Spatial diagnostic prototype
MSc foundation comparing structural and remote-sensing latent representations. Establishes the diagnostic base for concordance, stability and spatial interpretation.
Research agenda
7 papers one framework
A staged framework for turning mixed evidence views into local state spaces, global structure, temporal drift detection and decision-ready dashboards.
Local state-space construction
Builds the pipeline for entity preprocessing, feature construction and multi-view local state spaces.
Cross-view taxonomy concordance
Tests how local state spaces connect into a global structure and where labels, views and behaviours agree or diverge.
Temporal drift detection
Tracks how the global structure changes over time and where instability, transition or anomaly signals appear.
Entropy simulation breakdown
Simulates structural breakdown when labels, views or signals become noisy, unstable or unreliable.
Anonymisation protocol
Develops privacy-preserving synthetic or anonymised data while retaining useful structural patterns.
Multi-domain framework validation
Tests the same framework across different evidence views and domains.
- Crypto: market, on-chain, developer, infrastructure.
- Property: market, location, occupancy, infrastructure, planning.
- AI company: valuation, product usage, research, compute, infrastructure.
Research logic
From entities to dashboards.
The framework is built to move from raw entities into local state spaces, then into global metrics, temporal analysis and an interpretable dashboard.
Preprocess entities
Clean, align and standardise assets, sites, companies, regions or systems.
Build features
Create comparable attributes from the available datasets and evidence layers.
Create view vectors
Represent each entity across views such as market, usage, developer, infrastructure and geography.
Construct local state spaces
Localise each entity inside bounded neighbourhoods so similarity, isolation and structural position can be tested.
Measure global structure
Use global metrics to compare concordance, divergence, stability and taxonomy-level structure.
Detect temporal change
Track movement through time to identify drift, instability and structural breakdown.
Build the outputs
Turn the framework into an interpretable output for monitoring, comparison and decision support.