Who's On First — data model and gotchas

Who's On First (WOF) is the best open gazetteer available. It's also one of the strangest datasets you'll encounter as a developer. This article documents the gotchas — the structural quirks that trip up new consumers — and the tooling Mailwoman built to work around them.

If you just want to understand how Mailwoman's resolver queries WOF at runtime, see Resolver and Who's On First. For the build-time ingest pipeline, see WOF data pipeline. This article is about the data model itself.

What WOF is

WOF is a gazetteer — a structured database of places. Not addresses, not roads, not buildings. Places: countries, regions, counties, cities, neighbourhoods, venues. Each record gets:

A stable numeric ID
A parent-child hierarchy
Multilingual name variants
A polygon geometry (bounding box)

It was created by Mapzen (2018) as the successor to GeoPlanet (Yahoo's gazetteer). The data lives on GitHub as approximately 100 repositories under the whosonfirst-data org — several million individual GeoJSON files. Geocode Earth maintains the canonical SQLite distributions.

The key thing WOF gives you that no other open dataset provides: a consistent hierarchy with stable IDs. You can take a locality (Houston, id 85922029), follow its parent_id to a region (Texas, id 85688753), follow that to a country (United States, id 85633793), and know the chain is consistent. OpenStreetMap doesn't give you this. GeoNames gives you a partial version. WOF gives you the whole thing, with an opinion on how the world's administrative boundaries nest.

The gotchas

One file per place

WOF's canonical storage is one .geojson file per place, organized in a directory tree. A US admin subset has roughly 120,000 individual files. The French equivalent has about 80,000. Opening, parsing, and indexing 200,000 JSON files is a meaningful engineering problem before you've asked a single query.

This layout made sense for WOF's original use case — git-trackable changes to individual places. But for a geocoder that needs to query "all localities named Houston" across 120K files, it's the wrong access pattern. The SQLite distributions from Geocode Earth exist precisely to solve this.

The property namespace explosion

A WOF GeoJSON feature's properties object uses namespaced keys:

{
	"wof:id": 85830005,
	"wof:name": "Lawrence Corner",
	"wof:placetype": "neighbourhood",
	"wof:parent_id": 1729442683,
	"wof:country": "US",
	"wof:hierarchy": [
		{
			"continent_id": 102191575,
			"country_id": 85633793,
			"county_id": 102085493,
			"localadmin_id": 404477193,
			"locality_id": 1729442683,
			"neighbourhood_id": 85830005,
			"region_id": 85688689
		}
	],
	"name:eng_x_preferred": ["Lawrence Corner"],
	"name:eng_x_variant": ["Lawrence Cor"],
	"src:geom": "quattroshapes",
	"edtf:inception": "uuuu",
	"edtf:cessation": "uuuu",
	"geom:area": 0.000047,
	"geom:bbox": "-74.73,40.08,-74.72,40.09",
	"mz:hierarchy_label": 1
}

Key observations:

Namespaced keys everywhere. wof:, name:, src:, edtf:, geom:, mz: — each prefix is a different source or concern. The schema is flat (one object, no nesting) with meaning encoded in the key name.
Name variants are language-coded. name:eng_x_preferred is the preferred English name. name:fra_x_preferred is French. name:zho_x_preferred is Chinese. The _x_ separator splits the language code from the name kind.
Name kinds vary. preferred, variant, colloquial, abbr, short. A single place can have entries for several of these per language. A major city like Paris has name: entries in 50+ languages; a rural US neighbourhood might have only one.
The hierarchy is pre-computed. Instead of walking parent_id up the tree at query time, WOF bakes the full ancestry chain into each record. Convenient for display; redundant for storage; occasionally stale when a parent is reclassified.

Brooklyn Integers

WOF IDs are issued by a service called Brooklyn Integers — a distributed ID generator that guarantees uniqueness across the dataset. The IDs are not sequential, not geographically meaningful, and not sortable. They're just unique numbers. This is fine for lookup but means you can't reason about "nearby" places by ID proximity or infer anything from the numeric value.

Supersession chains

Places get deprecated: a neighbourhood is absorbed by a neighbouring one, a county boundary changes, a locality is merged. WOF tracks this via wof:superseded_by arrays. A query that doesn't check supersession may return a place that hasn't existed since 2015. Every consumer needs to follow the supersession chain to the current record.

Parent ID holes

parent_id: -1 — "we don't know the parent." The first French postalcode dataset was ingested with parent_id: -1 for every record, making hierarchy traversal useless until someone manually assigned parents. Some records still have -1.
parent_id: 0 — "no parent (this is a continent or Earth itself)."
parent_id: 1 — Earth (the root of all hierarchy).

A query that assumes every record has a valid parent chain will fail silently on these cases.

Name normalisation is critical

WOF stores "São Paulo" with the accent. User input might arrive as "Sao Paulo" or "SAO PAULO". Case folding and accent stripping are not optional — they are required for matching. The resolver's placename index normalises all names to a canonical form (NFC, lowercase, accent-stripped) before insertion.

How Mailwoman uses WOF

Mailwoman needs WOF for two distinct access patterns:

1. Rule classifiers — "is this token a known place name?"

The whos_on_first rule classifier answers "is this string a locality/region/country name in any language?" It doesn't need coordinates, hierarchy, or geometry — just the normalised string and which languages it's valid in.

WOFPlacenameCache builds this index by streaming GeoJSON files, extracting name:* properties, normalising them, and inserting into an in-memory Map<string, Set<language>> keyed by the normalised form.

2. Reconcile concordance — "do these components form a valid parent chain?"

Stage 5 joint decoding scores parse candidates against the gazetteer's hierarchy. The reconciler needs richer queries: "give me all localities named Houston with their parent_id chains" and "walk this locality's parent_id up to region — does it reach Texas?"

PlacetypeDataSource is a SQLite database per (placetype, language) pair:

CREATE TABLE records (
  id        INTEGER NOT NULL,
  src       TEXT NOT NULL,
  name      TEXT NOT NULL,
  preferred TEXT,
  variant   TEXT,
  colloquial TEXT,
  abbr      TEXT,
  short     TEXT,
  parent_id INTEGER,
  PRIMARY KEY (id, src, name)
);

One row per name variant. "Saint Petersburg", "St. Petersburg", and "St Petersburg" are three rows for the same id in different name/variant/short columns. The reconciler can match any variant form and get the same parent_id chain.

Ingest tooling

Processing 120K GeoJSON files is an embarrassingly-parallel problem. Mailwoman's WOF ingest pipeline uses Piscina (a Node.js worker-thread pool) to dispatch files across all available CPU cores. Each worker reads a GeoJSON file, calls pluckPlacetypeSpec to extract structured fields and name variants, and upserts into the appropriate PlacetypeDataSource.

When the data arrives as a single bulk NDJSON dump rather than individual files, AsyncSpliterator.asMany(source, delimiter, concurrency) splits the file into N byte-range chunks, snaps to delimiter boundaries, and returns N independent async iterators for parallel processing. It's built but not yet exercised at scale.

For full details on the ingest pipeline, see WOF data pipeline.

So why put up with WOF?

Every geocoder needs a gazetteer. The options are:

Pay for one — Google, HERE, Mapbox
Use an open one — WOF, GeoNames, OSM Nominatim
Build your own — BAN (France), NAD (US), TIGER (US)

WOF is the best open option for hierarchy and multilingual names. But it's hard to use raw. The per-file layout, flat namespace, supersession chains, and parent_id: -1 holes are each a trap for a naive consumer. The tooling Mailwoman built — WOFPlacenameCache, PlacetypeDataSource, the Piscina pipeline, AsyncSpliterator.asMany — closes the gap between "WOF exists" and "WOF is usable as a geocoder component."

What WOF is​

The gotchas​

One file per place​

The property namespace explosion​

Brooklyn Integers​

Supersession chains​

Parent ID holes​

Name normalisation is critical​

How Mailwoman uses WOF​

1. Rule classifiers — "is this token a known place name?"​

2. Reconcile concordance — "do these components form a valid parent chain?"​

Ingest tooling​

So why put up with WOF?​

See also​