Scientific grounding
Grounded in established methods and validated data contracts.
Method principles
Transparency
Sources, conditions and limitations remain visible.
Modularity
Components adapt to different city and risk contexts.
Comparability
Consistent identities and indicator definitions are used.
Decision relevance
Analysis links to local planning questions.
Methodology workflow
01
Data ingestion
Identify sources.
02
Preprocessing
Clean and align.
03
Indicator generation
Prepare component evidence.
04
Risk assessment
Assess spatial conditions.
05
Prioritisation
Identify priority places.
06
Scenarios
Compare assumptions.
07
Outputs
Deliver maps and tables.
Data sources
Earth observation
Surface and land evidence.
Climate data
Multi-year climate records.
.png&w=3840&q=75)
Terrain
Topographic context.
Land cover
Vegetation and surfaces.
Buildings
Built environment data.
Demographics
Social context.
Local data
City institution inputs.
From indicators to action
01
Hazard
Climate-related pressure.
02
Exposure and vulnerability
People and places at risk.
03
Adaptive capacity
Response and resilience context.
04
Actionable insight
Comparable planning output.
Quality, uncertainty and reproducibility
Quality assurance
Input and output validation.
Uncertainty
Communicate assumptions and limits.
Reproducibility
Project context and traceability.
Clear methods
Explain how decisions are formed.