Places filtering service — PointSav Documentation

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.

[edit]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.
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.
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.

[edit]Filtering Thresholds

The service applies attribute-weight filters to the raw civic data provided by `service-fs`:

Regional hospitals: minimum capacity threshold (50+ staffed beds).
Regional universities: minimum enrollment threshold (1,000+ full-time equivalent students).
Airports: validated as major regional transport hubs; general aviation facilities are excluded.

[edit]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.

[edit]Data Output

The resulting cleansed-places.jsonl provides the regional anchor dataset that app-orchestration-gis uses when awarding final co-location tier rankings.

[edit]See also

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
app-orchestration-gis — orchestration layer that assembles tier rankings from both services
co-location-methodology — the ranking methodology driving tier assignments

[edit]References

Point of interest — Wikipedia, accessed 2026-06-14