OUR APPROACH: STETA
Our Space-Time Encoded Training Architecture (STETA) embeds spatial and temporal relationships into training data as scalar values, allowing standard machine learning models to recognize patterns that would otherwise remain hidden.
Most spatial models address complexity by modifying the algorithm itself. STETA embeds the intelligence upstream, in the training set — so instead of learning from isolated data points, the model learns from meaningful groups of related information. The result is outputs that are more robust, more realistic, and more useful in practice.
Model-agnostic
Works with any standard machine learning model. No proprietary stack required.
Context-aware
Encodes distance, networks and spatial relationships directly into training data
Decision-focused
Outputs built for planning, resource allocation and operational decisions
Street-level precision
Analyze risk and demand at the scale where decisions actually happen
WHERE WE APPLY IT
GeoNexa supports decisions wherever location, time and local context affect risk, demand or performance.
WHY GEONEXA LABS?
Real-world risk does not follow neat boundaries
GeoNexa models the relationships between places, networks and time so local signals are not lost in broad averages.
Our work is underpinned by 30 years of spatial data science research, published in Geographical Analysis, Annals of the AAG, Computers, Environment and Urban Systems and other leading international journals, and presented at GEO Business 2025 and AGI Cambridge 2026.
Spatial context
Account for how nearby places and connected networks influence outcomes.
Operational relevance
Shape outputs around real planning, safety and resource questions.
Research-backed
Built on peer-reviewed spatial data science, published in leading international journals.
Interpretable outputs
Results built for planners and decision-makers, not just data scientists.