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Explore →CASE STUDYInfrastructure
Infrastructure
Gap Analysis
An applied GeoLayers workflow showing how to move from project evidence to a structured infrastructure gap analysis result.
Browser-based GISGeoAI + spatial analyticsSouth African context
THE DECISION
Where is infrastructure coverage insufficient?
The study starts by defining the service being assessed, the population or demand points that require access, and what counts as an acceptable service relationship. That definition matters: a gap based on a 5 km radius can look very different from a gap based on 20 minutes of network travel.
DATA FOUNDATION
Build the evidence layer by layer
Bring existing facilities, road/network data, demand or population points, administrative boundaries and any relevant capacity attributes into one project. Data quality checks should identify duplicate facilities, missing coordinates and inconsistent capacity fields before analysis.
ANALYSIS
Measure access and capacity, not just proximity
Use nearest-facility or service-area analysis to determine which demand points can reasonably reach an existing facility. Where capacity data exists, compare demand allocated to each facility against its available capacity. Map uncovered or overloaded areas as the primary gap layer.
PRIORITISATION
Turn gaps into candidate interventions
Overlay planning, environmental, land-availability or cost constraints to identify candidate locations for new facilities or upgrades. Multi-criteria suitability can rank candidates transparently instead of selecting a point only because it is geometrically central.
OUTPUT
Communicate the evidence for investment
Publish a map/dashboard showing existing assets, served areas, gap areas, demand and shortlisted interventions. Keep the assumptions—travel threshold, capacity, demand source and suitability weights—visible so the recommendation can be reviewed.
PROJECT INPUTS
Evidence required for a credible analysis
The quality of the result depends on the source evidence and the assumptions attached to it.
- Existing facility locations and service type
- Population, customers or other demand points
- Road/network data where accessibility matters
- Capacity, utilisation or service-volume fields where available
- Administrative/planning/environmental context for candidate interventions
DECISION OUTPUTS
What the workflow should produce
A useful case-study result should leave the project with evidence that can be inspected, compared and communicated—not only a final map.
- Coverage and travel-time distribution
- Demand allocated to each facility
- Unserved or poorly served demand
- Facility capacity pressure
- Shortlisted intervention areas with documented assumptions
INTERPRETATION & LIMITATIONS
Read the gap map as evidence, not as a final capital plan
A service gap can result from distance, travel barriers, insufficient capacity, uneven demand or poor source data. A candidate intervention should therefore be checked against land availability, operating model, capital cost, planned infrastructure, population change and the quality of the demand dataset. Where facility capacity is unknown, the analysis should state clearly that access does not prove service adequacy.
- The gap definition is explicit and tied to travel/capacity assumptions.
- Unreachable demand and overloaded facilities can be distinguished.
- Candidate interventions are screened against real planning and environmental constraints.
- A reviewer can trace the recommendation back to source layers and thresholds.
RELATED CAPABILITIES
Recreate the workflow with connected GeoLayers tools
The case study pattern can be adapted to your own project data rather than copied as a fixed recipe.