Location intelligence platform
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. All canonical datasets reside in a Totebox Archive as flat JSONL and GeoParquet files, applying the WORM ledger discipline to geospatial records.
Operational Capabilities
The platform transforms raw store locations into actionable commercial nodes by executing the Retail Co-location Methodology. It answers a fundamental commercial question: which geographic nodes possess the capital-validated density required to support adjacent development?
1. Five-Degree Cluster Identification
The platform computes co-location clusters around Primary Target anchors (e.g., Walmart Supercentres) using a deterministic spatial algorithm. Each cluster is scored based on the convergence of independent, capital-intensive operators (Costco, Home Depot, etc.) and supporting civic infrastructure (hospitals, universities).
2. Multi-Layer Interactive Interface
The interactive map at gis.woodfinegroup.com uses 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.
Sovereign Architecture
The platform adheres to the pointsav-gis-engine principles of customer-rooted 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: Uses 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.
Material assumptions for current platform performance include the continued availability of high-fidelity open geographic datasets. [osm-odbl] [overture-maps-cdla-2-0]
Future Roadmap
Planned enhancements to the platform surface include the integration of origin-destination (OD) mobility data for trade-area flow analysis and the expansion of the European institutional dataset. [ni-51-102] [osc-sn-51-721]
See also
- app-orchestration-gis — the stateless analytics engine that produces co-location rankings
- pointsav-gis-engine — the rendering layer that serves vector tiles to the map interface
- co-location-methodology — the scoring algorithm underlying cluster analysis
- location-intelligence-ux — the UX design philosophy for the interactive map surface
- totebox-archive — the flat-file archive that holds all canonical geospatial data