Location intelligence substrate

@@ -9,9 +9,11 @@ content_type: topic
quality: complete
status: active
bcsc_class: public-disclosure-safe
last_edited: 2026-05-09
last_edited: 2026-08-01
editor: pointsav-engineering
paired_with: location-intelligence-substrate.es.md
aliases:
  - pointsav-gis-engine
references:
  - id: 1
    text: "Overture Maps Foundation — GeoParquet places schema. overturemaps.org"
@@ -33,7 +35,7 @@ references:
    text: "OSC Staff Notice 51-721 Forward-Looking Information Disclosure"
---

The Location Intelligence Substrate is a flat-file, open-GIS architecture that lets [[customer-hostability|customers own their geographic datasets end-to-end]] — no tile API billing, no warehouse licensing, no cloud-vendor lock-in. The substrate is built on Apache-licensed open-data foundations (Overture Maps Foundation, Foursquare Open Source Places) and rendered via a Rust-aligned open-source stack (MapLibre GL JS, Martin tile server, PMTiles).[^1][^2]
A platform that depends on a running database and a live network connection is a platform a customer rents, not owns — outages, per-seat cost, and air-gap ineligibility follow. The Location Intelligence Substrate avoids that dependency by construction: it is a flat-file, open-GIS architecture that lets [[customer-hostability|customers own their geographic datasets end-to-end]] — no tile API billing, no warehouse licensing, no cloud-vendor lock-in. The substrate is built on Apache-licensed open-data foundations (Overture Maps Foundation, Foursquare Open Source Places) and rendered via a Rust-aligned open-source stack (MapLibre GL JS, Martin tile server, PMTiles).[^1][^2]

The first deployed surface is `gis.woodfinegroup.com` — a co-location map showing retail anchor co-presence across the United States, Canada, Mexico, and Spain.

@@ -83,18 +85,20 @@ A single record shape covers all three Ring 1 location services with discriminat

Brand-family normalisation lets co-location queries treat regional equivalents as one logical operator across countries. `service-places` carries a `place_type` field (hospital, higher-education, airport). `service-parking` carries a Polygon `geometry` (the lot geofence) rather than a Point, plus an `associated_business_id` linking the lot to its anchor business when known.

## Co-location analysis algorithm
## Tier rendering

The co-location query identifies locations from brand family A within 1 km, 2 km, and 3 km of locations from brand family B (and optionally a third family). Algorithm:
Cluster records carry a `tier` property once [[app-orchestration-gis]] applies the
[[retail-co-location-tier-methodology|tier methodology]] to the ingested data. The
substrate's job past that point is presentation: emit a GeoJSON `FeatureCollection` per
cluster (anchor points, a catchment-radius polygon, and the `tier` property), and let the
browser layers render it — POIs as circles colored by brand family (Layer 1), tiered
clusters with catchment haloes (Layer 2), and country boundaries with filter chips (Layer
3). Hover popovers surface brand, format, year opened, and tier without a page navigation.

1. Iterate every record in brand family A.
2. For each, find the nearest record in each other brand family using a haversine distance against an in-memory R-tree index. At tens of thousands of records, each lookup runs in microseconds.
3. Bucket each multi-brand tuple by the maximum pairwise distance: `<1 km`, `1–2 km`, `2–3 km`, `>3 km`.
4. Emit a GeoJSON FeatureCollection: tuple centroid, triangle polyline connecting the locations, radius circles, and a `cluster_grade` property.

Browser visualisation layers: POIs as circles coloured by brand family (Layer 1); co-location tuples with their radius haloes, toggled by grade (Layer 2); country boundaries and filter chips (Layer 3). Hover popovers show brand, format, year opened, distance to nearest co-located neighbours, and cluster grade.

At 15,000 POI records (combined coverage across four countries and three brand families), client-side rendering in MapLibre is well within comfortable operating range. Supercluster client-side clustering becomes relevant at approximately 50,000 records; server-side vector tile generation at 500,000+.
At 15,000 POI records (combined coverage across four countries and three brand
families), client-side rendering in MapLibre is well within comfortable operating range.
Supercluster client-side clustering becomes relevant at approximately 50,000 records;
server-side vector tile generation at 500,000+.

## Retail co-location research basis

@@ -125,3 +129,5 @@ Statements regarding deployment schedule, customer outcomes, and feature roadmap
- [[three-ring-architecture]] — `service-business`, `service-places`, and `service-parking` are Ring 1 services
- [[substrate-without-inference-base-case]] — GIS substrate functions fully without the AI ring
- [[customer-owned-graph-ip]] — geographic datasets owned by the customer, not the vendor
- [[retail-co-location-tier-methodology]] — the tier gates applied to the `tier` property rendered here
- [[app-orchestration-gis]] — the engine that computes tier assignment from ingested cluster data