geo-l3-antarctica-stations-geodesic
Map (reference ← swipe → agent)
0:00
I need to put together the cross-station logistics-overlap maps for the Antarctic Treaty consultative meeting next month. Each Antarctic research station has a notional 200 km operational radius. Please pull stations from Overture south of -60 latitude, along with the Antarctic landmass and water features.
For the first output I need the land-clipped station zones grouped into coalitions wherever they overlap. Each coalition gets an integer `coalition` id, and that column has to take more than one distinct value across the dataset. Every feature needs `station_id` and `station_name` as well. For the second output I need the over-water portion of each station's zone, attributed with the water features it intersects. Each row in that file must carry `station_id`, `station_name`, `water_id`, `water_name`, `water_subtype`, and a `water_source` column whose values are the literal Overture theme names `base.water` or `base.bathymetry`.
Please write both as GeoParquet in an appropriate projected coordinate system for Antarctica, with MultiPolygon geometry: `station_spheres.geoparquet` for the land-clipped coalition polygons, and `station_water_overlap.geoparquet` for the per-station over-water intersections with the water attribution described above.
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:03
Let me break down the task: 1. Get Antarctic research stations from Overture south of -60 latitude 2. Get Antarctic landmass and water features 3. Create 200 km operational radius buffers around each station 4. Clip those buffers by land vs water 5. For land-clipped zones: group overlapping ones into coalitions, assign coalition IDs 6. For water zones: intersect with water features, attribute with water info Let me start by exploring what Overture data is available and how to access it. First, let me check what tools/libraries are available for working with Overture data.
0:03
Bash
pip list 2>/dev/null | grep -iE "overture|duckdb|geopandas|shapely|pyproj|pyogri...
duckdb 1.5.3 fiona 1.10.1 geopandas 1.1.3 overturemaps 1.0.0 pandas 3.0.3 pyogrio 0.12.1 pyproj 3.7.2 shapely 2.1.2
0:04
Bash
ls /work/ 2>/dev/null; pwd
/work
0:10
Great, all the libraries are installed. Let me plan the approach: 1. **Fetch data from Overture**: - Research stations south of -60° latitude - Antarctic landmass (continent) - Water features (bathymetry, water areas) 2. **Create 200 km radius buffers** around each station - Use an appropriate projected CRS for Antarctica (likely EPSG:3031 - Antarctic Polar Stereographic) 3. **Clip the buffers by land** to get land-clipped station zones - Clip with Antarctic landmass 4. **Group into coalitions** by dissolving overlapping land-clipped zones 5. **For water overlap**: - Get the over-water portion (buffer minus land-clipped portion, or buffer clipped by water) - Intersect with water features, attributing each 6. **Write both GeoParquet files** Let me start by exploring what Overture data is available and how to query it. Actually, I should use the overturemaps Python library or direct DuckDB queries to Overture's S3 parquet files. Let me check the overturemaps library capabilities.
0:10
Bash
python3 -c "import overturemaps; help(overturemaps)" 2>&1 | head -50
Help on package overturemaps:
NAME
overturemaps
PACKAGE CONTENTS
__main__
changelog
cli
core
models
releases
state
writers
FILE
/usr/local/lib/python3.14/site-packages/overturemaps/__init__.py
0:11
Bash
python3 -c "import overturemaps; print(dir(overturemaps))"
['__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', 'core', 'geodataframe', 'get_all_overture_types', 'models', 'record_batch_reader']
0:14
Let me explore overturemaps to understand how to get data.
0:14
Bash
python3 -c "import overturemaps; help(overturemaps.core)" 2>&1 | head -100
Help on module overturemaps.core in overturemaps:
NAME
overturemaps.core
FUNCTIONS
count_rows(
overture_type,
bbox=None,
release=None,
connect_timeout=None,
request_timeout=None,
stac=False
) -> int
Return the number of rows matching the given parameters.
geoarrow_schema_adapter(schema: pa.Schema) -> pa.Schema
Convert a geoarrow-compatible schema to a proper geoarrow schema
This assumes there is a single "geometry" column with WKB formatting
Parameters
----------
schema: pa.Schema
Returns
-------
pa.Schema
A copy of the input schema with the geometry field replaced with
a new one with the proper geoarrow ARROW:extension metadata
geodataframe(
overture_type: str,
bbox: BBox | tuple[float, float, float, float] | list[float] | None = None,
release: str = None,
connect_timeout: int = None,
request_timeout: int = None,
stac: bool = False
) -> GeoDataFrame
Loads geoparquet for specified type into a geopandas dataframe
Parameters
----------
overture_type: type to load
bbox: optional bounding box for data fetch (xmin, ymin, xmax, ymax)
connect_timeout: optional connection timeout in seconds
request_timeout: optional request timeout in seconds
Returns
-------
GeoDataFrame with the optionally filtered theme data
get_all_overture_types() -> List[str]
get_available_releases() -> Tuple[List[str], str]
Fetch available releases from the STAC catalog.
Returns
-------
Tuple of (all_releases, latest_release) where:
- all_releases is a list of release version strings
- latest_release is the latest release version string
get_latest_release() -> str
Get the latest release version.
Returns
-------
str: The latest release version
query_gers_registry(gers_id: str) -> Optional[Tuple[str, BBox | None]]
Query the GERS registry to get the filepath and bbox for a given GERS ID.
The registry always uses the latest release.
Parameters
----------
gers_id: The GERS ID to look up
Returns
-------
Tuple of (filepath, bbox) where bbox is a BBox, or None if not found
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.
record_batch_reader_from_gers(
gers_id: str,
connect_timeout: int = None,
request_timeout: int = None,
registry_result: Optional[Tuple[str, BBox | None]] = None
) -> Optional[pa.RecordBatchReader]
Return a pyarrow RecordBatchReader for a specific GERS ID.
0:16
Bash
python3 -c "import overturemaps; print(overturemaps.get_all_overture_types())"
['address', 'bathymetry', 'building', 'building_part', 'division', 'division_area', 'division_boundary', 'place', 'segment', 'connector', 'infrastructure', 'land', 'land_cover', 'land_use', 'water']
3:22
Let me plan this out: 1. Need to get Antarctic research stations from Overture. The `place` theme likely has research stations. 2. Need to get Antarctic landmass - `land` theme? 3. Need water features - `water` and `bathymetry` themes. 4. Need to create 200 km buffer zones around each station. 5. Clip the buffer zones by land (to get land-clipped station spheres). 6. Group overlapping land-clipped zones into coalitions. 7. For water, get the over-water portion of each station's zone (the buffer minus land), intersected with water features. Let me first explore what themes are available and find the data.
3:22
Bash
python3 -c "
python3 -c " import overturemaps # Let's look at the place theme to find stations help(overturemaps.models) " 2>&1 | head -80
Help on module overturemaps.models in overturemaps:
NAME
overturemaps.models - Data models for the Overture toolkit.
CLASSES
builtins.object
BBox
PipelineState
enum.StrEnum(builtins.str, enum.ReprEnum)
Backend
class BBox(builtins.object)
| BBox(xmin: float, ymin: float, xmax: float, ymax: float) -> None
|
| Axis-aligned bounding box (WGS84 lon/lat).
|
| Methods defined here:
|
| __eq__(self, other)
| Return self==value.
|
| __init__(self, xmin: float, ymin: float, xmax: float, ymax: float) -> None
| Initialize self. See help(type(self)) for accurate signature.
|
| __replace__ = _replace(self, /, **changes) from dataclasses
|
| __repr__(self)
| Return repr(self).
|
| as_dict(self) -> dict[str, float]
| Return bbox as a dictionary.
|
| as_tuple(self) -> tuple[float, float, float, float]
| Return (xmin, ymin, xmax, ymax) tuple.
|
| ----------------------------------------------------------------------
| Class methods defined here:
|
| from_dict(data: dict[str, float]) -> BBox
| Create BBox from a dictionary.
|
| ----------------------------------------------------------------------
| Data descriptors defined here:
|
| __dict__
| dictionary for instance variables
|
| __weakref__
| list of weak references to the object
|
| ----------------------------------------------------------------------
| Data and other attributes defined here:
|
| __annotations__ = {'xmax': 'float', 'xmin': 'float', 'ymax': 'float', ...
|
| __dataclass_fields__ = {'xmax': Field(name='xmax',type='float',default...
|
| __dataclass_params__ = _DataclassParams(init=True,repr=True,eq=True,or...
|
| __hash__ = None
|
| __match_args__ = ('xmin', 'ymin', 'xmax', 'ymax')
class Backend(enum.StrEnum)
| Backend(*values)
|
| Storage backend for local Overture data.
|
| Method resolution order:
| Backend
| enum.StrEnum
| builtins.str
| enum.ReprEnum
| enum.Enum
| builtins.object
|
| Methods defined here:
|
| __format__(self, format_spec, /) from builtins.str
3:40
Let me look at the Overture data model for places to find research stations in Antarctica.
3:40
Bash
python3 -c "
python3 -c "
import overturemaps
df = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60))
print('Shape:', df.shape)
print('Columns:', df.columns.tolist())
print()
print(df['categories'].value_counts().head(30))
" 2>&1 | head -80
Shape: (2348, 17)
Columns: ['id', 'geometry', 'categories', 'confidence', 'websites', 'emails', 'socials', 'phones', 'brand', 'addresses', 'names', 'sources', 'operating_status', 'basic_category', 'taxonomy', 'version', 'bbox']
categories
{'primary': 'fast_food_restaurant', 'alternate': None} 127
{'primary': 'professional_services', 'alternate': None} 37
{'primary': 'real_estate', 'alternate': None} 28
{'primary': 'restaurant', 'alternate': None} 26
{'primary': 'hotel', 'alternate': None} 24
{'primary': 'shopping', 'alternate': None} 23
{'primary': 'landmark_and_historical_building', 'alternate': ['fountain']} 20
{'primary': 'school', 'alternate': None} 19
{'primary': 'beauty_and_spa', 'alternate': None} 18
{'primary': 'beauty_salon', 'alternate': None} 16
{'primary': 'beauty_salon', 'alternate': ['beauty_and_spa']} 15
{'primary': 'resort', 'alternate': ['hotel']} 13
{'primary': 'furniture_store', 'alternate': None} 11
{'primary': 'college_university', 'alternate': ['education']} 11
{'primary': 'shoe_store', 'alternate': None} 11
{'primary': 'landmark_and_historical_building', 'alternate': ['structure_and_geography']} 11
{'primary': 'health_and_medical', 'alternate': ['pharmacy']} 9
{'primary': 'clothing_store', 'alternate': ['womens_clothing_store']} 9
{'primary': 'arts_and_crafts', 'alternate': None} 9
{'primary': 'religious_organization', 'alternate': None} 9
{'primary': 'landmark_and_historical_building', 'alternate': ['mountain']} 9
{'primary': 'community_services_non_profits', 'alternate': None} 8
{'primary': 'automotive_repair', 'alternate': None} 8
{'primary': 'womens_clothing_store', 'alternate': None} 8
{'primary': 'school', 'alternate': ['education']} 8
{'primary': 'college_university', 'alternate': None} 7
{'primary': 'church_cathedral', 'alternate': None} 7
{'primary': 'motorcycle_dealer', 'alternate': None} 7
{'primary': 'mobile_phone_store', 'alternate': None} 7
{'primary': 'accommodation', 'alternate': None} 7
Name: count, dtype: int64
3:59
Bash
python3 -c "
python3 -c "
import overturemaps
# Check what's in the water theme for Antarctica
df = overturemaps.geodataframe('water', bbox=(-180, -90, 180, -60))
print('Water shape:', df.shape)
print('Water columns:', df.columns.tolist())
print()
if len(df) > 0:
print('subtype:', df['subtype'].value_counts().to_dict() if 'subtype' in df.columns else 'N/A')
print('class:', df['class'].value_counts().to_dict() if 'class' in df.columns else 'N/A')
" 2>&1 | head -60
Water shape: (13501, 13)
Water columns: ['id', 'names', 'subtype', 'class', 'sources', 'source_tags', 'level', 'wikidata', 'is_intermittent', 'is_salt', 'geometry', 'version', 'bbox']
subtype: {'stream': 5374, 'ocean': 4978, 'lake': 1746, 'physical': 809, 'pond': 372, 'water': 180, 'river': 34, 'canal': 5, 'human_made': 2, 'reservoir': 1}
class: {'stream': 5374, 'ocean': 4978, 'lake': 1745, 'cape': 411, 'pond': 372, 'bay': 350, 'water': 179, 'river': 34, 'strait': 30, 'sea': 12, 'canal': 4, 'shoal': 3, 'waterfall': 3, 'swimming_pool': 2, 'ditch': 1, 'lagoon': 1, 'water_storage': 1, 'fairway': 1}
4:52
Let me look for research stations. The place table has categories. I need to find "research" or "station" type places in Antarctica. Let me check the categories more carefully.
4:52
Bash
python3 -c "
python3 -c "
import overturemaps
df = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60))
# Check what categories exist for research/station type things
cats = df['categories'].dropna().unique()
for c in cats:
s = str(c)
if 'research' in s.lower() or 'station' in s.lower() or 'scientific' in s.lower():
print(s)
" 2>&1 | head -40
Traceback (most recent call last):
File "<string>", line 5, in <module>
cats = df['categories'].dropna().unique()
File "/usr/local/lib/python3.14/site-packages/pandas/core/series.py", line 2228, in unique
return super().unique()
~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/base.py", line 1159, in unique
result = algorithms.unique1d(values) # type: ignore[assignment]
File "/usr/local/lib/python3.14/site-packages/pandas/core/algorithms.py", line 433, in unique
return unique_with_mask(values)
File "/usr/local/lib/python3.14/site-packages/pandas/core/algorithms.py", line 476, in unique_with_mask
uniques = table.unique(values)
File "pandas/_libs/hashtable_class_helper.pxi", line 7840, in pandas._libs.hashtable.PyObjectHashTable.unique
File "pandas/_libs/hashtable_class_helper.pxi", line 7783, in pandas._libs.hashtable.PyObjectHashTable._unique
TypeError: unhashable type: 'dict'
5:13
Bash
python3 -c "
python3 -c "
import overturemaps
# Also check infrastructure
df_infra = overturemaps.geodataframe('infrastructure', bbox=(-180, -90, 180, -60))
print('Infra shape:', df_infra.shape)
print('Infra columns:', df_infra.columns.tolist())
if len(df_infra) > 0:
print('subtype:', df_infra['subtype'].value_counts().to_dict() if 'subtype' in df_infra.columns else 'N/A')
print('class:', df_infra['class'].value_counts().to_dict() if 'class' in df_infra.columns else 'N/A')
" 2>&1 | head -40
Infra shape: (944, 13)
Infra columns: ['id', 'geometry', 'sources', 'names', 'level', 'wikidata', 'source_tags', 'subtype', 'class', 'height', 'surface', 'version', 'bbox']
subtype: {'utility': 292, 'power': 181, 'airport': 134, 'pedestrian': 64, 'communication': 54, 'tower': 51, 'barrier': 44, 'bridge': 41, 'transportation': 27, 'transit': 20, 'pier': 13, 'water': 10, 'waste_management': 7, 'recreation': 4, 'quay': 1, 'aerialway': 1}
class: {'pipeline': 191, 'storage_tank': 99, 'power_line': 73, 'communication_tower': 52, 'power_pole': 51, 'airport': 49, 'helipad': 45, 'monitoring': 39, 'bollard': 30, 'generator': 29, 'runway': 28, 'bridge': 28, 'viewpoint': 26, 'information': 15, 'cable': 15, 'toilets': 13, 'pier': 13, 'boardwalk': 13, 'street_lamp': 13, 'parking': 9, 'crossing': 9, 'drinking_water': 9, 'radar': 7, 'taxiway': 6, 'ferry_terminal': 6, 'minor_line': 5, 'recycling': 5, 'observation': 4, 'camp_site': 4, 'artwork': 4, 'fence': 4, 'plant': 4, 'barrier': 4, 'apron': 3, 'milestone': 3, 'bicycle_parking': 3, 'jersey_barrier': 3, 'stop': 2, 'reservoir_covered': 2, 'mobile_phone_tower': 2, 'private_airport': 2, 'gate': 2, 'power_tower': 2, 'waste_disposal': 2, 'bus_stop': 2, 'bench': 2, 'post_box': 2, 'atm': 2, 'quay': 1, 'dam': 1, 'transformer': 1, 'military_airport': 1, 'wall': 1, 'lighting': 1, 'substation': 1, 'rope_tow': 1}
5:50
5:50
Bash
python3 -c "
python3 -c "
import overturemaps
# Let's look at the place data more carefully - check names for 'station' or 'research'
df = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60))
# Check names column structure
print('names sample:', df['names'].iloc[0])
print()
# Check categories structure
print('categories sample:', df['categories'].iloc[0])
print()
# Look for research stations
for idx, row in df.iterrows():
n = row['names']
if n and isinstance(n, dict):
for k, v in n.items():
if isinstance(v, str) and ('station' in v.lower() or 'research' in v.lower() or 'base' in v.lower()):
print(f'{idx}: {n} | cats: {row[\"categories\"]}')
break
" 2>&1 | head -60
names sample: {'primary': 'Tincorp Pa TI', 'common': None, 'rules': None}
categories sample: {'primary': 'information_technology_company', 'alternate': array(['professional_services'], dtype=object)}
439: {'primary': 'Master Bees Stationeries & Souvenirs', 'common': None, 'rules': None} | cats: {'primary': 'souvenir_shop', 'alternate': array(['bookstore'], dtype=object)}
460: {'primary': 'Brown Station, Antarctic Peninsula', 'common': None, 'rules': None} | cats: {'primary': 'landmark_and_historical_building', 'alternate': array(['fountain'], dtype=object)}
461: {'primary': 'Brown Station', 'common': None, 'rules': None} | cats: {'primary': 'landmark_and_historical_building', 'alternate': array(['fountain'], dtype=object)}
462: {'primary': 'González Videla Antarctic Base', 'common': None, 'rules': None} | cats: {'primary': 'airport', 'alternate': array(['travel'], dtype=object)}
473: {'primary': 'Palmer Station, Antarctica', 'common': None, 'rules': None} | cats: {'primary': 'home_developer', 'alternate': None}
490: {'primary': 'New Lucena Municipal Police Station', 'common': None, 'rules': None} | cats: {'primary': 'public_service_and_government', 'alternate': array(['community_center'], dtype=object)}
504: {'primary': 'Carlini Base', 'common': None, 'rules': None} | cats: {'primary': 'arts_and_entertainment', 'alternate': None}
511: {'primary': 'Bellingshausen Russian Antarctic Station', 'common': None, 'rules': None} | cats: {'primary': 'educational_research_institute', 'alternate': array(['public_and_government_association', 'college_university'],
dtype=object)}
563: {'primary': 'Base Marambio, Antartida Argentina', 'common': None, 'rules': None} | cats: {'primary': 'central_government_office', 'alternate': array(['accommodation'], dtype=object)}
564: {'primary': 'Marambio Base', 'common': None, 'rules': None} | cats: {'primary': 'airport', 'alternate': array(['armed_forces_branch', 'arts_and_entertainment'], dtype=object)}
565: {'primary': 'Base Antártica Marambio', 'common': None, 'rules': None} | cats: {'primary': 'public_and_government_association', 'alternate': array(['central_government_office', 'accommodation'], dtype=object)}
568: {'primary': 'Esperanza Base, Antarctica', 'common': None, 'rules': None} | cats: {'primary': 'landmark_and_historical_building', 'alternate': array(['fountain'], dtype=object)}
569: {'primary': 'Base Esperanza, Antartida Argentina', 'common': None, 'rules': None} | cats: {'primary': 'airport', 'alternate': array(['travel', 'accommodation'], dtype=object)}
1010: {'primary': 'Neumayer-Station II', 'common': None, 'rules': None} | cats: {'primary': 'landmark_and_historical_building', 'alternate': array(['restaurant'], dtype=object)}
1211: {'primary': 'Fuel Tech Micro Gas Station & Dispensers', 'common': None, 'rules': None} | cats: {'primary': 'industrial_equipment', 'alternate': None}
1418: {'primary': 'Plateau Station', 'common': None, 'rules': None} | cats: {'primary': 'landmark_and_historical_building', 'alternate': array(['armed_forces_branch', 'travel'], dtype=object)}
1484: {'primary': 'Pioneer - Gas Station', 'common': None, 'rules': None} | cats: {'primary': 'gas_station', 'alternate': None}
1825: {'primary': 'Base antarctique Mawson', 'common': None, 'rules': None} | cats: {'primary': 'landmark_and_historical_building', 'alternate': array(['train_station'], dtype=object)}
1904: {'primary': 'Antarctic Kunlun Station', 'common': None, 'rules': None} | cats: {'primary': 'landmark_and_historical_building', 'alternate': array(['train_station'], dtype=object)}
2101: {'primary': 'COFFE Station Old town', 'common': None, 'rules': None} | cats: {'primary': 'cafe', 'alternate': array(['coffee_shop'], dtype=object)}
2134: {'primary': 'Base antártica Concordia', 'common': None, 'rules': None} | cats: {'primary': 'train_station', 'alternate': array(['landmark_and_historical_building'], dtype=object)}
2135: {'primary': 'Base antarctique Concordia', 'common': None, 'rules': None} | cats: {'primary': 'landmark_and_historical_building', 'alternate': array(['train_station'], dtype=object)}
2183: {'primary': "Base antarctique Dumont-d'Urville", 'common': None, 'rules': None} | cats: {'primary': 'landmark_and_historical_building', 'alternate': array(['transportation'], dtype=object)}
2298: {'primary': 'Mario-Zucchelli-Station', 'common': None, 'rules': None} | cats: None
2308: {'primary': 'Base McMurdo', 'common': None, 'rules': None} | cats: {'primary': 'landmark_and_historical_building', 'alternate': array(['train_station', 'restaurant'], dtype=object)}
2310: {'primary': 'Base antarctique McMurdo', 'common': None, 'rules': None} | cats: {'primary': 'landmark_and_historical_building', 'alternate': array(['train_station'], dtype=object)}
2336: {'primary': 'Lakeview Clinical Research', 'common': None, 'rules': None} | cats: {'primary': 'medical_research_and_development', 'alternate': array(['doctor'], dtype=object)}
6:22
I can see there are research stations in the place data. But I need to find stations more systematically. Let me look for things with "station", "base", "research" etc in their names that are south of -60. Let me also check the land theme for the Antarctic landmass.
6:22
Bash
python3 -c "
python3 -c "
import overturemaps
# Let's get ALL places south of -60 and look for stations
df = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60))
# Extract names more carefully
stations = []
for idx, row in df.iterrows():
n = row['names']
if n and isinstance(n, dict):
primary = n.get('primary', '')
if isinstance(primary, str):
p_lower = primary.lower()
if any(kw in p_lower for kw in ['station', 'base ', 'base,', 'research']):
if any(kw in p_lower for kw in ['antarctic', 'antarctica', 'antartida', 'antártida', 'antarctique']):
stations.append((idx, primary))
print(f'Found {len(stations)} stations matching Antarctica keywords')
for s in stations:
print(f' {s[0]}: {s[1]}')
" 2>&1 | head -40
Found 11 stations matching Antarctica keywords 460: Brown Station, Antarctic Peninsula 473: Palmer Station, Antarctica 511: Bellingshausen Russian Antarctic Station 563: Base Marambio, Antartida Argentina 568: Esperanza Base, Antarctica 569: Base Esperanza, Antartida Argentina 1825: Base antarctique Mawson 1904: Antarctic Kunlun Station 2135: Base antarctique Concordia 2183: Base antarctique Dumont-d'Urville 2310: Base antarctique McMurdo
7:10
I see some Antarctic stations but I need a more comprehensive list. Let me also look for stations that might not have "Antarctic" in the name explicitly but are in Antarctica. Let me check what places exist south of -60 degrees latitude more broadly. Actually, I should also look at the `division` and `division_area` themes to find places. Also, let me check the Overture documentation for how to find research stations properly. Let me also check what categories are available and if there's a more systematic way to get research stations.
7:10
Bash
python3 -c "
python3 -c "
import overturemaps
df = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60))
# Look more broadly for anything Antarctic-station-like
stations = []
for idx, row in df.iterrows():
n = row['names']
if n and isinstance(n, dict):
primary = n.get('primary', '')
if isinstance(primary, str):
p_lower = primary.lower()
# Catch all kinds of research stations / bases in Antarctica
if any(kw in p_lower for kw in ['station', 'base ']):
stations.append((idx, primary, row.get('categories')))
print(f'Found {len(stations)} potential stations/bases')
for s in stations:
print(f' {s[0]}: {s[1]} | {s[2]}')
" 2>&1
Found 22 potential stations/bases
439: Master Bees Stationeries & Souvenirs | {'primary': 'souvenir_shop', 'alternate': array(['bookstore'], dtype=object)}
460: Brown Station, Antarctic Peninsula | {'primary': 'landmark_and_historical_building', 'alternate': array(['fountain'], dtype=object)}
461: Brown Station | {'primary': 'landmark_and_historical_building', 'alternate': array(['fountain'], dtype=object)}
473: Palmer Station, Antarctica | {'primary': 'home_developer', 'alternate': None}
490: New Lucena Municipal Police Station | {'primary': 'public_service_and_government', 'alternate': array(['community_center'], dtype=object)}
511: Bellingshausen Russian Antarctic Station | {'primary': 'educational_research_institute', 'alternate': array(['public_and_government_association', 'college_university'],
dtype=object)}
563: Base Marambio, Antartida Argentina | {'primary': 'central_government_office', 'alternate': array(['accommodation'], dtype=object)}
565: Base Antártica Marambio | {'primary': 'public_and_government_association', 'alternate': array(['central_government_office', 'accommodation'], dtype=object)}
569: Base Esperanza, Antartida Argentina | {'primary': 'airport', 'alternate': array(['travel', 'accommodation'], dtype=object)}
1010: Neumayer-Station II | {'primary': 'landmark_and_historical_building', 'alternate': array(['restaurant'], dtype=object)}
1211: Fuel Tech Micro Gas Station & Dispensers | {'primary': 'industrial_equipment', 'alternate': None}
1418: Plateau Station | {'primary': 'landmark_and_historical_building', 'alternate': array(['armed_forces_branch', 'travel'], dtype=object)}
1484: Pioneer - Gas Station | {'primary': 'gas_station', 'alternate': None}
1825: Base antarctique Mawson | {'primary': 'landmark_and_historical_building', 'alternate': array(['train_station'], dtype=object)}
1904: Antarctic Kunlun Station | {'primary': 'landmark_and_historical_building', 'alternate': array(['train_station'], dtype=object)}
2101: COFFE Station Old town | {'primary': 'cafe', 'alternate': array(['coffee_shop'], dtype=object)}
2134: Base antártica Concordia | {'primary': 'train_station', 'alternate': array(['landmark_and_historical_building'], dtype=object)}
2135: Base antarctique Concordia | {'primary': 'landmark_and_historical_building', 'alternate': array(['train_station'], dtype=object)}
2183: Base antarctique Dumont-d'Urville | {'primary': 'landmark_and_historical_building', 'alternate': array(['transportation'], dtype=object)}
2298: Mario-Zucchelli-Station | None
2308: Base McMurdo | {'primary': 'landmark_and_historical_building', 'alternate': array(['train_station', 'restaurant'], dtype=object)}
2310: Base antarctique McMurdo | {'primary': 'landmark_and_historical_building', 'alternate': array(['train_station'], dtype=object)}
8:02
Let me also check for stations that might be named differently - in Overture the place theme might not have every station. Let me also look at the `infrastructure` theme for airport/helipad locations which might indicate stations. And also check the `land` theme for the Antarctic continent. Actually, let me think about this differently. Antarctic research stations are places. Let me look more broadly for anything south of -60 that has station/base/research in the name, filtering out the clearly non-Antarctic ones (gas stations, police stations, souvenir shops, etc.). Let me also check what the `land` theme has.
8:02
Bash
python3 -c "
python3 -c "
import overturemaps
# Check land theme for Antarctica
df_land = overturemaps.geodataframe('land', bbox=(-180, -90, 180, -60))
print('Land shape:', df_land.shape)
print('Land columns:', df_land.columns.tolist())
if len(df_land) > 0:
print('subtype:', df_land['subtype'].value_counts().to_dict() if 'subtype' in df_land.columns else 'N/A')
print('class:', df_land['class'].value_counts().to_dict() if 'class' in df_land.columns else 'N/A')
" 2>&1 | head -30
Land shape: (93367, 13)
Land columns: ['id', 'names', 'subtype', 'class', 'sources', 'source_tags', 'level', 'wikidata', 'surface', 'elevation', 'geometry', 'version', 'bbox']
subtype: {'rock': 56249, 'land': 21921, 'physical': 14268, 'glacier': 842, 'reef': 36, 'sand': 32, 'wetland': 9, 'crater': 7, 'grass': 2, 'shrub': 1}
class: {'bare_rock': 54072, 'land': 14707, 'hill': 8337, 'islet': 6301, 'peak': 3261, 'cliff': 2229, 'scree': 2104, 'glacier': 842, 'island': 779, 'ridge': 167, 'archipelago': 134, 'valley': 90, 'saddle': 74, 'rock': 71, 'peninsula': 53, 'reef': 36, 'beach': 31, 'volcano': 22, 'mountain_range': 21, 'wetland': 9, 'cave_entrance': 8, 'volcanic_caldera_rim': 7, 'plateau': 6, 'stone': 2, 'tundra': 2, 'heath': 1, 'sand': 1}
9:13
Bash
python3 -c "
python3 -c "
import overturemaps
# Also look at land_cover
df_lc = overturemaps.geodataframe('land_cover', bbox=(-180, -90, 180, -60))
print('Land cover shape:', df_lc.shape)
print('Land cover columns:', df_lc.columns.tolist())
" 2>&1 | head -20
Land cover shape: (51, 7) Land cover columns: ['id', 'subtype', 'cartography', 'geometry', 'sources', 'version', 'bbox']
9:45
Bash
python3 -c "
python3 -c "
import overturemaps
# Also look at division_area for Antarctica
df_div = overturemaps.geodataframe('division_area', bbox=(-180, -90, 180, -60))
print('Division area shape:', df_div.shape)
print('Division area columns:', df_div.columns.tolist())
" 2>&1 | head -20
Division area shape: (3, 14) Division area columns: ['id', 'geometry', 'country', 'sources', 'subtype', 'admin_level', 'class', 'names', 'is_land', 'is_territorial', 'region', 'division_id', 'version', 'bbox']
10:10
Let me understand the task better. I need to: 1. Get Antarctic research stations from Overture (south of -60 latitude) 2. Get the Antarctic landmass (from Overture land theme) and water features 3. Create 200 km radius buffers around each station 4. Clip those buffers with the Antarctic landmass to get land-based operational zones 5. Group overlapping land-clipped zones into coalitions 6. Create a GeoParquet output of these coalition polygons 7. Create another output with over-water portions of each station's zone, attributed with water features they intersect Let me think about the approach more carefully. First, I need to identify the correct Antarctic research stations. The place theme has some but they're classified oddly. Let me also check if there's a better source - maybe the division_area has Antarctica boundaries, or maybe I need to look at the place data differently. Actually, looking at the results, we have quite a few Antarctic stations. Let me think about which ones are actual research stations south of -60: From the output, the ones that appear to be real Antarctic research stations: - Brown Station (Argentine) - 460, 461 - Palmer Station (US) - 473 - Bellingshausen Russian Antarctic Station - 511 - Base Marambio (Argentine) - 563, 565 - Esperanza Base (Argentine) - 568, 569 - Neumayer-Station II (German) - 1010 - Plateau Station (US) - 1418 - Base antarctique Mawson (Australian) - 1825 - Antarctic Kunlun Station (Chinese) - 1904 - Base antarctique Concordia (French/Italian) - 2134, 2135 - Base antarctique Dumont-d'Urville (French) - 2183 - Mario-Zucchelli-Station (Italian) - 2298 - Base McMurdo (US) - 2308, 2310 There might be more that I'm missing. Let me look more carefully at all the place points and their coordinates as well as check for other types. Actually, let me take a step back. Since this is for an Antarctic Treaty consultative meeting, I need the actual research stations. Let me look at all the place names more broadly to identify Antarctic stations. Let me also check the geometries to see where these points are located.
10:10
Bash
python3 -c "
python3 -c "
import overturemaps
df = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60))
# Let's check the geometries for the station-like entries
for idx, row in df.iterrows():
n = row['names']
if n and isinstance(n, dict):
primary = n.get('primary', '')
if isinstance(primary, str):
p_lower = primary.lower()
if any(kw in p_lower for kw in ['station', 'base ']) and not any(kw in p_lower for kw in ['gas', 'police', 'coffe', 'fuel', 'souvenir']):
geom = row.geometry
print(f'{idx}: {primary} | coords: ({geom.x:.4f}, {geom.y:.4f})')
" 2>&1
460: Brown Station, Antarctic Peninsula | coords: (-62.8724, -64.8921) 461: Brown Station | coords: (-62.8700, -64.8950) 473: Palmer Station, Antarctica | coords: (-64.0531, -64.7743) 511: Bellingshausen Russian Antarctic Station | coords: (-58.9624, -62.1998) 563: Base Marambio, Antartida Argentina | coords: (-56.6198, -64.2413) 565: Base Antártica Marambio | coords: (-56.6235, -64.2374) 569: Base Esperanza, Antartida Argentina | coords: (-56.9974, -63.3980) 1010: Neumayer-Station II | coords: (-8.2500, -70.6500) 1418: Plateau Station | coords: (40.5000, -79.2500) 1825: Base antarctique Mawson | coords: (62.8736, -67.6026) 1904: Antarctic Kunlun Station | coords: (77.1161, -80.4169) 2134: Base antártica Concordia | coords: (123.3333, -75.1000) 2135: Base antarctique Concordia | coords: (123.5580, -75.1000) 2183: Base antarctique Dumont-d'Urville | coords: (140.0013, -66.6628) 2298: Mario-Zucchelli-Station | coords: (164.1125, -74.6943) 2308: Base McMurdo | coords: (166.6667, -77.8500) 2310: Base antarctique McMurdo | coords: (166.6667, -77.8500)
11:05
11:05
Bash
python3 -c "
python3 -c "
import overturemaps
# Check bathymetry theme
df_bathy = overturemaps.geodataframe('bathymetry', bbox=(-180, -90, 180, -60))
print('Bathymetry shape:', df_bathy.shape)
print('Bathymetry columns:', df_bathy.columns.tolist())
if len(df_bathy) > 0:
print('subtype:', df_bathy['subtype'].value_counts().to_dict() if 'subtype' in df_bathy.columns else 'N/A')
print('class:', df_bathy['class'].value_counts().to_dict() if 'class' in df_bathy.columns else 'N/A')
" 2>&1 | head -30
Bathymetry shape: (3096, 7) Bathymetry columns: ['id', 'depth', 'cartography', 'geometry', 'sources', 'version', 'bbox'] subtype: N/A class: N/A
11:22
Let me understand the data better: 1. **Places** (stations): I have point locations for Antarctic research stations from the `place` theme 2. **Land**: I have polygon land features from the `land` theme - these cover Antarctica's landmass 3. **Water**: Water features from the `water` theme (ocean, sea, bay, etc.) 4. **Bathymetry**: Depth contours from the `bathymetry` theme For the task: - Each station has a 200 km radius buffer - Output 1: Land-clipped station zones grouped into coalitions wherever they overlap - Each coalition gets an integer `coalition` id (must have more than 1 distinct value) - Each feature needs `station_id`, `station_name`, `coalition` - MultiPolygon geometry - Write to `station_spheres.geoparquet` - Output 2: Over-water portion of each station's zone - Attributed with the water features it intersects - Columns: `station_id`, `station_name`, `water_id`, `water_name`, `water_subtype`, `water_source` (either `base.water` or `base.bathymetry`) - MultiPolygon geometry - Write to `station_water_overlap.geoparquet` Projected CRS for Antarctica: The standard is EPSG:3031 (Antarctic Polar Stereographic) or EPSG:32761 (UTM south). Let me use EPSG:3031 - Antarctic Polar Stereographic which is the most commonly used. Let me write a comprehensive script. First, let me think about station deduplication. Some stations have multiple entries (e.g., "Brown Station" and "Brown Station, Antarctic Peninsula" at slightly different coords). I should deduplicate by proximity or by name similarity. Let me think about the approach: 1. Fetch all places south of -60 2. Identify research stations (by keyword matching in names) 3. Deduplicate nearby stations 4. For each station, create a 200km buffer 5. Get Antarctica land polygons from the `land` theme - probably filter by `class` or `subtype` that corresponds to Antarctic continent 6. Clip station buffers to land (for first output) 7. Compute overlapping land-clipped buffers → union them into coalitions 8. For second output, clip station buffers to water features (intersection with water) Actually, let me think more carefully about what "land-clipped station zones" means. It means the 200km radius zones that are clipped to the Antarctic landmass - i.e., the portion of each station's 200km buffer that falls on land. And "over-water portion" is the portion of each station's 200km buffer that falls over water features. Let me write this script.
11:22
Bash
python3 -c "
python3 -c "
import overturemaps
# Check the land theme for Antarctic continent polygons
df_land = overturemaps.geodataframe('land', bbox=(-180, -90, 180, -60))
# Look at what 'land' subtype entries are - these might be the continent
land_subset = df_land[df_land['subtype'] == 'land']
print(f'Land subtype=land count: {len(land_subset)}')
# Check area of these
from shapely.geometry import shape
import pyproj
geodesic = pyproj.Geod(ellps='WGS84')
total_area = 0
for g in land_subset.geometry:
if g is not None:
# Simple area estimate
total_area += g.area
print(f'Total area (deg^2 approx): {total_area}')
print()
# Also check what class these are
print(land_subset['class'].value_counts().head(20))
" 2>&1
Land subtype=land count: 21921 Total area (deg^2 approx): 6837.066506579556 class land 14707 islet 6301 island 779 archipelago 134 Name: count, dtype: int64
12:49
I need to think about this more carefully. The "land" theme has many small features (islets, islands, etc.) plus the main continent. For the Antarctic landmass, I should dissolve all land features together, or maybe just use the "land" class which is the main continent. Let me also check what the division_area data gives us for Antarctica. Actually, let me step back and think about the approach: 1. Get research stations from Overture place data south of -60 2. Get the Antarctic landmass (land theme) 3. Get water features (water theme + bathymetry theme) 4. For each station, create a 200km radius buffer 5. Clip buffer to land -> station's land zone 6. Find overlapping land zones -> coalition groups 7. For water portion, intersect buffer with water features For the landmass, I should probably use the `land` theme and filter to the main Antarctic landmass. But given the complexity, I might want to use duckdb or a more efficient approach. Let me write a comprehensive script.
12:49
Write
/work/solve.py
content · 364 lines · py
#!/usr/bin/env python3
"""
Antarctic research station zones:
1. Land-clipped station zones grouped into coalitions (overlapping zones)
2. Over-water portion of each station's zone attributed with water features
"""
import warnings
warnings.filterwarnings('ignore')
import geopandas as gpd
import pandas as pd
import numpy as np
from shapely.geometry import Point, MultiPolygon, box
from shapely.ops import unary_union, polygonize
from shapely import wkb
import pyproj
from pyproj import CRS
import json
# ============================================================
# 1. Fetch data from Overture
# ============================================================
print("Fetching places (research stations)...")
places = gpd.read_parquet(
"s3://overturemaps-us-west-2/release/2025-04-08.0/theme=places/type=place/*",
bbox=(-180, -90, 180, -60)
)
print(f"Places fetched: {len(places)}")
# Extract station points south of -60
stations = []
for idx, row in places.iterrows():
n = row.get('names')
if not n or not isinstance(n, dict):
continue
primary = n.get('primary', '')
if not isinstance(primary, str):
continue
p_lower = primary.lower()
# Filter for Antarctic research stations
# Look for "station", "base" keywords in Antarctic context
is_station = any(kw in p_lower for kw in ['station', 'base '])
if not is_station:
continue
# Skip non-Antarctic things
if any(kw in p_lower for kw in ['gas station', 'police station', 'coffe', 'fuel', 'souvenir', 'micro']):
continue
geom = row.geometry
if geom is None:
continue
# Check it's a point
if geom.geom_type != 'Point':
continue
# Check it's south of -60
if geom.y > -60:
continue
# Use the primary name as station name
station_name = primary.strip()
station_id = row['id']
stations.append({
'station_id': station_id,
'station_name': station_name,
'geometry': geom
})
stations_df = gpd.GeoDataFrame(stations, crs='EPSG:4326')
print(f"Found {len(stations_df)} Antarctic research stations")
# Remove duplicates close together (same station with different names)
# Group by proximity (within 1 km)
if len(stations_df) > 0:
# Buffer a tiny bit and dissolve to group
buffered = stations_df.copy()
buffered['geometry'] = buffered.geometry.buffer(0.01) # ~1km at these latitudes
dissolved = buffered.dissolve()
print(f"After dedup: {len(stations_df)} stations")
# Let's look at the stations we have
for i, row in stations_df.iterrows():
print(f" {row['station_name']} @ ({row.geometry.x:.4f}, {row.geometry.y:.4f})")
# ============================================================
# 2. Fetch land data for Antarctic continent
# ============================================================
print("\nFetching land data...")
land_gdf = gpd.read_parquet(
"s3://overturemaps-us-west-2/release/2025-04-08.0/theme=land/type=land/*",
bbox=(-180, -90, 180, -60)
)
print(f"Land features: {len(land_gdf)}")
# Filter to the main Antarctic continent (class='land' that are large)
# The continent is the 'land' class entries
land_continent = land_gdf[land_gdf['class'] == 'land'].copy()
print(f"Land class='land' features: {len(land_continent)}")
# Dissolve into a single Antarctic landmass
if len(land_continent) > 0:
antarctica_land = land_continent.dissolve()
# Also add other land features (islands, etc.)
other_land = land_gdf[land_gdf['class'] != 'land'].copy()
if len(other_land) > 0:
other_dissolved = other_land.dissolve()
# Merge both
combined = gpd.GeoDataFrame(
pd.concat([antarctica_land, other_dissolved], ignore_index=True),
crs='EPSG:4326'
)
ant_land_union = combined.dissolve()
else:
ant_land_union = antarctica_land
ant_land_geom = ant_land_union.geometry.iloc[0]
print(f"Antarctic landmass area (approx deg^2): {ant_land_geom.area:.1f}")
else:
raise ValueError("No Antarctic land found!")
# ============================================================
# 3. Fetch water features
# ============================================================
print("\nFetching water features...")
water_gdf = gpd.read_parquet(
"s3://overturemaps-us-west-2/release/2025-04-08.0/theme=water/type=water/*",
bbox=(-180, -90, 180, -60)
)
print(f"Water features: {len(water_gdf)}")
print("\nFetching bathymetry...")
bathy_gdf = gpd.read_parquet(
"s3://overturemaps-us-west-2/release/2025-04-08.0/theme=bathymetry/type=bathymetry/*",
bbox=(-180, -90, 180, -60)
)
print(f"Bathymetry features: {len(bathy_gdf)}")
# ============================================================
# 4. Project to Antarctic Polar Stereographic (EPSG:3031)
# ============================================================
print("\nReprojecting to Antarctic Polar Stereographic (EPSG:3031)...")
ANTARCTIC_CRS = 'EPSG:3031' # Antarctic Polar Stereographic (meters)
stations_proj = stations_df.to_crs(ANTARCTIC_CRS)
land_proj = gpd.GeoDataFrame({'geometry': [ant_land_geom]}, crs='EPSG:4326').to_crs(ANTARCTIC_CRS)
water_proj = water_gdf.to_crs(ANTARCTIC_CRS)
bathy_proj = bathy_gdf.to_crs(ANTARCTIC_CRS)
ant_land_geom_proj = land_proj.geometry.iloc[0]
# ============================================================
# 5. Create 200 km buffer for each station
# ============================================================
print("Creating 200 km buffer zones...")
BUFFER_DIST = 200_000 # 200 km in meters
stations_proj['buffer'] = stations_proj.geometry.buffer(BUFFER_DIST)
print(f"Created {len(stations_proj)} buffer zones")
# ============================================================
# 6. Clip buffers to land -> station land zones
# ============================================================
print("Clipping buffer zones to land...")
land_zones = []
for idx, row in stations_proj.iterrows():
station_id = row['station_id']
station_name = row['station_name']
buffer_geom = row['buffer']
# Clip buffer to land (intersection with land)
land_zone = buffer_geom.intersection(ant_land_geom_proj)
if land_zone.is_empty or land_zone.area == 0:
print(f" WARNING: {station_name} has no land zone")
continue
# Ensure MultiPolygon
if land_zone.geom_type == 'Polygon':
land_zone = MultiPolygon([land_zone])
elif land_zone.geom_type == 'MultiPolygon':
pass
else:
# Try to convert
try:
land_zone = MultiPolygon([land_zone] if land_zone.geom_type == 'Polygon' else list(land_zone.geoms))
except:
print(f" WARNING: {station_name} unexpected geometry: {land_zone.geom_type}")
continue
land_zones.append({
'station_id': station_id,
'station_name': station_name,
'geometry': land_zone
})
land_zones_df = gpd.GeoDataFrame(land_zones, crs=ANTARCTIC_CRS)
print(f"Created {len(land_zones_df)} land-clipped zones")
if len(land_zones_df) == 0:
raise ValueError("No land zones created!")
# ============================================================
# 7. Find overlapping zones -> coalitions
# ============================================================
print("Computing coalition groups (overlapping zones)...")
# For each pair, check overlap
n = len(land_zones_df)
assigned = [-1] * n
next_coalition = 0
for i in range(n):
if assigned[i] == -1:
assigned[i] = next_coalition
# Find all that overlap with this one
changed = True
while changed:
changed = False
for j in range(n):
if assigned[j] == -1:
# Check if j overlaps with any already in this coalition
for k in range(n):
if assigned[k] == next_coalition and k != j:
if land_zones_df.geometry.iloc[j].intersects(land_zones_df.geometry.iloc[k]):
assigned[j] = next_coalition
changed = True
break
next_coalition += 1
land_zones_df['coalition'] = assigned
print(f"Found {next_coalition} coalition(s)")
print(f"Coalition membership: {land_zones_df['coalition'].value_counts().to_dict()}")
# ============================================================
# 8. Ensure MultiPolygon geometry, write first output
# ============================================================
land_zones_df['geometry'] = land_zones_df.geometry.apply(
lambda g: MultiPolygon([g]) if g.geom_type == 'Polygon' else g
)
# Keep only required columns
out1 = land_zones_df[['station_id', 'station_name', 'coalition', 'geometry']].copy()
out1 = out1.reset_index(drop=True)
print(f"\nWriting {len(out1)} features to station_spheres.geoparquet")
print(f"Coalition distinct values: {out1['coalition'].nunique()}")
out1.to_parquet('/work/station_spheres.geoparquet')
print("Done writing station_spheres.geoparquet")
# ============================================================
# 9. Over-water portion per station with water attribution
# ============================================================
print("\nComputing over-water portions...")
# Prepare water features with consistent columns
water_features = []
# From water theme
for idx, row in water_proj.iterrows():
geom = row.geometry
if geom is None or geom.is_empty:
continue
w_id = row.get('id', f'water_{idx}')
names = row.get('names', None)
w_name = None
if isinstance(names, dict):
w_name = names.get('primary', None)
w_subtype = row.get('subtype', None) or row.get('class', None) or 'unknown'
water_features.append({
'water_id': w_id,
'water_name': w_name,
'water_subtype': str(w_subtype),
'water_source': 'base.water',
'geometry': geom
})
# From bathymetry theme
for idx, row in bathy_proj.iterrows():
geom = row.geometry
if geom is None or geom.is_empty:
continue
w_id = row.get('id', f'bathy_{idx}')
names = row.get('sources', None)
w_name = None
if isinstance(names, dict):
w_name = names.get('primary', None)
w_subtype = 'bathymetry'
water_features.append({
'water_id': w_id,
'water_name': w_name,
'water_subtype': w_subtype,
'water_source': 'base.bathymetry',
'geometry': geom
})
water_features_df = gpd.GeoDataFrame(water_features, crs=ANTARCTIC_CRS)
print(f"Total water/bathymetry features: {len(water_features_df)}")
# For each station, compute intersection of buffer with water features
water_overlaps = []
for idx, row in stations_proj.iterrows():
station_id = row['station_id']
station_name = row['station_name']
buffer_geom = row['buffer']
# Intersect buffer with each water feature
for widx, wrow in water_features_df.iterrows():
wgeom = wrow.geometry
inter = buffer_geom.intersection(wgeom)
if inter.is_empty or inter.area == 0:
continue
# Ensure MultiPolygon
if inter.geom_type == 'Polygon':
inter = MultiPolygon([inter])
elif inter.geom_type == 'MultiPolygon':
pass
else:
try:
inter = MultiPolygon(list(inter.geoms))
except:
continue
water_overlaps.append({
'station_id': station_id,
'station_name': station_name,
'water_id': wrow['water_id'],
'water_name': wrow['water_name'],
'water_subtype': wrow['water_subtype'],
'water_source': wrow['water_source'],
'geometry': inter
})
if (idx + 1) % 5 == 0:
print(f" Processed {idx+1}/{len(stations_proj)} stations...")
water_overlaps_df = gpd.GeoDataFrame(water_overlaps, crs=ANTARCTIC_CRS)
print(f"Total water overlap features: {len(water_overlaps_df)}")
# Ensure MultiPolygon
water_overlaps_df['geometry'] = water_overlaps_df.geometry.apply(
lambda g: MultiPolygon([g]) if g.geom_type == 'Polygon' else g
)
# Keep only required columns
out2 = water_overlaps_df[['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source', 'geometry']].copy()
out2 = out2.reset_index(drop=True)
print(f"\nWriting {len(out2)} features to station_water_overlap.geoparquet")
out2.to_parquet('/work/station_water_overlap.geoparquet')
print("Done writing station_water_overlap.geoparquet")
print("\n=== SUMMARY ===")
print(f"station_spheres.geoparquet: {len(out1)} features, {out1['coalition'].nunique()} coalitions")
print(f"station_water_overlap.geoparquet: {len(out2)} features")
for c in sorted(out1['coalition'].unique()):
members = out1[out1['coalition'] == c]['station_name'].tolist()
print(f" Coalition {c}: {members}")Wrote /work/solve.py (12941 chars).
13:25
The script uses `gpd.read_parquet` with s3 paths, but that may not work directly. The overturemaps library handles the S3 access. Let me rewrite to use the overturemaps library for fetching data, and then do the processing.
13:25
Write
/work/solve.py
content · 411 lines · py
#!/usr/bin/env python3
"""
Antarctic research station zones:
1. Land-clipped station zones grouped into coalitions (overlapping zones)
2. Over-water portion of each station's zone attributed with water features
"""
import warnings
warnings.filterwarnings('ignore')
import geopandas as gpd
import pandas as pd
import numpy as np
from shapely.geometry import Point, MultiPolygon, box
from shapely.ops import unary_union, polygonize
import pyproj
import overturemaps
print("=" * 60)
print("Step 1: Fetching data from Overture")
print("=" * 60)
# Fetch places (research stations)
print("\nFetching places (the 'place' theme)...")
places = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60))
print(f"Places fetched: {len(places)}")
# Extract station points south of -60
print("\nExtracting Antarctic research stations...")
stations = []
for idx, row in places.iterrows():
n = row.get('names')
if not n or not isinstance(n, dict):
continue
primary = n.get('primary', '')
if not isinstance(primary, str):
continue
p_lower = primary.lower()
# Filter for Antarctic research stations
is_station = any(kw in p_lower for kw in ['station', 'base '])
if not is_station:
continue
# Skip non-research-station things
if any(kw in p_lower for kw in ['gas station', 'police station', 'coffe', 'fuel', 'souvenir', 'micro']):
continue
geom = row.geometry
if geom is None:
continue
if geom.geom_type != 'Point':
continue
if geom.y > -60:
continue
stations.append({
'station_id': row['id'],
'station_name': primary.strip(),
'geometry': geom
})
stations_df = gpd.GeoDataFrame(stations, crs='EPSG:4326')
print(f"Found {len(stations_df)} Antarctic research stations")
for _, row in stations_df.iterrows():
print(f" {row['station_name']} @ ({row.geometry.x:.4f}, {row.geometry.y:.4f})")
# Fetch land data
print("\nFetching land data (the 'land' theme)...")
land_gdf = overturemaps.geodataframe('land', bbox=(-180, -90, 180, -60))
print(f"Land features: {len(land_gdf)}")
# Fetch water data
print("\nFetching water data (the 'water' theme)...")
water_gdf = overturemaps.geodataframe('water', bbox=(-180, -90, 180, -60))
print(f"Water features: {len(water_gdf)}")
# Fetch bathymetry data
print("\nFetching bathymetry data (the 'bathymetry' theme)...")
bathy_gdf = overturemaps.geodataframe('bathymetry', bbox=(-180, -90, 180, -60))
print(f"Bathymetry features: {len(bathy_gdf)}")
print("\n" + "=" * 60)
print("Step 2: Building Antarctic landmass")
print("=" * 60)
# Filter to main Antarctic land
land_continent = land_gdf[land_gdf['class'] == 'land'].copy()
print(f"Land class='land' features: {len(land_continent)}")
# Also include islands and other land
other_land = land_gdf[land_gdf['class'] != 'land'].copy()
print(f"Other land features (islands, etc.): {len(other_land)}")
# Dissolve all land into one
all_land = gpd.GeoDataFrame(
pd.concat([land_continent, other_land], ignore_index=False),
crs='EPSG:4326'
)
ant_land_union = all_land.dissolve()
ant_land_geom = ant_land_union.geometry.iloc[0]
print(f"Antarctic landmass (union) area (approx deg^2): {ant_land_geom.area:.1f}")
print("\n" + "=" * 60)
print("Step 3: Reprojecting to EPSG:3031 (Antarctic Polar Stereographic)")
print("=" * 60)
ANTARCTIC_CRS = 'EPSG:3031'
stations_proj = stations_df.to_crs(ANTARCTIC_CRS)
land_proj = gpd.GeoDataFrame({'geometry': [ant_land_geom]}, crs='EPSG:4326').to_crs(ANTARCTIC_CRS)
water_proj = water_gdf.to_crs(ANTARCTIC_CRS)
bathy_proj = bathy_gdf.to_crs(ANTARCTIC_CRS)
ant_land_geom_proj = land_proj.geometry.iloc[0]
print("Reprojected to EPSG:3031")
print("\n" + "=" * 60)
print("Step 4: Creating 200 km buffer zones")
print("=" * 60)
BUFFER_DIST = 200_000 # 200 km in meters
stations_proj['buffer'] = stations_proj.geometry.buffer(BUFFER_DIST)
print(f"Created {len(stations_proj)} buffer zones of {BUFFER_DIST/1000} km radius")
print("\n" + "=" * 60)
print("Step 5: Clipping buffer zones to land -> land zones")
print("=" * 60)
land_zones = []
for idx, row in stations_proj.iterrows():
station_id = row['station_id']
station_name = row['station_name']
buffer_geom = row['buffer']
# Clip buffer to land (intersection with land)
land_zone = buffer_geom.intersection(ant_land_geom_proj)
if land_zone.is_empty or land_zone.area == 0:
print(f" SKIP: {station_name} has no land intersection")
continue
# Ensure MultiPolygon
if land_zone.geom_type == 'Polygon':
land_zone = MultiPolygon([land_zone])
elif land_zone.geom_type == 'MultiPolygon':
pass
elif land_zone.geom_type == 'GeometryCollection':
# Extract polygons
polys = [g for g in land_zone.geoms if g.geom_type in ('Polygon', 'MultiPolygon')]
if not polys:
print(f" SKIP: {station_name} no polygon geometries in intersection")
continue
final_polys = []
for p in polys:
if p.geom_type == 'Polygon':
final_polys.append(p)
elif p.geom_type == 'MultiPolygon':
final_polys.extend(list(p.geoms))
land_zone = MultiPolygon(final_polys)
else:
print(f" SKIP: {station_name} unexpected geometry: {land_zone.geom_type}")
continue
land_zones.append({
'station_id': station_id,
'station_name': station_name,
'geometry': land_zone
})
land_zones_df = gpd.GeoDataFrame(land_zones, crs=ANTARCTIC_CRS)
print(f"Created {len(land_zones_df)} land-clipped zones")
print("\n" + "=" * 60)
print("Step 6: Computing coalition groups (overlapping zones)")
print("=" * 60)
n = len(land_zones_df)
assigned = [-1] * n
next_coalition = 0
for i in range(n):
if assigned[i] == -1:
assigned[i] = next_coalition
# Expand this coalition: find all that overlap with any member
changed = True
while changed:
changed = False
for j in range(n):
if assigned[j] == -1:
# Check if j overlaps with any already in this coalition
for k in range(n):
if assigned[k] == next_coalition and k != j:
if land_zones_df.geometry.iloc[j].intersects(land_zones_df.geometry.iloc[k]):
assigned[j] = next_coalition
changed = True
break
next_coalition += 1
land_zones_df['coalition'] = assigned
print(f"Found {next_coalition} coalition(s)")
for c in sorted(land_zones_df['coalition'].unique()):
members = land_zones_df[land_zones_df['coalition'] == c]['station_name'].tolist()
print(f" Coalition {c}: {members}")
print(f"Coalition column has {land_zones_df['coalition'].nunique()} distinct values")
print("\n" + "=" * 60)
print("Step 7: Writing land-clipped coalition zones")
print("=" * 60)
# Ensure MultiPolygon geometry
def ensure_multipolygon(g):
if g.geom_type == 'Polygon':
return MultiPolygon([g])
elif g.geom_type == 'MultiPolygon':
return g
else:
# Try to extract polygons
try:
polys = [p for p in g.geoms if p.geom_type in ('Polygon', 'MultiPolygon')]
flat = []
for p in polys:
if p.geom_type == 'Polygon':
flat.append(p)
else:
flat.extend(list(p.geoms))
return MultiPolygon(flat) if flat else g
except:
return g
land_zones_df['geometry'] = land_zones_df.geometry.apply(ensure_multipolygon)
out1 = land_zones_df[['station_id', 'station_name', 'coalition', 'geometry']].copy()
out1 = out1.reset_index(drop=True)
out1.to_parquet('/work/station_spheres.geoparquet')
print(f"Written {len(out1)} features to station_spheres.geoparquet")
print(f" Columns: {out1.columns.tolist()}")
print(f" CRS: {out1.crs}")
print("\n" + "=" * 60)
print("Step 8: Computing over-water portions with water attribution")
print("=" * 60)
# Prepare water features with consistent columns
water_features = []
print(f"Processing {len(water_proj)} water features...")
for idx, row in water_proj.iterrows():
geom = row.geometry
if geom is None or geom.is_empty:
continue
w_id = row.get('id', f'water_{idx}')
names = row.get('names', None)
w_name = None
if isinstance(names, dict):
w_name = names.get('primary', None)
w_subtype = row.get('subtype', None) or 'water'
# Ensure class is used for more descriptive subtype if available
w_class = row.get('class', None)
if w_class:
w_subtype = str(w_class)
# Ensure MultiPolygon
if geom.geom_type == 'Polygon':
geom = MultiPolygon([geom])
elif geom.geom_type == 'MultiPolygon':
pass
else:
# Try to convert
try:
polys = [g for g in geom.geoms if g.geom_type in ('Polygon', 'MultiPolygon')]
flat = []
for p in polys:
if p.geom_type == 'Polygon':
flat.append(p)
else:
flat.extend(list(p.geoms))
if not flat:
continue
geom = MultiPolygon(flat)
except:
continue
water_features.append({
'water_id': w_id,
'water_name': w_name,
'water_subtype': str(w_subtype),
'water_source': 'base.water',
'geometry': geom
})
print(f"Processing {len(bathy_proj)} bathymetry features...")
for idx, row in bathy_proj.iterrows():
geom = row.geometry
if geom is None or geom.is_empty:
continue
w_id = row.get('id', f'bathy_{idx}')
# bathymetry has 'depth' but no names column in the typical sense
# sources might have names
sources = row.get('sources', None)
w_name = None
if isinstance(sources, dict):
w_name = sources.get('primary', None)
# Ensure MultiPolygon
if geom.geom_type == 'Polygon':
geom = MultiPolygon([geom])
elif geom.geom_type == 'MultiPolygon':
pass
else:
try:
polys = [g for g in geom.geoms if g.geom_type in ('Polygon', 'MultiPolygon')]
flat = []
for p in polys:
if p.geom_type == 'Polygon':
flat.append(p)
else:
flat.extend(list(p.geoms))
if not flat:
continue
geom = MultiPolygon(flat)
except:
continue
water_features.append({
'water_id': w_id,
'water_name': w_name,
'water_subtype': 'bathymetry',
'water_source': 'base.bathymetry',
'geometry': geom
})
water_features_df = gpd.GeoDataFrame(water_features, crs=ANTARCTIC_CRS)
print(f"Total water/bathymetry features: {len(water_features_df)}")
# For each station, compute intersection of buffer with water features
print("\nComputing station-water overlaps...")
water_overlaps = []
for idx, row in stations_proj.iterrows():
station_id = row['station_id']
station_name = row['station_name']
buffer_geom = row['buffer']
for widx, wrow in water_features_df.iterrows():
wgeom = wrow.geometry
try:
inter = buffer_geom.intersection(wgeom)
except:
continue
if inter.is_empty or inter.area == 0:
continue
# Ensure MultiPolygon
if inter.geom_type == 'Polygon':
inter = MultiPolygon([inter])
elif inter.geom_type == 'MultiPolygon':
pass
elif inter.geom_type == 'GeometryCollection':
polys = [g for g in inter.geoms if g.geom_type in ('Polygon', 'MultiPolygon')]
if not polys:
continue
flat = []
for p in polys:
if p.geom_type == 'Polygon':
flat.append(p)
else:
flat.extend(list(p.geoms))
if not flat:
continue
inter = MultiPolygon(flat)
else:
continue
water_overlaps.append({
'station_id': station_id,
'station_name': station_name,
'water_id': wrow['water_id'],
'water_name': wrow['water_name'],
'water_subtype': wrow['water_subtype'],
'water_source': wrow['water_source'],
'geometry': inter
})
if (idx + 1) % 5 == 0:
print(f" Processed {idx+1}/{len(stations_proj)} stations...")
water_overlaps_df = gpd.GeoDataFrame(water_overlaps, crs=ANTARCTIC_CRS)
print(f"Total water overlap features: {len(water_overlaps_df)}")
# Ensure MultiPolygon
water_overlaps_df['geometry'] = water_overlaps_df.geometry.apply(ensure_multipolygon)
out2 = water_overlaps_df[['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source', 'geometry']].copy()
out2 = out2.reset_index(drop=True)
out2.to_parquet('/work/station_water_overlap.geoparquet')
print(f"Written {len(out2)} features to station_water_overlap.geoparquet")
print(f" Columns: {out2.columns.tolist()}")
print("\n" + "=" * 60)
print("DONE! Summary:")
print("=" * 60)
print(f" station_spheres.geoparquet: {len(out1)} features, {out1['coalition'].nunique()} coalitions")
print(f" station_water_overlap.geoparquet: {len(out2)} features")
for c in sorted(out1['coalition'].unique()):
members = out1[out1['coalition'] == c]['station_name'].tolist()
print(f" Coalition {c}: {members}")
Wrote /work/solve.py (13682 chars).
13:36
Wait, I notice that the code uses `overturemaps.geodataframe()` which is a simple function call. Let me check if the `geodataframe` function is actually available in the module. Looking at the help output I got earlier, it showed `geodataframe` in the `core` module. Let me check.
Actually, looking at the output earlier:
```
overturemaps.geodataframe(
overture_type: str,
bbox: BBox | tuple[float, float, float, float] | list[float] | None = None,
...
```
So `overturemaps.geodataframe()` should work directly. But actually, the function was shown under `overturemaps.core.geodataframe`. Let me check.
Actually, looking at the `__init__` content:
```
['__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'core', 'geodataframe', 'get_all_overture_types', 'models', 'record_batch_reader']
```
It shows `geodataframe` is directly in the `overturemaps` namespace. So `overturemaps.geodataframe('place', ...)` should work.
But I also used `gpd.read_parquet` which might not work for S3. Let me use `overturemaps.geodataframe` for all data fetching.
Actually, looking at the earlier test:
```
python3 -c "
import overturemaps
df = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60))
```
This worked fine. So the code should be okay.
But I'm concerned about memory - the water features might be 13501 features and bathymetry 3096 features, and we're doing intersection with each station's buffer. That's potentially a lot of intersection operations. Let me also consider that the geometry processing could take a while.
Let me run it and see. But first, let me also handle another concern: the bbox parameter - for `land` theme, arrays=False might be needed... Actually in the earlier test it worked without arrays.
Wait, I notice in my first attempt I used `gpd.read_parquet` with an S3 path. Let me check if that works - it might not work without configuring S3 properly. Let me just use `overturemaps.geodataframe()` for everything.
Let me update the script.
13:36
Edit
/work/solve.py
Edited /work/solve.py.
13:53
Actually, let me check the Overture library more carefully. The `geodataframe` function might be using pyarrow under the hood to read from S3. Let me check if it works for reading from S3 directly, or if we need to use a different approach. Actually, looking at the overturemaps library, the `geodataframe` function reads from S3. Let me try running the script and see what happens. But first, let me check whether the overturemaps library works with these S3 paths directly.
13:53
Bash
cd /work && timeout 60 python3 -c "
cd /work && timeout 60 python3 -c "
import overturemaps
# Quick test: does geodataframe work?
print('Testing overturemaps.geodataframe...')
df = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60))
print(f'Success: {len(df)} rows')
" 2>&1 | tail -20
Testing overturemaps.geodataframe... Success: 2348 rows
14:49
The test works. But the full script might take a while. Let me consider optimizing by using record_batch_reader or reading parquet directly from S3 for better performance. Actually, overturemaps.geodataframe() should be fine. Let me run the full script. One concern: the script will try to intersect each station buffer with each water feature - that could be a lot of intersections. Let me think about optimization. Actually, I think the current approach should work. Let me run it and see.
14:49
Bash
cd /work && timeout 900 python3 solve.py 2>&1
============================================================
Step 1: Fetching data from Overture
============================================================
Fetching places (the 'place' theme)...
Places fetched: 2348
Place categories (sample):
Traceback (most recent call last):
File "/work/solve.py", line 30, in <module>
for c in places['categories'].dropna().unique()[:5]:
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/series.py", line 2228, in unique
return super().unique()
~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/base.py", line 1159, in unique
result = algorithms.unique1d(values) # type: ignore[assignment]
File "/usr/local/lib/python3.14/site-packages/pandas/core/algorithms.py", line 433, in unique
return unique_with_mask(values)
File "/usr/local/lib/python3.14/site-packages/pandas/core/algorithms.py", line 476, in unique_with_mask
uniques = table.unique(values)
File "pandas/_libs/hashtable_class_helper.pxi", line 7840, in pandas._libs.hashtable.PyObjectHashTable.unique
File "pandas/_libs/hashtable_class_helper.pxi", line 7783, in pandas._libs.hashtable.PyObjectHashTable._unique
TypeError: unhashable type: 'dict'
15:29
The categories column has dict values which can't be hashed. Let me remove that debug line and also just run the main script.
15:29
Edit
/work/solve.py
Edited /work/solve.py.
15:45
Bash
cd /work && timeout 900 python3 solve.py 2>&1
============================================================
Step 1: Fetching data from Overture
============================================================
Fetching places (the 'place' theme)...
Places fetched: 2348
Place categories (sample rows): ["{'primary': 'information_technology_company', 'alternate': array(['professional_services'], dtype=object)}", "{'primary': 'physical_therapy', 'alternate': array(['occupational_therapy', 'speech_therapist'], dtype=object)}", "{'primary': 'health_and_medical', 'alternate': array(['pharmacy'], dtype=object)}", "{'primary': 'school', 'alternate': None}", 'None']
Extracting Antarctic research stations...
Found 17 Antarctic research stations
Brown Station, Antarctic Peninsula @ (-62.8724, -64.8921)
Brown Station @ (-62.8700, -64.8950)
Palmer Station, Antarctica @ (-64.0531, -64.7743)
Bellingshausen Russian Antarctic Station @ (-58.9624, -62.1998)
Base Marambio, Antartida Argentina @ (-56.6198, -64.2413)
Base Antártica Marambio @ (-56.6235, -64.2374)
Base Esperanza, Antartida Argentina @ (-56.9974, -63.3980)
Neumayer-Station II @ (-8.2500, -70.6500)
Plateau Station @ (40.5000, -79.2500)
Base antarctique Mawson @ (62.8736, -67.6026)
Antarctic Kunlun Station @ (77.1161, -80.4169)
Base antártica Concordia @ (123.3333, -75.1000)
Base antarctique Concordia @ (123.5580, -75.1000)
Base antarctique Dumont-d'Urville @ (140.0013, -66.6628)
Mario-Zucchelli-Station @ (164.1125, -74.6943)
Base McMurdo @ (166.6667, -77.8500)
Base antarctique McMurdo @ (166.6667, -77.8500)
Fetching land data (the 'land' theme)...
Land features: 93367
Fetching water data (the 'water' theme)...
Water features: 13501
Fetching bathymetry data (the 'bathymetry' theme)...
Bathymetry features: 3096
============================================================
Step 2: Building Antarctic landmass
============================================================
Land class='land' features: 14707
Other land features (islands, etc.): 78660
Traceback (most recent call last):
File "/work/solve.py", line 98, in <module>
all_land = gpd.GeoDataFrame(
pd.concat([land_continent, other_land], ignore_index=False),
crs='EPSG:4326'
)
File "/usr/local/lib/python3.14/site-packages/geopandas/geodataframe.py", line 184, in __init__
raise ValueError(crs_mismatch_error)
ValueError: CRS mismatch between CRS of the passed geometries and 'crs'. Use 'GeoDataFrame.set_crs(crs, allow_override=True)' to overwrite CRS or 'GeoDataFrame.to_crs(crs)' to reproject geometries.
18:23
I need to fix the CRS issue. The land data might have different CRS values. Let me check and fix.
18:23
Edit
/work/solve.py
Edited /work/solve.py.
18:29
Bash
cd /work && timeout 900 python3 solve.py 2>&1
============================================================
Step 1: Fetching data from Overture
============================================================
Fetching places (the 'place' theme)...
Places fetched: 2348
Place categories (sample rows): ["{'primary': 'information_technology_company', 'alternate': array(['professional_services'], dtype=object)}", "{'primary': 'physical_therapy', 'alternate': array(['occupational_therapy', 'speech_therapist'], dtype=object)}", "{'primary': 'health_and_medical', 'alternate': array(['pharmacy'], dtype=object)}", "{'primary': 'school', 'alternate': None}", 'None']
Extracting Antarctic research stations...
Found 17 Antarctic research stations
Brown Station, Antarctic Peninsula @ (-62.8724, -64.8921)
Brown Station @ (-62.8700, -64.8950)
Palmer Station, Antarctica @ (-64.0531, -64.7743)
Bellingshausen Russian Antarctic Station @ (-58.9624, -62.1998)
Base Marambio, Antartida Argentina @ (-56.6198, -64.2413)
Base Antártica Marambio @ (-56.6235, -64.2374)
Base Esperanza, Antartida Argentina @ (-56.9974, -63.3980)
Neumayer-Station II @ (-8.2500, -70.6500)
Plateau Station @ (40.5000, -79.2500)
Base antarctique Mawson @ (62.8736, -67.6026)
Antarctic Kunlun Station @ (77.1161, -80.4169)
Base antártica Concordia @ (123.3333, -75.1000)
Base antarctique Concordia @ (123.5580, -75.1000)
Base antarctique Dumont-d'Urville @ (140.0013, -66.6628)
Mario-Zucchelli-Station @ (164.1125, -74.6943)
Base McMurdo @ (166.6667, -77.8500)
Base antarctique McMurdo @ (166.6667, -77.8500)
Fetching land data (the 'land' theme)...
Land features: 93367
Fetching water data (the 'water' theme)...
Water features: 13501
Fetching bathymetry data (the 'bathymetry' theme)...
Bathymetry features: 3096
============================================================
Step 2: Building Antarctic landmass
============================================================
Land class='land' features: 14707
Other land features (islands, etc.): 78660
Antarctic landmass (union) area (approx deg^2): 6661.6
============================================================
Step 3: Reprojecting to EPSG:3031 (Antarctic Polar Stereographic)
============================================================
Traceback (most recent call last):
File "/work/solve.py", line 118, in <module>
water_proj = water_gdf.to_crs(ANTARCTIC_CRS)
File "/usr/local/lib/python3.14/site-packages/geopandas/geodataframe.py", line 1842, in to_crs
geom = df.geometry.to_crs(crs=crs, epsg=epsg)
File "/usr/local/lib/python3.14/site-packages/geopandas/geoseries.py", line 1251, in to_crs
self.values.to_crs(crs=crs, epsg=epsg), index=self.index, name=self.name
~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/geopandas/_compat.py", line 88, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/geopandas/array.py", line 1114, in to_crs
raise ValueError(
...<2 lines>...
)
ValueError: Cannot transform naive geometries. Please set a crs on the object first.
21:51
The water data has no CRS set. I need to set it. Overture data comes in EPSG:4326.
21:51
Edit
/work/solve.py
old_string not found in /work/solve.py
21:58
Let me read the current file to see what's there.
21:58
Read
/work/solve.py
102 all_land = gpd.GeoDataFrame(
103 pd.concat([land_continent, other_land], ignore_index=True),
104 crs='EPSG:4326'
105 )
106 ant_land_union = all_land.dissolve()
107 ant_land_geom = ant_land_union.geometry.iloc[0]
108 print(f"Antarctic landmass (union) area (approx deg^2): {ant_land_geom.area:.1f}")
109
110 print("\n" + "=" * 60)
111 print("Step 3: Reprojecting to EPSG:3031 (Antarctic Polar Stereographic)")
112 print("=" * 60)
113
114 ANTARCTIC_CRS = 'EPSG:3031'
115
116 stations_proj = stations_df.to_crs(ANTARCTIC_CRS)
117 land_proj = gpd.GeoDataFrame({'geometry': [ant_land_geom]}, crs='EPSG:4326').to_crs(ANTARCTIC_CRS)
118 water_proj = water_gdf.to_crs(ANTARCTIC_CRS)
119 bathy_proj = bathy_gdf.to_crs(ANTARCTIC_CRS)
120
121 ant_land_geom_proj = land_proj.geometry.iloc[0]
122
123 print("Reprojected to EPSG:3031")
124
125 print("\n" + "=" * 60)
126 print("Step 4: Creating 200 km buffer zones")
127 print("=" * 60)
128
129 BUFFER_DIST = 200_000 # 200 km in meters
130
131 stations_proj['buffer'] = stations_proj.geometry.buffer(BUFFER_DIST)
<system-reminder>Showed lines 102-131 of 417. Re-call Read with offset=132 to continue, or raise limit.</system-reminder>
22:07
I need to fix the CRS issue. The water and bathymetry dataframes don't have CRS set. Let me fix that.
22:07
Edit
/work/solve.py
Edited /work/solve.py.
22:17
Wait, the `for` loop with `gdf = gdf.set_crs(...)` won't actually update the original variables. Let me fix that.
22:17
Edit
/work/solve.py
Edited /work/solve.py.
22:22
Wait, there's an issue with the land_proj variable. The code does `land_proj = gpd.GeoDataFrame(...)` and then `ant_land_geom_proj = land_proj.geometry.iloc[0]`. But earlier I named the variable `land_proj` which is also used for... wait let me trace through. The original code had:
```python
land_proj = gpd.GeoDataFrame({'geometry': [ant_land_geom]}, crs='EPSG:4326').to_crs(ANTARCTIC_CRS)
ant_land_geom_proj = land_proj.geometry.iloc[0]
```
So `land_proj` is a GeoDataFrame in EPSG:3031. That's fine - there's no conflict. Let me just run it.
22:22
Bash
cd /work && timeout 900 python3 solve.py 2>&1
============================================================
Step 1: Fetching data from Overture
============================================================
Fetching places (the 'place' theme)...
Places fetched: 2348
Place categories (sample rows): ["{'primary': 'information_technology_company', 'alternate': array(['professional_services'], dtype=object)}", "{'primary': 'physical_therapy', 'alternate': array(['occupational_therapy', 'speech_therapist'], dtype=object)}", "{'primary': 'health_and_medical', 'alternate': array(['pharmacy'], dtype=object)}", "{'primary': 'school', 'alternate': None}", 'None']
Extracting Antarctic research stations...
Found 17 Antarctic research stations
Brown Station, Antarctic Peninsula @ (-62.8724, -64.8921)
Brown Station @ (-62.8700, -64.8950)
Palmer Station, Antarctica @ (-64.0531, -64.7743)
Bellingshausen Russian Antarctic Station @ (-58.9624, -62.1998)
Base Marambio, Antartida Argentina @ (-56.6198, -64.2413)
Base Antártica Marambio @ (-56.6235, -64.2374)
Base Esperanza, Antartida Argentina @ (-56.9974, -63.3980)
Neumayer-Station II @ (-8.2500, -70.6500)
Plateau Station @ (40.5000, -79.2500)
Base antarctique Mawson @ (62.8736, -67.6026)
Antarctic Kunlun Station @ (77.1161, -80.4169)
Base antártica Concordia @ (123.3333, -75.1000)
Base antarctique Concordia @ (123.5580, -75.1000)
Base antarctique Dumont-d'Urville @ (140.0013, -66.6628)
Mario-Zucchelli-Station @ (164.1125, -74.6943)
Base McMurdo @ (166.6667, -77.8500)
Base antarctique McMurdo @ (166.6667, -77.8500)
Fetching land data (the 'land' theme)...
Land features: 93367
Fetching water data (the 'water' theme)...
Water features: 13501
Fetching bathymetry data (the 'bathymetry' theme)...
Bathymetry features: 3096
============================================================
Step 2: Building Antarctic landmass
============================================================
Land class='land' features: 14707
Other land features (islands, etc.): 78660
Antarctic landmass (union) area (approx deg^2): 6661.6
============================================================
Step 3: Reprojecting to EPSG:3031 (Antarctic Polar Stereographic)
============================================================
Setting CRS for water to EPSG:4326
Setting CRS for bathy to EPSG:4326
Reprojected to EPSG:3031
============================================================
Step 4: Creating 200 km buffer zones
============================================================
Created 17 buffer zones of 200.0 km radius
============================================================
Step 5: Clipping buffer zones to land -> land zones
============================================================
Created 17 land-clipped zones
============================================================
Step 6: Computing coalition groups (overlapping zones)
============================================================
Found 8 coalition(s)
Coalition 0: ['Brown Station, Antarctic Peninsula', 'Brown Station', 'Palmer Station, Antarctica', 'Bellingshausen Russian Antarctic Station', 'Base Marambio, Antartida Argentina', 'Base Antártica Marambio', 'Base Esperanza, Antartida Argentina']
Coalition 1: ['Neumayer-Station II']
Coalition 2: ['Plateau Station']
Coalition 3: ['Base antarctique Mawson']
Coalition 4: ['Antarctic Kunlun Station']
Coalition 5: ['Base antártica Concordia', 'Base antarctique Concordia']
Coalition 6: ["Base antarctique Dumont-d'Urville"]
Coalition 7: ['Mario-Zucchelli-Station', 'Base McMurdo', 'Base antarctique McMurdo']
Coalition column has 8 distinct values
============================================================
Step 7: Writing land-clipped coalition zones
============================================================
Written 17 features to station_spheres.geoparquet
Columns: ['station_id', 'station_name', 'coalition', 'geometry']
CRS: EPSG:3031
============================================================
Step 8: Computing over-water portions with water attribution
============================================================
Processing 13501 water features...
Processing 3096 bathymetry features...
Total water/bathymetry features: 10551
Computing station-water overlaps...
Processed 5/17 stations...
Processed 10/17 stations...
Processed 15/17 stations...
Total water overlap features: 4137
Written 4137 features to station_water_overlap.geoparquet
Columns: ['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source', 'geometry']
============================================================
DONE! Summary:
============================================================
station_spheres.geoparquet: 17 features, 8 coalitions
station_water_overlap.geoparquet: 4137 features
Coalition 0: ['Brown Station, Antarctic Peninsula', 'Brown Station', 'Palmer Station, Antarctica', 'Bellingshausen Russian Antarctic Station', 'Base Marambio, Antartida Argentina', 'Base Antártica Marambio', 'Base Esperanza, Antartida Argentina']
Coalition 1: ['Neumayer-Station II']
Coalition 2: ['Plateau Station']
Coalition 3: ['Base antarctique Mawson']
Coalition 4: ['Antarctic Kunlun Station']
Coalition 5: ['Base antártica Concordia', 'Base antarctique Concordia']
Coalition 6: ["Base antarctique Dumont-d'Urville"]
Coalition 7: ['Mario-Zucchelli-Station', 'Base McMurdo', 'Base antarctique McMurdo']
26:22
The script ran successfully. Let me now verify the outputs to make sure everything is correct: 1. `station_spheres.geoparquet` - 17 features, 8 coalitions, with station_id, station_name, coalition, geometry 2. `station_water_overlap.geoparquet` - 4137 features, with station_id, station_name, water_id, water_name, water_subtype, water_source, geometry Let me verify the output files are valid and check the CRS.
26:22
26:22
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
# Check output 1
df1 = gpd.read_parquet('station_spheres.geoparquet')
print('=== station_spheres.geoparquet ===')
print(f'Shape: {df1.shape}')
print(f'Columns: {df1.columns.tolist()}')
print(f'CRS: {df1.crs}')
print(f'Geometry types: {df1.geometry.geom_type.value_counts().to_dict()}')
print(f'Coalition unique values: {df1[\"coalition\"].nunique()}')
print(f'Coalition values: {sorted(df1[\"coalition\"].unique())}')
print(f'Rows: {len(df1)}')
print()
# Check output 2
df2 = gpd.read_parquet('station_water_overlap.geoparquet')
print('=== station_water_overlap.geoparquet ===')
print(f'Shape: {df2.shape}')
print(f'Columns: {df2.columns.tolist()}')
print(f'CRS: {df2.crs}')
print(f'Geometry types: {df2.geometry.geom_type.value_counts().to_dict()}')
print(f'Rows: {len(df2)}')
print(f'water_source values: {df2[\"water_source\"].value_counts().to_dict()}')
print(f'water_subtype values: {df2[\"water_subtype\"].value_counts().to_dict()}')
print()
# Show a few sample rows
print('Sample rows from station_water_overlap:')
print(df2.head(10).to_string())
" 2>&1
=== station_spheres.geoparquet ===
Shape: (17, 4)
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': 17}
Coalition unique values: 8
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)]
Rows: 17
=== station_water_overlap.geoparquet ===
Shape: (4137, 7)
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': 4137}
Rows: 4137
water_source values: {'base.water': 3290, 'base.bathymetry': 847}
water_subtype values: {'lake': 1638, 'bathymetry': 847, 'pond': 587, 'ocean': 584, 'water': 191, 'bay': 156, 'cape': 108, 'strait': 10, 'sea': 5, 'stream': 4, 'lagoon': 4, 'swimming_pool': 3}
Sample rows from station_water_overlap:
station_id station_name water_id water_name water_subtype water_source
[... truncated ...] MULTIPOLYGON (((-2412678.748 1083926.749, -2412678.751 1083913.132, -2412692.247 1083890.616, -2412706.304 1083876.561, -2412683.757 1083926.749, -2412678.748 1083926.749)))
26:30
The outputs look good! Let me also make sure there aren't duplicate station entries (Brown Station appears twice, Base Marambio appears twice, Concordia appears twice, McMurdo appears twice). The task says "stations from Overture" so getting them all is fine - they'll have different station_ids since they're different Overture features. But let me check if this is really desirable - Brown Station and Brown Station, Antarctic Peninsula are essentially the same station. Let me clean up duplicates by clustering stations that are very close to each other (within a few km). Actually, looking more carefully - the task says "Each Antarctic research station has a notional 200 km operational radius." So we need one zone per station. The same physical station appearing multiple times with slightly different coordinates should be deduplicated. Let me fix this by clustering points that are within ~5km of each other.
26:30
Read
/work/solve.py
33 stations = []
34 for idx, row in places.iterrows():
35 n = row.get('names')
36 if not n or not isinstance(n, dict):
37 continue
38 primary = n.get('primary', '')
39 if not isinstance(primary, str):
40 continue
41 p_lower = primary.lower()
42
43 # Filter for Antarctic research stations
44 is_station = any(kw in p_lower for kw in ['station', 'base '])
45 if not is_station:
46 continue
47 # Skip non-research-station things
48 if any(kw in p_lower for kw in ['gas station', 'police station', 'coffe', 'fuel', 'souvenir', 'micro']):
49 continue
50
51 geom = row.geometry
52 if geom is None:
53 continue
54 if geom.geom_type != 'Point':
55 continue
56 if geom.y > -60:
57 continue
58
59 stations.append({
60 'station_id': row['id'],
61 'station_name': primary.strip(),
62 'geometry': geom
63 })
64
65 stations_df = gpd.GeoDataFrame(stations, crs='EPSG:4326')
66 print(f"Found {len(stations_df)} Antarctic research stations")
67 for _, row in stations_df.iterrows():
68 print(f" {row['station_name']} @ ({row.geometry.x:.4f}, {row.geometry.y:.4f})")
69
70 # Fetch land data
71 print("\nFetching land data (the 'land' theme)...")
72 land_gdf = overturemaps.geodataframe('land', bbox=(-180, -90, 180, -60))
73 print(f"Land features: {len(land_gdf)}")
74
75 # Fetch water data
76 print("\nFetching water data (the 'water' theme)...")
77 water_gdf = overturemaps.geodataframe('water', bbox=(-180, -90, 180, -60))
78 print(f"Water features: {len(water_gdf)}")
79
80 # Fetch bathymetry data
81 print("\nFetching bathymetry data (the 'bathymetry' theme)...")
82 bathy_gdf = overturemaps.geodataframe('bathymetry', bbox=(-180, -90, 180, -60))
<system-reminder>Showed lines 33-82 of 428. Re-call Read with offset=83 to continue, or raise limit.</system-reminder>
26:44
The outputs look good! Let me also handle the deduplication of stations that are really the same (e.g., "Brown Station, Antarctic Peninsula" and "Brown Station" are the same station, as are "Base Marambio" and "Base Antártica Marambio", and "Base antártica Concordia" and "Base antarctique Concordia", and "Base McMurdo" and "Base antarctique McMurdo"). I should deduplicate stations that are very close to each other (within a few km) to avoid counting the same station twice. Let me update the script to handle this by grouping nearby stations. Actually, looking at the data more carefully: - Brown Station, Antarctic Peninsula (-62.8724, -64.8921) and Brown Station (-62.8700, -64.8950) -> same station, ~200m apart - Base Marambio (-56.6198, -64.2413) and Base Antártica Marambio (-56.6235, -64.2374) -> same station, ~400m apart - Base antártica Concordia (123.3333, -75.1000) and Base antarctique Concordia (123.5580, -75.1000) -> same station, ~8km apart - Base McMurdo (166.6667, -77.8500) and Base antarctique McMurdo (166.6667, -77.8500) -> same station, same coords These duplicates will cause the same station to have multiple zones. Let me deduplicate by proximity. Actually, the current output has 17 features with 8 coalitions. The duplicates make the zones overlap (since they're right next to each other). So Brown Station and Brown Station, Antarctic Peninsula are in the same coalition, etc. This is actually fine for the coalition grouping - they'll be grouped together. But each duplicate will have its own land zone polygon, so there are multiple features for the same station. Let me fix this by deduplicating stations that are close together. Let me update the script to group nearby stations (within 1 km) and merge them into one.
26:44
26:44
Edit
/work/solve.py
Edited /work/solve.py.
26:55
Now I also need to make sure we're including more Antarctic stations that might not have "station" or "base" in their name. Let me also check for places with names containing "antarctic" or "research" more broadly. But first let me re-run and see the deduped results. Also, I need to add the Esperanza Base (row 568) which might be separate from row 569. Let me check. Actually, looking at the stations I captured: - Brown Station, Antarctic Peninsula @ (-62.8724, -64.8921) - Brown Station @ (-62.8700, -64.8950) - these are the same place - Palmer Station, Antarctica @ (-64.0531, -64.7743) - Bellingshausen Russian Antarctic Station @ (-58.9624, -62.1998) - Base Marambio, Antartida Argentina @ (-56.6198, -64.2413) - Base Antártica Marambio @ (-56.6235, -64.2374) - same as above - Base Esperanza, Antartida Argentina @ (-56.9974, -63.3980) - Neumayer-Station II @ (-8.2500, -70.6500) - Plateau Station @ (40.5000, -79.2500) - Base antarctique Mawson @ (62.8736, -67.6026) - Antarctic Kunlun Station @ (77.1161, -80.4169) - Base antártica Concordia @ (123.3333, -75.1000) - Base antarctique Concordia @ (123.5580, -75.1000) - same - Base antarctique Dumont-d'Urville @ (140.0013, -66.6628) - Mario-Zucchelli-Station @ (164.1125, -74.6943) - Base McMurdo @ (166.6667, -77.8500) - Base antarctique McMurdo @ (166.6667, -77.8500) - same But wait - I notice I'm missing "Esperanza Base, Antarctica" (row 568, lat -63.398). Let me check if that one was caught - it's at the same coordinates as Base Esperanza. Let me also check for the Esperanza Base that was at -63.398. Actually, looking at my filter: `is_station = any(kw in p_lower for kw in ['station', 'base '])` - "Base " with a trailing space. "Esperanza Base, Antarctica" - the word "Base" is not followed by a space in that string... wait, "Esperanza Base" - "Base" appears at the end of a word. The check is `'base ' in p_lower` which would be "base " with a trailing space. For "Esperanza Base, Antarctica", p_lower would be "esperanza base, antarctica" - and `'base ' in "esperanza base, antarctica"` would be False because "base " doesn't match "base," (it's followed by a comma). So I need to fix the filter. Let me use `'base' in p_lower.split()` instead, or use a regex. Let me also check if I'm missing other stations. Let me look at what other Antarctic stations exist in the data but weren't caught.
26:55
Bash
python3 -c "
python3 -c "
import overturemaps
places = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60))
# Check for any place with 'base' or 'station' not caught by my filter
for idx, row in places.iterrows():
n = row.get('names')
if not n or not isinstance(n, dict):
continue
primary = n.get('primary', '')
if not isinstance(primary, str):
continue
p_lower = primary.lower()
geom = row.geometry
if geom is None or geom.geom_type != 'Point' or geom.y > -60:
continue
# Check if it's a research station we might miss
words = p_lower.split()
has_base = 'base' in words
has_station = 'station' in words
if (has_base or has_station) and 'esperanza' in p_lower:
print(f'ESPERANZA: {primary} | coords: ({geom.x:.4f}, {geom.y:.4f}) | words: {words}')
if (has_base or has_station) and not any(kw in p_lower for kw in ['gas station', 'police station', 'coffe', 'fuel', 'souvenir', 'micro']):
if 'base' in p_lower:
print(f'BASE: {primary} | coords: ({geom.x:.4f}, {geom.y:.4f})')
" 2>&1
BASE: González Videla Antarctic Base | coords: (-62.8575, -64.8234) BASE: Carlini Base | coords: (-58.6668, -62.2379) BASE: Base Marambio, Antartida Argentina | coords: (-56.6198, -64.2413) BASE: Marambio Base | coords: (-56.6268, -64.2410) BASE: Base Antártica Marambio | coords: (-56.6235, -64.2374) ESPERANZA: Base Esperanza, Antartida Argentina | coords: (-56.9974, -63.3980) | words: ['base', 'esperanza,', 'antartida', 'argentina'] BASE: Base Esperanza, Antartida Argentina | coords: (-56.9974, -63.3980) BASE: Base antarctique Mawson | coords: (62.8736, -67.6026) BASE: Base antártica Concordia | coords: (123.3333, -75.1000) BASE: Base antarctique Concordia | coords: (123.5580, -75.1000) BASE: Base antarctique Dumont-d'Urville | coords: (140.0013, -66.6628) BASE: Base McMurdo | coords: (166.6667, -77.8500) BASE: Base antarctique McMurdo | coords: (166.6667, -77.8500)
27:32
I notice "Base " with a space at the end is catching "Base " as a word. Let me check: - "Base Marambio, Antartida Argentina" → has "Base" as a word, then space, so "base " matches - "Marambio Base" → has "base" as a word... wait, "Base" is in the split words as "Base" (capitalized) Let me look more carefully. The filter `any(kw in p_lower for kw in ['station', 'base '])`: - "base " (with space) would match "base " anywhere in the string. So "base " matches "base marambio" because "base " appears in "base marambio, antartida argentina". That's correct. - But "marambio base" → "marambio base" does NOT contain "base " because "base" is at the end without a space after. So that would be missed! Wait, looking at the output again: ``` BASE: Marambio Base | coords: (-56.6268, -64.2410) ``` But my filter wouldn't catch "Marambio Base" because "base " (with trailing space) wouldn't match. Let me check... Actually it wouldn't, but "Marambio Base" was caught by the 'Base' check in that debug script, not by my actual filter. Let me fix the filter to also catch "base" as a complete word (end of string or followed by non-alphanumeric). But wait - actually I also need to check for "González Videla Antarctic Base" and "Carlini Base". Let me check: "González Videla Antarctic Base" has "Base" at the end. My filter `'base '` wouldn't catch that. Also "Carlini Base" wouldn't be caught. Let me fix the station detection. Also, let me check "Esperanza Base, Antarctica" - I saw this at index 568 in the earlier output. It was caught.
27:32
Bash
python3 -c "
python3 -c "
import overturemaps
places = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60))
# Find 'Esperanza Base, Antarctica'
for idx, row in places.iterrows():
n = row.get('names')
if n and isinstance(n, dict):
primary = n.get('primary', '')
if 'esperanza' in primary.lower() or 'gonzalez' in primary.lower() or 'carlini' in primary.lower():
print(f'{idx}: {primary} | ({row.geometry.x:.4f}, {row.geometry.y:.4f})')
# Check what I'm missing - 'base' at end of word
for idx, row in places.iterrows():
n = row.get('names')
if not n or not isinstance(n, dict):
continue
primary = n.get('primary', '')
if not isinstance(primary, str):
continue
p_lower = primary.lower()
geom = row.geometry
if geom is None or geom.geom_type != 'Point' or geom.y > -60:
continue
# Split into words and check
words = p_lower.split()
for w in words:
w_clean = w.strip(',.')
if w_clean in ('base', 'station'):
if 'gonzalez' in p_lower:
print(f'GONZALEZ: {primary} | {row.geometry.x:.4f}, {row.geometry.y:.4f}')
if 'carlini' in p_lower:
print(f'CARLINI: {primary} | {row.geometry.x:.4f}, {row.geometry.y:.4f}')
if 'neumayer' in p_lower:
print(f'NEUMAYER: {primary} | {row.geometry.x:.4f}, {row.geometry.y:.4f}')
break
" 2>&1
504: Carlini Base | (-58.6668, -62.2379) 568: Esperanza Base, Antarctica | (-56.9961, -63.3977) 569: Base Esperanza, Antartida Argentina | (-56.9974, -63.3980) CARLINI: Carlini Base | -58.6668, -62.2379
28:14
Let me check what's happening with my filter. The issue is with the string check `'base '` (with a space after it). Let me trace through: - "Carlini Base" - does not have ' base ' (has a space at the end but not at the beginning). Wait, my filter checks `any(kw in p_lower for kw in ['station', 'base '])`. So 'base ' would match 'Carlini Base' since 'base ' is not in 'carlini base'. Hmm, let me check: 'base ' in 'carlini base'? 'carlini base' has 'base' but not 'base ' (since 'base' is at the end). So the space after 'base' won't match. Similarly, "González Videla Antarctic Base" - 'base ' in 'gonzález videla antarctic base'? No, because 'base' is at the end with no trailing space. So my filter misses stations where "Base" is at the end of the name. Let me fix this. Actually wait, looking at the output more carefully: - I see "Carlini Base" was found (index 504 from earlier output) but it seems it wasn't captured by my filter. Let me check: "Carlini Base" → p_lower = "carlini base". Does 'base ' in 'carlini base'? 'carlini base' ends with 'base', so looking for 'base ' (with trailing space) won't match. So Carlini Base is not captured! Similarly "González Videla Antarctic Base" - the word 'base' is at the end, not followed by space. And "Esperanza Base, Antarctica" - wait, does 'base ' match in 'esperanza base, antarctica'? 'base ' is in 'base,'? No, because 'base ' has space but 'base,' has a comma. So no match. I need to fix my filter to also catch 'base' followed by end-of-string, comma, period, etc. Let me update the filter logic.
28:14
Bash
python3 -c "
python3 -c "
import overturemaps
places = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60))
# Find ALL stations/bases south of -60 that are Antarctic research stations
# Let's examine each one
candidates = []
for idx, row in places.iterrows():
n = row.get('names')
if not n or not isinstance(n, dict):
continue
primary = n.get('primary', '')
if not isinstance(primary, str):
continue
p_lower = primary.lower()
geom = row.geometry
if geom is None or geom.geom_type != 'Point' or geom.y > -60:
continue
words = p_lower.replace(',', ' ').replace('.', ' ').split()
if 'base' in words or 'station' in words:
if not any(kw in p_lower for kw in ['gas station', 'police station', 'coffe', 'fuel', 'souvenir']):
candidates.append((idx, primary, geom.x, geom.y))
print(f'Found {len(candidates)} candidates:')
for idx, name, x, y in candidates:
print(f' {idx}: {name} @ ({x:.4f}, {y:.4f})')
" 2>&1 | head -30
Found 19 candidates: 460: Brown Station, Antarctic Peninsula @ (-62.8724, -64.8921) 461: Brown Station @ (-62.8700, -64.8950) 462: González Videla Antarctic Base @ (-62.8575, -64.8234) 473: Palmer Station, Antarctica @ (-64.0531, -64.7743) 504: Carlini Base @ (-58.6668, -62.2379) 511: Bellingshausen Russian Antarctic Station @ (-58.9624, -62.1998) 563: Base Marambio, Antartida Argentina @ (-56.6198, -64.2413) 564: Marambio Base @ (-56.6268, -64.2410) 565: Base Antártica Marambio @ (-56.6235, -64.2374) 568: Esperanza Base, Antarctica @ (-56.9961, -63.3977) 569: Base Esperanza, Antartida Argentina @ (-56.9974, -63.3980) 1418: Plateau Station @ (40.5000, -79.2500) 1825: Base antarctique Mawson @ (62.8736, -67.6026) 1904: Antarctic Kunlun Station @ (77.1161, -80.4169) 2134: Base antártica Concordia @ (123.3333, -75.1000) 2135: Base antarctique Concordia @ (123.5580, -75.1000) 2183: Base antarctique Dumont-d'Urville @ (140.0013, -66.6628) 2308: Base McMurdo @ (166.6667, -77.8500) 2310: Base antarctique McMurdo @ (166.6667, -77.8500)
28:56
Let me check what else we're missing - also 'Neumayer-Station II' and 'Mario-Zucchelli-Station' and 'Esperanza Base, Antarctica' (which is 'base' not preceded by space). Let me also look for the missing ones.
28:56
Bash
python3 -c "
python3 -c "
import overturemaps
places = overturemaps.geodataframe('place', bbox=(-180, -90, 180, -60))
# Find Neumayer, Mario-Zucchelli, and others with 'base' as part of a word
for idx, row in places.iterrows():
n = row.get('names')
if not n or not isinstance(n, dict):
continue
primary = n.get('primary', '')
if not isinstance(primary, str):
continue
p_lower = primary.lower()
geom = row.geometry
if geom is None or geom.geom_type != 'Point' or geom.y > -60:
continue
# Check for 'station' anywhere (not just word boundary)
if 'station' in p_lower or 'base' in p_lower:
if not any(kw in p_lower for kw in ['gas station', 'police station', 'coffe', 'fuel', 'souvenir']):
# Check if I already have it
print(f' {idx}: {primary} @ ({geom.x:.4f}, {geom.y:.4f})')
" 2>&1
460: Brown Station, Antarctic Peninsula @ (-62.8724, -64.8921) 461: Brown Station @ (-62.8700, -64.8950) 462: González Videla Antarctic Base @ (-62.8575, -64.8234) 473: Palmer Station, Antarctica @ (-64.0531, -64.7743) 504: Carlini Base @ (-58.6668, -62.2379) 511: Bellingshausen Russian Antarctic Station @ (-58.9624, -62.1998) 563: Base Marambio, Antartida Argentina @ (-56.6198, -64.2413) 564: Marambio Base @ (-56.6268, -64.2410) 565: Base Antártica Marambio @ (-56.6235, -64.2374) 568: Esperanza Base, Antarctica @ (-56.9961, -63.3977) 569: Base Esperanza, Antartida Argentina @ (-56.9974, -63.3980) 1010: Neumayer-Station II @ (-8.2500, -70.6500) 1418: Plateau Station @ (40.5000, -79.2500) 1825: Base antarctique Mawson @ (62.8736, -67.6026) 1904: Antarctic Kunlun Station @ (77.1161, -80.4169) 2134: Base antártica Concordia @ (123.3333, -75.1000) 2135: Base antarctique Concordia @ (123.5580, -75.1000) 2183: Base antarctique Dumont-d'Urville @ (140.0013, -66.6628) 2298: Mario-Zucchelli-Station @ (164.1125, -74.6943) 2308: Base McMurdo @ (166.6667, -77.8500) 2310: Base antarctique McMurdo @ (166.6667, -77.8500)
29:34
I see - some stations got missed because: 1. "González Videla Antarctic Base" - has "Base" at the end of a word but "base " (with space) didn't match 2. "Carlini Base" - same issue 3. "Marambio Base" - same 4. "Esperanza Base, Antarctica" - has "Base" at word end 5. "Neumayer-Station II" - has "Station" but with hyphen, not "station " 6. "Mario-Zucchelli-Station" - same Let me fix the filter to use `in` instead of word matching.
29:34
29:34
Edit
/work/solve.py
Edited /work/solve.py.
29:40
Now we should also catch "Neumayer-Station II" and "Mario-Zucchelli-Station" which have "Station" as part of a hyphenated word but contain "station". Let me check if "station" in p_lower catches "neumayer-station" - yes it does, "station" is a substring of "neumayer-station ii". So this should work now. And "base" in p_lower catches "González Videla Antarctic Base" and "Carlini Base". Also I need to check if the deduplication works - "Esperanza Base, Antarctica" and "Base Esperanza, Antartida Argentina" are at different coordinates (-56.9961 vs -56.9974) but should still be close enough to be grouped as the same station. With 0.005 degree buffer (~500m), they should be grouped. Let me re-run.
29:40
Bash
cd /work && timeout 900 python3 solve.py 2>&1
============================================================
Step 1: Fetching data from Overture
============================================================
Fetching places (the 'place' theme)...
Places fetched: 2348
Place categories (sample rows): ["{'primary': 'information_technology_company', 'alternate': array(['professional_services'], dtype=object)}", "{'primary': 'physical_therapy', 'alternate': array(['occupational_therapy', 'speech_therapist'], dtype=object)}", "{'primary': 'health_and_medical', 'alternate': array(['pharmacy'], dtype=object)}", "{'primary': 'school', 'alternate': None}", 'None']
Extracting Antarctic research stations...
Found 21 Antarctic research stations
Brown Station, Antarctic Peninsula @ (-62.8724, -64.8921)
Brown Station @ (-62.8700, -64.8950)
González Videla Antarctic Base @ (-62.8575, -64.8234)
Palmer Station, Antarctica @ (-64.0531, -64.7743)
Carlini Base @ (-58.6668, -62.2379)
Bellingshausen Russian Antarctic Station @ (-58.9624, -62.1998)
Base Marambio, Antartida Argentina @ (-56.6198, -64.2413)
Marambio Base @ (-56.6268, -64.2410)
Base Antártica Marambio @ (-56.6235, -64.2374)
Esperanza Base, Antarctica @ (-56.9961, -63.3977)
Base Esperanza, Antartida Argentina @ (-56.9974, -63.3980)
Neumayer-Station II @ (-8.2500, -70.6500)
Plateau Station @ (40.5000, -79.2500)
Base antarctique Mawson @ (62.8736, -67.6026)
Antarctic Kunlun Station @ (77.1161, -80.4169)
Base antártica Concordia @ (123.3333, -75.1000)
Base antarctique Concordia @ (123.5580, -75.1000)
Base antarctique Dumont-d'Urville @ (140.0013, -66.6628)
Mario-Zucchelli-Station @ (164.1125, -74.6943)
Base McMurdo @ (166.6667, -77.8500)
Base antarctique McMurdo @ (166.6667, -77.8500)
Deduplicating nearby stations...
Grouped into 16 unique stations
Deduplicated to 16 unique stations
Brown Station, Antarctic Peninsula @ (-62.8724, -64.8921)
González Videla Antarctic Base @ (-62.8575, -64.8234)
Palmer Station, Antarctica @ (-64.0531, -64.7743)
Carlini Base @ (-58.6668, -62.2379)
Bellingshausen Russian Antarctic Station @ (-58.9624, -62.1998)
Base Marambio, Antartida Argentina @ (-56.6198, -64.2413)
Esperanza Base, Antarctica @ (-56.9961, -63.3977)
Neumayer-Station II @ (-8.2500, -70.6500)
Plateau Station @ (40.5000, -79.2500)
Base antarctique Mawson @ (62.8736, -67.6026)
Antarctic Kunlun Station @ (77.1161, -80.4169)
Base antártica Concordia @ (123.3333, -75.1000)
Base antarctique Concordia @ (123.5580, -75.1000)
Base antarctique Dumont-d'Urville @ (140.0013, -66.6628)
Mario-Zucchelli-Station @ (164.1125, -74.6943)
Base McMurdo @ (166.6667, -77.8500)
Fetching land data (the 'land' theme)...
Land features: 93367
Fetching water data (the 'water' theme)...
Water features: 13501
Fetching bathymetry data (the 'bathymetry' theme)...
Bathymetry features: 3096
============================================================
Step 2: Building Antarctic landmass
============================================================
Land class='land' features: 14707
Other land features (islands, etc.): 78660
Antarctic landmass (union) area (approx deg^2): 6661.6
============================================================
Step 3: Reprojecting to EPSG:3031 (Antarctic Polar Stereographic)
============================================================
Setting CRS for water to EPSG:4326
Setting CRS for bathy to EPSG:4326
Reprojected to EPSG:3031
============================================================
Step 4: Creating 200 km buffer zones
============================================================
Created 16 buffer zones of 200.0 km radius
============================================================
Step 5: Clipping buffer zones to land -> land zones
============================================================
Created 16 land-clipped zones
============================================================
Step 6: Computing coalition groups (overlapping zones)
============================================================
Found 8 coalition(s)
Coalition 0: ['Brown Station, Antarctic Peninsula', 'González Videla Antarctic Base', 'Palmer Station, Antarctica', 'Carlini Base', 'Bellingshausen Russian Antarctic Station', 'Base Marambio, Antartida Argentina', 'Esperanza Base, Antarctica']
Coalition 1: ['Neumayer-Station II']
Coalition 2: ['Plateau Station']
Coalition 3: ['Base antarctique Mawson']
Coalition 4: ['Antarctic Kunlun Station']
Coalition 5: ['Base antártica Concordia', 'Base antarctique Concordia']
Coalition 6: ["Base antarctique Dumont-d'Urville"]
Coalition 7: ['Mario-Zucchelli-Station', 'Base McMurdo']
Coalition column has 8 distinct values
============================================================
Step 7: Writing land-clipped coalition zones
============================================================
Written 16 features to station_spheres.geoparquet
Columns: ['station_id', 'station_name', 'coalition', 'geometry']
CRS: EPSG:3031
============================================================
Step 8: Computing over-water portions with water attribution
============================================================
Processing 13501 water features...
Processing 3096 bathymetry features...
Total water/bathymetry features: 10551
Computing station-water overlaps...
Processed 5/16 stations...
Processed 10/16 stations...
Processed 15/16 stations...
Total water overlap features: 3591
Written 3591 features to station_water_overlap.geoparquet
Columns: ['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source', 'geometry']
============================================================
DONE! Summary:
============================================================
station_spheres.geoparquet: 16 features, 8 coalitions
station_water_overlap.geoparquet: 3591 features
Coalition 0: ['Brown Station, Antarctic Peninsula', 'González Videla Antarctic Base', 'Palmer Station, Antarctica', 'Carlini Base', 'Bellingshausen Russian Antarctic Station', 'Base Marambio, Antartida Argentina', 'Esperanza Base, Antarctica']
Coalition 1: ['Neumayer-Station II']
Coalition 2: ['Plateau Station']
Coalition 3: ['Base antarctique Mawson']
Coalition 4: ['Antarctic Kunlun Station']
Coalition 5: ['Base antártica Concordia', 'Base antarctique Concordia']
Coalition 6: ["Base antarctique Dumont-d'Urville"]
Coalition 7: ['Mario-Zucchelli-Station', 'Base McMurdo']
33:47
I see an issue: Concordia has two names ("Base antártica Concordia" and "Base antarctique Concordia") that are at slightly different locations (123.3333 vs 123.5580) so they didn't get deduplicated. They're both the same station (Concordia Station). Let me check the distance between them.
Also, "Base McMurdo" and "Base antarctique McMurdo" - the dedup correctly kept one (Base McMurdo) since they're at the same location.
But Concordia has two nearby entries that are about 0.225 degrees apart (~a few km). Since the buffer is 0.005 degrees (~500m), they didn't merge. Let me increase the merge radius.
Actually, looking more carefully:
- Base antártica Concordia @ 123.3333, -75.1000
- Base antarctique Concordia @ 123.5580, -75.1000
That's about 0.225 degrees difference = about 8-10 km at -75 latitude. These are actually the same station with slightly different coordinates in Overture. Let me increase the dedup buffer.
Also, I should double-check: the task says "Each coalition gets an integer coalition id, and that column has to take more than one distinct value across the dataset." - 8 distinct values, that's fine.
Let me fix the dedup radius.
33:47
Edit
/work/solve.py
Edited /work/solve.py.
34:00
Let me check the deduplication more carefully now. The issue is that "Base antártica Concordia" and "Base antarctique Concordia" are the same station but they're ~25km apart (123.33 vs 123.56 longitude), so a 5km buffer wouldn't catch them. Let me think about this... Actually for the Antarctic Peninsula area, there are many stations close together (Brown, González Videla, Palmer, Carlini, Bellingshausen, Marambio, Esperanza) - these are genuinely separate stations that happen to be near each other. Their 200km buffers overlap so they should be in the same coalition. For Concordia, the two entries are at (123.3333, -75.1000) and (123.5580, -75.1000) - that's about 0.225 degrees apart. At latitude -75, 1 degree of longitude = 111320 * cos(75°) ≈ 28800m. So 0.225° ≈ 6.5km. These are probably the same station referenced by two names. Let me increase the dedup buffer to ~10km (0.1 degrees) for the Concordia area. Actually, looking more carefully at the output: - "Brown Station, Antarctic Peninsula" and "Brown Station" are the same station (different language names) - they're ~250m apart - "Base Marambio, Antartida Argentina" and "Marambio Base" and "Base Antártica Marambio" are same - within ~500m - "Esperanza Base, Antarctica" and "Base Esperanza, Antartida Argentina" are same - within ~100m - "Base antártica Concordia" and "Base antarctique Concordia" are same station - ~6.5km apart - "Base McMurdo" and "Base antarctique McMurdo" are same - 0 distance The current result shows: - Coalition 0 merged correctly (all the Antarctic Peninsula stations) - Concordia is still two separate entries in Coalition 5 (they should be deduped into one station) - McMurdo should have been deduped but there's only one now... wait, actually looking at the dedup output: "Deduplicated to 16 unique stations" and "Base McMurdo" is one entry. But "Base antarctique McMurdo" was in the original 21 and seems to have been merged. Good. For Concordia: "Base antártica Concordia" (123.3333, -75.1000) and "Base antarctique Concordia" (123.5580, -75.1000) are ~0.225 degrees apart. At latitude -75, 1 degree lon ≈ 28800m, so ~6.5km. With my new buffer of 0.05° (~5km), they won't merge. Let me increase to 0.1° to catch them. Wait, but the coalition step handles overlapping 200km zones anyway. So even if they're separate station entries, they'd be in the same coalition. Let me check the current output again - Coalition 5 has both entries. That's actually fine for the coalition output. But for the second output (water overlap), having two entries for the same station would double-count. Let me check if the dedup caught them... The output says "Deduplicated to 16 unique stations" and the list shows: - Base antártica Concordia (123.3333, -75.1000) - Base antarctique Concordia (123.5580, -75.1000) So they're both there. That's because the buffer was too small (0.005° ~ 500m, later changed to 0.05° ~ 5km). Let me increase to 0.1° (about 10km at these latitudes) which should catch the Concordia pair. Actually, let me increase the buffer to 0.1 degrees.
34:00
34:00
Edit
/work/solve.py
Edited /work/solve.py.
34:09
Hmm wait, I need to think about this more carefully. The Concordia pair (Base antártica Concordia and Base antarctique Concordia) are about 6.5 km apart (0.225 degrees difference in longitude). They might actually be the same station with two different name entries. Let me check. Actually, looking more carefully: - Base antártica Concordia @ (123.3333, -75.1000) - Base antarctique Concordia @ (123.5580, -75.1000) That's about 0.225 degrees apart - at -75° latitude, that's about 0.225 * 111km * cos(75°) ≈ 0.225 * 111 * 0.2588 ≈ 6.5 km. They might actually be the same station with different points. But I should be cautious - if they're actually different stations at the same location (Concordia is a joint French-Italian station), they'd be one station. The issue is: "Base antártica Concordia" and "Base antarctique Concordia" - "antártica" is Spanish/Portuguese and "antarctique" is French. It seems like the same station (Concordia Station) just with different language names. So they should be deduplicated. With a 0.1 degree buffer (~11 km), they'd be merged. Good. Let me also check the McMurdo and Mario-Zucchelli situation. They're far apart (164°E vs 166°E at -75°S - that's about 2 degrees * 111km * cos(75°) ≈ 57 km). They shouldn't merge... unless the buffering causes them to overlap through other stations' buffers. Let me think... Actually wait, looking at the coalition output: - Coalition 7: ['Mario-Zucchelli-Station', 'Base McMurdo'] These are separate stations that are about 160 km apart (at -74.7°S and -77.85°S). Their 200 km buffers would definitely overlap. That's correct - they form a coalition because their operational zones overlap. That's expected behavior for the coalition grouping. OK, so the dedup with 0.1 degree buffer should be fine. Let me finalize.
34:09
34:09
Bash
python3 -c "
python3 -c "
from math import cos, radians
# Distance between the two Concordia entries
dlon = 123.5580 - 123.3333 # 0.2247 deg
lat = -75.1
# At latitude -75, 1 deg lon = 111.32 * cos(lat) km
km_per_deg = 111.32 * cos(radians(-75.1))
dist_km = dlon * km_per_deg
print(f'Concordia distance: {dist_km:.1f} km')
print(f'0.1 deg at this latitude = {0.1 * km_per_deg:.1f} km')
# McMurdo vs Mario-Zucchelli
dlon2 = 166.6667 - 164.1125
lat2 = -74.6943
km_per_deg2 = 111.32 * cos(radians(lat2))
dist_km2 = dlon2 * km_per_deg2
print(f'McMurdo-Mario distance: {dist_km2:.1f} km')
"
Concordia distance: 6.4 km 0.1 deg at this latitude = 2.9 km McMurdo-Mario distance: 75.1 km
34:17
So the Concordia entries are 6.4 km apart and McMurdo-Mario is 75 km apart. The Concordia entries are: - Base antártica Concordia @ (123.3333, -75.1000) - Base antarctique Concordia @ (123.5580, -75.1000) These are the SAME station (Concordia Station) with different language names - they should be deduped. McMurdo (166.6667, -77.8500) and Mario-Zucchelli-Station (164.1125, -74.6943) are genuinely different stations 75 km apart. They should NOT be deduped. The previous buffer of 0.1 deg = 2.9 km at this latitude would NOT catch the Concordia pair (6.4 km apart). Let me use 0.25 deg buffer instead (~7 km), which would catch Concordia but not McMurdo-Mario. Wait, but actually McMurdo and Base McMurdo are the same station. Let me look more carefully: - Base McMurdo @ (166.6667, -77.8500) - Base antarctique McMurdo @ (166.6667, -77.8500) - same coords, already deduped And Mario-Zucchelli-Station @ (164.1125, -74.6943) is a different station 75 km away. So the Concordia entries should be deduped (6.4 km apart) while Mario-Zucchelli and McMurdo should not be. Let me use a buffer of 0.3 deg (~8.6 km at this latitude) to catch the Concordia pair.
34:17
Edit
/work/solve.py
Edited /work/solve.py.
34:25
Now let me think about this more carefully. The Concordia stations are about 6.4 km apart - they seem to be the same station (Concordia Station) with two different names (Spanish "Base antártica Concordia" and French "Base antarctique Concordia"). Let me check if they're actually the same station. Similarly, McMurdo and Mario-Zucchelli are ~75 km apart - those are definitely different stations, so they should NOT be merged. Let me set the dedup threshold to something like 20 km (0.7 degrees at these latitudes) to catch Concordia while not merging McMurdo and Mario. Wait, the Concordia distance is 6.4 km. At latitude -75, 0.3 deg of lon = 0.3 * 111.32 * cos(75°) = 0.3 * 28.8 = 8.6 km. That should work for Concordia. But the McMurdo (77.85°S) and Mario-Zucchelli (74.69°S) are at different latitudes too. Let me check the actual distance... McMurdo: (166.6667, -77.85) Mario: (164.1125, -74.6943) Using GIS: these are about 370 km apart, so they won't be grouped. Good. Let me just re-run.
34:25
34:25
Bash
python3 -c "
python3 -c "
from pyproj import Geod
g = Geod(ellps='WGS84')
# Concordia pair
az1, az2, dist1 = g.inv(123.3333, -75.1, 123.5580, -75.1)
print(f'Concordia pair distance: {dist1:.0f} m')
# Brown pair
az1, az2, dist2 = g.inv(-62.8724, -64.8921, -62.8700, -64.8950)
print(f'Brown pair distance: {dist2:.0f} m')
# Marambio pair (Marambio Base vs Base Antártica Marambio)
az1, az2, dist3 = g.inv(-56.6268, -64.2410, -56.6235, -64.2374)
print(f'Marambio pair distance: {dist3:.0f} m')
# McMurdo vs Mario
az1, az2, dist4 = g.inv(166.6667, -77.85, 164.1125, -74.6943)
print(f'McMurdo-Mario distance: {dist4:.0f} m')
# 0.3 degrees at different latitudes in meters
from math import cos, radians
for lat in [-64, -70, -75, -80]:
m_per_deg = 111320 * cos(radians(lat))
print(f' 0.3 deg at {lat:.0f}° = {0.3*m_per_deg:.0f} m')
"
Concordia pair distance: 6452 m Brown pair distance: 343 m Marambio pair distance: 432 m McMurdo-Mario distance: 358632 m 0.3 deg at -64° = 14640 m 0.3 deg at -70° = 11422 m 0.3 deg at -75° = 8644 m 0.3 deg at -80° = 5799 m
34:34
So 0.3 degrees at -75° is about 8.6 km, which should catch the Concordia pair (6.4 km apart). And it won't catch McMurdo-Mario (358 km apart). Good. But wait - the issue is that Mario-Zucchelli-Station and Base McMurdo are separate stations that happen to be in the same area (McMurdo is at -77.85, Mario-Zucchelli is at -74.69). They're 358 km apart, so they shouldn't be grouped together. Good. Let me re-run to confirm the dedup catches Concordia.
34:34
Bash
cd /work && timeout 900 python3 solve.py 2>&1 | head -50
============================================================
Step 1: Fetching data from Overture
============================================================
Fetching places (the 'place' theme)...
Places fetched: 2348
Place categories (sample rows): ["{'primary': 'information_technology_company', 'alternate': array(['professional_services'], dtype=object)}", "{'primary': 'physical_therapy', 'alternate': array(['occupational_therapy', 'speech_therapist'], dtype=object)}", "{'primary': 'health_and_medical', 'alternate': array(['pharmacy'], dtype=object)}", "{'primary': 'school', 'alternate': None}", 'None']
Extracting Antarctic research stations...
Found 21 Antarctic research stations
Brown Station, Antarctic Peninsula @ (-62.8724, -64.8921)
Brown Station @ (-62.8700, -64.8950)
González Videla Antarctic Base @ (-62.8575, -64.8234)
Palmer Station, Antarctica @ (-64.0531, -64.7743)
Carlini Base @ (-58.6668, -62.2379)
Bellingshausen Russian Antarctic Station @ (-58.9624, -62.1998)
Base Marambio, Antartida Argentina @ (-56.6198, -64.2413)
Marambio Base @ (-56.6268, -64.2410)
Base Antártica Marambio @ (-56.6235, -64.2374)
Esperanza Base, Antarctica @ (-56.9961, -63.3977)
Base Esperanza, Antartida Argentina @ (-56.9974, -63.3980)
Neumayer-Station II @ (-8.2500, -70.6500)
Plateau Station @ (40.5000, -79.2500)
Base antarctique Mawson @ (62.8736, -67.6026)
Antarctic Kunlun Station @ (77.1161, -80.4169)
Base antártica Concordia @ (123.3333, -75.1000)
Base antarctique Concordia @ (123.5580, -75.1000)
Base antarctique Dumont-d'Urville @ (140.0013, -66.6628)
Mario-Zucchelli-Station @ (164.1125, -74.6943)
Base McMurdo @ (166.6667, -77.8500)
Base antarctique McMurdo @ (166.6667, -77.8500)
Deduplicating nearby stations...
Grouped into 13 unique stations
Deduplicated to 13 unique stations
Brown Station, Antarctic Peninsula @ (-62.8724, -64.8921)
Palmer Station, Antarctica @ (-64.0531, -64.7743)
Carlini Base @ (-58.6668, -62.2379)
Base Marambio, Antartida Argentina @ (-56.6198, -64.2413)
Esperanza Base, Antarctica @ (-56.9961, -63.3977)
Neumayer-Station II @ (-8.2500, -70.6500)
Plateau Station @ (40.5000, -79.2500)
Base antarctique Mawson @ (62.8736, -67.6026)
Antarctic Kunlun Station @ (77.1161, -80.4169)
Base antártica Concordia @ (123.3333, -75.1000)
Base antarctique Dumont-d'Urville @ (140.0013, -66.6628)
Mario-Zucchelli-Station @ (164.1125, -74.6943)
Base McMurdo @ (166.6667, -77.8500)
Fetching land data (the 'land' theme)...
36:26
Wait - the dedup grouped McMurdo with Base antarctique McMurdo (same location), but Mario-Zucchelli is 359 km away from McMurdo, so those are separate. But they were in the same coalition before... Let me check the coalition groups now that we have 13 stations. Actually, looking back at the results - the first run (17 stations) had: - Coalition 7: ['Mario-Zucchelli-Station', 'Base McMurdo', 'Base antarctique McMurdo'] Now that Base McMurdo and Base antarctique McMurdo were merged (they're the same location), let's see what happens. The 200km buffer from McMurdo might still overlap with Mario-Zucchelli's buffer depending on distance. McMurdo-Mario distance = 358 km, and buffers are 200 km each. So they don't overlap. Good, they should be separate coalitions now. But wait - the previous run had McMurdo and Mario-Zucchelli in the same coalition (7). That was because of the duplicate McMurdo entries having overlapping buffers (they're at the same point). Now with dedup, they should be separate. Let me wait for the full run to complete.
36:26
Bash
cd /work && timeout 900 python3 solve.py 2>&1
============================================================
Step 1: Fetching data from Overture
============================================================
Fetching places (the 'place' theme)...
Places fetched: 2348
Place categories (sample rows): ["{'primary': 'information_technology_company', 'alternate': array(['professional_services'], dtype=object)}", "{'primary': 'physical_therapy', 'alternate': array(['occupational_therapy', 'speech_therapist'], dtype=object)}", "{'primary': 'health_and_medical', 'alternate': array(['pharmacy'], dtype=object)}", "{'primary': 'school', 'alternate': None}", 'None']
Extracting Antarctic research stations...
Found 21 Antarctic research stations
Brown Station, Antarctic Peninsula @ (-62.8724, -64.8921)
Brown Station @ (-62.8700, -64.8950)
González Videla Antarctic Base @ (-62.8575, -64.8234)
Palmer Station, Antarctica @ (-64.0531, -64.7743)
Carlini Base @ (-58.6668, -62.2379)
Bellingshausen Russian Antarctic Station @ (-58.9624, -62.1998)
Base Marambio, Antartida Argentina @ (-56.6198, -64.2413)
Marambio Base @ (-56.6268, -64.2410)
Base Antártica Marambio @ (-56.6235, -64.2374)
Esperanza Base, Antarctica @ (-56.9961, -63.3977)
Base Esperanza, Antartida Argentina @ (-56.9974, -63.3980)
Neumayer-Station II @ (-8.2500, -70.6500)
Plateau Station @ (40.5000, -79.2500)
Base antarctique Mawson @ (62.8736, -67.6026)
Antarctic Kunlun Station @ (77.1161, -80.4169)
Base antártica Concordia @ (123.3333, -75.1000)
Base antarctique Concordia @ (123.5580, -75.1000)
Base antarctique Dumont-d'Urville @ (140.0013, -66.6628)
Mario-Zucchelli-Station @ (164.1125, -74.6943)
Base McMurdo @ (166.6667, -77.8500)
Base antarctique McMurdo @ (166.6667, -77.8500)
Deduplicating nearby stations...
Grouped into 13 unique stations
Deduplicated to 13 unique stations
Brown Station, Antarctic Peninsula @ (-62.8724, -64.8921)
Palmer Station, Antarctica @ (-64.0531, -64.7743)
Carlini Base @ (-58.6668, -62.2379)
Base Marambio, Antartida Argentina @ (-56.6198, -64.2413)
Esperanza Base, Antarctica @ (-56.9961, -63.3977)
Neumayer-Station II @ (-8.2500, -70.6500)
Plateau Station @ (40.5000, -79.2500)
Base antarctique Mawson @ (62.8736, -67.6026)
Antarctic Kunlun Station @ (77.1161, -80.4169)
Base antártica Concordia @ (123.3333, -75.1000)
Base antarctique Dumont-d'Urville @ (140.0013, -66.6628)
Mario-Zucchelli-Station @ (164.1125, -74.6943)
Base McMurdo @ (166.6667, -77.8500)
Fetching land data (the 'land' theme)...
Land features: 93367
Fetching water data (the 'water' theme)...
Water features: 13501
Fetching bathymetry data (the 'bathymetry' theme)...
Bathymetry features: 3096
============================================================
Step 2: Building Antarctic landmass
============================================================
Land class='land' features: 14707
Other land features (islands, etc.): 78660
Antarctic landmass (union) area (approx deg^2): 6661.6
============================================================
Step 3: Reprojecting to EPSG:3031 (Antarctic Polar Stereographic)
============================================================
Setting CRS for water to EPSG:4326
Setting CRS for bathy to EPSG:4326
Reprojected to EPSG:3031
============================================================
Step 4: Creating 200 km buffer zones
============================================================
Created 13 buffer zones of 200.0 km radius
============================================================
Step 5: Clipping buffer zones to land -> land zones
============================================================
Created 13 land-clipped zones
============================================================
Step 6: Computing coalition groups (overlapping zones)
============================================================
Found 8 coalition(s)
Coalition 0: ['Brown Station, Antarctic Peninsula', 'Palmer Station, Antarctica', 'Carlini Base', 'Base Marambio, Antartida Argentina', 'Esperanza Base, Antarctica']
Coalition 1: ['Neumayer-Station II']
Coalition 2: ['Plateau Station']
Coalition 3: ['Base antarctique Mawson']
Coalition 4: ['Antarctic Kunlun Station']
Coalition 5: ['Base antártica Concordia']
Coalition 6: ["Base antarctique Dumont-d'Urville"]
Coalition 7: ['Mario-Zucchelli-Station', 'Base McMurdo']
Coalition column has 8 distinct values
============================================================
Step 7: Writing land-clipped coalition zones
============================================================
Written 13 features to station_spheres.geoparquet
Columns: ['station_id', 'station_name', 'coalition', 'geometry']
CRS: EPSG:3031
============================================================
Step 8: Computing over-water portions with water attribution
============================================================
Processing 13501 water features...
Processing 3096 bathymetry features...
Total water/bathymetry features: 10551
Computing station-water overlaps...
Processed 5/13 stations...
Processed 10/13 stations...
Total water overlap features: 2917
Written 2917 features to station_water_overlap.geoparquet
Columns: ['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source', 'geometry']
============================================================
DONE! Summary:
============================================================
station_spheres.geoparquet: 13 features, 8 coalitions
station_water_overlap.geoparquet: 2917 features
Coalition 0: ['Brown Station, Antarctic Peninsula', 'Palmer Station, Antarctica', 'Carlini Base', 'Base Marambio, Antartida Argentina', 'Esperanza Base, Antarctica']
Coalition 1: ['Neumayer-Station II']
Coalition 2: ['Plateau Station']
Coalition 3: ['Base antarctique Mawson']
Coalition 4: ['Antarctic Kunlun Station']
Coalition 5: ['Base antártica Concordia']
Coalition 6: ["Base antarctique Dumont-d'Urville"]
Coalition 7: ['Mario-Zucchelli-Station', 'Base McMurdo']
41:01
I notice that the deduplication with 0.3 degrees (~8-9 km) is dropping the Bellingshausen station and González Videla. Let me check what happened. The Brown Station dedup group (0.3 deg buffer) at lat -64 means ~14.6 km radius, so it probably absorbed González Videla. And Carlini and Bellingshausen might also be absorbed... Let me check the distances. Actually, looking at the output: - Brown Station, Antarctic Peninsula (-62.8724, -64.8921) - Brown Station (-62.8700, -64.8950) - this is the same station - González Videla Antarctic Base (-62.8575, -64.8234) - Palmer Station, Antarctica (-64.0531, -64.7743) - Carlini Base (-58.6668, -62.2379) - Bellingshausen Russian Antarctic Station (-58.9624, -62.1998) With 0.3 deg buffer at -64° latitude, the radius is ~14.6 km. Let me check distances: - Brown Station (first one) to Brown Station (second one): 0.34 km - merged - Brown Station to González Videla: distance? - Brown Station to Palmer Station: distance? Let me check if Bellingshausen is being accidentally merged into something.
41:01
Bash
python3 -c "
python3 -c "
from pyproj import Geod
g = Geod(ellps='WGS84')
pairs = [
('Brown (-62.87,-64.89)', 'González Videla (-62.86,-64.82)'),
('Brown (-62.87,-64.89)', 'Palmer (-64.05,-64.77)'),
('Brown (-62.87,-64.89)', 'Carlini (-58.67,-62.24)'),
('González Videla (-62.86,-64.82)', 'Palmer (-64.05,-64.77)'),
('Carlini (-58.67,-62.24)', 'Bellingshausen (-58.96,-62.20)'),
('Marambio (-56.62,-64.24)', 'Esperanza (-57.00,-63.40)'),
]
pts = {
'B1': (-62.8724, -64.8921),
'B2': (-62.8700, -64.8950),
'GV': (-62.8575, -64.8234),
'P': (-64.0531, -64.7743),
'C': (-58.6668, -62.2379),
'Bel': (-58.9624, -62.1998),
'M': (-56.6198, -64.2413),
'E': (-56.9961, -63.3977),
}
for (n1, (x1, y1)), (n2, (x2, y2)) in [('B1', pts['B1'], 'B2', pts['B2']),
('B1', pts['B1'], 'GV', pts['GV']),
('B1', pts['B1'], 'P', pts['P']),
('GV', pts['GV'], 'P', pts['P']),
('C', pts['C'], 'Bel', pts['Bel']),
('M', pts['M'], 'E', pts['E'])]:
_, _, d = g.inv(x1, y1, x2, y2)
print(f'{n1} -> {n2}: {d/1000:.1f} km')
" 2>&1
Traceback (most recent call last):
File "<string>", line 25, in <module>
for (n1, (x1, y1)), (n2, (x2, y2)) in [('B1', pts['B1'], 'B2', pts['B2']),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: too many values to unpack (expected 2, got 4)
41:17
I lost González Videla because it was grouped with Brown (they're ~6.4 km apart, within the 0.3 degree buffer which at that latitude is ~14 km). Let me check distances more carefully. Looking at the results: - Brown (B1): -62.8724, -64.8921 - Brown (B2): -62.8700, -64.8950 (343 m from B1) - González Videla: -62.8575, -64.8234 (should be ~7.7 km from Brown) - Palmer: -64.0531, -64.7743 (~140 km from Brown) - Carlini: -58.6668, -62.2379 - Bellingshausen: -58.9624, -62.1998 (~22 km from Carlini) - Marambio: -56.6198, -64.2413 - Esperanza: -56.9961, -63.3977 (~95 km from Marambio) So González Videla got absorbed into Brown's group because they're within 0.3 degrees (~14 km at that latitude). Hmm, that's wrong because they're genuinely different stations. Let me refine the dedup approach: I should group stations that have nearly identical coordinates (within 1 km or so), not those that are within 10+ km. Actually, looking at the list: - Brown Station, Antarctic Peninsula (-62.8724, -64.8921) and Brown Station (-62.8700, -64.8950) are 343m apart - same station - González Videla Antarctic Base (-62.8575, -64.8234) is ~7.7 km from Brown - different station - Marambio Base (-56.6268, -64.2410) and Base Marambio (-56.6198, -64.2413) - 432m apart - same - Base Antártica Marambio (-56.6235, -64.2374) - 432m from Marambio Base - same - Esperanza Base (-56.9961, -63.3977) and Base Esperanza (-56.9974, -63.3980) - 130m apart - same - Base antártica Concordia (123.3333, -75.1000) and Base antarctique Concordia (123.5580, -75.1000) - 6.4 km apart - same station different languages - Base McMurdo (166.6667, -77.8500) and Base antarctique McMurdo (166.6667, -77.8500) - same location - same - Bellingshausen Russian Antarctic Station (-58.9624, -62.1998) - no duplicates So the dedup should group by ~10 km or so. But González Videla is ~7.7 km from Brown, which is a different station. Let me use a smaller buffer - 0.1 degree (~5 km at the latitude of the peninsula) which would: - Group Brown with Brown (343m) ✓ - Group Marambio variants (432m) ✓ - Group Esperanza variants (130m) ✓ - Group Concordia variants (6.4 km) - might barely miss with 0.1 deg = ~2.9 km, so I need bigger - Group McMurdo variants (same point) ✓ Actually 0.3 deg is too big. Let me use 0.2 deg which at -75° is ~5.8 km, and at -64° is ~9.8 km. That should catch Concordia (6.4 km) but not González Videla (7.7 km) from Brown... hmm, it would. Better approach: group points based on proximity using a proper distance threshold in projected coordinates. Let me use EPSG:3031 and a 1 km buffer. Or simpler: just use a smaller buffer. 0.05° at -64° is ~2.4 km, too small for Concordia (6.4 km) but won't group Brown with González. 0.1° at -75° is ~2.9 km, too small for Concordia. Actually let me think differently: I should use a proper distance-based approach in projected CRS. Let me reproject the points first to 3031, then use a 10 km buffer for grouping.
41:17
Bash
python3 -c "
python3 -c "
from pyproj import Geod
g = Geod(ellps='WGS84')
pts = {
'Brown1': (-62.8724, -64.8921),
'Brown2': (-62.8700, -64.8950),
'GVidela': (-62.8575, -64.8234),
'Palmer': (-64.0531, -64.7743),
'Carlini': (-58.6668, -62.2379),
'Bellingshausen': (-58.9624, -62.1998),
'Marambio1': (-56.6198, -64.2413),
'Marambio2': (-56.6268, -64.2410),
'Marambio3': (-56.6235, -64.2374),
'Esperanza1': (-56.9961, -63.3977),
'Esperanza2': (-56.9974, -63.3980),
'Concordia1': (123.3333, -75.1000),
'Concordia2': (123.5580, -75.1000),
'McMurdo1': (166.6667, -77.8500),
'McMurdo2': (166.6667, -77.8500),
}
pairs = [('Brown1', 'Brown2'), ('Brown1', 'GVidela'),
('Marambio1', 'Marambio2'), ('Marambio1', 'Marambio3'),
('Esperanza1', 'Esperanza2'),
('Concordia1', 'Concordia2'),
('McMurdo1', 'McMurdo2'),
('Carlini', 'Bellingshausen'),
('Marambio1', 'Esperanza1')]
for a, b in pairs:
x1, y1 = pts[a]
x2, y2 = pts[b]
_, _, d = g.inv(x1, y1, x2, y2)
print(f'{a} -> {b}: {d/1000:.2f} km')
"
Brown1 -> Brown2: 0.34 km Brown1 -> GVidela: 7.69 km Marambio1 -> Marambio2: 0.34 km Marambio1 -> Marambio3: 0.47 km Esperanza1 -> Esperanza2: 0.07 km Concordia1 -> Concordia2: 6.45 km McMurdo1 -> McMurdo2: 0.00 km Carlini -> Bellingshausen: 15.95 km Marambio1 -> Esperanza1: 95.85 km
41:30
The dedup logic merged González Videla Antarctic Base with Brown Station because they're only 7.69 km apart and I was using a 0.3-degree buffer (~8.6 km at -75°, but ~14.6 km at -64°). Let me fix this - González Videla and Brown Station are separate stations, not duplicates. The buffer was too large. Let me use a smaller buffer for dedup - maybe 0.05 degrees (~2.5 km at -64°) which would group the obvious duplicates but not merge separate stations.
41:30
41:30
Edit
/work/solve.py
Edited /work/solve.py.
41:47
Wait, the deduplication algorithm groups stations that share overlapping buffer zones. With a 0.02 degree buffer (~2 km), Brown1 and Brown2 (340m apart) would be grouped together. But Brown1 and González Videla (7.7km apart) would not be grouped. Carlini and Bellingshausen (15.95km apart) would not be grouped. Good. But wait - the grouping algorithm does transitive closure. If Brown1-Brown2 overlap (0.02 buffer, 340m apart -> yes), and Brown2 doesn't overlap with anything else... Let's think about whether Carlini might get grouped with the Marambio/Esperanza group. Carlini is at (-58.67, -62.24) and Marambio at (-56.62, -64.24). Distance is about 228 km so no overlap. Let me check if we need to be more careful. Actually, the issue was that with 0.3 deg buffer, Brown Station (buffer covers ~30km radius) overlapped with González Videla (7.7km away). With 0.02 deg buffer (~2km), they won't overlap. Let me also check - did we lose González Videla and Bellingshausen in the dedup? The dedup only drops stations that are within 0.02 deg of another. Brown1 is 0.02 deg from... let me convert: 0.02 deg at 64°S = 0.02 * 111.32 * cos(64°) = 0.02 * 48.8 = 0.976 km ~ 1km. So Brown1 at (-62.872, -64.892) and Brown2 at (-62.870, -64.895) are 0.003 deg apart (340m), well within 0.02 deg. Good - they'll be grouped. But González Videla at (-62.8575, -64.8234) is about 0.015 deg lat from Brown (-64.8921 vs -64.8234 = 0.069 deg = 7.7 km). So with 0.02 deg buffer (~2km), they won't overlap. Good, GV stays separate! Carlini (-58.6668, -62.2379) and Bellingshausen (-58.9624, -62.1998) are about 0.296 deg lon apart = 15.95 km. Way more than 2km. They stay separate. Good. So with 0.02 deg buffer: Brown1+Brown2 merge (340m), Marambio1+Marambio2+Marambio3 merge (all ~500m), Esperanza1+Esperanza2 merge (70m), Concordia1+Concordia2... wait, they're 6.45 km apart. That's way more than 1km. So they WON'T be deduplicated. That's a problem - Base antártica Concordia and Base antarctique Concordia are both the same station (Concordia Station), just the name in different languages. Let me think about this differently. Maybe I should use a name-based dedup instead, or increase the buffer to 0.1 deg (~8km at these latitudes) but be smarter about it. Actually, let me reconsider. 0.02 deg at -75°S (Concordia latitude) = 0.02 * 111.32 * cos(75°) = 0.02 * 28.8 = 0.576 km = 576m. That's tiny. Concordia stations are 6.45 km apart - that won't merge. But actually, is it correct to merge them? They are two different entries for the same physical research station. So yes they should be merged. Let me use a smarter approach: just merge by name similarity using a dictionary of known station aliases, or use a slightly larger buffer like 0.15 deg which at -75° = 0.15 * 28.8 = 4.3 km... still not enough for 6.45 km. Let me use 0.25 deg which at -75° = 0.25 * 28.8 = 7.2 km. That should catch Concordia. But at -64°, 0.25 deg = 0.25 * 48.8 = 12.2 km. That would merge Brown with González Videla (7.7km apart), which we don't want because they're different stations! So pure distance-based dedup won't work for all cases. Let me use a smarter approach. Actually, let me reconsider: Brown Station and González Videla Base are actually different stations. Brown is an Argentine station and González Videla is a Chilean station that's about 5km away. So they should NOT be merged. Concordia - both entries are the same station (Concordia Station, French-Italian). So they SHOULD be merged. The issue is that I can't use a pure distance threshold. Let me use the Overture `id` field or something else? No, the IDs will be different since they're separate Overture entries. Let me take a different approach: I'll keep all stations as distinct entries from Overture, but group duplicates that are clearly the same station by looking at name similarity + distance. For the existing known Antarctic stations, the duplicates have similar names (e.g., "Base antártica Concordia" and "Base antarctique Concordia" - same station, different languages). Actually, the simplest approach: keep all the stations but DON'T deduplicate based on distance alone. Instead, use a manual mapping or a smarter approach. Actually, looking again - maybe it's OK to keep them as separate features. Each Overture place is a distinct feature. Even if it's the same physical research station, each entry represents something in the Overture data. The user asked for "stations from Overture" - so we should use the Overture records as they are. If there are duplicates in Overture, they represent multiple entries. Hmm, but then we'd have 21 stations and some would have nearly identical buffer zones. Let me just dedupe using a reasonable buffer that catches true duplicates (< 1km apart) and keeps everything else. The Concordia entries (6.45km) and Brown/GVidela (7.7km) can stay separate since one is clearly two names for the same station and the other is two different stations. With 0.02 deg: Brown-Brown2 merge (340m), Marambio triple merge (340-470m), Esperanza double merge (70m), McMurdo double merge (0m), Concordia (6450m -> won't merge), Brown-GVidela (7700m -> won't merge), Carlini-Bellingshausen (15950m -> won't merge). That's probably fine. The Concordia entries will both be in the same coalition anyway since their 200km buffers overlap (they're only 6.45km apart). Actually, let me just use a simple approach: dedupe at ~1km threshold and be done with it.
41:47
Bash
python3 -c "
python3 -c "
from pyproj import Geod
g = Geod(ellps='WGS84')
# Check if Concordia1 and Concordia2 200km buffers would overlap
# They're 6.45 km apart, so 200km buffers definitely overlap
print('Concordia pair: 6.45 km apart, buffers definitely overlap')
# Check if Brown and González Videla buffers overlap
_, _, d = g.inv(-62.8724, -64.8921, -62.8575, -64.8234)
print(f'Brown-GVidela: {d/1000:.1f} km apart')
# 7.69 km apart, both have 200km buffers, they overlap!
# So even without merging, Concordia pair would be in same coalition
# and Brown+GVidela would be in same coalition (which they should, they're on Antarctic Peninsula)
"
Concordia pair: 6.45 km apart, buffers definitely overlap Brown-GVidela: 7.7 km apart
42:16
Let me reconsider the approach. The issue is: 1. Brown Station and González Videla are 7.7 km apart - they should NOT be deduped as the same station, but their 200km buffers SHOULD overlap (they're on the Antarctic Peninsula) 2. Concordia1 and Concordia2 are 6.45 km apart, these ARE duplicates (same station, different names) and should be deduped So the dedup buffer needs to be less than 7.7 km but more than 6.45 km. Let's use ~7 km. 0.07 degrees at -75 deg = 0.07 * 111320 * cos(75°) = 0.07 * 28800 ≈ 2016 m 0.07 degrees at -64 deg = 0.07 * 111320 * cos(64°) = 0.07 * 48800 ≈ 3416 m Hmm, 0.07 degrees isn't enough for 6.45 km at -75 deg. Let me calculate: at -75°, 1 degree longitude = 111320 * cos(75°) = 111320 * 0.2588 = 28806 meters So 6.45 km = 6.45/28.8 = 0.224 degrees So I need a larger buffer for Concordia. But Brown and GVidela are 7.7 km apart. Let me think differently. Instead of buffering in degrees (which is latitude-dependent), let me use a proper geodesic distance buffer. Actually, the simplest fix: I can check which stations are truly duplicates by using a proper distance. Let me compute all pairwise distances and group items that are within say 1 km of each other (which would catch Brown-Brown, Marambio-Marambio, Esperanza-Esperanza, McMurdo-McMurdo but NOT Brown-GVidela). Or even better: use a proper geodesic distance.
42:16
Read
/work/solve.py
64 })
65
66 stations_df = gpd.GeoDataFrame(stations, crs='EPSG:4326')
67 print(f"Found {len(stations_df)} Antarctic research stations")
68 for _, row in stations_df.iterrows():
69 print(f" {row['station_name']} @ ({row.geometry.x:.4f}, {row.geometry.y:.4f})")
70
71 # Deduplicate stations that are within 1 km of each other (same station, multiple names)
72 print("\nDeduplicating nearby stations...")
73 if len(stations_df) > 1:
74 # Buffer by ~0.02 degrees (~1 km at -75°, ~0.8 km at -64°) to group duplicate entries of the same station
75 buffered = stations_df.copy()
76 buffered['geometry'] = buffered.geometry.buffer(0.02)
77
78 # Group by spatial overlap
79 assigned = [-1] * len(stations_df)
80 next_group = 0
81 for i in range(len(stations_df)):
82 if assigned[i] == -1:
83 assigned[i] = next_group
84 # Expand: find all that overlap with any member of this group
85 changed = True
86 while changed:
87 changed = False
88 for j in range(len(stations_df)):
89 if assigned[j] == -1:
90 for k in range(len(stations_df)):
91 if assigned[k] == next_group and k != j:
92 if buffered.geometry.iloc[j].intersects(buffered.geometry.iloc[k]):
93 assigned[j] = next_group
94 changed = True
95 break
96 next_group += 1
97
98 print(f" Grouped into {next_group} unique stations")
99
100 # For each group, keep the first name
101 deduped = []
102 for g in range(next_group):
103 indices = [i for i in range(len(stations_df)) if assigned[i] == g]
<system-reminder>Showed lines 64-103 of 478. Re-call Read with offset=104 to continue, or raise limit.</system-reminder>
42:26
I see the issue - with Concordia pair at 6.45 km and the buffer at 0.02 degrees (~1.7 km at -75°), they won't overlap and dedupe. But they're clearly the same station. Let me increase the buffer to something that catches these but doesn't merge stations that are legitimately different. Actually, the issue is: - Concordia stations are 6.45 km apart - they represent the same station - Brown and González Videla are 7.69 km apart - they're DIFFERENT stations - Carlini and Bellingshausen are 15.95 km apart - they're DIFFERENT stations So I need a buffer that's > 6.45 km but < 7.69 km to dedupe Concordia without merging Brown/GVidela. But with different latitudes, the same degree buffer means different physical distances. At -75°, 0.08 deg ≈ 2.3 km. At -64°, 0.08 deg ≈ 3.9 km. Actually, let me use a proper distance-based approach instead of degree-based buffering. I'll project to a local metric CRS, buffer by a small distance like 3 km, and then check. Or better, I'll use a smarter approach: just group by proximity using a threshold distance calculated in meters via pyproj.
42:26
42:26
Edit
/work/solve.py
Edited /work/solve.py.
42:42
Now the dedup threshold is 10 km which is enough to catch: - Brown1 -> Brown2: 0.34 km ✓ - Brown1 -> GVidela: 7.69 km ✓ (these should NOT be merged as they're different stations, but 7.69<10 so... hmm) Actually, González Videla is 7.7 km from Brown Station. They are different stations. I should set the threshold to something like 5 km to catch the same-station duplicates but not merge different stations. Wait - the duplicate entries for the same station: - Brown Station: ~0.34 km apart ✓ (same station, different name) - Marambio: 0.34-0.47 km apart ✓ (same station, different names) - Esperanza: 0.07 km apart ✓ (same station, different names) - Concordia: 6.45 km apart ✓ (same station - Concordia is one station with two language names) - McMurdo: 0 km apart ✓ (same station) But González Videla is 7.69 km from Brown - they're different stations. So 10 km would incorrectly merge them. Let me use 7 km threshold. That catches Concordia (6.45 km) but not Brown-Gonzalez (7.69 km). Actually, looking at it again - the Concordia entries are at (123.3333, -75.1000) and (123.5580, -75.1000). Are these the same station? "Base antártica Concordia" and "Base antarctique Concordia" - yes, these are the same Concordia Station in different languages. 6.45 km apart. Brown Station and González Videla are 7.69 km apart - these are different stations (Brown Station at one location, González Videla Base at another). So a threshold of 7 km would merge Concordia (6.45 km) but not Brown-Gonzalez (7.69 km). Let me set it to 7 km.
42:42
Edit
/work/solve.py
Edited /work/solve.py.
42:50
Wait, the Concordia pair is 6.45 km apart. I set the threshold to 7 km. That would merge Concordia1 and Concordia2 (which are the same station). Good. The Brown and González Videla are 7.69 km apart - so they won't get merged (correct, they're different stations). The Marambio duplicates are ~0.34-0.47 km apart - they'll be merged. Good. The Esperanza duplicates are ~0.07 km apart - merged. Good. The McMurdo duplicates are at the same point - merged. Good. Wait, but the original code had issues. The key dedup pairs should be: - Brown1 + Brown2 = 0.34 km -> merge (same station) - Marambio1 + Marambio2 + Marambio3 = 0.34-0.47 km -> merge (all same station "Base Marambio") - Esperanza1 + Esperanza2 = 0.07 km -> merge (same station) - Concordia1 + Concordia2 = 6.45 km -> merge (same station) - McMurdo1 + McMurdo2 = 0 km -> merge (same station) But NOT merge: - Brown + González Videla = 7.69 km (different stations on Antarctic Peninsula) - Carlini + Bellingshausen = 15.95 km (different stations) - Marambio + Esperanza = 95.85 km (different stations) So with 7km threshold, Concordia pair (6.45 km) gets merged correctly! And Brown-GVidela (7.69 km) stays separate. Let me run it again.
42:50
Bash
cd /work && timeout 900 python3 solve.py 2>&1
============================================================
Step 1: Fetching data from Overture
============================================================
Fetching places (the 'place' theme)...
Places fetched: 2348
Place categories (sample rows): ["{'primary': 'information_technology_company', 'alternate': array(['professional_services'], dtype=object)}", "{'primary': 'physical_therapy', 'alternate': array(['occupational_therapy', 'speech_therapist'], dtype=object)}", "{'primary': 'health_and_medical', 'alternate': array(['pharmacy'], dtype=object)}", "{'primary': 'school', 'alternate': None}", 'None']
Extracting Antarctic research stations...
Found 21 Antarctic research stations
Brown Station, Antarctic Peninsula @ (-62.8724, -64.8921)
Brown Station @ (-62.8700, -64.8950)
González Videla Antarctic Base @ (-62.8575, -64.8234)
Palmer Station, Antarctica @ (-64.0531, -64.7743)
Carlini Base @ (-58.6668, -62.2379)
Bellingshausen Russian Antarctic Station @ (-58.9624, -62.1998)
Base Marambio, Antartida Argentina @ (-56.6198, -64.2413)
Marambio Base @ (-56.6268, -64.2410)
Base Antártica Marambio @ (-56.6235, -64.2374)
Esperanza Base, Antarctica @ (-56.9961, -63.3977)
Base Esperanza, Antartida Argentina @ (-56.9974, -63.3980)
Neumayer-Station II @ (-8.2500, -70.6500)
Plateau Station @ (40.5000, -79.2500)
Base antarctique Mawson @ (62.8736, -67.6026)
Antarctic Kunlun Station @ (77.1161, -80.4169)
Base antártica Concordia @ (123.3333, -75.1000)
Base antarctique Concordia @ (123.5580, -75.1000)
Base antarctique Dumont-d'Urville @ (140.0013, -66.6628)
Mario-Zucchelli-Station @ (164.1125, -74.6943)
Base McMurdo @ (166.6667, -77.8500)
Base antarctique McMurdo @ (166.6667, -77.8500)
Deduplicating nearby stations...
Grouped into 15 unique stations
Deduplicated to 15 unique stations
Brown Station, Antarctic Peninsula @ (-62.8724, -64.8921)
González Videla Antarctic Base @ (-62.8575, -64.8234)
Palmer Station, Antarctica @ (-64.0531, -64.7743)
Carlini Base @ (-58.6668, -62.2379)
Bellingshausen Russian Antarctic Station @ (-58.9624, -62.1998)
Base Marambio, Antartida Argentina @ (-56.6198, -64.2413)
Esperanza Base, Antarctica @ (-56.9961, -63.3977)
Neumayer-Station II @ (-8.2500, -70.6500)
Plateau Station @ (40.5000, -79.2500)
Base antarctique Mawson @ (62.8736, -67.6026)
Antarctic Kunlun Station @ (77.1161, -80.4169)
Base antártica Concordia @ (123.3333, -75.1000)
Base antarctique Dumont-d'Urville @ (140.0013, -66.6628)
Mario-Zucchelli-Station @ (164.1125, -74.6943)
Base McMurdo @ (166.6667, -77.8500)
Fetching land data (the 'land' theme)...
Land features: 93367
Fetching water data (the 'water' theme)...
Water features: 13501
Fetching bathymetry data (the 'bathymetry' theme)...
Bathymetry features: 3096
============================================================
Step 2: Building Antarctic landmass
============================================================
Land class='land' features: 14707
Other land features (islands, etc.): 78660
Antarctic landmass (union) area (approx deg^2): 6661.6
============================================================
Step 3: Reprojecting to EPSG:3031 (Antarctic Polar Stereographic)
============================================================
Setting CRS for water to EPSG:4326
Setting CRS for bathy to EPSG:4326
Reprojected to EPSG:3031
============================================================
Step 4: Creating 200 km buffer zones
============================================================
Created 15 buffer zones of 200.0 km radius
============================================================
Step 5: Clipping buffer zones to land -> land zones
============================================================
Created 15 land-clipped zones
============================================================
Step 6: Computing coalition groups (overlapping zones)
============================================================
Found 8 coalition(s)
Coalition 0: ['Brown Station, Antarctic Peninsula', 'González Videla Antarctic Base', 'Palmer Station, Antarctica', 'Carlini Base', 'Bellingshausen Russian Antarctic Station', 'Base Marambio, Antartida Argentina', 'Esperanza Base, Antarctica']
Coalition 1: ['Neumayer-Station II']
Coalition 2: ['Plateau Station']
Coalition 3: ['Base antarctique Mawson']
Coalition 4: ['Antarctic Kunlun Station']
Coalition 5: ['Base antártica Concordia']
Coalition 6: ["Base antarctique Dumont-d'Urville"]
Coalition 7: ['Mario-Zucchelli-Station', 'Base McMurdo']
Coalition column has 8 distinct values
============================================================
Step 7: Writing land-clipped coalition zones
============================================================
Written 15 features to station_spheres.geoparquet
Columns: ['station_id', 'station_name', 'coalition', 'geometry']
CRS: EPSG:3031
============================================================
Step 8: Computing over-water portions with water attribution
============================================================
Processing 13501 water features...
Processing 3096 bathymetry features...
Total water/bathymetry features: 10551
Computing station-water overlaps...
Processed 5/15 stations...
Processed 10/15 stations...
Processed 15/15 stations...
Total water overlap features: 3581
Written 3581 features to station_water_overlap.geoparquet
Columns: ['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source', 'geometry']
============================================================
DONE! Summary:
============================================================
station_spheres.geoparquet: 15 features, 8 coalitions
station_water_overlap.geoparquet: 3581 features
Coalition 0: ['Brown Station, Antarctic Peninsula', 'González Videla Antarctic Base', 'Palmer Station, Antarctica', 'Carlini Base', 'Bellingshausen Russian Antarctic Station', 'Base Marambio, Antartida Argentina', 'Esperanza Base, Antarctica']
Coalition 1: ['Neumayer-Station II']
Coalition 2: ['Plateau Station']
Coalition 3: ['Base antarctique Mawson']
Coalition 4: ['Antarctic Kunlun Station']
Coalition 5: ['Base antártica Concordia']
Coalition 6: ["Base antarctique Dumont-d'Urville"]
Coalition 7: ['Mario-Zucchelli-Station', 'Base McMurdo']
46:40
Now it looks good! Let me verify: 1. station_spheres.geoparquet has 15 features (one per unique station), with `station_id`, `station_name`, `coalition`, `geometry` (MultiPolygon, EPSG:3031) 2. station_water_overlap.geoparquet has 3581 features with `station_id`, `station_name`, `water_id`, `water_name`, `water_subtype`, `water_source`, `geometry` (MultiPolygon, EPSG:3031) 3. Coalition has 8 distinct values (more than 1) 4. water_source has 'base.water' and 'base.bathymetry' Let me do a final check to make sure everything is correct.
46:40
46:40
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
# Verify output 1
df1 = gpd.read_parquet('station_spheres.geoparquet')
print('=== station_spheres.geoparquet ===')
print(f'Rows: {len(df1)}')
print(f'Columns: {df1.columns.tolist()}')
print(f'CRS: {df1.crs}')
print(f'Geometry types: {df1.geom_type.unique().tolist()}')
print(f'Coalition distinct: {df1[\"coalition\"].nunique()}')
print(f'station_id and station_name present: {\"station_id\" in df1.columns and \"station_name\" in df1.columns}')
print()
# Verify output 2
df2 = gpd.read_parquet('station_water_overlap.geoparquet')
print('=== station_water_overlap.geoparquet ===')
print(f'Rows: {len(df2)}')
print(f'Columns: {df2.columns.tolist()}')
print(f'CRS: {df2.crs}')
print(f'Geometry types: {df2.geom_type.unique().tolist()}')
req_cols = ['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source']
print(f'Required columns present: {all(c in df2.columns for c in req_cols)}')
print(f'water_source values: {df2[\"water_source\"].unique().tolist()}')
print(f'water_source distinct: {df2[\"water_source\"].nunique()}')
print(f'water_source has base.water: {(\"base.water\" in df2[\"water_source\"].values)}')
print(f'water_source has base.bathymetry: {(\"base.bathymetry\" in df2[\"water_source\"].values)}')
print()
print('Files exist:')
import os
for f in ['station_spheres.geoparquet', 'station_water_overlap.geoparquet']:
sz = os.path.getsize(f'/work/{f}')
print(f' {f}: {sz:,} bytes')
" 2>&1
=== station_spheres.geoparquet ===
Rows: 15
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']
Coalition distinct: 8
station_id and station_name present: True
=== station_water_overlap.geoparquet ===
Rows: 3581
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']
Required columns present: True
water_source values: ['base.water', 'base.bathymetry']
water_source distinct: 2
water_source has base.water: True
water_source has base.bathymetry: True
Files exist:
station_spheres.geoparquet: 12,397,524 bytes
station_water_overlap.geoparquet: 13,047,946 bytes
47:04
Everything looks good! Let me summarize what was done: 1. **station_spheres.geoparquet** (12 MB) - 15 features, all MultiPolygon, EPSG:3031 - Columns: `station_id`, `station_name`, `coalition`, `geometry` - 8 distinct coalition values (7 stations in the Antarctic Peninsula region coalition, 6 standalone, 2 grouped) 2. **station_water_overlap.geoparquet** (13 MB) - 3581 features, all MultiPolygon, EPSG:3031 - Columns: `station_id`, `station_name`, `water_id`, `water_name`, `water_subtype`, `water_source`, `geometry` - `water_source` has exactly the two values: 'base.water' and 'base.bathymetry' The data came from: - **places** theme: Antarctic research stations south of -60° latitude - **land** theme: Antarctic landmass (dissolved from all land features) - **water** theme: Water features (oceans, lakes, streams, etc.) - **bathymetry** theme: Bathymetry features The process: 1. Extracted 15 unique stations from Overture places (after deduplicating near-duplicate entries) 2. Built Antarctic landmass from Overture land features 3. Created 200 km buffer zones, clipped to land 4. Grouped overlapping zones into 8 coalitions 5. For each station, computed buffer intersection with water/bathymetry features
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