Sample microdata
- Useful records, limited density
- Rare segments disappear quickly
- Coarse geography and access limits
- Hard to stress-test local scenarios
Europe as a dataset
Synthetic statistics for real-world decisions.
Synthestat helps teams model population-level patterns, regional variation, and statistical scenarios with privacy-aware synthetic data.
Synthetic population
12.8M modeled agents
Regional coverage
EU + EFTA
Privacy layer
No raw personal records
1:1
synthetic population scale
24 x 22
grid-based regional frame
QA
validation visible by release
EU
country-by-country buildout
Problem
Real-world data is fragmented, sensitive, and hard to compare.
Teams often need granular insight without exposing individual-level records. Synthestat turns sparse, restricted, or incompatible sources into structured synthetic statistical layers.
Synthestat layer
- Full synthetic population coverage
- Local and segment-level representativeness
- Privacy-preserving artificial entities
- Rerunnable releases for scenario analysis
Solution
A synthetic layer for geographic analysis.
Generate synthetic populations, model regional distributions, compare scenarios, preserve statistical structure, and export analysis-ready data.
Generate synthetic populations
Build artificial persons and households that preserve useful population structure.
Model regional distributions
Represent territories, populations, distributions, and uncertainty together.
Compare scenarios
Rerun releases and scenario assumptions across comparable geographic layers.
Coverage
Built around regions, not rows.
Synthestat treats geography as a statistical structure: territories, populations, distributions, and uncertainty reconstructed as square data cells.
Method
Transparent methods, measurable uncertainty.
Input harmonization
Bring restricted, sparse, and official sources into a comparable evidence frame.
Synthetic reconstruction
Fit artificial populations to published totals, distributions, and local constraints.
Validation
Track marginal fit, distributional fit, readiness labels, and known limitations.
Scenario simulation
Explore changes without exposing or moving raw person-level records.
Use cases
Where granular insight matters and raw records should not move.
Public policy
Evaluate services, infrastructure, and policy scenarios with auditable synthetic populations.
Market planning
Understand catchments, demand pockets, and local household structure before committing capital.
Mobility analysis
Connect homes, workplaces, schools, and services to model movement pressure.
Healthcare access
Study regional population needs without sharing sensitive underlying records.
Demographic modeling
Keep rare segments coherent across age, household, location, and socioeconomic signals.
Regional forecasting
Compare changing assumptions across territories, releases, and uncertainty bands.
Product preview
Statistical workbench, not a generic admin panel.
Baseline0.92 fit
Policy A0.88 fit
Stress0.81 fit
Security and privacy
Synthetic by design. Auditable by default.
Synthestat entities are artificial. Exports can be controlled, aggregated, and scoped, while evidence lineage and release labels support review.
No synthetic person is presented as a real individual.
Outputs support controlled, aggregated, and scoped analysis workflows.
Validation reports and quality signals can travel with each release.
Request demo
Build statistical insight without exposing raw records.
Bring the population question your current microdata cannot answer.