GIS orchestration application
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.
@@ -11,7 +11,7 @@ audience: public short_description: "Stateless spatial analytics engine producing the Woodfine co-location rankings and interactive map — a pure function holding no canonical data." bcsc_class: public-disclosure-safe language_protocol: PROSE-TOPIC last_edited: 2026-05-08 last_edited: 2026-08-01 editor: pointsav-engineering paired_with: app-orchestration-gis.es.md cites: @@ -21,14 +21,13 @@ cites: `app-orchestration-gis` is the stateless spatial analytics engine that performs linear-geometry calculations and coordinate mapping to produce the Woodfine co-location rankings and the interactive map at [gis.woodfinegroup.com](https://gis.woodfinegroup.com). The application holds no canonical data — it operates as a pure function from cleansed cluster files to ranked geo-tiles, so a lost instance can be re-provisioned by pointing a fresh process at the immutable [[totebox-archive|Totebox data layer]] with no state migration. It runs on [[os-orchestration|`os-orchestration`]] and composes with [[service-business-clustering]] and [[service-places-filtering]] to produce its input datasets. ## Scoring Algorithm ## Tier Assignment The engine implements a linear geometric decay model using the Haversine formula. For every Alpha Anchor in the cleansed data layers, it calculates two proximity scores: - **Secondary proximity (3.0 km radius):** score = max(0, 100 × (3.0 − distance_km) / 3.0) - **Tertiary proximity (5.0 km radius):** score = max(0, 100 × (5.0 − distance_km) / 5.0) The two scores combine to produce a continuous co-location score ranging from 0 to 400. Higher scores reflect greater convergence of capital-intensive operators within the defined catchment radii. The engine assigns every cluster one of four tiers by testing it against the [[retail-co-location-tier-methodology|retail co-location tier methodology]] — composition, catchment-population rank, civic support, and non-overlap with stronger neighboring clusters. Tier assignment is a pass/fail classification against fixed gates, not a composite numeric score. ## Tile Generation @@ -36,7 +35,7 @@ The engine compiles scored output into vector tile assets for delivery to the in - **Vector tiles:** PMTiles format for client-side rendering without a dedicated tile server [pmtiles-spec] - **Rendering:** MapLibre GL JS processes the tiles client-side at high performance [maplibre-gl-js] - **Visual tiers:** Spatial convergence across anchor categories (primary, hardware, warehouse, civic) maps to a four-tier visual classification on the map surface, expressed through the [[co-location-methodology|co-location scoring methodology]] - **Visual tiers:** Spatial convergence across anchor categories (primary, hardware, warehouse, civic) maps to the four-tier visual classification on the map surface, per the [[retail-co-location-tier-methodology|tier methodology]] above ## Stateless Architecture @@ -44,12 +43,8 @@ The application holds no canonical data. It operates as a pure function: cleanse ## See also - [[pointsav-gis-engine]] — the rendering layer that serves tiles produced by this engine - [[location-intelligence-substrate]] — the rendering layer that serves tiles produced by this engine - [[service-business-clustering]] — the clustering service that groups POI data into co-location clusters - [[service-places-filtering]] — the places filtering service that prepares cleansed input data - [[co-location-methodology]] — the scoring and ranking methodology implemented by the engine - [[retail-co-location-tier-methodology]] — the tier methodology implemented by the engine - [[location-intelligence-platform]] — the platform article covering the full GIS deployment ## References - [Haversine formula](https://en.wikipedia.org/wiki/Haversine_formula) — Wikipedia, accessed 2026-06-14