cterdam/iata-metro

IATA Metro Codes with Lang, Hash, and Coordinates

IATA metropolitan area codes with language selection, deterministic hashing, and geographic coordinates.

Fields

• code (string): IATA metro code (e.g., 'NYC', 'MOW', 'SVO')
• region (string): ISO 3166-1 alpha-2 country code
• subdiv (string): ISO 3166-2 subdivision code (if applicable)
• name (string): Primary name in selected language
• name_en (string): English name
• name_ru (string): Russian name
• name_zh (string): Chinese name
• lang (string): Language of primary name field (en, ru, or zh)
  • Determined by region's language rule
• hash (string): SHA256 hash of name field (64 hex chars)
  • Used for sharding and collision detection
• lat (float): Latitude coordinate (decimal degrees, -90 to 90)
• lon (float): Longitude coordinate (decimal degrees, -180 to 180)
• names_en_alt (list): Alternative English names
• names_ru_alt (list): Alternative Russian names
• names_zh_alt (list): Alternative Chinese names
• names_other (list): Names in other languages
• links (list): Reference links
• note (string): Additional notes

Language Rule

Languages are determined by region using hardcoded mapping (same as iso_3166-1_alpha-2).

Hash Field

The hash field is the SHA256 hash of the name field:

• Format: 64-character hexadecimal string
• Deterministic: same name always produces same hash
• Used for entry sharding in doc repositories (first 6 hex chars = shard ID)
• Enables collision detection

Geographic Coverage

• Coordinates: 99.4% coverage (9026/9081 entries)
• Format: WGS84 (EPSG:4326)
• Precision: Approximately city-level

Usage

from datasets import load_dataset

ds = load_dataset('cterdam/iata-metro', split='train')

# Get Moscow metro
moscow = ds.filter(lambda x: x['code'] == 'MOW')[0]
print(moscow)
# {
#   'code': 'MOW',
#   'region': 'RU',
#   'subdiv': 'RU-MOW',
#   'name': 'Москва',
#   'lang': 'ru',
#   'lat': 55.7558,
#   'lon': 37.6173,
#   'hash': '...'
# }

# Find metros in China
china_metros = ds.filter(lambda x: x['region'] == 'CN')
print(f"Found {len(china_metros)} metro areas in China")

# Geographic queries
nearby = ds.filter(lambda x: abs(x['lat'] - 40.7) < 1 and abs(x['lon'] + 74.0) < 1)

See Also

• cterdam/iso_3166-1_alpha-2: Countries/regions with lang and hash
• cterdam/iso_3166-2: Subdivisions with lang and hash

Invariants

All entries in this dataset maintain the following invariants (verified by test/test_invariants.py):

Name Field

• Required: Every entry has a name field
• Constraint: name always equals one of name_en, name_ru, or name_zh (based on lang)
• Example: If lang: ru, then name == name_ru

Lang Field

• Required: Every entry has a lang field
• Values: One of en, ru, or zh
• Determination: Hardcoded by region (not inferred from text)
  • Slavic regions (RU, UA, BY, KZ, etc.) → lang: ru
  • Sinophone regions (CN, TW, HK, JP, etc.) → lang: zh
  • All others → lang: en

Hash Field

• Required: Every entry with a non-null name has a hash field
• Format: 64-character hexadecimal string (SHA256)
• Constraint: hash == SHA256(name)
• Usage: First 6 hex digits used for sharding in source repositories

Verify Invariants

Clone the dataset and run tests:

```bash git clone https://huggingface.co/datasets/cterdam/iso_3166-1_alpha-2 cd iso_3166-1_alpha-2

Install dependencies

pip install datasets

Run invariant tests

pytest test/test_invariants.py -v ```

Expected output: All tests pass, verifying dataset integrity.