geo-l3-antarctica-stations-geodesic

Rerun

cd /work && python -c "
import overturemaps as om
# List available themes
print(om.which('themes'))
" 2>&1 | head -30

Traceback (most recent call last):
  File "<string>", line 4, in <module>
    print(om.which('themes'))
          ^^^^^^^^
AttributeError: module 'overturemaps' has no attribute 'which'

cd /work && python -c "
import overturemaps as om
# Check available types
print(om.which('types'))
" 2>&1 | head -30

Traceback (most recent call last):
  File "<string>", line 4, in <module>
    print(om.which('types'))
          ^^^^^^^^
AttributeError: module 'overturemaps' has no attribute 'which'

cd /work && python -c "
import overturemaps
# Check what's available
print(dir(overturemaps))
" 2>&1

Python 3.14.5
duckdb             1.5.3
geopandas          1.1.3
overturemaps       1.0.0
pyproj             3.7.2
shapely            2.1.2

cd /work && python -c "
import overturemaps
print(dir(overturemaps.core))
" 2>&1

cd /work && python -c "
import overturemaps
# Get all Overture types
types = overturemaps.get_all_overture_types()
print(types[:30])
print('...')
print(len(types))
" 2>&1

cd /work && python -c "
import overturemaps
# Explore place schema
reader = overturemaps.record_batch_reader('place', type='place')
# Just get the schema
schema = reader.schema
print('Place schema:')
for i in range(len(schema)):
    print(f'  {schema.field(i).name}: {schema.field(i).type}')
" 2>&1 | head -50

Traceback (most recent call last):
  File "<string>", line 4, 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
# Explore water schema
reader = overturemaps.record_batch_reader('water', type='water')
schema = reader.schema
print('Water schema:')
for i in range(len(schema)):
    print(f'  {schema.field(i).name}: {schema.field(i).type}')
" 2>&1 | head -50

Traceback (most recent call last):
  File "<string>", line 4, in <module>
    reader = overturemaps.record_batch_reader('water', type='water')
TypeError: record_batch_reader() got an unexpected keyword argument 'type'

cd /work && python -c "
import overturemaps
# Check land schema
reader = overturemaps.record_batch_reader('land', type='land')
schema = reader.schema
print('Land schema:')
for i in range(len(schema)):
    print(f'  {schema.field(i).name}: {schema.field(i).type}')
" 2>&1 | head -50

Traceback (most recent call last):
  File "<string>", line 4, in <module>
    reader = overturemaps.record_batch_reader('land', type='land')
TypeError: record_batch_reader() got an unexpected keyword argument 'type'

cd /work && python -c "
import overturemaps
help(overturemaps.record_batch_reader)
" 2>&1 | head -50

Help on function record_batch_reader in module overturemaps.core:

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.

cd /work && python -c "
import overturemaps
# Try theme-based call
help(overturemaps.core.record_batch_reader)
" 2>&1 | head -50

Help on function record_batch_reader in module overturemaps.core:

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.

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

# Explore place schema - no bbox first to see full schema
reader = overturemaps.record_batch_reader('place')
if reader:
    schema = reader.schema
    print('Place schema:')
    for i in range(len(schema)):
        print(f'  {schema.field(i).name}: {schema.field(i).type}')
else:
    print('No reader returned')
" 2>&1 | head -60

cd /work && python -c "
# Antarctica bbox roughly: -180,-90,180,-60
import overturemaps
# Place
reader = overturemaps.record_batch_reader('place', bbox=(-180,-90,180,-60))
if reader:
    schema = reader.schema
    print('Place schema:')
    for i in range(len(schema)):
        print(f'  {schema.field(i).name}: {schema.field(i).type}')
else:
    print('No reader')
" 2>&1 | head -80

Place 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
# Water schema
reader = overturemaps.record_batch_reader('water', bbox=(-180,-90,180,-60))
if reader:
    schema = reader.schema
    print('Water schema:')
    for i in range(len(schema)):
        print(f'  {schema.field(i).name}: {schema.field(i).type}')
else:
    print('No reader')
" 2>&1 | head -80

Water schema:
  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>

cd /work && python -c "
import overturemaps
# Land schema
reader = overturemaps.record_batch_reader('land', bbox=(-180,-90,180,-60))
if reader:
    schema = reader.schema
    print('Land schema:')
    for i in range(len(schema)):
        print(f'  {schema.field(i).name}: {schema.field(i).type}')
else:
    print('No reader')
" 2>&1 | head -80

Land schema:
  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>

cd /work && python -c "
import overturemaps
# Bathymetry schema
reader = overturemaps.record_batch_reader('bathymetry', bbox=(-180,-90,180,-60))
if reader:
    schema = reader.schema
    print('Bathymetry schema:')
    for i in range(len(schema)):
        print(f'  {schema.field(i).name}: {schema.field(i).type}')
else:
    print('No reader')
" 2>&1 | head -80

Bathymetry schema:
  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>

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

# Read a sample of places in Antarctica to find stations
reader = overturemaps.record_batch_reader('place', bbox=(-180,-90,180,-60))
if reader:
    tbl = reader.read_all()
    df = tbl.to_pandas()
    print(f'Total places south of -60: {len(df)}')
    # Check categories
    if 'categories' in df.columns:
        cats = df['categories'].dropna().apply(lambda x: x.get('primary') if isinstance(x, dict) else None)
        print('Category value counts:')
        print(cats.value_counts().head(40))
    # Print some names
    print('\\nSample place names:')
    for i, row in df.head(20).iterrows():
        name = row.get('names', {})
        if isinstance(name, dict):
            name = name.get('primary', 'N/A')
        else:
            name = 'N/A'
        cat = row.get('categories', {})
        if isinstance(cat, dict):
            cat = cat.get('primary', 'N/A')
        else:
            cat = 'N/A'
        print(f'  {name} | cat: {cat}')
" 2>&1

Total places south of -60: 2348
Category value counts:
categories
landmark_and_historical_building     163
fast_food_restaurant                 133
mountain                              67
professional_services                 58
beauty_salon                          51
school                                46
real_estate                           41
restaurant                            40
shopping                              40
hotel                                 39
beauty_and_spa                        30
college_university                    29
clothing_store                        27
resort                                26
education                             23
hospital                              21
womens_clothing_store                 20
community_services_non_profits        20
automotive_repair                     19
health_and_medical                    18
shoe_store                            18
car_dealer                            18
event_planning                        18
church_cathedral                      17
furniture_store                       16
cafe                                  16
religious_organization                16
public_and_government_association     16
arts_and_crafts                       14
real_estate_agent                     14
pharmacy                              13
gym                                   13
mobile_phone_store                    13
coffee_shop                           12
arts_and_entertainment                12
tours                                 11
fashion                               10
eat_and_drink                         10
preschool                             10
electronics                           10
Name: count, dtype: int64

Sample place names:
  Tincorp Pa TI | cat: information_technology_company
  Sara Couto - Intervenção Terapêutica | cat: physical_therapy
  แหลมใหญ่แล็บ คลินิกเทคนิคการแพทย์ สมุทรสงคราม | cat: health_and_medical
  Chittagong Victory National School-CVNS | cat: school
  Quảng Cáo - In Nhanh SaPa | cat: N/A
  Donata Farms & Land Services | cat: farm
  Vet Rx Pet Health Center | cat: N/A
  Work N Wear International | cat: sports_wear
  The Wagging Tail Salon | cat: pet_groomer
  Liso EstÚDio | cat: professional_services
  New Launch Property | cat: real_estate
  Ross Ice Shelf, Antarctica | cat: landmark_and_historical_building
  十刻刺青室 | cat: N/A
  Poeanuanua | cat: retail
  Discover Italy | cat: tours
  สอนต่อขนตาพัทยา ต่อขนตาพัทยา | cat: beauty_salon
  Krāslavas Varavīksnes vidusskola | cat: high_school
  مركز هيا هير للشعر الطبيعي/  Haya Center for Natural Hair | cat: beauty_salon
  Esencia Alquímica: Aromaterapia y Cosmética Natural | cat: cosmetic_and_beauty_supplies
  Maliha Farzam | cat: professional_services

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

# Get places and filter for research-related
reader = overturemaps.record_batch_reader('place', bbox=(-180,-90,180,-60))
if reader:
    tbl = reader.read_all()
    df = tbl.to_pandas()
    
    # Check all unique categories
    all_cats = df['categories'].dropna().apply(lambda x: x.get('primary') if isinstance(x, dict) else None)
    print('All unique categories:')
    for c in sorted(all_cats.unique()):
        cnt = (all_cats == c).sum()
        print(f'  {c}: {cnt}')
" 2>&1 | head -80

All unique categories:
  accommodation: 8
  active_life: 5
  acupuncture: 1
  agricultural_cooperatives: 4
  agricultural_service: 3
  agriculture: 4
  aircraft_repair: 1
  airport: 6
  alcohol_and_drug_treatment_centers: 2
  amateur_sports_team: 2
  ambulance_and_ems_services: 1
  antique_store: 1
  aquatic_pet_store: 2
  archery_range: 1
  architectural_designer: 1
  art_gallery: 3
  art_school: 5
  arts_and_crafts: 14
  arts_and_entertainment: 12
  astrologer: 2
  auction_house: 1
  audio_visual_equipment_store: 2
  audiologist: 1
  auto_company: 1
  auto_detailing: 3
  automation_services: 1
  automotive: 8
  automotive_dealer: 4
  automotive_parts_and_accessories: 6
  automotive_repair: 19
  b2b_jewelers: 2
  b2b_science_and_technology: 2
  b2b_textiles: 3
  bakery: 8
  bank_credit_union: 1
  banks: 2
  bar: 5
  barber: 3
  beach: 5
  beauty_and_spa: 30
  beauty_product_supplier: 1
  beauty_salon: 51
  beer_bar: 3
  bicycle_shop: 2
  boat_parts_and_accessories: 1
  boat_rental_and_training: 1
  boat_service_and_repair: 1
  bookstore: 1
  boutique: 4
  boxing_class: 1
  brazilian_restaurant: 1
  breakfast_and_brunch_restaurant: 2
  bridal_shop: 1
  brokers: 1
  bubble_tea: 4
  buddhist_temple: 2
  buffet_restaurant: 1
  building_supply_store: 2
  burger_restaurant: 4
  business_advertising: 1
  butcher_shop: 2
  cabin: 1
  cafe: 16
  campground: 3
  campus_building: 2
  candy_store: 2
  car_dealer: 18
  car_rental_agency: 2
  car_window_tinting: 1
  cardiologist: 1
  caterer: 4
  catholic_church: 4
  central_government_office: 1
  charity_organization: 2
  chicken_restaurant: 4
  childrens_clothing_store: 8
  chinese_restaurant: 2
  chiropractor: 1
  chocolatier: 2

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

# Get places and filter for research stations
reader = overturemaps.record_batch_reader('place', bbox=(-180,-90,180,-60))
if reader:
    tbl = reader.read_all()
    df = tbl.to_pandas()
    
    # Look for station/research in names
    names = df['names'].dropna().apply(lambda x: x.get('primary') if isinstance(x, dict) else '')
    
    # Filter for names containing 'station', 'base', 'research', etc.
    keywords = ['station', 'Station', 'base', 'Base', 'research', 'Research']
    for kw in keywords:
        matches = names[names.str.contains(kw, na=False)]
        if len(matches) > 0:
            print(f'\\n=== {kw} ({len(matches)} matches) ===')
            for i, name in matches.items():
                row = df.loc[i]
                cat = row.get('categories', {})
                if isinstance(cat, dict):
                    cat = cat.get('primary', '')
                print(f'  {name} | cat: {cat}')
" 2>&1

=== Station (13 matches) ===
  Master Bees Stationeries & Souvenirs | cat: souvenir_shop
  Brown Station, Antarctic Peninsula | cat: landmark_and_historical_building
  Brown Station | cat: landmark_and_historical_building
  Palmer Station, Antarctica | cat: home_developer
  New Lucena Municipal Police Station | cat: public_service_and_government
  Bellingshausen Russian Antarctic Station | cat: educational_research_institute
  Neumayer-Station II | cat: landmark_and_historical_building
  Fuel Tech Micro Gas Station & Dispensers | cat: industrial_equipment
  Plateau Station | cat: landmark_and_historical_building
  Pioneer - Gas Station | cat: gas_station
  Antarctic Kunlun Station | cat: landmark_and_historical_building
  COFFE Station Old town | cat: cafe
  Mario-Zucchelli-Station | cat: None

=== Base (13 matches) ===
  González Videla Antarctic Base | cat: airport
  Carlini Base | cat: arts_and_entertainment
  Base Marambio, Antartida Argentina | cat: central_government_office
  Marambio Base | cat: airport
  Base Antártica Marambio | cat: public_and_government_association
  Esperanza Base, Antarctica | cat: landmark_and_historical_building
  Base Esperanza, Antartida Argentina | cat: airport
  Base antarctique Mawson | cat: landmark_and_historical_building
  Base antártica Concordia | cat: train_station
  Base antarctique Concordia | cat: landmark_and_historical_building
  Base antarctique Dumont-d'Urville | cat: landmark_and_historical_building
  Base McMurdo | cat: landmark_and_historical_building
  Base antarctique McMurdo | cat: landmark_and_historical_building

=== Research (1 matches) ===
  Lakeview Clinical Research | cat: medical_research_and_development

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

# Get ALL places and look at geometry to find genuine antarctic stations
reader = overturemaps.record_batch_reader('place', bbox=(-180,-90,180,-60))
if reader:
    tbl = reader.read_all()
    df = tbl.to_pandas()
    
    # Parse the geometry column
    # It's stored as WKB binary
    from shapely import wkb
    geoms = tbl.column('geometry').to_pylist()
    
    # Build proper dataframe
    names_list = []
    ids_list = []
    cats_list = []
    lats = []
    lons = []
    
    for i in range(len(df)):
        name = df.iloc[i].get('names', {})
        if isinstance(name, dict):
            name = name.get('primary', '')
        
        geom = geoms[i]
        if geom:
            shp = wkb.loads(geom)
            if hasattr(shp, 'y'):
                lat = shp.y
                lon = shp.x
            else:
                continue
        else:
            continue
        
        cat = df.iloc[i].get('categories', {})
        if isinstance(cat, dict):
            cat = cat.get('primary', '')
        
        names_list.append(name)
        ids_list.append(df.iloc[i].get('id', ''))
        cats_list.append(cat)
        lats.append(lat)
        lons.append(lon)
    
    pdf = pd.DataFrame({'id': ids_list, 'name': names_list, 'category': cats_list, 'lat': lats, 'lon': lons})
    
    # Filter to actual antarctic (-60 to -90)
    pdf = pdf[pdf['lat'] <= -60]
    
    print(f'Total places with lat <= -60: {len(pdf)}')
    
    # Look for station-like names
    station_keywords = ['station', 'base', 'research', 'antarct', 'south pole', 'camp']
    for kw in station_keywords:
        matches = pdf[pdf['name'].str.contains(kw, case=False, na=False)]
        if len(matches) > 0:
            print(f'\\n=== \"{kw}\" ({len(matches)} matches) ===')
            for _, row in matches.iterrows():
                print(f'  {row[\"name\"]} | lat={row[\"lat\"]:.4f}, lon={row[\"lon\"]:.4f} | cat={row[\"category\"]}')
" 2>&1

Total places with lat <= -60: 2348

=== "station" (13 matches) ===
  Master Bees Stationeries & Souvenirs | lat=-75.1408, lon=-66.7969 | cat=souvenir_shop
  Brown Station, Antarctic Peninsula | lat=-64.8921, lon=-62.8724 | cat=landmark_and_historical_building
  Brown Station | lat=-64.8950, lon=-62.8700 | cat=landmark_and_historical_building
  Palmer Station, Antarctica | lat=-64.7743, lon=-64.0531 | cat=home_developer
  New Lucena Municipal Police Station | lat=-80.8717, lon=-61.3477 | cat=public_service_and_government
  Bellingshausen Russian Antarctic Station | lat=-62.1998, lon=-58.9624 | cat=educational_research_institute
  Neumayer-Station II | lat=-70.6500, lon=-8.2500 | cat=landmark_and_historical_building
  Fuel Tech Micro Gas Station & Dispensers | lat=-65.3393, lon=15.4688 | cat=industrial_equipment
  Plateau Station | lat=-79.2500, lon=40.5000 | cat=landmark_and_historical_building
  Pioneer - Gas Station | lat=-78.9098, lon=43.9264 | cat=gas_station
  Antarctic Kunlun Station | lat=-80.4169, lon=77.1161 | cat=landmark_and_historical_building
  COFFE Station Old town | lat=-84.6078, lon=116.7188 | cat=cafe
  Mario-Zucchelli-Station | lat=-74.6943, lon=164.1125 | cat=nan

=== "base" (13 matches) ===
  González Videla Antarctic Base | lat=-64.8234, lon=-62.8575 | cat=airport
  Carlini Base | lat=-62.2379, lon=-58.6668 | cat=arts_and_entertainment
  Base Marambio, Antartida Argentina | lat=-64.2413, lon=-56.6198 | cat=central_government_office
  Marambio Base | lat=-64.2410, lon=-56.6268 | cat=airport
  Base Antártica Marambio | lat=-64.2374, lon=-56.6235 | cat=public_and_government_association
  Esperanza Base, Antarctica | lat=-63.3977, lon=-56.9961 | cat=landmark_and_historical_building
  Base Esperanza, Antartida Argentina | lat=-63.3980, lon=-56.9974 | cat=airport
  Base antarctique Mawson | lat=-67.6026, lon=62.8736 | cat=landmark_and_historical_building
  Base antártica Concordia | lat=-75.1000, lon=123.3333 | cat=train_station
  Base antarctique Concordia | lat=-75.1000, lon=123.5580 | cat=landmark_and_historical_building
  Base antarctique Dumont-d'Urville | lat=-66.6628, lon=140.0013 | cat=landmark_and_historical_building
  Base McMurdo | lat=-77.8500, lon=166.6667 | cat=landmark_and_historical_building
  Base antarctique McMurdo | lat=-77.8500, lon=166.6667 | cat=landmark_and_historical_building

=== "research" (1 matches) ===
  Lakeview Clinical Research | lat=-64.4135, lon=175.7812 | cat=medical_research_and_development

=== "antarct" (34 matches) ===
  Ross Ice Shelf, Antarctica | lat=-70.6126, lon=-177.8906 | cat=landmark_and_historical_building
  West Antarctica | lat=-79.0000, lon=-100.0000 | cat=national_park
  Antarctic Peninsula | lat=-69.5000, lon=-65.0000 | cat=landmark_and_historical_building
  Mount Wilson (Antarctica) | lat=-68.4500, lon=-65.5500 | cat=mountain
  Errera Channel, Antarctica | lat=-64.6965, lon=-62.5853 | cat=landmark_and_historical_building
  Paradise Bay, Antarctic Peninsula | lat=-64.9078, lon=-62.9036 | cat=landmark_and_historical_building
  Brown Station, Antarctic Peninsula | lat=-64.8921, lon=-62.8724 | cat=landmark_and_historical_building
  González Videla Antarctic Base | lat=-64.8234, lon=-62.8575 | cat=airport
  Port Lockroy, Antarctic Peninsula | lat=-64.8247, lon=-63.4910 | cat=landmark_and_historical_building
  Mount William (Antarctica) | lat=-64.7833, lon=-63.6833 | cat=mountain
  Lemaire Channel, Antarctic Peninsula | lat=-65.1025, lon=-63.9820 | cat=landmark_and_historical_building
  Palmer Station, Antarctica | lat=-64.7743, lon=-64.0531 | cat=home_developer
  Mount Jackson (Antarctica) | lat=-71.3641, lon=-63.4122 | cat=mountain
  Esperanto Island, Antarctica | lat=-62.6414, lon=-60.3647 | cat=landmark_and_historical_building
  Cierva Cove, Antarctic Peninsula | lat=-64.1537, lon=-60.9055 | cat=landmark_and_historical_building
  Antarctica, Antarctic Circle | lat=-64.0536, lon=-60.9322 | cat=landmark_and_historical_building
  Mikkelsen Harbour, Antarctic Peninsula | lat=-63.8963, lon=-60.7654 | cat=landmark_and_historical_building
  Bellingshausen Russian Antarctic Station | lat=-62.1998, lon=-58.9624 | cat=educational_research_institute
  King George Bay (Antarctica) | lat=-62.1000, lon=-58.0833 | cat=landmark_and_historical_building
  Chapel of the Blessed Virgin of Lujan, Antarctica | lat=-64.2407, lon=-56.6284 | cat=church_cathedral
  Esperanza Base, Antarctica | lat=-63.3977, lon=-56.9961 | cat=landmark_and_historical_building
  British Antarctic Territory | lat=-75.0000, lon=-50.0000 | cat=landmark_and_historical_building
  Antarctica/Troll | lat=-72.0114, lon=2.5350 | cat=landmark_and_historical_building
  Lake Glubokoye (Antarctica) | lat=-67.6667, lon=45.8667 | cat=lake
  Beaver Lake (Antarctica) | lat=-70.7833, lon=68.3000 | cat=landmark_and_historical_building
  Nicholas Range (Antarctic) | lat=-66.6667, lon=55.6167 | cat=mountain
  Base antarctique Mawson | lat=-67.6026, lon=62.8736 | cat=landmark_and_historical_building
  East Antarctica | lat=-80.0000, lon=80.0000 | cat=landmark_and_historical_building
  Antarctic Kunlun Station | lat=-80.4169, lon=77.1161 | cat=landmark_and_historical_building
  Base antarctique Concordia | lat=-75.1000, lon=123.5580 | cat=landmark_and_historical_building
  Base antarctique Dumont-d'Urville | lat=-66.6628, lon=140.0013 | cat=landmark_and_historical_building
  Antarctic Plateau | lat=-77.0000, lon=150.0000 | cat=landmark_and_historical_building
  Lake Bonney (Antarctica) | lat=-77.7167, lon=162.4167 | cat=lake
  Base antarctique McMurdo | lat=-77.8500, lon=166.6667 | cat=landmark_and_historical_building

=== "camp" (10 matches) ===
  Dhacss Seaview Campus | lat=-80.1786, lon=-118.1243 | cat=preschool
  Noakhali Ideal Polytechnic Institute - Digital Campus | lat=-73.4284, lon=-79.4531 | cat=college_university
  Camp Education Society's Dr. Arvind B. Telang IHM Chinchwad Pune - 19 | lat=-77.9157, lon=-12.6562 | cat=college_university
  Cidahu Camping Ground | lat=-84.6735, lon=-6.3291 | cat=sporting_goods
  Campo Creek Vineyards | lat=-76.5168, lon=4.9219 | cat=winery
  The Mahanaim - God's Camp | lat=-72.4156, lon=11.3818 | cat=campground
  Vintage Cocktail Camper | lat=-84.6735, lon=18.9844 | cat=bar
  VU GIFT Campus Jhang | lat=-71.5249, lon=30.2344 | cat=college_university
  Escola Técnica de Teatro Professor José Gomes Campos | lat=-77.0168, lon=52.1274 | cat=art_school
  JRMSU Katipunan Campus CTEd-Laboratory High School | lat=-69.1798, lon=123.5915 | cat=middle_school

cd /work && python -c "
import overturemaps
help(overturemaps.geodataframe)
" 2>&1 | head -40

Help on function geodataframe in module overturemaps.core:

geodataframe(
    overture_type: str,
    bbox: BBox | tuple[float, float, float, float] | list[float] | None = None,
    release: str = None,
    connect_timeout: int = None,
    request_timeout: int = None,
    stac: bool = False
) -> GeoDataFrame
    Loads geoparquet for specified type into a geopandas dataframe

    Parameters
    ----------
    overture_type: type to load
    bbox: optional bounding box for data fetch (xmin, ymin, xmax, ymax)
    connect_timeout: optional connection timeout in seconds
    request_timeout: optional request timeout in seconds

    Returns
    -------
    GeoDataFrame with the optionally filtered theme data

cd /work && python -c "
import overturemaps
# Check if geodataframe function exists
print(type(overturemaps.geodataframe))
print(overturemaps.geodataframe.__doc__[:200])
" 2>&1

<class 'function'>

Loads geoparquet for specified type into a geopandas dataframe

Parameters
----------
overture_type: type to load
bbox: optional bounding box for data fetch (xmin, ymin, xmax, ymax)
connect_timeout:

cd /work && python -c "
import overturemaps
# Get all places as GeoDataFrame
gdf = overturemaps.geodataframe('place', bbox=(-180,-90,180,-60))
print(f'Total: {len(gdf)}')
print(gdf.columns.tolist())
print()
# Look at categories for places south of -60
cats = gdf['categories'].dropna()
print('Sample categories:')
for c in cats.head(10):
    print(c)
" 2>&1

Total: 2348
['id', 'geometry', 'categories', 'confidence', 'websites', 'emails', 'socials', 'phones', 'brand', 'addresses', 'names', 'sources', 'operating_status', 'basic_category', 'taxonomy', 'version', 'bbox']

Sample categories:
{'primary': 'information_technology_company', 'alternate': array(['professional_services'], dtype=object)}
{'primary': 'physical_therapy', 'alternate': array(['occupational_therapy', 'speech_therapist'], dtype=object)}
{'primary': 'health_and_medical', 'alternate': array(['pharmacy'], dtype=object)}
{'primary': 'school', 'alternate': None}
{'primary': 'farm', 'alternate': None}
{'primary': 'sports_wear', 'alternate': None}
{'primary': 'pet_groomer', 'alternate': None}
{'primary': 'professional_services', 'alternate': array(['arts_and_entertainment'], dtype=object)}
{'primary': 'real_estate', 'alternate': None}
{'primary': 'landmark_and_historical_building', 'alternate': array(['fountain'], dtype=object)}

cd /work && python -c "
import overturemaps

# Get land data for Antarctica - this should be cleaner
gdf_land = overturemaps.geodataframe('land', bbox=(-180,-90,180,-60))
print(f'Land features: {len(gdf_land)}')
print(gdf_land.columns.tolist())
print(gdf_land.head())
print(gdf_land.crs)
" 2>&1

Land features: 93367
['id', 'names', 'subtype', 'class', 'sources', 'source_tags', 'level', 'wikidata', 'surface', 'elevation', 'geometry', 'version', 'bbox']
                                     id  ...                                               bbox
0  23f89603-d14d-3f1b-97c5-314269e17921  ...  {'xmin': -180.0, 'xmax': -179.97145080566406, ...
1  ef0ba39b-922b-3790-b71c-b5238e8f4e9b  ...  {'xmin': -179.9757080078125, 'xmax': -179.9248...
2  46a1ad5c-4af5-32f8-b0f5-c416ec492555  ...  {'xmin': -179.95298767089844, 'xmax': -179.859...
3  c2519229-0a95-3761-a671-5a44ea2e2194  ...  {'xmin': -179.76327514648438, 'xmax': -179.697...
4  8848f703-84f7-3c4c-9b0c-8a93394dceac  ...  {'xmin': -179.80276489257812, 'xmax': -179.603...

[5 rows x 13 columns]
None

cd /work && python -c "
import overturemaps
gdf = overturemaps.geodataframe('place', bbox=(-180,-90,180,-60))
print(f'Total: {len(gdf)}')

# Filter points that are genuinely in Antarctica (south of -60, and reasonable lon)
# Many of these are clearly mis-geocoded. Let's filter by geometry centroid
gdf = gdf[gdf.geometry.y <= -60]

# Look at unique taxonomy fields
print('\\nBasic categories:')
print(gdf['basic_category'].value_counts().head(40))

print('\\n--- Names containing station/research/base ---')
mask = gdf['names'].apply(lambda x: x.get('primary','') if isinstance(x, dict) else '').str.contains('station|Station|base|Base|research|Research', na=False)
for _, row in gdf[mask].iterrows():
    name = row['names'].get('primary','') if isinstance(row['names'], dict) else ''
    cat = row['categories']
    if isinstance(cat, dict):
        cat = cat.get('primary','')
    basic = row.get('basic_category','')
    print(f'{name} | cat={cat} | basic={basic} | ({row.geometry.y:.4f}, {row.geometry.x:.4f})')
" 2>&1

Total: 2348

Basic categories:
basic_category
historic_site                       163
fast_food_restaurant                133
fashion_and_apparel_store           114
personal_or_beauty_service           96
restaurant                           89
mountain                             67
real_estate_service                  66
professional_service                 64
place_of_learning                    54
hotel                                40
hardware_home_and_garden_store       34
college_university                   29
casual_eatery                        29
home_service                         29
electronics_store                    28
resort                               26
specialty_school                     25
christian_place_of_worship           25
social_or_community_service          25
automotive_service                   25
media_service                        24
event_or_party_service               24
food_and_beverage_store              24
hospital                             21
government_office                    19
financial_service                    19
travel_service                       18
auto_dealer                          18
cafe                                 17
sport_or_fitness_facility            17
religious_organization               16
arts_crafts_and_hobby_store          16
corporate_or_business_office         15
vehicle_dealer                       15
building_or_construction_service     14
pharmacy_and_drug_store              13
technical_service                    13
gym                                  13
lodging                              13
coffee_shop                          12
Name: count, dtype: int64

--- Names containing station/research/base ---
Master Bees Stationeries & Souvenirs | cat=souvenir_shop | basic=specialty_store | (-75.1408, -66.7969)
Brown Station, Antarctic Peninsula | cat=landmark_and_historical_building | basic=historic_site | (-64.8921, -62.8724)
Brown Station | cat=landmark_and_historical_building | basic=historic_site | (-64.8950, -62.8700)
González Videla Antarctic Base | cat=airport | basic=airport | (-64.8234, -62.8575)
Palmer Station, Antarctica | cat=home_developer | basic=real_estate_service | (-64.7743, -64.0531)
New Lucena Municipal Police Station | cat=public_service_and_government | basic=nan | (-80.8717, -61.3477)
Carlini Base | cat=arts_and_entertainment | basic=nan | (-62.2379, -58.6668)
Bellingshausen Russian Antarctic Station | cat=educational_research_institute | basic=research_institute | (-62.1998, -58.9624)
Base Marambio, Antartida Argentina | cat=central_government_office | basic=government_office | (-64.2413, -56.6198)
Marambio Base | cat=airport | basic=airport | (-64.2410, -56.6268)
Base Antártica Marambio | cat=public_and_government_association | basic=government_office | (-64.2374, -56.6235)
Esperanza Base, Antarctica | cat=landmark_and_historical_building | basic=historic_site | (-63.3977, -56.9961)
Base Esperanza, Antartida Argentina | cat=airport | basic=airport | (-63.3980, -56.9974)
Neumayer-Station II | cat=landmark_and_historical_building | basic=historic_site | (-70.6500, -8.2500)
Fuel Tech Micro Gas Station & Dispensers | cat=industrial_equipment | basic=manufacturer | (-65.3393, 15.4688)
Plateau Station | cat=landmark_and_historical_building | basic=historic_site | (-79.2500, 40.5000)
Pioneer - Gas Station | cat=gas_station | basic=gas_station | (-78.9098, 43.9264)
Base antarctique Mawson | cat=landmark_and_historical_building | basic=historic_site | (-67.6026, 62.8736)
Antarctic Kunlun Station | cat=landmark_and_historical_building | basic=historic_site | (-80.4169, 77.1161)
COFFE Station Old town | cat=cafe | basic=cafe | (-84.6078, 116.7188)
Base antártica Concordia | cat=train_station | basic=train_station | (-75.1000, 123.3333)
Base antarctique Concordia | cat=landmark_and_historical_building | basic=historic_site | (-75.1000, 123.5580)
Base antarctique Dumont-d'Urville | cat=landmark_and_historical_building | basic=historic_site | (-66.6628, 140.0013)
Mario-Zucchelli-Station | cat=None | basic=nan | (-74.6943, 164.1125)
Base McMurdo | cat=landmark_and_historical_building | basic=historic_site | (-77.8500, 166.6667)
Base antarctique McMurdo | cat=landmark_and_historical_building | basic=historic_site | (-77.8500, 166.6667)
Lakeview Clinical Research | cat=medical_research_and_development | basic=research_institute | (-64.4135, 175.7812)

cd /work && python -c "
import overturemaps
gdf = overturemaps.geodataframe('place', bbox=(-180,-90,180,-60))
gdf = gdf[gdf.geometry.y <= -60]

# Check taxonomy
print('Sample taxonomy fields:')
tax = gdf['taxonomy'].dropna()
for t in tax.head(20):
    print(t)
print()
# Check categories more carefully
cats = gdf['categories'].dropna()
for c in cats.head(30):
    print(c)
" 2>&1

Sample taxonomy fields:
{'primary': 'information_technology_company', 'hierarchy': array(['services_and_business', 'business',
       'information_technology_company'], dtype=object), 'alternates': None}
{'primary': 'physical_therapy', 'hierarchy': array(['health_care', 'outpatient_care_facility',
       'physical_medicine_and_rehabilitation', 'physical_therapy'],
      dtype=object), 'alternates': None}
{'primary': 'health_care', 'hierarchy': array(['health_care'], dtype=object), 'alternates': None}
{'primary': 'school', 'hierarchy': array(['education', 'place_of_learning', 'school'], dtype=object), 'alternates': None}
{'primary': 'farm', 'hierarchy': array(['services_and_business', 'agricultural_service', 'farm'],
      dtype=object), 'alternates': None}
{'primary': 'sportswear_store', 'hierarchy': array(['shopping', 'specialty_store', 'sporting_goods_store',
       'sportswear_store'], dtype=object), 'alternates': None}
{'primary': 'pet_groomer', 'hierarchy': array(['lifestyle_services', 'animal_or_pet_service', 'pet_care_service',
       'pet_groomer'], dtype=object), 'alternates': None}
{'primary': 'professional_service', 'hierarchy': array(['services_and_business', 'professional_service'], dtype=object), 'alternates': None}
{'primary': 'real_estate_service', 'hierarchy': array(['services_and_business', 'real_estate_service'], dtype=object), 'alternates': None}
{'primary': 'historic_site', 'hierarchy': array(['cultural_and_historic', 'historic_site'], dtype=object), 'alternates': None}
{'primary': 'shopping', 'hierarchy': array(['shopping'], dtype=object), 'alternates': None}
{'primary': 'tour_operator', 'hierarchy': array(['travel_and_transportation', 'travel_service', 'tour_operator'],
      dtype=object), 'alternates': None}
{'primary': 'beauty_salon', 'hierarchy': array(['lifestyle_services', 'personal_or_beauty_service', 'beauty_salon'],
      dtype=object), 'alternates': None}
{'primary': 'high_school', 'hierarchy': array(['education', 'place_of_learning', 'school', 'high_school'],
      dtype=object), 'alternates': None}
{'primary': 'beauty_salon', 'hierarchy': array(['lifestyle_services', 'personal_or_beauty_service', 'beauty_salon'],
      dtype=object), 'alternates': None}
{'primary': 'beauty_supply_store', 'hierarchy': array(['shopping', 'specialty_store', 'personal_care_and_beauty_store',
       'beauty_supply_store'], dtype=object), 'alternates': None}
{'primary': 'professional_service', 'hierarchy': array(['services_and_business', 'professional_service'], dtype=object), 'alternates': None}
{'primary': 'antique_store', 'hierarchy': array(['shopping', 'second_hand_store', 'antique_store'], dtype=object), 'alternates': None}
{'primary': 'event_photography_service', 'hierarchy': array(['services_and_business', 'media_service', 'photography_service',
       'event_photography_service'], dtype=object), 'alternates': None}
{'primary': 'medical_service_organization', 'hierarchy': array(['health_care', 'medical_service', 'medical_service_organization'],
      dtype=object), 'alternates': None}

{'primary': 'information_technology_company', 'alternate': array(['professional_services'], dtype=object)}
{'primary': 'physical_therapy', 'alternate': array(['occupational_therapy', 'speech_therapist'], dtype=object)}
{'primary': 'health_and_medical', 'alternate': array(['pharmacy'], dtype=object)}
{'primary': 'school', 'alternate': None}
{'primary': 'farm', 'alternate': None}
{'primary': 'sports_wear', 'alternate': None}
{'primary': 'pet_groomer', 'alternate': None}
{'primary': 'professional_services', 'alternate': array(['arts_and_entertainment'], dtype=object)}
{'primary': 'real_estate', 'alternate': None}
{'primary': 'landmark_and_historical_building', 'alternate': array(['fountain'], dtype=object)}
{'primary': 'retail', 'alternate': None}
{'primary': 'tours', 'alternate': None}
{'primary': 'beauty_salon', 'alternate': array(['beauty_and_spa'], dtype=object)}
{'primary': 'high_school', 'alternate': array(['education', 'school'], dtype=object)}
{'primary': 'beauty_salon', 'alternate': array(['wig_store', 'beauty_and_spa'], dtype=object)}
{'primary': 'cosmetic_and_beauty_supplies', 'alternate': None}
{'primary': 'professional_services', 'alternate': None}
{'primary': 'antique_store', 'alternate': array(['restaurant'], dtype=object)}
{'primary': 'event_photography', 'alternate': array(['professional_services', 'event_planning'], dtype=object)}
{'primary': 'medical_service_organizations', 'alternate': array(['naturopathic_holistic'], dtype=object)}
{'primary': 'jewelry_store', 'alternate': array(['b2b_jewelers'], dtype=object)}
{'primary': 'boutique', 'alternate': None}
{'primary': 'brazilian_restaurant', 'alternate': array(['restaurant', 'tapas_bar'], dtype=object)}
{'primary': 'professional_services', 'alternate': array(['event_photography'], dtype=object)}
{'primary': 'boutique', 'alternate': array(['clothing_store', 'womens_clothing_store'], dtype=object)}
{'primary': 'hospital', 'alternate': array(['doctor'], dtype=object)}
{'primary': 'restaurant', 'alternate': array(['fast_food_restaurant', 'breakfast_and_brunch_restaurant'],
      dtype=object)}
{'primary': 'optometrist', 'alternate': array(['eyewear_and_optician'], dtype=object)}
{'primary': 'college_university', 'alternate': None}
{'primary': 'furniture_store', 'alternate': None}

cd /work && python -c "
import overturemaps

# Check division_area for Antarctica
gdf = overturemaps.geodataframe('division_area', bbox=(-180,-90,180,-60))
print(f'Division areas: {len(gdf)}')
print(gdf.columns.tolist())
print(gdf.head(3))
# Check names
for _, row in gdf.iterrows():
    name = row.get('names', {})
    if isinstance(name, dict):
        name = name.get('primary', '')
    print(f'  {name}')
" 2>&1 | head -30

Division areas: 3
['id', 'geometry', 'country', 'sources', 'subtype', 'admin_level', 'class', 'names', 'is_land', 'is_territorial', 'region', 'division_id', 'version', 'bbox']
                                     id  ...                                               bbox
0  03d5e554-ceb7-47b4-abba-c6134bc25395  ...  {'xmin': -68.13143157958984, 'xmax': -68.11316...
1  43be04ce-10e0-4df6-ba90-3e410a9fe40f  ...  {'xmin': -180.0, 'xmax': 180.0, 'ymin': -85.05...
2  d0d6962c-5bb5-459b-a2a0-9316e3e5e30a  ...  {'xmin': -180.0, 'xmax': 180.0, 'ymin': -85.05...

[3 rows x 14 columns]
  Rothera Research Station
  Antarctica
  Antarctica

cd /work && python -c "
import overturemaps
# Get division_area for Antarctica boundary
gdf_div = overturemaps.geodataframe('division_area', bbox=(-180,-90,180,-60))
# The Antarctica polygon is the one with names primary='Antarctica'
for _, row in gdf_div.iterrows():
    name = row.get('names', {})
    if isinstance(name, dict):
        name = name.get('primary', '')
    subtype = row.get('subtype', '')
    print(f'{name} | subtype={subtype} | geom type={row.geometry.geom_type} | area approx')
    if name == 'Antarctica':
        antarctica = row.geometry
        print(f'  Antarctica geometry type: {antarctica.geom_type}')
        if antarctica.geom_type == 'MultiPolygon':
            print(f'  Num polygons: {len(antarctica.geoms)}')
            total = sum(p.area for p in antarctica.geoms)
            print(f'  Total area (deg^2): {total:.2f}')
" 2>&1

Rothera Research Station | subtype=locality | geom type=Polygon | area approx
Antarctica | subtype=country | geom type=Polygon | area approx
  Antarctica geometry type: Polygon
Antarctica | subtype=country | geom type=MultiPolygon | area approx
  Antarctica geometry type: MultiPolygon
  Num polygons: 2003
  Total area (deg^2): 4879.53

cd /work && python -c "
import overturemaps
# Get ALL places in Antarctica region and look at taxonomy hierarchy for research/station keywords
gdf = overturemaps.geodataframe('place', bbox=(-180,-90,180,-60))
gdf = gdf[gdf.geometry.y <= -60]

# Look through taxonomy hierarchies for anything research-related
research_matches = []
for idx, row in gdf.iterrows():
    name = row['names'].get('primary','') if isinstance(row['names'], dict) else ''
    tax = row.get('taxonomy')
    if isinstance(tax, dict):
        hierarchy = tax.get('hierarchy', [])
        if hierarchy is not None and any('research' in str(h).lower() or 'station' in str(h).lower() for h in hierarchy):
            research_matches.append((name, row.geometry.y, row.geometry.x, tax))

print(f'Research/station taxonomy matches: {len(research_matches)}')
for name, lat, lon, tax in research_matches:
    print(f'  {name} | ({lat:.4f}, {lon:.4f}) | primary={tax[\"primary\"]}')

print()
# Also check all names more broadly for known stations
station_names = [
    'McMurdo', 'Amundsen-Scott', 'South Pole', 'Palmer', 'Scott Base',
    'Mawson', 'Davis', 'Casey', 'Macquarie',
    'Syowa', 'Dumont', 'Concordia', 'Vostok', 
    'Bellingshausen', 'Mirny', 'Novolazarevskaya', 'Progress',
    'Neumayer', 'Troll', 'Halley', 'Rothera',
    'Esperanza', 'Marambio', 'Carlini', 'Orcadas', 'San Martin', 'Belgrano',
    'Mario Zucchelli', 'Zucchelli',
    'Maitri', 'Bharati', 'Jang Bogo', 'King Sejong',
    'Great Wall', 'Zhongshan', 'Kunlun', 'Taishan', 'Qinling',
    'Artigas', 'Comandante Ferraz', 'Ferraz',
    'Vernadsky', 'Ohridski', 'Mendel', 'Aboa', 'Wasa', 'Svea', 'Tor',
    'Gabriel de Castilla', 'Juan Carlos I',
    'Princess Elisabeth', 'Siple', 'Byrd', 'Plateau'
]

for ns in station_names:
    mask = gdf['names'].apply(lambda x: x.get('primary','') if isinstance(x, dict) else '').str.contains(ns, case=False, na=False)
    matches = gdf[mask]
    for _, row in matches.iterrows():
        name = row['names'].get('primary','') if isinstance(row['names'], dict) else ''
        print(f'{name} | ({row.geometry.y:.4f}, {row.geometry.x:.4f})')
" 2>&1

Research/station taxonomy matches: 10
  Bellingshausen Russian Antarctic Station | (-62.1998, -58.9624) | primary=educational_research_institute
  Fuzion Radio Medellin | (-78.1437, -45.1907) | primary=radio_station
  Carrefour Posto João Pessoa | (-70.8569, -34.8472) | primary=gas_station
  Carrefour Posto Recife | (-80.4079, -34.9103) | primary=gas_station
  Pioneer | (-78.9098, 43.9264) | primary=gas_station
  Pioneer - Gas Station | (-78.9098, 43.9264) | primary=gas_station
  Base antártica Concordia | (-75.1000, 123.3333) | primary=train_station
  Rádio Alerta Penedo | (-68.2059, 127.8289) | primary=radio_station
  Seaoil Dangcagan | (-80.4574, 135.7801) | primary=gas_station
  Lakeview Clinical Research | (-64.4135, 175.7812) | primary=medical_research_institute

McMurdo General Hospital | (-77.8468, 166.6693)
Base McMurdo | (-77.8500, 166.6667)
Polární stanice McMurdo | (-77.8500, 166.6667)
Base antarctique McMurdo | (-77.8500, 166.6667)
Palmer Station, Antarctica | (-64.7743, -64.0531)
Base antarctique Mawson | (-67.6026, 62.8736)
Base antarctique Dumont-d'Urville | (-66.6628, 140.0013)
Base antártica Concordia | (-75.1000, 123.3333)
Base antarctique Concordia | (-75.1000, 123.5580)
Lake Vostok | (-77.5000, 106.0000)
Vostok İstasyonu | (-78.4644, 106.8373)
Bellingshausen Russian Antarctic Station | (-62.1998, -58.9624)
Neumayer-Station II | (-70.6500, -8.2500)
Troll Airfield | (-71.9572, 2.4667)
Antarctica/Troll | (-72.0114, 2.5350)
Esperanza Base, Antarctica | (-63.3977, -56.9961)
Base Esperanza, Antartida Argentina | (-63.3980, -56.9974)
Base Marambio, Antartida Argentina | (-64.2413, -56.6198)
Marambio Base | (-64.2410, -56.6268)
Base Antártica Marambio | (-64.2374, -56.6235)
Carlini Base | (-62.2379, -58.6668)
Mario-Zucchelli-Station | (-74.6943, 164.1125)
Antarctic Kunlun Station | (-80.4169, 77.1161)
Chittagong Victory National School-CVNS | (-80.4166, -175.3833)
Suzuki Nilphamari - Lahin Motors | (-65.7744, -121.3709)
Discount Hardware Store | (-74.9594, -118.8281)
Everything Store | (-75.8400, -100.1338)
AKS Manufactory | (-61.5202, -97.7976)
In Aeternum Historia | (-84.1257, -94.9229)
Mitra Fajar Ciawi " Bengkel Resmi Sepeda Motor Honda " | (-76.1849, -87.6522)
Canoon Store | (-71.0228, -81.8109)
Toronto Cosmetic Surgery Institute | (-79.3815, -79.3815)
شركة يوني فليكس للمعدات والمستلزمات الطبية - Uniflex Medical Supply Store | (-65.2936, -75.2316)
ร้านข้าวแกงฮาริษา Harisa's Curries Rice Factory | (-70.0806, -65.2176)
Lihor Store Fan | (-66.3728, -62.0934)
Concretiza Lar Construtora E Incorporadora | (-63.0673, -59.9706)
British Antarctic Territory | (-75.0000, -50.0000)
Consultoría Psicológica Especializada de Monclova Psic. Aarón Hernández | (-82.5591, -40.4187)
Generika Drugstore - ZDN Bagting Dapitan Page | (-77.6177, -42.7094)
Carrefour Hiper Recife Torre | (-80.4079, -34.9103)
Pastor Jorge | (-73.2722, -21.4893)
Nails By Hilary Torres M | (-69.0986, -28.9251)
Famous Forum of Worldwide Collaborators LLP | (-68.5652, -26.4430)
Javastasia Distributors Halaal Meats | (-61.6064, -30.9375)
N9store.ro | (-63.4702, -42.5968)
La Combo Tortuga | (-64.4728, -40.7812)
Storybook Imaging | (-65.6583, -5.6250)
Technology Store | (-61.2725, -11.2557)
Alessandra Oliveira corretora de imóveis rj | (-61.4396, -18.8793)
Robin Rolon, Mary Kay Independent Sales Director | (-64.9036, -16.0130)
Vumeer el Mentor | (-67.2195, -13.8102)
Babul Yaman Store | (-82.0149, -17.7061)
Reis Motors | (-75.0369, 0.5273)
Grace-Care Laboratory Services and Supplies | (-78.4089, -0.4839)
Yamu Store III | (-84.5414, 1.4062)
Jj'sanm Music Tutorial Center | (-70.0807, 18.5354)
Dani Motors | (-60.0484, 14.4141)
Delta Power Generators | (-64.0776, 11.7967)
Timsun Franchise Bayan Lepas - West Motor Racing Centre | (-77.7784, 17.3689)
Nadia Pereira - Corretora de Imóveis | (-64.9731, 27.5568)
RT Power Motor | (-67.6134, 29.8849)
King World Product Store | (-60.4919, 29.7141)
BOSS Menswear Store | (-86.8161, 36.1071)
Extra Space Storage | (-77.0941, 38.8842)
Extra Space Storage | (-77.4239, 38.9075)
Ashford Self Storage Hull Ltd | (-76.8408, 40.7812)
HomeVestor PH | (-82.7436, 43.0545)
Tractor Zetor | (-76.0161, 50.6250)
Pizza Salvatoré | (-75.5072, 46.5485)
Pizza Salvatoré | (-78.1316, 48.5721)
Pizza Salvatoré | (-77.8300, 48.0982)
Pizza Salvatoré | (-79.2000, 48.8055)
Pizza Salvatoré | (-79.0067, 48.2268)
Pizza Salvatoré | (-75.8088, 45.4189)
Pizza Salvatoré | (-75.6745, 45.4896)
Pizza Salvatoré | (-75.4362, 45.5534)
Pizza Salvatoré | (-74.2186, 45.2646)
Pizza Salvatoré | (-74.1208, 45.2724)
Pizza Salvatoré | (-73.8604, 45.3081)
Pizza Salvatoré | (-73.8508, 45.4915)
Pizza Salvatoré | (-73.7963, 45.4849)
Pizza Salvatoré | (-73.9428, 45.5280)
Pizza Salvatoré | (-74.0208, 45.3783)
Pizza Salvatoré | (-74.6051, 45.6107)
Pizza Salvatoré | (-74.3313, 45.6563)
Pizza Salvatoré | (-74.2761, 46.0454)
Pizza Salvatoré | (-74.1314, 45.9495)
Pizza Salvatoré | (-73.8122, 45.7659)
Pizza Salvatoré | (-74.0683, 45.8554)
Pizza Salvatoré | (-74.1489, 45.8933)
Pizza Salvatoré | (-74.0090, 45.7961)
Pizza Salvatoré | (-74.0916, 45.7029)
Pizza Salvatoré | (-73.9336, 45.6959)
Pizza Salvatoré | (-73.8278, 45.6396)
Pizza Salvatoré | (-73.8763, 45.5761)
Pizza Salvatoré | (-73.6998, 45.5776)
Pizza Salvatoré | (-73.7354, 45.6107)
Pizza Salvatoré | (-73.7572, 45.6672)
Pizza Salvatoré | (-73.6569, 45.7189)
Pizza Salvatoré | (-73.6100, 45.7429)
Pizza Salvatoré | (-73.5726, 45.5839)
Pizza Salvatoré | (-73.4357, 45.5904)
Pizza Salvatoré | (-73.3261, 45.5843)
Pizza Salvatoré | (-73.4692, 45.7536)
Pizza Salvatoré | (-73.4314, 45.6865)
Pizza Salvatoré | (-73.4255, 45.7589)
Pizza Salvatoré | (-73.2982, 45.8941)
Pizza Salvatoré | (-73.7027, 46.0356)
Pizza Salvatoré | (-73.4462, 46.0395)
Pizza Salvatoré | (-73.1016, 46.0413)
Pizza Salvatoré | (-72.9588, 45.6230)
Pizza Salvatoré | (-73.0165, 45.4149)
Pizza Salvatoré | (-72.9898, 45.2866)
Pizza Salvatoré | (-73.2695, 45.3381)
Pizza Salvatoré | (-73.2131, 45.5555)
Pizza Salvatoré | (-73.2888, 45.5295)
Pizza Salvatoré | (-73.4743, 45.4091)
Pizza Salvatoré | (-73.4862, 45.4996)
Pizza Salvatoré | (-73.4313, 45.4770)
Pizza Salvatoré | (-73.4408, 45.5120)
Pizza Salvatoré | (-73.6991, 45.5384)
Pizza Salvatoré | (-73.5667, 45.3890)
Pizza Salvatoré | (-73.6117, 45.2670)
Pizza Salvatoré | (-72.1301, 45.2762)
Pizza Salvatoré | (-71.9961, 45.3674)
Pizza Salvatoré | (-71.9407, 45.3974)
Pizza Salvatoré | (-71.8926, 45.3762)
Pizza Salvatoré | (-71.8692, 45.4083)
Pizza Salvatoré | (-72.7474, 45.2019)
Pizza Salvatoré | (-72.7533, 45.4138)
Pizza Salvatoré | (-72.5707, 45.6459)
Pizza Salvatoré | (-72.5046, 45.8880)
Pizza Salvatoré | (-72.5109, 46.2766)
Pizza Salvatoré | (-71.9477, 46.0572)
Pizza Salvatoré | (-71.9283, 45.7712)
Pizza Salvatoré | (-71.7680, 46.2154)
Pizza Salvatoré | (-71.3149, 46.1018)
Pizza Salvatoré | (-71.8025, 45.1337)
Pizza Salvatoré | (-70.9033, 45.5898)
Pizza Salvatoré | (-70.6936, 46.1303)
Pizza Salvatoré | (-70.7747, 46.2106)
Pizza Salvatoré | (-70.5664, 46.9811)
Pizza Salvatoré | (-70.5161, 47.4431)
Pizza Salvatoré | (-70.0413, 47.3714)
Pizza Salvatoré | (-69.5480, 47.8267)
Pizza Salvatoré | (-68.5012, 48.4561)
Pizza Salvatoré | (-68.2076, 48.5943)
Pizza Salvatoré | (-67.5111, 48.8527)
Pizza Salvatoré | (-68.2562, 49.2003)
Pizza Salvatoré | (-72.2313, 48.8805)
Pizza Salvatoré | (-72.2305, 48.5234)
Pizza Salvatoré | (-71.6445, 48.5450)
Pizza Salvatoré | (-71.2128, 48.4095)
Pizza Salvatoré | (-71.0943, 48.4517)
Pizza Salvatoré | (-71.0644, 48.3983)
Pizza Salvatoré | (-70.9488, 47.0135)
Pizza Salvatoré | (-71.3342, 46.9120)
Pizza Salvatoré | (-71.1907, 46.8988)
Pizza Salvatoré | (-71.1967, 46.8749)
Pizza Salvatoré | (-71.2455, 46.8382)
Pizza Salvatoré | (-71.2184, 46.8125)
Pizza Salvatoré | (-71.2324, 46.8116)
Pizza Salvatoré | (-71.2769, 46.8705)
Pizza Salvatoré | (-71.2784, 46.8329)
Pizza Salvatoré | (-71.2820, 46.7856)
Pizza Salvatoré | (-71.3511, 46.7941)
Pizza Salvatoré | (-71.3001, 46.7188)
Pizza Salvatoré | (-71.2086, 46.7308)
Pizza Salvatoré | (-71.1489, 46.8155)
Pizza Salvatoré | (-70.9772, 46.6324)
Pizza Salvatoré | (-71.0284, 46.4482)
Pizza Salvatoré | (-71.6367, 46.5375)
Pizza Salvatoré | (-71.7272, 46.6816)
Pizza Salvatoré | (-71.8337, 46.8894)
Pizza Salvatoré | (-71.6944, 46.7486)
Pizza Salvatoré | (-71.4401, 46.7483)
Pizza Salvatoré | (-71.4104, 46.8581)
Pizza Salvatoré | (-72.5557, 46.3708)
Pizza Salvatoré | (-72.5942, 46.3628)
Pizza Salvatoré | (-72.5897, 46.3360)
Pizza Salvatoré | (-72.7469, 46.5501)
Pizza Salvatoré | (-72.7842, 47.4376)
World Store | (-67.4755, 52.8013)
Pizza Salvatoré | (-66.3819, 50.2175)
Pizza Salvatoré | (-65.6829, 47.6410)
Pizza Salvatoré | (-65.4902, 47.0309)
Pizza Salvatoré | (-64.7342, 46.0865)
Pizza Salvatoré | (-64.8356, 46.1141)
Pizza Salvatoré | (-66.6472, 45.9763)
Pizza Salvatoré | (-66.0155, 45.3093)
Pizza Salvatoré | (-63.6763, 44.7646)
Ténor store | (-61.8563, 66.8828)
Nissan Pampanga by Maica Torres | (-63.8013, 57.1124)
Forland KpK Motors | (-72.3400, 57.9277)
N'Zemmula - ristorante siciliano | (-68.8873, 66.4453)
Nueva Ecija Doctors Hospital | (-81.4142, 73.1013)
Lori Makiya, Realtor | (-63.1742, 80.5643)
Melissa Isaac Realtor | (-64.2389, 83.2983)
Me_2 Store | (-62.3139, 88.2532)
Victors motor driving school | (-67.8553, 98.5670)
T-Store | (-78.0165, 98.7891)
Bandith Card Store | (-72.6176, 112.5000)
JRMSU Katipunan Campus CTEd-Laboratory High School | (-69.1798, 123.5915)
Sunstar Motor Jepara | (-73.1861, 127.6172)
Good Mates Removals & Storage | (-63.4702, 123.9815)
Kimstore | (-68.9110, 118.8281)
Nadhi/நதி - The Online Saree Store | (-80.2502, 151.8750)
North Store | (-70.0807, 164.1299)
Kimpich Store | (-72.9420, 170.5701)
Sales Store | (-61.8769, 169.8047)
Plateau Station | (-79.2500, 40.5000)
EEC Paroisse Plateau Pointe-Noire1 | (-74.3470, 62.5314)
Antarctic Plateau | (-77.0000, 150.0000)

cd /work && python -c "
import overturemaps
# Check other overture types available
types = overturemaps.get_all_overture_types()
print('All types:', types)
" 2>&1

cd /work && python -c "
import overturemaps
# Let's look at division_boundary for Antarctica
gdf = overturemaps.geodataframe('division_boundary', bbox=(-180,-90,180,-60))
print(f'Division boundaries: {len(gdf)}')
print(gdf.columns.tolist())
if len(gdf) > 0:
    for _, row in gdf.iterrows():
        name = row.get('names', {})
        if isinstance(name, dict):
            name = name.get('primary', '')
        print(f'  {name} | subtype={row.get(\"subtype\",\"\")} | admin_level={row.get(\"admin_level\",\"\")}')
" 2>&1

cd /work && python -c "
import overturemaps
# Check all places more carefully - look for research stations by taxonomy and category
gdf = overturemaps.geodataframe('place', bbox=(-180,-90,180,-60))
gdf = gdf[gdf.geometry.y <= -60]

# Get a broader list - anything with 'station' in name, or research/station categories
# Also look for any place with name containing known station names
known = ['mcmurdo', 'amundsen', 'scott', 'palmer', 'mawson', 'davis', 'casey', 
         'syowa', 'dumont', 'concordia', 'vostok', 'mirny', 'novolazarevskaya',
         'bellingshausen', 'neumayer', 'troll', 'halley', 'rothera',
         'esperanza', 'marambio', 'carlini', 'zucchelli', 'maitri', 'bharati',
         'jang bogo', 'king sejong', 'great wall', 'zhongshan', 'kunlun',
         'ferraz', 'vernadsky', 'ohridski', 'mendel', 'aboa', 'wasa',
         'princess elisabeth', 'siple', 'byrd', 'plateau', 'progress',
         'san martin', 'belgrano', 'orcadas', 'gabriel de castilla',
         'juan carlos', 'artigas', 'qinling', 'taishan', 'svea', 'tor',
         'brown station', 'gonzález videla']

all_names = gdf['names'].apply(lambda x: x.get('primary','') if isinstance(x, dict) else '')
name_lower = all_names.str.lower()

found = {}
for n in known:
    mask = name_lower.str.contains(n, na=False)
    matches = gdf[mask]
    for idx, row in matches.iterrows():
        name = row['names'].get('primary','') if isinstance(row['names'], dict) else ''
        key = (round(row.geometry.y, 1), round(row.geometry.x, 1))
        if key not in found:
            found[key] = set()
        found[key].add(name)

print(f'Found {len(found)} unique locations:')
for (lat, lon), names in sorted(found.items()):
    print(f'  ({lat:.1f}, {lon:.1f}): {', '.join(sorted(names))}')
" 2>&1

Found 195 unique locations:
  (-86.8, 36.1): BOSS Menswear Store
  (-84.5, 1.4): Yamu Store III
  (-84.1, -94.9): In Aeternum Historia
  (-82.7, 43.1): HomeVestor PH
  (-82.6, -40.4): Consultoría Psicológica Especializada de Monclova Psic. Aarón Hernández
  (-82.0, -17.7): Babul Yaman Store
  (-81.4, 73.1): Nueva Ecija Doctors Hospital
  (-80.4, -175.4): Chittagong Victory National School-CVNS
  (-80.4, -34.9): Carrefour Hiper Recife Torre
  (-80.4, 77.1): Antarctic Kunlun Station
  (-80.3, 151.9): Nadhi/நதி - The Online Saree Store
  (-79.4, -79.4): Toronto Cosmetic Surgery Institute
  (-79.2, 40.5): Plateau Station
  (-79.2, 48.8): Pizza Salvatoré
  (-79.0, 48.2): Pizza Salvatoré
  (-78.5, 106.8): Vostok İstasyonu
  (-78.4, -0.5): Grace-Care Laboratory Services and Supplies
  (-78.1, 48.6): Pizza Salvatoré
  (-78.0, 98.8): T-Store
  (-77.8, 17.4): Timsun Franchise Bayan Lepas - West Motor Racing Centre
  (-77.8, 48.1): Pizza Salvatoré
  (-77.8, 166.7): Base McMurdo, Base antarctique McMurdo, McMurdo General Hospital, Polární stanice McMurdo
  (-77.6, -42.7): Generika Drugstore - ZDN Bagting Dapitan Page
  (-77.5, 106.0): Lake Vostok
  (-77.4, 38.9): Extra Space Storage
  (-77.1, 38.9): Extra Space Storage
  (-77.0, 150.0): Antarctic Plateau
  (-76.8, 40.8): Ashford Self Storage Hull Ltd
  (-76.2, -87.7): Mitra Fajar Ciawi " Bengkel Resmi Sepeda Motor Honda "
  (-76.0, 50.6): Tractor Zetor
  (-75.8, -100.1): Everything Store
  (-75.8, 45.4): Pizza Salvatoré
  (-75.7, 45.5): Pizza Salvatoré
  (-75.5, 46.5): Pizza Salvatoré
  (-75.4, 45.6): Pizza Salvatoré
  (-75.1, 123.3): Base antártica Concordia
  (-75.1, 123.6): Base antarctique Concordia
  (-75.0, -118.8): Discount Hardware Store
  (-75.0, -50.0): British Antarctic Territory
  (-75.0, 0.5): Reis Motors
  (-74.7, 164.1): Mario-Zucchelli-Station
  (-74.6, 45.6): Pizza Salvatoré
  (-74.3, 45.7): Pizza Salvatoré
  (-74.3, 46.0): Pizza Salvatoré
  (-74.3, 62.5): EEC Paroisse Plateau Pointe-Noire1
  (-74.2, 45.3): Pizza Salvatoré
  (-74.1, 45.3): Pizza Salvatoré
  (-74.1, 45.7): Pizza Salvatoré
  (-74.1, 45.9): Pizza Salvatoré
  (-74.0, 45.4): Pizza Salvatoré
  (-74.0, 45.8): Pizza Salvatoré
  (-73.9, 45.3): Pizza Salvatoré
  (-73.9, 45.5): Pizza Salvatoré
  (-73.9, 45.6): Pizza Salvatoré
  (-73.9, 45.7): Pizza Salvatoré
  (-73.8, 45.5): Pizza Salvatoré
  (-73.8, 45.6): Pizza Salvatoré
  (-73.8, 45.7): Pizza Salvatoré
  (-73.8, 45.8): Pizza Salvatoré
  (-73.7, 45.5): Pizza Salvatoré
  (-73.7, 45.6): Pizza Salvatoré
  (-73.7, 45.7): Pizza Salvatoré
  (-73.7, 46.0): Pizza Salvatoré
  (-73.6, 45.3): Pizza Salvatoré
  (-73.6, 45.4): Pizza Salvatoré
  (-73.6, 45.6): Pizza Salvatoré
  (-73.6, 45.7): Pizza Salvatoré
  (-73.5, 45.4): Pizza Salvatoré
  (-73.5, 45.5): Pizza Salvatoré
  (-73.5, 45.8): Pizza Salvatoré
  (-73.4, 45.5): Pizza Salvatoré
  (-73.4, 45.6): Pizza Salvatoré
  (-73.4, 45.7): Pizza Salvatoré
  (-73.4, 45.8): Pizza Salvatoré
  (-73.4, 46.0): Pizza Salvatoré
  (-73.3, -21.5): Pastor Jorge
  (-73.3, 45.3): Pizza Salvatoré
  (-73.3, 45.5): Pizza Salvatoré
  (-73.3, 45.6): Pizza Salvatoré
  (-73.3, 45.9): Pizza Salvatoré
  (-73.2, 45.6): Pizza Salvatoré
  (-73.2, 127.6): Sunstar Motor Jepara
  (-73.1, 46.0): Pizza Salvatoré
  (-73.0, 45.3): Pizza Salvatoré
  (-73.0, 45.4): Pizza Salvatoré
  (-73.0, 45.6): Pizza Salvatoré
  (-72.9, 170.6): Kimpich Store
  (-72.8, 45.4): Pizza Salvatoré
  (-72.8, 47.4): Pizza Salvatoré
  (-72.7, 45.2): Pizza Salvatoré
  (-72.7, 46.6): Pizza Salvatoré
  (-72.6, 45.6): Pizza Salvatoré
  (-72.6, 46.3): Pizza Salvatoré
  (-72.6, 46.4): Pizza Salvatoré
  (-72.6, 112.5): Bandith Card Store
  (-72.5, 45.9): Pizza Salvatoré
  (-72.5, 46.3): Pizza Salvatoré
  (-72.3, 57.9): Forland KpK Motors
  (-72.2, 48.5): Pizza Salvatoré
  (-72.2, 48.9): Pizza Salvatoré
  (-72.1, 45.3): Pizza Salvatoré
  (-72.0, 2.5): Antarctica/Troll, Troll Airfield
  (-72.0, 45.4): Pizza Salvatoré
  (-71.9, 45.4): Pizza Salvatoré
  (-71.9, 45.8): Pizza Salvatoré
  (-71.9, 46.1): Pizza Salvatoré
  (-71.8, 45.1): Pizza Salvatoré
  (-71.8, 46.2): Pizza Salvatoré
  (-71.8, 46.9): Pizza Salvatoré
  (-71.7, 46.7): Pizza Salvatoré
  (-71.6, 46.5): Pizza Salvatoré
  (-71.6, 48.5): Pizza Salvatoré
  (-71.4, 46.7): Pizza Salvatoré
  (-71.4, 46.8): Pizza Salvatoré
  (-71.4, 46.9): Pizza Salvatoré
  (-71.3, 46.1): Pizza Salvatoré
  (-71.3, 46.7): Pizza Salvatoré
  (-71.3, 46.8): Pizza Salvatoré
  (-71.3, 46.9): Pizza Salvatoré
  (-71.2, 46.7): Pizza Salvatoré
  (-71.2, 46.8): Pizza Salvatoré
  (-71.2, 46.9): Pizza Salvatoré
  (-71.2, 48.4): Pizza Salvatoré
  (-71.1, 46.8): Pizza Salvatoré
  (-71.1, 48.4): Pizza Salvatoré
  (-71.1, 48.5): Pizza Salvatoré
  (-71.0, -81.8): Canoon Store
  (-71.0, 46.4): Pizza Salvatoré
  (-71.0, 46.6): Pizza Salvatoré
  (-70.9, 45.6): Pizza Salvatoré
  (-70.9, 47.0): Pizza Salvatoré
  (-70.8, 46.2): Pizza Salvatoré
  (-70.7, -8.2): Neumayer-Station II
  (-70.7, 46.1): Pizza Salvatoré
  (-70.6, 47.0): Pizza Salvatoré
  (-70.5, 47.4): Pizza Salvatoré
  (-70.1, -65.2): ร้านข้าวแกงฮาริษา Harisa's Curries Rice Factory
  (-70.1, 18.5): Jj'sanm Music Tutorial Center
  (-70.1, 164.1): North Store
  (-70.0, 47.4): Pizza Salvatoré
  (-69.5, 47.8): Pizza Salvatoré
  (-69.2, 123.6): JRMSU Katipunan Campus CTEd-Laboratory High School
  (-69.1, -28.9): Nails By Hilary Torres M
  (-68.9, 66.4): N'Zemmula - ristorante siciliano
  (-68.9, 118.8): Kimstore
  (-68.6, -26.4): Famous Forum of Worldwide Collaborators LLP
  (-68.5, 48.5): Pizza Salvatoré
  (-68.3, 49.2): Pizza Salvatoré
  (-68.2, 48.6): Pizza Salvatoré
  (-67.9, 98.6): Victors motor driving school
  (-67.6, 29.9): RT Power Motor
  (-67.6, 62.9): Base antarctique Mawson
  (-67.5, 48.9): Pizza Salvatoré
  (-67.5, 52.8): World Store
  (-67.2, -13.8): Vumeer el Mentor
  (-67.1, 51.4): Scott Mountains
  (-66.7, 140.0): Base antarctique Dumont-d'Urville
  (-66.6, 46.0): Pizza Salvatoré
  (-66.4, -62.1): Lihor Store Fan
  (-66.4, 50.2): Pizza Salvatoré
  (-66.0, 45.3): Pizza Salvatoré
  (-65.8, -121.4): Suzuki Nilphamari - Lahin Motors
  (-65.7, -5.6): Storybook Imaging
  (-65.7, 47.6): Pizza Salvatoré
  (-65.5, 47.0): Pizza Salvatoré
  (-65.3, -75.2): شركة يوني فليكس للمعدات والمستلزمات الطبية - Uniflex Medical Supply Store
  (-65.0, 27.6): Nadia Pereira - Corretora de Imóveis
  (-64.9, -62.9): Brown Station, Brown Station, Antarctic Peninsula
  (-64.9, -16.0): Robin Rolon, Mary Kay Independent Sales Director
  (-64.8, -64.1): Palmer Station, Antarctica
  (-64.8, -62.9): González Videla Antarctic Base
  (-64.8, 46.1): Pizza Salvatoré
  (-64.7, 46.1): Pizza Salvatoré
  (-64.5, -40.8): La Combo Tortuga
  (-64.2, -56.6): Base Antártica Marambio, Base Marambio, Antartida Argentina, Marambio Base
  (-64.2, 83.3): Melissa Isaac Realtor
  (-64.1, 11.8): Delta Power Generators
  (-63.8, 57.1): Nissan Pampanga by Maica Torres
  (-63.7, 44.8): Pizza Salvatoré
  (-63.5, -42.6): N9store.ro
  (-63.5, 124.0): Good Mates Removals & Storage
  (-63.4, -57.0): Base Esperanza, Antartida Argentina, Esperanza Base, Antarctica
  (-63.2, 80.6): Lori Makiya, Realtor
  (-63.1, -60.0): Concretiza Lar Construtora E Incorporadora
  (-62.3, 88.3): Me_2 Store
  (-62.2, -59.0): Bellingshausen Russian Antarctic Station
  (-62.2, -58.7): Carlini Base
  (-61.9, 66.9): Ténor store
  (-61.9, 169.8): Sales Store
  (-61.6, -30.9): Javastasia Distributors Halaal Meats
  (-61.5, -97.8): AKS Manufactory
  (-61.4, -18.9): Alessandra Oliveira corretora de imóveis rj
  (-61.3, -11.3): Technology Store
  (-60.5, 29.7): King World Product Store
  (-60.0, 14.4): Dani Motors

cd /work && python -c "
import overturemaps
# Check division type
gdf = overturemaps.geodataframe('division', bbox=(-180,-90,180,-60))
print(f'Divisions: {len(gdf)}')
if len(gdf) > 0:
    print(gdf.columns.tolist())
    for _, row in gdf.head(20).iterrows():
        name = row.get('names', {})
        if isinstance(name, dict):
            name = name.get('primary', '')
        print(f'  {name}')
" 2>&1 | head -40

Divisions: 49
['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']
  Amundsen–Scott South Pole
  Base Antártica San Martín
  Rothera Research Station
  Base Marambio
  Base Artigas
  长城站
  Arctowski
  남극세종과학기지
  Palmer Station
  Base Bernardo O’Higgins
  Vernadsky Research Base
  Deutsche Antarktis-Empfangsstation O'Higgins
  Villa Las Estrellas
  Fortín Sargento Cabral
  Base Esperanza
  Base Profesor Julio Escudero
  Беллинсгаузен
  Base Naval Antartica Arturo Prat
  Base Carlini
  Base Presidente Eduardo Frei Montalva

cd /work && python -c "
import overturemaps
gdf = overturemaps.geodataframe('division', bbox=(-180,-90,180,-60))
print(f'Total divisions: {len(gdf)}')

# Also filter by geometry
gdf = gdf[gdf.geometry.y <= -60] if len(gdf) > 0 else gdf

for _, row in gdf.iterrows():
    name = row.get('names', {})
    if isinstance(name, dict):
        name = name.get('primary', '')
    subtype = row.get('subtype', '')
    wikidata = row.get('wikidata', '')
    geom_type = row.geometry.geom_type if row.geometry else 'None'
    print(f'{name} | subtype={subtype} | wikidata={wikidata} | geom={geom_type}')
" 2>&1

Total divisions: 49
Amundsen–Scott South Pole | subtype=locality | wikidata=Q243307 | geom=Point
Base Antártica San Martín | subtype=locality | wikidata=Q923120 | geom=Point
Rothera Research Station | subtype=locality | wikidata=Q1635867 | geom=Point
Base Marambio | subtype=locality | wikidata=Q141493 | geom=Point
Base Artigas | subtype=locality | wikidata=Q809827 | geom=Point
长城站 | subtype=locality | wikidata=Q1548160 | geom=Point
Arctowski | subtype=locality | wikidata=Q638234 | geom=Point
남극세종과학기지 | subtype=locality | wikidata=Q246364 | geom=Point
Palmer Station | subtype=locality | wikidata=Q1333903 | geom=Point
Base Bernardo O’Higgins | subtype=locality | wikidata=Q2318477 | geom=Point
Vernadsky Research Base | subtype=locality | wikidata=Q1469808 | geom=Point
Deutsche Antarktis-Empfangsstation O'Higgins | subtype=locality | wikidata=nan | geom=Point
Villa Las Estrellas | subtype=locality | wikidata=Q2745332 | geom=Point
Fortín Sargento Cabral | subtype=locality | wikidata=Q5864270 | geom=Point
Base Esperanza | subtype=neighborhood | wikidata=Q1368174 | geom=Point
Base Profesor Julio Escudero | subtype=neighborhood | wikidata=Q2714138 | geom=Point
Беллинсгаузен | subtype=locality | wikidata=Q815995 | geom=Point
Base Naval Antartica Arturo Prat | subtype=locality | wikidata=Q715071 | geom=Point
Base Carlini | subtype=locality | wikidata=Q1710747 | geom=Point
Base Presidente Eduardo Frei Montalva | subtype=neighborhood | wikidata=Q1928160 | geom=Point
Estação Antártica Comandante Ferraz | subtype=locality | wikidata=Q683143 | geom=Point
Halley VI Research Station | subtype=locality | wikidata=Q1571679 | geom=Point
Base Antártica Orcadas | subtype=locality | wikidata=Q1440159 | geom=Point
Base Antártica Belgrano II | subtype=locality | wikidata=Q2357907 | geom=Point
Nordenskiöld Base | subtype=neighborhood | wikidata=Q3654639 | geom=Point
Neumayer-Station III | subtype=locality | wikidata=Q7933 | geom=Point
Antarctica | subtype=country | wikidata=Q51 | geom=Point
Troll | subtype=locality | wikidata=Q1082371 | geom=Point
Maitri Station | subtype=locality | wikidata=Q626852 | geom=Point
Новолазаревская | subtype=locality | wikidata=Q1411423 | geom=Point
昭和基地 | subtype=locality | wikidata=Q1550784 | geom=Point
Davis Station | subtype=locality | wikidata=Q1315213 | geom=Point
Mawson Station | subtype=locality | wikidata=Q1434388 | geom=Point
中山站 | subtype=locality | wikidata=Q197899 | geom=Point
Bharati Research Station | subtype=locality | wikidata=Q627152 | geom=Point
昆仑站 | subtype=locality | wikidata=Q3235369 | geom=Point
Прогресс | subtype=locality | wikidata=Q1469332 | geom=Point
Мирный | subtype=locality | wikidata=Q646492 | geom=Point
Stacja im. A.B. Dobrowolskiego | subtype=locality | wikidata=Q1965470 | geom=Point
Восток | subtype=locality | wikidata=Q211014 | geom=Point
Casey Station | subtype=locality | wikidata=Q1047169 | geom=Point
Concordia Station | subtype=locality | wikidata=Q403127 | geom=Point
Camp d'été | subtype=neighborhood | wikidata=nan | geom=Point
Scott Base | subtype=locality | wikidata=Q1136714 | geom=Point
秦岭站 | subtype=locality | wikidata=Q60793962 | geom=Point
Stazione Mario Zucchelli | subtype=locality | wikidata=Q1677142 | geom=Point
Base Dumont d'Urville | subtype=locality | wikidata=Q403901 | geom=Point
McMurdo Station | subtype=locality | wikidata=Q498627 | geom=Point
남극장보고과학기지 | subtype=locality | wikidata=Q245875 | geom=Point

cd /work && python -c "
import overturemaps
# Check division_area for Antarctica boundary
gdf = overturemaps.geodataframe('division_area', bbox=(-180,-90,180,-60))
print(f'Total: {len(gdf)}')
for _, row in gdf.iterrows():
    name = row.get('names', {})
    if isinstance(name, dict):
        name = name.get('primary', '')
    subtype = row.get('subtype', '')
    geom = row.geometry
    print(f'{name} | subtype={subtype} | geom_type={geom.geom_type}')
" 2>&1

Total: 3
Rothera Research Station | subtype=locality | geom_type=Polygon
Antarctica | subtype=country | geom_type=Polygon
Antarctica | subtype=country | geom_type=MultiPolygon

cd /work && python -c "
import overturemaps
# Get all stations from division
gdf = overturemaps.geodataframe('division', bbox=(-180,-90,180,-60))
gdf = gdf[gdf.geometry.y <= -60]

# Get names for all
for _, row in gdf.iterrows():
    name = row.get('names', {})
    if isinstance(name, dict):
        name = name.get('primary', '')
    wikidata = row.get('wikidata', '')
    geom_type = row.geometry.geom_type if row.geometry else 'None'
    print(f'{name} | wikidata={wikidata} | geom={geom_type}')
" 2>&1 | head -60

Amundsen–Scott South Pole | wikidata=Q243307 | geom=Point
Base Antártica San Martín | wikidata=Q923120 | geom=Point
Rothera Research Station | wikidata=Q1635867 | geom=Point
Base Marambio | wikidata=Q141493 | geom=Point
Base Artigas | wikidata=Q809827 | geom=Point
长城站 | wikidata=Q1548160 | geom=Point
Arctowski | wikidata=Q638234 | geom=Point
남극세종과학기지 | wikidata=Q246364 | geom=Point
Palmer Station | wikidata=Q1333903 | geom=Point
Base Bernardo O’Higgins | wikidata=Q2318477 | geom=Point
Vernadsky Research Base | wikidata=Q1469808 | geom=Point
Deutsche Antarktis-Empfangsstation O'Higgins | wikidata=nan | geom=Point
Villa Las Estrellas | wikidata=Q2745332 | geom=Point
Fortín Sargento Cabral | wikidata=Q5864270 | geom=Point
Base Esperanza | wikidata=Q1368174 | geom=Point
Base Profesor Julio Escudero | wikidata=Q2714138 | geom=Point
Беллинсгаузен | wikidata=Q815995 | geom=Point
Base Naval Antartica Arturo Prat | wikidata=Q715071 | geom=Point
Base Carlini | wikidata=Q1710747 | geom=Point
Base Presidente Eduardo Frei Montalva | wikidata=Q1928160 | geom=Point
Estação Antártica Comandante Ferraz | wikidata=Q683143 | geom=Point
Halley VI Research Station | wikidata=Q1571679 | geom=Point
Base Antártica Orcadas | wikidata=Q1440159 | geom=Point
Base Antártica Belgrano II | wikidata=Q2357907 | geom=Point
Nordenskiöld Base | wikidata=Q3654639 | geom=Point
Neumayer-Station III | wikidata=Q7933 | geom=Point
Antarctica | wikidata=Q51 | geom=Point
Troll | wikidata=Q1082371 | geom=Point
Maitri Station | wikidata=Q626852 | geom=Point
Новолазаревская | wikidata=Q1411423 | geom=Point
昭和基地 | wikidata=Q1550784 | geom=Point
Davis Station | wikidata=Q1315213 | geom=Point
Mawson Station | wikidata=Q1434388 | geom=Point
中山站 | wikidata=Q197899 | geom=Point
Bharati Research Station | wikidata=Q627152 | geom=Point
昆仑站 | wikidata=Q3235369 | geom=Point
Прогресс | wikidata=Q1469332 | geom=Point
Мирный | wikidata=Q646492 | geom=Point
Stacja im. A.B. Dobrowolskiego | wikidata=Q1965470 | geom=Point
Восток | wikidata=Q211014 | geom=Point
Casey Station | wikidata=Q1047169 | geom=Point
Concordia Station | wikidata=Q403127 | geom=Point
Camp d'été | wikidata=nan | geom=Point
Scott Base | wikidata=Q1136714 | geom=Point
秦岭站 | wikidata=Q60793962 | geom=Point
Stazione Mario Zucchelli | wikidata=Q1677142 | geom=Point
Base Dumont d'Urville | wikidata=Q403901 | geom=Point
McMurdo Station | wikidata=Q498627 | geom=Point
남극장보고과학기지 | wikidata=Q245875 | geom=Point

cd /work && python -c "
import overturemaps
# Quick test of division geodataframe
gdf = overturemaps.geodataframe('division', bbox=(-180,-90,180,-60))
print(f'Shape: {gdf.shape}')
print(f'CRS: {gdf.crs}')
print(f'Dtypes: {gdf.dtypes}')
print(f'Geometry sample: {gdf.geometry.head()}')
print(f'Geometry type: {gdf.geometry.geom_type.head()}')
" 2>&1 | head -30

Shape: (49, 21)
CRS: None
Dtypes: id                           str
geometry                geometry
country                      str
sources                   object
subtype                      str
admin_level              float64
class                        str
names                     object
wikidata                     str
perspectives              object
local_type                object
region                       str
hierarchies               object
parent_division_id           str
norms                     object
population               float64
capital_division_ids      object
capital_of_divisions      object
cartography               object
version                    int32
bbox                      object
dtype: object
Geometry sample: 0      POINT (-139.2728 -89.998)
1    POINT (-67.10132 -68.13006)
2    POINT (-68.12217 -67.56924)
3    POINT (-56.62769 -64.24062)
4     POINT (-58.90385 -62.1844)
Name: geometry, dtype: geometry

=== Loading stations from Overture 'division' ===
  Raw divisions in bbox: 49
  Stations found: 40
    Amundsen–Scott South Pole  (-89.9980, -139.2728)
    Base Antártica San Martín  (-68.1301, -67.1013)
    Rothera Research Station  (-67.5692, -68.1222)
    Base Marambio  (-64.2406, -56.6277)
    Base Artigas  (-62.1844, -58.9039)
    长城站  (-62.2177, -58.9612)
    Arctowski  (-62.1590, -58.4719)
    남극세종과학기지  (-62.2230, -58.7873)
    Palmer Station  (-64.7743, -64.0531)
    Base Bernardo O’Higgins  (-63.3208, -57.8999)
    Vernadsky Research Base  (-65.2455, -64.2577)
    Беллинсгаузен  (-62.1982, -58.9599)
    Base Naval Antartica Arturo Prat  (-62.4789, -59.6643)
    Base Carlini  (-62.2379, -58.6670)
    Estação Antártica Comandante Ferraz  (-62.0842, -58.3908)
    Halley VI Research Station  (-75.6125, -26.1977)
    Base Antártica Orcadas  (-60.7381, -44.7377)
    Base Antártica Belgrano II  (-77.8736, -34.6275)
    Neumayer-Station III  (-70.6744, -8.2742)
    Troll  (-72.0120, 2.5344)
    Maitri Station  (-70.7674, 11.7328)
    Новолазаревская  (-70.7766, 11.8245)
    昭和基地  (-69.0042, 39.5820)
    Davis Station  (-68.5769, 77.9684)
    Mawson Station  (-67.6029, 62.8738)
    中山站  (-69.3736, 76.3717)
    Bharati Research Station  (-69.4068, 76.1953)
    昆仑站  (-80.4174, 77.1174)
    Прогресс  (-69.3801, 76.3886)
    Мирный  (-66.5530, 93.0093)
    Stacja im. A.B. Dobrowolskiego  (-66.2746, 100.7497)
    Восток  (-78.4643, 106.8383)
    Casey Station  (-66.2821, 110.5241)
    Concordia Station  (-75.1000, 123.3333)
    Scott Base  (-77.8495, 166.7683)
    秦岭站  (-74.9343, 163.7153)
    Stazione Mario Zucchelli  (-74.6944, 164.1131)
    Base Dumont d'Urville  (-66.6633, 140.0011)
    McMurdo Station  (-77.8483, 166.6683)
    남극장보고과학기지  (-74.6241, 164.2288)

Clean stations count: 40

=== Loading Antarctica landmass ===
  Antarctica geometry type: Polygon
  Loading Overture 'land' features for coastline detail...
  Land features south of -60: 93367

=== Loading water features ===
  Water features: 13501
Loading bathymetry...
  Bathymetry features: 3095

=== Reprojecting to EPSG:3031 ===
  Dissolving land features...
Traceback (most recent call last):
  File "/work/solve.py", line 143, in <module>
    land_3031 = gdf_land.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.

=== Loading stations from Overture 'division' ===
  Raw divisions in bbox: 49
  Stations found: 40
    Amundsen–Scott South Pole  (-89.9980, -139.2728)
    Base Antártica San Martín  (-68.1301, -67.1013)
    Rothera Research Station  (-67.5692, -68.1222)
    Base Marambio  (-64.2406, -56.6277)
    Base Artigas  (-62.1844, -58.9039)
    长城站  (-62.2177, -58.9612)
    Arctowski  (-62.1590, -58.4719)
    남극세종과학기지  (-62.2230, -58.7873)
    Palmer Station  (-64.7743, -64.0531)
    Base Bernardo O’Higgins  (-63.3208, -57.8999)
    Vernadsky Research Base  (-65.2455, -64.2577)
    Беллинсгаузен  (-62.1982, -58.9599)
    Base Naval Antartica Arturo Prat  (-62.4789, -59.6643)
    Base Carlini  (-62.2379, -58.6670)
    Estação Antártica Comandante Ferraz  (-62.0842, -58.3908)
    Halley VI Research Station  (-75.6125, -26.1977)
    Base Antártica Orcadas  (-60.7381, -44.7377)
    Base Antártica Belgrano II  (-77.8736, -34.6275)
    Neumayer-Station III  (-70.6744, -8.2742)
    Troll  (-72.0120, 2.5344)
    Maitri Station  (-70.7674, 11.7328)
    Новолазаревская  (-70.7766, 11.8245)
    昭和基地  (-69.0042, 39.5820)
    Davis Station  (-68.5769, 77.9684)
    Mawson Station  (-67.6029, 62.8738)
    中山站  (-69.3736, 76.3717)
    Bharati Research Station  (-69.4068, 76.1953)
    昆仑站  (-80.4174, 77.1174)
    Прогресс  (-69.3801, 76.3886)
    Мирный  (-66.5530, 93.0093)
    Stacja im. A.B. Dobrowolskiego  (-66.2746, 100.7497)
    Восток  (-78.4643, 106.8383)
    Casey Station  (-66.2821, 110.5241)
    Concordia Station  (-75.1000, 123.3333)
    Scott Base  (-77.8495, 166.7683)
    秦岭站  (-74.9343, 163.7153)
    Stazione Mario Zucchelli  (-74.6944, 164.1131)
    Base Dumont d'Urville  (-66.6633, 140.0011)
    McMurdo Station  (-77.8483, 166.6683)
    남극장보고과학기지  (-74.6241, 164.2288)

Clean stations count: 40

=== Loading Antarctica landmass ===
  Antarctica geometry type: Polygon

=== Loading water features ===
  Water features: 13501
Loading bathymetry...
  Bathymetry features: 3095

=== Reprojecting to EPSG:3031 ===
  Land geometry type: Polygon
  Combined water/bathy features: 16596

=== Buffering stations by 200 km ===
  Buffered 40 stations

=== Clipping buffers to land ===
  Land-clipped zones: 39

=== Computing coalitions (overlap groups) ===
  Number of coalitions: 15
  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)]

  Output land features: 39
  Distinct coalition values: 15

=== Computing over-water intersections ===
  Water overlap features: 37042

=== Writing outputs ===
  ✔ Written station_spheres.geoparquet (39 features)
  ✔ Written station_water_overlap.geoparquet (37042 features)

=== Verification ===
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}}
  Columns: ['station_id', 'station_name', 'coalition', 'geometry']
  Features: 39
  Distinct coalition values: 15
  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)]
  Geometry types: {'MultiPolygon': 39}

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}}
  Columns: ['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source', 'geometry']
  Features: 37042
  water_source values: <ArrowStringArray>
['base.bathymetry', 'base.water']
Length: 2, dtype: str
  Geometry types: {'LineString': 24356, 'MultiPolygon': 10872, 'Point': 1810, 'MultiLineString': 4}

=== DONE ===

cd /work && python -c "
import overturemaps
gdf_w = overturemaps.geodataframe('water', bbox=(-180,-90,180,-60))
gdf_w = gdf_w[gdf_w.geometry.notna()]
print('Water geom types:')
print(gdf_w.geometry.geom_type.value_counts())
print()
gdf_b = overturemaps.geodataframe('bathymetry', bbox=(-180,-90,180,-60))
gdf_b = gdf_b[gdf_b.geometry.notna()]
print('Bathymetry geom types:')
print(gdf_b.geometry.geom_type.value_counts())
" 2>&1 | head -30

Water geom types:
Polygon         7445
LineString      5421
Point            625
MultiPolygon      10
Name: count, dtype: int64

Bathymetry geom types:
MultiPolygon    3077
Polygon           19
Name: count, dtype: int64

=== Loading stations from Overture 'division' ===
  Raw divisions in bbox: 49
  Stations found: 40
    Amundsen–Scott South Pole  (-89.9980, -139.2728)
    Base Antártica San Martín  (-68.1301, -67.1013)
    Rothera Research Station  (-67.5692, -68.1222)
    Base Marambio  (-64.2406, -56.6277)
    Base Artigas  (-62.1844, -58.9039)
    长城站  (-62.2177, -58.9612)
    Arctowski  (-62.1590, -58.4719)
    남극세종과학기지  (-62.2230, -58.7873)
    Palmer Station  (-64.7743, -64.0531)
    Base Bernardo O’Higgins  (-63.3208, -57.8999)
    Vernadsky Research Base  (-65.2455, -64.2577)
    Беллинсгаузен  (-62.1982, -58.9599)
    Base Naval Antartica Arturo Prat  (-62.4789, -59.6643)
    Base Carlini  (-62.2379, -58.6670)
    Estação Antártica Comandante Ferraz  (-62.0842, -58.3908)
    Halley VI Research Station  (-75.6125, -26.1977)
    Base Antártica Orcadas  (-60.7381, -44.7377)
    Base Antártica Belgrano II  (-77.8736, -34.6275)
    Neumayer-Station III  (-70.6744, -8.2742)
    Troll  (-72.0120, 2.5344)
    Maitri Station  (-70.7674, 11.7328)
    Новолазаревская  (-70.7766, 11.8245)
    昭和基地  (-69.0042, 39.5820)
    Davis Station  (-68.5769, 77.9684)
    Mawson Station  (-67.6029, 62.8738)
    中山站  (-69.3736, 76.3717)
    Bharati Research Station  (-69.4068, 76.1953)
    昆仑站  (-80.4174, 77.1174)
    Прогресс  (-69.3801, 76.3886)
    Мирный  (-66.5530, 93.0093)
    Stacja im. A.B. Dobrowolskiego  (-66.2746, 100.7497)
    Восток  (-78.4643, 106.8383)
    Casey Station  (-66.2821, 110.5241)
    Concordia Station  (-75.1000, 123.3333)
    Scott Base  (-77.8495, 166.7683)
    秦岭站  (-74.9343, 163.7153)
    Stazione Mario Zucchelli  (-74.6944, 164.1131)
    Base Dumont d'Urville  (-66.6633, 140.0011)
    McMurdo Station  (-77.8483, 166.6683)
    남극장보고과학기지  (-74.6241, 164.2288)

Clean stations count: 40

=== Loading Antarctica landmass ===
  Antarctica geometry type: Polygon

=== Loading water features ===
  Water features: 13501
Loading bathymetry...
  Bathymetry features: 3095

=== Reprojecting to EPSG:3031 ===
  Land geometry type: Polygon
  Combined water/bathy features: 16596

=== Buffering stations by 200 km ===
  Buffered 40 stations

=== Clipping buffers to land ===
  Land-clipped zones: 39

=== Computing coalitions (overlap groups) ===
  Number of coalitions: 15
  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)]

  Output land features: 39
  Distinct coalition values: 15

=== Computing over-water intersections ===
  Water overlap features: 10872

=== Writing outputs ===
  ✔ Written station_spheres.geoparquet (39 features)
  ✔ Written station_water_overlap.geoparquet (10872 features)

=== Verification ===
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}}
  Columns: ['station_id', 'station_name', 'coalition', 'geometry']
  Features: 39
  Distinct coalition values: 15
  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)]
  Geometry types: {'MultiPolygon': 39}

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}}
  Columns: ['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source', 'geometry']
  Features: 10872
  water_source values: <ArrowStringArray>
['base.bathymetry', 'base.water']
Length: 2, dtype: str
  Geometry types: {'MultiPolygon': 10872}

=== DONE ===

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

# Quick sanity check on coordinate magnitudes
gdf1 = gpd.read_parquet('station_spheres.geoparquet')
bounds1 = gdf1.total_bounds
print(f'Station spheres bounds (EPSG:3031 meters):')
print(f'  x: {bounds1[0]:.0f} to {bounds1[2]:.0f}')
print(f'  y: {bounds1[1]:.0f} to {bounds1[3]:.0f}')
# Should be in the range of 10^5 to 10^6 for Antarctic region

gdf2 = gpd.read_parquet('station_water_overlap.geoparquet')
bounds2 = gdf2.total_bounds
print(f'Water overlap bounds (EPSG:3031 meters):')
print(f'  x: {bounds2[0]:.0f} to {bounds2[2]:.0f}')
print(f'  y: {bounds2[1]:.0f} to {bounds2[3]:.0f}')

# Check station_spheres has >1 coalition values
print(f'\\nCoalition nunique: {gdf1[\"coalition\"].nunique()}')
print(f'Coalition values: {sorted(gdf1[\"coalition\"].unique())}')

# Spot-check a few station names
print(f'\\nSample stations:')
for _, row in gdf1.head(5).iterrows():
    print(f'  id={row[\"station_id\"][:20]:20s} name={row[\"station_name\"]:30s} coalition={row[\"coalition\"]}')

# Check water_source values
print(f'\\nWater source values: {gdf2[\"water_source\"].unique().tolist()}')
print(f'Water overlap count: {len(gdf2)}')

import os
for f in ['station_spheres.geoparquet', 'station_water_overlap.geoparquet']:
    size = os.path.getsize(f)
    print(f'{f}: {size:,} bytes ({size/1024/1024:.1f} MB)')
" 2>&1

Station spheres bounds (EPSG:3031 meters):
  x: -2838141 to 2778729
  y: -2168628 to 2482732
Water overlap bounds (EPSG:3031 meters):
  x: -2838141 to 2778729
  y: -2168628 to 2506851

Coalition nunique: 15
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)]

Sample stations:
  id=2921924c-7938-4ef0-9 name=Base Antártica San Martín      coalition=0
  id=ed7d4df0-fb65-49a7-a name=Rothera Research Station       coalition=0
  id=0bd9aea9-7334-4ae1-a name=Base Marambio                  coalition=0
  id=4463ec21-3f98-4603-8 name=Base Artigas                   coalition=0
  id=1ae1d16b-e347-4085-8 name=长城站                            coalition=0

Water source values: ['base.bathymetry', 'base.water']
Water overlap count: 10872
station_spheres.geoparquet: 47,338 bytes (0.0 MB)
station_water_overlap.geoparquet: 42,612,173 bytes (40.6 MB)

cd /work && python -c "
import overturemaps
gdf = overturemaps.geodataframe('division_area', bbox=(-180,-90,180,-60))
print(f'Total division areas: {len(gdf)}')
for _, row in gdf.iterrows():
    name = row.get('names', {})
    if isinstance(name, dict):
        name = name.get('primary', '')
    subtype = row.get('subtype', '')
    geom = row.geometry
    print(f'  {name} | subtype={subtype} | geom_type={geom.geom_type}')
    if geom.geom_type == 'MultiPolygon':
        print(f'    num_polys={len(geom.geoms)}')
" 2>&1

Total division areas: 3
  Rothera Research Station | subtype=locality | geom_type=Polygon
  Antarctica | subtype=country | geom_type=Polygon
  Antarctica | subtype=country | geom_type=MultiPolygon
    num_polys=2003

=== Loading stations from Overture 'division' ===
  Raw divisions in bbox: 49
  Stations found: 40
    Amundsen–Scott South Pole  (-89.9980, -139.2728)
    Base Antártica San Martín  (-68.1301, -67.1013)
    Rothera Research Station  (-67.5692, -68.1222)
    Base Marambio  (-64.2406, -56.6277)
    Base Artigas  (-62.1844, -58.9039)
    长城站  (-62.2177, -58.9612)
    Arctowski  (-62.1590, -58.4719)
    남극세종과학기지  (-62.2230, -58.7873)
    Palmer Station  (-64.7743, -64.0531)
    Base Bernardo O’Higgins  (-63.3208, -57.8999)
    Vernadsky Research Base  (-65.2455, -64.2577)
    Беллинсгаузен  (-62.1982, -58.9599)
    Base Naval Antartica Arturo Prat  (-62.4789, -59.6643)
    Base Carlini  (-62.2379, -58.6670)
    Estação Antártica Comandante Ferraz  (-62.0842, -58.3908)
    Halley VI Research Station  (-75.6125, -26.1977)
    Base Antártica Orcadas  (-60.7381, -44.7377)
    Base Antártica Belgrano II  (-77.8736, -34.6275)
    Neumayer-Station III  (-70.6744, -8.2742)
    Troll  (-72.0120, 2.5344)
    Maitri Station  (-70.7674, 11.7328)
    Новолазаревская  (-70.7766, 11.8245)
    昭和基地  (-69.0042, 39.5820)
    Davis Station  (-68.5769, 77.9684)
    Mawson Station  (-67.6029, 62.8738)
    中山站  (-69.3736, 76.3717)
    Bharati Research Station  (-69.4068, 76.1953)
    昆仑站  (-80.4174, 77.1174)
    Прогресс  (-69.3801, 76.3886)
    Мирный  (-66.5530, 93.0093)
    Stacja im. A.B. Dobrowolskiego  (-66.2746, 100.7497)
    Восток  (-78.4643, 106.8383)
    Casey Station  (-66.2821, 110.5241)
    Concordia Station  (-75.1000, 123.3333)
    Scott Base  (-77.8495, 166.7683)
    秦岭站  (-74.9343, 163.7153)
    Stazione Mario Zucchelli  (-74.6944, 164.1131)
    Base Dumont d'Urville  (-66.6633, 140.0011)
    McMurdo Station  (-77.8483, 166.6683)
    남극장보고과학기지  (-74.6241, 164.2288)

Clean stations count: 40

=== Loading Antarctica landmass ===
  Antarctica geometry type: MultiPolygon

=== Loading water features ===
  Water features: 13501
Loading bathymetry...
  Bathymetry features: 3095

=== Reprojecting to EPSG:3031 ===
  Land geometry type: MultiPolygon
  Combined water/bathy features: 16596

=== Buffering stations by 200 km ===
  Buffered 40 stations

=== Clipping buffers to land ===
  Land-clipped zones: 39

=== Computing coalitions (overlap groups) ===
  Number of coalitions: 15
  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)]

  Output land features: 39
  Distinct coalition values: 15

=== Computing over-water intersections ===
  Water overlap features: 10872

=== Writing outputs ===
  ✔ Written station_spheres.geoparquet (39 features)
  ✔ Written station_water_overlap.geoparquet (10872 features)

=== Verification ===
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}}
  Columns: ['station_id', 'station_name', 'coalition', 'geometry']
  Features: 39
  Distinct coalition values: 15
  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)]
  Geometry types: {'MultiPolygon': 39}

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}}
  Columns: ['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source', 'geometry']
  Features: 10872
  water_source values: <ArrowStringArray>
['base.bathymetry', 'base.water']
Length: 2, dtype: str
  Geometry types: {'MultiPolygon': 10872}

=== DONE ===

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

# Final verification
print('=' * 60)
print('FINAL VERIFICATION')
print('=' * 60)

# File 1: station_spheres.geoparquet
s1 = gpd.read_parquet('station_spheres.geoparquet')
print('\n--- station_spheres.geoparquet ---')
print(f'  CRS: {s1.crs}')
print(f'  Columns: {s1.columns.tolist()}')
print(f'  Features: {len(s1)}')
print(f'  Geometry: all {s1.geometry.geom_type.unique().tolist()}')
print(f'  coalition nunique: {s1[\"coalition\"].nunique()}')
print(f'  coalition values: {sorted(s1[\"coalition\"].unique())}')
print(f'  station_id dtype: {s1[\"station_id\"].dtype}')
print(f'  station_name dtype: {s1[\"station_name\"].dtype}')
print(f'  coalition dtype: {s1[\"coalition\"].dtype}')
print(f'  Has station_id: {\"station_id\" in s1.columns}')
print(f'  Has station_name: {\"station_name\" in s1.columns}')
print(f'  Has coalition: {\"coalition\" in s1.columns}')

# File 2: station_water_overlap.geoparquet
s2 = gpd.read_parquet('station_water_overlap.geoparquet')
print('\n--- station_water_overlap.geoparquet ---')
print(f'  CRS: {s2.crs}')
print(f'  Columns: {s2.columns.tolist()}')
print(f'  Features: {len(s2)}')
print(f'  Geometry: all {s2.geometry.geom_type.unique().tolist()}')
print(f'  water_source values: {s2[\"water_source\"].unique().tolist()}')
print(f'  Has station_id: {\"station_id\" in s2.columns}')
print(f'  Has station_name: {\"station_name\" in s2.columns}')
print(f'  Has water_id: {\"water_id\" in s2.columns}')
print(f'  Has water_name: {\"water_name\" in s2.columns}')
print(f'  Has water_subtype: {\"water_subtype\" in s2.columns}')
print(f'  Has water_source: {\"water_source\" in s2.columns}')
print(f'  water_source types in dataset: {s2[\"water_source\"].dropna().unique()}')

# Check coordinate magnitudes are in meters
print('\n--- Coordinate sanity ---')
b1 = s1.total_bounds
print(f'  station_spheres bounds: x=[{b1[0]:.0f}, {b1[2]:.0f}]  y=[{b1[1]:.0f}, {b1[3]:.0f}]')
print(f'  Values are ~10⁵-10⁶ (meters in EPSG:3031): OK' if abs(b1[0]) > 1000 else 'SUSPICIOUS')
b2 = s2.total_bounds
print(f'  station_water_overlap bounds: x=[{b2[0]:.0f}, {b2[2]:.0f}]  y=[{b2[1]:.0f}, {b2[3]:.0f}]')
print(f'  Values are ~10⁵-10⁶ (meters in EPSG:3031): OK' if abs(b2[0]) > 1000 else 'SUSPICIOUS')

print('\nAll requirements met!')
" 2>&1

============================================================
FINAL VERIFICATION
============================================================

--- 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}}
  Columns: ['station_id', 'station_name', 'coalition', 'geometry']
  Features: 39
  Geometry: all ['MultiPolygon']
  coalition nunique: 15
  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)]
  station_id dtype: str
  station_name dtype: str
  coalition dtype: int64
  Has station_id: True
  Has station_name: True
  Has coalition: 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}}
  Columns: ['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source', 'geometry']
  Features: 10872
  Geometry: all ['MultiPolygon']
  water_source values: ['base.bathymetry', 'base.water']
  Has station_id: True
  Has station_name: True
  Has water_id: True
  Has water_name: True
  Has water_subtype: True
  Has water_source: True
  water_source types in dataset: <ArrowStringArray>
['base.bathymetry', 'base.water']
Length: 2, dtype: str

--- Coordinate sanity ---
  station_spheres bounds: x=[-2661800, 2718736]  y=[-2058943, 2321720]
  Values are ~10⁵-10⁶ (meters in EPSG:3031): OK
  station_water_overlap bounds: x=[-2838141, 2778729]  y=[-2168628, 2506851]
  Values are ~10⁵-10⁶ (meters in EPSG:3031): OK

All requirements met!

cd /work && python -c "
import geopandas as pd
s1 = pd.read_parquet('station_spheres.geoparquet')
stations_present = set(s1['station_name'].values)

# Read from the script output - check Amundsen-Scott
print('Stations in land-clipped output:')
for n in sorted(stations_present):
    print(f'  {n}')

# The station names from the log - 40 stations
all_stations = [
    'Amundsen–Scott South Pole', 'Base Antártica San Martín', 'Rothera Research Station',
    'Base Marambio', 'Base Artigas', '长城站', 'Arctowski', '남극세종과학기지',
    'Palmer Station', 'Base Bernardo O\u2019Higgins', 'Vernadsky Research Base',
    'Беллинсгаузен', 'Base Naval Antartica Arturo Prat', 'Base Carlini',
    'Estação Antártica Comandante Ferraz', 'Halley VI Research Station',
    'Base Antártica Orcadas', 'Base Antártica Belgrano II', 'Neumayer-Station III',
    'Troll', 'Maitri Station', 'Новолазаревская', '昭和基地', 'Davis Station',
    'Mawson Station', '中山站', 'Bharati Research Station', '昆仑站', 'Прогресс',
    'Мирный', 'Stacja im. A.B. Dobrowolskiego', 'Восток', 'Casey Station',
    'Concordia Station', 'Scott Base', '秦岭站', 'Stazione Mario Zucchelli',
    'Base Dumont d\'Urville', 'McMurdo Station', '남극장보고과학기지'
]
missing = [s for s in all_stations if s not in stations_present]
print(f'\\nMissing from land-clipped: {missing}')
" 2>&1

Stations in land-clipped output:
  Arctowski
  Base Antártica Belgrano II
  Base Antártica Orcadas
  Base Antártica San Martín
  Base Artigas
  Base Bernardo O’Higgins
  Base Carlini
  Base Dumont d'Urville
  Base Marambio
  Base Naval Antartica Arturo Prat
  Bharati Research Station
  Casey Station
  Concordia Station
  Davis Station
  Estação Antártica Comandante Ferraz
  Halley VI Research Station
  Maitri Station
  Mawson Station
  McMurdo Station
  Neumayer-Station III
  Palmer Station
  Rothera Research Station
  Scott Base
  Stacja im. A.B. Dobrowolskiego
  Stazione Mario Zucchelli
  Troll
  Vernadsky Research Base
  Беллинсгаузен
  Восток
  Мирный
  Новолазаревская
  Прогресс
  中山站
  昆仑站
  昭和基地
  秦岭站
  长城站
  남극세종과학기지
  남극장보고과학기지

Missing from land-clipped: ['Amundsen–Scott South Pole']

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

# Check where Amundsen-Scott went
gdf = overturemaps.geodataframe('division', bbox=(-180,-90,180,-60))
as_row = gdf[gdf['names'].apply(lambda x: x.get('primary','') if isinstance(x,dict) else '') == 'Amundsen–Scott South Pole']
print('Amundsen-Scott Point:')
print(as_row.geometry)
print(f'Y={as_row.geometry.y.values[0]:.4f}')

# Check Antarctica polygon bounds
gdf_area = overturemaps.geodataframe('division_area', bbox=(-180,-90,180,-60))
for _, row in gdf_area.iterrows():
    name = row.get('names', {})
    name_p = name.get('primary', '') if isinstance(name, dict) else ''
    if name_p == 'Antarctica' and row.get('subtype') == 'country':
        if row.geometry.geom_type == 'MultiPolygon':
            # Check bounds
            print(f'Antarctica MultiPolygon bounds: {row.geometry.bounds}')
            # Check if it covers -90
            print(f'Y min: {row.geometry.bounds[1]}')
            # Check if the South Pole point is within Antarctica
            pt = shapely.Point(-139.2728, -89.998)
            antarctica_3031 = gpd.GeoSeries([row.geometry], crs='EPSG:4326').to_crs('EPSG:3031').iloc[0]
            pt_3031 = gpd.GeoSeries([pt], crs='EPSG:4326').to_crs('EPSG:3031').iloc[0]
            print(f'Point in Antarctica (3031): {antarctica_3031.contains(pt_3031)}')
            print(f'Buffer 200km intersects: {antarctica_3031.intersects(pt_3031.buffer(200000))}')
" 2>&1

Amundsen-Scott Point:
0    POINT (-139.2728 -89.998)
Name: geometry, dtype: geometry
Y=-89.9980
Antarctica MultiPolygon bounds: (-180.0, -85.0511289, 180.0, -60.5094997)
Y min: -85.0511289
Point in Antarctica (3031): False
Buffer 200km intersects: False