazard mechanisms and asset vulnerability drivers
• Cost-effectiveness comparisons support drainage, protection, relocation and redundancy choices
• Residual risk estimated after proposed controls and adaptation measures
• Implementation roadmaps assign owners, milestones, evidence and review triggers

Participants can communicate infrastructure risk findings through clear spatial products and reports.

• Map layouts apply visual hierarchy, scale bars, legends and source notes
• Risk symbology distinguishes likelihood, consequence, confidence and treatment priorities
• Web maps and dashboards present filtered asset-risk information for managers
• Technical reports document methods, datasets, assumptions and validation findings
• Executive briefings connect mapped risk patterns to investment decisions
• Version control procedures preserve approved maps, models and source data

Participants can build, review and present a complete GIS-based infrastructure risk assessment for a defined asset portfolio.

• Define the asset portfolio, decision question, scenarios and assessment boundaries
• Prepare documented asset, hazard, terrain and consequence data layers
• Build exposure, vulnerability and consequence models using selected GIS methods
• Calculate prioritised risk scores and test sensitivity to key assumptions
• Produce risk maps, treatment priorities and an implementation roadmap
• Present findings, limitations and recommendations to a technical review panel