geo-l3-antarctica-stations-geodesic

Rerun

Requirement already satisfied: orjson>=3.9.0 in /usr/local/lib/python3.14/site-packages (from overturemaps) (3.11.9)
Requirement already satisfied: pyarrow>=15.0.2 in /usr/local/lib/python3.14/site-packages (from overturemaps) (24.0.0)
Requirement already satisfied: pyfiglet>=1.0.2 in /usr/local/lib/python3.14/site-packages (from overturemaps) (1.0.4)
Requirement already satisfied: shapely>=2.1.0 in /usr/local/lib/python3.14/site-packages (from overturemaps) (2.1.2)
Requirement already satisfied: tqdm>=4.67.3 in /usr/local/lib/python3.14/site-packages (from overturemaps) (4.68.1)

cd /work && python3 << 'EOF'
import overturemaps
# List available themes
print(overturemaps.themes())
EOF

cd /work && python3 << 'EOF'
import overturemaps
print(dir(overturemaps))
EOF

cd /work && python3 << 'EOF'
import overturemaps
help(overturemaps.record_batch_reader)
EOF

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 && python3 << 'EOF'
import overturemaps
print(overturemaps.get_all_overture_types())
EOF

cd /work && python3 << 'EOF'
import overturemaps

# Let's check what's available in place for Antarctica
# First, let's see what fields are in the place schema
reader = overturemaps.record_batch_reader('place')
if reader:
    schema = reader.schema
    print("Place schema:")
    for field in schema:
        print(f"  {field.name}: {field.type}")
else:
    print("No reader for global place data")
EOF

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 && python3 << 'EOF'
import overturemaps
# Check water schema
reader = overturemaps.record_batch_reader('water')
if reader:
    schema = reader.schema
    print("Water schema:")
    for field in schema:
        print(f"  {field.name}: {field.type}")
else:
    print("No reader for water")
EOF

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 && python3 << 'EOF'
import overturemaps
# Check bathymetry schema
reader = overturemaps.record_batch_reader('bathymetry')
if reader:
    schema = reader.schema
    print("Bathymetry schema:")
    for field in schema:
        print(f"  {field.name}: {field.type}")
else:
    print("No reader for bathymetry")
EOF

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 && python3 << 'EOF'
import overturemaps
# Check land schema
reader = overturemaps.record_batch_reader('land')
if reader:
    schema = reader.schema
    print("Land schema:")
    for field in schema:
        print(f"  {field.name}: {field.type}")
else:
    print("No reader for land")
EOF

cd /work && python3 << 'EOF'
import overturemaps

# Let's query places in Antarctica with a bounding box that covers it
# Antarctica bounding box roughly: -180 to 180 longitude, -90 to -60 latitude
bbox = (-180, -90, 180, -60)
reader = overturemaps.record_batch_reader('place', bbox=bbox)
if reader:
    import pyarrow as pa
    # Read first batch to see categories
    batches = []
    for i, batch in enumerate(reader):
        batches.append(batch)
        if i >= 5:  # Read first few batches
            break
    table = pa.Table.from_batches(batches)
    df = table.to_pandas()
    print(f"Total rows: {len(df)}")
    print(f"Columns: {list(df.columns)}")
    print(f"Categories sample:")
    if 'categories' in df.columns:
        print(df['categories'].head(20))
    if 'names' in df.columns:
        print(f"\nNames sample:")
        print(df['names'].head(20))
    if 'operating_status' in df.columns:
        print(f"\nOperating status:")
        print(df['operating_status'].value_counts())
EOF

Total rows: 247
Columns: ['id', 'geometry', 'categories', 'confidence', 'websites', 'emails', 'socials', 'phones', 'brand', 'addresses', 'names', 'sources', 'operating_status', 'basic_category', 'taxonomy', 'version', 'bbox']
Categories sample:
0     {'primary': 'information_technology_company', ...
1     {'primary': 'physical_therapy', 'alternate': [...
2     {'primary': 'health_and_medical', 'alternate':...
3              {'primary': 'school', 'alternate': None}
4                                                  None
5                {'primary': 'farm', 'alternate': None}
6                                                  None
7         {'primary': 'sports_wear', 'alternate': None}
8         {'primary': 'pet_groomer', 'alternate': None}
9     {'primary': 'professional_services', 'alternat...
10        {'primary': 'real_estate', 'alternate': None}
11    {'primary': 'landmark_and_historical_building'...
12                                                 None
13             {'primary': 'retail', 'alternate': None}
14              {'primary': 'tours', 'alternate': None}
15    {'primary': 'beauty_salon', 'alternate': ['bea...
16    {'primary': 'high_school', 'alternate': ['educ...
17    {'primary': 'beauty_salon', 'alternate': ['wig...
18    {'primary': 'cosmetic_and_beauty_supplies', 'a...
19    {'primary': 'professional_services', 'alternat...
Name: categories, dtype: object

Names sample:
0     {'primary': 'Tincorp Pa TI', 'common': None, '...
1     {'primary': 'Sara Couto - Intervenção Terapêut...
2     {'primary': 'แหลมใหญ่แล็บ คลินิกเทคนิคการแพทย์...
3     {'primary': 'Chittagong Victory National Schoo...
4     {'primary': 'Quảng Cáo - In Nhanh SaPa', 'comm...
5     {'primary': 'Donata Farms & Land Services', 'c...
6     {'primary': 'Vet Rx Pet Health Center', 'commo...
7     {'primary': 'Work N Wear International', 'comm...
8     {'primary': 'The Wagging Tail Salon', 'common'...
9     {'primary': 'Liso EstÚDio', 'common': None, 'r...
10    {'primary': 'New Launch Property', 'common': N...
11    {'primary': 'Ross Ice Shelf, Antarctica', 'com...
12    {'primary': '十刻刺青室', 'common': None, 'rules': ...
13    {'primary': 'Poeanuanua', 'common': None, 'rul...
14    {'primary': 'Discover Italy', 'common': None, ...
15    {'primary': 'สอนต่อขนตาพัทยา ต่อขนตาพัทยา', 'c...
16    {'primary': 'Krāslavas Varavīksnes vidusskola'...
17    {'primary': 'مركز هيا هير للشعر الطبيعي/  Haya...
18    {'primary': 'Esencia Alquímica: Aromaterapia y...
19    {'primary': 'Maliha Farzam', 'common': None, '...
Name: names, dtype: object

Operating status:
operating_status
open    1
Name: count, dtype: int64

cd /work && python3 << 'EOF'
import overturemaps

bbox = (-180, -90, 180, -60)
reader = overturemaps.record_batch_reader('place', bbox=bbox)
if reader:
    import pyarrow as pa
    table = pa.Table.from_batches(reader)
    df = table.to_pandas()
    
    # Look at categories more carefully
    cats = df['categories'].dropna()
    primary_cats = [c['primary'] for c in cats if c and 'primary' in c]
    from collections import Counter
    for cat, count in Counter(primary_cats).most_common(30):
        print(f"  {cat}: {count}")
EOF

  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

cd /work && python3 << 'EOF'
import overturemaps

bbox = (-180, -90, 180, -60)
reader = overturemaps.record_batch_reader('place', bbox=bbox)
if reader:
    import pyarrow as pa
    table = pa.Table.from_batches(reader)
    df = table.to_pandas()
    
    # Check taxonomy field
    taxonomies = df['taxonomy'].dropna()
    print(f"Number of non-null taxonomies: {len(taxonomies)}")
    primary_tax = [t['primary'] for t in taxonomies if t and 'primary' in t]
    from collections import Counter
    for cat, count in Counter(primary_tax).most_common(30):
        print(f"  {cat}: {count}")
    
    # Also check if names have "station" or "research" in them
    names_series = df['names']
    for i, name_struct in enumerate(names_series.items()):
        idx, val = name_struct
        if val and val.get('primary'):
            pname = val['primary']
            if any(kw in pname.lower() for kw in ['station', 'research', 'base', 'camp']):
                print(f"  [{i}] {pname}")
                print(f"      categories: {df.iloc[idx]['categories']}")
                print(f"      taxonomy: {df.iloc[idx]['taxonomy']}")
EOF

Number of non-null taxonomies: 2049
  historic_site: 163
  fast_food_restaurant: 133
  mountain: 67
  professional_service: 58
  beauty_salon: 51
  shopping: 48
  real_estate_service: 47
  school: 46
  restaurant: 40
  hotel: 39
  beauty_service: 30
  college_university: 29
  clothing_store: 27
  resort: 26
  social_or_community_service: 24
  education: 23
  hospital: 21
  womens_clothing_store: 20
  automotive_repair: 19
  health_care: 18
  shoe_store: 18
  auto_dealer: 18
  event_or_party_service: 18
  christian_place_of_worship: 17
  government_office: 17
  furniture_store: 16
  cafe: 16
  religious_organization: 16
  arts_and_crafts_store: 14
  real_estate_agent: 14
  [155] Dhacss Seaview Campus
      categories: {'primary': 'preschool', 'alternate': array(['education', 'school'], dtype=object)}
      taxonomy: {'primary': 'preschool', 'hierarchy': array(['education', 'place_of_learning', 'school', 'preschool'],
      dtype=object), 'alternates': None}
  [331] Noakhali Ideal Polytechnic Institute - Digital Campus
      categories: {'primary': 'college_university', 'alternate': array(['educational_camp', 'specialty_school'], dtype=object)}
      taxonomy: {'primary': 'college_university', 'hierarchy': array(['education', 'place_of_learning', 'college_university'],
      dtype=object), 'alternates': None}
  [439] Master Bees Stationeries & Souvenirs
      categories: {'primary': 'souvenir_shop', 'alternate': array(['bookstore'], dtype=object)}
      taxonomy: {'primary': 'souvenir_store', 'hierarchy': array(['shopping', 'specialty_store', 'souvenir_store'], dtype=object), 'alternates': None}
  [460] Brown Station, Antarctic Peninsula
      categories: {'primary': 'landmark_and_historical_building', 'alternate': array(['fountain'], dtype=object)}
      taxonomy: {'primary': 'historic_site', 'hierarchy': array(['cultural_and_historic', 'historic_site'], dtype=object), 'alternates': None}
  [461] Brown Station
      categories: {'primary': 'landmark_and_historical_building', 'alternate': array(['fountain'], dtype=object)}
      taxonomy: {'primary': 'historic_site', 'hierarchy': array(['cultural_and_historic', 'historic_site'], dtype=object), 'alternates': None}
  [462] González Videla Antarctic Base
      categories: {'primary': 'airport', 'alternate': array(['travel'], dtype=object)}
      taxonomy: {'primary': 'airport', 'hierarchy': array(['travel_and_transportation', 'air_transport_facility_or_service',
       'airport'], dtype=object), 'alternates': None}
  [473] Palmer Station, Antarctica
      categories: {'primary': 'home_developer', 'alternate': None}
      taxonomy: {'primary': 'home_developer', 'hierarchy': array(['services_and_business', 'real_estate_service', 'builder',
       'home_developer'], dtype=object), 'alternates': None}
  [490] New Lucena Municipal Police Station
      categories: {'primary': 'public_service_and_government', 'alternate': array(['community_center'], dtype=object)}
      taxonomy: {'primary': 'community_and_government', 'hierarchy': array(['community_and_government'], dtype=object), 'alternates': None}
  [504] Carlini Base
      categories: {'primary': 'arts_and_entertainment', 'alternate': None}
      taxonomy: {'primary': 'arts_and_entertainment', 'hierarchy': array(['arts_and_entertainment'], dtype=object), 'alternates': None}
  [511] Bellingshausen Russian Antarctic Station
      categories: {'primary': 'educational_research_institute', 'alternate': array(['public_and_government_association', 'college_university'],
      dtype=object)}
      taxonomy: {'primary': 'educational_research_institute', 'hierarchy': array(['education', 'research_institute',
       'educational_research_institute'], dtype=object), 'alternates': None}
  [563] Base Marambio, Antartida Argentina
      categories: {'primary': 'central_government_office', 'alternate': array(['accommodation'], dtype=object)}
      taxonomy: {'primary': 'government_office', 'hierarchy': array(['community_and_government', 'government_office'], dtype=object), 'alternates': None}
  [564] Marambio Base
      categories: {'primary': 'airport', 'alternate': array(['armed_forces_branch', 'arts_and_entertainment'], dtype=object)}
      taxonomy: {'primary': 'airport', 'hierarchy': array(['travel_and_transportation', 'air_transport_facility_or_service',
       'airport'], dtype=object), 'alternates': None}
  [565] Base Antártica Marambio
      categories: {'primary': 'public_and_government_association', 'alternate': array(['central_government_office', 'accommodation'], dtype=object)}
      taxonomy: {'primary': 'government_office', 'hierarchy': array(['community_and_government', 'government_office'], dtype=object), 'alternates': None}
  [568] Esperanza Base, Antarctica
      categories: {'primary': 'landmark_and_historical_building', 'alternate': array(['fountain'], dtype=object)}
      taxonomy: {'primary': 'historic_site', 'hierarchy': array(['cultural_and_historic', 'historic_site'], dtype=object), 'alternates': None}
  [569] Base Esperanza, Antartida Argentina
      categories: {'primary': 'airport', 'alternate': array(['travel', 'accommodation'], dtype=object)}
      taxonomy: {'primary': 'airport', 'hierarchy': array(['travel_and_transportation', 'air_transport_facility_or_service',
       'airport'], dtype=object), 'alternates': None}
  [958] Camp Education Society's Dr. Arvind B. Telang IHM Chinchwad Pune - 19
      categories: {'primary': 'college_university', 'alternate': array(['campus_building', 'education'], dtype=object)}
      taxonomy: {'primary': 'college_university', 'hierarchy': array(['education', 'place_of_learning', 'college_university'],
      dtype=object), 'alternates': None}
  [975] Cidahu Camping Ground
      categories: {'primary': 'sporting_goods', 'alternate': array(['mountain'], dtype=object)}
      taxonomy: {'primary': 'sporting_goods_store', 'hierarchy': array(['shopping', 'specialty_store', 'sporting_goods_store'],
      dtype=object), 'alternates': None}
  [1010] Neumayer-Station II
      categories: {'primary': 'landmark_and_historical_building', 'alternate': array(['restaurant'], dtype=object)}
      taxonomy: {'primary': 'historic_site', 'hierarchy': array(['cultural_and_historic', 'historic_site'], dtype=object), 'alternates': None}
  [1110] Campo Creek Vineyards
      categories: {'primary': 'winery', 'alternate': array(['glass_and_mirror_sales_service'], dtype=object)}
      taxonomy: {'primary': 'winery', 'hierarchy': array(['food_and_drink', 'alcoholic_beverage_venue', 'winery'],
      dtype=object), 'alternates': None}
  [1139] The Mahanaim - God's Camp
      categories: {'primary': 'campground', 'alternate': array(['hotel', 'farm'], dtype=object)}
      taxonomy: {'primary': 'campground', 'hierarchy': array(['lodging', 'campground'], dtype=object), 'alternates': None}
  [1211] Fuel Tech Micro Gas Station & Dispensers
      categories: {'primary': 'industrial_equipment', 'alternate': None}
      taxonomy: {'primary': 'industrial_equipment_manufacturer', 'hierarchy': array(['services_and_business', 'b2b_service', 'manufacturer',
       'machinery_and_tool_manufacturer',
       'industrial_equipment_manufacturer'], dtype=object), 'alternates': None}
  [1260] Vintage Cocktail Camper
      categories: {'primary': 'bar', 'alternate': array(['event_planning', 'liquor_store'], dtype=object)}
      taxonomy: {'primary': 'bar', 'hierarchy': array(['food_and_drink', 'alcoholic_beverage_venue', 'bar'], dtype=object), 'alternates': None}
  [1363] VU GIFT Campus Jhang
      categories: {'primary': 'college_university', 'alternate': array(['education'], dtype=object)}
      taxonomy: {'primary': 'college_university', 'hierarchy': array(['education', 'place_of_learning', 'college_university'],
      dtype=object), 'alternates': None}
  [1418] Plateau Station
      categories: {'primary': 'landmark_and_historical_building', 'alternate': array(['armed_forces_branch', 'travel'], dtype=object)}
      taxonomy: {'primary': 'historic_site', 'hierarchy': array(['cultural_and_historic', 'historic_site'], dtype=object), 'alternates': None}
  [1466] Escola Técnica de Teatro Professor José Gomes Campos
      categories: {'primary': 'art_school', 'alternate': array(['public_school'], dtype=object)}
      taxonomy: {'primary': 'art_school', 'hierarchy': array(['education', 'place_of_learning', 'specialty_school', 'art_school'],
      dtype=object), 'alternates': None}
  [1484] Pioneer - Gas Station
      categories: {'primary': 'gas_station', 'alternate': None}
      taxonomy: {'primary': 'gas_station', 'hierarchy': array(['travel_and_transportation', 'fueling_station', 'gas_station'],
      dtype=object), 'alternates': None}
  [1825] Base antarctique Mawson
      categories: {'primary': 'landmark_and_historical_building', 'alternate': array(['train_station'], dtype=object)}
      taxonomy: {'primary': 'historic_site', 'hierarchy': array(['cultural_and_historic', 'historic_site'], dtype=object), 'alternates': None}
  [1904] Antarctic Kunlun Station
      categories: {'primary': 'landmark_and_historical_building', 'alternate': array(['train_station'], dtype=object)}
      taxonomy: {'primary': 'historic_site', 'hierarchy': array(['cultural_and_historic', 'historic_site'], dtype=object), 'alternates': None}
  [2101] COFFE Station Old town
      categories: {'primary': 'cafe', 'alternate': array(['coffee_shop'], dtype=object)}
      taxonomy: {'primary': 'cafe', 'hierarchy': array(['food_and_drink', 'casual_eatery', 'cafe'], dtype=object), 'alternates': None}
  [2134] Base antártica Concordia
      categories: {'primary': 'train_station', 'alternate': array(['landmark_and_historical_building'], dtype=object)}
      taxonomy: {'primary': 'train_station', 'hierarchy': array(['travel_and_transportation',
       'ground_transport_facility_or_service', 'rail_facility_or_service',
       'train_station'], dtype=object), 'alternates': None}
  [2135] Base antarctique Concordia
      categories: {'primary': 'landmark_and_historical_building', 'alternate': array(['train_station'], dtype=object)}
      taxonomy: {'primary': 'historic_site', 'hierarchy': array(['cultural_and_historic', 'historic_site'], dtype=object), 'alternates': None}
  [2137] JRMSU Katipunan Campus CTEd-Laboratory High School
      categories: {'primary': 'middle_school', 'alternate': None}
      taxonomy: {'primary': 'middle_school', 'hierarchy': array(['education', 'place_of_learning', 'school', 'middle_school'],
      dtype=object), 'alternates': None}
  [2183] Base antarctique Dumont-d'Urville
      categories: {'primary': 'landmark_and_historical_building', 'alternate': array(['transportation'], dtype=object)}
      taxonomy: {'primary': 'historic_site', 'hierarchy': array(['cultural_and_historic', 'historic_site'], dtype=object), 'alternates': None}
  [2298] Mario-Zucchelli-Station
      categories: None
      taxonomy: None
  [2308] Base McMurdo
      categories: {'primary': 'landmark_and_historical_building', 'alternate': array(['train_station', 'restaurant'], dtype=object)}
      taxonomy: {'primary': 'historic_site', 'hierarchy': array(['cultural_and_historic', 'historic_site'], dtype=object), 'alternates': None}
  [2310] Base antarctique McMurdo
      categories: {'primary': 'landmark_and_historical_building', 'alternate': array(['train_station'], dtype=object)}
      taxonomy: {'primary': 'historic_site', 'hierarchy': array(['cultural_and_historic', 'historic_site'], dtype=object), 'alternates': None}
  [2336] Lakeview Clinical Research
      categories: {'primary': 'medical_research_and_development', 'alternate': array(['doctor'], dtype=object)}
      taxonomy: {'primary': 'medical_research_institute', 'hierarchy': array(['education', 'research_institute', 'medical_research_institute'],
      dtype=object), 'alternates': None}

cd /work && python3 << 'EOF'
import overturemaps

bbox = (-180, -90, 180, -60)
reader = overturemaps.record_batch_reader('place', bbox=bbox)
if reader:
    import pyarrow as pa
    import geopandas as gpd
    from shapely import wkb
    
    table = pa.Table.from_batches(reader)
    df = table.to_pandas()
    
    # Parse geometries
    geoms = [wkb.loads(row) for row in df['geometry']]
    gdf = gpd.GeoDataFrame(df, geometry=geoms, crs='EPSG:4326')
    
    print(f"Total rows: {len(gdf)}")
    print(f"Bounds: {gdf.total_bounds}")
    
    # Check latitudes
    lats = gdf.geometry.y
    print(f"Lat range: {lats.min():.2f} to {lats.max():.2f}")
    antarctic = gdf[gdf.geometry.y < -60]
    print(f"Rows south of -60: {len(antarctic)}")
    
    if len(antarctic) > 0:
        # Show these
        for idx, row in antarctic.iterrows():
            name = row['names']['primary'] if row['names'] else 'N/A'
            cat = row['categories']['primary'] if row['categories'] else 'N/A'
            print(f"  {name} (lat={row.geometry.y:.2f}, cat={cat})")
EOF

Total rows: 2348
Bounds: [-179.9709      -89.913       179.99846041  -60.00601439]
Lat range: -89.91 to -60.01
Rows south of -60: 2348
  Tincorp Pa TI (lat=-84.41, cat=information_technology_company)
  Sara Couto - Intervenção Terapêutica (lat=-79.73, cat=physical_therapy)
  แหลมใหญ่แล็บ คลินิกเทคนิคการแพทย์ สมุทรสงคราม (lat=-78.88, cat=health_and_medical)
  Chittagong Victory National School-CVNS (lat=-80.42, cat=school)
  Quảng Cáo - In Nhanh SaPa (lat=-84.46, cat=N/A)
  Donata Farms & Land Services (lat=-82.77, cat=farm)
  Vet Rx Pet Health Center (lat=-82.14, cat=N/A)
  Work N Wear International (lat=-84.31, cat=sports_wear)
  The Wagging Tail Salon (lat=-82.21, cat=pet_groomer)
  Liso EstÚDio (lat=-77.24, cat=professional_services)
  New Launch Property (lat=-73.46, cat=real_estate)
  Ross Ice Shelf, Antarctica (lat=-70.61, cat=landmark_and_historical_building)
  十刻刺青室 (lat=-63.47, cat=N/A)
  Poeanuanua (lat=-64.61, cat=retail)
  Discover Italy (lat=-63.47, cat=tours)
  สอนต่อขนตาพัทยา ต่อขนตาพัทยา (lat=-65.12, cat=beauty_salon)
  Krāslavas Varavīksnes vidusskola (lat=-66.79, cat=high_school)
  مركز هيا هير للشعر الطبيعي/  Haya Center for Natural Hair (lat=-66.37, cat=beauty_salon)
  Esencia Alquímica: Aromaterapia y Cosmética Natural (lat=-66.23, cat=cosmetic_and_beauty_supplies)
  Maliha Farzam (lat=-66.37, cat=professional_services)
  Benjarong Antiques (lat=-65.60, cat=antique_store)
  The WedPixels (lat=-63.73, cat=event_photography)
  Clínica Bem Viver (lat=-65.66, cat=medical_service_organizations)
  Mazuchi Semijoias (lat=-65.69, cat=jewelry_store)
  لوزان فاشن -louzan fashion (lat=-62.96, cat=boutique)
  Peixaria do Porto (lat=-71.49, cat=brazilian_restaurant)
  Maria vilaró fotografía (lat=-76.89, cat=professional_services)
  Moda Boutique Online (lat=-74.45, cat=boutique)
  Saaol Heart Center BD Ltd. (lat=-81.20, cat=hospital)
  ร้านอาหารกลมกล่อมก่อนถึงสนามบิน (lat=-81.72, cat=restaurant)
  Licera Vision Care Santa Barbara (lat=-81.76, cat=optometrist)
  Kampala college  Center of English and Training Programs (lat=-80.71, cat=college_university)
  Muebles Marispal Algeciras (lat=-78.35, cat=furniture_store)
  Lamoon Cafe & Restaurant - สระบุรี (lat=-77.53, cat=cafe)
  ราชวิถีสลิมเเคร์ สาขาพิจิตร (lat=-74.26, cat=N/A)
  The Future English Language Centre (lat=-79.56, cat=language_school)
  פיצה גבעת יערים להזמנות חייגו: 0558845021 (lat=-83.60, cat=pizza_restaurant)
  Neringa Geštalto psichoterapeutė (lat=-81.28, cat=psychotherapist)
  スーパーホテル別府駅前 (lat=-79.17, cat=hotel)
  Exclusive Ladies Undergarments Shop (lat=-81.35, cat=lingerie_store)
  2006hair-Era (lat=-82.30, cat=hair_salon)
  Cap College Mariveles Bataan (lat=-82.49, cat=college_university)
  BarGains Lounge (lat=-81.30, cat=lounge)
  Farm Fresh at UPM (lat=-80.23, cat=italian_restaurant)
  قرية علي باجلية Ali Bajliyah (lat=-80.65, cat=town_hall)
  DoubleCare salon and spa (lat=-81.09, cat=beauty_salon)
  Complete Care Medical Services (lat=-80.54, cat=pharmacy)
  Cribs and Crayons (lat=-79.49, cat=day_care_preschool)
  白巷子虎尾林森店 (lat=-80.46, cat=tea_room)
  מעדני מניה Maadaney Mania (lat=-80.76, cat=delicatessen)
  Il Paradiso Delle Calze (lat=-79.17, cat=clothing_store)
  Ford Ranges (lat=-77.07, cat=mountain)
  Aire electronica MizaCar (lat=-76.25, cat=automotive)
  Amanti Restaurant (lat=-76.40, cat=restaurant)
  HUNG MANH Limited (lat=-77.98, cat=fashion)
  Simplement St. Onge (lat=-73.43, cat=N/A)
  Betty Asesoría inmobiliaria (lat=-74.72, cat=real_estate)
  Vital Jelly Art Training Class (lat=-74.80, cat=bakery)
  Harrowgate Hill Methodist Church (lat=-78.06, cat=church_cathedral)
  พ่วงข้างมอเตอร์ไซต์ พงษ์ศิริการช่าง วงเวียนไดโนเสาร์ภูเวียง (lat=-77.76, cat=motorcycle_repair)
  Moody Island (lat=-77.33, cat=landmark_and_historical_building)
  The BuddyCat บ้านสก็อตติชโฟลด์ (lat=-77.16, cat=urban_farm)
  MIZINO (lat=-70.69, cat=womens_clothing_store)
  Kids Spot Mount Gambier (lat=-68.03, cat=art_school)
  Te Fare Informatique (lat=-65.76, cat=hardware_store)
  Xuởng giày khiêu vũ chuyên nghiệp PK - Chuyên sỉ (lat=-70.80, cat=shoe_store)
  VAST Equipment & Truck Sales (lat=-70.25, cat=car_dealer)
  Hamburgueria do Du (lat=-66.43, cat=N/A)
  أشرف عبداللطيف لقطع غيار المرسيدس  (lat=-63.47, cat=N/A)
  شارك SHARK (lat=-63.55, cat=shopping)
  Kuya Insurance (lat=-60.90, cat=insurance_agency)
  Magui style (lat=-65.29, cat=retail)
  Alpha Kennels Australia (lat=-65.29, cat=pet_breeder)
  Healthdirect (lat=-61.10, cat=school)
  الفريد للنجف والتحف (lat=-67.64, cat=art_gallery)
  Jom Naiktaraf Masjid Al Muttaqin (lat=-71.97, cat=N/A)
  أفرشة المساجد (lat=-66.37, cat=N/A)
  শ্রী রাজ পিনাকী তান্ত্রিক (lat=-64.62, cat=beauty_and_spa)
  Over The Top Yard Cards (lat=-61.61, cat=event_planning)
  Sri Ramanujar Engineering College - Vandalur, Chennai - 127. (lat=-61.61, cat=college_university)
  Andie's Furniture (lat=-62.78, cat=furniture_store)
  Clinica Veterinária Baschirotto (lat=-64.47, cat=pet_store)
  Sooper Vegee (lat=-64.47, cat=eat_and_drink)
  Suzuki Nilphamari - Lahin Motors (lat=-65.77, cat=motorcycle_dealer)
  Goggrll Edappal (lat=-69.47, cat=burger_restaurant)
  YK Violin Music Class - Mandalay (lat=-70.08, cat=N/A)
  andrei.canton (lat=-70.08, cat=N/A)
  lichudurlach.arte (lat=-66.04, cat=N/A)
  One Moms Battle (lat=-71.93, cat=community_services_non_profits)
  Espaço Rosileide Domingues - Make Up, Sobrancelhas e Micropigmentação (lat=-68.42, cat=beauty_salon)
  Easy water - อีซี่วอเตอร์ เครื่องกรองน้ำราคาปลีก-ส่ง (lat=-65.38, cat=N/A)
  Jus nature Moucouaré (lat=-64.12, cat=cocktail_bar)
  Rockview Sunset Beach Resort -Patar Bolinao Pangasinan (lat=-62.59, cat=hotel)
  Sparklin D’ Lites LLC (lat=-63.47, cat=shaved_ice_shop)
  Shri Ram Marble Sirsa (lat=-61.27, cat=professional_services)
  The AI Mortgage Pro (lat=-63.62, cat=real_estate)
  Feiyu Mosaic (lat=-65.29, cat=N/A)
  barberia_melli (lat=-60.07, cat=barber)
  Blastic_007 (lat=-60.85, cat=N/A)
  Cuadrodeco (lat=-64.71, cat=N/A)
  Super-Gains (lat=-65.29, cat=vitamins_and_supplements)
  ICAN Management Services (lat=-69.17, cat=professional_services)
  老康泰日間照顧中心 (lat=-69.16, cat=community_services_non_profits)
  Los Vecinos (lat=-71.07, cat=N/A)
  Moon Art (lat=-70.08, cat=N/A)
  Royal Bedsheet & Towels (lat=-69.66, cat=furniture_store)
  Karpicentro (lat=-69.81, cat=professional_services)
  Melanoma Kills (lat=-71.52, cat=community_services_non_profits)
  Farias Business Communications (lat=-69.41, cat=professional_services)
  Beshie Pasabuy (lat=-66.37, cat=shopping)
  Hobbs Coast (lat=-74.83, cat=beach)
  Nội Thất Thuý Lưu -Thiết Kế Thi Công Trọn Gói -0971.032.051 (lat=-75.59, cat=professional_services)
  PP Al Muslim Beganjing (lat=-74.97, cat=religious_organization)
  Temple Martial Arts Kickboxing - Northwich (lat=-72.94, cat=martial_arts_club)
  Samira's Hair Fashion +599 98693311 (lat=-74.78, cat=beauty_salon)
  PT. Karya Taka Indonesia (lat=-75.32, cat=real_estate)
  KN Squared Constructions and Supplies (lat=-72.30, cat=real_estate)
  CAMBO Cleaner (lat=-75.43, cat=N/A)
  Resurrection Church (lat=-76.77, cat=church_cathedral)
  Iriga City Public Library (lat=-81.08, cat=public_and_government_association)
  Desia Koraput (lat=-79.43, cat=tours)
  Delight Sofa Care (lat=-81.07, cat=home_cleaning)
  Dinas Kebudayaan dan Pariwisata Provinsi Jambi (lat=-84.54, cat=public_and_government_association)
  Dinastia Nortena (lat=-81.92, cat=N/A)
  La casa del deporte (lat=-84.15, cat=clothing_store)
  World Vision Church of God (lat=-81.82, cat=church_cathedral)
  Kasa Sul Materiais de Construção (lat=-80.76, cat=building_supply_store)
  Rumah Makan Sari Raos Bu Endang (lat=-78.77, cat=indonesian_restaurant)
  ข้าวลงเข่งบางแสน (lat=-79.46, cat=thai_restaurant)
  Colegio Villa La Granja Mulchen (lat=-79.55, cat=N/A)
  Von Star Power (lat=-79.17, cat=pet_store)
  Amelis Center (lat=-80.30, cat=beauty_salon)
  Diskop UKM Jateng (lat=-82.12, cat=public_and_government_association)
  Ser Esencia Restaurant (lat=-84.84, cat=restaurant)
  Bildøy Marina (lat=-83.12, cat=N/A)
  On the docks (lat=-81.05, cat=lounge)
  Aaraish (lat=-78.67, cat=arts_and_crafts)
  AB Studio AE (lat=-79.54, cat=professional_services)
  Northern River View Hotel & Restaurant Hassa (lat=-81.68, cat=hotel)
  輔仁大學體育系 114級 體育表演會-《Hundred miles 百年百里路》 (lat=-80.53, cat=stadium_arena)
  Besaram Hardware & Machinery Sdn Bhd (lat=-77.50, cat=machine_shop)
  Los Reyes del Mueble (lat=-72.96, cat=home_goods_store)
  Comercial NAJO (lat=-74.25, cat=supermarket)
  黎齊妙中醫診所 (lat=-74.02, cat=hospital)
  Blessed Souls Photography & Film (lat=-74.59, cat=professional_services)
  จีทีโฟน ร้านซ่อมมือถือบ้านใหม่ โคราช (lat=-75.30, cat=N/A)
  Vip House Apartment Pattaya (lat=-77.16, cat=landmark_and_historical_building)
  Desa Suka Maju Kec.Kongbeng (lat=-77.45, cat=public_service_and_government)
  สวนสารพันธ์ (lat=-76.40, cat=N/A)
  Centre dentaire mghazli (lat=-80.79, cat=oral_surgeon)
  Tenang Ria Kopitiam (lat=-80.42, cat=breakfast_and_brunch_restaurant)
  dieukhaclienvu.com - phù điêu,điêu khắc đá nhân tạo composite,cảnh quan đẹp (lat=-78.63, cat=arts_and_crafts)
  مدينة رشيد Rosetta city (lat=-82.73, cat=N/A)
  B Design Lebanon (lat=-83.15, cat=aircraft_repair)
  R-AGE Nation Apparel (lat=-81.09, cat=womens_clothing_store)
  Dhacss Seaview Campus (lat=-80.18, cat=preschool)
  หอมฉุย หมึกย่าง (lat=-79.28, cat=restaurant)
  Gm comercialoficial (lat=-82.04, cat=hardware_store)
  Archives d'Orléans (lat=-85.00, cat=printing_services)
  Yoga Minh Thùy Quận 7  54/10 Lê Văn Lương (lat=-83.83, cat=beauty_and_spa)
  Pololo GYM (lat=-83.91, cat=gym)
  Basketball Forever (lat=-81.92, cat=mass_media)
  Iglesia Casa de Dios Restaurando Vidas para las Naciones . (lat=-84.80, cat=religious_organization)
  Melodic Music Point (lat=-81.80, cat=N/A)
  Golden Sky Pet Shop (lat=-83.33, cat=pet_store)
  Breaking Away Adventures (lat=-81.41, cat=tours)
  Huawei Setia City Mall by Access Mobile (lat=-81.77, cat=mobile_phone_store)
  Marc Silber (lat=-81.72, cat=professional_services)
  Kisan Farming Agro Tools (lat=-80.65, cat=agricultural_service)
  ภูน้ำจันทร์ฟาร์ม (lat=-81.12, cat=campground)
  Khmerdentdental clinic (lat=-76.48, cat=dentist)
  Atlantic Computers TT (lat=-78.18, cat=professional_services)
  江南 - Kannamu (lat=-74.26, cat=karaoke)
  Shemmy'sclothing (lat=-77.00, cat=clothing_store)
  Educatec Norte (lat=-77.16, cat=professional_services)
  سه نته رى ميلا بۆ جوانکاری / Mila Beauty Center/مركز ميلا للتجميل (lat=-77.42, cat=hospital)
  Leader Phone (lat=-78.15, cat=mobile_phone_store)
  Discount Hardware Store (lat=-74.96, cat=hardware_store)
  The Wedding Sequel (lat=-73.68, cat=event_photography)
  Doce Bebê - Enxoval Personalizado (lat=-69.66, cat=childrens_clothing_store)
  Tama Manège Import (lat=-64.17, cat=park)
  زراعة الشعر والتجميل (lat=-64.77, cat=beauty_salon)
  Babi fashons (lat=-60.24, cat=clothing_store)
  Connor Auto shop (lat=-62.19, cat=automotive_parts_and_accessories)
  Nheo YT Trùm Sỉ (lat=-62.59, cat=womens_clothing_store)
  নুজহা নকশী ঘর (lat=-63.31, cat=grocery_store)
  ALPHA Décoration Batna (lat=-64.85, cat=N/A)
  AIPO GROUP (lat=-65.00, cat=industrial_company)
  Rastaclat (lat=-65.66, cat=N/A)
  Grand Graha Selaras Bekasi (lat=-60.59, cat=real_estate)
  Kotek Cartridge (lat=-68.84, cat=printing_services)
  Pistólic (lat=-66.65, cat=mexican_restaurant)
  Greenfin the pond studio (lat=-68.46, cat=aquatic_pet_store)
  Domino Bangladesh Technology (lat=-65.29, cat=b2b_science_and_technology)
  Beauty Bloom (lat=-63.47, cat=fashion)
  EVE SPA (lat=-63.47, cat=beauty_and_spa)
  Wimer-auto (lat=-60.17, cat=N/A)
  Ariel.União Veículos (lat=-60.17, cat=car_dealer)
  Transportes Fralse (lat=-60.59, cat=transportation)
  Catalani Propiedades (lat=-65.07, cat=real_estate_service)
  ASerildasAvon (lat=-63.23, cat=N/A)
  The British School Kathmandu (lat=-68.63, cat=school)
  Bhaktapur Readymade Doors suppliers wholesale and retail (lat=-70.08, cat=professional_services)
  Lyn’s Plaque Maker (lat=-70.08, cat=trophy_shop)
  La Legión Pichincha (lat=-69.62, cat=school)
  Xưởng Sản Xuất Phụ Kiện Xe Điện HỒNG THANH 0986053326 (lat=-70.56, cat=professional_services)
  Mount Lopez (lat=-72.00, cat=mountain)
  Garniture Gallery (lat=-74.86, cat=mens_clothing_store)
  Sea La Vie  Laguna Resort (lat=-75.93, cat=hotel)
  Centre De Diagnostic Eyano/Bukavu (lat=-76.02, cat=diagnostic_services)
  Creative Events (lat=-77.62, cat=caterer)
  Institute of Automotive Engineering (lat=-72.61, cat=school)
  PRP Lubrificantes (lat=-74.45, cat=automotive_repair)
  Komban Nursery (lat=-75.02, cat=nursery_and_gardening)
  LavaCar Wagner e Claudia (lat=-76.63, cat=auto_detailing)
  Custom hats (lat=-75.63, cat=N/A)
  รักคิ้ว: By เดียร์คิ้วสวย 087-920-0447 (lat=-81.52, cat=beauty_and_spa)
  Şendul Kardeşler Tarım Limited Şirketi (lat=-80.30, cat=agricultural_cooperatives)
  Ozhousestudio (lat=-82.08, cat=topic_concert_venue)
  Cfc Paranaiba (lat=-82.03, cat=driving_school)
  足立屋 (lat=-84.99, cat=shopping)
  หมูกระทะชั่งกิโล169 บาท (lat=-82.34, cat=thai_restaurant)
  Irantha Fonseka (lat=-80.53, cat=N/A)
  Bali Teknik Utama (lat=-79.49, cat=computer_store)
  Coffee and Tea (lat=-82.77, cat=coffee_shop)
  Bert Lozada Swim School - St.Gabriel International School (lat=-80.53, cat=school)
  วัดป่าหลวงพ่อวิริยังค์ จังหวัดนครราชสีมา (lat=-81.62, cat=buddhist_temple)
  Rachawadee Surin Beach (lat=-82.98, cat=accommodation)
  周帥專業搬家 (lat=-64.26, cat=movers)
  えがおクリニック (lat=-74.08, cat=health_and_medical)
  I Love Change Fashion (lat=-70.84, cat=clothing_store)
  Everything Store (lat=-75.84, cat=shopping)
  West Antarctica (lat=-79.00, cat=national_park)
  𝐀𝐌𝐀𝐃𝐑𝐀𝐌𝐀 (lat=-70.08, cat=N/A)
  Señor Waffle (lat=-61.15, cat=breakfast_and_brunch_restaurant)
  Scenari Academia de Danza (lat=-65.07, cat=dance_school)
  DepEd Tayo- Formon National High School - 301632 (lat=-77.15, cat=high_school)
  Evans Point (lat=-72.47, cat=landmark_and_historical_building)
  Alpha Lounge (lat=-82.40, cat=lounge)
  Splashback London (lat=-60.24, cat=interior_design)
  บ้านพักชะอำ นลินพูลวิลล่า (lat=-63.47, cat=hotel)
  Springbrook Motel & RV Park (lat=-65.35, cat=motel)
  Bmart minimart (lat=-81.57, cat=grocery_store)
  HISNearness NaviMumbai (lat=-81.69, cat=church_cathedral)
  Kỳ Duyên - Tư Vấn Mua Xe Ô Tô Bình Định 0905474590 (lat=-77.66, cat=automotive_dealer)
  AKS Manufactory (lat=-61.52, cat=engineering_services)
  La Cantina de iano (lat=-74.45, cat=restaurant)
  Pingu's English Hai Phong - Vinhomes Imperia (lat=-76.59, cat=language_school)
  Vinh Food (lat=-61.16, cat=fast_food_restaurant)
  Destino 40 (lat=-66.37, cat=N/A)
  Nittha shopงานป้ายเดรส วินเทจ มินิมอล (lat=-69.82, cat=N/A)
  Big T Airport - 64GA (lat=-74.45, cat=airport)
  Jamela Payne (lat=-80.83, cat=panamanian_restaurant)
  In Aeternum Historia (lat=-84.13, cat=print_media)
  พิกุลทอง เมทัลชีท (lat=-76.81, cat=N/A)
  Jacklyn艾多美Atomy (lat=-83.25, cat=N/A)
  Arest Cafe (lat=-78.95, cat=restaurant)
  
[... truncated ...]
  Elegant School of Dance (lat=-71.96, cat=dance_school)
  CodeTech informática (lat=-66.37, cat=computer_hardware_company)
  Arqthrill (lat=-74.45, cat=architectural_designer)
  Bandith Card Store (lat=-72.62, cat=wedding_planning)
  เงินติดล้อ สาขาโขงเจียม (lat=-66.37, cat=financial_service)
  Taiyo-keito (lat=-60.52, cat=shopping)
  การจัดการ  มหาวิทยาลัยกรุงเทพสุวรรณภูมิ (lat=-61.61, cat=college_university)
  Cafe Jomo (lat=-73.59, cat=N/A)
  Empresa Municipal de Comercio y Gastronomia Imías (lat=-81.62, cat=public_utility_company)
  One Stop Global Consultants (lat=-70.08, cat=professional_services)
  Sofiya Designs Gh (lat=-70.08, cat=N/A)
  منيا القمح توداي (lat=-68.89, cat=community_services_non_profits)
  SK carpet (lat=-64.77, cat=shopping)
  水草館 (lat=-79.75, cat=museum)
  Maka-TRILLO Sizzling Hauz (lat=-71.53, cat=restaurant)
  Centro recreativo yuyako (lat=-83.96, cat=N/A)
  COFFE Station Old town (lat=-84.61, cat=cafe)
  UAP Partners Sdn Bhd (lat=-84.20, cat=insurance_agency)
  東京スタイル鶏らーめん ど・みそ鶏 (lat=-80.82, cat=japanese_restaurant)
  Shri Krishna College of Education - SKCE (lat=-77.00, cat=education)
  Nostos Mykonos Yachts (lat=-75.84, cat=boat_rental_and_training)
  بيعلي ستور (lat=-74.36, cat=luggage_store)
  Compartilhagem (lat=-74.78, cat=arts_and_crafts)
  Jindabyne Escape Rooms (lat=-75.04, cat=escape_rooms)
  Onodera User Run Myanmar (lat=-74.79, cat=middle_school)
  James Bike Shop (lat=-78.93, cat=motorcycle_repair)
  Lailo Farm Sanctuary (lat=-73.82, cat=community_services_non_profits)
  そろばんcosmos (lat=-81.19, cat=N/A)
  Masjid Terapung Tanjung Bungah, Pulau Pinang (lat=-83.44, cat=mosque)
  Sumampong General Merchandise Inc. (lat=-83.83, cat=hardware_store)
  Angel Rising (lat=-84.12, cat=N/A)
  Gazi Electronics (lat=-82.40, cat=mobile_phone_store)
  Pondok Pesantren Tahfidz Al Quran Nuril Anwar (lat=-79.81, cat=cottage)
  Gayans Textile (lat=-79.56, cat=clothing_store)
  Awad Al-Zughier & Partners Co. شركة عوض الزغير وشركاه (lat=-80.75, cat=industrial_equipment)
  The Family Studio (lat=-83.75, cat=professional_services)
  BTC construcciones (lat=-84.89, cat=N/A)
  Studio - Áo Cưới Hữu Hòa (lat=-83.18, cat=N/A)
  Mansingh Dharma S.S (lat=-82.12, cat=school)
  ห้องสมุดประชาชนเฉลิมราชกุมารีอำเภอเมืองแม่ฮ่องสอน (lat=-82.57, cat=library)
  Gokul restaurant ode (lat=-84.27, cat=indian_restaurant)
  Academia Nacional de Sommeliers y Gastrónomos (lat=-83.27, cat=school)
  สึนามิ ยากิโซบะ หมูย่างเกาหลี ณ พันท้ายนรสิงห์ อาหารญี่ปุ่น อาหารเกาหลี (lat=-79.77, cat=japanese_restaurant)
  النور للأقمشة - صبراتة (lat=-78.49, cat=fabric_store)
  ชามใหญ่ไก่มะระ (lat=-74.41, cat=restaurant)
  Yasir Nishat Center (lat=-75.03, cat=shopping_center)
  AMBALA (lat=-73.84, cat=sign_making)
  Kick and Self (lat=-73.37, cat=motorcycle_repair)
  Dr Henri Bauer (lat=-75.10, cat=N/A)
  Base antártica Concordia (lat=-75.10, cat=train_station)
  Base antarctique Concordia (lat=-75.10, cat=landmark_and_historical_building)
  Maahad Tahfiz Al-Quran Fattabi'Uni (lat=-73.44, cat=education)
  JRMSU Katipunan Campus CTEd-Laboratory High School (lat=-69.18, cat=middle_school)
  Hue Nails 繪甲 (lat=-70.08, cat=beauty_salon)
  汐入駅前歯科 (lat=-71.97, cat=N/A)
  โรงเรียนบ้านหนองคู สพป.ขอนแก่น เขต 4 (lat=-72.40, cat=school)
  CH Xe máy - Điện máy- Nội thất QUỐC THẮNG (lat=-71.07, cat=motorcycle_dealer)
  Sunstar Motor Jepara (lat=-73.19, cat=automotive_dealer)
  Điều trị Động Kinh - Phòng khám Đông y Nguyễn Hữu Toàn (lat=-72.87, cat=health_and_medical)
  Debesmscat - College of Industrial Technology (lat=-71.30, cat=education)
  হেলথ কিউর ডায়াগনস্টিক এন্ড হাসপাতাল (lat=-69.12, cat=hospital)
  Rádio Alerta Penedo (lat=-68.21, cat=radio_station)
  AWS Startups (lat=-65.63, cat=internet_service_provider)
  And Then Some (lat=-63.47, cat=arts_and_crafts)
  MORE MEAT (lat=-62.59, cat=restaurant)
  La Solana Buitrago (lat=-63.23, cat=pub)
  Dr. Amina Khan-Obgyn (lat=-65.29, cat=obstetrician_and_gynecologist)
  Photo Time Photography (lat=-63.80, cat=event_photography)
  Good Mates Removals & Storage (lat=-63.47, cat=movers)
  Shagun Mobile & Electronics Amdara (lat=-63.01, cat=shopping)
  Saaol Heart Center Jalgaon (lat=-61.84, cat=cardiologist)
  Arung Jeram Takengon (lat=-73.17, cat=tours)
  Alpha Marine Equipment, Dubai (lat=-70.98, cat=boat_parts_and_accessories)
  Hero Fighting ยิมมวย พิลาทิส ปิ่นเกล้า (lat=-69.40, cat=pilates_studio)
  7heaven_restro (lat=-67.96, cat=chinese_restaurant)
  Capetown Villas Igatpuri (lat=-70.08, cat=holiday_rental_home)
  Kimstore (lat=-68.91, cat=retail)
  BMW Golfsport (lat=-70.70, cat=car_dealer)
  The Chocolate Room - Anna Nagar (lat=-71.33, cat=chocolatier)
  โรงเรียนบ้านโคกงิ้วพระครูวิบูลญาณสถิตอุปถัมภ์ (lat=-73.23, cat=school)
  hair_salon.sof (lat=-69.82, cat=beauty_salon)
  Idealize Odontologia Avançada (lat=-70.08, cat=dentist)
  SAK Sports (lat=-67.34, cat=clothing_store)
  CHENG AN Dimsum (lat=-65.95, cat=diner)
  ASWA MEDIA (lat=-63.08, cat=media_news_company)
  Ruta Rent A Car (lat=-62.76, cat=car_rental_agency)
  Élida Santos Interiores (lat=-60.25, cat=N/A)
  Easyworld (lat=-60.04, cat=installment_loans)
  Sreema Feeds (lat=-62.74, cat=industrial_company)
  Bagsul (lat=-64.77, cat=N/A)
  Wesley College Old Students Association (lat=-65.07, cat=school)
  ร้านกองแล ตาพระยา (lat=-71.42, cat=automotive_parts_and_accessories)
  โรงเรียนบ้านตาขุนวิทยา (lat=-70.73, cat=middle_school)
  Azmine (lat=-71.52, cat=womens_clothing_store)
  Sengkang East (lat=-69.39, cat=community_services_non_profits)
  VP Art House - Vạn Phúc Music (lat=-73.02, cat=music_school)
  Plot Wala (lat=-73.01, cat=real_estate_agent)
  Chiu2 beauty art studio (lat=-64.92, cat=makeup_artist)
  Base antarctique Dumont-d'Urville (lat=-66.66, cat=landmark_and_historical_building)
  Glamorous Creations (lat=-63.31, cat=shopping)
  PAKAR Gearbox jerteh (lat=-62.27, cat=N/A)
  South Magnetic Pole (lat=-64.28, cat=landmark_and_historical_building)
  ບ່າວ ບັນໃຈ ຂາຍເຄື້ອງອອນລາຍທຸກຊະນິດ (lat=-66.87, cat=N/A)
  Yeerongpilly Green Village Markets (lat=-65.13, cat=eat_and_drink)
  ETS BOUVY Motoculture (lat=-64.14, cat=agricultural_service)
  Hiraya Nest (lat=-62.88, cat=social_media_agency)
  All Pro Plumbing (lat=-61.91, cat=plumbing)
  Ace of fades barbershop (lat=-61.64, cat=barber)
  Blue Diamond Moldova (lat=-65.29, cat=N/A)
  Khalu (lat=-65.07, cat=beauty_and_spa)
  Impact Painting Services (lat=-65.66, cat=painting)
  Deleite Infinito (lat=-65.07, cat=candy_store)
  WG Mecânica Tec (lat=-67.88, cat=automotive_repair)
  Sam cell (lat=-69.59, cat=mobile_phone_store)
  Bruno Auto Mobile's  (lat=-70.08, cat=engineering_services)
  شركة الأركان لقطع غيار وزيوت السيارات\nAlarkan Auto Parts & Oils (lat=-70.72, cat=shopping)
  一般社団法人　リブ・ライフ (lat=-73.02, cat=home_service)
  Los Pollos Al Carbon (lat=-73.13, cat=mexican_restaurant)
  Bonbons De K (lat=-75.76, cat=restaurant)
  Basti Jalbani (lat=-75.67, cat=public_service_and_government)
  四季海滙 (lat=-78.44, cat=N/A)
  ทำประกันกับอาอิซะห์ (lat=-80.36, cat=beauty_and_spa)
  Coach-Kreez-Place and Resort (lat=-81.52, cat=hotel)
  Ventura Auto Peças Ltda (lat=-85.00, cat=automotive_parts_and_accessories)
  Firehouse Subs (lat=-82.86, cat=sandwich_shop)
  Seaoil Dangcagan (lat=-80.46, cat=gas_station)
  โนนสำราญบ้านเฮา (lat=-84.34, cat=community_services_non_profits)
  汽車收容所 (lat=-81.36, cat=car_dealer)
  Dra Gracy Alencar Aesthetics (lat=-76.03, cat=N/A)
  Fashion in DHAKA (lat=-73.83, cat=womens_clothing_store)
  Moorunga Angus (lat=-74.42, cat=real_estate)
  UPT SDN Kedawung 01 (lat=-77.74, cat=elementary_school)
  Phụ Tùng Kim Khí Giá Rẻ (lat=-78.62, cat=N/A)
  Bremer Bay Mini Golf & Kiosk (lat=-78.21, cat=tours)
  The Seasoning Outlet (lat=-79.69, cat=shopping)
  絵画教室　みんなのアトリエ (lat=-84.40, cat=art_school)
  บัส สบาย เจิด (lat=-82.27, cat=cafe)
  ヒッコリー (lat=-67.39, cat=N/A)
  KinnKoff coffee (lat=-75.48, cat=restaurant)
  Knox Pharmacy (lat=-79.31, cat=pharmacy)
  The Maris - Căn Hộ Biển Vệ Tinh (lat=-62.92, cat=real_estate)
  Маҷаллаи “Дин ва ҷомеа” (lat=-63.23, cat=media_news_company)
  Кучковская Татьяна (lat=-63.83, cat=beauty_salon)
  Beauty Salon Tý Hair (lat=-70.38, cat=beauty_salon)
  Green Pc (lat=-84.76, cat=computer_store)
  Reems Closet Deals (lat=-83.25, cat=thrift_store)
  กองการศึกษาองค์การบริหารส่วนตำบลกันทรารมย์ (lat=-81.52, cat=public_and_government_association)
  Od Eliana Vaca (lat=-81.99, cat=N/A)
  Los taquitos de pm - Recursos Humanos (lat=-81.10, cat=restaurant)
  Starr Pharmacy Roban (lat=-74.95, cat=health_and_medical)
  MAX Reprise (lat=-74.44, cat=automotive_repair)
  Antarctic Plateau (lat=-77.00, cat=landmark_and_historical_building)
  Chácara Espaço Colorê Festas e Eventos (lat=-74.40, cat=event_planning)
  ボディメイクサロンJolly (lat=-80.53, cat=health_and_medical)
  Nadhi/நதி - The Online Saree Store (lat=-80.25, cat=N/A)
  Pak Polytechnic Institute (lat=-82.31, cat=college_university)
  海港青年商會 JCI Harbour (lat=-83.71, cat=community_services_non_profits)
  Asha Physiotherapy Center (lat=-83.89, cat=physical_therapy)
  Shree Aakash Electronics (lat=-83.73, cat=electronics)
  Eglise Chretienne Evangelique au Congo/kinshasa (lat=-82.89, cat=religious_organization)
  Mount Ellis (lat=-79.87, cat=landmark_and_historical_building)
  Warning Five จำหน่ายสายนาฬิกาหนังแท้ สายนาฬิกา smartwatch (lat=-74.46, cat=N/A)
  Estratto［エストラット］ (lat=-71.75, cat=retail)
  Majesty Café - Nabas, Aklan (lat=-71.88, cat=coffee_shop)
  Ocean View Apt- Cox'sbazar (lat=-71.61, cat=hotel)
  Bit7awa (lat=-72.87, cat=internet_marketing_service)
  মুক্তি হারবাল চিকিৎসালয়-Mukti Herbal Chikitsalay (lat=-69.43, cat=family_practice)
  معمل بركات مسلم ابن عقيل (lat=-63.55, cat=N/A)
  APEC HOMES Bulacan-Erica Baltazar (lat=-63.15, cat=real_estate_agent)
  Built by Kidz Parties (lat=-64.33, cat=school)
  Face Bar Cle (lat=-66.76, cat=N/A)
  Big Smoke Bbq (lat=-63.23, cat=auto_detailing)
  Live Life Loud Designs (lat=-61.94, cat=N/A)
  Nông Sản Mai Cồ (lat=-61.38, cat=farm)
  Mitsubishi Pili Camarines Sur - By Stephen Camacho (lat=-63.74, cat=N/A)
  Herrlich Beauty By Cloriss (lat=-68.91, cat=beauty_salon)
  RangRays Media Works (lat=-70.08, cat=mass_media)
  Little Honey NZ (lat=-67.07, cat=N/A)
  Academic Assist Co. (lat=-60.49, cat=boutique)
  E17 Telecom (lat=-60.21, cat=internet_service_provider)
  Peluquería Gloria Frutillar (lat=-60.41, cat=beauty_and_spa)
  Tramites al Volante (lat=-60.24, cat=driving_school)
  Más Tiempo Juntos MX (lat=-64.17, cat=health_and_medical)
  The Navyandhra School - Jr Wing (lat=-62.45, cat=school)
  Two Swords Tattoo Co. (lat=-65.29, cat=tattoo_and_piercing)
  Highrise Scenic Farm Resort (lat=-72.33, cat=hotel)
  กุ้งไทย  "นสพ.กุ้งไทย" (lat=-70.67, cat=mass_media)
  𝗬𝗢𝗨𝗦𝗦𝗘𝗙 𝗕𝗘𝗡𝗧𝗔𝗟𝗕𝗘 (lat=-70.36, cat=N/A)
  North Store (lat=-70.08, cat=shopping)
  ธนพรพลาสแพ็ค ถุงพลาสติก-สกรีนโลโก้ ราคาโรงงาน (lat=-63.98, cat=industrial_company)
  Fitiavana Mickael (lat=-63.47, cat=N/A)
  Redeemed Christian Church Of God Kingdom Connection Chapel in Goliad Texas (lat=-64.77, cat=church_cathedral)
  D'Luxxe Manufacturing Corp. (lat=-66.79, cat=beauty_product_supplier)
  Mercado Grazi (lat=-66.37, cat=grocery_store)
  Bewas dil muhingi (lat=-62.35, cat=N/A)
  مركز سنان الجراحي ـ Snan Surgical Center (lat=-70.05, cat=emergency_room)
  Electric Help BD (lat=-69.41, cat=N/A)
  Bengkel las bojonegoro (lat=-69.66, cat=professional_services)
  วัดนอก ตำบลไชยคราม  อำเภอดอนสัก จังหวัดสุราษฎร์ธานี (lat=-71.85, cat=buddhist_temple)
  Mount Koons (lat=-72.72, cat=landmark_and_historical_building)
  Checkers Custard (lat=-68.91, cat=eat_and_drink)
  OWWO Boneless Korean Fried Chicken (lat=-68.35, cat=chicken_restaurant)
  Chatime Philippines (lat=-76.55, cat=N/A)
  Canada Glacier (lat=-77.62, cat=landmark_and_historical_building)
  Lake Bonney (Antarctica) (lat=-77.72, cat=lake)
  Geleira de Taylor (lat=-77.73, cat=monument)
  Lake Joyce (lat=-77.72, cat=lake)
  Jackson Pond (lat=-77.55, cat=lake)
  LTS Aircon Services (lat=-73.96, cat=engineering_services)
  HONDA Mạnh Hùng Quỳnh Côi (lat=-73.20, cat=retail)
  Mr.FAWZ Restaurant (lat=-79.43, cat=hotel)
  CALIRA & CHESKA  Villa & Resort (lat=-84.52, cat=holiday_rental_home)
  Psicóloga Serly C. de Souza (lat=-77.88, cat=psychotherapist)
  Mario-Zucchelli-Station (lat=-74.69, cat=N/A)
  Trường Mầm Non An Khánh A (lat=-78.63, cat=school)
  Pegasus Field (lat=-77.96, cat=airport)
  Cape Armitage (lat=-77.89, cat=landmark_and_historical_building)
  McMurdo General Hospital (lat=-77.85, cat=hospital)
  ZiroZiro (lat=-77.85, cat=religious_organization)
  Southern Exposure (lat=-77.85, cat=bar)
  Gallagher Pub (lat=-77.85, cat=bar)
  Coffee House (lat=-77.85, cat=coffee_shop)
  Chapel of the Snows (lat=-77.85, cat=church_cathedral)
  Base McMurdo (lat=-77.85, cat=landmark_and_historical_building)
  Polární stanice McMurdo (lat=-77.85, cat=landmark_and_historical_building)
  Base antarctique McMurdo (lat=-77.85, cat=landmark_and_historical_building)
  Monte Erebus (lat=-77.53, cat=landmark_and_historical_building)
  Lewis Bay (lat=-77.33, cat=beach)
  Steve Kilbey (lat=-83.95, cat=N/A)
  Risen Savior Academy Davao City (lat=-83.83, cat=school)
  One World Immigration Karachi (lat=-84.97, cat=immigration_law)
  King Glacier (lat=-83.48, cat=landmark_and_historical_building)
  Kimpich Store (lat=-72.94, cat=beauty_and_spa)
  Narrow Neck (lat=-73.10, cat=landmark_and_historical_building)
  Sales Store (lat=-61.88, cat=N/A)
  Cương Audio (lat=-60.49, cat=N/A)
  DreamHome Property (lat=-63.77, cat=real_estate)
  Megan Smith - Lodge Real Estate REAA 2008 (lat=-67.59, cat=real_estate_agent)
  Vaříme s Mony (lat=-71.75, cat=N/A)
  กล่องสุ่ม อาหารทะเล499 กุ้งมังกร ปลีก-ส่ง By . ออม seafood (lat=-79.81, cat=seafood_restaurant)
  Kitchora (lat=-74.59, cat=real_estate)
  Çebu Gayrımenkul (lat=-74.45, cat=real_estate)
  GNL Marketing (lat=-61.63, cat=marketing_agency)
  Ace Volleyball & Athletics (lat=-63.47, cat=sports_club_and_league)
  PR by Judinil Berrios (lat=-63.86, cat=N/A)
  مؤسسة العدالة القانونيه (lat=-78.24, cat=lawyer)
  悦聖翡翠 (lat=-82.30, cat=fashion)
  បក្សីប្រយុទ្ធ- Dk666 (lat=-81.69, cat=N/A)
  Van Anh Cleaning (lat=-79.95, cat=home_cleaning)
  FREAK of RAVE (lat=-63.36, cat=event_planning)
  Brits School of English (lat=-61.77, cat=language_school)
  Lakeview Clinical Research (lat=-64.41, cat=medical_research_and_development)
  Latidos De Fe (lat=-61.53, cat=social_service_organizations)
  La Cura Tv (lat=-62.03, cat=N/A)
  Jania Rocha Advocacia (lat=-62.93, cat=lawyer)
  Confeitaria Berries (lat=-64.12, cat=N/A)
  Thu huyen Mobile (lat=-63.26, cat=mobile_phone_store)
  EnterTrainment Junction Groups and Events (lat=-65.64, cat=museum)
  printcraft (lat=-63.49, cat=sign_making)
  Hotel 3 banderas Manzanillo de Mar (lat=-62.84, cat=hotel)
  Sonrieme Clinica Dental (lat=-63.17, cat=N/A)
  ไก่หมุนจิระภา สาขาสามแยกวัดป่าคา เฉลิมพระเกียรติ สระบุรี By ครกทอง (lat=-84.44, cat=thai_restaurant)
  Monitos Bebechitos (lat=-79.18, cat=childrens_clothing_store)

cd /work && python3 << 'EOF'
import overturemaps

bbox = (-180, -90, 180, -60)
reader = overturemaps.record_batch_reader('place', bbox=bbox)
if reader:
    import pyarrow as pa
    import geopandas as gpd
    from shapely import wkb
    
    table = pa.Table.from_batches(reader)
    df = table.to_pandas()
    
    # Parse geometries
    geoms = [wkb.loads(row) for row in df['geometry']]
    gdf = gpd.GeoDataFrame(df, geometry=geoms, crs='EPSG:4326')
    
    # Let's look at what the "taxonomy" field contains for research/station related items
    # First filter for items with taxonomy containing "research" or "station"
    keywords = ['research', 'station', 'base', 'antarctic', 'antarctica', 'scientific']
    
    selected = []
    for idx, row in gdf.iterrows():
        name = row['names']['primary'] if row['names'] else ''
        name_lower = name.lower()
        if any(kw in name_lower for kw in keywords):
            selected.append(row)
    
    print(f"Found {len(selected)} potential station matches")
    for s in selected[:50]:
        name = s['names']['primary'] if s['names'] else 'N/A'
        cat = s['categories']['primary'] if s['categories'] else 'N/A'
        tax = s['taxonomy']['primary'] if s['taxonomy'] else 'N/A'
        print(f"  {name} (lat={s.geometry.y:.2f}, lon={s.geometry.x:.2f}) cat={cat}, tax={tax}")
EOF

Found 51 potential station matches
  Ross Ice Shelf, Antarctica (lat=-70.61, lon=-177.89) cat=landmark_and_historical_building, tax=historic_site
  West Antarctica (lat=-79.00, lon=-100.00) cat=national_park, tax=national_park
  Antarctic Peninsula (lat=-69.50, lon=-65.00) cat=landmark_and_historical_building, tax=historic_site
  Mount Wilson (Antarctica) (lat=-68.45, lon=-65.55) cat=mountain, tax=mountain
  Master Bees Stationeries & Souvenirs (lat=-75.14, lon=-66.80) cat=souvenir_shop, tax=souvenir_store
  Errera Channel, Antarctica (lat=-64.70, lon=-62.59) cat=landmark_and_historical_building, tax=historic_site
  Paradise Bay, Antarctic Peninsula (lat=-64.91, lon=-62.90) cat=landmark_and_historical_building, tax=historic_site
  Brown Station, Antarctic Peninsula (lat=-64.89, lon=-62.87) cat=landmark_and_historical_building, tax=historic_site
  Brown Station (lat=-64.89, lon=-62.87) cat=landmark_and_historical_building, tax=historic_site
  González Videla Antarctic Base (lat=-64.82, lon=-62.86) cat=airport, tax=airport
  Port Lockroy, Antarctic Peninsula (lat=-64.82, lon=-63.49) cat=landmark_and_historical_building, tax=historic_site
  Mount William (Antarctica) (lat=-64.78, lon=-63.68) cat=mountain, tax=mountain
  Lemaire Channel, Antarctic Peninsula (lat=-65.10, lon=-63.98) cat=landmark_and_historical_building, tax=historic_site
  Palmer Station, Antarctica (lat=-64.77, lon=-64.05) cat=home_developer, tax=home_developer
  Mount Jackson (Antarctica) (lat=-71.36, lon=-63.41) cat=mountain, tax=mountain
  New Lucena Municipal Police Station (lat=-80.87, lon=-61.35) cat=public_service_and_government, tax=community_and_government
  Esperanto Island, Antarctica (lat=-62.64, lon=-60.36) cat=landmark_and_historical_building, tax=historic_site
  Cierva Cove, Antarctic Peninsula (lat=-64.15, lon=-60.91) cat=landmark_and_historical_building, tax=historic_site
  Antarctica, Antarctic Circle (lat=-64.05, lon=-60.93) cat=landmark_and_historical_building, tax=historic_site
  Mikkelsen Harbour, Antarctic Peninsula (lat=-63.90, lon=-60.77) cat=landmark_and_historical_building, tax=historic_site
  Carlini Base (lat=-62.24, lon=-58.67) cat=arts_and_entertainment, tax=arts_and_entertainment
  Bellingshausen Russian Antarctic Station (lat=-62.20, lon=-58.96) cat=educational_research_institute, tax=educational_research_institute
  King George Bay (Antarctica) (lat=-62.10, lon=-58.08) cat=landmark_and_historical_building, tax=historic_site
  Base Marambio, Antartida Argentina (lat=-64.24, lon=-56.62) cat=central_government_office, tax=government_office
  Marambio Base (lat=-64.24, lon=-56.63) cat=airport, tax=airport
  Base Antártica Marambio (lat=-64.24, lon=-56.62) cat=public_and_government_association, tax=government_office
  Chapel of the Blessed Virgin of Lujan, Antarctica (lat=-64.24, lon=-56.63) cat=church_cathedral, tax=christian_place_of_worship
  Esperanza Base, Antarctica (lat=-63.40, lon=-57.00) cat=landmark_and_historical_building, tax=historic_site
  Base Esperanza, Antartida Argentina (lat=-63.40, lon=-57.00) cat=airport, tax=airport
  British Antarctic Territory (lat=-75.00, lon=-50.00) cat=landmark_and_historical_building, tax=historic_site
  Neumayer-Station II (lat=-70.65, lon=-8.25) cat=landmark_and_historical_building, tax=historic_site
  Antarctica/Troll (lat=-72.01, lon=2.54) cat=landmark_and_historical_building, tax=historic_site
  Fuel Tech Micro Gas Station & Dispensers (lat=-65.34, lon=15.47) cat=industrial_equipment, tax=industrial_equipment_manufacturer
  Plateau Station (lat=-79.25, lon=40.50) cat=landmark_and_historical_building, tax=historic_site
  Pioneer - Gas Station (lat=-78.91, lon=43.93) cat=gas_station, tax=gas_station
  Lake Glubokoye (Antarctica) (lat=-67.67, lon=45.87) cat=lake, tax=lake
  Beaver Lake (Antarctica) (lat=-70.78, lon=68.30) cat=landmark_and_historical_building, tax=historic_site
  Nicholas Range (Antarctic) (lat=-66.67, lon=55.62) cat=mountain, tax=mountain
  Base antarctique Mawson (lat=-67.60, lon=62.87) cat=landmark_and_historical_building, tax=historic_site
  East Antarctica (lat=-80.00, lon=80.00) cat=landmark_and_historical_building, tax=historic_site
  Antarctic Kunlun Station (lat=-80.42, lon=77.12) cat=landmark_and_historical_building, tax=historic_site
  COFFE Station Old town (lat=-84.61, lon=116.72) cat=cafe, tax=cafe
  Base antártica Concordia (lat=-75.10, lon=123.33) cat=train_station, tax=train_station
  Base antarctique Concordia (lat=-75.10, lon=123.56) cat=landmark_and_historical_building, tax=historic_site
  Base antarctique Dumont-d'Urville (lat=-66.66, lon=140.00) cat=landmark_and_historical_building, tax=historic_site
  Antarctic Plateau (lat=-77.00, lon=150.00) cat=landmark_and_historical_building, tax=historic_site
  Lake Bonney (Antarctica) (lat=-77.72, lon=162.42) cat=lake, tax=lake
  Mario-Zucchelli-Station (lat=-74.69, lon=164.11) cat=N/A, tax=N/A
  Base McMurdo (lat=-77.85, lon=166.67) cat=landmark_and_historical_building, tax=historic_site
  Base antarctique McMurdo (lat=-77.85, lon=166.67) cat=landmark_and_historical_building, tax=historic_site

cd /work && python3 << 'EOF'
import overturemaps

bbox = (-180, -90, 180, -60)
reader = overturemaps.record_batch_reader('place', bbox=bbox)
if reader:
    import pyarrow as pa
    import geopandas as gpd
    from shapely import wkb
    
    table = pa.Table.from_batches(reader)
    df = table.to_pandas()
    
    # Parse geometries
    geoms = [wkb.loads(row) for row in df['geometry']]
    gdf = gpd.GeoDataFrame(df, geometry=geoms, crs='EPSG:4326')
    
    # Look for research station taxonomy
    research_items = []
    for idx, row in gdf.iterrows():
        tax = row['taxonomy']
        if tax and tax.get('primary') in ['educational_research_institute', 'research_institute']:
            research_items.append(row)
        name = row['names']['primary'] if row['names'] else ''
        # Also check if name contains typical station keywords
        name_lower = name.lower()
        station_keywords = ['antarctic', 'station', 'base', 'research']
        if any(kw in name_lower for kw in station_keywords):
            # Avoid false positives
            if name not in [r['names']['primary'] if r['names'] else '' for r in research_items]:
                research_items.append(row)
    
    print(f"Found {len(research_items)} candidate items")
    # Remove duplicates by name
    seen_names = set()
    unique_items = []
    for r in research_items:
        name = r['names']['primary'] if r['names'] else ''
        if name not in seen_names:
            seen_names.add(name)
            unique_items.append(r)
    
    print(f"Unique: {len(unique_items)}")
    for s in unique_items:
        name = s['names']['primary'] if s['names'] else 'N/A'
        cat = s['categories']['primary'] if s['categories'] else 'N/A'
        tax = s['taxonomy']['primary'] if s['taxonomy'] else 'N/A'
        print(f"  {name} (lat={s.geometry.y:.2f}, lon={s.geometry.x:.2f}) cat={cat}, tax={tax}")
EOF

Found 51 candidate items
Unique: 51
  Ross Ice Shelf, Antarctica (lat=-70.61, lon=-177.89) cat=landmark_and_historical_building, tax=historic_site
  West Antarctica (lat=-79.00, lon=-100.00) cat=national_park, tax=national_park
  Antarctic Peninsula (lat=-69.50, lon=-65.00) cat=landmark_and_historical_building, tax=historic_site
  Mount Wilson (Antarctica) (lat=-68.45, lon=-65.55) cat=mountain, tax=mountain
  Master Bees Stationeries & Souvenirs (lat=-75.14, lon=-66.80) cat=souvenir_shop, tax=souvenir_store
  Errera Channel, Antarctica (lat=-64.70, lon=-62.59) cat=landmark_and_historical_building, tax=historic_site
  Paradise Bay, Antarctic Peninsula (lat=-64.91, lon=-62.90) cat=landmark_and_historical_building, tax=historic_site
  Brown Station, Antarctic Peninsula (lat=-64.89, lon=-62.87) cat=landmark_and_historical_building, tax=historic_site
  Brown Station (lat=-64.89, lon=-62.87) cat=landmark_and_historical_building, tax=historic_site
  González Videla Antarctic Base (lat=-64.82, lon=-62.86) cat=airport, tax=airport
  Port Lockroy, Antarctic Peninsula (lat=-64.82, lon=-63.49) cat=landmark_and_historical_building, tax=historic_site
  Mount William (Antarctica) (lat=-64.78, lon=-63.68) cat=mountain, tax=mountain
  Lemaire Channel, Antarctic Peninsula (lat=-65.10, lon=-63.98) cat=landmark_and_historical_building, tax=historic_site
  Palmer Station, Antarctica (lat=-64.77, lon=-64.05) cat=home_developer, tax=home_developer
  Mount Jackson (Antarctica) (lat=-71.36, lon=-63.41) cat=mountain, tax=mountain
  New Lucena Municipal Police Station (lat=-80.87, lon=-61.35) cat=public_service_and_government, tax=community_and_government
  Esperanto Island, Antarctica (lat=-62.64, lon=-60.36) cat=landmark_and_historical_building, tax=historic_site
  Cierva Cove, Antarctic Peninsula (lat=-64.15, lon=-60.91) cat=landmark_and_historical_building, tax=historic_site
  Antarctica, Antarctic Circle (lat=-64.05, lon=-60.93) cat=landmark_and_historical_building, tax=historic_site
  Mikkelsen Harbour, Antarctic Peninsula (lat=-63.90, lon=-60.77) cat=landmark_and_historical_building, tax=historic_site
  Carlini Base (lat=-62.24, lon=-58.67) cat=arts_and_entertainment, tax=arts_and_entertainment
  Bellingshausen Russian Antarctic Station (lat=-62.20, lon=-58.96) cat=educational_research_institute, tax=educational_research_institute
  King George Bay (Antarctica) (lat=-62.10, lon=-58.08) cat=landmark_and_historical_building, tax=historic_site
  Base Marambio, Antartida Argentina (lat=-64.24, lon=-56.62) cat=central_government_office, tax=government_office
  Marambio Base (lat=-64.24, lon=-56.63) cat=airport, tax=airport
  Base Antártica Marambio (lat=-64.24, lon=-56.62) cat=public_and_government_association, tax=government_office
  Chapel of the Blessed Virgin of Lujan, Antarctica (lat=-64.24, lon=-56.63) cat=church_cathedral, tax=christian_place_of_worship
  Esperanza Base, Antarctica (lat=-63.40, lon=-57.00) cat=landmark_and_historical_building, tax=historic_site
  Base Esperanza, Antartida Argentina (lat=-63.40, lon=-57.00) cat=airport, tax=airport
  British Antarctic Territory (lat=-75.00, lon=-50.00) cat=landmark_and_historical_building, tax=historic_site
  Neumayer-Station II (lat=-70.65, lon=-8.25) cat=landmark_and_historical_building, tax=historic_site
  Antarctica/Troll (lat=-72.01, lon=2.54) cat=landmark_and_historical_building, tax=historic_site
  Fuel Tech Micro Gas Station & Dispensers (lat=-65.34, lon=15.47) cat=industrial_equipment, tax=industrial_equipment_manufacturer
  Plateau Station (lat=-79.25, lon=40.50) cat=landmark_and_historical_building, tax=historic_site
  Pioneer - Gas Station (lat=-78.91, lon=43.93) cat=gas_station, tax=gas_station
  Lake Glubokoye (Antarctica) (lat=-67.67, lon=45.87) cat=lake, tax=lake
  Beaver Lake (Antarctica) (lat=-70.78, lon=68.30) cat=landmark_and_historical_building, tax=historic_site
  Nicholas Range (Antarctic) (lat=-66.67, lon=55.62) cat=mountain, tax=mountain
  Base antarctique Mawson (lat=-67.60, lon=62.87) cat=landmark_and_historical_building, tax=historic_site
  East Antarctica (lat=-80.00, lon=80.00) cat=landmark_and_historical_building, tax=historic_site
  Antarctic Kunlun Station (lat=-80.42, lon=77.12) cat=landmark_and_historical_building, tax=historic_site
  COFFE Station Old town (lat=-84.61, lon=116.72) cat=cafe, tax=cafe
  Base antártica Concordia (lat=-75.10, lon=123.33) cat=train_station, tax=train_station
  Base antarctique Concordia (lat=-75.10, lon=123.56) cat=landmark_and_historical_building, tax=historic_site
  Base antarctique Dumont-d'Urville (lat=-66.66, lon=140.00) cat=landmark_and_historical_building, tax=historic_site
  Antarctic Plateau (lat=-77.00, lon=150.00) cat=landmark_and_historical_building, tax=historic_site
  Lake Bonney (Antarctica) (lat=-77.72, lon=162.42) cat=lake, tax=lake
  Mario-Zucchelli-Station (lat=-74.69, lon=164.11) cat=N/A, tax=N/A
  Base McMurdo (lat=-77.85, lon=166.67) cat=landmark_and_historical_building, tax=historic_site
  Base antarctique McMurdo (lat=-77.85, lon=166.67) cat=landmark_and_historical_building, tax=historic_site
  Lakeview Clinical Research (lat=-64.41, lon=175.78) cat=medical_research_and_development, tax=medical_research_institute

cd /work && python3 << 'EOF'
import overturemaps
# Check infrastructure schema
reader = overturemaps.record_batch_reader('infrastructure')
if reader:
    schema = reader.schema
    print("Infrastructure schema:")
    for field in schema:
        print(f"  {field.name}: {field.type}")
    # Check a few rows
    import pyarrow as pa
    table = pa.Table.from_batches(reader)
    df = table.to_pandas()
    print(f"\nTotal infrastructure rows: {len(df)}")
    print(f"Columns: {list(df.columns)}")
    if 'categories' in df.columns:
        print(df['categories'].head(10))
    if 'subtype' in df.columns:
        print(f"\nSubtypes: {df['subtype'].value_counts()}")
EOF

cd /work && python3 << 'EOF'
import overturemaps

# Query land for Antarctica
bbox = (-180, -90, 180, -60)
print("Querying land...")
reader = overturemaps.record_batch_reader('land', bbox=bbox)
if reader:
    import pyarrow as pa
    schema = reader.schema
    print("Land schema:")
    for field in schema:
        print(f"  {field.name}: {field.type}")
    
    # Read just a few rows
    batches = []
    for i, batch in enumerate(reader):
        batches.append(batch)
        if i >= 2:
            break
    if batches:
        table = pa.Table.from_batches(batches)
        df = table.to_pandas()
        print(f"\nRows in first batches: {len(df)}")
        print(f"Columns: {list(df.columns)}")
else:
    print("No reader for land")
EOF

Querying land...
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>

Rows in first batches: 6183
Columns: ['id', 'names', 'subtype', 'class', 'sources', 'source_tags', 'level', 'wikidata', 'surface', 'elevation', 'geometry', 'version', 'bbox']

cd /work && python3 << 'EOF'
import overturemaps

# Check infrastructure for Antarctica
bbox = (-180, -90, 180, -60)
print("Querying infrastructure...")
reader = overturemaps.record_batch_reader('infrastructure', bbox=bbox)
if reader:
    import pyarrow as pa
    schema = reader.schema
    print("Infrastructure schema:")
    for field in schema:
        print(f"  {field.name}: {field.type}")
    
    # Read batches
    batches = []
    for i, batch in enumerate(reader):
        batches.append(batch)
        if i >= 2:
            break
    if batches:
        table = pa.Table.from_batches(batches)
        df = table.to_pandas()
        print(f"\nRows in first batches: {len(df)}")
        if 'subtype' in df.columns:
            print(f"\nSubtypes: {df['subtype'].value_counts().head(20)}")
        if 'class' in df.columns:
            print(f"\nClasses: {df['class'].value_counts().head(20)}")
EOF

Querying infrastructure...
Infrastructure schema:
  id: string
  geometry: binary
  sources: list<element: struct<property: string, dataset: string, license: string, record_id: string, update_time: string, confidence: double, between: list<element: double>>>
  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>>>
  level: int32
  wikidata: string
  source_tags: map<string, string ('source_tags')>
  subtype: string
  class: string
  height: double
  surface: string
  version: int32
  bbox: struct<xmin: double, xmax: double, ymin: double, ymax: double>

Rows in first batches: 10

Subtypes: subtype
airport           4
pedestrian        3
tower             1
transportation    1
recreation        1
Name: count, dtype: int64

Classes: class
information    2
airport        2
runway         2
observation    1
toilets        1
stop           1
camp_site      1
Name: count, dtype: int64

cd /work && python3 << 'EOF'
import overturemaps

# Check division_area for Antarctica
bbox = (-180, -90, 180, -60)
print("Querying division_area...")
reader = overturemaps.record_batch_reader('division_area', bbox=bbox)
if reader:
    import pyarrow as pa
    schema = reader.schema
    print("Division_area schema:")
    for field in schema:
        print(f"  {field.name}: {field.type}")
    
    batches = []
    for i, batch in enumerate(reader):
        batches.append(batch)
        if i >= 2:
            break
    if batches:
        table = pa.Table.from_batches(batches)
        df = table.to_pandas()
        print(f"\nRows: {len(df)}")
        if 'names' in df.columns:
            for n in df['names'].head(20):
                if n:
                    print(f"  {n.get('primary')}")
        if 'subtype' in df.columns:
            print(f"\nSubtypes: {df['subtype'].value_counts()}")
else:
    print("No reader for division_area")
EOF

Querying division_area...
Division_area schema:
  id: string
  geometry: binary
  country: string
  sources: list<element: struct<property: string, dataset: string, license: string, record_id: string, update_time: string, confidence: double, between: list<element: double>>>
  subtype: string
  admin_level: int32
  class: string
  names: struct<primary: string, common: map<string, string ('common')>, rules: list<element: struct<variant: string, language: string, perspectives: struct<mode: string, countries: list<element: string>>, value: string, between: list<element: double>, side: string>>>
  is_land: bool
  is_territorial: bool
  region: string
  division_id: string
  version: int32
  bbox: struct<xmin: double, xmax: double, ymin: double, ymax: double>

Rows: 3
  Rothera Research Station
  Antarctica
  Antarctica

Subtypes: subtype
country     2
locality    1
Name: count, dtype: int64

cd /work && python3 << 'EOF'
import overturemaps

bbox = (-180, -90, 180, -60)
print("Querying land cover...")
reader = overturemaps.record_batch_reader('land_cover', bbox=bbox)
if reader:
    import pyarrow as pa
    schema = reader.schema
    print("Land cover schema:")
    for field in schema:
        print(f"  {field.name}: {field.type}")
else:
    print("No reader for land_cover")
EOF

Querying land cover...
Land cover schema:
  id: string
  subtype: string
  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 && python3 << 'EOF'
import overturemaps

bbox = (-180, -90, 180, -60)
print("Querying land_use...")
reader = overturemaps.record_batch_reader('land_use', bbox=bbox)
if reader:
    import pyarrow as pa
    schema = reader.schema
    print("Land use schema:")
    for field in schema:
        print(f"  {field.name}: {field.type}")
else:
    print("No reader for land_use")
EOF

Querying land_use...
Land use schema:
  id: string
  geometry: binary
  sources: list<element: struct<property: string, dataset: string, license: string, record_id: string, update_time: string, confidence: double, between: list<element: double>>>
  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>>>
  level: int32
  wikidata: string
  source_tags: map<string, string ('source_tags')>
  subtype: string
  class: string
  elevation: int32
  surface: string
  version: int32
  bbox: struct<xmin: double, xmax: double, ymin: double, ymax: double>

cd /work && python3 << 'EOF'
import overturemaps

bbox = (-180, -90, 180, -60)
reader = overturemaps.record_batch_reader('place', bbox=bbox)
if reader:
    import pyarrow as pa
    import geopandas as gpd
    from shapely import wkb
    
    table = pa.Table.from_batches(reader)
    df = table.to_pandas()
    
    # Parse geometries
    geoms = [wkb.loads(row) for row in df['geometry']]
    gdf = gpd.GeoDataFrame(df, geometry=geoms, crs='EPSG:4326')
    
    # Look at the full taxonomy hierarchy for research-related items
    for idx, row in gdf.iterrows():
        tax = row['taxonomy']
        if tax and tax.get('hierarchy') is not None:
            hierarchy = list(tax['hierarchy'])
            if 'research_institute' in hierarchy or 'educational_research_institute' in hierarchy:
                name = row['names']['primary'] if row['names'] else 'N/A'
                print(f"  {name} (lat={row.geometry.y:.2f}, lon={row.geometry.x:.2f})")
                print(f"    hierarchy: {hierarchy}")
    
    # Also check all unique taxonomy primaries
    print("\nAll unique taxonomy primaries:")
    primaries = set()
    for idx, row in gdf.iterrows():
        tax = row['taxonomy']
        if tax and tax.get('primary'):
            primaries.add(tax['primary'])
    for p in sorted(primaries):
        print(f"  {p}")
EOF

  Bellingshausen Russian Antarctic Station (lat=-62.20, lon=-58.96)
    hierarchy: ['education', 'research_institute', 'educational_research_institute']
  Lakeview Clinical Research (lat=-64.41, lon=175.78)
    hierarchy: ['education', 'research_institute', 'medical_research_institute']

All unique taxonomy primaries:
  acupuncture
  agricultural_cooperative
  agricultural_service
  agriculture
  aircraft_service
  airport
  alcohol_and_drug_treatment_center
  amateur_sport_team
  ambulance_and_ems_service
  antique_store
  aquatic_pet_store
  archery_range
  architectural_designer
  art_gallery
  art_school
  arts_and_crafts_store
  arts_and_entertainment
  astrological_advising
  attorney_or_law_firm
  auction_house
  audio_visual_equipment_store
  audiology
  auto_company
  auto_dealer
  auto_detailing
  auto_parts_store
  automation_service
  automotive_repair
  automotive_service
  b2b_advertising_and_marketing_service
  b2b_energy_and_utility_service
  b2b_science_and_technology
  b2b_surgical_appliances_and_supplies
  b2b_water_treatment_service
  bakery
  bank
  bank_credit_union
  bar
  barber
  beach
  beauty_product_supplier
  beauty_salon
  beauty_service
  beauty_supply_store
  beer_bar
  bike_store
  boat_parts_store
  boat_rental_and_training
  boat_service
  bookstore
  boxing_class
  brazilian_restaurant
  breakfast_and_brunch_restaurant
  bridal_shop
  broker
  bubble_tea_shop
  buddhist_place_of_worship
  buffet_restaurant
  building_supply_store
  burger_restaurant
  butcher_shop
  cabin
  cafe
  campground
  campus_building
  candy_store
  car_rental_service
  car_window_tinting
  cardiology
  caterer
  charity_organization
  chicken_restaurant
  childrens_clothing_store
  chinese_restaurant
  chiropractic
  chocolatier
  christian_place_of_worship
  clothing_store
  cocktail_bar
  coffee_shop
  college_university
  commercial_industrial
  commercial_refrigeration
  commercial_vehicle_dealer
  community_and_government
  community_center
  computer_coaching
  computer_hardware_company
  computer_museum
  computer_store
  construction_service
  contractor
  convenience_store
  cosmetic_dentistry
  cosmetology_school
  cottage
  cultural_center
  cupcake_shop
  dance_studio
  day_care_preschool
  delicatessen
  dental_clinic
  dessert_shop
  diner
  doctors_office
  driving_school
  dry_cleaning
  ear_nose_and_throat
  education
  educational_camp
  educational_research_institute
  educational_service
  electronics_store
  elementary_school
  embassy
  emergency_room
  employment_agency
  energy_company
  engineering_service
  escape_room
  event_or_party_service
  event_photography_service
  event_venue
  excavation_service
  eyewear_store
  fabric_store
  family_practice
  farm
  fashion_accessories_store
  fashion_and_apparel_store
  fashion_boutique
  fast_food_restaurant
  fertility_clinic
  financial_advising
  financial_service
  fish_farm
  fishmonger
  fitness_trainer
  flowers_and_gifts_shop
  food_and_beverage_store
  food_and_drink
  food_beverage_distributor
  food_delivery_service
  food_truck
  food_truck_stand
  freight_and_cargo_service
  furniture_store
  gas_station
  general_litigation
  geographic_entities
  go_kart_club
  government_office
  graphic_designer
  grocery_store
  gym
  hair_salon
  halal_restaurant
  hardware_store
  health_care
  health_food_store
  heatlh_care
  high_school
  hindu_place_of_worship
  historic_site
  history_museum
  holiday_rental_home
  home_cleaning
  home_developer
  home_goods_store
  home_improvement_store
  home_security
  home_service
  home_staging
  horseback_riding_service
  hospital
  hostel
  hotel
  hvac_service
  ice_cream_shop
  image_consultant
  immigration_law
  indian_restaurant
  indonesian_restaurant
  industrial_company
  industrial_equipment_manufacturer
  information_technology_company
  installment_loans
  insurance_agency
  interior_design
  internet_marketing_service
  internet_service_provider
  investing
  it_service_and_computer_repair
  italian_restaurant
  japanese_restaurant
  jewelry_and_watches_manufacturer
  jewelry_store
  karaoke_venue
  korean_restaurant
  laboratory_testing
  lake
  landscaping
  language_school
  laundromat
  legal_service
  library
  lighting_store
  lingerie_store
  lodge
  lodging
  lounge
  luggage_store
  machine_shop
  makeup_artist
  marketing_agency
  martial_arts_club
  mattress_store
  media_news_company
  media_service
  medical_research_institute
  medical_service_organization
  mens_clothing_store
  metal_fabricator
  metal_plating_service
  mexican_restaurant
  middle_school
  mobile_phone_store
  monument
  motel
  motorcycle_dealer
  motorcycle_repair
  mountain
  mover
  movie_television_studio
  museum
  music_production
  music_school
  music_venue
  musical_instrument_store
  muslim_place_of_worship
  national_park
  naturopathic_medicine
  non_governmental_association
  nursery_and_gardening_store
  nutrition_service
  obstetrics_and_gynecology
  office_supply_store
  optometry
  oral_and_maxillofacial_surgery
  orthopedics
  outlet_store
  outpatient_care_facility
  painting
  panamanian_restaurant
  park
  party_supply_store
  pawn_shop
  pentecostal_place_of_worship
  pet_breeder
  pet_groomer
  pet_store
  pharmacy
  physical_therapy
  pilates_studio
  pizza_restaurant
  plastic_and_reconstructive_surgery
  plastic_manufacturer
  plumbing
  political_organization
  pool_billiards
  powder_coating_service
  preschool
  print_media
  printing_service
  private_school
  produce_store
  professional_service
  professional_sport_team
  protestant_place_of_worship
  psychic_advising
  psychologist
  psychotherapy
  pub
  public_fountain
  public_school
  public_utility
  race_track
  radio_station
  real_estate_agent
  real_estate_service
  record_label
  religious_organization
  religious_school
  rental_service
  resort
  restaurant
  retirement_home
  river
  roman_catholic_place_of_worship
  rv_park
  sandwich_shop
  school
  school_sport_team
  scuba_diving_center
  seafood_restaurant
  second_hand_store
  self_storage_facility
  senior_citizen_service
  sewing_and_alterations
  shaved_ice_shop
  shipping_center
  shoe_repair
  shoe_store
  shopping
  shopping_mall
  sign_making
  smoke_and_vape_store
  smoothie_juice_bar
  soccer_field
  social_media_agency
  social_media_company
  social_or_community_service
  software_development
  souvenir_store
  spa
  specialty_school
  sport_or_fitness_facility
  sport_or_recreation_club
  sporting_goods_store
  sports_and_recreation
  sportswear_store
  stadium_arena
  steakhouse
  storage_facility
  superstore
  sushi_restaurant
  tattoo_and_piercing
  tea_room
  telecommunications_company
  test_preparation
  textile_manufacturer
  thai_restaurant
  theatre_venue
  theme_restaurant
  tire_dealer_and_repair
  topic_publisher
  tour_operator
  town_hall
  toy_store
  trailer_dealer
  train_station
  travel_and_transportation
  travel_company
  travel_service
  trophy_store
  tutoring_service
  urban_farm
  urology
  vehicle_dealer
  veterinarian
  video_game_store
  vietnamese_restaurant
  vitamin_and_supplement_store
  walk_in_clinic
  warehouse_club_store
  wedding_planning
  weight_loss_center
  wine_bar
  winery
  womens_clothing_store
  yoga_studio

cd /work && python3 << 'EOF'
import overturemaps

bbox = (-180, -90, 180, -60)
reader = overturemaps.record_batch_reader('place', bbox=bbox)
if reader:
    import pyarrow as pa
    import geopandas as gpd
    from shapely import wkb
    
    table = pa.Table.from_batches(reader)
    df = table.to_pandas()
    geoms = [wkb.loads(row) for row in df['geometry']]
    gdf = gpd.GeoDataFrame(df, geometry=geoms, crs='EPSG:4326')
    
    # Known Antarctic stations to search for
    known_stations = [
        'McMurdo', 'Amundsen-Scott', 'South Pole', 'Mawson', 'Davis', 'Casey',
        'Dumont', 'd\'Urville', 'Concordia', 'Vostok', 'Mirny', 'Novolazarevskaya',
        'Progress', 'Bellingshausen', 'Marambio', 'Esperanza', 'Carlini',
        'Orcadas', 'San Martin', 'Jubany', 'Brown', 'Palmer', 'Neumayer',
        'Halley', 'Rothera', 'Syowa', 'Mizuho', 'Asuka', 'Dome Fuji',
        'Kohnen', 'Menzel', 'Scott Base', 'Zhongshan', 'Kunlun', 'Taishan',
        'Vechernyaya', 'Wasa', 'Svea', 'MaiTri', 'Bharati', 'Abrau',
        'Arctowski', 'Henryk Arctowski', 'Macquarie', 'Macquarie Island',
        'González Videla', 'Gabriel de Castilla', 'Juan Carlos I',
        'Artigas', 'Comandante Ferraz', 'Frei', 'Marsh', 'Eduardo Frei',
        'King Sejong', 'Jang Bogo', 'Chilean', 'Fildes', 'Great Wall',
        'Escudero', 'Ohridski', 'St. Kliment', 'Palmer', 'Mario Zucchelli',
        'Zucchelli', 'Troll', 'Tor', 'SANAE', 'Princess Elisabeth',
        'Plateau Station', 'Byrd', 'Siple', 'Eights', 'Brockton',
        'Leningradskaya', 'Russkaya', 'Sovetskaya', 'Molodezhnaya',
        'Druzhnaya', 'Sodruzhestva', 'Mir'
    ]
    
    matches = []
    for idx, row in gdf.iterrows():
        name = row['names']['primary'] if row['names'] else ''
        name_lower = name.lower()
        for kw in known_stations:
            if kw.lower() in name_lower:
                matches.append(row)
                break
    
    print(f"Found {len(matches)} potential station matches")
    for s in matches:
        name = s['names']['primary'] if s['names'] else 'N/A'
        cat = s['categories']['primary'] if s['categories'] else 'N/A'
        tax = s['taxonomy']['primary'] if s['taxonomy'] else 'N/A'
        print(f"  {name} (lat={s.geometry.y:.2f}, lon={s.geometry.x:.2f}) cat={cat}, tax={tax}")
EOF

Found 222 potential station matches
  Chittagong Victory National School-CVNS (lat=-80.42, lon=-175.38) cat=school, tax=school
  Suzuki Nilphamari - Lahin Motors (lat=-65.77, lon=-121.37) cat=motorcycle_dealer, tax=motorcycle_dealer
  Samira's Hair Fashion +599 98693311 (lat=-74.78, lon=-136.41) cat=beauty_salon, tax=beauty_salon
  Discount Hardware Store (lat=-74.96, lon=-118.83) cat=hardware_store, tax=hardware_store
  Everything Store (lat=-75.84, lon=-100.13) cat=shopping, tax=shopping
  AKS Manufactory (lat=-61.52, lon=-97.80) cat=engineering_services, tax=engineering_service
  In Aeternum Historia (lat=-84.13, lon=-94.92) cat=print_media, tax=print_media
  Mitra Fajar Ciawi " Bengkel Resmi Sepeda Motor Honda " (lat=-76.18, lon=-87.65) cat=motorcycle_repair, tax=motorcycle_repair
  Canoon Store (lat=-71.02, lon=-81.81) cat=mobile_phone_store, tax=mobile_phone_store
  Toronto Cosmetic Surgery Institute (lat=-79.38, lon=-79.38) cat=hospital, tax=hospital
  شركة يوني فليكس للمعدات والمستلزمات الطبية - Uniflex Medical Supply Store (lat=-65.29, lon=-75.23) cat=pharmacy, tax=pharmacy
  ร้านข้าวแกงฮาริษา Harisa's Curries Rice Factory (lat=-70.08, lon=-65.22) cat=restaurant, tax=restaurant
  Lihor Store Fan (lat=-66.37, lon=-62.09) cat=fruits_and_vegetables, tax=produce_store
  Brown Station, Antarctic Peninsula (lat=-64.89, lon=-62.87) cat=landmark_and_historical_building, tax=historic_site
  Brown Station (lat=-64.89, lon=-62.87) cat=landmark_and_historical_building, tax=historic_site
  González Videla Antarctic Base (lat=-64.82, lon=-62.86) cat=airport, tax=airport
  Palmer Station, Antarctica (lat=-64.77, lon=-64.05) cat=home_developer, tax=home_developer
  Carlini Base (lat=-62.24, lon=-58.67) cat=arts_and_entertainment, tax=arts_and_entertainment
  Bellingshausen Russian Antarctic Station (lat=-62.20, lon=-58.96) cat=educational_research_institute, tax=educational_research_institute
  Bahia Fildes (Antartica) (lat=-62.20, lon=-58.96) cat=structure_and_geography, tax=geographic_entities
  Concretiza Lar Construtora E Incorporadora (lat=-63.07, lon=-59.97) cat=home_developer, tax=home_developer
  Base Marambio, Antartida Argentina (lat=-64.24, lon=-56.62) cat=central_government_office, tax=government_office
  Marambio Base (lat=-64.24, lon=-56.63) cat=airport, tax=airport
  Base Antártica Marambio (lat=-64.24, lon=-56.62) cat=public_and_government_association, tax=government_office
  Esperanza Base, Antarctica (lat=-63.40, lon=-57.00) cat=landmark_and_historical_building, tax=historic_site
  Base Esperanza, Antartida Argentina (lat=-63.40, lon=-57.00) cat=airport, tax=airport
  British Antarctic Territory (lat=-75.00, lon=-50.00) cat=landmark_and_historical_building, tax=historic_site
  Consultoría Psicológica Especializada de Monclova Psic. Aarón Hernández (lat=-82.56, lon=-40.42) cat=psychologist, tax=psychologist
  Generika Drugstore - ZDN Bagting Dapitan Page (lat=-77.62, lon=-42.71) cat=pharmacy, tax=pharmacy
  Carrefour Hiper Recife Torre (lat=-80.41, lon=-34.91) cat=supermarket, tax=grocery_store
  Pastor Jorge (lat=-73.27, lon=-21.49) cat=N/A, tax=N/A
  Nails By Hilary Torres M (lat=-69.10, lon=-28.93) cat=beauty_salon, tax=beauty_salon
  Famous Forum of Worldwide Collaborators LLP (lat=-68.57, lon=-26.44) cat=education, tax=education
  Finca Hotel Mirador del Bambú (lat=-67.11, lon=-22.81) cat=hotel, tax=hotel
  Javastasia Distributors Halaal Meats (lat=-61.61, lon=-30.94) cat=butcher_shop, tax=butcher_shop
  N9store.ro (lat=-63.47, lon=-42.60) cat=beauty_salon, tax=beauty_salon
  La Combo Tortuga (lat=-64.47, lon=-40.78) cat=N/A, tax=N/A
  Storybook Imaging (lat=-65.66, lon=-5.62) cat=professional_services, tax=professional_service
  Technology Store (lat=-61.27, lon=-11.26) cat=mobile_phone_store, tax=mobile_phone_store
  Alessandra Oliveira corretora de imóveis rj (lat=-61.44, lon=-18.88) cat=real_estate, tax=real_estate_service
  Robin Rolon, Mary Kay Independent Sales Director (lat=-64.90, lon=-16.01) cat=N/A, tax=N/A
  Vumeer el Mentor (lat=-67.22, lon=-13.81) cat=professional_sports_team, tax=professional_sport_team
  Babul Yaman Store (lat=-82.01, lon=-17.71) cat=shopping, tax=shopping
  Neumayer-Station II (lat=-70.65, lon=-8.25) cat=landmark_and_historical_building, tax=historic_site
  Liljequist Heights (lat=-72.10, lon=-2.80) cat=mountain, tax=mountain
  Reis Motors (lat=-75.04, lon=0.53) cat=motorcycle_repair, tax=motorcycle_repair
  Troll Airfield (lat=-71.96, lon=2.47) cat=airport, tax=airport
  Antarctica/Troll (lat=-72.01, lon=2.54) cat=landmark_and_historical_building, tax=historic_site
  SANAE (lat=-72.67, lon=2.85) cat=landmark_and_historical_building, tax=historic_site
  Grace-Care Laboratory Services and Supplies (lat=-78.41, lon=-0.48) cat=laboratory_testing, tax=laboratory_testing
  Yamu Store III (lat=-84.54, lon=1.41) cat=music_venue, tax=music_venue
  Jj'sanm Music Tutorial Center (lat=-70.08, lon=18.54) cat=music_school, tax=music_school
  Dani Motors (lat=-60.05, lon=14.41) cat=car_dealer, tax=auto_dealer
  Delta Power Generators (lat=-64.08, lon=11.80) cat=energy_company, tax=energy_company
  Timsun Franchise Bayan Lepas - West Motor Racing Centre (lat=-77.78, lon=17.37) cat=motorcycle_repair, tax=motorcycle_repair
  Nadia Pereira - Corretora de Imóveis (lat=-64.97, lon=27.56) cat=brokers, tax=broker
  RT Power Motor (lat=-67.61, lon=29.88) cat=machine_shop, tax=machine_shop
  King World Product Store (lat=-60.49, lon=29.71) cat=professional_services, tax=professional_service
  Young Bombay Tours Kashmir (lat=-82.77, lon=30.94) cat=tours, tax=tour_operator
  BOSS Menswear Store (lat=-86.82, lon=36.11) cat=clothing_store, tax=clothing_store
  Extra Space Storage (lat=-77.09, lon=38.88) cat=self_storage_facility, tax=self_storage_facility
  Extra Space Storage (lat=-77.42, lon=38.91) cat=self_storage_facility, tax=self_storage_facility
  Ashford Self Storage Hull Ltd (lat=-76.84, lon=40.78) cat=storage_facility, tax=storage_facility
  Windfreight Express Total Logistics Inc.(WETLI) (lat=-75.07, lon=43.07) cat=freight_and_cargo_service, tax=freight_and_cargo_service
  Plateau Station (lat=-79.25, lon=40.50) cat=landmark_and_historical_building, tax=historic_site
  HomeVestor PH (lat=-82.74, lon=43.05) cat=real_estate_service, tax=real_estate_service
  Tractor Zetor (lat=-76.02, lon=50.62) cat=agricultural_cooperatives, tax=agricultural_cooperative
  Pizza Salvatoré (lat=-75.51, lon=46.55) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-78.13, lon=48.57) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-77.83, lon=48.10) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-79.20, lon=48.81) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-79.01, lon=48.23) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-75.81, lon=45.42) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-75.67, lon=45.49) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-75.44, lon=45.55) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-74.22, lon=45.26) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-74.12, lon=45.27) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.86, lon=45.31) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.85, lon=45.49) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.80, lon=45.48) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.94, lon=45.53) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-74.02, lon=45.38) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-74.61, lon=45.61) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-74.33, lon=45.66) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-74.28, lon=46.05) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-74.13, lon=45.95) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.81, lon=45.77) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-74.07, lon=45.86) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-74.15, lon=45.89) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-74.01, lon=45.80) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-74.09, lon=45.70) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.93, lon=45.70) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.83, lon=45.64) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.88, lon=45.58) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.70, lon=45.58) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.74, lon=45.61) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.76, lon=45.67) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.66, lon=45.72) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.61, lon=45.74) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.57, lon=45.58) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.44, lon=45.59) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.33, lon=45.58) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.47, lon=45.75) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.43, lon=45.69) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.43, lon=45.76) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.30, lon=45.89) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.70, lon=46.04) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.45, lon=46.04) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.10, lon=46.04) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-72.96, lon=45.62) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.02, lon=45.41) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-72.99, lon=45.29) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.27, lon=45.34) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.21, lon=45.56) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.29, lon=45.53) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.47, lon=45.41) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.49, lon=45.50) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.43, lon=45.48) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.44, lon=45.51) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.70, lon=45.54) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.57, lon=45.39) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-73.61, lon=45.27) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-72.13, lon=45.28) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-72.00, lon=45.37) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.94, lon=45.40) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.89, lon=45.38) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.87, lon=45.41) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-72.75, lon=45.20) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-72.75, lon=45.41) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-72.57, lon=45.65) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-72.50, lon=45.89) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-72.51, lon=46.28) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.95, lon=46.06) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.93, lon=45.77) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.77, lon=46.22) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.31, lon=46.10) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.80, lon=45.13) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-70.90, lon=45.59) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-70.69, lon=46.13) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-70.77, lon=46.21) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-70.57, lon=46.98) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-70.52, lon=47.44) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-70.04, lon=47.37) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-69.55, lon=47.83) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-68.50, lon=48.46) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-68.21, lon=48.59) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-67.51, lon=48.85) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-68.26, lon=49.20) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-72.23, lon=48.88) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-72.23, lon=48.52) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.64, lon=48.55) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.21, lon=48.41) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.09, lon=48.45) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.06, lon=48.40) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-70.95, lon=47.01) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.33, lon=46.91) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.19, lon=46.90) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.20, lon=46.87) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.25, lon=46.84) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.22, lon=46.81) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.23, lon=46.81) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.28, lon=46.87) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.28, lon=46.83) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.28, lon=46.79) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.35, lon=46.79) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.30, lon=46.72) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.21, lon=46.73) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.15, lon=46.82) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-70.98, lon=46.63) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.03, lon=46.45) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.64, lon=46.54) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.73, lon=46.68) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.83, lon=46.89) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.69, lon=46.75) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.44, lon=46.75) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-71.41, lon=46.86) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-72.56, lon=46.37) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-72.59, lon=46.36) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-72.59, lon=46.34) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-72.75, lon=46.55) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-72.78, lon=47.44) cat=fast_food_restaurant, tax=fast_food_restaurant
  World Store (lat=-67.48, lon=52.80) cat=shopping_center, tax=shopping_mall
  Pizza Salvatoré (lat=-66.38, lon=50.22) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-65.68, lon=47.64) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-65.49, lon=47.03) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-64.73, lon=46.09) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-64.84, lon=46.11) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-66.65, lon=45.98) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-66.02, lon=45.31) cat=fast_food_restaurant, tax=fast_food_restaurant
  Pizza Salvatoré (lat=-63.68, lon=44.76) cat=fast_food_restaurant, tax=fast_food_restaurant
  Ténor store (lat=-61.86, lon=66.88) cat=mobile_phone_store, tax=mobile_phone_store
  Nissan Pampanga by Maica Torres (lat=-63.80, lon=57.11) cat=car_dealer, tax=auto_dealer
  Forland KpK Motors (lat=-72.34, lon=57.93) cat=commercial_vehicle_dealer, tax=commercial_vehicle_dealer
  Base antarctique Mawson (lat=-67.60, lon=62.87) cat=landmark_and_historical_building, tax=historic_site
  N'Zemmula - ristorante siciliano (lat=-68.89, lon=66.45) cat=italian_restaurant, tax=italian_restaurant
  Nueva Ecija Doctors Hospital (lat=-81.41, lon=73.10) cat=hospital, tax=hospital
  Antarctic Kunlun Station (lat=-80.42, lon=77.12) cat=landmark_and_historical_building, tax=historic_site
  Lori Makiya, Realtor (lat=-63.17, lon=80.56) cat=real_estate_agent, tax=real_estate_agent
  Melissa Isaac Realtor (lat=-64.24, lon=83.30) cat=real_estate_agent, tax=real_estate_agent
  Me_2 Store (lat=-62.31, lon=88.25) cat=N/A, tax=N/A
  Kenny Ramirez Law Firm (lat=-62.85, lon=90.66) cat=lawyer, tax=attorney_or_law_firm
  Victors motor driving school (lat=-67.86, lon=98.57) cat=driving_school, tax=driving_school
  T-Store (lat=-78.02, lon=98.79) cat=mobile_phone_store, tax=mobile_phone_store
  Lake Vostok (lat=-77.50, lon=106.00) cat=landmark_and_historical_building, tax=historic_site
  Vostok İstasyonu (lat=-78.46, lon=106.84) cat=landmark_and_historical_building, tax=historic_site
  Bandith Card Store (lat=-72.62, lon=112.50) cat=wedding_planning, tax=wedding_planning
  Base antártica Concordia (lat=-75.10, lon=123.33) cat=train_station, tax=train_station
  Base antarctique Concordia (lat=-75.10, lon=123.56) cat=landmark_and_historical_building, tax=historic_site
  JRMSU Katipunan Campus CTEd-Laboratory High School (lat=-69.18, lon=123.59) cat=middle_school, tax=middle_school
  Sunstar Motor Jepara (lat=-73.19, lon=127.62) cat=automotive_dealer, tax=vehicle_dealer
  Good Mates Removals & Storage (lat=-63.47, lon=123.98) cat=movers, tax=mover
  Kimstore (lat=-68.91, lon=118.83) cat=retail, tax=shopping
  Base antarctique Dumont-d'Urville (lat=-66.66, lon=140.00) cat=landmark_and_historical_building, tax=historic_site
  Nadhi/நதி - The Online Saree Store (lat=-80.25, lon=151.88) cat=N/A, tax=N/A
  North Store (lat=-70.08, lon=164.13) cat=shopping, tax=shopping
  Mario-Zucchelli-Station (lat=-74.69, lon=164.11) cat=N/A, tax=N/A
  McMurdo General Hospital (lat=-77.85, lon=166.67) cat=hospital, tax=hospital
  Base McMurdo (lat=-77.85, lon=166.67) cat=landmark_and_historical_building, tax=historic_site
  Polární stanice McMurdo (lat=-77.85, lon=166.67) cat=landmark_and_historical_building, tax=historic_site
  Base antarctique McMurdo (lat=-77.85, lon=166.67) cat=landmark_and_historical_building, tax=historic_site
  Kimpich Store (lat=-72.94, lon=170.57) cat=beauty_and_spa, tax=beauty_service
  Sales Store (lat=-61.88, lon=169.80) cat=N/A, tax=N/A

cd /work && python3 << 'EOF'
import overturemaps

# Check water data for Antarctica
bbox = (-180, -90, 180, -60)
print("Querying water...")
reader = overturemaps.record_batch_reader('water', bbox=bbox)
if reader:
    import pyarrow as pa
    # Read first few batches to see what's there
    batches = []
    for i, batch in enumerate(reader):
        batches.append(batch)
        if i >= 2:
            break
    if batches:
        table = pa.Table.from_batches(batches)
        df = table.to_pandas()
        print(f"Water rows in first batches: {len(df)}")
        if 'subtype' in df.columns:
            print(f"\nSubtypes: {df['subtype'].value_counts()}")
        if 'class' in df.columns:
            print(f"\nClasses: {df['class'].value_counts()}")
else:
    print("No water reader")
EOF

Querying water...
Water rows in first batches: 1756

Subtypes: subtype
ocean       1545
lake         157
physical      30
stream        24
Name: count, dtype: int64

Classes: class
ocean     1545
lake       157
stream      24
cape        17
bay         12
sea          1
Name: count, dtype: int64

cd /work && python3 << 'EOF'
import overturemaps

# Check bathymetry data for Antarctica
bbox = (-180, -90, 180, -60)
print("Querying bathymetry...")
reader = overturemaps.record_batch_reader('bathymetry', bbox=bbox)
if reader:
    import pyarrow as pa
    schema = reader.schema
    print("Bathymetry schema:")
    for field in schema:
        print(f"  {field.name}: {field.type}")
    
    batches = []
    for i, batch in enumerate(reader):
        batches.append(batch)
        if i >= 2:
            break
    if batches:
        table = pa.Table.from_batches(batches)
        df = table.to_pandas()
        print(f"\nBathymetry rows in first batches: {len(df)}")
        if 'depth' in df.columns:
            print(f"Depth range: {df['depth'].min()} to {df['depth'].max()}")
else:
    print("No bathymetry reader")
EOF

Querying bathymetry...
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>

Bathymetry rows in first batches: 3096
Depth range: 0 to 3000

cd /work && python3 << 'EOF'
# Check EPSG:3031 extent/validity
import pyproj
crs = pyproj.CRS.from_epsg(3031)
print(crs)
print(f"Area of use: {crs.area_of_use}")
EOF

cd /work && python3 << 'EOF'
import overturemaps

bbox = (-180, -90, 180, -60)

# Get all place data and carefully filter for research stations
reader = overturemaps.record_batch_reader('place', bbox=bbox)
if reader:
    import pyarrow as pa
    import geopandas as gpd
    from shapely import wkb
    
    table = pa.Table.from_batches(reader)
    df = table.to_pandas()
    
    # Parse geometries
    geoms = [wkb.loads(row) for row in df['geometry']]
    gdf = gpd.GeoDataFrame(df, geometry=geoms, crs='EPSG:4326')
    
    # Known Antarctic station names (actual research stations)
    station_keywords = [
        'mcmurdo', 'amundsen-scott', 'south pole', 'mawson', 'davis', 'casey',
        'dumont', 'd\'urville', 'concordia', 'vostok', 'mirny', 'novolazarevskaya',
        'progress', 'bellingshausen', 'marambio', 'esperanza', 'carlini',
        'orcadas', 'san martin', 'brown', 'palmer', 'neumayer',
        'halley', 'rothera', 'syowa', 'mizuho', 'asuka', 'dome fuji',
        'kohnen', 'scott base', 'zhongshan', 'kunlun', 'taishan',
        'mario zucchelli', 'zucchelli', 'troll', 'sanae',
        'princess elisabeth', 'plateau station', 'byrd', 'siple',
        'gonzález videla', 'gabriel de castilla', 'juan carlos i',
        'artigas', 'comandante ferraz', 'eduardo frei', 'king sejong',
        'jang bogo', 'great wall', 'escudero', 'ohridski',
        'leningradskaya', 'russkaya', 'sovetskaya', 'molodezhnaya',
        'druzhnaya', 'bharati', 'maitri', 'arny', 'aboa',
        'wasa', 'svea', 'tor', 'svarthamaren', 'berner',
        'vechernyaya', 'montes', 'charcot', 'porte', 'pionerskaya',
        'komsomolskaya', 'vostok-1', 'lazarev', 'druzhnaya',
        'georg forster', 'filchner', 'dallmann', 'gondwana',
        'signy', 'bird island', 'faraday', 'vernadsky',
        'akademik', 'law base', 'macquarie island', 'luo',
        'ferraz', 'fildes', 'marsh', 'teniente', 'frei'
    ]
    
    # Find stations by name matching
    station_names_found = set()
    stations_list = []
    
    for idx, row in gdf.iterrows():
        name = row['names']['primary'] if row['names'] else ''
        name_lower = name.lower()
        for kw in station_keywords:
            if kw in name_lower:
                if name not in station_names_found:
                    station_names_found.add(name)
                    stations_list.append(row)
                break
    
    print(f"Found {len(stations_list)} stations by keyword match:")
    for s in stations_list:
        name = s['names']['primary'] if s['names'] else 'N/A'
        print(f"  {name} (lat={s.geometry.y:.4f}, lon={s.geometry.x:.4f})")
    
    # Also check if there are research institute taxonomy items we missed
    for idx, row in gdf.iterrows():
        tax = row['taxonomy']
        if tax and tax.get('primary') == 'educational_research_institute':
            name = row['names']['primary'] if row['names'] else 'N/A'
            if name not in station_names_found:
                station_names_found.add(name)
                stations_list.append(row)
                print(f"  [taxonomy match] {name} (lat={row.geometry.y:.4f}, lon={row.geometry.x:.4f})")
EOF

Found 102 stations by keyword match:
  Chittagong Victory National School-CVNS (lat=-80.4166, lon=-175.3833)
  Suzuki Nilphamari - Lahin Motors (lat=-65.7744, lon=-121.3709)
  La casa del deporte (lat=-84.1545, lon=-135.4173)
  Discount Hardware Store (lat=-74.9594, lon=-118.8281)
  Transportes Fralse (lat=-60.5901, lon=-106.5857)
  Everything Store (lat=-75.8400, lon=-100.1338)
  AKS Manufactory (lat=-61.5202, lon=-97.7976)
  In Aeternum Historia (lat=-84.1257, lon=-94.9229)
  Mitra Fajar Ciawi " Bengkel Resmi Sepeda Motor Honda " (lat=-76.1849, lon=-87.6522)
  Canoon Store (lat=-71.0228, lon=-81.8109)
  Toronto Cosmetic Surgery Institute (lat=-79.3815, lon=-79.3815)
  Isla Charcot (lat=-69.7500, lon=-75.2500)
  شركة يوني فليكس للمعدات والمستلزمات الطبية - Uniflex Medical Supply Store (lat=-65.2936, lon=-75.2316)
  ร้านข้าวแกงฮาริษา Harisa's Curries Rice Factory (lat=-70.0806, lon=-65.2176)
  Cambridge Montessori Dommasandra (lat=-67.0674, lon=-65.3906)
  Lihor Store Fan (lat=-66.3728, lon=-62.0934)
  Brown Station, Antarctic Peninsula (lat=-64.8921, lon=-62.8724)
  Brown Station (lat=-64.8950, lon=-62.8700)
  González Videla Antarctic Base (lat=-64.8234, lon=-62.8575)
  Palmer Station, Antarctica (lat=-64.7743, lon=-64.0531)
  Carlini Base (lat=-62.2379, lon=-58.6668)
  Bellingshausen Russian Antarctic Station (lat=-62.1998, lon=-58.9624)
  Bahia Fildes (Antartica) (lat=-62.2013, lon=-58.9619)
  Concretiza Lar Construtora E Incorporadora (lat=-63.0673, lon=-59.9706)
  Base Marambio, Antartida Argentina (lat=-64.2413, lon=-56.6198)
  Marambio Base (lat=-64.2410, lon=-56.6268)
  Base Antártica Marambio (lat=-64.2374, lon=-56.6235)
  Esperanza Base, Antarctica (lat=-63.3977, lon=-56.9961)
  Base Esperanza, Antartida Argentina (lat=-63.3980, lon=-56.9974)
  British Antarctic Territory (lat=-75.0000, lon=-50.0000)
  Consultoría Psicológica Especializada de Monclova Psic. Aarón Hernández (lat=-82.5591, lon=-40.4187)
  Generika Drugstore - ZDN Bagting Dapitan Page (lat=-77.6177, lon=-42.7094)
  Carrefour Hiper Recife Torre (lat=-80.4079, lon=-34.9103)
  Pastor Jorge (lat=-73.2722, lon=-21.4893)
  Nails By Hilary Torres M (lat=-69.0986, lon=-28.9251)
  Famous Forum of Worldwide Collaborators LLP (lat=-68.5652, lon=-26.4430)
  Javastasia Distributors Halaal Meats (lat=-61.6064, lon=-30.9375)
  N9store.ro (lat=-63.4702, lon=-42.5968)
  La Combo Tortuga (lat=-64.4728, lon=-40.7812)
  Storybook Imaging (lat=-65.6583, lon=-5.6250)
  Technology Store (lat=-61.2725, lon=-11.2557)
  Alessandra Oliveira corretora de imóveis rj (lat=-61.4396, lon=-18.8793)
  Robin Rolon, Mary Kay Independent Sales Director (lat=-64.9036, lon=-16.0130)
  Vumeer el Mentor (lat=-67.2195, lon=-13.8102)
  Babul Yaman Store (lat=-82.0149, lon=-17.7061)
  Neumayer-Station II (lat=-70.6500, lon=-8.2500)
  Reis Motors (lat=-75.0369, lon=0.5273)
  Troll Airfield (lat=-71.9572, lon=2.4667)
  Antarctica/Troll (lat=-72.0114, lon=2.5350)
  SANAE (lat=-72.6667, lon=2.8500)
  Grace-Care Laboratory Services and Supplies (lat=-78.4089, lon=-0.4839)
  Yamu Store III (lat=-84.5414, lon=1.4062)
  Mount Dallmann (lat=-71.7500, lon=10.3000)
  Filchner Mountains (lat=-72.0500, lon=7.6667)
  Jj'sanm Music Tutorial Center (lat=-70.0807, lon=18.5354)
  Dani Motors (lat=-60.0484, lon=14.4141)
  Delta Power Generators (lat=-64.0776, lon=11.7967)
  Timsun Franchise Bayan Lepas - West Motor Racing Centre (lat=-77.7784, lon=17.3689)
  Nadia Pereira - Corretora de Imóveis (lat=-64.9731, lon=27.5568)
  RT Power Motor (lat=-67.6134, lon=29.8849)
  King World Product Store (lat=-60.4919, lon=29.7141)
  BOSS Menswear Store (lat=-86.8161, lon=36.1071)
  Extra Space Storage (lat=-77.0941, lon=38.8842)
  Ashford Self Storage Hull Ltd (lat=-76.8408, lon=40.7812)
  Windfreight Express Total Logistics Inc.(WETLI) (lat=-75.0730, lon=43.0664)
  Plateau Station (lat=-79.2500, lon=40.5000)
  HomeVestor PH (lat=-82.7436, lon=43.0545)
  Tractor Zetor (lat=-76.0161, lon=50.6250)
  Pizza Salvatoré (lat=-75.5072, lon=46.5485)
  World Store (lat=-67.4755, lon=52.8013)
  Żłobek i Przedszkole Montessori Biała Sowa (lat=-73.6844, lon=38.9983)
  Ténor store (lat=-61.8563, lon=66.8828)
  Nissan Pampanga by Maica Torres (lat=-63.8013, lon=57.1124)
  Forland KpK Motors (lat=-72.3400, lon=57.9277)
  Base antarctique Mawson (lat=-67.6026, lon=62.8736)
  N'Zemmula - ristorante siciliano (lat=-68.8873, lon=66.4453)
  Nueva Ecija Doctors Hospital (lat=-81.4142, lon=73.1013)
  Antarctic Kunlun Station (lat=-80.4169, lon=77.1161)
  Lori Makiya, Realtor (lat=-63.1742, lon=80.5643)
  Melissa Isaac Realtor (lat=-64.2389, lon=83.2983)
  Me_2 Store (lat=-62.3139, lon=88.2532)
  Victors motor driving school (lat=-67.8553, lon=98.5670)
  T-Store (lat=-78.0165, lon=98.7891)
  Lake Vostok (lat=-77.5000, lon=106.0000)
  Vostok İstasyonu (lat=-78.4644, lon=106.8373)
  Bandith Card Store (lat=-72.6176, lon=112.5000)
  Base antártica Concordia (lat=-75.1000, lon=123.3333)
  Base antarctique Concordia (lat=-75.1000, lon=123.5580)
  JRMSU Katipunan Campus CTEd-Laboratory High School (lat=-69.1798, lon=123.5915)
  Sunstar Motor Jepara (lat=-73.1861, lon=127.6172)
  Good Mates Removals & Storage (lat=-63.4702, lon=123.9815)
  Kimstore (lat=-68.9110, lon=118.8281)
  Base antarctique Dumont-d'Urville (lat=-66.6628, lon=140.0013)
  Nadhi/நதி - The Online Saree Store (lat=-80.2502, lon=151.8750)
  North Store (lat=-70.0807, lon=164.1299)
  Mario-Zucchelli-Station (lat=-74.6943, lon=164.1125)
  McMurdo General Hospital (lat=-77.8468, lon=166.6693)
  Base McMurdo (lat=-77.8500, lon=166.6667)
  Polární stanice McMurdo (lat=-77.8500, lon=166.6667)
  Base antarctique McMurdo (lat=-77.8500, lon=166.6667)
  Kimpich Store (lat=-72.9420, lon=170.5701)
  Sales Store (lat=-61.8769, lon=169.8047)

cd /work && python3 << 'EOF'
# Let me manually curate the list of known Antarctic research stations
# from the Overture data and use more specific matching

import overturemaps

bbox = (-180, -90, 180, -60)
reader = overturemaps.record_batch_reader('place', bbox=bbox)
if reader:
    import pyarrow as pa
    import geopandas as gpd
    from shapely import wkb
    
    table = pa.Table.from_batches(reader)
    df = table.to_pandas()
    geoms = [wkb.loads(row) for row in df['geometry']]
    gdf = gpd.GeoDataFrame(df, geometry=geoms, crs='EPSG:4326')
    
    # Specific station names to look for (case insensitive)
    # These are the actual research stations I can identify from the data
    exact_matches = {}
    
    # Check specific names
    for idx, row in gdf.iterrows():
        name = row['names']['primary'] if row['names'] else ''
        name_lower = name.lower().strip()
        
        # Specific Antarctic station names we want
        station_patterns = [
            'brown station', 'gonzález videla', 'palmer station',
            'carlini base', 'bellingshausen', 'marambio', 'esperanza base',
            'neumayer-station', 'troll', 'sanae', 'plateau station',
            'mawson', 'kunlun station', 'concordia', 'dumont-d\'urville',
            'mario-zucchelli', 'mcmurdo', 'vostok',
            'base mcmurdo', 'base antártica marambio', 'base antártica concordia',
            'base antarctique mawson', 'base antarctique dumont',
            'base antarctique concordia', 'base antarctique mcmurdo',
            'base esperanza', 'base marambio',
            'polární stanice mcmurdo'
        ]
        
        # Check if name matches any station pattern
        for pattern in station_patterns:
            if pattern in name_lower or name_lower == pattern:
                key = name  # use full name as key
                if key not in exact_matches:
                    exact_matches[key] = row
                break
        
        # Also check taxonomy
        tax = row['taxonomy']
        if tax and tax.get('primary') == 'educational_research_institute':
            if name not in exact_matches:
                exact_matches[name] = row
    
    print(f"Found {len(exact_matches)} station matches:")
    for name, row in exact_matches.items():
        print(f"  {name} (lat={row.geometry.y:.4f}, lon={row.geometry.x:.4f})")
EOF

Found 28 station matches:
  Brown Station, Antarctic Peninsula (lat=-64.8921, lon=-62.8724)
  Brown Station (lat=-64.8950, lon=-62.8700)
  González Videla Antarctic Base (lat=-64.8234, lon=-62.8575)
  Palmer Station, Antarctica (lat=-64.7743, lon=-64.0531)
  Carlini Base (lat=-62.2379, lon=-58.6668)
  Bellingshausen Russian Antarctic Station (lat=-62.1998, lon=-58.9624)
  Base Marambio, Antartida Argentina (lat=-64.2413, lon=-56.6198)
  Marambio Base (lat=-64.2410, lon=-56.6268)
  Base Antártica Marambio (lat=-64.2374, lon=-56.6235)
  Esperanza Base, Antarctica (lat=-63.3977, lon=-56.9961)
  Base Esperanza, Antartida Argentina (lat=-63.3980, lon=-56.9974)
  Neumayer-Station II (lat=-70.6500, lon=-8.2500)
  Troll Airfield (lat=-71.9572, lon=2.4667)
  Antarctica/Troll (lat=-72.0114, lon=2.5350)
  SANAE (lat=-72.6667, lon=2.8500)
  Plateau Station (lat=-79.2500, lon=40.5000)
  Base antarctique Mawson (lat=-67.6026, lon=62.8736)
  Antarctic Kunlun Station (lat=-80.4169, lon=77.1161)
  Lake Vostok (lat=-77.5000, lon=106.0000)
  Vostok İstasyonu (lat=-78.4644, lon=106.8373)
  Base antártica Concordia (lat=-75.1000, lon=123.3333)
  Base antarctique Concordia (lat=-75.1000, lon=123.5580)
  Base antarctique Dumont-d'Urville (lat=-66.6628, lon=140.0013)
  Mario-Zucchelli-Station (lat=-74.6943, lon=164.1125)
  McMurdo General Hospital (lat=-77.8468, lon=166.6693)
  Base McMurdo (lat=-77.8500, lon=166.6667)
  Polární stanice McMurdo (lat=-77.8500, lon=166.6667)
  Base antarctique McMurdo (lat=-77.8500, lon=166.6667)

cd /work && python3 << 'EOF'
import overturemaps

bbox = (-180, -90, 180, -60)
reader = overturemaps.record_batch_reader('place', bbox=bbox)
if reader:
    import pyarrow as pa
    import geopandas as gpd
    from shapely import wkb
    
    table = pa.Table.from_batches(reader)
    df = table.to_pandas()
    geoms = [wkb.loads(row) for row in df['geometry']]
    gdf = gpd.GeoDataFrame(df, geometry=geoms, crs='EPSG:4326')
    
    # More patterns for research stations
    more_patterns = [
        'davis station', 'casey station', 'syowa', 'zhongshan',
        'progress station', 'mirny', 'novolazarevskaya', 'progress',
        'halley', 'rothera', 'scott base', 'amundsen-scott',
        'amundsen', 'south pole station', 'mizuho', 'asuka',
        'dome fuji', 'kohnen', 'taishan', 'bharati', 'maitri',
        'king sejong', 'jang bogo', 'great wall', 'frei',
        'comandante ferraz', 'ferraz', 'arcitowski',
        'gabriel de castilla', 'juan carlos i', 'artigas',
        'escudero', 'ohridski', 'princess elisabeth',
        'aboa', 'wasa', 'tor', 'svea', 'law base',
        'vernadsky', 'signy', 'faraday', 'bird island',
        'macquarie island', 'gondwana', 'georg forster',
        'dallmann', 'filchner', 'byrd station', 'siple station',
        'leningradskaya', 'russkaya', 'sovetskaya', 'molodezhnaya',
        'rn', '30 de julio', 'estación', 'base antártica',
        'base antarctica', 'base antartica', 'polární stanice',
        'istasyonu', 'station -', '- station'
    ]
    
    more_matches = {}
    for idx, row in gdf.iterrows():
        name = row['names']['primary'] if row['names'] else ''
        name_lower = name.lower().strip()
        
        for pattern in more_patterns:
            if pattern in name_lower:
                if name not in more_matches:
                    more_matches[name] = row
                break
    
    print(f"Additional matches: {len(more_matches)}")
    for name, row in more_matches.items():
        print(f"  {name} (lat={row.geometry.y:.4f}, lon={row.geometry.x:.4f})")
EOF

Additional matches: 118
  Chittagong Victory National School-CVNS (lat=-80.4166, lon=-175.3833)
  Work N Wear International (lat=-84.3112, lon=-171.6963)
  Andie's Furniture (lat=-62.7780, lon=-125.1094)
  Suzuki Nilphamari - Lahin Motors (lat=-65.7744, lon=-121.3709)
  Northern River View Hotel & Restaurant Hassa (lat=-81.6789, lon=-129.0196)
  Discount Hardware Store (lat=-74.9594, lon=-118.8281)
  Garniture Gallery (lat=-74.8618, lon=-101.8928)
  Bert Lozada Swim School - St.Gabriel International School (lat=-80.5321, lon=-101.2500)
  Everything Store (lat=-75.8400, lon=-100.1338)
  HISNearness NaviMumbai (lat=-81.6853, lon=-98.3503)
  AKS Manufactory (lat=-61.5202, lon=-97.7976)
  In Aeternum Historia (lat=-84.1257, lon=-94.9229)
  Complejo Educativo Profesor José Arnoldo Sermeño (lat=-81.4139, lon=-92.1094)
  CORA Physical Therapy Hales Corners (lat=-88.0494, lon=-88.0494)
  Mitra Fajar Ciawi " Bengkel Resmi Sepeda Motor Honda " (lat=-76.1849, lon=-87.6522)
  RMC Furniture Manufacturing (lat=-70.0807, lon=-84.8586)
  Iglesia Centro Internacional Maranatha Honduras (lat=-73.8248, lon=-84.3750)
  Canoon Store (lat=-71.0228, lon=-81.8109)
  Toronto Cosmetic Surgery Institute (lat=-79.3815, lon=-79.3815)
  شركة يوني فليكس للمعدات والمستلزمات الطبية - Uniflex Medical Supply Store (lat=-65.2936, lon=-75.2316)
  The Learning Center at SUNY Cortland (lat=-67.3399, lon=-68.2031)
  Dikshita Pet Corner (lat=-72.4198, lon=-64.8853)
  ร้านข้าวแกงฮาริษา Harisa's Curries Rice Factory (lat=-70.0806, lon=-65.2176)
  Lihor Store Fan (lat=-66.3728, lon=-62.0934)
  Obec Horní Studénky (lat=-83.4403, lon=-61.1719)
  Concretiza Lar Construtora E Incorporadora (lat=-63.0673, lon=-59.9706)
  Bestiario Moderno (lat=-78.1542, lon=-58.0165)
  Base Antártica Marambio (lat=-64.2374, lon=-56.6235)
  British Antarctic Territory (lat=-75.0000, lon=-50.0000)
  Consultoría Psicológica Especializada de Monclova Psic. Aarón Hernández (lat=-82.5591, lon=-40.4187)
  Hurricane On Saturn (lat=-78.1487, lon=-40.7299)
  Generika Drugstore - ZDN Bagting Dapitan Page (lat=-77.6177, lon=-42.7094)
  Carrefour Hiper Recife Torre (lat=-80.4079, lon=-34.9103)
  Modern American Schools of Egypt 2000 (lat=-84.9901, lon=-36.5625)
  Church of Jesus Christ United Faith Apostolic/Tavern Bible Way Apostolic (lat=-79.7789, lon=-31.3150)
  DRK Kreisverband Gelnhausen-Schlüchtern e.V. (lat=-83.1649, lon=-28.8171)
  Pastor Jorge (lat=-73.2722, lon=-21.4893)
  บ้านน็อคดาวน์ King Modern Home สาขาพัทยา (lat=-71.9340, lon=-24.3365)
  Shah Door And Furniture (lat=-65.2936, lon=-33.4936)
  Nails By Hilary Torres M (lat=-69.0986, lon=-28.9251)
  Famous Forum of Worldwide Collaborators LLP (lat=-68.5652, lon=-26.4430)
  Javastasia Distributors Halaal Meats (lat=-61.6064, lon=-30.9375)
  N9store.ro (lat=-63.4702, lon=-42.5968)
  La Combo Tortuga (lat=-64.4728, lon=-40.7812)
  Smart Home Furnishings (lat=-67.8755, lon=-5.6250)
  Storybook Imaging (lat=-65.6583, lon=-5.6250)
  Lafourcade Sap - Services informatiques à domicile en Béarn (lat=-65.3668, lon=-5.6250)
  Technology Store (lat=-61.2725, lon=-11.2557)
  Alessandra Oliveira corretora de imóveis rj (lat=-61.4396, lon=-18.8793)
  Robin Rolon, Mary Kay Independent Sales Director (lat=-64.9036, lon=-16.0130)
  Vumeer el Mentor (lat=-67.2195, lon=-13.8102)
  ERNSTform.se (lat=-71.2392, lon=-17.1307)
  Babul Yaman Store (lat=-82.0149, lon=-17.7061)
  Arteco Furniture (lat=-77.3549, lon=-3.5248)
  Reis Motors (lat=-75.0369, lon=0.5273)
  Grace-Care Laboratory Services and Supplies (lat=-78.4089, lon=-0.4839)
  Yamu Store III (lat=-84.5414, lon=1.4062)
  Mount Dallmann (lat=-71.7500, lon=10.3000)
  Filchner Mountains (lat=-72.0500, lon=7.6667)
  Jj'sanm Music Tutorial Center (lat=-70.0807, lon=18.5354)
  Dani Motors (lat=-60.0484, lon=14.4141)
  Delta Power Generators (lat=-64.0776, lon=11.7967)
  St. Isidore Catholic Learning Centre (lat=-84.2672, lon=12.6562)
  Wijaya Furniture (lat=-79.0392, lon=14.7578)
  Timsun Franchise Bayan Lepas - West Motor Racing Centre (lat=-77.7784, lon=17.3689)
  Southern Ocean (lat=-80.7606, lon=25.3125)
  Tattoos by Mauro Cornejo (lat=-60.6733, lon=25.5240)
  Nadia Pereira - Corretora de Imóveis (lat=-64.9731, lon=27.5568)
  RT Power Motor (lat=-67.6134, lon=29.8849)
  King World Product Store (lat=-60.4919, lon=29.7141)
  BOSS Menswear Store (lat=-86.8161, lon=36.1071)
  Extra Space Storage (lat=-77.0941, lon=38.8842)
  Ashford Self Storage Hull Ltd (lat=-76.8408, lon=40.7812)
  Windfreight Express Total Logistics Inc.(WETLI) (lat=-75.0730, lon=43.0664)
  HomeVestor PH (lat=-82.7436, lon=43.0545)
  Kernel existence (lat=-79.3026, lon=54.1406)
  Tractor Zetor (lat=-76.0161, lon=50.6250)
  Pizza Salvatoré (lat=-75.5072, lon=46.5485)
  Lake Lagernoye (lat=-67.6667, lon=45.8500)
  American Ribs & Wings - Barangay 4-A, Guiuan, Eastern Samar (lat=-69.2927, lon=46.5915)
  World Store (lat=-67.4755, lon=52.8013)
  Learn Languages & Dialects (lat=-67.6092, lon=34.4531)
  In Love Furniture - ศูนย์รวมเฟอร์นิเจอร์ชุดห้องนอน และ เฟอร์นิเจอร์สำนักงาน (lat=-72.8161, lon=32.3438)
  Creative Learners Art Studio (lat=-61.2659, lon=72.0418)
  Ténor store (lat=-61.8563, lon=66.8828)
  Adult Learning Center, Bridgeport, CT (lat=-63.2020, lon=57.3047)
  Nissan Pampanga by Maica Torres (lat=-63.8013, lon=57.1124)
  Alvlyn International School (lat=-67.3399, lon=56.6125)
  Forland KpK Motors (lat=-72.3400, lon=57.9277)
  N'Zemmula - ristorante siciliano (lat=-68.8873, lon=66.4453)
  Delhi International Academy (lat=-76.8408, lon=59.0625)
  Snacks Corner (lat=-76.1028, lon=57.0541)
  V.P Furniture (lat=-79.8783, lon=57.7653)
  Kidventure Early Learning (lat=-80.2944, lon=61.1678)
  Jyomogang International Academy (lat=-84.9901, lon=66.7969)
  Nueva Ecija Doctors Hospital (lat=-81.4142, lon=73.1013)
  Lori Makiya, Realtor (lat=-63.1742, lon=80.5643)
  Melissa Isaac Realtor (lat=-64.2389, lon=83.2983)
  Me_2 Store (lat=-62.3139, lon=88.2532)
  Terapias Espirituales y Alternativas (lat=-61.8760, lon=93.2562)
  albertopiernas (lat=-65.3010, lon=96.3082)
  Victors motor driving school (lat=-67.8553, lon=98.5670)
  T-Store (lat=-78.0165, lon=98.7891)
  Sydney Amigurumi Corner 手勾公仔 (lat=-81.9232, lon=99.1406)
  Sajid Overseas international Ltd (lat=-80.4288, lon=104.2544)
  Bandith Card Store (lat=-72.6176, lon=112.5000)
  Base antártica Concordia (lat=-75.1000, lon=123.3333)
  JRMSU Katipunan Campus CTEd-Laboratory High School (lat=-69.1798, lon=123.5915)
  Sunstar Motor Jepara (lat=-73.1861, lon=127.6172)
  Good Mates Removals & Storage (lat=-63.4702, lon=123.9815)
  Kimstore (lat=-68.9110, lon=118.8281)
  Nadhi/நதி - The Online Saree Store (lat=-80.2502, lon=151.8750)
  Warning Five จำหน่ายสายนาฬิกาหนังแท้ สายนาฬิกา smartwatch (lat=-74.4635, lon=154.4062)
  North Store (lat=-70.0807, lon=164.1299)
  Southern Exposure (lat=-77.8454, lon=166.6700)
  Polární stanice McMurdo (lat=-77.8500, lon=166.6667)
  Kimpich Store (lat=-72.9420, lon=170.5701)
  Sales Store (lat=-61.8769, lon=169.8047)

Fetching places …
Found 25 research stations
Stations CRS: EPSG:3031
Fetching land …
Traceback (most recent call last):
  File "/work/solve.py", line 123, in <module>
    land_ant = land[land.geometry.y.min() > -90]  # already within bbox
                    ^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/geopandas/geoseries.py", line 301, in y
    return _delegate_property("y", self)
  File "/usr/local/lib/python3.14/site-packages/geopandas/base.py", line 93, in _delegate_property
    data = getattr(a_this, op)
  File "/usr/local/lib/python3.14/site-packages/geopandas/array.py", line 1251, in y
    raise ValueError(message)
ValueError: y attribute access only provided for Point geometries

Fetching places …
Found 25 research stations
Stations CRS: EPSG:3031
Fetching land …
Dissolving Antarctic land …
Land area (km²): 13637523
Fetching water features …
  Ocean water features: 4978
Fetching bathymetry …
  Bathymetry features: 3096
Total water features: 8074
Creating 200 km buffers …
Clipping buffers with Antarctic landmass …
Land-clipped zones: 9
Computing overlap coalitions …
  Found 7 coalition(s)
Output 1 rows: 9
Coalition values: [np.int64(0), np.int64(1), np.int64(2), np.int64(3), np.int64(4), np.int64(5), np.int64(6)]
Computing over-water portions …
Output 2 rows: 1975
Writing GeoParquet files …
  ✓ station_spheres.geoparquet
  ✓ station_water_overlap.geoparquet
Done.

cd /work && python3 << 'EOF'
import geopandas as gpd
import pandas as pd

# Check output 1
print("=" * 60)
print("station_spheres.geoparquet")
print("=" * 60)
gdf1 = gpd.read_parquet("/work/station_spheres.geoparquet")
print(f"Rows: {len(gdf1)}")
print(f"Columns: {list(gdf1.columns)}")
print(f"CRS: {gdf1.crs}")
print(f"Geometry types: {gdf1.geometry.geom_type.value_counts()}")
print(f"\nCoalitions: {sorted(gdf1['coalition'].unique())}")
print(f"Distinct coalition values: {gdf1['coalition'].nunique()}")
print(f"\nSample data:")
for idx, row in gdf1.iterrows():
    print(f"  station_id={row['station_id']}, station_name={row['station_name']}, coalition={row['coalition']}, area_km2={row.geometry.area/1e6:.0f}")

print("\n" + "=" * 60)
print("station_water_overlap.geoparquet")
print("=" * 60)
gdf2 = gpd.read_parquet("/work/station_water_overlap.geoparquet")
print(f"Rows: {len(gdf2)}")
print(f"Columns: {list(gdf2.columns)}")
print(f"CRS: {gdf2.crs}")
print(f"Geometry types: {gdf2.geometry.geom_type.value_counts()}")
print(f"\nWater sources: {gdf2['water_source'].value_counts().to_dict()}")
print(f"\nSample (first 10):")
print(gdf2[['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source']].head(10))
EOF

============================================================
station_spheres.geoparquet
============================================================
Rows: 9
Columns: ['station_id', 'station_name', 'coalition', 'geometry']
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}}
Geometry types: MultiPolygon    9
Name: count, dtype: int64

Coalitions: [np.int64(0), np.int64(1), np.int64(2), np.int64(3), np.int64(4), np.int64(5), np.int64(6)]
Distinct coalition values: 7

Sample data:
  station_id=11, station_name=Neumayer-Station II, coalition=0, area_km2=57232
  station_id=12, station_name=Antarctica/Troll, coalition=1, area_km2=125462
  station_id=13, station_name=SANAE, coalition=1, area_km2=125462
  station_id=14, station_name=Plateau Station, coalition=2, area_km2=125462
  station_id=15, station_name=Base antarctique Mawson, coalition=3, area_km2=64125
  station_id=16, station_name=Antarctic Kunlun Station, coalition=4, area_km2=125462
  station_id=17, station_name=Vostok İstasyonu, coalition=5, area_km2=125462
  station_id=18, station_name=Base antártica Concordia, coalition=6, area_km2=125462
  station_id=19, station_name=Base antarctique Concordia, coalition=6, area_km2=125462

============================================================
station_water_overlap.geoparquet
============================================================
Rows: 1975
Columns: ['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source', 'geometry']
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}}
Geometry types: MultiPolygon    1975
Name: count, dtype: int64

Water sources: {'base.bathymetry': 1181, 'base.water': 794}

Sample (first 10):
   station_id                        station_name  ... water_subtype water_source
0           0  Brown Station, Antarctic Peninsula  ...         ocean   base.water
1           0  Brown Station, Antarctic Peninsula  ...         ocean   base.water
2           0  Brown Station, Antarctic Peninsula  ...         ocean   base.water
3           0  Brown Station, Antarctic Peninsula  ...         ocean   base.water
4           0  Brown Station, Antarctic Peninsula  ...         ocean   base.water
5           0  Brown Station, Antarctic Peninsula  ...         ocean   base.water
6           0  Brown Station, Antarctic Peninsula  ...         ocean   base.water
7           0  Brown Station, Antarctic Peninsula  ...         ocean   base.water
8           0  Brown Station, Antarctic Peninsula  ...         ocean   base.water
9           0  Brown Station, Antarctic Peninsula  ...         ocean   base.water

[10 rows x 6 columns]

cd /work && python3 << 'EOF'
import geopandas as gpd
import pandas as pd

# Check which stations were in the original set
sph = gpd.read_parquet("/work/station_spheres.geoparquet")
print("Stations in land-clipped output:")
print(sph[["station_id", "station_name", "coalition"]].to_string())

print("\n\nAll station names in over-water output:")
wat = gpd.read_parquet("/work/station_water_overlap.geoparquet")
print(wat["station_name"].unique())
EOF

Stations in land-clipped output:
   station_id                station_name  coalition
0          11         Neumayer-Station II          0
1          12            Antarctica/Troll          1
2          13                       SANAE          1
3          14             Plateau Station          2
4          15     Base antarctique Mawson          3
5          16    Antarctic Kunlun Station          4
6          17            Vostok İstasyonu          5
7          18    Base antártica Concordia          6
8          19  Base antarctique Concordia          6


All station names in over-water output:
<ArrowStringArray>
[      'Brown Station, Antarctic Peninsula',
                            'Brown Station',
           'González Videla Antarctic Base',
               'Palmer Station, Antarctica',
                             'Carlini Base',
 'Bellingshausen Russian Antarctic Station',
       'Base Marambio, Antartida Argentina',
                            'Marambio Base',
                  'Base Antártica Marambio',
               'Esperanza Base, Antarctica',
      'Base Esperanza, Antartida Argentina',
                      'Neumayer-Station II',
                  'Base antarctique Mawson',
        'Base antarctique Dumont-d'Urville',
                  'Mario-Zucchelli-Station',
                             'Base McMurdo',
                  'Polární stanice McMurdo',
                 'Base antarctique McMurdo']
Length: 18, dtype: str

cd /work && python3 << 'EOF'
import overturemaps
import pyarrow as pa
import geopandas as gpd
from shapely import wkb
from shapely.ops import unary_union

bbox = (-180, -90, 180, -60)

# Check what land features exist around the Antarctic Peninsula
reader = overturemaps.record_batch_reader("land", bbox=bbox)
table = pa.Table.from_batches(reader)
land_df = table.to_pandas()
land_geoms = [wkb.loads(row) for row in land_df["geometry"]]
land = gpd.GeoDataFrame(land_df, geometry=land_geoms, crs="EPSG:4326")

print(f"Total land features: {len(land)}")

# Check bounds
print(f"Land bounds: {land.total_bounds}")

# Check if there are land features around the Antarctic Peninsula region
# e.g. lat around -62 to -65, lon around -55 to -65
peninsula = land.cx[-70:-55, -65:-60]
print(f"Land features in Antarctic Peninsula region: {len(peninsula)}")
if len(peninsula) > 0:
    print(f"  Types: {peninsula.geometry.geom_type.value_counts()}")
    print(f"  Area: {peninsula.area.sum():.2f} sq deg")

# Check Brown Station location
brown_lat, brown_lon = -64.895, -62.870
# Check if any land feature contains or is near this point
from shapely import Point
pt = Point(brown_lon, brown_lat)
for idx, row in land.iterrows():
    if row.geometry.distance(pt) < 0.01:  # within ~1km
        print(f"  Land feature near Brown: subtype={row.get('subtype')}, geom_type={row.geometry.geom_type}")
        break
else:
    print("  No land feature near Brown Station!")
EOF

Total land features: 93367
Land bounds: [-180.         -90.         180.         -60.3551539]
Land features in Antarctic Peninsula region: 9850
  Types: Polygon         9103
Point            652
MultiPolygon      66
LineString        29
Name: count, dtype: int64
  Area: 10.50 sq deg
  Land feature near Brown: subtype=rock, geom_type=Polygon

cd /work && python3 << 'EOF'
import overturemaps
import pyarrow as pa
import geopandas as gpd
import pandas as pd
from shapely import wkb, Point
from shapely.ops import unary_union

bbox = (-180, -90, 180, -60)

# Get stations
reader = overturemaps.record_batch_reader("place", bbox=bbox)
table = pa.Table.from_batches(reader)
pdf = table.to_pandas()
geoms = [wkb.loads(row) for row in pdf["geometry"]]
places = gpd.GeoDataFrame(pdf, geometry=geoms, crs="EPSG:4326")

# Get land
reader_land = overturemaps.record_batch_reader("land", bbox=bbox)
table_land = pa.Table.from_batches(reader_land)
land_df = table_land.to_pandas()
land_geoms = [wkb.loads(row) for row in land_df["geometry"]]
land = gpd.GeoDataFrame(land_df, geometry=land_geoms, crs="EPSG:4326")

# Brown station
brown = places[places["id"].str.contains("Brown", case=False, na=False)]
if not brown.empty:
    print(f"Brown found: {brown.iloc[0]['names']}")
    pt = brown.iloc[0].geometry
    print(f"Brown at: {pt.x}, {pt.y}")
    buf = pt.buffer(200/111.32)  # rough 200km in deg
    # Check intersection with land
    land_sub = land[land.geometry.intersects(buf)]
    print(f"Land features intersecting Brown's 200km buffer: {len(land_sub)}")
    if len(land_sub) > 0:
        print(f"  Area of land in buffer: {land_sub.area.sum():.2f} sq deg")

# Check all stations
KNOWN_STATION_NAMES = {
    "Brown Station",
    "Brown Station, Antarctic Peninsula",
    "González Videla Antarctic Base",
    "Palmer Station, Antarctica",
    "Carlini Base",
    "Bellingshausen Russian Antarctic Station",
    "Base Marambio, Antartida Argentina",
    "Marambio Base",
    "Base Antártica Marambio",
    "Esperanza Base, Antarctica",
    "Base Esperanza, Antartida Argentina",
    "Neumayer-Station II",
    "Antarctica/Troll",
    "SANAE",
    "Plateau Station",
    "Base antarctique Mawson",
    "Antarctic Kunlun Station",
    "Vostok İstasyonu",
    "Base antártica Concordia",
    "Base antarctique Concordia",
    "Base antarctique Dumont-d'Urville",
    "Mario-Zucchelli-Station",
    "Base McMurdo",
    "Polární stanice McMurdo",
    "Base antarctique McMurdo",
}

for idx, row in places.iterrows():
    name = row["names"]["primary"] if row["names"] else ""
    if name in KNOWN_STATION_NAMES:
        pt = row.geometry
        # Check if 200km buffer (rough) intersects any land
        buf = pt.buffer(200/111.32)
        land_intersect = land[land.geometry.intersects(buf)]
        has_land = len(land_intersect) > 0
        print(f"  {name}: lat={pt.y:.2f} lon={pt.x:.2f} land_in_buffer={has_land}")
EOF

  Brown Station, Antarctic Peninsula: lat=-64.89 lon=-62.87 land_in_buffer=True
  Brown Station: lat=-64.89 lon=-62.87 land_in_buffer=True
  González Videla Antarctic Base: lat=-64.82 lon=-62.86 land_in_buffer=True
  Palmer Station, Antarctica: lat=-64.77 lon=-64.05 land_in_buffer=True
  Carlini Base: lat=-62.24 lon=-58.67 land_in_buffer=True
  Bellingshausen Russian Antarctic Station: lat=-62.20 lon=-58.96 land_in_buffer=True
  Base Marambio, Antartida Argentina: lat=-64.24 lon=-56.62 land_in_buffer=True
  Marambio Base: lat=-64.24 lon=-56.63 land_in_buffer=True
  Base Antártica Marambio: lat=-64.24 lon=-56.62 land_in_buffer=True
  Esperanza Base, Antarctica: lat=-63.40 lon=-57.00 land_in_buffer=True
  Base Esperanza, Antartida Argentina: lat=-63.40 lon=-57.00 land_in_buffer=True
  Neumayer-Station II: lat=-70.65 lon=-8.25 land_in_buffer=True
  Antarctica/Troll: lat=-72.01 lon=2.54 land_in_buffer=True
  SANAE: lat=-72.67 lon=2.85 land_in_buffer=True
  Plateau Station: lat=-79.25 lon=40.50 land_in_buffer=True
  Base antarctique Mawson: lat=-67.60 lon=62.87 land_in_buffer=True
  Antarctic Kunlun Station: lat=-80.42 lon=77.12 land_in_buffer=True
  Vostok İstasyonu: lat=-78.46 lon=106.84 land_in_buffer=True
  Base antártica Concordia: lat=-75.10 lon=123.33 land_in_buffer=True
  Base antarctique Concordia: lat=-75.10 lon=123.56 land_in_buffer=True
  Base antarctique Dumont-d'Urville: lat=-66.66 lon=140.00 land_in_buffer=True
  Mario-Zucchelli-Station: lat=-74.69 lon=164.11 land_in_buffer=True
  Base McMurdo: lat=-77.85 lon=166.67 land_in_buffer=True
  Polární stanice McMurdo: lat=-77.85 lon=166.67 land_in_buffer=True
  Base antarctique McMurdo: lat=-77.85 lon=166.67 land_in_buffer=True

cd /work && python3 << 'EOF'
import geopandas as gpd

sph = gpd.read_parquet("/work/station_spheres.geoparquet")
print(f"Total stations in output 1: {len(sph)}")
for _, r in sph.iterrows():
    print(f"  id={r['station_id']}, name='{r['station_name']}', coalition={r['coalition']}")
EOF

Total stations in output 1: 9
  id=11, name='Neumayer-Station II', coalition=0
  id=12, name='Antarctica/Troll', coalition=1
  id=13, name='SANAE', coalition=1
  id=14, name='Plateau Station', coalition=2
  id=15, name='Base antarctique Mawson', coalition=3
  id=16, name='Antarctic Kunlun Station', coalition=4
  id=17, name='Vostok İstasyonu', coalition=5
  id=18, name='Base antártica Concordia', coalition=6
  id=19, name='Base antarctique Concordia', coalition=6

cd /work && python3 << 'EOF'
import overturemaps
import pyarrow as pa
import geopandas as gpd
import pandas as pd
import numpy as np
from shapely import wkb, Point, MultiPolygon
from shapely.ops import unary_union

bbox = (-180, -90, 180, -60)

# Get stations
reader = overturemaps.record_batch_reader("place", bbox=bbox)
table = pa.Table.from_batches(reader)
pdf = table.to_pandas()
geoms = [wkb.loads(row) for row in pdf["geometry"]]
places = gpd.GeoDataFrame(pdf, geometry=geoms, crs="EPSG:4326")

KNOWN_STATION_NAMES = {
    "Brown Station",
    "Brown Station, Antarctic Peninsula",
    "González Videla Antarctic Base",
    "Palmer Station, Antarctica",
    "Carlini Base",
    "Bellingshausen Russian Antarctic Station",
    "Base Marambio, Antartida Argentina",
    "Marambio Base",
    "Base Antártica Marambio",
    "Esperanza Base, Antarctica",
    "Base Esperanza, Antartida Argentina",
    "Neumayer-Station II",
    "Antarctica/Troll",
    "SANAE",
    "Plateau Station",
    "Base antarctique Mawson",
    "Antarctic Kunlun Station",
    "Vostok İstasyonu",
    "Base antártica Concordia",
    "Base antarctique Concordia",
    "Base antarctique Dumont-d'Urville",
    "Mario-Zucchelli-Station",
    "Base McMurdo",
    "Polární stanice McMurdo",
    "Base antarctique McMurdo",
}

rows = []
seen = set()
for idx, row in places.iterrows():
    name = row["names"]["primary"] if row["names"] else ""
    if not name:
        continue
    if name in KNOWN_STATION_NAMES:
        if name not in seen:
            seen.add(name)
            rows.append(row)

stations = gpd.GeoDataFrame(rows, columns=places.columns, crs="EPSG:4326")
stations["station_id"] = stations.reset_index(drop=True).index.astype(int)
stations["station_name"] = stations["names"].apply(lambda n: n["primary"] if n else "Unknown")

# Re-project
stations_3031 = stations.to_crs("EPSG:3031")

# Get land
reader_land = overturemaps.record_batch_reader("land", bbox=bbox)
table_land = pa.Table.from_batches(reader_land)
land_df = table_land.to_pandas()
land_geoms = [wkb.loads(row) for row in land_df["geometry"]]
land = gpd.GeoDataFrame(land_df, geometry=land_geoms, crs="EPSG:4326")
land_union = unary_union(land.geometry.to_list())
land_all = gpd.GeoDataFrame(geometry=[land_union], crs="EPSG:4326")
land_3031 = land_all.to_crs("EPSG:3031")
land_geom = land_3031.geometry.iloc[0]

BUFFER = 200_000

# Test each station
for idx, row in stations_3031.iterrows():
    name = row["station_name"]
    buf = row.geometry.buffer(BUFFER)
    clipped = buf.intersection(land_geom)
    print(f"id={row['station_id']}, name={name}")
    print(f"  buffer area: {buf.area/1e6:.0f} km²")
    print(f"  intersection valid: {not clipped.is_empty}")
    if not clipped.is_empty:
        print(f"  intersection area: {clipped.area/1e6:.0f} km²")
    else:
        print(f"  Location: {row.geometry}")
EOF

cd /work && python3 << 'EOF'
# Quick check: why are some stations missing from land-clipped output?
import overturemaps
import pyarrow as pa
import geopandas as gpd
import pandas as pd
from shapely import wkb, MultiPolygon
from shapely.ops import unary_union

bbox = (-180, -90, 180, -60)

# Get stations
reader = overturemaps.record_batch_reader("place", bbox=bbox)
table = pa.Table.from_batches(reader)
pdf = table.to_pandas()
geoms = [wkb.loads(row) for row in pdf["geometry"]]
places = gpd.GeoDataFrame(pdf, geometry=geoms, crs="EPSG:4326")

KNOWN_STATION_NAMES = {
    "Brown Station",
    "Brown Station, Antarctic Peninsula",
    "González Videla Antarctic Base",
    "Palmer Station, Antarctica",
    "Carlini Base",
    "Bellingshausen Russian Antarctic Station",
    "Base Marambio, Antartida Argentina",
    "Marambio Base",
    "Base Antártica Marambio",
    "Esperanza Base, Antarctica",
    "Base Esperanza, Antartida Argentina",
    "Neumayer-Station II",
    "Antarctica/Troll",
    "SANAE",
    "Plateau Station",
    "Base antarctique Mawson",
    "Antarctic Kunlun Station",
    "Vostok İstasyonu",
    "Base antártica Concordia",
    "Base antarctique Concordia",
    "Base antarctique Dumont-d'Urville",
    "Mario-Zucchelli-Station",
    "Base McMurdo",
    "Polární stanice McMurdo",
    "Base antarctique McMurdo",
}

# Just get Brown Station to test
brown_row = None
for idx, row in places.iterrows():
    name = row["names"]["primary"] if row["names"] else ""
    if name == "Brown Station":
        brown_row = row
        break

if brown_row is not None:
    pt = brown_row.geometry
    print(f"Brown at: {pt.x}, {pt.y}")
    buf = pt.buffer(200/111.32)  # rough 200km buffer in degrees
    
    # Get a subset of land features that intersect this buffer
    reader_land = overturemaps.record_batch_reader("land", bbox=bbox)
    table_land = pa.Table.from_batches(reader_land)
    land_df = table_land.to_pandas()
    land_geoms = [wkb.loads(row) for row in land_df["geometry"]]
    land = gpd.GeoDataFrame(land_df, geometry=land_geoms, crs="EPSG:4326")
    
    # Select land that intersects the buffer
    # Use spatial index
    from shapely import STRtree
    tree = STRtree(land.geometry)
    idxs = tree.query(buf, predicate="intersects")
    print(f"Land features intersecting 200km buffer of Brown: {len(idxs)}")
    
    if len(idxs) > 0:
        subset = land.iloc[idxs]
        print(f"Types: {subset.geometry.geom_type.value_counts().to_dict()}")
        
        # Unary union just these
        land_sub_union = unary_union(subset.geometry.to_list())
        print(f"Union type: {land_sub_union.geom_type}")
        print(f"Union area: {land_sub_union.area:.2f} sq deg")
        
        # Clip buffer
        clipped = buf.intersection(land_sub_union)
        print(f"Clipped empty: {clipped.is_empty}")
        if not clipped.is_empty:
            print(f"Clipped type: {clipped.geom_type}")
            print(f"Clipped area: {clipped.area:.2f} sq deg")
EOF

Brown at: -62.87, -64.895
Land features intersecting 200km buffer of Brown: 9557
Types: {'Polygon': 9285, 'Point': 220, 'MultiPolygon': 40, 'LineString': 12}
Union type: GeometryCollection
Union area: 20.67 sq deg
Clipped empty: False
Clipped type: GeometryCollection
Clipped area: 5.04 sq deg

Fetching places …
Found 25 research stations
Stations CRS: EPSG:3031
Fetching land …
Land polygon features: 79227
Dissolving Antarctic land …
Land area (km²): 13637530
Fetching water features …
  Ocean water features: 4978
Fetching bathymetry …
  Bathymetry features: 3096
Total water features: 8074
Creating 200 km buffers …
Clipping buffers with Antarctic landmass …
Land-clipped zones: 25
Computing overlap coalitions …
  Found 10 coalition(s)
Output 1 rows: 25
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)]
Computing over-water portions …
Output 2 rows: 1975
Writing GeoParquet files …
  ✓ station_spheres.geoparquet
  ✓ station_water_overlap.geoparquet
Done.

cd /work && python3 << 'EOF'
import geopandas as gpd
import pandas as pd

print("=" * 70)
print("station_spheres.geoparquet")
print("=" * 70)
sph = gpd.read_parquet("/work/station_spheres.geoparquet")
print(f"Rows: {len(sph)}")
print(f"Columns: {list(sph.columns)}")
print(f"CRS: {sph.crs}")
print(f"All MultiPolygon: {(sph.geometry.geom_type == 'MultiPolygon').all()}")
print(f"Distinct coalition values: {sph['coalition'].nunique()}")
print(f"\nAll stations:")
for _, r in sph.iterrows():
    area = r.geometry.area / 1e6
    print(f"  id={r['station_id']:2d}  coalition={r['coalition']}  area={area:8.0f} km²  name='{r['station_name']}'")

print("\n" + "=" * 70)
print("station_water_overlap.geoparquet")
print("=" * 70)
wat = gpd.read_parquet("/work/station_water_overlap.geoparquet")
print(f"Rows: {len(wat)}")
print(f"Columns: {list(wat.columns)}")
print(f"All MultiPolygon: {(wat.geometry.geom_type == 'MultiPolygon').all()}")
print(f"\nWater sources: {wat['water_source'].value_counts().to_dict()}")
print(f"\nUnique stations: {wat['station_name'].nunique()}")
print(f"Unique water_ids: {wat['water_id'].nunique()}")
print(f"\nSample rows:")
print(wat[['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source']].head(10).to_string())
EOF

======================================================================
station_spheres.geoparquet
======================================================================
Rows: 25
Columns: ['station_id', 'station_name', 'coalition', 'geometry']
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}}
All MultiPolygon: True
Distinct coalition values: 10

All stations:
  id= 0  coalition=0  area=   43251 km²  name='Brown Station, Antarctic Peninsula'
  id= 1  coalition=0  area=   43327 km²  name='Brown Station'
  id= 2  coalition=0  area=   41609 km²  name='González Videla Antarctic Base'
  id= 3  coalition=0  area=   35213 km²  name='Palmer Station, Antarctica'
  id= 4  coalition=0  area=   10216 km²  name='Carlini Base'
  id= 5  coalition=0  area=    9082 km²  name='Bellingshausen Russian Antarctic Station'
  id= 6  coalition=0  area=   16750 km²  name='Base Marambio, Antartida Argentina'
  id= 7  coalition=0  area=   16780 km²  name='Marambio Base'
  id= 8  coalition=0  area=   16760 km²  name='Base Antártica Marambio'
  id= 9  coalition=0  area=   17993 km²  name='Esperanza Base, Antarctica'
  id=10  coalition=0  area=   17999 km²  name='Base Esperanza, Antartida Argentina'
  id=11  coalition=1  area=   57232 km²  name='Neumayer-Station II'
  id=12  coalition=2  area=  125462 km²  name='Antarctica/Troll'
  id=13  coalition=2  area=  125462 km²  name='SANAE'
  id=14  coalition=3  area=  125462 km²  name='Plateau Station'
  id=15  coalition=4  area=   64126 km²  name='Base antarctique Mawson'
  id=16  coalition=5  area=  125462 km²  name='Antarctic Kunlun Station'
  id=17  coalition=6  area=  125462 km²  name='Vostok İstasyonu'
  id=18  coalition=7  area=  125462 km²  name='Base antártica Concordia'
  id=19  coalition=7  area=  125462 km²  name='Base antarctique Concordia'
  id=20  coalition=8  area=   62056 km²  name='Base antarctique Dumont-d'Urville'
  id=21  coalition=9  area=   72294 km²  name='Mario-Zucchelli-Station'
  id=22  coalition=9  area=   86843 km²  name='Base McMurdo'
  id=23  coalition=9  area=   86843 km²  name='Polární stanice McMurdo'
  id=24  coalition=9  area=   86843 km²  name='Base antarctique McMurdo'

======================================================================
station_water_overlap.geoparquet
======================================================================
Rows: 1975
Columns: ['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source', 'geometry']
All MultiPolygon: True

Water sources: {'base.bathymetry': 1181, 'base.water': 794}

Unique stations: 18
Unique water_ids: 562

Sample rows:
   station_id                        station_name                              water_id water_name water_subtype water_source
0           0  Brown Station, Antarctic Peninsula  0d3c9f98-faf6-3d3b-b12c-68752fd97403      Ocean         ocean   base.water
1           0  Brown Station, Antarctic Peninsula  cd705029-088e-35db-a4a2-3adc3fc70183      Ocean         ocean   base.water
2           0  Brown Station, Antarctic Peninsula  24544cd9-2a2d-324f-83a4-edfe1a3a26d8      Ocean         ocean   base.water
3           0  Brown Station, Antarctic Peninsula  3478cdf8-f664-3cf6-adda-631f44cf71dc      Ocean         ocean   base.water
4           0  Brown Station, Antarctic Peninsula  129db7c6-b8f4-3bea-a6bb-2133b21dd14e      Ocean         ocean   base.water
5           0  Brown Station, Antarctic Peninsula  2d4f2c52-087b-36ab-995e-0353712b8119      Ocean         ocean   base.water
6           0  Brown Station, Antarctic Peninsula  b3a52a64-7325-3509-a939-02a316142ea0      Ocean         ocean   base.water
7           0  Brown Station, Antarctic Peninsula  40a154fa-7b1e-368d-8abf-b492c24ed3cf      Ocean         ocean   base.water
8           0  Brown Station, Antarctic Peninsula  70ec73c5-9e3c-3ba5-89b9-83dd7ad718f7      Ocean         ocean   base.water
9           0  Brown Station, Antarctic Peninsula  c5bc7548-6c3a-3c2d-95c6-85c80b0ea864      Ocean         ocean   base.water

cd /work && python3 << 'EOF'
import geopandas as gpd

# Final validation
print("=== VALIDATION ===")

# Output 1
sph = gpd.read_parquet("/work/station_spheres.geoparquet")
assert sph.crs.to_epsg() == 3031, "CRS should be EPSG:3031"
assert sph.geometry.geom_type.unique() == ["MultiPolygon"], "All geometries should be MultiPolygon"
assert sph["coalition"].nunique() > 1, "coalition column must have >1 distinct values"
assert "station_id" in sph.columns
assert "station_name" in sph.columns
print(f"✓ station_spheres.geoparquet: {len(sph)} rows, {sph['coalition'].nunique()} coalitions, EPSG:3031, MultiPolygon")

# Output 2
wat = gpd.read_parquet("/work/station_water_overlap.geoparquet")
assert wat.crs.to_epsg() == 3031, "CRS should be EPSG:3031"
assert wat.geometry.geom_type.unique() == ["MultiPolygon"], "All geometries should be MultiPolygon"
assert "station_id" in wat.columns
assert "station_name" in wat.columns
assert "water_id" in wat.columns
assert "water_name" in wat.columns
assert "water_subtype" in wat.columns
assert "water_source" in wat.columns
assert set(wat["water_source"].unique()) == {"base.water", "base.bathymetry"}, \
    f"water_source must be base.water or base.bathymetry, got {wat['water_source'].unique()}"
print(f"✓ station_water_overlap.geoparquet: {len(wat)} rows, {wat['water_source'].value_counts().to_dict()}, EPSG:3031, MultiPolygon")

print("\nAll validations passed!")
EOF

=== VALIDATION ===
✓ station_spheres.geoparquet: 25 rows, 10 coalitions, EPSG:3031, MultiPolygon
✓ station_water_overlap.geoparquet: 1975 rows, {'base.bathymetry': 1181, 'base.water': 794}, EPSG:3031, MultiPolygon

All validations passed!