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--- ---
schema: foundry-doc-v1 schema: foundry-doc-v1
title: "PointSav GIS engine" title: "PointSav GIS engine"
slug: pointsav-gis-engine slug: pointsav-gis-engine
category: services category: services
type: topic type: topic
content_type: topic
quality: complete quality: complete
status: active status: active
audience: public audience: public
bcsc_class: public-disclosure-safe bcsc_class: public-disclosure-safe
language_protocol: PROSE-TOPIC language_protocol: PROSE-TOPIC
last_edited: 2026-05-08 last_edited: 2026-05-08
editor: pointsav-engineering editor: pointsav-engineering
short_description: "The PointSav GIS Engine is a high-performance, customer-owned location intelligence platform built in Rust for offline-first, flat-file operation — a structural departure from traditional geographic information systems that rely on centralised database instances and continuous network connectivity." short_description: "The PointSav GIS Engine is a high-performance, customer-owned location intelligence platform built in Rust for offline-first, flat-file operation — a structural departure from traditional geographic information systems that rely on centralised database instances and continuous network connectivity."
paired_with: pointsav-gis-engine.es.md paired_with: pointsav-gis-engine.es.md
cites: cites:
- maplibre-gl-js - maplibre-gl-js
- pmtiles-spec - pmtiles-spec
- tippecanoe-tool - tippecanoe-tool
--- ---
The PointSav GIS Engine is a high-performance, customer-owned location intelligence platform built in Rust for offline-first, flat-file operation — a structural departure from traditional geographic information systems that rely on centralised database instances and continuous network connectivity. The engine reads from a static PMTiles archive on the customer's own [[service-fs-data-lake|filesystem]], renders interactively through MapLibre GL JS in the browser, and serves every query without an external dependency. The map data lives in the customer's archive; nothing leaves the deployment unless the operator explicitly chooses to publish. The PointSav GIS Engine is a high-performance, customer-owned location intelligence platform built in Rust for offline-first, flat-file operation — a structural departure from traditional geographic information systems that rely on centralised database instances and continuous network connectivity. The engine reads from a static PMTiles archive on the customer's own [[service-fs-data-lake|filesystem]], renders interactively through MapLibre GL JS in the browser, and serves every query without an external dependency. The map data lives in the customer's archive; nothing leaves the deployment unless the operator explicitly chooses to publish.
## Architectural Principles ## Architectural Principles
The engine is engineered to operate as a stateless application surface, adhering to the PointSav principle of complete data sovereignty. The engine is engineered to operate as a stateless application surface, adhering to the PointSav principle of complete data sovereignty.
### Flat-File Substrate ### Flat-File Substrate
Unlike traditional GIS stacks (e.g., PostGIS, Esri) which require persistent database management, the PointSav engine uses a flat-file substrate. It consumes geographic data directly from `JSONL`, `GeoParquet`, and `YAML` formats versioned within a Totebox Archive. This architecture ensures the data layer remains entirely decoupled from the application logic, eliminating database maintenance overhead and preventing vendor lock-in. Unlike traditional GIS stacks (e.g., PostGIS, Esri) which require persistent database management, the PointSav engine uses a flat-file substrate. It consumes geographic data directly from `JSONL`, `GeoParquet`, and `YAML` formats versioned within a Totebox Archive. This architecture ensures the data layer remains entirely decoupled from the application logic, eliminating database maintenance overhead and preventing vendor lock-in.
### Sovereign Rendering Stack ### Sovereign Rendering Stack
The platform avoids commercial SaaS mapping dependencies by using a high-performance, open-source rendering stack: The platform avoids commercial SaaS mapping dependencies by using a high-performance, open-source rendering stack:
- **PMTiles:** A single-file archive format for tiled data that enables maps to be served directly from standard web servers (Nginx) or blob storage without a dedicated tile server. [pmtiles-spec] - **PMTiles:** A single-file archive format for tiled data that enables maps to be served directly from standard web servers (Nginx) or blob storage without a dedicated tile server. [pmtiles-spec]
- **MapLibre GL JS:** A WebGL-based library for rendering interactive vector maps with high client-side performance. [maplibre-gl-js] - **MapLibre GL JS:** A WebGL-based library for rendering interactive vector maps with high client-side performance. [maplibre-gl-js]
- **Tippecanoe:** A tool used to compile massive flat-file datasets into optimized vector tiles, ensuring rapid delivery of complex co-location clusters. [tippecanoe-tool] - **Tippecanoe:** A tool used to compile massive flat-file datasets into optimized vector tiles, ensuring rapid delivery of complex co-location clusters. [tippecanoe-tool]
## Spatial Processing and Orchestration ## Spatial Processing and Orchestration
The engine's core logic resides in the [[app-orchestration-gis]] service. This component executes the [[co-location-methodology|Woodfine co-location methodology]] deterministically: The engine's core logic resides in the [[app-orchestration-gis]] service. This component executes the [[co-location-methodology|Woodfine co-location methodology]] deterministically:
1. **Ingestion:** Reads retail and civic infrastructure records from the [[totebox-os|Totebox]] Archive via [[service-business-clustering]] and [[service-places-filtering]]. 1. **Ingestion:** Reads retail and civic infrastructure records from the [[totebox-os|Totebox]] Archive via [[service-business-clustering]] and [[service-places-filtering]].
2. **Analysis:** Executes spatial joins and proximity queries to identify co-location clusters across 1.0 km, 3.0 km, and 5.0 km radii. 2. **Analysis:** Executes spatial joins and proximity queries to identify co-location clusters across 1.0 km, 3.0 km, and 5.0 km radii.
3. **Ranking:** Applies the 12-rank named-anchor matrix to generate site quality tiers. 3. **Ranking:** Applies the 12-rank named-anchor matrix to generate site quality tiers.
4. **Serialization:** Outputs the processed results as tiled data for the visual interface at [gis.woodfinegroup.com](https://gis.woodfinegroup.com). 4. **Serialization:** Outputs the processed results as tiled data for the visual interface at [gis.woodfinegroup.com](https://gis.woodfinegroup.com).
This stateless approach ensures that the entire GIS environment can be re-provisioned instantly from the immutable data layer, providing maximum service resilience and auditability. This stateless approach ensures that the entire GIS environment can be re-provisioned instantly from the immutable data layer, providing maximum service resilience and auditability.
## See also ## See also
- [[co-location-methodology]] — the ranking methodology that drives tier assignment in the GIS engine - [[co-location-methodology]] — the ranking methodology that drives tier assignment in the GIS engine
- [[app-orchestration-gis]] — the orchestration layer that runs the spatial analysis pipeline - [[app-orchestration-gis]] — the orchestration layer that runs the spatial analysis pipeline
- [[service-business-clustering]] — retail clustering service feeding the GIS tier computation - [[service-business-clustering]] — retail clustering service feeding the GIS tier computation
- [[service-places-filtering]] — civic infrastructure filtering service feeding the GIS tier computation - [[service-places-filtering]] — civic infrastructure filtering service feeding the GIS tier computation
- [[service-fs-data-lake]] — the flat-file data lake that backs all GIS source data - [[service-fs-data-lake]] — the flat-file data lake that backs all GIS source data