Leisure Facilities Data Providers: Site Inventories, Live Sessions and Demand Economics · Head-to-head
Overpass API — OSM Leisure Query Engine vs Recreation Information Database (RIDB) / Recreation.gov
Which leisure facilities data providers: site inventories, live sessions and demand economics data fits your job: Overpass API — OSM Leisure Query Engine, or Recreation Information Database / Recreation.gov. API, files, or your warehouse. Daily, weekly, or hourly.
Overpass API — OSM Leisure Query Engine
Recreation Information Database (RIDB) / Recreation.gov
Coverage, side by side
| Overpass API — OSM Leisure Query Engine | Recreation Information Database / Recreation.gov | |
|---|---|---|
| Geographic | Global OpenStreetMap coverage | United States federal lands and waters (USFS, NPS, BLM, USACE, FWS and other agencies) |
| Temporal | Live OSM data, minutes behind edits; history queries possible but changeset-based criteria unsupported | Continuously maintained; LastUpdatedDate per record supports incremental pulls |
| Granularity | Individual OSM elements (nodes/ways/relations) with all tags | RecArea > Facility > Campsite/Tour/Permit Entrance hierarchy with point coordinates and GeoJSON |
What each contains
They tie on 1 attribute. Pick by fit, not by loyalty.
| Overpass API — OSM Leisure Query Engine | Recreation Information Database / Recreation.gov | |
|---|---|---|
| Publisher | OpenStreetMap Overpass | Recreation.gov (RIDB) |
| Geographic coverage | Global OpenStreetMap coverage | United States federal lands and waters (USFS, NPS, BLM, USACE, FWS and other agencies) |
| Temporal coverage | Live OSM data, minutes behind edits; history queries possible but changeset-based criteria unsupported | Continuously maintained; LastUpdatedDate per record supports incremental pulls |
| Granularity | Individual OSM elements (nodes/ways/relations) with all tags | RecArea > Facility > Campsite/Tour/Permit Entrance hierarchy with point coordinates and GeoJSON |
| Field dictionary | 8 documented fields, verified during research | 22 documented fields, verified during research |
| Formats | JSON, OSM XML, CSV, GeoJSON | JSON, GeoJSON, CSV |
| Scale | Full OSM planet scale on demand; tens to millions of elements per scoped query | Tens of thousands of facilities across hundreds of recreation areas |
| Best for | Global tag-driven leisure mapping: gyms, parks, pools, playgrounds by place or category | US federal recreation inventory: campgrounds, fees, accessibility, reservability |
What each does better
Overpass API
Global reach with arbitrary slicing. Coverage is worldwide OpenStreetMap, and the query language selects by tag, by bounding box or by named area, with regular-expression matching on top. The record shows the shape: node["leisure"="fitness_centre"](51.49,-0.14,51.51,-0.11) returns every mapped gym in central London in one statement — single queries scale from tens to millions of elements.
Any private amenity, not just official ones. Because mappers tag what exists, the engine sees commercial gyms by brand — the sample rows carry Virgin Active, Fitness First and Bannatynes — alongside playgrounds, pitches and pools. RIDB cannot see any of that: its remit stops at the federal estate boundary.
Element-level geometry. Responses come back as nodes, ways and relations with all their tags, so a swimming pool arrives as its actual outline rather than a centroid point, rendered as JSON, OSM XML, CSV or GeoJSON. Each response embeds osm3s.timestamp_osm_base, the snapshot timestamp that answered you — currency stated inside the payload itself.
RIDB
Operational depth per facility. Twenty-two documented fields reach past location into operations: FacilityUseFeeDescription for charges, FacilityAdaAccess and FacilityAccessibilityText for wheelchair access, StayLimit rules, Reservable and Enabled switches, FacilityPhone, plus GEOJSON footprints and LastUpdatedDate. The sample row proves it: UNION CREEK CAMPGROUND, FacilityID 233115 (legacy 71992, agency ID AN371992), Campground at -118.0186111, 44.6969444, Reservable false, Enabled true.
A governed hierarchy instead of a flat cloud of points. Records nest RecArea > Facility > Campsite/Tour/Permit Entrance, with address records and media attached, so a campground rolls up to its recreation area cleanly — structure OSM never guarantees, because any mapper can tag anything anywhere.
Bookability. Reservable and FacilityReservationURL mark which facilities can actually be booked through Recreation.gov, and OrgFacilityID ties each row back to the owning agency's internal identifier. For capacity or demand planning around federal recreation, those fields turn a map dot into a unit of supply.
Where they're equivalent
More than the field counts suggest. Both dictionaries were verified during research, both score 9/10, and both are point-of-interest records keyed by a stable identifier and located by decimal-degree coordinates, so rows from either side drop onto one map. Both publish JSON and CSV among their formats, both carry descriptive naming, and both state their own freshness — Overpass through osm3s.timestamp_osm_base, RIDB through LastUpdatedDate.
They share limits too. Neither retains a historical series for trend work: Overpass answers some history queries but supports no changeset-based criteria, and RIDB keeps only the latest update stamp per record. Neither vouches for completeness either — OSM leisure tagging is volunteer-shaped, while RIDB notes agencies remain responsible for their own data quality.
The verdict
Verdict: sample both, pick by fit — they map different halves of the same leisure economy.
Accept its frame: attribute depth rides on what volunteers tagged, so a gym may be a named point and nothing more.
Take Recreation Information Database (RIDB) / Recreation.gov if your question lives on US federal land. Campground supply and reservable-capacity studies, ADA-access coverage gaps, fee-description comparison across agencies, permit-entrance and tour inventories, incremental syncs keyed on LastUpdatedDate — anything answered by reading an inventory. Accept its frame: 22 fields of operational detail, zero visibility beyond the federal estate.
Sample both, pick by fit. See Overpass API — OSM Leisure Query Engine · See Recreation Information Database / Recreation.gov
Or take both in one feed
Yes — they stack into one leisure picture neither completes alone. Sketch the demand side globally with Overpass: every fitness centre, swimming pool, playground and sports pitch in a region, sliced by tag and area, exported as GeoJSON. Then overlay the US supply side: campgrounds, ranger stations and trails on federal land with fees, accessibility text, stay limits and reservable flags attached.
Three alignments decide whether the merge holds. First, identity: Overpass's type-plus-id pairs never equal RIDB's FacilityID, so match on normalized names plus coordinates. Second, geometry: node lat/lon versus facility points and GeoJSON footprints — buffer-match points against polygons. Third, classification: open tags.leisure vocabularies must map onto RIDB's fixed FacilityTypeDescription list before any roll-up is honest. Do that, and the pair covers private and public leisure in one schema. Or take both in one feed.
API, files, or your warehouse. Daily, weekly, or hourly.
Fair questions
Which dataset documents more fields per record?
RIDB, nearly three times over: 22 verified fields against Overpass's 8. The gap is operational rather than positional - both carry coordinates and identifiers, but RIDB adds FacilityUseFeeDescription, FacilityAccessibilityText, StayLimit, Reservable, Enabled, GEOJSON geometry and LastUpdatedDate. Overpass spends its fields on element type, id, lat, lon, tags.leisure, tags.name and the snapshot timestamp.
Can the two datasets be joined in one analysis?
Yes, with three alignment rules. Match records on normalized names plus nearby coordinates, since Overpass type-plus-id keys never equal RIDB FacilityIDs. Treat node lat/lon and GeoJSON footprints as buffer-matching problems, not exact-coordinate joins. Map open tags.leisure vocabularies onto RIDB's fixed FacilityTypeDescription list before aggregating. Done properly, one schema spans private gyms and federal campgrounds alike.
Can Datadory deliver both datasets together?
Yes. Both records score 9/10 in Datadory's Leisure Facilities slice - two of 16 pooled there out of 1,744 cataloged overall - and both ship with verified field dictionaries and sample rows. Datadory delivers them as one feed, daily, weekly, or hourly - your call, aligned to whichever cadence your leisure analytics run on.