Home▸Data Science▸Government Record Linkage — Probabilistic Entity Resolution

Government Record Linkage — Probabilistic Entity Resolution

Interactive Fellegi-Sunter probabilistic record-linkage simulator: match citizen records across two siloed government databases by comparing name, date of birth, address and ID fields, tune the decision threshold, and watch precision and recall respond live.

Data Science3DAdvanced60 FPS

Government data analytics rarely starts with a clean, unified dataset — it starts with dozens of agency databases that all describe the same citizens under slightly different names, addresses and typos, with no common key to join on. This simulator implements the actual algorithm used to solve that: Fellegi–Sunter probabilistic record linkage. Synthetic citizen records are generated for two agencies, corrupted by a tunable data-entry error rate, and every Agency-A-to-Agency-B pair is scored by comparing name, date of birth, address and a 4-digit ID suffix, each weighted by its real discriminating power. Drag the decision threshold and watch precision and recall trade off live in 3D as declared matches — correct in green, false in red — connect the two record columns.

⚙ Under the hood

Match synthetic citizen records across two siloed government databases using the real Fellegi-Sunter probabilistic record-linkage algorithm, tune the match threshold, and watch precision and recall trade off live in 3D.

data sciencegovernmententity resolutionrecord linkageprobabilityAI

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install