Location intelligence platform
docs(2g): corpus-wide Doctrine claim vocabulary removal (227 files)
@@ -14,10 +14,10 @@ last_edited: 2026-05-08 editor: pointsav-engineering paired_with: location-intelligence-platform.es.md cites: - osm-odbl - overture-maps-cdla-2-0 - ni-51-102 - osc-sn-51-721 - osm-odbl - overture-maps-cdla-2-0 - ni-51-102 - osc-sn-51-721 --- The PointSav Location Intelligence platform is a customer-owned flat-file GIS application designed for retail cluster analysis and strategic site selection — composed of [[app-orchestration-gis]] (the analytics engine) and [[pointsav-gis-engine]] (the rendering layer), with every dataset, algorithm, and rendering decision under the customer's direct control. The platform answers a fundamental commercial question — *which geographic nodes possess the capital-validated density required to support adjacent development?* — by transforming raw store locations into actionable commercial nodes through the [[co-location-methodology]]. @@ -31,23 +31,23 @@ The platform computes co-location clusters around Primary Target anchors (e.g., ### 2. Multi-Layer Interactive Interface The interactive map at [gis.woodfinegroup.com](https://gis.woodfinegroup.com) utilizes a three-layer architecture: - **Layer 1 — Global POIs:** Toggled view of 31,000+ individual retail locations, color-coded by brand family. - **Layer 2 — Co-location Clusters:** The primary analytical view, encoding cluster strength through visual saturation and size. - **Layer 3 — Catchment Radii:** Visualized proximity boundaries (default 3.0 km) that define the scope for trade-area analysis and mobility data procurement. - **Layer 1 — Global POIs:** Toggled view of 31,000+ individual retail locations, color-coded by brand family. - **Layer 2 — Co-location Clusters:** The primary analytical view, encoding cluster strength through visual saturation and size. - **Layer 3 — Catchment Radii:** Visualized proximity boundaries (default 3.0 km) that define the scope for trade-area analysis and mobility data procurement. ## Sovereign Architecture The platform adheres to the [PointSav GIS Engine](pointsav-gis-engine) principles of data sovereignty: - **Flat-File Operation:** All data persists as versioned JSONL and GeoParquet files within a Totebox Archive, rather than a running database daemon. - **Open Standards Rendering:** Utilizes PMTiles and MapLibre GL JS to serve vector maps directly from standard web servers, eliminating proprietary tile-API dependencies. - **Reproducible Build:** If a gateway node is destroyed, the application surface can be re-provisioned instantly by pointing a fresh instance at the immutable data layer. - **Flat-File Operation:** All data persists as versioned JSONL and GeoParquet files within a Totebox Archive, rather than a running database daemon. - **Open Standards Rendering:** Utilizes PMTiles and MapLibre GL JS to serve vector maps directly from standard web servers, eliminating proprietary tile-API dependencies. - **Reproducible Build:** If a gateway node is destroyed, the application surface can be re-provisioned instantly by pointing a fresh instance at the immutable data layer. ## Data Foundations and Licensing The platform integrates high-fidelity open data sources to ensure transparency and auditability: - **Retail Data:** Sourced from OpenStreetMap contributors and the Overture Maps Foundation. - **Civic Infrastructure:** Healthcare and institutional records from the Overture Maps Foundation Places dataset. - **Sovereign Basemap:** OpenFreeMap liberty tiles served via the PointSav infrastructure. - **Retail Data:** Sourced from OpenStreetMap contributors and the Overture Maps Foundation. - **Civic Infrastructure:** Healthcare and institutional records from the Overture Maps Foundation Places dataset. - **Sovereign Basemap:** OpenFreeMap liberty tiles served via the PointSav infrastructure. *Material assumptions for current platform performance include the continued availability of high-fidelity open geographic datasets. [osm-odbl] [overture-maps-cdla-2-0]*