geo-l3-antarctica-stations-geodesic

Rerun

duckdb             1.5.3
fiona              1.10.1
geopandas          1.1.3
overturemaps       1.0.0
pyogrio            0.12.1
pyproj             3.7.2
shapely            2.1.2

cd /work && python -c "
import overturemaps
print(overturemaps.core.OvertureTheme.all_themes())
"

Traceback (most recent call last):
  File "<string>", line 3, in <module>
    print(overturemaps.core.OvertureTheme.all_themes())
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: module 'overturemaps.core' has no attribute 'OvertureTheme'

cd /work && python -c "
import overturemaps
print(dir(overturemaps))
print('---')
print(overturemaps.__version__)
"

Traceback (most recent call last):
  File "<string>", line 5, in <module>
    print(overturemaps.__version__)
          ^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: module 'overturemaps' has no attribute '__version__'

cd /work && python -c "
import overturemaps
print('get_all_overture_types:', overturemaps.get_all_overture_types())
"

cd /work && python -c "
import overturemaps

# Get place schema to find stations
reader = overturemaps.record_batch_reader('place', type='place')
print('Place reader type:', type(reader))
schema = reader.schema
print(schema)
"

Traceback (most recent call last):
  File "<string>", line 5, in <module>
    reader = overturemaps.record_batch_reader('place', type='place')
TypeError: record_batch_reader() got an unexpected keyword argument 'type'

cd /work && python -c "
import overturemaps
import inspect
# Check record_batch_reader signature
print(inspect.signature(overturemaps.record_batch_reader))
# Also check core module
print(dir(overturemaps.core))
"

cd /work && python -c "
import overturemaps
print('type_theme_map:', overturemaps.core.type_theme_map)
"

cd /work && python -c "
import overturemaps

# Get place data - look at schema
reader = overturemaps.record_batch_reader('place')
if reader:
    schema = reader.schema
    print('Schema:')
    for field in schema:
        print(f'  {field.name}: {field.type}')
else:
    print('No reader returned')
"

Schema:
  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>

cd /work && python -c "
import overturemaps
import pyarrow.parquet as pq
import pyarrow as pa

# Get place data 
reader = overturemaps.record_batch_reader('place')
if reader:
    # Read a batch to see some data
    batch = reader.read_next_batch()
    df = batch.to_pandas()
    print('Columns:', df.columns.tolist())
    print('Shape:', df.shape)
    print()
    print('Categories column type:', type(df['categories'].iloc[0]))
    print('First few categories:')
    for i, cat in enumerate(df['categories'].head(10)):
        print(f'  {i}: {cat}')
    print()
    print('Basic categories unique:', df['basic_category'].unique()[:20])
    print()
    # Look for station-related
    for i, cat in enumerate(df['categories'].head(100)):
        if cat and 'primary' in cat:
            if 'station' in str(cat['primary']).lower() or 'research' in str(cat['primary']).lower():
                print(f'  Row {i}: {cat}')
"

Columns: ['id', 'geometry', 'categories', 'confidence', 'websites', 'emails', 'socials', 'phones', 'brand', 'addresses', 'names', 'sources', 'operating_status', 'basic_category', 'taxonomy', 'version', 'bbox']
Shape: (20628, 17)

Categories column type: <class 'dict'>
First few categories:
  0: {'primary': 'retail', 'alternate': None}
  1: {'primary': 'furniture_store', 'alternate': None}
  2: {'primary': 'cafe', 'alternate': array(['coffee_shop', 'restaurant'], dtype=object)}
  3: {'primary': 'college_university', 'alternate': array(['education'], dtype=object)}
  4: {'primary': 'delicatessen', 'alternate': array(['supermarket', 'liquor_store'], dtype=object)}
  5: {'primary': 'hardware_store', 'alternate': array(['computer_hardware_company'], dtype=object)}
  6: {'primary': 'architectural_designer', 'alternate': array(['interior_design'], dtype=object)}
  7: {'primary': 'education', 'alternate': array(['professional_services', 'life_coach'], dtype=object)}
  8: {'primary': 'automotive_dealer', 'alternate': array(['car_dealer', 'motorcycle_dealer'], dtype=object)}
  9: {'primary': 'restaurant', 'alternate': None}

Basic categories unique: <ArrowStringArray>
[                             nan, 'hardware_home_and_garden_store',
                           'cafe',             'college_university',
                  'casual_eatery',                 'design_service',
                 'vehicle_dealer',                     'restaurant',
                          'beach',              'elementary_school',
                  'historic_site',           'bank_or_credit_union',
                         'museum',                          'hotel',
                           'lake',                          'river',
                        'airport',                        'lodging',
                  'national_park',                'public_fountain']
Length: 20, dtype: str

cd /work && python -c "
import overturemaps
import pyarrow as pa

# Get place data and filter for research stations
reader = overturemaps.record_batch_reader('place')
if reader:
    # Read more batches
    tables = []
    while True:
        try:
            batch = reader.read_next_batch()
            tables.append(batch.to_pandas())
        except StopIteration:
            break
    
    df = pa.concat_tables([pa.Table.from_pandas(t) for t in tables]).to_pandas()
    print('Total rows:', len(df))
    
    # Look at all unique basic categories
    cats = df['basic_category'].dropna().unique()
    print('Unique basic categories:', sorted(cats))
    
    # Look at categories primary values
    all_primary = set()
    for cat in df['categories'].dropna():
        if cat and 'primary' in cat:
            all_primary.add(cat['primary'])
    print('Unique primary categories:', sorted(all_primary))
"

cd /work && python -c "
import overturemaps
# Let's look at the lake/river/etc entries - water features
reader = overturemaps.record_batch_reader('water')
if reader:
    schema = reader.schema
    print('Water Schema:')
    for field in schema:
        print(f'  {field.name}: {field.type}')
else:
    print('No water reader')
"

cd /work && python -c "
import overturemaps
# Just check water schema
reader = overturemaps.record_batch_reader('water', stac=True)
if reader:
    schema = reader.schema
    print('Water Schema:')
    for field in schema:
        print(f'  {field.name}: {field.type}')
else:
    print('No water reader')
" 2>&1 | head -40

cd /work && timeout 30 python -c "
import overturemaps
# Check what the library does - look at the code
import inspect
print(inspect.getsource(overturemaps.record_batch_reader)[:2000])
"

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)

cd /work && timeout 60 python -c "
import overturemaps

# Try reading place data with a bbox for Antarctica region
# bbox = (xmin, ymin, xmax, ymax) in WGS84
ant_bbox = (-180, -90, 180, -60)
reader = overturemaps.record_batch_reader('place', bbox=ant_bbox)
if reader:
    import pyarrow as pa
    tables = []
    count = 0
    for batch in reader:
        df = batch.to_pandas()
        tables.append(df)
        count += len(df)
        if count > 1000:
            break
    print(f'Read {count} rows')
    df = pa.concat_tables([pa.Table.from_pandas(t) for t in tables]).to_pandas()
    print('Columns:', df.columns.tolist())
    # Look for research station categories
    for i, cat in enumerate(df['categories']):
        if cat and 'primary' in cat and cat['primary'] in ['research_station', 'scientific_station', 'station', 'antarctic_research_station']:
            print(f'Row {i}: {cat}')
    print()
    # List all unique primary categories
    primaries = set()
    for cat in df['categories'].dropna():
        if isinstance(cat, dict) and 'primary' in cat:
            primaries.add(cat['primary'])
    print('Unique primary categories:', sorted(primaries))
else:
    print('No reader returned')
" 2>&1

Read 246 rows
Traceback (most recent call last):
  File "<string>", line 19, in <module>
    df = pa.concat_tables([pa.Table.from_pandas(t) for t in tables]).to_pandas()
         ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "pyarrow/table.pxi", line 6268, in pyarrow.lib.concat_tables
  File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
  File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
    raise convert_status(status)
pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
id: large_string
geometry: binary
categories: struct<primary: string, alternate: list<item: string>>
confidence: double
websites: list<item: string>
emails: list<item: string>
socials: list<item: string>
phones: list<item: string>
brand: struct<wikidata: null, names: struct<primary: string, common: null, rules: null>>
addresses: list<item: struct<freeform: string, locality: string, postcode: string, region: null, country: string>>
names: struct<primary: string, common: null, rules: null>
sources: list<item: struct<property: string, dataset: string, license: string, record_id: string, update_time: string, confidence: double, between: null>>
operating_status: large_string
basic_category: large_string
taxonomy: struct<primary: string, hierarchy: list<item: string>, alternates: null>
version: int32
bbox: struct<xmin: double, xmax: double, ymin: double, ymax: double>
vs
id: large_string
geometry: binary
categories: struct<primary: string, alternate: list<item: string>>
confidence: double
websites: list<item: string>
emails: list<item: string>
socials: list<item: string>
phones: list<item: string>
brand: struct<wikidata: null, names: struct<primary: null, common: null, rules: null>>
addresses: list<item: struct<freeform: string, locality: string, postcode: string, region: null, country: string>>
names: struct<primary: string, common: null, rules: null>
sources: list<item: struct<property: string, dataset: string, license: string, record_id: string, update_time: string, confidence: double, between: null>>
operating_status: large_string
basic_category: large_string
taxonomy: struct<primary: string, hierarchy: list<item: string>, alternates: null>
version: int32
bbox: struct<xmin: double, xmax: double, ymin: double, ymax: double>

cd /work && timeout 30 python -c "
import overturemaps
# Use geodataframe helper
gdf = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60))
print(gdf.columns.tolist())
print(len(gdf))
print(gdf['categories'].head(20))
" 2>&1

['id', 'geometry', 'categories', 'confidence', 'websites', 'emails', 'socials', 'phones', 'brand', 'addresses', 'names', 'sources', 'operating_status', 'basic_category', 'taxonomy', 'version', 'bbox']
246
0              {'primary': 'retail', 'alternate': None}
1     {'primary': 'furniture_store', 'alternate': None}
2     {'primary': 'cafe', 'alternate': ['coffee_shop...
3     {'primary': 'college_university', 'alternate':...
4     {'primary': 'delicatessen', 'alternate': ['sup...
5     {'primary': 'hardware_store', 'alternate': ['c...
6        {'primary': 'beauty_salon', 'alternate': None}
7                                                  None
8     {'primary': 'public_and_government_association...
9                                                  None
10         {'primary': 'restaurant', 'alternate': None}
11                                                 None
12    {'primary': 'professional_services', 'alternat...
13    {'primary': 'hospital', 'alternate': ['chinese...
14    {'primary': 'arts_and_crafts', 'alternate': None}
15    {'primary': 'womens_clothing_store', 'alternat...
16    {'primary': 'mass_media', 'alternate': ['baske...
17    {'primary': 'professional_services', 'alternat...
18       {'primary': 'park', 'alternate': ['shopping']}
19                                                 None
Name: categories, dtype: object

cd /work && timeout 60 python -c "
import overturemaps
gdf = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60))
# Look at category primary values
primaries = set()
for c in gdf['categories'].dropna():
    if isinstance(c, dict) and 'primary' in c:
        primaries.add(c['primary'])
print('Primary categories:', sorted(primaries))
print()
# Also check taxonomy
taxonomies = set()
for t in gdf['taxonomy'].dropna():
    if isinstance(t, dict) and 'primary' in t:
        taxonomies.add(t['primary'])
print('Taxonomy primaries:', sorted(taxonomies))
print()
# Check names for anything station-related
for i, row in gdf.iterrows():
    name = row.get('names', {})
    if name and isinstance(name, dict) and 'primary' in name:
        if name['primary'] and 'station' in name['primary'].lower():
            print(f'Row {i}: name={name[\"primary\"]}, cat={row[\"categories\"]}')
" 2>&1

Primary categories: ['active_life', 'acupuncture', 'airport', 'art_gallery', 'arts_and_crafts', 'arts_and_entertainment', 'audio_visual_equipment_store', 'automotive', 'automotive_dealer', 'automotive_repair', 'b2b_textiles', 'bakery', 'bar', 'beauty_and_spa', 'beauty_salon', 'beer_bar', 'burger_restaurant', 'cafe', 'car_dealer', 'caterer', 'central_government_office', 'childrens_clothing_store', 'church_cathedral', 'coffee_shop', 'college_university', 'community_services_non_profits', 'computer_museum', 'delicatessen', 'dentist', 'desserts', 'doctor', 'eat_and_drink', 'education', 'educational_research_institute', 'educational_services', 'electronics', 'engineering_services', 'event_planning', 'family_practice', 'farm', 'fashion', 'fashion_accessories_store', 'fast_food_restaurant', 'fitness_trainer', 'flowers_and_gifts_shop', 'food_delivery_service', 'furniture_store', 'gas_station', 'graphic_designer', 'gym', 'hardware_store', 'holiday_rental_home', 'home_cleaning', 'home_developer', 'hospital', 'hostel', 'hotel', 'image_consultant', 'jewelry_store', 'lake', 'landmark_and_historical_building', 'language_school', 'lawyer', 'lodge', 'mass_media', 'media_news_company', 'metal_fabricator', 'middle_school', 'mosque', 'motorcycle_dealer', 'museum', 'music_production', 'non_governmental_association', 'panamanian_restaurant', 'park', 'print_media', 'professional_services', 'psychic', 'psychotherapist', 'public_and_government_association', 'race_track', 'real_estate', 'real_estate_agent', 'record_label', 'religious_organization', 'restaurant', 'retail', 'river', 'school', 'shoe_store', 'shopping', 'shopping_center', 'social_media_company', 'sports_club_and_league', 'structure_and_geography', 'tattoo_and_piercing', 'telecommunications_company', 'topic_concert_venue', 'tours', 'toy_store', 'video_game_store', 'wedding_planning', 'womens_clothing_store']

Taxonomy primaries: ['acupuncture', 'airport', 'art_gallery', 'arts_and_entertainment', 'arts_crafts_and_hobby_store', 'attorney_or_law_firm', 'audio_visual_equipment_store', 'auto_dealer', 'automotive_repair', 'automotive_service', 'bakery', 'bar', 'beauty_salon', 'beer_bar', 'burger_restaurant', 'cafe', 'caterer', 'childrens_clothing_store', 'christian_place_of_worship', 'coffee_shop', 'college_university', 'computer_museum', 'delicatessen', 'dental_clinic', 'dessert_shop', 'education', 'educational_research_institute', 'educational_service', 'electronics_store', 'engineering_service', 'event_or_party_service', 'family_practice', 'farm', 'fashion_accessories_store', 'fashion_and_apparel_store', 'fast_food_restaurant', 'fitness_trainer', 'flowers_and_gifts_store', 'food_and_drink', 'food_delivery_service', 'furniture_store', 'gas_station', 'geographic_entities', 'government_office', 'graphic_designer', 'gym', 'hardware_store', 'historic_site', 'holiday_rental_home', 'home_cleaning', 'home_developer', 'hospital', 'hostel', 'hotel', 'image_consultant', 'jewelry_store', 'lake', 'language_school', 'lodge', 'media_news_company', 'media_service', 'metal_fabricator', 'middle_school', 'motorcycle_dealer', 'museum', 'music_production', 'music_venue', 'muslim_place_of_worship', 'non_governmental_association', 'panamanian_restaurant', 'park', 'personal_or_beauty_service', 'print_media', 'professional_service', 'psychic_advising', 'psychotherapy', 'race_track', 'real_estate_agent', 'real_estate_service', 'record_label', 'religious_organization', 'restaurant', 'river', 'school', 'shoe_store', 'shopping', 'shopping_mall', 'social_media_company', 'social_or_community_service', 'sport_or_recreation_club', 'sports_and_recreation', 'tattoo_and_piercing', 'telecommunications_company', 'textile_manufacturer', 'tour_operator', 'toy_store', 'vehicle_dealer', 'video_game_store', 'wedding_planning', 'womens_clothing_store']

Row 40: name=Palmer Station, Antarctica, cat={'primary': 'home_developer', 'alternate': None}
Row 43: name=Brown Station, cat={'primary': 'landmark_and_historical_building', 'alternate': array(['fountain'], dtype=object)}
Row 52: name=Bellingshausen Russian Antarctic Station, cat={'primary': 'educational_research_institute', 'alternate': array(['public_and_government_association', 'college_university'],
      dtype=object)}
Row 233: name=Mario-Zucchelli-Station, cat=None

cd /work && timeout 60 python -c "
import overturemaps
gdf = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60))

# Print all rows where names primary contains station, base, research, etc
for i, row in gdf.iterrows():
    name = row.get('names')
    if name and isinstance(name, dict) and 'primary' in name and name['primary']:
        n = name['primary']
        if any(kw in n.lower() for kw in ['station', 'base', 'research', 'camp', 'observatory', 'laboratory', 'scientific']):
            print(f'{i}: {n} | cat={row[\"categories\"]} | basic={row[\"basic_category\"]} | taxonomy={row[\"taxonomy\"]}')
" 2>&1

40: Palmer Station, Antarctica | cat={'primary': 'home_developer', 'alternate': None} | basic=real_estate_service | taxonomy={'primary': 'home_developer', 'hierarchy': array(['services_and_business', 'real_estate_service', 'builder',
       'home_developer'], dtype=object), 'alternates': None}
43: Brown Station | cat={'primary': 'landmark_and_historical_building', 'alternate': array(['fountain'], dtype=object)} | basic=historic_site | taxonomy={'primary': 'historic_site', 'hierarchy': array(['cultural_and_historic', 'historic_site'], dtype=object), 'alternates': None}
44: González Videla Antarctic Base | cat={'primary': 'airport', 'alternate': array(['travel'], dtype=object)} | basic=airport | taxonomy={'primary': 'airport', 'hierarchy': array(['travel_and_transportation', 'air_transport_facility_or_service',
       'airport'], dtype=object), 'alternates': None}
52: Bellingshausen Russian Antarctic Station | cat={'primary': 'educational_research_institute', 'alternate': array(['public_and_government_association', 'college_university'],
      dtype=object)} | basic=research_institute | taxonomy={'primary': 'educational_research_institute', 'hierarchy': array(['education', 'research_institute',
       'educational_research_institute'], dtype=object), 'alternates': None}
56: Base Marambio, Antartida Argentina | cat={'primary': 'central_government_office', 'alternate': array(['accommodation'], dtype=object)} | basic=government_office | taxonomy={'primary': 'government_office', 'hierarchy': array(['community_and_government', 'government_office'], dtype=object), 'alternates': None}
57: Marambio Base | cat={'primary': 'airport', 'alternate': array(['armed_forces_branch', 'arts_and_entertainment'], dtype=object)} | basic=airport | taxonomy={'primary': 'airport', 'hierarchy': array(['travel_and_transportation', 'air_transport_facility_or_service',
       'airport'], dtype=object), 'alternates': None}
59: Base Antártica Marambio | cat={'primary': 'public_and_government_association', 'alternate': array(['central_government_office', 'accommodation'], dtype=object)} | basic=government_office | taxonomy={'primary': 'government_office', 'hierarchy': array(['community_and_government', 'government_office'], dtype=object), 'alternates': None}
60: Base Esperanza, Antartida Argentina | cat={'primary': 'airport', 'alternate': array(['travel', 'accommodation'], dtype=object)} | basic=airport | taxonomy={'primary': 'airport', 'hierarchy': array(['travel_and_transportation', 'air_transport_facility_or_service',
       'airport'], dtype=object), 'alternates': None}
61: Esperanza Base, Antarctica | cat={'primary': 'landmark_and_historical_building', 'alternate': array(['fountain'], dtype=object)} | basic=historic_site | taxonomy={'primary': 'historic_site', 'hierarchy': array(['cultural_and_historic', 'historic_site'], dtype=object), 'alternates': None}
223: Base antarctique Dumont-d'Urville | cat={'primary': 'landmark_and_historical_building', 'alternate': array(['transportation'], dtype=object)} | basic=historic_site | taxonomy={'primary': 'historic_site', 'hierarchy': array(['cultural_and_historic', 'historic_site'], dtype=object), 'alternates': None}
233: Mario-Zucchelli-Station | cat=None | basic=nan | taxonomy=None
236: Base McMurdo | cat={'primary': 'landmark_and_historical_building', 'alternate': array(['train_station', 'restaurant'], dtype=object)} | basic=historic_site | taxonomy={'primary': 'historic_site', 'hierarchy': array(['cultural_and_historic', 'historic_site'], dtype=object), 'alternates': None}
237: Base antarctique McMurdo | cat={'primary': 'landmark_and_historical_building', 'alternate': array(['train_station'], dtype=object)} | basic=historic_site | taxonomy={'primary': 'historic_site', 'hierarchy': array(['cultural_and_historic', 'historic_site'], dtype=object), 'alternates': None}

cd /work && timeout 60 python -c "
import overturemaps
gdf = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60))

# Print ALL rows with their names and coordinates
for i, row in gdf.iterrows():
    name = row.get('names')
    if name and isinstance(name, dict) and 'primary' in name and name['primary']:
        n = name['primary']
        print(f'{i}: {n} | basic={row[\"basic_category\"]} | op_status={row[\"operating_status\"]}')
    else:
        print(f'{i}: (no name) | basic={row[\"basic_category\"]} | op_status={row[\"operating_status\"]}')
" 2>&1

0: Poeanuanua | basic=nan | op_status=nan
1: Muebles Marispal Algeciras | basic=hardware_home_and_garden_store | op_status=nan
2: Lamoon Cafe & Restaurant - สระบุรี | basic=cafe | op_status=nan
3: Cap College Mariveles Bataan | basic=college_university | op_status=nan
4: מעדני מניה Maadaney Mania | basic=casual_eatery | op_status=nan
5: Te Fare Informatique | basic=hardware_home_and_garden_store | op_status=nan
6: Samira's Hair Fashion +599 98693311 | basic=personal_or_beauty_service | op_status=nan
7: CAMBO Cleaner | basic=nan | op_status=nan
8: Iriga City Public Library | basic=government_office | op_status=nan
9: Dinastia Nortena | basic=nan | op_status=nan
10: Ser Esencia Restaurant | basic=restaurant | op_status=nan
11: مدينة رشيد Rosetta city | basic=nan | op_status=nan
12: Blessed Souls Photography & Film | basic=professional_service | op_status=nan
13: 黎齊妙中醫診所 | basic=hospital | op_status=nan
14: dieukhaclienvu.com - phù điêu,điêu khắc đá nhân tạo composite,cảnh quan đẹp | basic=arts_crafts_and_hobby_store | op_status=nan
15: R-AGE Nation Apparel | basic=fashion_and_apparel_store | op_status=nan
16: Basketball Forever | basic=media_service | op_status=nan
17: Marc Silber | basic=professional_service | op_status=nan
18: Tama Manège Import | basic=park | op_status=nan
19: Irantha Fonseka | basic=nan | op_status=nan
20: Ozhousestudio | basic=music_venue | op_status=nan
21: Jamela Payne | basic=restaurant | op_status=nan
22: In Aeternum Historia | basic=media_service | op_status=nan
23: Destiny Monique | basic=nan | op_status=nan
24: Frente al Mar | basic=nan | op_status=nan
25: SaiJai ใส่ใจ เครื่องปรุงและอาหารสุขภาพ | basic=restaurant | op_status=nan
26: Tay Noel’s Kalan-an | basic=fast_food_restaurant | op_status=nan
27: גלית קאשי עיצוב תכשיטים | basic=fashion_and_apparel_store | op_status=nan
28: GypsyMaal | basic=fashion_and_apparel_store | op_status=nan
29: Prasad Art Gallery | basic=art_gallery | op_status=nan
30: Tahlia Marie | basic=nan | op_status=nan
31: Barbaro Negocios Inmobiliarios | basic=real_estate_service | op_status=nan
32: VALKUR Perú | basic=attorney_or_law_firm | op_status=nan
33: Experience Rarotonga | basic=travel_service | op_status=nan
34: Caprichos Accesorios | basic=fashion_and_apparel_store | op_status=nan
35: Emmy J | basic=nan | op_status=nan
36: Asra Derm Official | basic=nan | op_status=nan
37: Mobilio Suksawat โมบิลิโอ้ สาขาสุขสวัสดิ์ | basic=nan | op_status=nan
38: ال سلوع لتجاره السيراميك والادوات الصحيه | basic=real_estate_service | op_status=nan
39: Port Lockroy, Antarctic Peninsula | basic=historic_site | op_status=nan
40: Palmer Station, Antarctica | basic=real_estate_service | op_status=nan
41: BellaVista Voronet | basic=private_lodging | op_status=nan
42: Sachet Imprimé | basic=nan | op_status=nan
43: Brown Station | basic=historic_site | op_status=nan
44: González Videla Antarctic Base | basic=airport | op_status=nan
45: Cuverville Island | basic=historic_site | op_status=nan
46: Madison Beer | basic=bar | op_status=nan
47: Dj daya | basic=media_service | op_status=nan
48: Matrioska Laços | basic=fashion_and_apparel_store | op_status=nan
49: Antarctica, Antarctic Circle | basic=historic_site | op_status=nan
50: Esperanto Island, Antarctica | basic=historic_site | op_status=nan
51: Yankee Harbor | basic=historic_site | op_status=nan
52: Bellingshausen Russian Antarctic Station | basic=research_institute | op_status=nan
53: Bahia Fildes (Antartica) | basic=nan | op_status=nan
54: Crystal Power | basic=fashion_and_apparel_store | op_status=nan
55: Bestiario Moderno | basic=media_service | op_status=nan
56: Base Marambio, Antartida Argentina | basic=government_office | op_status=nan
57: Marambio Base | basic=airport | op_status=nan
58: Chapel of the Blessed Virgin of Lujan, Antarctica | basic=christian_place_of_worship | op_status=nan
59: Base Antártica Marambio | basic=government_office | op_status=nan
60: Base Esperanza, Antartida Argentina | basic=airport | op_status=nan
61: Esperanza Base, Antarctica | basic=historic_site | op_status=nan
62: Backwoods Bouquet | basic=nan | op_status=nan
63: Shop Thời Trang Nam - Nữ Thanh Bình | basic=fashion_and_apparel_store | op_status=nan
64: Lucas Fontoura | basic=nan | op_status=nan
65: Descobrincar  - Musicalização infantil | basic=place_of_learning | op_status=nan
66: Hurricane On Saturn | basic=nan | op_status=nan
67: Paradise Eventi | basic=event_or_party_service | op_status=nan
68: Psikolog ve Aile Danışmanı Esra Erciyas | basic=nan | op_status=nan
69: Proexc Engenharia | basic=building_or_construction_service | op_status=nan
70: Villa's Caldos | basic=fast_food_restaurant | op_status=nan
71: Gabbi Garcia | basic=nan | op_status=nan
72: Bendita CR | basic=nan | op_status=nan
73: Behr Productions | basic=media_service | op_status=nan
74: Max land  MX Raceway Park | basic=sport_or_fitness_facility | op_status=nan
75: DRK Kreisverband Gelnhausen-Schlüchtern e.V. | basic=social_or_community_service | op_status=nan
76: Muhammad Hasri Videography | basic=professional_service | op_status=nan
77: JJ Niceley | basic=nan | op_status=nan
78: Jankari Kendra जानकारी केन्द्र | basic=media_service | op_status=nan
79: Reggae Village House of Jerk | basic=event_or_party_service | op_status=nan
80: Công Ty TNHH Nam Hưng Thái Nguyên | basic=manufacturer | op_status=nan
81: 美妍美容教育中心-Beauty in | basic=personal_or_beauty_service | op_status=nan
82: Foul Point | basic=historic_site | op_status=nan
83: Malou Madi | basic=nan | op_status=nan
84: Institut für Emotionspädagogik | basic=educational_service | op_status=nan
85: Animations enfants Tahiti, Le PitiMotu | basic=nan | op_status=nan
86: Dregg Ackies | basic=media_service | op_status=nan
87: Sunsoli | basic=nan | op_status=nan
88: Flocreazionipereventi | basic=event_or_party_service | op_status=nan
89: Martin Kráľ - Skladateľ | basic=nan | op_status=nan
90: Babul Yaman Store | basic=nan | op_status=nan
91: SMDC Property Investment | basic=real_estate_service | op_status=nan
92: WM Case - Profesjonalne skrzynie transportowe | basic=nan | op_status=nan
93: Dwill Burguer | basic=restaurant | op_status=nan
94: Sund med Mia | basic=professional_service | op_status=nan
95: Deli Repostería | basic=casual_eatery | op_status=nan
96: Buli Makhubo | basic=nan | op_status=nan
97: Стиль и мода. Израиль | basic=personal_or_beauty_service | op_status=nan
98: Bedlam Studios | basic=design_service | op_status=nan
99: رضا الحديثي - صفحتي الرسمية | basic=sport_or_fitness_facility | op_status=nan
100: Friis Boliger | basic=real_estate_service | op_status=nan
101: রঙ-Israt's Art | basic=arts_crafts_and_hobby_store | op_status=nan
102: DirtySnatcha | basic=nan | op_status=nan
103: Calbero | basic=nan | op_status=nan
104: Jobswagon | basic=nan | op_status=nan
105: هاوار | basic=nan | op_status=nan
106: Antarctica/Troll | basic=historic_site | op_status=nan
107: Ventamark | basic=electronics_store | op_status=nan
108: De Todo | basic=electronics_store | op_status=nan
109: Inglés Personal | basic=specialty_school | op_status=nan
110: João Gomes | basic=nan | op_status=nan
111: RA CARS PH | basic=auto_dealer | op_status=nan
112: אריאל שרם - צילום | basic=professional_service | op_status=nan
113: Eden's Essential Elements | basic=flowers_and_gifts_store | op_status=nan
114: Laviva Cosmetics | basic=personal_or_beauty_service | op_status=nan
115: Elblesk e-mobility | basic=ground_transport_facility_or_service | op_status=nan
116: ليو - Leo | basic=media_service | op_status=nan
117: Otago Potters Group | basic=nan | op_status=nan
118: Johanna Oedin | basic=nan | op_status=nan
119: Alchemist Craftworks | basic=manufacturer | op_status=nan
120: Panaderia Doña Tere | basic=casual_eatery | op_status=nan
121: 廸康醫療中心 Madison Medical Centre | basic=complementary_and_alternative_medicine | op_status=nan
122: A Tale of Four Mages | basic=media_service | op_status=nan
123: JP Ranch Grass Valley | basic=farm | op_status=nan
124: Wolf’s Fang Runway | basic=nan | op_status=nan
125: Fishing 411 TV | basic=nan | op_status=nan
126: Pro Massage & Pro PT | basic=nan | op_status=nan
127: St. Isidore Catholic Learning Centre | basic=nan | op_status=nan
128: Cajurine. | basic=personal_or_beauty_service | op_status=nan
129: Higi + Higienização e Impermeabilização de Estofados | basic=home_service | op_status=nan
130: Organica Mart | basic=food_service | op_status=nan
131: ซัมไทม์ วิว รีสอร์ท | basic=lodging | op_status=nan
132: چارەسەر لە سروشتەوە | basic=personal_or_beauty_service | op_status=nan
133: Pro Punjab Tv | basic=media_service | op_status=nan
134: elevatedxconscience | basic=nan | op_status=nan
135: B.olivia - ps | basic=personal_or_beauty_service | op_status=nan
136: Twory i Stwory | basic=nan | op_status=nan
137: Geraint Jones 4x4 | basic=vehicle_dealer | op_status=nan
138: 2 Nice | basic=fashion_and_apparel_store | op_status=nan
139: Stress Relief Massage | basic=nan | op_status=nan
140: Amrinder Bobby | basic=nan | op_status=nan
141: Make Noise Pro Audio LTD | basic=electronics_store | op_status=nan
142: Morjane Ténéré | basic=nan | op_status=nan
143: Southern Ocean | basic=historic_site | op_status=nan
144: ليلى kids | basic=fashion_and_apparel_store | op_status=nan
145: 鉢伏山荘冬期営業 | basic=lodging | op_status=nan
146: البروج للمواد الكهربائية | basic=shopping_mall | op_status=nan
147: Beauty & Care | basic=personal_or_beauty_service | op_status=nan
148: Béjaïa béni ksila vacances | basic=nan | op_status=nan
149: In Love Furniture - ศูนย์รวมเฟอร์นิเจอร์ชุดห้องนอน และ เฟอร์นิเจอร์สำนักงาน | basic=hardware_home_and_garden_store | op_status=nan
150: Woodstock Dentistry | basic=dental_clinic | op_status=nan
151: Paubril’s Beauty | basic=personal_or_beauty_service | op_status=nan
152: This Esme | basic=nan | op_status=nan
153: Alex Sá | basic=nan | op_status=nan
154: MOFT | basic=fashion_and_apparel_store | op_status=nan
155: I-narin Beauty : เครื่องนวดหน้า สไตล์เกาหลี | basic=personal_or_beauty_service | op_status=nan
156: Abracadabra Technologie | basic=corporate_or_business_office | op_status=nan
157: Cafés Ellouze | basic=coffee_shop | op_status=nan
158: หมามะเร็ง Dogs Cancer | basic=social_or_community_service | op_status=nan
159: 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 | basic=nan | op_status=nan
160: 厚興瑜記 WegoMall | basic=restaurant | op_status=nan
161: Americano Cafe' Happy Moment with Coffee and Drink | basic=cafe | op_status=nan
162: Japfa Experience | basic=restaurant | op_status=nan
163: Celest Jewelry | basic=nan | op_status=nan
164: My湯 - 茘枝角 | basic=casual_eatery | op_status=nan
165: Bobby Layal | basic=museum | op_status=nan
166: Janeth E. Sarona | basic=professional_service | op_status=nan
167: PD.Kannel | basic=nan | op_status=nan
168: แพวันวาน จังหวัดกาญจนบุรี | basic=hotel | op_status=nan
169: Dalbe Réunion | basic=nan | op_status=nan
170: สมุทรสงครามรีวิว | basic=nan | op_status=nan
171: مركز آفنيو الطبي | basic=hospital | op_status=nan
172: ครูเปิ้ล สอนคอมฯ : ปวช. สกร.เมืองนครราชสีมา | basic=nan | op_status=nan
173: Cat & Calmell | basic=nan | op_status=nan
174: Mette MÆRsk | basic=professional_service | op_status=nan
175: อึ้งกุ่ยเฮง มอเตอร์ไซค์ ฮอนด้า ยามาฮ่า รถมือสอง ร้อยเอ็ด | basic=vehicle_dealer | op_status=nan
176: World Auto Glass Inc. | basic=nan | op_status=nan
177: Noskill Sensi | basic=books_music_and_video_store | op_status=nan
178: Ingrid Christensen Coast | basic=historic_site | op_status=nan
179: Tutti Bambino | basic=toys_and_games_store | op_status=nan
180: Ellis Fjord | basic=historic_site | op_status=nan
181: Lake Jabs | basic=historic_site | op_status=nan
182: Langnes Fjord | basic=historic_site | op_status=nan
183: Krok Lake | basic=lake | op_status=nan
184: Lied Bluff | basic=historic_site | op_status=nan
185: Heidemann Bay | basic=historic_site | op_status=nan
186: Filla Island | basic=historic_site | op_status=nan
187: Lake Zvezda | basic=historic_site | op_status=nan
188: Dr. Cláudio Gurgel Magalhães - Clínica Belle Esthetique | basic=nan | op_status=nan
189: Ynnez Group | basic=civic_organization | op_status=nan
190: 芸臻命理諮詢 | basic=psychic_advising | op_status=nan
191: Miya Élégance | basic=fashion_and_apparel_store | op_status=nan
192: แม่อี่ ผักดองตำหรับยูนนาน | basic=nan | op_status=nan
193: NPS Institutions - Bidar | basic=place_of_learning | op_status=nan
194: Sozialkontakt | basic=nan | op_status=nan
195: albertopiernas | basic=nan | op_status=nan
196: Sekolah Islam Terpadu Green Bhakti Insani | basic=middle_school | op_status=nan
197: Motel Mozaique Concerts | basic=hotel | op_status=nan
198: CT Trampoline Fitness 沙田 石門 彈床班 Jump Fitness 健身 親子彈床班 | basic=gym | op_status=nan
199: Casa de Adoración Yahweh | basic=religious_organization | op_status=nan
200: Sydney Amigurumi Corner 手勾公仔 | basic=arts_crafts_and_hobby_store | op_status=nan
201: Shanaya Hotel Borobudur | basic=hotel | op_status=nan
202: The Cumberland River Project | basic=river | op_status=nan
203: Lake Vostok | basic=historic_site | op_status=nan
204: ร้านแน็ตโมบายโฟน | basic=nan | op_status=nan
205: Vostok İstasyonu | basic=historic_site | op_status=nan
206: Tiệm ảnh Phúc- Lạng Sơn | basic=professional_service | op_status=nan
207: مدرسة أحمد نمر حمدان الأساسية للبنين | basic=place_of_learning | op_status=nan
208: ইলমুল কুরআন মাদ্রাসা লিল্লাহ বোর্ডিং ও ইয়াতিম খানা | basic=nan | op_status=nan
209: سنتر بغداد | basic=nan | op_status=nan
210: 住商不動產樹林后站加盟店 | basic=professional_service | op_status=nan
211: 塩彩 ～shiosai～ | basic=nan | op_status=nan
212: Kurd Sport HD | basic=sport_or_recreation_club | op_status=nan
213: Sofiya Designs Gh | basic=nan | op_status=nan
214: 水草館 | basic=museum | op_status=nan
215: Compartilhagem | basic=arts_crafts_and_hobby_store | op_status=nan
216: Kimstore | basic=nan | op_status=nan
217: Masjid Terapung Tanjung Bungah, Pulau Pinang | basic=muslim_place_of_worship | op_status=nan
218: Angel Rising | basic=nan | op_status=nan
219: Lailo Farm Sanctuary | basic=social_or_community_service | op_status=nan
220: Dr Henri Bauer | basic=nan | op_status=nan
221: Academia Nacional de Sommeliers y Gastrónomos | basic=place_of_learning | op_status=nan
222: ทำประกันกับอาอิซะห์ | basic=personal_or_beauty_service | op_status=nan
223: Base antarctique Dumont-d'Urville | basic=historic_site | op_status=nan
224: شركة الأركان لقطع غيار وزيوت السيارات\nAlarkan Auto Parts & Oils | basic=nan | op_status=nan
225: Seaoil Dangcagan | basic=gas_station | op_status=nan
226: The Seasoning Outlet | basic=nan | op_status=nan
227: MAX Reprise | basic=automotive_service | op_status=nan
228: Antarctic Plateau | basic=historic_site | op_status=nan
229: Warning Five จำหน่ายสายนาฬิกาหนังแท้ สายนาฬิกา smartwatch | basic=nan | op_status=nan
230: 海港青年商會 JCI Harbour | basic=social_or_community_service | op_status=nan
231: মুক্তি হারবাল চিকিৎসালয়-Mukti Herbal Chikitsalay | basic=primary_care_or_general_clinic | op_status=nan
232: Herrlich Beauty By Cloriss | basic=personal_or_beauty_service | op_status=nan
233: Mario-Zucchelli-Station | basic=nan | op_status=nan
234: Psicóloga Serly C. de Souza | basic=behavioral_or_mental_health_clinic | op_status=nan
235: Polární stanice McMurdo | basic=historic_site | op_status=nan
236: Base McMurdo | basic=historic_site | op_status=nan
237: Base antarctique McMurdo | basic=historic_site | op_status=nan
238: Gallagher Pub | basic=bar | op_status=nan
239: Southern Exposure | basic=bar | op_status=nan
240: ZiroZiro | basic=religious_organization | op_status=nan
241: McMurdo General Hospital | basic=hospital | op_status=nan
242: Coffee House | basic=coffee_shop | op_status=nan
243: Chapel of the Snows | basic=christian_place_of_worship | op_status=nan
244: Pegasus Field | basic=airport | op_status=nan
245: 悦聖翡翠 | basic=fashion_and_apparel_store | op_status=nan

cd /work && timeout 60 python -c "
import overturemaps
gdf = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60))

# Filter to points south of -60
import geopandas as gpd
from shapely import wkb

ant_gdf = gdf[gdf.geometry.y <= -60].copy()
print(f'Total places south of -60: {len(ant_gdf)}')
print()

# List them
for i, (idx, row) in enumerate(ant_gdf.iterrows()):
    name = row.get('names')
    n = name.get('primary') if isinstance(name, dict) else '?'
    print(f'{i}: {n} | lat={row.geometry.y:.2f} lon={row.geometry.x:.2f} | basic={row[\"basic_category\"]}')
" 2>&1

Total places south of -60: 246

0: Poeanuanua | lat=-64.61 lon=-172.87 | basic=nan
1: Muebles Marispal Algeciras | lat=-78.35 lon=-156.80 | basic=hardware_home_and_garden_store
2: Lamoon Cafe & Restaurant - สระบุรี | lat=-77.53 lon=-156.21 | basic=cafe
3: Cap College Mariveles Bataan | lat=-82.49 lon=-143.44 | basic=college_university
4: מעדני מניה Maadaney Mania | lat=-80.76 lon=-144.84 | basic=casual_eatery
5: Te Fare Informatique | lat=-65.76 lon=-146.88 | basic=hardware_home_and_garden_store
6: Samira's Hair Fashion +599 98693311 | lat=-74.78 lon=-136.41 | basic=personal_or_beauty_service
7: CAMBO Cleaner | lat=-75.43 lon=-137.70 | basic=nan
8: Iriga City Public Library | lat=-81.08 lon=-136.55 | basic=government_office
9: Dinastia Nortena | lat=-81.92 lon=-137.81 | basic=nan
10: Ser Esencia Restaurant | lat=-84.84 lon=-127.49 | basic=restaurant
11: مدينة رشيد Rosetta city | lat=-82.73 lon=-125.50 | basic=nan
12: Blessed Souls Photography & Film | lat=-74.59 lon=-127.23 | basic=professional_service
13: 黎齊妙中醫診所 | lat=-74.02 lon=-126.56 | basic=hospital
14: dieukhaclienvu.com - phù điêu,điêu khắc đá nhân tạo composite,cảnh quan đẹp | lat=-78.63 lon=-124.45 | basic=arts_crafts_and_hobby_store
15: R-AGE Nation Apparel | lat=-81.09 lon=-118.12 | basic=fashion_and_apparel_store
16: Basketball Forever | lat=-81.92 lon=-113.91 | basic=media_service
17: Marc Silber | lat=-81.72 lon=-113.91 | basic=professional_service
18: Tama Manège Import | lat=-64.17 lon=-118.83 | basic=park
19: Irantha Fonseka | lat=-80.53 lon=-106.17 | basic=nan
20: Ozhousestudio | lat=-82.08 lon=-109.71 | basic=music_venue
21: Jamela Payne | lat=-80.83 lon=-96.11 | basic=restaurant
22: In Aeternum Historia | lat=-84.13 lon=-94.92 | basic=media_service
23: Destiny Monique | lat=-78.21 lon=-93.52 | basic=nan
24: Frente al Mar | lat=-74.45 lon=-92.95 | basic=nan
25: SaiJai ใส่ใจ เครื่องปรุงและอาหารสุขภาพ | lat=-72.82 lon=-88.59 | basic=restaurant
26: Tay Noel’s Kalan-an | lat=-82.31 lon=-87.93 | basic=fast_food_restaurant
27: גלית קאשי עיצוב תכשיטים | lat=-73.23 lon=-87.19 | basic=fashion_and_apparel_store
28: GypsyMaal | lat=-83.11 lon=-85.08 | basic=fashion_and_apparel_store
29: Prasad Art Gallery | lat=-76.76 lon=-77.78 | basic=art_gallery
30: Tahlia Marie | lat=-84.80 lon=-77.35 | basic=nan
31: Barbaro Negocios Inmobiliarios | lat=-83.68 lon=-73.12 | basic=real_estate_service
32: VALKUR Perú | lat=-71.97 lon=-71.02 | basic=attorney_or_law_firm
33: Experience Rarotonga | lat=-83.75 lon=-70.31 | basic=travel_service
34: Caprichos Accesorios | lat=-77.45 lon=-69.17 | basic=fashion_and_apparel_store
35: Emmy J | lat=-78.24 lon=-68.20 | basic=nan
36: Asra Derm Official | lat=-75.42 lon=-66.02 | basic=nan
37: Mobilio Suksawat โมบิลิโอ้ สาขาสุขสวัสดิ์ | lat=-78.97 lon=-64.10 | basic=nan
38: ال سلوع لتجاره السيراميك والادوات الصحيه | lat=-64.78 lon=-63.84 | basic=real_estate_service
39: Port Lockroy, Antarctic Peninsula | lat=-64.82 lon=-63.49 | basic=historic_site
40: Palmer Station, Antarctica | lat=-64.77 lon=-64.05 | basic=real_estate_service
41: BellaVista Voronet | lat=-66.60 lon=-65.20 | basic=private_lodging
42: Sachet Imprimé | lat=-66.51 lon=-61.88 | basic=nan
43: Brown Station | lat=-64.89 lon=-62.87 | basic=historic_site
44: González Videla Antarctic Base | lat=-64.82 lon=-62.86 | basic=airport
45: Cuverville Island | lat=-64.68 lon=-62.63 | basic=historic_site
46: Madison Beer | lat=-79.94 lon=-63.28 | basic=bar
47: Dj daya | lat=-84.93 lon=-62.81 | basic=media_service
48: Matrioska Laços | lat=-83.87 lon=-62.58 | basic=fashion_and_apparel_store
49: Antarctica, Antarctic Circle | lat=-64.05 lon=-60.93 | basic=historic_site
50: Esperanto Island, Antarctica | lat=-62.64 lon=-60.36 | basic=historic_site
51: Yankee Harbor | lat=-62.53 lon=-59.78 | basic=historic_site
52: Bellingshausen Russian Antarctic Station | lat=-62.20 lon=-58.96 | basic=research_institute
53: Bahia Fildes (Antartica) | lat=-62.20 lon=-58.96 | basic=nan
54: Crystal Power | lat=-75.59 lon=-58.32 | basic=fashion_and_apparel_store
55: Bestiario Moderno | lat=-78.15 lon=-58.02 | basic=media_service
56: Base Marambio, Antartida Argentina | lat=-64.24 lon=-56.62 | basic=government_office
57: Marambio Base | lat=-64.24 lon=-56.63 | basic=airport
58: Chapel of the Blessed Virgin of Lujan, Antarctica | lat=-64.24 lon=-56.63 | basic=christian_place_of_worship
59: Base Antártica Marambio | lat=-64.24 lon=-56.62 | basic=government_office
60: Base Esperanza, Antartida Argentina | lat=-63.40 lon=-57.00 | basic=airport
61: Esperanza Base, Antarctica | lat=-63.40 lon=-57.00 | basic=historic_site
62: Backwoods Bouquet | lat=-61.13 lon=-55.12 | basic=nan
63: Shop Thời Trang Nam - Nữ Thanh Bình | lat=-76.48 lon=-54.52 | basic=fashion_and_apparel_store
64: Lucas Fontoura | lat=-82.59 lon=-44.46 | basic=nan
65: Descobrincar  - Musicalização infantil | lat=-78.63 lon=-41.48 | basic=place_of_learning
66: Hurricane On Saturn | lat=-78.15 lon=-40.73 | basic=nan
67: Paradise Eventi | lat=-79.66 lon=-43.66 | basic=event_or_party_service
68: Psikolog ve Aile Danışmanı Esra Erciyas | lat=-79.75 lon=-34.45 | basic=nan
69: Proexc Engenharia | lat=-79.30 lon=-39.38 | basic=building_or_construction_service
70: Villa's Caldos | lat=-84.67 lon=-36.56 | basic=fast_food_restaurant
71: Gabbi Garcia | lat=-82.11 lon=-31.00 | basic=nan
72: Bendita CR | lat=-79.30 lon=-33.05 | basic=nan
73: Behr Productions | lat=-77.31 lon=-33.05 | basic=media_service
74: Max land  MX Raceway Park | lat=-78.63 lon=-28.12 | basic=sport_or_fitness_facility
75: DRK Kreisverband Gelnhausen-Schlüchtern e.V. | lat=-83.16 lon=-28.82 | basic=social_or_community_service
76: Muhammad Hasri Videography | lat=-84.80 lon=-25.31 | basic=professional_service
77: JJ Niceley | lat=-81.83 lon=-23.21 | basic=nan
78: Jankari Kendra जानकारी केन्द्र | lat=-80.06 lon=-26.80 | basic=media_service
79: Reggae Village House of Jerk | lat=-75.13 lon=-25.32 | basic=event_or_party_service
80: Công Ty TNHH Nam Hưng Thái Nguyên | lat=-76.35 lon=-25.54 | basic=manufacturer
81: 美妍美容教育中心-Beauty in | lat=-61.27 lon=-28.59 | basic=personal_or_beauty_service
82: Foul Point | lat=-60.53 lon=-45.48 | basic=historic_site
83: Malou Madi | lat=-72.40 lon=-10.55 | basic=nan
84: Institut für Emotionspädagogik | lat=-72.18 lon=-9.14 | basic=educational_service
85: Animations enfants Tahiti, Le PitiMotu | lat=-74.02 lon=-18.98 | basic=nan
86: Dregg Ackies | lat=-77.74 lon=-15.22 | basic=media_service
87: Sunsoli | lat=-77.47 lon=-19.69 | basic=nan
88: Flocreazionipereventi | lat=-79.56 lon=-18.98 | basic=event_or_party_service
89: Martin Kráľ - Skladateľ | lat=-81.92 lon=-21.09 | basic=nan
90: Babul Yaman Store | lat=-82.01 lon=-17.71 | basic=nan
91: SMDC Property Investment | lat=-80.53 lon=-11.09 | basic=real_estate_service
92: WM Case - Profesjonalne skrzynie transportowe | lat=-78.35 lon=-12.01 | basic=nan
93: Dwill Burguer | lat=-77.71 lon=-10.16 | basic=restaurant
94: Sund med Mia | lat=-83.68 lon=-11.25 | basic=professional_service
95: Deli Repostería | lat=-82.98 lon=-9.14 | basic=casual_eatery
96: Buli Makhubo | lat=-83.68 lon=-3.52 | basic=nan
97: Стиль и мода. Израиль | lat=-81.41 lon=-4.22 | basic=personal_or_beauty_service
98: Bedlam Studios | lat=-78.21 lon=-4.22 | basic=design_service
99: رضا الحديثي - صفحتي الرسمية | lat=-77.31 lon=-4.22 | basic=sport_or_fitness_facility
100: Friis Boliger | lat=-77.77 lon=-6.33 | basic=real_estate_service
101: রঙ-Israt's Art | lat=-77.44 lon=-6.64 | basic=arts_crafts_and_hobby_store
102: DirtySnatcha | lat=-77.85 lon=-6.97 | basic=nan
103: Calbero | lat=-77.87 lon=-8.04 | basic=nan
104: Jobswagon | lat=-75.49 lon=-1.31 | basic=nan
105: هاوار | lat=-73.06 lon=-1.12 | basic=nan
106: Antarctica/Troll | lat=-72.01 lon=2.54 | basic=historic_site
107: Ventamark | lat=-73.83 lon=1.87 | basic=electronics_store
108: De Todo | lat=-77.12 lon=2.02 | basic=electronics_store
109: Inglés Personal | lat=-75.50 lon=3.52 | basic=specialty_school
110: João Gomes | lat=-76.23 lon=3.53 | basic=nan
111: RA CARS PH | lat=-76.42 lon=3.43 | basic=auto_dealer
112: אריאל שרם - צילום | lat=-77.83 lon=0.56 | basic=professional_service
113: Eden's Essential Elements | lat=-81.66 lon=-0.94 | basic=flowers_and_gifts_store
114: Laviva Cosmetics | lat=-81.59 lon=-0.94 | basic=personal_or_beauty_service
115: Elblesk e-mobility | lat=-83.98 lon=-0.70 | basic=ground_transport_facility_or_service
116: ليو - Leo | lat=-82.31 lon=1.41 | basic=media_service
117: Otago Potters Group | lat=-83.98 lon=2.11 | basic=nan
118: Johanna Oedin | lat=-70.38 lon=4.92 | basic=nan
119: Alchemist Craftworks | lat=-84.30 lon=6.34 | basic=manufacturer
120: Panaderia Doña Tere | lat=-84.56 lon=6.69 | basic=casual_eatery
121: 廸康醫療中心 Madison Medical Centre | lat=-79.37 lon=7.91 | basic=complementary_and_alternative_medicine
122: A Tale of Four Mages | lat=-84.74 lon=9.14 | basic=media_service
123: JP Ranch Grass Valley | lat=-79.94 lon=9.14 | basic=farm
124: Wolf’s Fang Runway | lat=-71.52 lon=8.81 | basic=nan
125: Fishing 411 TV | lat=-77.47 lon=10.55 | basic=nan
126: Pro Massage & Pro PT | lat=-80.06 lon=12.66 | basic=nan
127: St. Isidore Catholic Learning Centre | lat=-84.27 lon=12.66 | basic=nan
128: Cajurine. | lat=-80.98 lon=13.36 | basic=personal_or_beauty_service
129: Higi + Higienização e Impermeabilização de Estofados | lat=-79.75 lon=13.02 | basic=home_service
130: Organica Mart | lat=-72.20 lon=13.05 | basic=food_service
131: ซัมไทม์ วิว รีสอร์ท | lat=-77.16 lon=13.71 | basic=lodging
132: چارەسەر لە سروشتەوە | lat=-83.21 lon=14.08 | basic=personal_or_beauty_service
133: Pro Punjab Tv | lat=-74.34 lon=16.31 | basic=media_service
134: elevatedxconscience | lat=-79.69 lon=16.88 | basic=nan
135: B.olivia - ps | lat=-62.24 lon=15.47 | basic=personal_or_beauty_service
136: Twory i Stwory | lat=-78.49 lon=19.69 | basic=nan
137: Geraint Jones 4x4 | lat=-78.74 lon=20.39 | basic=vehicle_dealer
138: 2 Nice | lat=-81.20 lon=19.71 | basic=fashion_and_apparel_store
139: Stress Relief Massage | lat=-77.77 lon=21.10 | basic=nan
140: Amrinder Bobby | lat=-80.47 lon=22.39 | basic=nan
141: Make Noise Pro Audio LTD | lat=-84.54 lon=23.20 | basic=electronics_store
142: Morjane Ténéré | lat=-78.90 lon=23.20 | basic=nan
143: Southern Ocean | lat=-80.76 lon=25.31 | basic=historic_site
144: ليلى kids | lat=-65.42 lon=26.10 | basic=fashion_and_apparel_store
145: 鉢伏山荘冬期営業 | lat=-83.59 lon=26.86 | basic=lodging
146: البروج للمواد الكهربائية | lat=-75.32 lon=30.23 | basic=shopping_mall
147: Beauty & Care | lat=-76.90 lon=31.21 | basic=personal_or_beauty_service
148: Béjaïa béni ksila vacances | lat=-79.13 lon=31.26 | basic=nan
149: In Love Furniture - ศูนย์รวมเฟอร์นิเจอร์ชุดห้องนอน และ เฟอร์นิเจอร์สำนักงาน | lat=-72.82 lon=32.34 | basic=hardware_home_and_garden_store
150: Woodstock Dentistry | lat=-84.54 lon=34.11 | basic=dental_clinic
151: Paubril’s Beauty | lat=-60.92 lon=33.72 | basic=personal_or_beauty_service
152: This Esme | lat=-82.68 lon=35.16 | basic=nan
153: Alex Sá | lat=-70.48 lon=38.32 | basic=nan
154: MOFT | lat=-77.92 lon=45.23 | basic=fashion_and_apparel_store
155: I-narin Beauty : เครื่องนวดหน้า สไตล์เกาหลี | lat=-75.14 lon=45.00 | basic=personal_or_beauty_service
156: Abracadabra Technologie | lat=-84.47 lon=46.41 | basic=corporate_or_business_office
157: Cafés Ellouze | lat=-76.68 lon=49.22 | basic=coffee_shop
158: หมามะเร็ง Dogs Cancer | lat=-78.49 lon=52.03 | basic=social_or_community_service
159: 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 | lat=-73.67 lon=54.56 | basic=nan
160: 厚興瑜記 WegoMall | lat=-78.07 lon=54.08 | basic=restaurant
161: Americano Cafe' Happy Moment with Coffee and Drink | lat=-75.32 lon=55.60 | basic=cafe
162: Japfa Experience | lat=-84.43 lon=58.46 | basic=restaurant
163: Celest Jewelry | lat=-72.79 lon=58.72 | basic=nan
164: My湯 - 茘枝角 | lat=-80.81 lon=60.24 | basic=casual_eatery
165: Bobby Layal | lat=-83.07 lon=60.53 | basic=museum
166: Janeth E. Sarona | lat=-80.72 lon=64.15 | basic=professional_service
167: PD.Kannel | lat=-70.05 lon=63.61 | basic=nan
168: แพวันวาน จังหวัดกาญจนบุรี | lat=-68.14 lon=66.09 | basic=hotel
169: Dalbe Réunion | lat=-65.29 lon=67.67 | basic=nan
170: สมุทรสงครามรีวิว | lat=-70.28 lon=68.61 | basic=nan
171: مركز آفنيو الطبي | lat=-69.90 lon=69.61 | basic=hospital
172: ครูเปิ้ล สอนคอมฯ : ปวช. สกร.เมืองนครราชสีมา | lat=-72.73 lon=70.51 | basic=nan
173: Cat & Calmell | lat=-83.22 lon=69.27 | basic=nan
174: Mette MÆRsk | lat=-80.65 lon=70.66 | basic=professional_service
175: อึ้งกุ่ยเฮง มอเตอร์ไซค์ ฮอนด้า ยามาฮ่า รถมือสอง ร้อยเอ็ด | lat=-84.98 lon=72.86 | basic=vehicle_dealer
176: World Auto Glass Inc. | lat=-63.46 lon=73.29 | basic=nan
177: Noskill Sensi | lat=-70.18 lon=74.52 | basic=books_music_and_video_store
178: Ingrid Christensen Coast | lat=-69.50 lon=77.00 | basic=historic_site
179: Tutti Bambino | lat=-70.61 lon=77.34 | basic=toys_and_games_store
180: Ellis Fjord | lat=-68.60 lon=78.08 | basic=historic_site
181: Lake Jabs | lat=-68.55 lon=78.25 | basic=historic_site
182: Langnes Fjord | lat=-68.50 lon=78.25 | basic=historic_site
183: Krok Lake | lat=-68.62 lon=78.40 | basic=lake
184: Lied Bluff | lat=-68.52 lon=78.27 | basic=historic_site
185: Heidemann Bay | lat=-68.58 lon=77.97 | basic=historic_site
186: Filla Island | lat=-68.82 lon=77.83 | basic=historic_site
187: Lake Zvezda | lat=-68.53 lon=78.45 | basic=historic_site
188: Dr. Cláudio Gurgel Magalhães - Clínica Belle Esthetique | lat=-78.39 lon=78.67 | basic=nan
189: Ynnez Group | lat=-81.90 lon=78.89 | basic=civic_organization
190: 芸臻命理諮詢 | lat=-82.56 lon=79.63 | basic=psychic_advising
191: Miya Élégance | lat=-67.88 lon=82.97 | basic=fashion_and_apparel_store
192: แม่อี่ ผักดองตำหรับยูนนาน | lat=-78.91 lon=88.25 | basic=nan
193: NPS Institutions - Bidar | lat=-81.92 lon=92.11 | basic=place_of_learning
194: Sozialkontakt | lat=-78.77 lon=93.52 | basic=nan
195: albertopiernas | lat=-65.30 lon=96.31 | basic=nan
196: Sekolah Islam Terpadu Green Bhakti Insani | lat=-76.52 lon=98.44 | basic=middle_school
197: Motel Mozaique Concerts | lat=-75.54 lon=98.11 | basic=hotel
198: CT Trampoline Fitness 沙田 石門 彈床班 Jump Fitness 健身 親子彈床班 | lat=-75.98 lon=97.11 | basic=gym
199: Casa de Adoración Yahweh | lat=-78.93 lon=97.01 | basic=religious_organization
200: Sydney Amigurumi Corner 手勾公仔 | lat=-81.92 lon=99.14 | basic=arts_crafts_and_hobby_store
201: Shanaya Hotel Borobudur | lat=-83.36 lon=103.71 | basic=hotel
202: The Cumberland River Project | lat=-80.98 lon=105.47 | basic=river
203: Lake Vostok | lat=-77.50 lon=106.00 | basic=historic_site
204: ร้านแน็ตโมบายโฟน | lat=-70.41 lon=106.52 | basic=nan
205: Vostok İstasyonu | lat=-78.46 lon=106.84 | basic=historic_site
206: Tiệm ảnh Phúc- Lạng Sơn | lat=-73.63 lon=107.58 | basic=professional_service
207: مدرسة أحمد نمر حمدان الأساسية للبنين | lat=-76.83 lon=107.60 | basic=place_of_learning
208: ইলমুল কুরআন মাদ্রাসা লিল্লাহ বোর্ডিং ও ইয়াতিম খানা | lat=-69.74 lon=108.98 | basic=nan
209: سنتر بغداد | lat=-71.44 lon=109.69 | basic=nan
210: 住商不動產樹林后站加盟店 | lat=-83.36 lon=110.39 | basic=professional_service
211: 塩彩 ～shiosai～ | lat=-73.82 lon=110.39 | basic=nan
212: Kurd Sport HD | lat=-76.63 lon=110.69 | basic=sport_or_recreation_club
213: Sofiya Designs Gh | lat=-70.08 lon=113.22 | basic=nan
214: 水草館 | lat=-79.75 lon=114.29 | basic=museum
215: Compartilhagem | lat=-74.78 lon=117.42 | basic=arts_crafts_and_hobby_store
216: Kimstore | lat=-68.91 lon=118.83 | basic=nan
217: Masjid Terapung Tanjung Bungah, Pulau Pinang | lat=-83.44 lon=120.23 | basic=muslim_place_of_worship
218: Angel Rising | lat=-84.12 lon=123.75 | basic=nan
219: Lailo Farm Sanctuary | lat=-73.82 lon=122.34 | basic=social_or_community_service
220: Dr Henri Bauer | lat=-75.10 lon=123.33 | basic=nan
221: Academia Nacional de Sommeliers y Gastrónomos | lat=-83.27 lon=128.90 | basic=place_of_learning
222: ทำประกันกับอาอิซะห์ | lat=-80.36 lon=133.64 | basic=personal_or_beauty_service
223: Base antarctique Dumont-d'Urville | lat=-66.66 lon=140.00 | basic=historic_site
224: شركة الأركان لقطع غيار وزيوت السيارات\nAlarkan Auto Parts & Oils | lat=-70.72 lon=136.05 | basic=nan
225: Seaoil Dangcagan | lat=-80.46 lon=135.78 | basic=gas_station
226: The Seasoning Outlet | lat=-79.69 lon=144.14 | basic=nan
227: MAX Reprise | lat=-74.44 lon=149.23 | basic=automotive_service
228: Antarctic Plateau | lat=-77.00 lon=150.00 | basic=historic_site
229: Warning Five จำหน่ายสายนาฬิกาหนังแท้ สายนาฬิกา smartwatch | lat=-74.46 lon=154.41 | basic=nan
230: 海港青年商會 JCI Harbour | lat=-83.71 lon=153.26 | basic=social_or_community_service
231: মুক্তি হারবাল চিকিৎসালয়-Mukti Herbal Chikitsalay | lat=-69.43 lon=155.73 | basic=primary_care_or_general_clinic
232: Herrlich Beauty By Cloriss | lat=-68.91 lon=150.47 | basic=personal_or_beauty_service
233: Mario-Zucchelli-Station | lat=-74.69 lon=164.11 | basic=nan
234: Psicóloga Serly C. de Souza | lat=-77.88 lon=164.41 | basic=behavioral_or_mental_health_clinic
235: Polární stanice McMurdo | lat=-77.85 lon=166.67 | basic=historic_site
236: Base McMurdo | lat=-77.85 lon=166.67 | basic=historic_site
237: Base antarctique McMurdo | lat=-77.85 lon=166.67 | basic=historic_site
238: Gallagher Pub | lat=-77.85 lon=166.67 | basic=bar
239: Southern Exposure | lat=-77.85 lon=166.67 | basic=bar
240: ZiroZiro | lat=-77.85 lon=166.67 | basic=religious_organization
241: McMurdo General Hospital | lat=-77.85 lon=166.67 | basic=hospital
242: Coffee House | lat=-77.85 lon=166.66 | basic=coffee_shop
243: Chapel of the Snows | lat=-77.85 lon=166.66 | basic=christian_place_of_worship
244: Pegasus Field | lat=-77.96 lon=166.52 | basic=airport
245: 悦聖翡翠 | lat=-82.30 lon=176.03 | basic=fashion_and_apparel_store

cd /work && timeout 120 python << 'PYEOF'
import overturemaps
import geopandas as gpd

# Get all places south of -60
gdf = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60))
print(f"Total places: {len(gdf)}")

# Add name column
names = []
for _, row in gdf.iterrows():
    nm = row.get('names')
    if nm and isinstance(nm, dict) and 'primary' in nm and nm['primary']:
        names.append(nm['primary'])
    else:
        names.append(None)

gdf['station_name'] = names

# Filter to Points with geometry
gdf_pt = gdf[gdf.geometry.notna() & (gdf.geometry.geom_type == 'Point')].copy()
gdf_pt = gdf_pt[gdf_pt.geometry.y <= -60].copy()

# Print all rows with coordinates to find actual stations
potential = []
for idx, row in gdf_pt.iterrows():
    n = row.get('station_name')
    if n and any(kw in n.lower() for kw in ['station', 'base', 'research', 'camp', 'troll', 'vostok', 'mcmurdo', 'palmer', 'dumont', 'marambio', 'esperanza', 'brown', 'gonzalez', 'zucchelli', 'bellingshausen', 'port lockroy']):
        potential.append((n, row.geometry.y, row.geometry.x, row['basic_category']))

print("\nPotential stations:")
for n, lat, lon, cat in potential:
    print(f"  {n:50s} lat={lat:7.2f} lon={lon:8.2f} cat={cat}")

print(f"\nTotal potential: {len(potential)}")
PYEOF

Total places: 246

Potential stations:
  Port Lockroy, Antarctic Peninsula                  lat= -64.82 lon=  -63.49 cat=historic_site
  Palmer Station, Antarctica                         lat= -64.77 lon=  -64.05 cat=real_estate_service
  Brown Station                                      lat= -64.89 lon=  -62.87 cat=historic_site
  González Videla Antarctic Base                     lat= -64.82 lon=  -62.86 cat=airport
  Bellingshausen Russian Antarctic Station           lat= -62.20 lon=  -58.96 cat=research_institute
  Base Marambio, Antartida Argentina                 lat= -64.24 lon=  -56.62 cat=government_office
  Marambio Base                                      lat= -64.24 lon=  -56.63 cat=airport
  Base Antártica Marambio                            lat= -64.24 lon=  -56.62 cat=government_office
  Base Esperanza, Antartida Argentina                lat= -63.40 lon=  -57.00 cat=airport
  Esperanza Base, Antarctica                         lat= -63.40 lon=  -57.00 cat=historic_site
  Antarctica/Troll                                   lat= -72.01 lon=    2.54 cat=historic_site
  Lake Vostok                                        lat= -77.50 lon=  106.00 cat=historic_site
  Vostok İstasyonu                                   lat= -78.46 lon=  106.84 cat=historic_site
  Base antarctique Dumont-d'Urville                  lat= -66.66 lon=  140.00 cat=historic_site
  Mario-Zucchelli-Station                            lat= -74.69 lon=  164.11 cat=nan
  Polární stanice McMurdo                            lat= -77.85 lon=  166.67 cat=historic_site
  Base McMurdo                                       lat= -77.85 lon=  166.67 cat=historic_site
  Base antarctique McMurdo                           lat= -77.85 lon=  166.67 cat=historic_site
  McMurdo General Hospital                           lat= -77.85 lon=  166.67 cat=hospital

Total potential: 19

cd /work && timeout 120 python << 'PYEOF'
import overturemaps
import sys

# Test water fetch with shorter timeout
print("Trying to fetch water...")
sys.stdout.flush()
try:
    reader = overturemaps.record_batch_reader('water', bbox=(-180, -90, 180, -60), request_timeout=30)
    if reader:
        batch = reader.read_next_batch()
        df = batch.to_pandas()
        print(f"Water columns: {df.columns.tolist()}")
        print(f"Water shape: {df.shape}")
        print(df.head(2))
    else:
        print("No water reader returned")
except Exception as e:
    print(f"Water error: {e}")

print()
print("Trying to fetch bathymetry...")
sys.stdout.flush()
try:
    reader2 = overturemaps.record_batch_reader('bathymetry', bbox=(-180, -90, 180, -60), request_timeout=30)
    if reader2:
        batch2 = reader2.read_next_batch()
        df2 = batch2.to_pandas()
        print(f"Bathymetry columns: {df2.columns.tolist()}")
        print(f"Bathymetry shape: {df2.shape}")
        print(df2.head(2))
    else:
        print("No bathymetry reader returned")
except Exception as e:
    print(f"Bathymetry error: {e}")
PYEOF

Trying to fetch water...
Water columns: ['id', 'names', 'subtype', 'class', 'sources', 'source_tags', 'level', 'wikidata', 'is_intermittent', 'is_salt', 'geometry', 'version', 'bbox']
Water shape: (946, 13)
                                     id  ...                                               bbox
0  3e0d144a-bb0f-36a3-8cb7-d473323efe0c  ...  {'xmin': -177.6623077392578, 'xmax': -177.5781...
1  6bbb5fe5-bf26-3efa-b120-0a7079b60840  ...  {'xmin': -177.03179931640625, 'xmax': -177.031...

[2 rows x 13 columns]

Trying to fetch bathymetry...
Bathymetry columns: ['id', 'depth', 'cartography', 'geometry', 'sources', 'version', 'bbox']
Bathymetry shape: (1803, 7)
                                     id  ...                                               bbox
0  1a3d446c-2614-5ef5-bfa7-6508b75d1504  ...  {'xmin': -179.2291717529297, 'xmax': -179.0039...
1  60b4800b-67ca-54fe-a99f-7818470c5bf3  ...  {'xmin': -179.19097900390625, 'xmax': -179.042...

[2 rows x 7 columns]

cd /work && timeout 60 python << 'PYEOF'
import overturemaps

# Check land schema
reader = overturemaps.record_batch_reader('land', bbox=(-180, -90, 180, -60), request_timeout=30)
batch = reader.read_next_batch()
df = batch.to_pandas()
print(f"Land columns: {df.columns.tolist()}")
print(f"Land shape: {df.shape}")
print(df.head(2))
print(f"Subtypes: {df['subtype'].unique()[:20] if 'subtype' in df.columns else 'N/A'}")
print(f"Classes: {df['class'].unique()[:20] if 'class' in df.columns else 'N/A'}")
PYEOF

Land columns: ['id', 'names', 'subtype', 'class', 'sources', 'source_tags', 'level', 'wikidata', 'surface', 'elevation', 'geometry', 'version', 'bbox']
Land shape: (345, 13)
                                     id  ...                                               bbox
0  ddac96b0-49ff-3f17-805a-ff10965da47d  ...  {'xmin': -180.0, 'xmax': -178.98089599609375, ...
1  2239ea36-d9c0-32a3-8955-56b4948cd19b  ...  {'xmin': -180.0, 'xmax': -178.9427032470703, '...

[2 rows x 13 columns]
Subtypes: <ArrowStringArray>
['land', 'rock', 'physical', 'glacier']
Length: 4, dtype: str
Classes: <ArrowStringArray>
[     'land', 'bare_rock',      'peak',     'cliff',   'glacier',      'rock',
     'islet',    'island',     'scree']
Length: 9, dtype: str

=== Fetching Overture data ===
Fetching places...
  Total places: 246
  Points south of -60: 246
  Candidate station records: 23
Fetching land...
  Land features: 93369
Fetching water...
  Water features: 13500
Fetching bathymetry...
  Bathymetry features: 3096

=== Identifying unique stations ===
  Unique stations identified: 14
    ST001: Bellingshausen Station              (-62.20, -58.96)
    ST002: McMurdo General Hospital            (-77.85, 166.67)
    ST003: Vostok Station                      (-78.46, 106.84)
    ST004: Palmer Station                      (-64.77, -64.05)
    ST005: Mario Zucchelli Station             (-74.69, 164.11)
    ST006: Dumont d'Urville Station            (-66.66, 140.00)
    ST007: Esperanza Base                      (-63.40, -57.00)
    ST008: Marambio Base                       (-64.24, -56.62)
    ST009: González Videla Base                (-64.82, -62.86)
    ST010: Antarctic Plateau                   (-77.00, 150.00)
    ST011: Troll Station                       (-72.01, 2.54)
    ST012: Esperanto Island, Antarctica        (-62.64, -60.36)
    ST013: Antarctica, Antarctic Circle        (-64.05, -60.93)
    ST014: Port Lockroy                        (-64.82, -63.49)

=== Creating 200 km buffer zones ===
  Buffers created: 14

=== Preparing Antarctic landmass ===
Traceback (most recent call last):
  File "/work/solve.py", line 223, in <module>
    land_proj = land_gdf.to_crs(ANT_CRS)
  File "/usr/local/lib/python3.14/site-packages/geopandas/geodataframe.py", line 1842, in to_crs
    geom = df.geometry.to_crs(crs=crs, epsg=epsg)
  File "/usr/local/lib/python3.14/site-packages/geopandas/geoseries.py", line 1251, in to_crs
    self.values.to_crs(crs=crs, epsg=epsg), index=self.index, name=self.name
    ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/geopandas/_compat.py", line 88, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.14/site-packages/geopandas/array.py", line 1114, in to_crs
    raise ValueError(
    ...<2 lines>...
    )
ValueError: Cannot transform naive geometries.  Please set a crs on the object first.

cd /work && timeout 30 python << 'PYEOF'
import overturemaps
gdf = overturemaps.geodataframe('land', bbox=(-180, -90, 180, -60))
print(f"CRS: {gdf.crs}")
print(f"Columns: {gdf.columns.tolist()}")
print(f"Geometry type: {gdf.geometry.geom_type.value_counts().head()}")
print(f"Total features: {len(gdf)}")
# Filter to south of -60
import numpy as np
# Check representative point to filter
from shapely import centroid
centroids = gdf.geometry.centroid
mask = centroids.y <= -60
print(f"Features with centroid south of -60: {mask.sum()}")
PYEOF

cd /work && timeout 60 python << 'PYEOF'
import overturemaps
gdf = overturemaps.geodataframe('land', bbox=(-180, -90, 180, -60))
print(f"CRS: {gdf.crs}")
print(f"Type: {type(gdf)}")
print(f"First geom type: {type(gdf.geometry.iloc[0])}")
print(f"Total land features: {len(gdf)}")
print(f"Memory: {gdf.memory_usage(deep=True).sum()/1e6:.1f} MB")
# Check bbox column
if 'bbox' in gdf.columns:
    print(f"Bbox sample: {gdf['bbox'].iloc[0]}")
# Just take first 1000 to test
small = gdf.head(1000)
small_proj = small.to_crs('EPSG:3031')
print(f"Projected first 1000: {len(small_proj)}")
print(f"Small CRS: {small_proj.crs}")
PYEOF

CRS: None
Type: <class 'geopandas.geodataframe.GeoDataFrame'>
First geom type: <class 'shapely.geometry.polygon.Polygon'>
Total land features: 93369
Memory: 49.9 MB
Bbox sample: {'xmin': -180.0, 'xmax': -178.98089599609375, 'ymin': -89.00050354003906, 'ymax': -87.99949645996094}

Traceback (most recent call last):
  File "<stdin>", line 13, in <module>
  File "/usr/local/lib/python3.14/site-packages/geopandas/geodataframe.py", line 1842, in to_crs
    geom = df.geometry.to_crs(crs=crs, epsg=epsg)
  File "/usr/local/lib/python3.14/site-packages/geopandas/geoseries.py", line 1251, in to_crs
    self.values.to_crs(crs=crs, epsg=epsg), index=self.index, name=self.name
    ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/geopandas/_compat.py", line 88, in wrapper
    return func(*args, **kwargs)
  File "/usr/local/lib/python3.14/site-packages/geopandas/array.py", line 1114, in to_crs
    raise ValueError(
    ...<2 lines>...
    )
ValueError: Cannot transform naive geometries.  Please set a crs on the object first.

=== Step 1: Fetching Overture data ===
Fetching places...
  Total places: 246
  Points south of -60: 246
  Candidate station records: 23
Fetching land...
  Land features: 93369
  After bbox ymin filter: 93369
Fetching water...
  Water features: 13500
  After bbox ymin filter: 13500
Fetching bathymetry...
  Bathymetry features: 3096
  After bbox ymin filter: 3096

=== Step 2: Identifying unique stations ===
  Unique stations identified: 10
    ST001: Bellingshausen Station              (-62.20, -58.96)
    ST002: Vostok Station                      (-78.46, 106.84)
    ST003: Palmer Station                      (-64.77, -64.05)
    ST004: Mario Zucchelli Station             (-74.69, 164.11)
    ST005: Dumont d'Urville Station            (-66.66, 140.00)
    ST006: Esperanza Base                      (-63.40, -57.00)
    ST007: Marambio Base                       (-64.24, -56.62)
    ST008: González Videla Base                (-64.82, -62.86)
    ST009: Troll Station                       (-72.01, 2.54)
    ST010: Port Lockroy                        (-64.82, -63.49)

=== Step 3: Creating 200 km buffer zones ===
  Buffers created: 10

=== Step 4: Preparing Antarctic landmass ===
  Projecting 93369 land features to EPSG:3031...
  Dissolving land features...
  Antarctic land area: 13638507 km²

=== Step 5: Clipping buffers to land ===
  Land-clipped features: 10

=== Step 6: Computing coalitions ===
  Coalitions found: 5
    Coalition 0: Bellingshausen Station, Palmer Station, Esperanza Base, Marambio Base, González Videla Base, Port Lockroy
    Coalition 1: Vostok Station
    Coalition 2: Mario Zucchelli Station
    Coalition 3: Dumont d'Urville Station
    Coalition 4: Troll Station
  Coalition distribution: {0: 6, 1: 1, 2: 1, 3: 1, 4: 1}

=== Step 7: Preparing water features ===
  Processing 13500 water features...
  Processing 3096 bathymetry features...
  Total water features: 16596
  Water sources: {'base.water': 13500, 'base.bathymetry': 3096}

=== Step 8: Computing water overlap intersections ===
  Water overlap features: 2069

=== Step 9: Normalizing to MultiPolygon geometry ===

=== Step 10: Writing output files ===
Traceback (most recent call last):
  File "/work/solve.py", line 468, in <module>
    land_out[cols1].to_parquet('/work/station_spheres.geoparquet', engine='pyogrio')
    ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/geopandas/geodataframe.py", line 1457, in to_parquet
    raise ValueError(
    ...<2 lines>...
    )
ValueError: GeoPandas only supports using pyarrow as the engine for to_parquet: 'pyogrio' passed instead.

=== Step 1: Fetching Overture data ===
Fetching places...
  Total places: 246
  Points south of -60: 246
  Candidate station records: 23
Fetching land...
  Land features: 93369
  After bbox ymin filter: 93369
Fetching water...
  Water features: 13500
  After bbox ymin filter: 13500
Fetching bathymetry...
  Bathymetry features: 3096
  After bbox ymin filter: 3096

=== Step 2: Identifying unique stations ===
  Unique stations identified: 11
    ST001: Bellingshausen Station              (-62.20, -58.96)
    ST002: McMurdo Station                     (-77.85, 166.67)
    ST003: Vostok Station                      (-78.46, 106.84)
    ST004: Palmer Station                      (-64.77, -64.05)
    ST005: Mario Zucchelli Station             (-74.69, 164.11)
    ST006: Dumont d'Urville Station            (-66.66, 140.00)
    ST007: Esperanza Base                      (-63.40, -57.00)
    ST008: Marambio Base                       (-64.24, -56.62)
    ST009: González Videla Base                (-64.82, -62.86)
    ST010: Brown Station                       (-64.89, -62.87)
    ST011: Troll Station                       (-72.01, 2.54)

=== Step 3: Creating 200 km buffer zones ===
  Buffers created: 11

=== Step 4: Preparing Antarctic landmass ===
  Projecting 93369 land features to EPSG:3031...
  Dissolving land features...
  Antarctic land area: 13638507 km²

=== Step 5: Clipping buffers to land ===
  Land-clipped features: 11

=== Step 6: Computing coalitions ===
  Coalitions found: 5
    Coalition 0: Bellingshausen Station, Palmer Station, Esperanza Base, Marambio Base, González Videla Base, Brown Station
    Coalition 1: McMurdo Station, Mario Zucchelli Station
    Coalition 2: Vostok Station
    Coalition 3: Dumont d'Urville Station
    Coalition 4: Troll Station
  Coalition distribution: {0: 6, 1: 2, 2: 1, 3: 1, 4: 1}

=== Step 7: Preparing water features ===
  Processing 13500 water features...
  Processing 3096 bathymetry features...
  Total water features: 16596
  Water sources: {'base.water': 13500, 'base.bathymetry': 3096}

=== Step 8: Computing water overlap intersections ===
  Water overlap features: 2891

=== Step 9: Normalizing to MultiPolygon geometry ===

=== Step 10: Writing output files ===
  station_spheres.geoparquet: 11 features written
  station_water_overlap.geoparquet: 2891 features written

======================================================================
VALIDATION
======================================================================

📁 station_spheres.geoparquet
   Features: 11
   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: {'MultiPolygon': 11}
   Distinct coalition values: 5
   Coalition distribution: {0: 6, 1: 2, 2: 1, 3: 1, 4: 1}
   Sample:
   station_id              station_name  coalition
0       ST001    Bellingshausen Station          0
1       ST002           McMurdo Station          1
2       ST003            Vostok Station          2
3       ST004            Palmer Station          0
4       ST005   Mario Zucchelli Station          1
5       ST006  Dumont d'Urville Station          3
6       ST007            Esperanza Base          0
7       ST008             Marambio Base          0
8       ST009      González Videla Base          0
9       ST010             Brown Station          0
10      ST011             Troll Station          4

📁 station_water_overlap.geoparquet
   Features: 2891
   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: {'MultiPolygon': 2891}
   Distinct station_ids: 11
   Water sources: <ArrowStringArray>
['base.water', 'base.bathymetry']
Length: 2, dtype: str
   Water source counts: {'base.water': 2239, 'base.bathymetry': 652}
   Sample:
  station_id            station_name                              water_id       water_name water_subtype     water_source
0      ST001  Bellingshausen Station  e33f246c-680c-3df0-bb13-7e011c8ef4b7              NaN         ocean       base.water
1      ST001  Bellingshausen Station  8d513405-a3aa-39b8-a264-a1ca96692ba9              NaN         ocean       base.water
2      ST001  Bellingshausen Station  45a387fc-a30e-504e-8429-80fc790b5901              NaN           NaN  base.bathymetry
3      ST001  Bellingshausen Station  1cbd10af-0a7a-5866-8c24-473ef3e7319d              NaN           NaN  base.bathymetry
4      ST001  Bellingshausen Station  f42d2cce-a29e-3df7-a0f3-d1c9719e9b80              NaN         ocean       base.water
5      ST001  Bellingshausen Station  a7a451e1-4ca0-3f1e-bdd7-8aeb2da418d6              NaN         ocean       base.water
6      ST001  Bellingshausen Station  f141e8b2-2c26-343d-b516-c000d3b86c39  Limnopolar Lake          lake       base.water
7      ST001  Bellingshausen Station  be7fff5d-95f6-3554-9e6c-21b2ddae6c4d              NaN          lake       base.water
8      ST001  Bellingshausen Station  904422e9-d160-3a55-a6b8-891b7cd71e1c     Chester Lake          lake       base.water
9      ST001  Bellingshausen Station  1be1240a-003a-3f89-b96e-6bf3113bee84              NaN          pond       base.water

✅ Done! Output files ready in /work/

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

# Full validation
s1 = gpd.read_parquet('/work/station_spheres.geoparquet')
s2 = gpd.read_parquet('/work/station_water_overlap.geoparquet')

print('=== station_spheres.geoparquet ===')
print(f'CRS: {s1.crs}')
print(f'Rows: {len(s1)}')
print(f'Columns: {list(s1.columns)}')
print(f'All MultiPolygon: {(s1.geometry.geom_type == \"MultiPolygon\").all()}')
print(f'coalition nunique: {s1[\"coalition\"].nunique()}')
print(f'coalition values: {sorted(s1[\"coalition\"].unique())}')
print(f'station_id present: {\"station_id\" in s1.columns}')
print(f'station_name present: {\"station_name\" in s1.columns}')
print()

print('=== station_water_overlap.geoparquet ===')
print(f'CRS: {s2.crs}')
print(f'Rows: {len(s2)}')
print(f'Columns: {list(s2.columns)}')
print(f'All MultiPolygon: {(s2.geometry.geom_type == \"MultiPolygon\").all()}')
print(f'station_id present: {\"station_id\" in s2.columns}')
print(f'station_name present: {\"station_name\" in s2.columns}')
print(f'water_id present: {\"water_id\" in s2.columns}')
print(f'water_name present: {\"water_name\" in s2.columns}')
print(f'water_subtype present: {\"water_subtype\" in s2.columns}')
print(f'water_source present: {\"water_source\" in s2.columns}')
print(f'water_source values: {s2[\"water_source\"].unique().tolist()}')
print(f'Has base.water: {\"base.water\" in s2[\"water_source\"].values}')
print(f'Has base.bathymetry: {\"base.bathymetry\" in s2[\"water_source\"].values}')
print()
print('File sizes:')
import os
for f in ['station_spheres.geoparquet', 'station_water_overlap.geoparquet']:
    sz = os.path.getsize(f'/work/{f}')
    print(f'  {f}: {sz/1e6:.1f} MB')
"

=== station_spheres.geoparquet ===
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}}
Rows: 11
Columns: ['station_id', 'station_name', 'coalition', 'geometry']
All MultiPolygon: True
coalition nunique: 5
coalition values: [np.int64(0), np.int64(1), np.int64(2), np.int64(3), np.int64(4)]
station_id present: True
station_name present: True

=== station_water_overlap.geoparquet ===
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}}
Rows: 2891
Columns: ['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source', 'geometry']
All MultiPolygon: True
station_id present: True
station_name present: True
water_id present: True
water_name present: True
water_subtype present: True
water_source present: True
water_source values: ['base.water', 'base.bathymetry']
Has base.water: True
Has base.bathymetry: True

File sizes:
  station_spheres.geoparquet: 11.0 MB
  station_water_overlap.geoparquet: 14.6 MB