Places filtering
content(gis): GIS/location-intelligence cluster consolidation (final of 5 flagged clusters) — merged the one genuine duplicate (pointsav-gis-engine.md into location-intelligence-substrate.md; its own body said its core logic lives in app-orchestration-gis). More significant: found and fixed a corpus-wide wikilink collision — 7 articles cited [[co-location-methodology]] as the retail-cluster tier-scoring methodology, but that slug resolves to a real, unrelated article about data-center FACILITY co-location siting. No article for the real retail methodology existed under any slug, and the one place a scoring mechanism WAS described in detail (app-orchestration-gis.md) described a Haversine formula the real SCORING-METHODOLOGY.md V3 confirms was replaced by a 4-tier gate system — with 2 more articles describing the mechanism differently again, none matching each other or reality. Created substrate/retail-co-location-tier-methodology.md as the single source of truth (facts from the verified real methodology doc) and repointed all 7 citing articles (14 files EN+ES) to it. Fixed architecture/_index.md's MOC description, which had also mischaracterized the real co-location-methodology.md article. All content-matrix clusters now resolved.
@@ -11,13 +11,13 @@ audience: public short_description: "service-places filters raw civic and institutional data to retain only regional-grade facilities, so GIS tier rankings reflect institutional concentration." bcsc_class: public-disclosure-safe language_protocol: PROSE-TOPIC last_edited: 2026-05-08 last_edited: 2026-08-01 editor: pointsav-engineering paired_with: service-places-filtering.es.md cites: [] --- [[co-location-methodology|GIS tier rankings]] depend on knowing where regional institutions sit, not where every clinic and community college sits. **`service-places`** filters raw civic data to retain only regional-grade facilities — hospitals with at least 50 staffed beds, universities with at least 1,000 full-time-equivalent students, validated major regional transport hubs — and applies a 200 m spatial buffer to consolidate large institutional campuses into single regional anchors. Local-service density is filtered out at this stage; downstream rankings reflect institutional concentration rather than facility count. [[retail-co-location-tier-methodology|GIS tier rankings]] depend on knowing where regional institutions sit, not where every clinic and community college sits. **`service-places`** filters raw civic data to retain only regional-grade facilities — hospitals with at least 50 staffed beds, universities with at least 1,000 full-time-equivalent students, validated major regional transport hubs — and applies a 200 m spatial buffer to consolidate large institutional campuses into single regional anchors. Local-service density is filtered out at this stage; downstream rankings reflect institutional concentration rather than facility count. ## Key Takeaways @@ -40,14 +40,14 @@ Large institutional campuses frequently appear in raw open geospatial data as mu ## Data Output The resulting `cleansed-places.jsonl` provides the regional anchor dataset that [[app-orchestration-gis]] uses when awarding final [[co-location-methodology|co-location tier rankings]]. The resulting `cleansed-places.jsonl` provides the regional anchor dataset that [[app-orchestration-gis]] uses when awarding final [[retail-co-location-tier-methodology|co-location tier rankings]]. ## See also - [[service-fs-data-lake]] — GIS data lake supplying the raw civic and retail data - [[service-business-clustering]] — retail clustering service that consumes the filtered place data - [[app-orchestration-gis]] — orchestration layer that assembles tier rankings from both services - [[co-location-methodology]] — the ranking methodology driving tier assignments - [[retail-co-location-tier-methodology]] — the tier methodology driving tier assignments ## References