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| schema: foundry-doc-v1 | schema: foundry-doc-v1 |
| title: "Places filtering service" | title: "Places filtering service" |
| slug: service-places-filtering | slug: service-places-filtering |
| category: services | category: services |
| type: topic | type: topic |
| content_type: topic | content_type: topic |
| quality: complete | quality: complete |
| status: active | status: active |
| audience: public | audience: public |
| short_description: "service-places filters raw civic and institutional infrastructure data to retain only regional-grade facilities — hospitals, universities, and major transport hubs — so GIS tier rankings reflect institutional-level concentration rather than local-service density." | short_description: "service-places filters raw civic and institutional infrastructure data to retain only regional-grade facilities — hospitals, universities, and major transport hubs — so GIS tier rankings reflect institutional-level concentration rather than local-service density." |
| 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-places-filtering.es.md | paired_with: service-places-filtering.es.md |
| cites: [] | cites: [] |
| --- | --- |
| [[co-location-methodology|GIS tier rankings]] depend on knowing where regional institutions sit, not where every clinic and community college sits. **`service-places`** filters raw civic data to retain only regional-grade facilities — hospitals with at least 50 staffed beds, universities with at least 1,000 full-time-equivalent students, validated major regional transport hubs — and applies a 200 m spatial buffer to consolidate large institutional campuses into single regional anchors. Local-service density is filtered out at this stage; downstream rankings reflect institutional concentration rather than facility count. | [[co-location-methodology|GIS tier rankings]] depend on knowing where regional institutions sit, not where every clinic and community college sits. **`service-places`** filters raw civic data to retain only regional-grade facilities — hospitals with at least 50 staffed beds, universities with at least 1,000 full-time-equivalent students, validated major regional transport hubs — and applies a 200 m spatial buffer to consolidate large institutional campuses into single regional anchors. Local-service density is filtered out at this stage; downstream rankings reflect institutional concentration rather than facility count. |
| ## Key Takeaways | ## Key Takeaways |
| - Regional-grade thresholds are hard-coded into the service: hospitals require ≥50 staffed beds, universities require ≥1,000 full-time-equivalent students, and general aviation facilities are excluded from airport scoring. These filters are structural, not configurable parameters. | - Regional-grade thresholds are hard-coded into the service: hospitals require ≥50 staffed beds, universities require ≥1,000 full-time-equivalent students, and general aviation facilities are excluded from airport scoring. These filters are structural, not configurable parameters. |
| - A 200 m spatial buffer consolidates multi-point campus records — a large hospital campus often appears as dozens of OSM nodes — into a single regional anchor with a unified centroid. This prevents tier over-counting on large institutional footprints. | - A 200 m spatial buffer consolidates multi-point campus records — a large hospital campus often appears as dozens of OSM nodes — into a single regional anchor with a unified centroid. This prevents tier over-counting on large institutional footprints. |
| - Output is `cleansed-places.jsonl`, consumed by `[[app-orchestration-gis]]` alongside the retail cluster dataset from `[[service-business-clustering]]` when assigning final co-location tiers. | - Output is `cleansed-places.jsonl`, consumed by `[[app-orchestration-gis]]` alongside the retail cluster dataset from `[[service-business-clustering]]` when assigning final co-location tiers. |
| - The filtering stage is deliberately upstream of tier scoring. Once local-service density is removed here, all downstream scoring operates on a clean regional-institution signal. | - The filtering stage is deliberately upstream of tier scoring. Once local-service density is removed here, all downstream scoring operates on a clean regional-institution signal. |
| ## Filtering Thresholds | ## Filtering Thresholds |
| The service applies attribute-weight filters to the raw civic data provided by [[service-fs-data-lake|`service-fs`]]: | The service applies attribute-weight filters to the raw civic data provided by [[service-fs-data-lake|`service-fs`]]: |
| - **Regional hospitals:** minimum capacity threshold (50+ staffed beds). | - **Regional hospitals:** minimum capacity threshold (50+ staffed beds). |
| - **Regional universities:** minimum enrollment threshold (1,000+ full-time equivalent students). | - **Regional universities:** minimum enrollment threshold (1,000+ full-time equivalent students). |
| - **Airports:** validated as major regional transport hubs; general aviation facilities are excluded. | - **Airports:** validated as major regional transport hubs; general aviation facilities are excluded. |
| ## Spatial Aggregation | ## Spatial Aggregation |
| Large institutional campuses frequently appear in raw open geospatial data as multiple separate points. `service-places` applies a 200 m spatial buffer to cluster these into a single regional anchor with a unified center of gravity, preventing over-counting of large campus footprints. | Large institutional campuses frequently appear in raw open geospatial data as multiple separate points. `service-places` applies a 200 m spatial buffer to cluster these into a single regional anchor with a unified center of gravity, preventing over-counting of large campus footprints. |
| ## Data Output | ## Data Output |
| The resulting `cleansed-places.jsonl` provides the regional anchor dataset that [[app-orchestration-gis]] uses when awarding final [[co-location-methodology|co-location tier rankings]]. | The resulting `cleansed-places.jsonl` provides the regional anchor dataset that [[app-orchestration-gis]] uses when awarding final [[co-location-methodology|co-location tier rankings]]. |
| ## See also | ## See also |
| - [[service-fs-data-lake]] — GIS data lake supplying the raw civic and retail data | - [[service-fs-data-lake]] — GIS data lake supplying the raw civic and retail data |
| - [[service-business-clustering]] — retail clustering service that consumes the filtered place data | - [[service-business-clustering]] — retail clustering service that consumes the filtered place data |
| - [[app-orchestration-gis]] — orchestration layer that assembles tier rankings from both services | - [[app-orchestration-gis]] — orchestration layer that assembles tier rankings from both services |
| - [[co-location-methodology]] — the ranking methodology driving tier assignments | - [[co-location-methodology]] — the ranking methodology driving tier assignments |
| ## References | ## References |
| - [Point of interest](https://en.wikipedia.org/wiki/Point_of_interest) — Wikipedia, accessed 2026-06-14 | - [Point of interest](https://en.wikipedia.org/wiki/Point_of_interest) — Wikipedia, accessed 2026-06-14 |