Diff: services/service-business-clustering
From c4d1fb1 to c4d1fb1
+0 / −0 lines
| Before | After |
|---|---|
| --- | --- |
| schema: foundry-doc-v1 | schema: foundry-doc-v1 |
| title: "service-business" | title: "service-business" |
| slug: service-business-clustering | slug: service-business-clustering |
| category: services | category: services |
| type: topic | type: topic |
| quality: complete | quality: complete |
| status: active | status: active |
| audience: public | audience: public |
| short_description: "service-business turns raw retail data points into actionable commercial clusters by applying a parent-child spatial schema — so when multiple distinct operators share a single physical site, the GIS engine receives one commercial entity per site rather than several overlapping points." | short_description: "service-business turns raw retail data points into actionable commercial clusters by applying a parent-child spatial schema — so when multiple distinct operators share a single physical site, the GIS engine receives one commercial entity per site rather than several overlapping points." |
| 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 |
| paired_with: service-business-clustering.es.md | paired_with: service-business-clustering.es.md |
| cites: [] | cites: [] |
| --- | --- |
| Retail data is inherently messy — a single commercial site often contains multiple distinct points, such as a big-box anchor, a nested pharmacy, and a fuel outlet sharing the same parking area. **`service-business`** turns those raw points into actionable commercial clusters using a parent-child spatial schema, so the GIS engine receives one unified commercial entity per physical site rather than several overlapping records. The service iterates the `service-fs` raw data lake, groups entities that share a footprint within a 100 m proximity threshold, and assigns the highest-weight named anchor as the parent. | Retail data is inherently messy — a single commercial site often contains multiple distinct points, such as a big-box anchor, a nested pharmacy, and a fuel outlet sharing the same parking area. **`service-business`** turns those raw points into actionable commercial clusters using a parent-child spatial schema, so the GIS engine receives one unified commercial entity per physical site rather than several overlapping records. The service iterates the `service-fs` raw data lake, groups entities that share a footprint within a 100 m proximity threshold, and assigns the highest-weight named anchor as the parent. |
| ## The Clustering Logic | ## The Clustering Logic |
| Retail data is inherently messy. A single commercial node often contains multiple distinct points — for example, a big-box anchor store, a nested pharmacy, and a fuel outlet in the same parking area. `service-business` processes these points so that the GIS engine produces a single, unified commercial entity per physical site. | Retail data is inherently messy. A single commercial node often contains multiple distinct points — for example, a big-box anchor store, a nested pharmacy, and a fuel outlet in the same parking area. `service-business` processes these points so that the GIS engine produces a single, unified commercial entity per physical site. |
| ### Grid-Based Spatial Indexing | ### Grid-Based Spatial Indexing |
| To perform this at scale, the service uses a grid-based spatial index (approximately 1 km cells). It iterates through the `service-fs` raw data lake and groups entities that share a physical footprint within a 100 m proximity threshold. | To perform this at scale, the service uses a grid-based spatial index (approximately 1 km cells). It iterates through the `service-fs` raw data lake and groups entities that share a physical footprint within a 100 m proximity threshold. |
| ### Parent-Child Schema | ### Parent-Child Schema |
| - **Parent node:** The primary commercial driver — typically the highest-weight named anchor at the site. | - **Parent node:** The primary commercial driver — typically the highest-weight named anchor at the site. |
| - **Children (sub-entities):** Secondary operators located within the same spatial node. | - **Children (sub-entities):** Secondary operators located within the same spatial node. |
| ## Cleansed Data Output | ## Cleansed Data Output |
| The output is a refined `cleansed-clusters.jsonl` file. This processed dataset is consumed by the downstream `app-orchestration-gis` to build the regional co-location index. | The output is a refined `cleansed-clusters.jsonl` file. This processed dataset is consumed by the downstream `app-orchestration-gis` to build the regional co-location index. |
| ## See Also | ## See Also |
| - [[app-orchestration-gis]] | - [[app-orchestration-gis]] |
| - [[service-fs-data-lake]] | - [[service-fs-data-lake]] |
| - [[service-places-filtering]] | - [[service-places-filtering]] |