Matching and Conflation of Open Government Data with OpenStreetMap Data

This thesis addresses the absence of a reliable, openly licensed, repeatable workflow for conflating authoritative POI from Swiss OGD sources with heterogeneous OSM data—a gap that increases duplication,staleness, and manual curation cost. The objective was to design and evaluate an auditable end-to-end pipeline (DiffedPlaces) that ingests brand and retailer feeds from ATP together with contemporaneous OSM extracts, generates spatially and semantically blocked candidate pairs for Switzerland (Oct 2024–Aug 2025), and resolves matches via a tunable rule-based scorer and a supervised machine learning (ML) RandomForestClassifier. Two golden datasets underpinned evaluation: a brand-focused Aldi Süd Switzerland subset (246 outlets) and a stratified multi-category random sample (200 POI). The evolved ML matcher achieved Precision 1.0000, Recall 0.9957 (F1 0.9978) on the brand subset and improved F1 on the heterogeneous sample while substantially lowering false positives versus the tuned rule-based approach. The resulting workflow delivers reproducible, high-precision conflation, reduces audit workload, and provides a transferable governance template for integrating additional authoritative OGD feeds into OSM with transparent quality controls.