geo-l3-antarctica-stations-geodesic

Rerun

Help on package overturemaps:

NAME
    overturemaps

PACKAGE CONTENTS
    __main__
    changelog
    cli
    core
    models
    releases
    state
    writers

FILE
    /usr/local/lib/python3.14/site-packages/overturemaps/__init__.py

python -c "
import overturemaps
print(dir(overturemaps))
print('---')
print(overturemaps.__version__ if hasattr(overturemaps, '__version__') else 'no version')
"

python -c "
from overturemaps.core import available_themes, available_types
themes = available_themes()
print('Themes:', themes)
for t in themes:
    types = available_types(t)
    print(f'  {t}: {list(types.keys())[:10]}')
"

Traceback (most recent call last):
  File "<string>", line 2, in <module>
    from overturemaps.core import available_themes, available_types
ImportError: cannot import name 'available_themes' from 'overturemaps.core' (/usr/local/lib/python3.14/site-packages/overturemaps/core.py)

python -c "
from overturemaps.core import record_batch_reader
import inspect
print(inspect.signature(record_batch_reader))
print('---')
print(inspect.getsource(record_batch_reader))
" 2>&1 | head -60

(overture_type, bbox=None, release=None, connect_timeout=None, request_timeout=None, stac=False) -> pyarrow.lib.RecordBatchReader | None
---
def record_batch_reader(
    overture_type,
    bbox=None,
    release=None,
    connect_timeout=None,
    request_timeout=None,
    stac=False,
) -> Optional[pa.RecordBatchReader]:
    """Return a pyarrow RecordBatchReader for the desired bounding box and s3 path, or None on error."""
    result = _prepare_query(
        overture_type, bbox, release, connect_timeout, request_timeout, stac
    )
    if result is None:
        return None
    dataset, filter_expr = result
    return _record_batch_reader_from_dataset(dataset, filter_expr=filter_expr)

python -c "
import overturemaps.core as c
print([x for x in dir(c) if not x.startswith('_')])
"

python -c "
from overturemaps.core import get_all_overture_types
types = get_all_overture_types()
for t in sorted(types):
    print(t)
"

address
bathymetry
building
building_part
connector
division
division_area
division_boundary
infrastructure
land
land_cover
land_use
place
segment
water

python -c "
import pyarrow as pa
from overturemaps.core import record_batch_reader

# Check place schema
r = record_batch_reader('place')
if r:
    print('Place schema:')
    print(r.schema)
else:
    print('place failed')
"

Place schema:
id: string
geometry: binary
  -- field metadata --
  ARROW:extension:name: 'geoarrow.wkb'
categories: struct<primary: string, alternate: list<element: string>>
  child 0, primary: string
  child 1, alternate: list<element: string>
      child 0, element: string
confidence: double
websites: list<element: string>
  child 0, element: string
emails: list<element: string>
  child 0, element: string
socials: list<element: string>
  child 0, element: string
phones: list<element: string>
  child 0, element: string
brand: struct<wikidata: string, names: struct<primary: string, common: map<string, string ('common')>, rule (... 192 chars omitted)
  child 0, wikidata: string
  child 1, names: struct<primary: string, common: map<string, string ('common')>, rules: list<element: struct<variant: (... 159 chars omitted)
      child 0, primary: string
      child 1, common: map<string, string ('common')>
          child 0, common: struct<key: string not null, value: string> not null
              child 0, key: string not null
              child 1, value: string
      child 2, rules: list<element: struct<variant: string, language: string, perspectives: struct<mode: string, countries (... 87 chars omitted)
          child 0, element: struct<variant: string, language: string, perspectives: struct<mode: string, countries: list<element (... 72 chars omitted)
              child 0, variant: string
              child 1, language: string
              child 2, perspectives: struct<mode: string, countries: list<element: string>>
                  child 0, mode: string
                  child 1, countries: list<element: string>
                      child 0, element: string
              child 3, value: string
              child 4, between: list<element: double>
                  child 0, element: double
              child 5, side: string
addresses: list<element: struct<freeform: string, locality: string, postcode: string, region: string, country:  (... 8 chars omitted)
  child 0, element: struct<freeform: string, locality: string, postcode: string, region: string, country: string>
      child 0, freeform: string
      child 1, locality: string
      child 2, postcode: string
      child 3, region: string
      child 4, country: string
names: struct<primary: string, common: map<string, string ('common')>, rules: list<element: struct<variant: (... 159 chars omitted)
  child 0, primary: string
  child 1, common: map<string, string ('common')>
      child 0, common: struct<key: string not null, value: string> not null
          child 0, key: string not null
          child 1, value: string
  child 2, rules: list<element: struct<variant: string, language: string, perspectives: struct<mode: string, countries (... 87 chars omitted)
      child 0, element: struct<variant: string, language: string, perspectives: struct<mode: string, countries: list<element (... 72 chars omitted)
          child 0, variant: string
          child 1, language: string
          child 2, perspectives: struct<mode: string, countries: list<element: string>>
              child 0, mode: string
              child 1, countries: list<element: string>
                  child 0, element: string
          child 3, value: string
          child 4, between: list<element: double>
              child 0, element: double
          child 5, side: string
sources: list<element: struct<property: string, dataset: string, license: string, record_id: string, update_t (... 65 chars omitted)
  child 0, element: struct<property: string, dataset: string, license: string, record_id: string, update_time: string, c (... 50 chars omitted)
      child 0, property: string
      child 1, dataset: string
      child 2, license: string
      child 3, record_id: string
      child 4, update_time: string
      child 5, confidence: double
      child 6, between: list<element: double>
          child 0, element: double
operating_status: string
basic_category: string
taxonomy: struct<primary: string, hierarchy: list<element: string>, alternates: list<element: string>>
  child 0, primary: string
  child 1, hierarchy: list<element: string>
      child 0, element: string
  child 2, alternates: list<element: string>
      child 0, element: string
version: int32
bbox: struct<xmin: double, xmax: double, ymin: double, ymax: double>
  child 0, xmin: double
  child 1, xmax: double
  child 2, ymin: double
  child 3, ymax: double
-- schema metadata --
geo: '{"version": "1.1.0", "primary_column": "geometry", "columns": {"geo' + 275

python -c "
from overturemaps.core import record_batch_reader

for t in ['land', 'water', 'bathymetry']:
    r = record_batch_reader(t)
    if r:
        print(f'{t} schema:')
        print(r.schema)
        print('---')
"

python -c "
from overturemaps.core import record_batch_reader, get_all_overture_types

# Just get the schema without downloading much data
for t in ['place', 'water', 'land', 'bathymetry']:
    try:
        r = record_batch_reader(t, bbox=(-180, -90, 180, -60))
        if r:
            print(f'{t} schema columns:')
            for field in r.schema:
                print(f'  {field.name}: {field.type}')
            print()
    except Exception as e:
        print(f'{t}: {e}')
" 2>&1

place schema columns:
  id: string
  geometry: binary
  categories: struct<primary: string, alternate: list<element: string>>
  confidence: double
  websites: list<element: string>
  emails: list<element: string>
  socials: list<element: string>
  phones: list<element: string>
  brand: struct<wikidata: string, names: struct<primary: string, common: map<string, string ('common')>, rules: list<element: struct<variant: string, language: string, perspectives: struct<mode: string, countries: list<element: string>>, value: string, between: list<element: double>, side: string>>>>
  addresses: list<element: struct<freeform: string, locality: string, postcode: string, region: string, country: string>>
  names: struct<primary: string, common: map<string, string ('common')>, rules: list<element: struct<variant: string, language: string, perspectives: struct<mode: string, countries: list<element: string>>, value: string, between: list<element: double>, side: string>>>
  sources: list<element: struct<property: string, dataset: string, license: string, record_id: string, update_time: string, confidence: double, between: list<element: double>>>
  operating_status: string
  basic_category: string
  taxonomy: struct<primary: string, hierarchy: list<element: string>, alternates: list<element: string>>
  version: int32
  bbox: struct<xmin: double, xmax: double, ymin: double, ymax: double>

water schema columns:
  id: string
  names: struct<primary: string, common: map<string, string ('common')>, rules: list<element: struct<variant: string, language: string, perspectives: struct<mode: string, countries: list<element: string>>, value: string, between: list<element: double>, side: string>>>
  subtype: string
  class: string
  sources: list<element: struct<property: string, dataset: string, license: string, record_id: string, update_time: string, confidence: double, between: list<element: double>>>
  source_tags: map<string, string ('source_tags')>
  level: int32
  wikidata: string
  is_intermittent: bool
  is_salt: bool
  geometry: binary
  version: int32
  bbox: struct<xmin: double, xmax: double, ymin: double, ymax: double>

land schema columns:
  id: string
  names: struct<primary: string, common: map<string, string ('common')>, rules: list<element: struct<variant: string, language: string, perspectives: struct<mode: string, countries: list<element: string>>, value: string, between: list<element: double>, side: string>>>
  subtype: string
  class: string
  sources: list<element: struct<property: string, dataset: string, license: string, record_id: string, update_time: string, confidence: double, between: list<element: double>>>
  source_tags: map<string, string ('source_tags')>
  level: int32
  wikidata: string
  surface: string
  elevation: int32
  geometry: binary
  version: int32
  bbox: struct<xmin: double, xmax: double, ymin: double, ymax: double>

bathymetry schema columns:
  id: string
  depth: int32
  cartography: struct<prominence: int32, min_zoom: int32, max_zoom: int32, sort_key: int32>
  geometry: binary
  sources: list<element: struct<property: string, dataset: string, license: string, record_id: string, update_time: string, confidence: double, between: list<element: double>>>
  version: int32
  bbox: struct<xmin: double, xmax: double, ymin: double, ymax: double>

python -c "
from overturemaps.core import record_batch_reader
import pyarrow as pa
import pyarrow.compute as pc

# Get a sample of places south of -60 to see what categories exist
r = record_batch_reader('place', bbox=(-180, -90, 180, -60))
if r:
    tbl = r.read_next_batch()
    print(f'Got {len(tbl)} rows')
    # Check categories
    cats = tbl.column('categories')
    print(type(cats))
    print(cats[0])
else:
    print('Failed')
" 2>&1

python -c "
from overturemaps.core import record_batch_reader
import pyarrow as pa
import pyarrow.compute as pc

# Get all places south of -60 to see what's there
r = record_batch_reader('place', bbox=(-180, -90, 180, -60))
if r:
    tbl = r.read_all()
    print(f'Total: {len(tbl)} rows')
    for i in range(len(tbl)):
        row = tbl.slice(i, 1)
        names = row.column('names')[0]
        cats = row.column('categories')[0]
        idv = row.column('id')[0].as_py()
        primary = cats['primary'].as_py()
        name = names['primary'].as_py() if names else None
        print(f'{idv}: name={name}, category={primary}')
else:
    print('Failed')
" 2>&1

Total: 246 rows
ff93e6a3-b7fc-4691-980a-c36820a301c4: name=Poeanuanua, category=retail
e4cd796b-b4c6-47e1-927b-79edfc50c48c: name=Muebles Marispal Algeciras, category=furniture_store
52dd18ca-aaae-4f06-aa53-06349b302c66: name=Lamoon Cafe & Restaurant - สระบุรี, category=cafe
b01d6401-437f-4be8-bb25-21055d6881fa: name=Cap College Mariveles Bataan, category=college_university
d61f38d3-a73b-44d6-ae69-1120a1518aaf: name=מעדני מניה Maadaney Mania, category=delicatessen
855105af-cb9a-4c55-a72e-c5ae356f1c5e: name=Te Fare Informatique, category=hardware_store
237cf98b-d9b0-4756-82ee-528c55f41e29: name=Samira's Hair Fashion +599 98693311, category=beauty_salon
43c099d7-9d1b-41a3-8e43-320e2eacf6fa: name=CAMBO Cleaner, category=None
d6149cd5-5a22-43e1-ae86-e7577501d0db: name=Iriga City Public Library, category=public_and_government_association
06cbc176-ade9-45fa-bf4d-1e1047961cd3: name=Dinastia Nortena, category=None
37b38556-e96f-4489-8369-198afd14715f: name=Ser Esencia Restaurant, category=restaurant
a8fae705-fa37-4416-9500-53834b942c97: name=مدينة رشيد Rosetta city, category=None
e926755f-2666-4c23-b411-a44ec025a072: name=Blessed Souls Photography & Film, category=professional_services
2a57ff3e-de19-4d8c-9a80-894baa013331: name=黎齊妙中醫診所, category=hospital
cae3dffc-923f-486f-a9dd-b3978352cd3a: name=dieukhaclienvu.com - phù điêu,điêu khắc đá nhân tạo composite,cảnh quan đẹp, category=arts_and_crafts
6c026c49-4a4f-44a9-b18b-2e858b4176bd: name=R-AGE Nation Apparel, category=womens_clothing_store
d586b57b-b36c-41c8-8c6b-0121de8150d0: name=Basketball Forever, category=mass_media
f4ed31ee-3bc1-49e9-8a28-3005e0e22ac1: name=Marc Silber, category=professional_services
f105891e-42e1-4c5f-90c2-c2932966b6e9: name=Tama Manège Import, category=park
4acb8ee7-5b68-48c7-b77c-a1849ac3280b: name=Irantha Fonseka, category=None
34c0cba6-9f9d-4eeb-bb37-63e4fd235441: name=Ozhousestudio, category=topic_concert_venue
830e25c9-3ec0-488e-921c-11670b9d5c98: name=Jamela Payne, category=panamanian_restaurant
b9f600a7-98dd-4d9b-b1a2-70d1d79ed9b2: name=In Aeternum Historia, category=print_media
a09c28e0-2005-42d5-b55e-4b2dda79609a: name=Destiny Monique, category=arts_and_entertainment
aec03ab5-bce4-484f-a042-9f6b49c3ea93: name=Frente al Mar, category=eat_and_drink
7ae461e2-5c10-4eab-b2aa-b253d5c9cba6: name=SaiJai ใส่ใจ เครื่องปรุงและอาหารสุขภาพ, category=restaurant
eb5e3c92-2c72-48a2-832d-9bf7e6ae3192: name=Tay Noel’s Kalan-an, category=fast_food_restaurant
d4d2c7e1-1e50-4362-90ae-bf74cd3646e8: name=גלית קאשי עיצוב תכשיטים, category=jewelry_store
028cd213-5f11-4b3e-bd5f-7ed9e22e3e88: name=GypsyMaal, category=fashion_accessories_store
83847f48-3480-47a9-878f-132b275c2880: name=Prasad Art Gallery, category=art_gallery
de2b1fec-574c-4461-8bd6-6a2c2b2e3c63: name=Tahlia Marie, category=None
0f605256-4adc-4d04-bdab-44b6e8ca17e1: name=Barbaro Negocios Inmobiliarios, category=real_estate
ff41a340-bd7c-49c8-9eb8-be5903a3653a: name=VALKUR Perú, category=lawyer
09e9bea6-d40f-41c2-a88f-561cba558ad3: name=Experience Rarotonga, category=tours
2a30eed0-ca25-4aa6-9147-cece509f4f09: name=Caprichos Accesorios, category=fashion_accessories_store
d8a1c3a5-fadc-448d-a543-22067b69d36e: name=Emmy J, category=None
f0847dff-07cd-422a-81c9-f20a40b26106: name=Asra Derm Official, category=None
1c2353f9-bc96-4304-8b30-35e0b4ffc40e: name=Mobilio Suksawat โมบิลิโอ้ สาขาสุขสวัสดิ์, category=shopping
c2eb4ea6-634e-4cf6-9f53-fe826b4dcb61: name=ال سلوع لتجاره السيراميك والادوات الصحيه, category=real_estate
2a100ade-be10-41c0-b6c2-a49e921a24e7: name=Port Lockroy, Antarctic Peninsula, category=landmark_and_historical_building
f73848f3-abaa-432b-b3ed-8092a6b39b9e: name=Palmer Station, Antarctica, category=home_developer
f4491389-9904-4c46-ad66-18c0e736e547: name=BellaVista Voronet, category=holiday_rental_home
ea957818-ac33-4951-9f52-8845ee4419b3: name=Sachet Imprimé, category=None
eb858253-acd6-4231-9ce9-e0e4c799d7a0: name=Brown Station, category=landmark_and_historical_building
ba9d8ef6-32f3-4098-a010-b5b485e4e75f: name=González Videla Antarctic Base, category=airport
90087bb3-b8ba-4507-886b-104fe36bc40c: name=Cuverville Island, category=landmark_and_historical_building
49b9a6e2-2e53-4040-8cea-39b554a52b66: name=Madison Beer, category=beer_bar
ca45ce1c-ca5e-4cd8-8f98-5975f5706828: name=Dj daya, category=music_production
a593665a-c1c6-4acd-b0c0-d25735d6dd4c: name=Matrioska Laços, category=childrens_clothing_store
9d364bf8-eaaf-4f79-ad45-195f74f49a93: name=Antarctica, Antarctic Circle, category=landmark_and_historical_building
3bf8e968-6050-4ee6-8a60-f046db54cc02: name=Esperanto Island, Antarctica, category=landmark_and_historical_building
ddfff404-5576-4082-8528-ef0d79f2dbb0: name=Yankee Harbor, category=landmark_and_historical_building
6240b821-30cb-4fe4-a08a-6fddb4fe2978: name=Bellingshausen Russian Antarctic Station, category=educational_research_institute
e4b318d7-4b12-4b00-806c-e21f1ca962ac: name=Bahia Fildes (Antartica), category=structure_and_geography
eebb9028-32bd-4b7c-a8c2-7a620a35a072: name=Crystal Power, category=jewelry_store
b5dbce59-268a-4e42-9bb2-211da08f53e5: name=Bestiario Moderno, category=print_media
18414d58-4e4c-480f-bfab-ee3e224c5b5e: name=Base Marambio, Antartida Argentina, category=central_government_office
015df87e-1265-4d80-844b-97a4c1af44a5: name=Marambio Base, category=airport
4775fc70-d68b-48b4-af40-377af7e9a457: name=Chapel of the Blessed Virgin of Lujan, Antarctica, category=church_cathedral
11037d14-337b-4a53-a23b-85566484f016: name=Base Antártica Marambio, category=public_and_government_association
b1662153-d852-4a90-b8a7-5d5e113439bb: name=Base Esperanza, Antartida Argentina, category=airport
8536af5a-82ba-46ed-bd6c-069082cbfc5d: name=Esperanza Base, Antarctica, category=landmark_and_historical_building
a13cf580-a927-4f97-a2d6-7f12f7ac954b: name=Backwoods Bouquet, category=None
93371fbe-1368-4cba-96be-b7c04397a8d4: name=Shop Thời Trang Nam - Nữ Thanh Bình, category=fashion
6097b50e-58bc-4b7d-aec4-0161d86e0c26: name=Lucas Fontoura, category=arts_and_entertainment
ff744642-189b-47c5-8b59-02176f30b03f: name=Descobrincar  - Musicalização infantil, category=school
7cbecc8f-af4f-459e-a503-da31f4e15db1: name=Hurricane On Saturn, category=None
0c99398b-501d-41dd-8dd0-5bc68dd2beec: name=Paradise Eventi, category=event_planning
3cb2a3a2-6b4c-4c0c-9b3c-fe52b1f5ef49: name=Psikolog ve Aile Danışmanı Esra Erciyas, category=None
578ca710-182a-4fa4-94d7-acafa067c6a0: name=Proexc Engenharia, category=engineering_services
ec210e56-697b-4e50-8e88-f73a9ab3becf: name=Villa's Caldos, category=fast_food_restaurant
be7f69c3-12f2-49d0-8b88-be2abefdc457: name=Gabbi Garcia, category=None
a090a904-2767-409a-adf6-92bd9ed0b843: name=Bendita CR, category=None
850678ce-ad91-4646-acc9-645885dbae45: name=Behr Productions, category=music_production
b56ce88f-0fb8-42d2-8dd4-9b19447bf9b6: name=Max land  MX Raceway Park, category=race_track
a7ef9c17-90f8-436b-ac3b-2a6c1f4ec6d5: name=DRK Kreisverband Gelnhausen-Schlüchtern e.V., category=community_services_non_profits
8314e13e-dfe1-4d29-aa98-7f4dfff59e2b: name=Muhammad Hasri Videography, category=professional_services
846206ab-ab1d-4e02-ad32-54acf08bcf73: name=JJ Niceley, category=None
626a584b-87d5-4dc2-a893-a5baa337590f: name=Jankari Kendra जानकारी केन्द्र, category=mass_media
c5c5a237-039d-48ee-858d-ed070520875f: name=Reggae Village House of Jerk, category=caterer
e266b928-982b-4279-8d49-342930116b3b: name=Công Ty TNHH Nam Hưng Thái Nguyên, category=b2b_textiles
f8357f19-e91b-409e-8328-0a3ce09f9250: name=美妍美容教育中心-Beauty in, category=beauty_and_spa
67dc407b-ab3c-4a98-9c4b-021635ec8000: name=Foul Point, category=landmark_and_historical_building
2cd19d2b-bafa-4bef-9841-e89d7e9ffe92: name=Malou Madi, category=None
1f4cbf9e-f030-4e05-b3d0-7645989883b9: name=Institut für Emotionspädagogik, category=educational_services
4bf6997e-7821-40d4-ac35-9c7aa62380cd: name=Animations enfants Tahiti, Le PitiMotu, category=arts_and_entertainment
827514b2-1b65-4edb-abe5-3e54390b67c7: name=Dregg Ackies, category=record_label
0b2d13d5-befe-4a1f-af71-deb7ec35537d: name=Sunsoli, category=None
c6dfca54-43db-4011-96e6-5edf7c897bf7: name=Flocreazionipereventi, category=wedding_planning
e3fdc1df-ec12-481c-acab-020dccb21d53: name=Martin Kráľ - Skladateľ, category=None
cd40e85b-48bf-4a95-b777-2310ce51de03: name=Babul Yaman Store, category=shopping
a049cb22-9538-4fe0-b0db-b8211dcde371: name=SMDC Property Investment, category=real_estate_agent
8d5d6b50-56ec-411b-b6c3-9f01c5f85fec: name=WM Case - Profesjonalne skrzynie transportowe, category=None
c455a0c7-92a1-4dfa-b4fb-e529e19bf03b: name=Dwill Burguer, category=burger_restaurant
5f194203-4c23-4c00-911b-13f14c6035a1: name=Sund med Mia, category=professional_services
09a01e2c-128a-435c-8330-05134d7a6cb1: name=Deli Repostería, category=desserts
644fd3f9-14f5-475e-bb40-06a9a631b76d: name=Buli Makhubo, category=None
37186c0b-2c16-495d-aac4-f8884e45e352: name=Стиль и мода. Израиль, category=image_consultant
e98978c0-2318-4cbe-a1f5-b5ee7c08a287: name=Bedlam Studios, category=graphic_designer
c4d32583-0c39-402f-b67b-4b6d3ff66bbc: name=رضا الحديثي - صفحتي الرسمية, category=fitness_trainer
e8ada447-c200-445d-b6fb-9b2e272cb0d3: name=Friis Boliger, category=real_estate
fa437eeb-d4ac-41ff-884b-5ff86bec8c84: name=রঙ-Israt's Art, category=arts_and_crafts
26e9c69b-1c79-473e-bd85-7291e6c9a8d6: name=DirtySnatcha, category=None
03d664ce-c46e-40c4-ac7a-9748b7fa6f57: name=Calbero, category=None
7977131d-ad69-4f47-a2d0-bb29f8c98906: name=Jobswagon, category=None
6d6ac2ae-2e8a-49f5-abb2-7f631521eb63: name=هاوار, category=education
e3417e97-f283-4ecc-a32b-ae164e69903f: name=Antarctica/Troll, category=landmark_and_historical_building
fe4a1bd1-1875-422d-85e0-311802f9bcb2: name=Ventamark, category=electronics
1b314288-8b0b-473f-b449-bee33f727f96: name=De Todo, category=electronics
afa11645-f1f6-4dbf-9c14-75cf67300532: name=Inglés Personal, category=language_school
f813783f-faa6-427f-bb27-bda6ff276a7d: name=João Gomes, category=None
143bfe4e-e494-4e7b-a912-b969a23279d9: name=RA CARS PH, category=car_dealer
f0b0d468-b504-4461-bdec-61bcc74bb60b: name=אריאל שרם - צילום, category=professional_services
90b1c6f6-5e99-466e-a342-b6a9a6c0146e: name=Eden's Essential Elements, category=flowers_and_gifts_shop
324d76ee-74f4-42b6-ba46-21444ce2783e: name=Laviva Cosmetics, category=beauty_and_spa
e4574119-d881-4681-9afc-219482f6c6c9: name=Elblesk e-mobility, category=automotive
f57896bc-008a-48d0-a073-b47eecadae81: name=ليو - Leo, category=social_media_company
4004650a-a32e-4431-9838-3a4265f24b99: name=Otago Potters Group, category=None
30804109-2513-487f-bcd2-1f747515516a: name=Johanna Oedin, category=None
da025271-a268-4b31-8993-b3c358bf44b0: name=Alchemist Craftworks, category=metal_fabricator
2fa973bc-63f4-413a-891a-4e224f5b046d: name=Panaderia Doña Tere, category=bakery
faad9628-792c-4834-979d-e541a29b9da8: name=廸康醫療中心 Madison Medical Centre, category=acupuncture
6573068f-744b-4a97-ab54-e671178c2365: name=A Tale of Four Mages, category=print_media
c132ba4b-db4c-41fd-89b5-347450ef4a65: name=JP Ranch Grass Valley, category=farm
ea2f0e50-4251-43f3-89ab-ad1f141f2069: name=Wolf’s Fang Runway, category=None
0df43561-08b3-4e0d-8668-e6d59ae4e11c: name=Fishing 411 TV, category=active_life
94711a0f-334f-4d8a-8f29-81ddbacf2c40: name=Pro Massage & Pro PT, category=None
34ffb80a-2ec5-411c-981b-14d04d3c0c5e: name=St. Isidore Catholic Learning Centre, category=None
6a62900c-ce96-41b5-98de-949d46e5e1fe: name=Cajurine., category=tattoo_and_piercing
8e4c5cd6-402c-44fe-bf75-e0fb4b70391a: name=Higi + Higienização e Impermeabilização de Estofados, category=home_cleaning
33d2cad5-5329-4a8e-8258-48c34338043d: name=Organica Mart, category=food_delivery_service
b521d3e2-aa7b-437b-a6a0-bd00d079012c: name=ซัมไทม์ วิว รีสอร์ท, category=hostel
5a95abd1-f5ad-4fa2-a410-092e92160347: name=چارەسەر لە سروشتەوە, category=beauty_and_spa
7a2ef32d-41b7-4fd1-b05a-136b89e6afae: name=Pro Punjab Tv, category=media_news_company
2f0009a4-41f0-4b6d-8b79-9f8329bca235: name=elevatedxconscience, category=None
e69c8fa0-b370-431b-9877-9f138003c9b3: name=B.olivia - ps, category=beauty_and_spa
239f485e-336d-4bd7-abb1-a6084783b953: name=Twory i Stwory, category=None
b3327bca-f823-4092-8f57-ff4909fc43d6: name=Geraint Jones 4x4, category=automotive_dealer
dc072c6e-6745-46d5-a91d-111f47962ec4: name=2 Nice, category=shoe_store
120d0f93-bd72-4fc6-8908-5341a23c816d: name=Stress Relief Massage, category=None
61401ba7-85fa-4e7f-8469-95264ffbbf37: name=Amrinder Bobby, category=None
c5755c5c-49bd-4f95-a278-c0036ad3d378: name=Make Noise Pro Audio LTD, category=audio_visual_equipment_store
2166b3fc-617c-4fa8-8f52-825470debd21: name=Morjane Ténéré, category=None
cdb4c8c8-9073-435b-8ef3-b91afd117006: name=Southern Ocean, category=landmark_and_historical_building
45e21b66-c772-42d6-81d6-87042c06d118: name=ليلى kids, category=childrens_clothing_store
9edd01f2-2e51-4c81-b1c2-b05ffcd1b00b: name=鉢伏山荘冬期営業, category=lodge
db034fd2-6a0b-4eeb-b8df-bb8c6729a4c9: name=البروج للمواد الكهربائية, category=shopping_center
50d11940-d8ac-4b59-9de4-0a0fa17ae0fe: name=Beauty & Care, category=beauty_and_spa
60c01170-1149-4946-95f3-9f5ac9000e94: name=Béjaïa béni ksila vacances, category=None
6f2ca4c1-e58e-4479-b25f-1a9909f66248: name=In Love Furniture - ศูนย์รวมเฟอร์นิเจอร์ชุดห้องนอน และ เฟอร์นิเจอร์สำนักงาน, category=furniture_store
8665150b-23b3-4db4-a036-6e092eb83eef: name=Woodstock Dentistry, category=dentist
b2c33d2b-e914-4ff5-aefc-d21b94134b66: name=Paubril’s Beauty, category=beauty_salon
81279d7e-ebc0-451f-acab-d16abb26dfb8: name=This Esme, category=None
1221efca-01ab-4a63-b7cf-b7e57aa4d7de: name=Alex Sá, category=None
a54c1fc9-e821-41fc-9676-2217c0cb08b9: name=MOFT, category=fashion_accessories_store
aa5ed046-938d-4250-ab80-763dec0a7973: name=I-narin Beauty : เครื่องนวดหน้า สไตล์เกาหลี, category=beauty_and_spa
ed8b1616-8305-419a-9c31-7103f33e561b: name=Abracadabra Technologie, category=telecommunications_company
d8f0007b-7bd4-49c1-a313-f6edceb1ae0c: name=Cafés Ellouze, category=coffee_shop
f2e30c5a-d360-4768-a665-bb4b5e315b92: name=หมามะเร็ง Dogs Cancer, category=community_services_non_profits
e99e67d0-44df-42cc-a037-ac56083ab35c: name=Catiline Kindergarten - Whampoa カティライン幼稚園・インターナショナルプレスクール, category=education
e8239c37-81ea-4157-bdbe-4886a5d8acc5: name=厚興瑜記 WegoMall, category=restaurant
e7042319-227a-4aa2-a1de-418bafdf50a8: name=Americano Cafe' Happy Moment with Coffee and Drink, category=cafe
d8a7c84f-87d0-4dbc-8ef7-c005f4815f74: name=Japfa Experience, category=restaurant
5142358e-838a-466f-9ef1-341e2583a0e7: name=Celest Jewelry, category=None
8e8bb400-aef6-4570-a362-c6dc671c8def: name=My湯 - 茘枝角, category=desserts
4e6c21fc-d140-42bd-b03f-fff591b8b1a4: name=Bobby Layal, category=computer_museum
749ef775-0275-444e-afcf-3eeea68632cd: name=Janeth E. Sarona, category=professional_services
93b19196-0fd7-4030-8e31-7bcc6828b12f: name=PD.Kannel, category=None
5bcb716a-3179-4db9-83ff-f23ee47f5708: name=แพวันวาน จังหวัดกาญจนบุรี, category=hotel
8e90e81e-f21e-42f8-a49d-91e007402806: name=Dalbe Réunion, category=None
d1f40180-a2a5-46ff-aea5-e421ad771c4b: name=สมุทรสงครามรีวิว, category=None
c8d8dd6a-66d4-4f69-ab1b-0d07a2db2c1f: name=مركز آفنيو الطبي, category=hospital
c375f9a7-de0b-4c35-a6b2-e0749c6c1fbc: name=ครูเปิ้ล สอนคอมฯ : ปวช. สกร.เมืองนครราชสีมา, category=shopping
1d715c37-7e51-4377-b4c1-f99b08682650: name=Cat & Calmell, category=None
e4d040bc-4aeb-47eb-b1c3-2e192284399e: name=Mette MÆRsk, category=professional_services
04a0ba90-1b7f-4a57-9ec8-91fde8e4ba63: name=อึ้งกุ่ยเฮง มอเตอร์ไซค์ ฮอนด้า ยามาฮ่า รถมือสอง ร้อยเอ็ด, category=motorcycle_dealer
f6fbba53-4e47-43ba-aa3a-11669c106c27: name=World Auto Glass Inc., category=None
788c50a6-a275-4f65-a4e8-51ff804e2c76: name=Noskill Sensi, category=video_game_store
6ef9d71e-0a81-4353-88c5-0fe3a6298c8a: name=Ingrid Christensen Coast, category=landmark_and_historical_building
bfbe1e51-dafc-4aed-b84c-b93cafa9fec7: name=Tutti Bambino, category=toy_store
863c4e6c-5fe1-45d6-a5fd-8a6f0b006e51: name=Ellis Fjord, category=landmark_and_historical_building
ecfca81b-f2f7-42c3-a81c-5a7a6c840795: name=Lake Jabs, category=landmark_and_historical_building
a088edca-c206-4689-a20e-c6da1adcf298: name=Langnes Fjord, category=landmark_and_historical_building
1f68ade1-0f5a-4f35-908d-e06d98fa5c8a: name=Krok Lake, category=lake
81cfb3fa-b152-4763-ab34-047f7e238b3f: name=Lied Bluff, category=landmark_and_historical_building
064348b3-0c46-412e-89ef-99b5d89a9629: name=Heidemann Bay, category=landmark_and_historical_building
830bdae6-0e87-49f5-8d9f-d09eb55f2ae1: name=Filla Island, category=landmark_and_historical_building
aa330079-2239-4210-abce-3f0189a302c9: name=Lake Zvezda, category=landmark_and_historical_building
1d7407a1-6de8-4ada-b682-77b0581f9b5a: name=Dr. Cláudio Gurgel Magalhães - Clínica Belle Esthetique, category=doctor
fa828383-ccae-4bc6-8280-dedc9bec84de: name=Ynnez Group, category=non_governmental_association
375a785a-cbe8-483f-9892-411d65e6a048: name=芸臻命理諮詢, category=psychic
e4a5838b-2744-4bbb-b631-7d6329f03e87: name=Miya Élégance, category=womens_clothing_store
9611bace-e1ea-41f6-948d-d148653c9189: name=แม่อี่ ผักดองตำหรับยูนนาน, category=None
657610cc-fcce-4cf0-b464-6156bb51b2ab: name=NPS Institutions - Bidar, category=school
4f7ecec0-deee-478f-8695-c5834263ca8d: name=Sozialkontakt, category=None
cdb16257-a06c-42d3-a607-2595832bcb1d: name=albertopiernas, category=None
95f38586-af0c-4833-90d6-8691b4cac1af: name=Sekolah Islam Terpadu Green Bhakti Insani, category=middle_school
76a651dd-ec31-40b9-ab8a-9db09f41ad5d: name=Motel Mozaique Concerts, category=hotel
1f8440b3-8f1f-4b26-9f9b-a66f1124bedf: name=CT Trampoline Fitness 沙田 石門 彈床班 Jump Fitness 健身 親子彈床班, category=gym
48a4811a-3075-4054-b63f-3178e564f9e7: name=Casa de Adoración Yahweh, category=religious_organization
9ada4a7f-d61c-4ea0-80a0-fb8e77427b36: name=Sydney Amigurumi Corner 手勾公仔, category=arts_and_crafts
bd2419af-7105-4f9d-b663-aa9a409fb43f: name=Shanaya Hotel Borobudur, category=hotel
316d32e2-dd75-40a0-82ca-07edb2866724: name=The Cumberland River Project, category=river
63b91770-28a5-4a5c-bea8-0e5842150734: name=Lake Vostok, category=landmark_and_historical_building
af82bf4b-f128-4169-a1c5-17003b6079b3: name=ร้านแน็ตโมบายโฟน, category=None
bfd01a42-92a7-43f9-a7d3-0cc7fb3157b4: name=Vostok İstasyonu, category=landmark_and_historical_building
d7827f14-512a-4e6e-b797-9d66a117f488: name=Tiệm ảnh Phúc- Lạng Sơn, category=professional_services
360518d5-1db3-4636-a7a1-c365891ae149: name=مدرسة أحمد نمر حمدان الأساسية للبنين, category=school
20ed15c3-ed4a-459d-9ba0-07aad85c2749: name=ইলমুল কুরআন মাদ্রাসা লিল্লাহ বোর্ডিং ও ইয়াতিম খানা, category=None
d2de84a6-d20f-463e-8a77-7affecc053fc: name=سنتر بغداد, category=shopping
f64a2822-9dfa-4267-b634-1912aeeca8b4: name=住商不動產樹林后站加盟店, category=professional_services
bc8d2cd3-b5ce-46c9-81f5-6c52d700166b: name=塩彩 ～shiosai～, category=shopping
1c18608c-90c2-4657-b19e-7326bb9e41b6: name=Kurd Sport HD, category=sports_club_and_league
445f1366-e41d-4ceb-ad94-c556ca440987: name=Sofiya Designs Gh, category=None
0fa972cb-d0a0-4322-8dbe-bad88268f96b: name=水草館, category=museum
639c6258-52e6-4b6b-8cef-c840a33ecefc: name=Compartilhagem, category=arts_and_crafts
b62b8f0a-8a79-49c4-9ae2-462cdb5a345c: name=Kimstore, category=retail
7857e963-4baa-4107-83f5-49c554d73305: name=Masjid Terapung Tanjung Bungah, Pulau Pinang, category=mosque
fb8eb545-4390-45bf-b407-340592033b54: name=Angel Rising, category=None
ecf6de82-41c3-4de0-b65a-094affdfd2d1: name=Lailo Farm Sanctuary, category=community_services_non_profits
d345d139-6ba1-4085-a0c8-3de8043ca6d4: name=Dr Henri Bauer, category=None
36a428fd-122e-4c13-8060-8d8d36162018: name=Academia Nacional de Sommeliers y Gastrónomos, category=school
65458250-4795-48f1-b740-41fe80b96871: name=ทำประกันกับอาอิซะห์, category=beauty_and_spa
04d8258a-e765-4a4d-96c0-20cd3b11a9c3: name=Base antarctique Dumont-d'Urville, category=landmark_and_historical_building
fc899431-4ab7-4890-9900-78c44dc030ce: name=شركة الأركان لقطع غيار وزيوت السيارات\nAlarkan Auto Parts & Oils, category=shopping
ba6072a7-e330-4288-9e7f-b345f2e18d7b: name=Seaoil Dangcagan, category=gas_station
e2e091af-6137-403e-bbd4-6b67f7c55102: name=The Seasoning Outlet, category=shopping
4c24258b-5017-4b9b-b97c-fb85810468cf: name=MAX Reprise, category=automotive_repair
26f0564a-e108-46da-b7aa-a4f3aca0daa2: name=Antarctic Plateau, category=landmark_and_historical_building
83fcb72b-6a48-45ed-b8ba-86133e5c0a81: name=Warning Five จำหน่ายสายนาฬิกาหนังแท้ สายนาฬิกา smartwatch, category=None
e53f80a9-1a41-4215-84b6-6b71e2e2cbbd: name=海港青年商會 JCI Harbour, category=community_services_non_profits
696feff0-bd87-4a7b-b778-479dced0681a: name=মুক্তি হারবাল চিকিৎসালয়-Mukti Herbal Chikitsalay, category=family_practice
ce4a2fc8-49de-48b1-babe-ad78bf4328ac: name=Herrlich Beauty By Cloriss, category=beauty_salon
fcdd73f9-5cb8-4812-be88-099727bf3936: name=Mario-Zucchelli-Station, category=None
693626b4-7ef6-41ee-bc0a-ae604b3351d2: name=Psicóloga Serly C. de Souza, category=psychotherapist
bb48b222-57c4-4c85-8743-752df4bba2ca: name=Polární stanice McMurdo, category=landmark_and_historical_building
d5882de0-07ff-46b9-a3c0-bbfb0cc9552c: name=Base McMurdo, category=landmark_and_historical_building
d74e5a73-042c-4694-9e16-fa9622f203b3: name=Base antarctique McMurdo, category=landmark_and_historical_building
fd4c334a-7431-4d8c-888b-3f8c7adda8d1: name=Gallagher Pub, category=bar
b3790f8f-7451-4a53-abff-62ab3af51607: name=Southern Exposure, category=bar
9e4a3d08-61ff-4b24-b19f-ff6701d7f012: name=ZiroZiro, category=religious_organization
82b40435-6b61-4d8e-9caa-7ff1d248b8a1: name=McMurdo General Hospital, category=hospital
2390d849-b20b-4b63-9be3-0209ee70518b: name=Coffee House, category=coffee_shop
64a5e802-0687-4df3-8389-5a4f84e649a0: name=Chapel of the Snows, category=church_cathedral
e0e03e63-936d-427e-92bc-815a9a44b6a5: name=Pegasus Field, category=airport
512534a4-cc70-416d-89f0-9144564048d3: name=悦聖翡翠, category=fashion

python -c "
from overturemaps.core import record_batch_reader
import pyarrow as pa
import pyarrow.compute as pc

# Get geometries for a few rows to check coordinates
r = record_batch_reader('place', bbox=(-180, -90, 180, -60))
if r:
    tbl = r.read_all()
    print(f'Total: {len(tbl)}')
    # Look at bbox column for first 10
    bboxes = tbl.column('bbox')
    for i in range(min(10, len(tbl))):
        bb = bboxes[i]
        print(f'{i}: xmin={bb[\"xmin\"]}, ymin={bb[\"ymin\"]}, xmax={bb[\"xmax\"]}, ymax={bb[\"ymax\"]}')
" 2>&1

Total: 246
0: xmin=-172.8719482421875, ymin=-64.6113510131836, xmax=-172.87191772460938, ymax=-64.61134338378906
1: xmin=-156.796875, ymin=-78.34941101074219, xmax=-156.796875, ymax=-78.34940338134766
2: xmin=-156.20516967773438, ymin=-77.52698516845703, xmax=-156.2051544189453, ymax=-77.5269775390625
3: xmin=-143.4375, ymin=-82.49482727050781, xmax=-143.4375, ymax=-82.49481201171875
4: xmin=-144.84375, ymin=-80.76062774658203, xmax=-144.84375, ymax=-80.76061248779297
5: xmin=-146.87623596191406, ymin=-65.75599670410156, xmax=-146.876220703125, ymax=-65.75598907470703
6: xmin=-136.40625, ymin=-74.77584838867188, xmax=-136.40625, ymax=-74.77584075927734
7: xmin=-137.6954345703125, ymin=-75.42521667480469, xmax=-137.69540405273438, ymax=-75.42520904541016
8: xmin=-136.546875, ymin=-81.08232116699219, xmax=-136.54685974121094, ymax=-81.08231353759766
9: xmin=-137.8125, ymin=-81.92318725585938, xmax=-137.8125, ymax=-81.92317962646484

python -c "
from overturemaps.core import record_batch_reader
import pyarrow as pa
import pyarrow.compute as pc
import json

r = record_batch_reader('place', bbox=(-180, -90, 180, -60))
if r:
    tbl = r.read_all()
    # Print those with station/base/antarctic in name
    names = tbl.column('names')
    ids = tbl.column('id')
    cats = tbl.column('categories')
    for i in range(len(tbl)):
        n = names[i]
        if n:
            name = n['primary'].as_py()
            if name and any(k in (name.lower() or '') for k in ['station', 'base', 'antarctic', 'antarct', 'vostok', 'mcmurdo', 'dumont', 'marambio', 'esperanza', 'bellingshausen', 'palmer', 'brown', 'troll', 'zucchelli', 'halley', 'rothschild']):
                cat = cats[i]
                primary = cat['primary'].as_py() if cat else None
                cid = ids[i].as_py()
                # Get geometry
                print(f'{cid}: \"{name}\" -> cat={primary}')
" 2>&1

2a100ade-be10-41c0-b6c2-a49e921a24e7: "Port Lockroy, Antarctic Peninsula" -> cat=landmark_and_historical_building
f73848f3-abaa-432b-b3ed-8092a6b39b9e: "Palmer Station, Antarctica" -> cat=home_developer
eb858253-acd6-4231-9ce9-e0e4c799d7a0: "Brown Station" -> cat=landmark_and_historical_building
ba9d8ef6-32f3-4098-a010-b5b485e4e75f: "González Videla Antarctic Base" -> cat=airport
9d364bf8-eaaf-4f79-ad45-195f74f49a93: "Antarctica, Antarctic Circle" -> cat=landmark_and_historical_building
3bf8e968-6050-4ee6-8a60-f046db54cc02: "Esperanto Island, Antarctica" -> cat=landmark_and_historical_building
6240b821-30cb-4fe4-a08a-6fddb4fe2978: "Bellingshausen Russian Antarctic Station" -> cat=educational_research_institute
18414d58-4e4c-480f-bfab-ee3e224c5b5e: "Base Marambio, Antartida Argentina" -> cat=central_government_office
015df87e-1265-4d80-844b-97a4c1af44a5: "Marambio Base" -> cat=airport
4775fc70-d68b-48b4-af40-377af7e9a457: "Chapel of the Blessed Virgin of Lujan, Antarctica" -> cat=church_cathedral
11037d14-337b-4a53-a23b-85566484f016: "Base Antártica Marambio" -> cat=public_and_government_association
b1662153-d852-4a90-b8a7-5d5e113439bb: "Base Esperanza, Antartida Argentina" -> cat=airport
8536af5a-82ba-46ed-bd6c-069082cbfc5d: "Esperanza Base, Antarctica" -> cat=landmark_and_historical_building
e3417e97-f283-4ecc-a32b-ae164e69903f: "Antarctica/Troll" -> cat=landmark_and_historical_building
63b91770-28a5-4a5c-bea8-0e5842150734: "Lake Vostok" -> cat=landmark_and_historical_building
bfd01a42-92a7-43f9-a7d3-0cc7fb3157b4: "Vostok İstasyonu" -> cat=landmark_and_historical_building
04d8258a-e765-4a4d-96c0-20cd3b11a9c3: "Base antarctique Dumont-d'Urville" -> cat=landmark_and_historical_building
26f0564a-e108-46da-b7aa-a4f3aca0daa2: "Antarctic Plateau" -> cat=landmark_and_historical_building
fcdd73f9-5cb8-4812-be88-099727bf3936: "Mario-Zucchelli-Station" -> cat=None
bb48b222-57c4-4c85-8743-752df4bba2ca: "Polární stanice McMurdo" -> cat=landmark_and_historical_building
d5882de0-07ff-46b9-a3c0-bbfb0cc9552c: "Base McMurdo" -> cat=landmark_and_historical_building
d74e5a73-042c-4694-9e16-fa9622f203b3: "Base antarctique McMurdo" -> cat=landmark_and_historical_building
82b40435-6b61-4d8e-9caa-7ff1d248b8a1: "McMurdo General Hospital" -> cat=hospital

python -c "
from overturemaps.core import record_batch_reader
import pyarrow as pa
import pyarrow.compute as pc

r = record_batch_reader('place', bbox=(-180, -90, 180, -60))
if r:
    tbl = r.read_all()
    taxonomies = tbl.column('taxonomy')
    for i in range(len(tbl)):
        tax = taxonomies[i]
        if tax:
            primary = tax['primary'].as_py()
            name = tbl.column('names')[i]
            n = name['primary'].as_py() if name else None
            if n and any(k in (n.lower() or '') for k in ['station', 'base', 'antarct', 'vostok', 'mcmurdo', 'dumont', 'marambio', 'esperanza', 'bellingshausen', 'palmer', 'brown', 'troll', 'zucchelli']):
                print(f'{n}: taxonomy={primary}, hierarchy={tax[\"hierarchy\"].as_py()}')
" 2>&1

Port Lockroy, Antarctic Peninsula: taxonomy=historic_site, hierarchy=['cultural_and_historic', 'historic_site']
Palmer Station, Antarctica: taxonomy=home_developer, hierarchy=['services_and_business', 'real_estate_service', 'builder', 'home_developer']
Brown Station: taxonomy=historic_site, hierarchy=['cultural_and_historic', 'historic_site']
González Videla Antarctic Base: taxonomy=airport, hierarchy=['travel_and_transportation', 'air_transport_facility_or_service', 'airport']
Antarctica, Antarctic Circle: taxonomy=historic_site, hierarchy=['cultural_and_historic', 'historic_site']
Esperanto Island, Antarctica: taxonomy=historic_site, hierarchy=['cultural_and_historic', 'historic_site']
Bellingshausen Russian Antarctic Station: taxonomy=educational_research_institute, hierarchy=['education', 'research_institute', 'educational_research_institute']
Base Marambio, Antartida Argentina: taxonomy=government_office, hierarchy=['community_and_government', 'government_office']
Marambio Base: taxonomy=airport, hierarchy=['travel_and_transportation', 'air_transport_facility_or_service', 'airport']
Chapel of the Blessed Virgin of Lujan, Antarctica: taxonomy=christian_place_of_worship, hierarchy=['cultural_and_historic', 'religious_organization', 'place_of_worship', 'christian_place_of_worship']
Base Antártica Marambio: taxonomy=government_office, hierarchy=['community_and_government', 'government_office']
Base Esperanza, Antartida Argentina: taxonomy=airport, hierarchy=['travel_and_transportation', 'air_transport_facility_or_service', 'airport']
Esperanza Base, Antarctica: taxonomy=historic_site, hierarchy=['cultural_and_historic', 'historic_site']
Antarctica/Troll: taxonomy=historic_site, hierarchy=['cultural_and_historic', 'historic_site']
Lake Vostok: taxonomy=historic_site, hierarchy=['cultural_and_historic', 'historic_site']
Vostok İstasyonu: taxonomy=historic_site, hierarchy=['cultural_and_historic', 'historic_site']
Base antarctique Dumont-d'Urville: taxonomy=historic_site, hierarchy=['cultural_and_historic', 'historic_site']
Antarctic Plateau: taxonomy=historic_site, hierarchy=['cultural_and_historic', 'historic_site']
Mario-Zucchelli-Station: taxonomy=None, hierarchy=None
Polární stanice McMurdo: taxonomy=historic_site, hierarchy=['cultural_and_historic', 'historic_site']
Base McMurdo: taxonomy=historic_site, hierarchy=['cultural_and_historic', 'historic_site']
Base antarctique McMurdo: taxonomy=historic_site, hierarchy=['cultural_and_historic', 'historic_site']
McMurdo General Hospital: taxonomy=hospital, hierarchy=['health_care', 'hospital']

python -c "
from overturemaps.core import record_batch_reader
import pyarrow as pa
import pyarrow.compute as pc

r = record_batch_reader('division', bbox=(-180, -90, 180, -60))
if r:
    tbl = r.read_all()
    print(f'Total divisions: {len(tbl)}')
    for field in tbl.schema:
        print(f'  {field.name}: {field.type}')
" 2>&1

Total divisions: 49
  id: string
  geometry: binary
  country: string
  sources: list<element: struct<property: string, dataset: string, license: string, record_id: string, update_time: string, confidence: double, between: list<element: double>>>
  subtype: string
  admin_level: int32
  class: string
  names: struct<primary: string, common: map<string, string ('common')>, rules: list<element: struct<variant: string, language: string, perspectives: struct<mode: string, countries: list<element: string>>, value: string, between: list<element: double>, side: string>>>
  wikidata: string
  perspectives: struct<mode: string, countries: list<element: string>>
  local_type: map<string, string ('local_type')>
  region: string
  hierarchies: list<element: list<element: struct<division_id: string, subtype: string, name: string>>>
  parent_division_id: string
  norms: struct<driving_side: string>
  population: int32
  capital_division_ids: list<element: string>
  capital_of_divisions: list<element: struct<division_id: string, subtype: string>>
  cartography: struct<prominence: int32, min_zoom: int32, max_zoom: int32, sort_key: int32>
  version: int32
  bbox: struct<xmin: double, xmax: double, ymin: double, ymax: double>

python -c "
from overturemaps.core import record_batch_reader
import pyarrow as pa

r = record_batch_reader('division', bbox=(-180, -90, 180, -60))
if r:
    tbl = r.read_all()
    names = tbl.column('names')
    subtypes = tbl.column('subtype')
    for i in range(len(tbl)):
        n = names[i]
        name = n['primary'].as_py() if n else 'None'
        sub = subtypes[i].as_py()
        print(f'{i}: {name} ({sub})')
" 2>&1

0: Amundsen–Scott South Pole (locality)
1: Rothera Research Station (locality)
2: Base Antártica San Martín (locality)
3: Vernadsky Research Base (locality)
4: Palmer Station (locality)
5: 남극세종과학기지 (locality)
6: Base Artigas (locality)
7: Беллинсгаузен (locality)
8: Base Profesor Julio Escudero (neighborhood)
9: Base Presidente Eduardo Frei Montalva (neighborhood)
10: Villa Las Estrellas (locality)
11: 长城站 (locality)
12: Base Naval Antartica Arturo Prat (locality)
13: Base Carlini (locality)
14: Arctowski (locality)
15: Estação Antártica Comandante Ferraz (locality)
16: Base Bernardo O’Higgins (locality)
17: Deutsche Antarktis-Empfangsstation O'Higgins (locality)
18: Fortín Sargento Cabral (locality)
19: Base Esperanza (neighborhood)
20: Base Marambio (locality)
21: Base Antártica Orcadas (locality)
22: Base Antártica Belgrano II (locality)
23: Halley VI Research Station (locality)
24: Nordenskiöld Base (neighborhood)
25: Neumayer-Station III (locality)
26: Antarctica (country)
27: Troll (locality)
28: Новолазаревская (locality)
29: Maitri Station (locality)
30: 昭和基地 (locality)
31: Mawson Station (locality)
32: Bharati Research Station (locality)
33: 中山站 (locality)
34: Прогресс (locality)
35: 昆仑站 (locality)
36: Davis Station (locality)
37: Мирный (locality)
38: Stacja im. A.B. Dobrowolskiego (locality)
39: Восток (locality)
40: Casey Station (locality)
41: Concordia Station (locality)
42: Camp d'été (neighborhood)
43: Base Dumont d'Urville (locality)
44: 秦岭站 (locality)
45: 남극장보고과학기지 (locality)
46: Stazione Mario Zucchelli (locality)
47: McMurdo Station (locality)
48: Scott Base (locality)

python -c "
from overturemaps.core import record_batch_reader, geodataframe
import pyarrow as pa

r = record_batch_reader('division', bbox=(-180, -90, 180, -60))
if r:
    tbl = r.read_all()
    print(f'Total: {len(tbl)}')
    print(f'Columns: {tbl.column_names}')
    # Check the geometry column binary type and try converting
    print(f'Geometry type: {tbl.schema.field(\"geometry\").type}')
    # Try to get a geopandas dataframe
    gdf = geodataframe(tbl)
    print(gdf.head())
    print(f'Geometry types: {gdf.geom_type.unique()}')
    print(f'CRS: {gdf.crs}')
" 2>&1

Total: 49
Columns: ['id', 'geometry', 'country', 'sources', 'subtype', 'admin_level', 'class', 'names', 'wikidata', 'perspectives', 'local_type', 'region', 'hierarchies', 'parent_division_id', 'norms', 'population', 'capital_division_ids', 'capital_of_divisions', 'cartography', 'version', 'bbox']
Geometry type: binary
Traceback (most recent call last):
  File "<string>", line 13, in <module>
    gdf = geodataframe(tbl)
  File "/usr/local/lib/python3.14/site-packages/overturemaps/core.py", line 342, in geodataframe
    reader = record_batch_reader(
        overture_type,
    ...<4 lines>...
        stac=stac,
    )
  File "/usr/local/lib/python3.14/site-packages/overturemaps/core.py", line 307, in record_batch_reader
    result = _prepare_query(
        overture_type, bbox, release, connect_timeout, request_timeout, stac
    )
  File "/usr/local/lib/python3.14/site-packages/overturemaps/core.py", line 244, in _prepare_query
    path = _dataset_path(overture_type, release)
  File "/usr/local/lib/python3.14/site-packages/overturemaps/core.py", line 409, in _dataset_path
    theme = type_theme_map[overture_type]
            ~~~~~~~~~~~~~~^^^^^^^^^^^^^^^
TypeError: cannot use 'pyarrow.lib.Table' as a dict key (unhashable type: 'pyarrow.lib.Table')

python -c "
from overturemaps.core import record_batch_reader
import geopandas as gpd
from shapely import wkb
import pyarrow as pa

r = record_batch_reader('division', bbox=(-180, -90, 180, -60))
if r:
    tbl = r.read_all()
    print(f'Rows: {len(tbl)}')
    
    # Convert WKB geometry column
    geoms = []
    for i in range(len(tbl)):
        geom_bytes = tbl.column('geometry')[i].as_py()
        geom = wkb.loads(geom_bytes)
        geoms.append(geom)
    
    names_col = tbl.column('names')
    ids_col = tbl.column('id')
    
    names = []
    ids = []
    for i in range(len(tbl)):
        n = names_col[i]
        name = n['primary'].as_py() if n else None
        names.append(name)
        ids.append(ids_col[i].as_py())
    
    gdf = gpd.GeoDataFrame({'station_id': ids, 'station_name': names, 'geometry': geoms}, crs='EPSG:4326')
    print(gdf)
    print(f'Geometry types: {gdf.geom_type.unique()}')
" 2>&1

Rows: 49
                              station_id  ...                     geometry
0   23e81262-d6ed-45a3-a1a0-4bc6a2a887d8  ...    POINT (-139.2728 -89.998)
1   ed7d4df0-fb65-49a7-a46e-4d3595ab8f9e  ...  POINT (-68.12217 -67.56924)
2   2921924c-7938-4ef0-9cd2-cd6ac0947c6c  ...  POINT (-67.10132 -68.13006)
3   90c7bf02-04af-423b-8f54-8ffb4bfa6c45  ...  POINT (-64.25769 -65.24551)
4   f6b7b583-191a-449b-95dc-7bd90ecddef7  ...  POINT (-64.05311 -64.77429)
5   d76b5d42-78a1-49aa-a015-be14688c29d9  ...  POINT (-58.78731 -62.22305)
6   4463ec21-3f98-4603-8174-c2c2ea68bd71  ...   POINT (-58.90385 -62.1844)
7   c38eadd2-432f-443b-a45d-0dd6f970b03a  ...   POINT (-58.95992 -62.1982)
8   06cc197e-bade-42aa-9dfc-39a687d41a78  ...   POINT (-58.96152 -62.2014)
9   e89b5561-840e-4ebc-99ca-3d748a78a60a  ...  POINT (-58.96317 -62.19985)
10  3a6a5d5a-3040-49a1-a0aa-a1c408a60146  ...  POINT (-58.96537 -62.20043)
11  1ae1d16b-e347-4085-89be-feb8f5151c50  ...  POINT (-58.96118 -62.21775)
12  3554e829-89ff-4681-942f-e361e9a93acf  ...  POINT (-59.66434 -62.47889)
13  7ed44c37-1664-42da-9e1a-fbc1774a7367  ...  POINT (-58.66701 -62.23793)
14  5cd11342-bb88-4ffb-a8cb-d12bc90635c6  ...  POINT (-58.47194 -62.15901)
15  8f17d23a-553a-4641-a5d1-e67ca1c7bacc  ...  POINT (-58.39083 -62.08424)
16  9d034b39-095a-45bf-8463-9b89102bf6a2  ...  POINT (-57.89986 -63.32083)
17  de5df6e3-3b22-468c-ba43-4d243d721c9f  ...  POINT (-57.90117 -63.32089)
18  59fb7587-704a-4a4c-8a80-4de39fc865eb  ...  POINT (-56.99717 -63.39834)
19  96ed2dba-5a05-4d96-bdbf-a162ee11b655  ...  POINT (-56.99637 -63.39806)
20  0bd9aea9-7334-4ae1-afe0-3954079e80f8  ...  POINT (-56.62769 -64.24062)
21  e2a72a14-ea5e-47c7-8f7c-303b9fd47f73  ...  POINT (-44.73769 -60.73807)
22  256f7a41-8ba5-42e3-9544-73621992f5c6  ...  POINT (-34.62745 -77.87361)
23  5a06bf03-22f9-46a0-a805-dfd7d56c54eb  ...   POINT (-26.19771 -75.6125)
24  13f83c85-ac47-43aa-a11f-248c14eb5d7e  ...   POINT (-13.4077 -73.04279)
25  76fc8784-4217-4eb7-951e-77a7fc986ec3  ...   POINT (-8.27417 -70.67444)
26  2ba5ad8b-6784-4088-87bb-50c14f2cbdc0  ...    POINT (0.31493 -79.40631)
27  01e06782-a4dc-4d9e-a850-242c94f1dbc2  ...    POINT (2.53443 -72.01198)
28  864311b8-c1ba-4818-8b15-402122495a65  ...    POINT (11.82453 -70.7766)
29  30a92d5d-700e-4501-8ad8-d7b373d07e90  ...   POINT (11.73276 -70.76741)
30  47f64194-6f2c-4cd5-bf8a-0fd5ac49d24b  ...   POINT (39.58203 -69.00424)
31  d4a35fd4-5e6c-4c00-9c36-8249179b5641  ...    POINT (62.87375 -67.6029)
32  c8b9875f-3051-4b51-bc75-e5fd03507bcd  ...   POINT (76.19533 -69.40683)
33  0471ebb0-e185-48f0-8146-ce23d4a5a931  ...   POINT (76.37175 -69.37361)
34  ad792a75-c72e-4204-8fda-a14235a2791d  ...   POINT (76.38864 -69.38006)
35  66fc254c-4bd6-4f3d-8f95-c5c0aaafa206  ...   POINT (77.11737 -80.41738)
36  61fa8848-c708-48c8-8842-817124296b41  ...   POINT (77.96837 -68.57691)
37  3a151f12-0ce5-4a2f-b17e-d5740a5b5a21  ...   POINT (93.00928 -66.55305)
38  656c3136-8b2a-4ef6-870e-e98b961e8638  ...  POINT (100.74975 -66.27459)
39  19aeddf3-edd8-456d-8171-4c902e794824  ...  POINT (106.83829 -78.46435)
40  e02d07a5-de40-4997-8062-2adecb7640d8  ...  POINT (110.52408 -66.28209)
41  42d554ba-4456-42f8-b7df-3faa71e6daf6  ...  POINT (123.33333 -75.10001)
42  ca812f6a-ae44-4d83-8c80-1d55d4b1d2f1  ...  POINT (123.34713 -75.10122)
43  81f11e7c-fc8f-4e71-8dec-40c625e64689  ...   POINT (140.0011 -66.66328)
44  a2b3c483-20d5-4044-a098-3a9eeb5e1a2b  ...  POINT (163.71527 -74.93434)
45  81ab83f2-39a1-4289-8674-2c6111c3280d  ...  POINT (164.22882 -74.62409)
46  65e16630-0264-40b3-b21f-2df6716d5c7e  ...  POINT (164.11311 -74.69439)
47  a1a1291c-4171-4768-8b8e-4df289c7ed30  ...  POINT (166.66833 -77.84833)
48  2069a892-5e6c-4b45-9659-53f0967cc370  ...  POINT (166.76834 -77.84945)

[49 rows x 3 columns]
Geometry types: <ArrowStringArray>
['Point']
Length: 1, dtype: str

python -c "
from overturemaps.core import record_batch_reader
import pyarrow as pa

# Check land data volume
r = record_batch_reader('land', bbox=(-180, -90, 180, -60))
if r:
    tbl = r.read_all()
    print(f'Land features: {len(tbl)}')
    subtypes = tbl.column('subtype')
    for s in subtypes.unique():
        print(f'  subtype: {s.as_py()}')
else:
    print('land failed')

r2 = record_batch_reader('water', bbox=(-180, -90, 180, -60))
if r2:
    tbl2 = r2.read_all()
    print(f'Water features: {len(tbl2)}')
    subtypes = tbl2.column('subtype')
    for s in subtypes.unique():
        print(f'  subtype: {s.as_py()}')
else:
    print('water failed')

r3 = record_batch_reader('bathymetry', bbox=(-180, -90, 180, -60))
if r3:
    tbl3 = r3.read_all()
    print(f'Bathymetry features: {len(tbl3)}')
else:
    print('bathymetry failed')
" 2>&1

Land features: 93369
  subtype: land
  subtype: rock
  subtype: physical
  subtype: glacier
  subtype: crater
  subtype: sand
  subtype: reef
  subtype: wetland
  subtype: shrub
  subtype: grass
Water features: 13500
  subtype: lake
  subtype: physical
  subtype: stream
  subtype: ocean
  subtype: human_made
  subtype: canal
  subtype: water
  subtype: pond
  subtype: river
  subtype: reservoir
Bathymetry features: 3096

=== Fetching data ===
Fetching divisions (research stations) ...
  division: 49 features
Fetching land...
  land: 93369 features
Fetching water...
  water: 13500 features
Fetching bathymetry...
  bathymetry: 3096 features

=== Processing stations ===
Total divisions: 49
Station candidates: 48
Final station count: 48
  Amundsen–Scott South Pole @ (-139.2728, -89.9980)
  Rothera Research Station @ (-68.1222, -67.5692)
  Base Antártica San Martín @ (-67.1013, -68.1301)
  Vernadsky Research Base @ (-64.2577, -65.2455)
  Palmer Station @ (-64.0531, -64.7743)
  남극세종과학기지 @ (-58.7873, -62.2230)
  Base Artigas @ (-58.9039, -62.1844)
  Беллинсгаузен @ (-58.9599, -62.1982)
  Base Profesor Julio Escudero @ (-58.9615, -62.2014)
  Base Presidente Eduardo Frei Montalva @ (-58.9632, -62.1998)
  Villa Las Estrellas @ (-58.9654, -62.2004)
  长城站 @ (-58.9612, -62.2177)
  Base Naval Antartica Arturo Prat @ (-59.6643, -62.4789)
  Base Carlini @ (-58.6670, -62.2379)
  Arctowski @ (-58.4719, -62.1590)
  Estação Antártica Comandante Ferraz @ (-58.3908, -62.0842)
  Base Bernardo O’Higgins @ (-57.8999, -63.3208)
  Deutsche Antarktis-Empfangsstation O'Higgins @ (-57.9012, -63.3209)
  Fortín Sargento Cabral @ (-56.9972, -63.3983)
  Base Esperanza @ (-56.9964, -63.3981)
  Base Marambio @ (-56.6277, -64.2406)
  Base Antártica Orcadas @ (-44.7377, -60.7381)
  Base Antártica Belgrano II @ (-34.6275, -77.8736)
  Halley VI Research Station @ (-26.1977, -75.6125)
  Nordenskiöld Base @ (-13.4077, -73.0428)
  Neumayer-Station III @ (-8.2742, -70.6744)
  Troll @ (2.5344, -72.0120)
  Новолазаревская @ (11.8245, -70.7766)
  Maitri Station @ (11.7328, -70.7674)
  昭和基地 @ (39.5820, -69.0042)
  Mawson Station @ (62.8738, -67.6029)
  Bharati Research Station @ (76.1953, -69.4068)
  中山站 @ (76.3717, -69.3736)
  Прогресс @ (76.3886, -69.3801)
  昆仑站 @ (77.1174, -80.4174)
  Davis Station @ (77.9684, -68.5769)
  Мирный @ (93.0093, -66.5530)
  Stacja im. A.B. Dobrowolskiego @ (100.7497, -66.2746)
  Восток @ (106.8383, -78.4643)
  Casey Station @ (110.5241, -66.2821)
  Concordia Station @ (123.3333, -75.1000)
  Camp d'été @ (123.3471, -75.1012)
  Base Dumont d'Urville @ (140.0011, -66.6633)
  秦岭站 @ (163.7153, -74.9343)
  남극장보고과학기지 @ (164.2288, -74.6241)
  Stazione Mario Zucchelli @ (164.1131, -74.6944)
  McMurdo Station @ (166.6683, -77.8483)
  Scott Base @ (166.7683, -77.8495)

=== Projecting to EPSG:3031 ===
Stations CRS: EPSG:3031

=== Creating 200 km buffers ===
Buffers created.

=== Processing land ===
Land union type: GeometryCollection

=== Clipping station buffers to land ===
Land-clipped spheres: 16

=== Grouping into coalitions ===
Traceback (most recent call last):
  File "/work/solve.py", line 187, in <module>
    i = row.index_left
        ^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/pandas/core/generic.py", line 6206, in __getattr__
    return object.__getattribute__(self, name)
           ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
AttributeError: 'Series' object has no attribute 'index_left'. Did you mean: 'index_right'?

cd /work && python -c "
import geopandas as gpd
# Quick test
s1 = gpd.read_parquet('station_spheres.geoparquet') if False else None
# Test sjoin column names
from shapely.geometry import Point
import pandas as pd
gdf1 = gpd.GeoDataFrame({'a': [1,2]}, geometry=[Point(0,0), Point(1,1)], crs='EPSG:3031')
gdf2 = gpd.GeoDataFrame({'b': [3,4]}, geometry=[Point(0.1,0.1), Point(2,2)], crs='EPSG:3031')
sj = gpd.sjoin(gdf1, gdf2, how='inner', predicate='intersects')
print('Columns:', list(sj.columns))
print(sj)
"

Columns: ['a', 'geometry', 'index_right', 'b']
Empty GeoDataFrame
Columns: [a, geometry, index_right, b]
Index: []

=== Fetching data ===
Fetching divisions (research stations) ...
  division: 49 features
Fetching land...
  land: 93369 features
Fetching water...
  water: 13500 features
Fetching bathymetry...
  bathymetry: 3096 features

=== Processing stations ===
Total divisions: 49
Station candidates: 48
Final station count: 48
  Amundsen–Scott South Pole @ (-139.2728, -89.9980)
  Rothera Research Station @ (-68.1222, -67.5692)
  Base Antártica San Martín @ (-67.1013, -68.1301)
  Vernadsky Research Base @ (-64.2577, -65.2455)
  Palmer Station @ (-64.0531, -64.7743)
  남극세종과학기지 @ (-58.7873, -62.2230)
  Base Artigas @ (-58.9039, -62.1844)
  Беллинсгаузен @ (-58.9599, -62.1982)
  Base Profesor Julio Escudero @ (-58.9615, -62.2014)
  Base Presidente Eduardo Frei Montalva @ (-58.9632, -62.1998)
  Villa Las Estrellas @ (-58.9654, -62.2004)
  长城站 @ (-58.9612, -62.2177)
  Base Naval Antartica Arturo Prat @ (-59.6643, -62.4789)
  Base Carlini @ (-58.6670, -62.2379)
  Arctowski @ (-58.4719, -62.1590)
  Estação Antártica Comandante Ferraz @ (-58.3908, -62.0842)
  Base Bernardo O’Higgins @ (-57.8999, -63.3208)
  Deutsche Antarktis-Empfangsstation O'Higgins @ (-57.9012, -63.3209)
  Fortín Sargento Cabral @ (-56.9972, -63.3983)
  Base Esperanza @ (-56.9964, -63.3981)
  Base Marambio @ (-56.6277, -64.2406)
  Base Antártica Orcadas @ (-44.7377, -60.7381)
  Base Antártica Belgrano II @ (-34.6275, -77.8736)
  Halley VI Research Station @ (-26.1977, -75.6125)
  Nordenskiöld Base @ (-13.4077, -73.0428)
  Neumayer-Station III @ (-8.2742, -70.6744)
  Troll @ (2.5344, -72.0120)
  Новолазаревская @ (11.8245, -70.7766)
  Maitri Station @ (11.7328, -70.7674)
  昭和基地 @ (39.5820, -69.0042)
  Mawson Station @ (62.8738, -67.6029)
  Bharati Research Station @ (76.1953, -69.4068)
  中山站 @ (76.3717, -69.3736)
  Прогресс @ (76.3886, -69.3801)
  昆仑站 @ (77.1174, -80.4174)
  Davis Station @ (77.9684, -68.5769)
  Мирный @ (93.0093, -66.5530)
  Stacja im. A.B. Dobrowolskiego @ (100.7497, -66.2746)
  Восток @ (106.8383, -78.4643)
  Casey Station @ (110.5241, -66.2821)
  Concordia Station @ (123.3333, -75.1000)
  Camp d'été @ (123.3471, -75.1012)
  Base Dumont d'Urville @ (140.0011, -66.6633)
  秦岭站 @ (163.7153, -74.9343)
  남극장보고과학기지 @ (164.2288, -74.6241)
  Stazione Mario Zucchelli @ (164.1131, -74.6944)
  McMurdo Station @ (166.6683, -77.8483)
  Scott Base @ (166.7683, -77.8495)

=== Projecting to EPSG:3031 ===
Stations CRS: EPSG:3031

=== Creating 200 km buffers ===
Buffers created.

=== Processing land ===
Land union type: MultiPolygon

=== Clipping station buffers to land ===
Land-clipped spheres: 48

=== Grouping into coalitions ===
Coalitions formed: 16, distinct coalition ids: 16
Coalition counts:
coalition
0     20
1      2
2      2
3      3
4      4
5      2
6      2
7      5
8      1
9      1
10     1
11     1
12     1
13     1
14     1
15     1
Name: count, dtype: int64

=== Writing station_spheres.geoparquet ===
Written 48 features.

=== Computing water overlaps ===
Combined water features: 16596
Water overlap features: 14212

=== Writing station_water_overlap.geoparquet ===
Written 14212 features.

=== Final sanity checks ===
station_spheres.geoparquet: 48 features, CRS={"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "ProjectedCRS", "name": "WGS 84 / Antarctic Polar Stereographic", "base_crs": {"name": "WGS 84", "datum_ensemble": {"name": "World Geodetic System 1984 ensemble", "members": [{"name": "World Geodetic System 1984 (Transit)"}, {"name": "World Geodetic System 1984 (G730)"}, {"name": "World Geodetic System 1984 (G873)"}, {"name": "World Geodetic System 1984 (G1150)"}, {"name": "World Geodetic System 1984 (G1674)"}, {"name": "World Geodetic System 1984 (G1762)"}, {"name": "World Geodetic System 1984 (G2139)"}, {"name": "World Geodetic System 1984 (G2296)"}], "ellipsoid": {"name": "WGS 84", "semi_major_axis": 6378137, "inverse_flattening": 298.257223563}, "accuracy": "2.0", "id": {"authority": "EPSG", "code": 6326}}, "coordinate_system": {"subtype": "ellipsoidal", "axis": [{"name": "Geodetic latitude", "abbreviation": "Lat", "direction": "north", "unit": "degree"}, {"name": "Geodetic longitude", "abbreviation": "Lon", "direction": "east", "unit": "degree"}]}, "id": {"authority": "EPSG", "code": 4326}}, "conversion": {"name": "Antarctic Polar Stereographic", "method": {"name": "Polar Stereographic (variant B)", "id": {"authority": "EPSG", "code": 9829}}, "parameters": [{"name": "Latitude of standard parallel", "value": -71, "unit": "degree", "id": {"authority": "EPSG", "code": 8832}}, {"name": "Longitude of origin", "value": 0, "unit": "degree", "id": {"authority": "EPSG", "code": 8833}}, {"name": "False easting", "value": 0, "unit": "metre", "id": {"authority": "EPSG", "code": 8806}}, {"name": "False northing", "value": 0, "unit": "metre", "id": {"authority": "EPSG", "code": 8807}}]}, "coordinate_system": {"subtype": "Cartesian", "axis": [{"name": "Easting", "abbreviation": "E", "direction": "north", "meridian": {"longitude": 90}, "unit": "metre"}, {"name": "Northing", "abbreviation": "N", "direction": "north", "meridian": {"longitude": 0}, "unit": "metre"}]}, "scope": "Antarctic Digital Database and small scale topographic mapping.", "area": "Antarctica.", "bbox": {"south_latitude": -90, "west_longitude": -180, "north_latitude": -60, "east_longitude": 180}, "id": {"authority": "EPSG", "code": 3031}}
  Columns: ['station_id', 'station_name', 'coalition', 'geometry']
  Coalition distinct values: 16
  Geometry types: <ArrowStringArray>
['MultiPolygon']
Length: 1, dtype: str
station_water_overlap.geoparquet: 14212 features, CRS={"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "ProjectedCRS", "name": "WGS 84 / Antarctic Polar Stereographic", "base_crs": {"name": "WGS 84", "datum_ensemble": {"name": "World Geodetic System 1984 ensemble", "members": [{"name": "World Geodetic System 1984 (Transit)"}, {"name": "World Geodetic System 1984 (G730)"}, {"name": "World Geodetic System 1984 (G873)"}, {"name": "World Geodetic System 1984 (G1150)"}, {"name": "World Geodetic System 1984 (G1674)"}, {"name": "World Geodetic System 1984 (G1762)"}, {"name": "World Geodetic System 1984 (G2139)"}, {"name": "World Geodetic System 1984 (G2296)"}], "ellipsoid": {"name": "WGS 84", "semi_major_axis": 6378137, "inverse_flattening": 298.257223563}, "accuracy": "2.0", "id": {"authority": "EPSG", "code": 6326}}, "coordinate_system": {"subtype": "ellipsoidal", "axis": [{"name": "Geodetic latitude", "abbreviation": "Lat", "direction": "north", "unit": "degree"}, {"name": "Geodetic longitude", "abbreviation": "Lon", "direction": "east", "unit": "degree"}]}, "id": {"authority": "EPSG", "code": 4326}}, "conversion": {"name": "Antarctic Polar Stereographic", "method": {"name": "Polar Stereographic (variant B)", "id": {"authority": "EPSG", "code": 9829}}, "parameters": [{"name": "Latitude of standard parallel", "value": -71, "unit": "degree", "id": {"authority": "EPSG", "code": 8832}}, {"name": "Longitude of origin", "value": 0, "unit": "degree", "id": {"authority": "EPSG", "code": 8833}}, {"name": "False easting", "value": 0, "unit": "metre", "id": {"authority": "EPSG", "code": 8806}}, {"name": "False northing", "value": 0, "unit": "metre", "id": {"authority": "EPSG", "code": 8807}}]}, "coordinate_system": {"subtype": "Cartesian", "axis": [{"name": "Easting", "abbreviation": "E", "direction": "north", "meridian": {"longitude": 90}, "unit": "metre"}, {"name": "Northing", "abbreviation": "N", "direction": "north", "meridian": {"longitude": 0}, "unit": "metre"}]}, "scope": "Antarctic Digital Database and small scale topographic mapping.", "area": "Antarctica.", "bbox": {"south_latitude": -90, "west_longitude": -180, "north_latitude": -60, "east_longitude": 180}, "id": {"authority": "EPSG", "code": 3031}}
  Columns: ['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source', 'geometry']
  water_source values: <ArrowStringArray>
['base.bathymetry', 'base.water']
Length: 2, dtype: str

Done!

cd /work && python -c "
import geopandas as gpd

s1 = gpd.read_parquet('station_spheres.geoparquet')
print('=== station_spheres sample ===')
print(s1.head(10).to_string())
print()

s2 = gpd.read_parquet('station_water_overlap.geoparquet')
print('=== station_water_overlap sample ===')
print(s2.head(10).to_string())
print()

# Check specific station
for stn in ['McMurdo Station', 'Palmer Station', 'Base Esperanza']:
    sub = s2[s2['station_name'] == stn]
    print(f'{stn}: {len(sub)} water overlap features')
    if len(sub) > 0:
        print(f'  water_source values: {sub[\"water_source\"].unique()}')
        print(f'  water_subtype values: {sub[\"water_subtype\"].unique()}')
"

=== station_spheres sample ===
                             station_id                           station_name  coalition                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                
[... truncated ...]6129.243, -94421.13 176219.574, -76678.469 184611.227, -58198.718 191223.388, -39159.847 195992.377, -19745.21 198872.266, -141.782 199835.321, 19461.646 198872.266, 38876.282 195992.377, 57915.153 191223.388, 76394.904 184611.227, 94137.565 176219.574, 110972.264 166129.243, 126736.874 154437.412, 141279.574 141256.677, 154460.308 126713.978, 166152.14 110949.368, 176242.471 94114.668, 184634.124 76372.007, 191246.285 57892.256, 196015.274 38853.385, 198895.163 19438.749, 199858.218 -164.679, 198895.163 -19768.107)))
2  23e81262-d6ed-45a3-a1a0-4bc6a2a887d8  Amundsen–Scott South Pole  d1548a3a-2bc5-502a-bd2d-dd985969e9dd        NaN                base.bathymetry  MULTIPOLYGON (((198895.163 -19768.107, 196015.274 -39182.743, 191246.285 -58221.614, 184634.124 -76701.365, 176242.471 -94444.026, 166152.14 -111278.726, 154460.308 -127043.336, 141279.574 -141586.035, 126736.874 -154766.77, 110972.264 -166458.601, 94137.565 -176548.932, 85482.349 -180642.544, 93589.026 -140112.009, 84187.457 -56269.591, 50594.837 -4989.799, 15194.438 8119.344, 161.518 389.831, -161.755 390.597, -15073.829 8058.316, -50189.075 -4940.835, -83515.863 -55799.373, -92853.525 -138957.74, -84393.081 -181291.846, -94421.13 -176548.932, -111255.829 -166458.601, -127020.439 -154766.77, -141563.139 -141586.035, -154743.873 -127043.336, -166435.705 -111278.726, -176526.035 -94444.026, -184917.689 -76701.365, -191529.85 -58221.614, -196298.838 -39182.743, -199178.728 -19768.107, -200141.782 -164.679, -199178.728 19438.749, -196298.838 38853.385, -191529.85 57892.256, -184917.689 76372.007, -176526.035 94114.668, -166435.705 110949.368, -154743.873 126713.978, -141563.139 141256.677, -127020.439 154437.412, -111255.829 166129.243, -94421.13 176219.574, -76678.469 184611.227, -58198.718 191223.388, -39159.847 195992.377, -19745.21 198872.266, -141.782 199835.321, 19461.646 198872.266, 38876.282 195992.377, 57915.153 191223.388, 76394.904 184611.227, 94137.565 176219.574, 110972.264 166129.243, 126736.874 154437.412, 141279.574 141256.677, 154460.308 126713.978, 166152.14 110949.368, 176242.471 94114.668, 184634.124 76372.007, 191246.285 57892.256, 196015.274 38853.385, 198895.163 19438.749, 199858.218 -164.679, 198895.163 -19768.107)))
3  23e81262-d6ed-45a3-a1a0-4bc6a2a887d8  Amundsen–Scott South Pole  c10a3ab1-cda7-595c-8769-99cef4f67429        NaN                base.bathymetry                          MULTIPOLYGON (((198895.163 -19768.107, 196015.274 -39182.743, 191246.285 -58221.614, 184634.124 -76701.365, 176242.471 -94444.026, 166152.14 -111278.726, 154460.308 -127043.336, 141279.574 -141586.035, 126736.874 -154766.77, 110972.264 -166458.601, 94993.134 -176036.123, 100197.556 -150005.63, 90121.747 -60235.978, 54146.32 -5340.056, 16243.595 8679.975, 161.518 389.831, -161.755 390.597, -15665.605 8374.674, -52192.509 -5138.062, -86863.796 -58036.224, -96582.367 -144538.05, -89744.557 -178760.786, -94421.13 -176548.932, -111255.829 -166458.601, -127020.439 -154766.77, -141563.139 -141586.035, -154743.873 -127043.336, -166435.705 -111278.726, -176526.035 -94444.026, -184917.689 -76701.365, -191529.85 -58221.614, -196298.838 -39182.743, -199178.728 -19768.107, -200141.782 -164.679, -199178.728 19438.749, -196298.838 38853.385, -191529.85 57892.256, -184917.689 76372.007, -176526.035 94114.668, -166435.705 110949.368, -154743.873 126713.978, -141563.139 141256.677, -127020.439 154437.412, -111255.829 166129.243, -94421.13 176219.574, -76678.469 184611.227, -58198.718 191223.388, -39159.847 195992.377, -19745.21 198872.266, -141.782 199835.321, 19461.646 198872.266, 38876.282 195992.377, 57915.153 191223.388, 76394.904 184611.227, 94137.565 176219.574, 110972.264 166129.243, 126736.874 154437.412, 141279.574 141256.677, 154460.308 126713.978, 166152.14 110949.368, 176242.471 94114.668, 184634.124 76372.007, 191246.285 57892.256, 196015.274 38853.385, 198895.163 19438.749, 199858.218 -164.679, 198895.163 -19768.107)))
4  23e81262-d6ed-45a3-a1a0-4bc6a2a887d8  Amundsen–Scott South Pole  002c89a3-1f5e-56ee-a399-a4bb9cf6d081        NaN                base.bathymetry                                                                                                                                                                                                                                                                     MULTIPOLYGON (((198895.163 -19768.107, 196015.274 -39182.743, 191246.285 -58221.614, 184634.124 -76701.365, 176242.471 -94444.026, 166152.14 -111278.726, 164875.268 -113000.388, 164533.007 -109971.25, 98675.533 -9731.621, 29397.007 15708.68, 161 388.582, -161.352 389.625, -26854.737 14356.265, -90075.187 -8867.413, -150173.071 -100335.027, -153364.789 -128564.921, -154743.873 -127043.336, -166435.705 -111278.726, -176526.035 -94444.026, -184917.689 -76701.365, -191529.85 -58221.614, -196298.838 -39182.743, -199178.728 -19768.107, -200141.782 -164.679, -199178.728 19438.749, -196298.838 38853.385, -191529.85 57892.256, -184917.689 76372.007, -176526.035 94114.668, -166435.705 110949.368, -154743.873 126713.978, -141563.139 141256.677, -127020.439 154437.412, -111255.829 166129.243, -94421.13 176219.574, -76678.469 184611.227, -58198.718 191223.388, -39159.847 195992.377, -19745.21 198872.266, -141.782 199835.321, 19461.646 198872.266, 38876.282 195992.377, 57915.153 191223.388, 76394.904 184611.227, 94137.565 176219.574, 110972.264 166129.243, 126736.874 154437.412, 141279.574 141256.677, 154460.308 126713.978, 166152.14 110949.368, 176242.471 94114.668, 184634.124 76372.007, 191246.285 57892.256, 196015.274 38853.385, 198895.163 19438.749, 199858.218 -164.679, 198895.163 -19768.107)))
5  23e81262-d6ed-45a3-a1a0-4bc6a2a887d8  Amundsen–Scott South Pole  d8eecaa0-c85c-59c2-94c3-e31c00a8fdd7        NaN                base.bathymetry                                                                                                                                                                                                                                                                                                                                                               MULTIPOLYGON (((198895.163 -19768.107, 196015.274 -39182.743, 191246.285 -58221.614, 184634.124 -76701.365, 184398.52 -77199.509, 142896.754 -14092.891, 42462.93 22690.604, 162.893 393.149, -162.824 393.178, -29574.079 15810.006, -99275.187 -9773.087, -165546.714 -110606.581, -165730.393 -112229.729, -166435.705 -111278.726, -176526.035 -94444.026, -184917.689 -76701.365, -191529.85 -58221.614, -196298.838 -39182.743, -199178.728 -19768.107, -200141.782 -164.679, -199178.728 19438.749, -196298.838 38853.385, -191529.85 57892.256, -184917.689 76372.007, -176526.035 94114.668, -166435.705 110949.368, -154743.873 126713.978, -141563.139 141256.677, -127020.439 154437.412, -111255.829 166129.243, -94421.13 176219.574, -76678.469 184611.227, -58198.718 191223.388, -39159.847 195992.377, -19745.21 198872.266, -141.782 199835.321, 19461.646 198872.266, 38876.282 195992.377, 57915.153 191223.388, 76394.904 184611.227, 94137.565 176219.574, 110972.264 166129.243, 126736.874 154437.412, 141279.574 141256.677, 154460.308 126713.978, 166152.14 110949.368, 176242.471 94114.668, 184634.124 76372.007, 191246.285 57892.256, 196015.274 38853.385, 198895.163 19438.749, 199858.218 -164.679, 198895.163 -19768.107)))
6  23e81262-d6ed-45a3-a1a0-4bc6a2a887d8  Amundsen–Scott South Pole  c5dba2bc-a386-5655-afec-6fab1456dcb1        NaN                base.bathymetry                                                                                                                                                                                                                                                                                            MULTIPOLYGON (((198895.163 -19768.107, 196015.274 -39182.743, 191246.285 -58221.614, 184634.124 -76701.365, 176242.471 -94444.026, 166152.14 -111278.726, 165973.196 -111520.003, 165901.539 -110885.874, 99493.9 -9812.297, 29638.181 15837.566, 160.668 387.78, -161.094 389.001, -29396.393 15715.003, -98681.306 -9714.645, -164556.83 -109945.229, -164936.481 -113300.195, -166435.705 -111278.726, -176526.035 -94444.026, -184917.689 -76701.365, -191529.85 -58221.614, -196298.838 -39182.743, -199178.728 -19768.107, -200141.782 -164.679, -199178.728 19438.749, -196298.838 38853.385, -191529.85 57892.256, -184917.689 76372.007, -176526.035 94114.668, -166435.705 110949.368, -154743.873 126713.978, -141563.139 141256.677, -127020.439 154437.412, -111255.829 166129.243, -94421.13 176219.574, -76678.469 184611.227, -58198.718 191223.388, -39159.847 195992.377, -19745.21 198872.266, -141.782 199835.321, 19461.646 198872.266, 38876.282 195992.377, 57915.153 191223.388, 76394.904 184611.227, 94137.565 176219.574, 110972.264 166129.243, 126736.874 154437.412, 141279.574 141256.677, 154460.308 126713.978, 166152.14 110949.368, 176242.471 94114.668, 184634.124 76372.007, 191246.285 57892.256, 196015.274 38853.385, 198895.163 19438.749, 199858.218 -164.679, 198895.163 -19768.107)))
7  23e81262-d6ed-45a3-a1a0-4bc6a2a887d8  Amundsen–Scott South Pole  2ca98fcf-27c9-5086-ac07-1729ffe0546e        NaN                base.bathymetry                                                                                                                                                                                                                                             MULTIPOLYGON (((198895.163 -19768.107, 196015.274 -39182.743, 191246.285 -58221.614, 184634.124 -76701.365, 176242.471 -94444.026, 166152.14 -111278.726, 154460.308 -127043.336, 151054.4 -130801.177, 147411.434 -98527.271, 88429.216 -8721.033, 26369.432 14090.878, 160.553 387.503, -161.005 388.786, -26060.62 13931.727, -87388.19 -8602.914, -145682.924 -97335.062, -149673.189 -132637.974, -154743.873 -127043.336, -166435.705 -111278.726, -176526.035 -94444.026, -184917.689 -76701.365, -191529.85 -58221.614, -196298.838 -39182.743, -199178.728 -19768.107, -200141.782 -164.679, -199178.728 19438.749, -196298.838 38853.385, -191529.85 57892.256, -184917.689 76372.007, -176526.035 94114.668, -166435.705 110949.368, -154743.873 126713.978, -141563.139 141256.677, -127020.439 154437.412, -111255.829 166129.243, -94421.13 176219.574, -76678.469 184611.227, -58198.718 191223.388, -39159.847 195992.377, -19745.21 198872.266, -141.782 199835.321, 19461.646 198872.266, 38876.282 195992.377, 57915.153 191223.388, 76394.904 184611.227, 94137.565 176219.574, 110972.264 166129.243, 126736.874 154437.412, 141279.574 141256.677, 154460.308 126713.978, 166152.14 110949.368, 176242.471 94114.668, 184634.124 76372.007, 191246.285 57892.256, 196015.274 38853.385, 198895.163 19438.749, 199858.218 -164.679, 198895.163 -19768.107)))
8  23e81262-d6ed-45a3-a1a0-4bc6a2a887d8  Amundsen–Scott South Pole  d97068b2-8f46-5a0e-9a93-803579a73525        NaN                base.bathymetry                                                                                                                                                                                          MULTIPOLYGON (((198895.163 -19768.107, 196015.274 -39182.743, 191246.285 -58221.614, 184634.124 -76701.365, 176242.471 -94444.026, 166152.14 -111278.726, 154460.308 -127043.336, 149051.635 -133010.887, 144976.189 -96899.623, 86971.943 -8577.32, 25938.946 13860.841, 160.553 387.503, -161.005 388.786, -18165.913 9711.302, -60660.554 -5971.714, -101016.027 -67491.773, -112088.119 -165841.333, -127020.439 -154766.77, -141563.139 -141586.035, -154743.873 -127043.336, -166435.705 -111278.726, -176526.035 -94444.026, -184917.689 -76701.365, -191529.85 -58221.614, -196298.838 -39182.743, -199178.728 -19768.107, -200141.782 -164.679, -199178.728 19438.749, -196298.838 38853.385, -191529.85 57892.256, -184917.689 76372.007, -176526.035 94114.668, -166435.705 110949.368, -154743.873 126713.978, -141563.139 141256.677, -127020.439 154437.412, -111255.829 166129.243, -94421.13 176219.574, -76678.469 184611.227, -58198.718 191223.388, -39159.847 195992.377, -19745.21 198872.266, -141.782 199835.321, 19461.646 198872.266, 38876.282 195992.377, 57915.153 191223.388, 76394.904 184611.227, 94137.565 176219.574, 110972.264 166129.243, 126736.874 154437.412, 141279.574 141256.677, 154460.308 126713.978, 166152.14 110949.368, 176242.471 94114.668, 184634.124 76372.007, 191246.285 57892.256, 196015.274 38853.385, 198895.163 19438.749, 199858.218 -164.679, 198895.163 -19768.107)))
9  23e81262-d6ed-45a3-a1a0-4bc6a2a887d8  Amundsen–Scott South Pole  5637be2d-a08a-59c9-8239-d900a3bc3212        NaN                base.bathymetry                                                                                                                                                                                                                                                                                                                                                                                                                                    MULTIPOLYGON (((198895.163 -19768.107, 196015.274 -39182.743, 191246.285 -58221.614, 189284.097 -63705.563, 157602.609 -15543.301, 46804.384 25010.479, 161.074 388.758, -160.485 387.532, -30966.523 16554.439, -104003.045 -10238.386, -168337.665 -108105.497, -176526.035 -94444.026, -184917.689 -76701.365, -191529.85 -58221.614, -196298.838 -39182.743, -199178.728 -19768.107, -200141.782 -164.679, -199178.728 19438.749, -196298.838 38853.385, -191529.85 57892.256, -184917.689 76372.007, -176526.035 94114.668, -166435.705 110949.368, -154743.873 126713.978, -141563.139 141256.677, -127020.439 154437.412, -111255.829 166129.243, -94421.13 176219.574, -76678.469 184611.227, -58198.718 191223.388, -39159.847 195992.377, -19745.21 198872.266, -141.782 199835.321, 19461.646 198872.266, 38876.282 195992.377, 57915.153 191223.388, 76394.904 184611.227, 94137.565 176219.574, 110972.264 166129.243, 126736.874 154437.412, 141279.574 141256.677, 154460.308 126713.978, 166152.14 110949.368, 176242.471 94114.668, 184634.124 76372.007, 191246.285 57892.256, 196015.274 38853.385, 198895.163 19438.749, 199858.218 -164.679, 198895.163 -19768.107)))

McMurdo Station: 817 water overlap features
  water_source values: <ArrowStringArray>
['base.water', 'base.bathymetry']
Length: 2, dtype: str
  water_subtype values: <ArrowStringArray>
['physical', 'lake', 'pond', 'ocean', 'water', '']
Length: 6, dtype: str
Palmer Station: 166 water overlap features
  water_source values: <ArrowStringArray>
['base.water', 'base.bathymetry']
Length: 2, dtype: str
  water_subtype values: <ArrowStringArray>
['ocean', 'human_made', 'physical', '']
Length: 4, dtype: str
Base Esperanza: 596 water overlap features
  water_source values: <ArrowStringArray>
['base.water', 'base.bathymetry']
Length: 2, dtype: str
  water_subtype values: <ArrowStringArray>
['physical', 'ocean', 'lake', 'water', 'pond', 'stream', '']
Length: 7, dtype: str

cd /work && python -c "
import geopandas as gpd

# Check station_spheres
s1 = gpd.read_parquet('station_spheres.geoparquet')
print('=== station_spheres.geoparquet ===')
print(f'Rows: {len(s1)}')
print(f'CRS: {s1.crs}')
print(f'Columns: {list(s1.columns)}')
print(f'Geometry types: {s1.geom_type.unique()}')
print(f'Coalition distinct: {s1[\"coalition\"].nunique()}')
print(f'Coalition values: {sorted(s1[\"coalition\"].unique())}')
print(f'Null station_id: {s1[\"station_id\"].isna().sum()}')
print(f'Null station_name: {s1[\"station_name\"].isna().sum()}')
print()

# Check station_water_overlap
s2 = gpd.read_parquet('station_water_overlap.geoparquet')
print('=== station_water_overlap.geoparquet ===')
print(f'Rows: {len(s2)}')
print(f'CRS: {s2.crs}')
print(f'Columns: {list(s2.columns)}')
print(f'Geometry types: {s2.geom_type.unique()}')
print(f'water_source values: {list(s2[\"water_source\"].unique())}')
print(f'Null station_id: {s2[\"station_id\"].isna().sum()}')
print(f'Null station_name: {s2[\"station_name\"].isna().sum()}')
print(f'Null water_id: {s2[\"water_id\"].isna().sum()}')
print(f'Null water_subtype: {s2[\"water_subtype\"].isna().sum()}')
print(f'Null water_name: {s2[\"water_name\"].isna().sum()}')
print(f'Sample water_name values (non-null): {list(s2[s2[\"water_name\"].notna()][\"water_name\"].head(10))}')

# Check that all stations from spheres appear in water overlap
stations_in_spheres = set(s1['station_id'].unique())
stations_in_water = set(s2['station_id'].unique())
missing = stations_in_spheres - stations_in_water
print(f'\nStations in spheres but NOT in water overlap: {len(missing)}')
if missing:
    print(f'  {missing}')
"

=== station_spheres.geoparquet ===
Rows: 48
CRS: {"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "ProjectedCRS", "name": "WGS 84 / Antarctic Polar Stereographic", "base_crs": {"name": "WGS 84", "datum_ensemble": {"name": "World Geodetic System 1984 ensemble", "members": [{"name": "World Geodetic System 1984 (Transit)"}, {"name": "World Geodetic System 1984 (G730)"}, {"name": "World Geodetic System 1984 (G873)"}, {"name": "World Geodetic System 1984 (G1150)"}, {"name": "World Geodetic System 1984 (G1674)"}, {"name": "World Geodetic System 1984 (G1762)"}, {"name": "World Geodetic System 1984 (G2139)"}, {"name": "World Geodetic System 1984 (G2296)"}], "ellipsoid": {"name": "WGS 84", "semi_major_axis": 6378137, "inverse_flattening": 298.257223563}, "accuracy": "2.0", "id": {"authority": "EPSG", "code": 6326}}, "coordinate_system": {"subtype": "ellipsoidal", "axis": [{"name": "Geodetic latitude", "abbreviation": "Lat", "direction": "north", "unit": "degree"}, {"name": "Geodetic longitude", "abbreviation": "Lon", "direction": "east", "unit": "degree"}]}, "id": {"authority": "EPSG", "code": 4326}}, "conversion": {"name": "Antarctic Polar Stereographic", "method": {"name": "Polar Stereographic (variant B)", "id": {"authority": "EPSG", "code": 9829}}, "parameters": [{"name": "Latitude of standard parallel", "value": -71, "unit": "degree", "id": {"authority": "EPSG", "code": 8832}}, {"name": "Longitude of origin", "value": 0, "unit": "degree", "id": {"authority": "EPSG", "code": 8833}}, {"name": "False easting", "value": 0, "unit": "metre", "id": {"authority": "EPSG", "code": 8806}}, {"name": "False northing", "value": 0, "unit": "metre", "id": {"authority": "EPSG", "code": 8807}}]}, "coordinate_system": {"subtype": "Cartesian", "axis": [{"name": "Easting", "abbreviation": "E", "direction": "north", "meridian": {"longitude": 90}, "unit": "metre"}, {"name": "Northing", "abbreviation": "N", "direction": "north", "meridian": {"longitude": 0}, "unit": "metre"}]}, "scope": "Antarctic Digital Database and small scale topographic mapping.", "area": "Antarctica.", "bbox": {"south_latitude": -90, "west_longitude": -180, "north_latitude": -60, "east_longitude": 180}, "id": {"authority": "EPSG", "code": 3031}}
Columns: ['station_id', 'station_name', 'coalition', 'geometry']
Geometry types: <ArrowStringArray>
['MultiPolygon']
Length: 1, dtype: str
Coalition distinct: 16
Coalition values: [np.int64(0), np.int64(1), np.int64(2), np.int64(3), np.int64(4), np.int64(5), np.int64(6), np.int64(7), np.int64(8), np.int64(9), np.int64(10), np.int64(11), np.int64(12), np.int64(13), np.int64(14), np.int64(15)]
Null station_id: 0
Null station_name: 0

=== station_water_overlap.geoparquet ===
Rows: 14212
CRS: {"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "ProjectedCRS", "name": "WGS 84 / Antarctic Polar Stereographic", "base_crs": {"name": "WGS 84", "datum_ensemble": {"name": "World Geodetic System 1984 ensemble", "members": [{"name": "World Geodetic System 1984 (Transit)"}, {"name": "World Geodetic System 1984 (G730)"}, {"name": "World Geodetic System 1984 (G873)"}, {"name": "World Geodetic System 1984 (G1150)"}, {"name": "World Geodetic System 1984 (G1674)"}, {"name": "World Geodetic System 1984 (G1762)"}, {"name": "World Geodetic System 1984 (G2139)"}, {"name": "World Geodetic System 1984 (G2296)"}], "ellipsoid": {"name": "WGS 84", "semi_major_axis": 6378137, "inverse_flattening": 298.257223563}, "accuracy": "2.0", "id": {"authority": "EPSG", "code": 6326}}, "coordinate_system": {"subtype": "ellipsoidal", "axis": [{"name": "Geodetic latitude", "abbreviation": "Lat", "direction": "north", "unit": "degree"}, {"name": "Geodetic longitude", "abbreviation": "Lon", "direction": "east", "unit": "degree"}]}, "id": {"authority": "EPSG", "code": 4326}}, "conversion": {"name": "Antarctic Polar Stereographic", "method": {"name": "Polar Stereographic (variant B)", "id": {"authority": "EPSG", "code": 9829}}, "parameters": [{"name": "Latitude of standard parallel", "value": -71, "unit": "degree", "id": {"authority": "EPSG", "code": 8832}}, {"name": "Longitude of origin", "value": 0, "unit": "degree", "id": {"authority": "EPSG", "code": 8833}}, {"name": "False easting", "value": 0, "unit": "metre", "id": {"authority": "EPSG", "code": 8806}}, {"name": "False northing", "value": 0, "unit": "metre", "id": {"authority": "EPSG", "code": 8807}}]}, "coordinate_system": {"subtype": "Cartesian", "axis": [{"name": "Easting", "abbreviation": "E", "direction": "north", "meridian": {"longitude": 90}, "unit": "metre"}, {"name": "Northing", "abbreviation": "N", "direction": "north", "meridian": {"longitude": 0}, "unit": "metre"}]}, "scope": "Antarctic Digital Database and small scale topographic mapping.", "area": "Antarctica.", "bbox": {"south_latitude": -90, "west_longitude": -180, "north_latitude": -60, "east_longitude": 180}, "id": {"authority": "EPSG", "code": 3031}}
Columns: ['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source', 'geometry']
Geometry types: <ArrowStringArray>
['MultiPolygon']
Length: 1, dtype: str
water_source values: ['base.bathymetry', 'base.water']
Null station_id: 0
Null station_name: 0
Null water_id: 0
Null water_subtype: 0
Null water_name: 10499
Sample water_name values (non-null): ['Bellingshausen Sea', 'Bellingshausen Sea', 'Hot Tub', 'Port-Charcot', 'Flanders Bay', 'Oscar Pinochet Strait', 'Gerritsz Bay', 'Neko Harbour', 'Buls Bay', 'Canal Almirante Merino']

Stations in spheres but NOT in water overlap: 0