Business clustering service
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editorial(services+architecture+systems): enrich 3 thin articles + Phase 7 alias fix — Key Takeaways for service-business-clustering, service-places-filtering, mailbox-atomicity (EN+ES); aliases: [os-totebox] on totebox-os — Phase 6+7
@@ -19,6 +19,13 @@ cites: [] Retail data is inherently messy — a single commercial site often contains multiple distinct points, such as a big-box anchor, a nested pharmacy, and a fuel outlet sharing the same parking area. **`service-business`** turns those raw points into actionable commercial clusters using a parent-child spatial schema, so the GIS engine receives one unified commercial entity per physical site rather than several overlapping records. The service iterates the `service-fs` raw data lake, groups entities that share a footprint within a 100 m proximity threshold, and assigns the highest-weight named anchor as the parent. ## Key Takeaways - `service-business` uses a grid-based spatial index at roughly 1 km cell resolution to group raw retail points. Points within 100 m of each other are collapsed into one commercial cluster before any tier score is computed. - The parent-child schema assigns the highest-weight named anchor as the parent. Every other operator at the same site becomes a child record. Without this step, co-located fuel outlets and pharmacies would each count as independent tier signals. - Output is `cleansed-clusters.jsonl`, consumed directly by `[[app-orchestration-gis]]` when building the regional co-location index. The clustering step is the boundary between raw POI data and ranked commercial intelligence. - The 100 m proximity threshold is calibrated for large-format retail parks — close enough to capture genuine co-location, far enough to separate adjacent but structurally distinct shopping destinations. ## The Clustering Logic `service-business` processes raw commercial nodes so that the GIS engine produces a single, unified commercial entity per physical site.