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PointSav Documentation

The engineering library for the PointSav platform — operating systems and services for regulated businesses that own their data, their AI, and their record-keeping outright. Where the monorepo holds the code, this wiki holds the reasoning: architecture, services, security, and the governance commitments that bind future development.

PointSav Digital Systems

Location intelligence platform

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bdda5fc9 · PointSav Digital Systems ·

editorial(applications): rewrite location-intelligence-platform (Track-B) — fixed stale claims against confirmed-real facts from sibling app-orchestration-gis/location-intelligence-ux rewrites this session: pipeline is a nightly cron rebuild not a stateless engine; real UI is 4-tier Regional/District/Local/Fringe + Clusters/Catchment/OD-Study drawer toggles (confirmed live on gis.woodfinegroup.com), not the old 3-layer Global-POIs/Clusters/Catchment structure; fixed catchment radius (real DEFAULT_CATCHMENT_KM=150.0/DENSE=27.0 confirmed in project-gis/app-orchestration-gis/config.py, resolving the article's own already-flagged 3.0km discrepancy, described generically without citing the config file per register-documentation.yaml); dropped fabricated 'Five-Degree Cluster Identification' algorithm name (real method is two-pass DBSCAN, described generically); fixed Future-Roadmap section claiming OD mobility data and Europe coverage as unbuilt when both are confirmed live; dropped an unverifiable stale POI count; removed 2 mismatched securities-citation IDs (ni-51-102/osc-sn-51-721) with no body use; full ES rewrite to match; register-clean

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@@ -7,59 +7,65 @@ type: topic
content_type: topic
quality: complete
index_group: location-intelligence-applications
short_description: "Customer-owned flat-file GIS application for retail cluster analysis and strategic site selection, pairing an analytics engine with a rendering layer."
short_description: "Customer-owned flat-file GIS application for retail cluster analysis and strategic site selection, pairing a nightly scoring pipeline with an interactive rendering layer."
status: active
audience: public
bcsc_class: public-disclosure-safe
language_protocol: PROSE-TOPIC
last_edited: 2026-08-01
last_edited: 2026-08-22
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
---

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 [[location-intelligence-substrate]] (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 [[retail-co-location-tier-methodology]]. All canonical datasets reside in a [[totebox-archive|Totebox Archive]] as flat JSONL and GeoParquet files, applying the [[worm-ledger-design|WORM ledger discipline]] to geospatial records.
The PointSav Location Intelligence platform is a customer-owned flat-file GIS application for retail cluster analysis and strategic site selection — a nightly [[app-orchestration-gis|pipeline]] that scores and tiers commercial nodes, paired with [[location-intelligence-substrate|a rendering layer]] that serves the result as an interactive map, 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 [[retail-co-location-tier-methodology]]. All canonical datasets reside in a [[totebox-archive|Totebox Archive]] as flat JSONL and GeoParquet files.

## Operational Capabilities
## Operational capabilities

The platform transforms raw store locations into actionable commercial nodes by executing the [[retail-co-location-tier-methodology|Retail Co-location Tier 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).
### Cluster identification

### 2. Multi-Layer Interactive Interface
The interactive map at [gis.woodfinegroup.com](https://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 — Correction, 2026-08-02: the real value used by `build-radius.py` is `DEFAULT_CATCHMENT_KM = 150.0` (27.0 for dense urban corridors), roughly 50× the figure stated here; the 3.0 km figure appears to conflate an unrelated DBSCAN cluster-tightness parameter with the trade-area catchment radius. Flagged, not resolved.) that define the scope for trade-area analysis and mobility data procurement.
The pipeline groups nearby capital-intensive operators (Walmart, Costco, Home Depot, and
similar anchors) and supporting civic infrastructure (hospitals, universities) into clusters
using a spatial clustering algorithm, then scores each cluster and assigns it one of four
tiers per the tier methodology.

## Sovereign Architecture
### Interactive map interface

The platform adheres to [[location-intelligence-substrate|the substrate's]] principles of [[customer-hostability|customer-rooted data sovereignty]]:
- **Flat-File Operation:** All data persists as versioned JSONL and GeoParquet files within a [[totebox-archive|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.
The interactive map at [gis.woodfinegroup.com](https://gis.woodfinegroup.com) renders tier
conclusions rather than individual data points, per the platform's
[[location-intelligence-ux|Conclusion-First design philosophy]]. Analytical layers —
Clusters, Catchment, and OD Study — are presented as primary navigation toggles inside a
drawer component, with the four-tier Regional/District/Local/Fringe color scheme surfacing
the strongest nodes at a national zoom level before a user drills into individual sites.
Trade-area catchment radii default to approximately 150 km for standard regional analysis,
narrowing to roughly 27 km in dense urban corridors.

## Data Foundations and Licensing
## Data sovereignty

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.
The platform's data model is deliberately flat-file rather than a running database daemon:
- **Flat-file operation:** All data persists as versioned JSONL and GeoParquet files within a [[totebox-archive|Totebox Archive]].
- **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:** The application surface can be re-provisioned by pointing a fresh instance at the immutable data layer and re-running the nightly pipeline.

*Material assumptions for current platform performance include the continued availability of high-fidelity open geographic datasets. [osm-odbl] [overture-maps-cdla-2-0]*
## Data foundations and licensing

## Future Roadmap
The platform integrates open data sources for 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.
- **Basemap:** Vector tiles served via the platform's own infrastructure, not a third-party tile API.

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]
Coverage already spans both North American and European markets — origin-destination mobility
data for trade-area flow analysis is live today, not a future addition (the "OD Study" layer
described above). *Material assumptions for current platform performance include the continued
availability of high-fidelity open geographic datasets. [osm-odbl] [overture-maps-cdla-2-0]*

## See also

- [[app-orchestration-gis]] — the stateless analytics engine that produces co-location rankings
- [[app-orchestration-gis]] — the pipeline that produces co-location rankings
- [[location-intelligence-substrate]] — the rendering layer that serves vector tiles to the map interface
- [[retail-co-location-tier-methodology]] — the tier methodology underlying cluster analysis
- [[location-intelligence-ux]] — the UX design philosophy for the interactive map surface
Important Information

Corporate structure. PointSav Digital Systems ("PointSav") is currently a trade name of Woodfine Capital Projects Inc. ("Woodfine"), planned to become a wholly-owned Woodfine subsidiary upon incorporation. PointSav does not itself offer, sell, or solicit any security. Any securities offering associated with Woodfine's real-property direct-hold solutions is made exclusively by Woodfine, and only by means of the applicable Private Placement Memorandum.

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