geo-l3-antarctica-stations-geodesic

Rerun

Traceback (most recent call last):
  File "<string>", line 1, in <module>
    import overturemaps; help(overturemaps.record_type)
                              ^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: module 'overturemaps' has no attribute 'record_type'

Traceback (most recent call last):
  File "<string>", line 1, in <module>
    import overturemaps; print(overturemaps.__version__)
                               ^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: module 'overturemaps' has no attribute '__version__'

Help on function record_batch_reader in module overturemaps.core:

record_batch_reader(
    overture_type,
    bbox=None,
    release=None,
    connect_timeout=None,
    request_timeout=None,
    stac=False
) -> Optional[pa.RecordBatchReader]
    Return a pyarrow RecordBatchReader for the desired bounding box and s3 path, or None on error.

python -c "
import overturemaps
# Check what the 'place' type offers
reader = overturemaps.record_batch_reader('place', release='2025-02-15-alpha')
if reader:
    import pyarrow as pa
    schema = reader.schema
    print('Place schema:')
    for f in schema:
        print(f'  {f.name}: {f.type}')
else:
    print('No reader returned')
" 2>&1

Traceback (most recent call last):
  File "<string>", line 4, in <module>
    reader = overturemaps.record_batch_reader('place', release='2025-02-15-alpha')
  File "/usr/local/lib/python3.14/site-packages/overturemaps/core.py", line 307, in record_batch_reader
    result = _prepare_query(
        overture_type, bbox, release, connect_timeout, request_timeout, stac
    )
  File "/usr/local/lib/python3.14/site-packages/overturemaps/core.py", line 266, in _prepare_query
    dataset = ds.dataset(
        intersecting_files if intersecting_files is not None else path,
    ...<5 lines>...
        ),
    )
  File "/usr/local/lib/python3.14/site-packages/pyarrow/dataset.py", line 790, in dataset
    return _filesystem_dataset(source, **kwargs)
  File "/usr/local/lib/python3.14/site-packages/pyarrow/dataset.py", line 472, in _filesystem_dataset
    fs, paths_or_selector = _ensure_single_source(source, filesystem)
                            ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/pyarrow/dataset.py", line 437, in _ensure_single_source
    raise FileNotFoundError(path)
FileNotFoundError: overturemaps-us-west-2/release/2025-02-15-alpha/theme=places/type=place/

python -c "
import overturemaps
# Check what 'land' type offers
reader = overturemaps.record_batch_reader('land', release='2025-02-15-alpha')
if reader:
    import pyarrow as pa
    schema = reader.schema
    print('Land schema:')
    for f in schema:
        print(f'  {f.name}: {f.type}')
else:
    print('No reader returned')
" 2>&1

Traceback (most recent call last):
  File "<string>", line 4, in <module>
    reader = overturemaps.record_batch_reader('land', release='2025-02-15-alpha')
  File "/usr/local/lib/python3.14/site-packages/overturemaps/core.py", line 307, in record_batch_reader
    result = _prepare_query(
        overture_type, bbox, release, connect_timeout, request_timeout, stac
    )
  File "/usr/local/lib/python3.14/site-packages/overturemaps/core.py", line 266, in _prepare_query
    dataset = ds.dataset(
        intersecting_files if intersecting_files is not None else path,
    ...<5 lines>...
        ),
    )
  File "/usr/local/lib/python3.14/site-packages/pyarrow/dataset.py", line 790, in dataset
    return _filesystem_dataset(source, **kwargs)
  File "/usr/local/lib/python3.14/site-packages/pyarrow/dataset.py", line 472, in _filesystem_dataset
    fs, paths_or_selector = _ensure_single_source(source, filesystem)
                            ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/pyarrow/dataset.py", line 437, in _ensure_single_source
    raise FileNotFoundError(path)
FileNotFoundError: overturemaps-us-west-2/release/2025-02-15-alpha/theme=base/type=land/

python -c "
import overturemaps
# Check what 'water' type offers
reader = overturemaps.record_batch_reader('water', release='2025-02-15-alpha')
if reader:
    import pyarrow as pa
    schema = reader.schema
    print('Water schema:')
    for f in schema:
        print(f'  {f.name}: {f.type}')
else:
    print('No reader returned')
" 2>&1

Traceback (most recent call last):
  File "<string>", line 4, in <module>
    reader = overturemaps.record_batch_reader('water', release='2025-02-15-alpha')
  File "/usr/local/lib/python3.14/site-packages/overturemaps/core.py", line 307, in record_batch_reader
    result = _prepare_query(
        overture_type, bbox, release, connect_timeout, request_timeout, stac
    )
  File "/usr/local/lib/python3.14/site-packages/overturemaps/core.py", line 266, in _prepare_query
    dataset = ds.dataset(
        intersecting_files if intersecting_files is not None else path,
    ...<5 lines>...
        ),
    )
  File "/usr/local/lib/python3.14/site-packages/pyarrow/dataset.py", line 790, in dataset
    return _filesystem_dataset(source, **kwargs)
  File "/usr/local/lib/python3.14/site-packages/pyarrow/dataset.py", line 472, in _filesystem_dataset
    fs, paths_or_selector = _ensure_single_source(source, filesystem)
                            ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/pyarrow/dataset.py", line 437, in _ensure_single_source
    raise FileNotFoundError(path)
FileNotFoundError: overturemaps-us-west-2/release/2025-02-15-alpha/theme=base/type=water/

python -c "
import overturemaps
# Check what 'bathymetry' type offers
reader = overturemaps.record_batch_reader('bathymetry', release='2025-02-15-alpha')
if reader:
    import pyarrow as pa
    schema = reader.schema
    print('Bathymetry schema:')
    for f in schema:
        print(f'  {f.name}: {f.type}')
else:
    print('No reader returned')
" 2>&1

Traceback (most recent call last):
  File "<string>", line 4, in <module>
    reader = overturemaps.record_batch_reader('bathymetry', release='2025-02-15-alpha')
  File "/usr/local/lib/python3.14/site-packages/overturemaps/core.py", line 307, in record_batch_reader
    result = _prepare_query(
        overture_type, bbox, release, connect_timeout, request_timeout, stac
    )
  File "/usr/local/lib/python3.14/site-packages/overturemaps/core.py", line 266, in _prepare_query
    dataset = ds.dataset(
        intersecting_files if intersecting_files is not None else path,
    ...<5 lines>...
        ),
    )
  File "/usr/local/lib/python3.14/site-packages/pyarrow/dataset.py", line 790, in dataset
    return _filesystem_dataset(source, **kwargs)
  File "/usr/local/lib/python3.14/site-packages/pyarrow/dataset.py", line 472, in _filesystem_dataset
    fs, paths_or_selector = _ensure_single_source(source, filesystem)
                            ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/pyarrow/dataset.py", line 437, in _ensure_single_source
    raise FileNotFoundError(path)
FileNotFoundError: overturemaps-us-west-2/release/2025-02-15-alpha/theme=base/type=bathymetry/

python -c "
import overturemaps
print(overturemaps.core.__file__)
" 2>&1

python -c "
import overturemaps.core as c
import inspect
src = inspect.getsource(c)
print(src[:3000])
" 2>&1

import io
import json
import sys
from typing import List, Optional, Tuple
from urllib.request import urlopen

import pyarrow as pa
import pyarrow.compute as pc
import pyarrow.dataset as ds
import pyarrow.fs as fs
import pyarrow.parquet as pq

from .models import BBox

STAC_CATALOG_URL = "https://stac.overturemaps.org/catalog.json"

# Cache for STAC catalog to avoid repeated network calls
_cached_stac_catalog = None

# Allows for optional import of additional dependencies
try:
    from geopandas import GeoDataFrame

    HAS_GEOPANDAS = True
except ImportError:
    HAS_GEOPANDAS = False

    class GeoDataFrame:
        pass


def _get_stac_catalog() -> dict:
    """
    Fetch and cache the STAC catalog.

    Returns
    -------
    dict: The STAC catalog JSON
    """
    global _cached_stac_catalog

    if _cached_stac_catalog is not None:
        return _cached_stac_catalog

    try:
        with urlopen(STAC_CATALOG_URL) as response:
            catalog = json.load(response)

        # Cache the catalog
        _cached_stac_catalog = catalog
        return catalog

    except Exception as e:
        raise Exception(f"Could not fetch STAC catalog: {e}") from e


def 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
    """
    catalog = _get_stac_catalog()

    latest_release = catalog.get("latest")

    # Extract release versions from the child links
    releases = []
    for link in catalog.get("links", []):
        if link.get("rel") == "child":
            href = link.get("href", "")
            # href format is "./2025-09-24.0/catalog.json"
            release_version = href.strip("./").split("/")[0]
            if release_version:
                releases.append(release_version)

    return releases, latest_release


def get_latest_release() -> str:
    """
    Get the latest release version.

    Returns
    -------
    str: The latest release version
    """
    _, latest = get_available_releases()
    return latest


# For backwards compatibility, expose ALL_RELEASES as a list
# This will be populated dynamically when first accessed
def _get_all_releases():
    releases, _ = get_available_releases()
    return releases


# Lazy evaluation property-like access
class _ReleasesProxy:
    def __getitem__(self, index):
        return _get_all_releases()[index]

    def __iter__(self):
        return iter(_get_all_releases())

    def __len__(self):
        return len(_get_all_releases())

    def __repr__(self):
        return repr(_get_all_releases())


ALL_RELEASES = _ReleasesProxy()


def _coerce_bbox(
    bbox: BBox | tuple[float, float, float, float] | list[float] | None,
) -> BBox | None:
    """Normalize bbox input to a BBox instance."""
    if bbox is None:
        return None

python -c "
import overturemaps.core as c
import inspect
src = inspect.getsource(c)
print(src[3000:6000])
" 2>&1

 if isinstance(bbox, BBox):
        return bbox

    if len(bbox) != 4:
        raise ValueError("bbox must contain exactly 4 numeric values")

    return BBox(
        xmin=float(bbox[0]),
        ymin=float(bbox[1]),
        xmax=float(bbox[2]),
        ymax=float(bbox[3]),
    )


def _get_files_from_stac(
    theme: str, overture_type: str, bbox: BBox, release: str
) -> Optional[List[str]]:
    """
    Returns a list of bucket/key paths using the STAC-geoparquet index
    """
    stac_url = f"https://stac.overturemaps.org/{release}/collections.parquet"
    try:
        # Arrow can't read HTTP URLs directly; read into memory first
        with urlopen(stac_url) as response:
            data = response.read()
            buffer = io.BytesIO(data)
            stac_table = pq.read_table(buffer)

        feature_type_filter = (pc.field("collection") == overture_type) & (
            pc.field("type") == "Feature"
        )

        xmin, ymin, xmax, ymax = bbox.as_tuple()
        bbox_filter = (
            (pc.field("bbox", "xmin") < xmax)
            & (pc.field("bbox", "xmax") > xmin)
            & (pc.field("bbox", "ymin") < ymax)
            & (pc.field("bbox", "ymax") > ymin)
        )

        combined_filter = feature_type_filter & bbox_filter
        table = stac_table.filter(combined_filter)

        if table.num_rows > 0:
            file_paths = table.column("assets").to_pylist()

            # clip out the "s3://" prefix
            s3_paths = [
                path["aws"]["alternate"]["s3"]["href"][len("s3://") :]
                for path in file_paths
            ]
            return s3_paths
        else:
            print(
                f"No data found for release {release} in query bbox {bbox.as_tuple()}."
            )
            return []

    except Exception as e:
        print(f"Error reading STAC index at {stac_url}: {e}")
        return None


def _record_batch_reader_from_dataset(
    dataset: ds.Dataset,
    filter_expr=None,
) -> Optional[pa.RecordBatchReader]:
    """
    Create a RecordBatchReader from an S3 dataset with optional filtering.

    Parameters
    ----------
    dataset: pyarrow dataset
        Dataset to read from
    filter_expr: pyarrow expression, optional
        Filter to apply when reading the dataset

    Returns
    -------
    RecordBatchReader with the feature data, or None if error occurs
    """
    try:
        batches = dataset.to_batches(
            filter=filter_expr,
            use_threads=True,
            batch_readahead=16,
            fragment_readahead=4,
        )

        # Filter out empty batches to avoid downstream issues
        non_empty_batches = (b for b in batches if b.num_rows > 0)

        geoarrow_schema = geoarrow_schema_adapter(dataset.schema)
        return pa.RecordBatchReader.from_batches(geoarrow_schema, non_empty_batches)

    except Exception as e:
        print(f"Error reading dataset: {e}")
        return None


def _prepare_query(
    overture_type,
    b

python -c "
import overturemaps.core as c
import inspect
src = inspect.getsource(c)
print(src[6000:])
" 2>&1

box: BBox | tuple[float, float, float, float] | list[float] | None = None,
    release=None,
    connect_timeout=None,
    request_timeout=None,
    stac=False,
) -> Optional[Tuple[ds.Dataset, Optional[pc.Expression]]]:
    """
    Resolve the S3 dataset and filter expression for a given query.

    Returns the dataset and filter expression ready for counting or streaming,
    or None if STAC reports no files intersect the bbox.
    """
    if release is None:
        release = get_latest_release()
    path = _dataset_path(overture_type, release)
    bbox_obj = _coerce_bbox(bbox)

    intersecting_files = None
    if bbox_obj and stac:
        intersecting_files = _get_files_from_stac(
            type_theme_map[overture_type], overture_type, bbox_obj, release
        )
        if intersecting_files is not None and len(intersecting_files) == 0:
            return None

    if bbox_obj:
        xmin, ymin, xmax, ymax = bbox_obj.as_tuple()
        filter_expr = (
            (pc.field("bbox", "xmin") < xmax)
            & (pc.field("bbox", "xmax") > xmin)
            & (pc.field("bbox", "ymin") < ymax)
            & (pc.field("bbox", "ymax") > ymin)
        )
    else:
        filter_expr = None

    dataset = ds.dataset(
        intersecting_files if intersecting_files is not None else path,
        filesystem=fs.S3FileSystem(
            anonymous=True,
            region="us-west-2",
            connect_timeout=connect_timeout,
            request_timeout=request_timeout,
        ),
    )

    return dataset, filter_expr


def 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."""
    result = _prepare_query(
        overture_type, bbox, release, connect_timeout, request_timeout, stac
    )
    if result is None:
        return 0
    dataset, filter_expr = result
    return dataset.count_rows(filter=filter_expr)



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


def 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

    """
    if not HAS_GEOPANDAS:
        raise ImportError("geopandas is required to use this function")

    reader = record_batch_reader(
        overture_type,
        bbox=bbox,
        release=release,
        connect_timeout=connect_timeout,
        request_timeout=request_timeout,
        stac=stac,
    )
    return GeoDataFrame.from_arrow(reader)


def 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

    """
    geometry_field_index = schema.get_field_index("geometry")
    geometry_field = schema.field(geometry_field_index)
    geoarrow_geometry_field = geometry_field.with_metadata(
        {b"ARROW:extension:name": b"geoarrow.wkb"}
    )

    geoarrow_schema = schema.set(geometry_field_index, geoarrow_geometry_field)

    return geoarrow_schema


type_theme_map = {
    "address": "addresses",
    "bathymetry": "base",
    "building": "buildings",
    "building_part": "buildings",
    "division": "divisions",
    "division_area": "divisions",
    "division_boundary": "divisions",
    "place": "places",
    "segment": "transportation",
    "connector": "transportation",
    "infrastructure": "base",
    "land": "base",
    "land_cover": "base",
    "land_use": "base",
    "water": "base",
}


def _dataset_path(overture_type: str, release: str) -> str:
    """
    Returns the s3 path of the Overture dataset to use. This assumes overture_type has
    been validated, e.g. by the CLI

    """
    # Map of sub-partition "type" to parent partition "theme" for forming the
    # complete s3 path. Could be discovered by reading from the top-level s3
    # location but this allows to only read the files in the necessary partition.
    theme = type_theme_map[overture_type]
    return (
        f"overturemaps-us-west-2/release/{release}/theme={theme}/type={overture_type}/"
    )


def get_all_overture_types() -> List[str]:
    return list(type_theme_map.keys())


# Registry manifest is now part of the STAC catalog
# Access via catalog.json -> registry property -> manifest field


def _binary_search_manifest(
    manifest_tuples: List[Tuple[str, str]], gers_id: str
) -> Optional[str]:
    """
    Binary search through manifest tuples to find the file containing the given GERS ID.

    Parameters
    ----------
    manifest_tuples: List of (filename, max_id) tuples, sorted by max_id
    gers_id: The GERS ID to search for (lowercase)

    Returns
    -------
    Filename containing the ID, or None if not found
    """
    left, right = 0, len(manifest_tuples) - 1

    while left <= right:
        mid = (left + right) // 2
        filename, max_id = manifest_tuples[mid]

        if gers_id <= max_id:
            # Check if this is the first file where max_id >= gers_id
            if mid == 0 or manifest_tuples[mid - 1][1] < gers_id:
                return filename
            else:
                # Search in the left half
                right = mid - 1
        else:
            # Search in the right half
            left = mid + 1

    return None


def 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
    """
    import sys

    release = get_latest_release()
    release_path = f"overturemaps-us-west-2/release/{release}"
    gers_id_lower = gers_id.lower()

    try:
        # Get the cached STAC catalog
        catalog = _get_stac_catalog()

        # Get the registry object from the catalog
        registry = catalog.get("registry")
        if registry is None:
            print("Registry configuration not found in STAC catalog", file=sys.stderr)
            return None

        # The registry contains 'path' and 'manifest'
        # manifest is a list of [filename, max_id] tuples
        registry_path = registry.get("path", "")
        manifest_tuples = registry.get("manifest", [])

        if not manifest_tuples:
            print("Registry manifest is empty in STAC catalog", file=sys.stderr)
            return None

        # Use binary search to find the file containing this GERS ID
        registry_file = _binary_search_manifest(manifest_tuples, gers_id_lower)

        if registry_file is None:
            print(f"{gers_id} does not exist in the GERS Registry.", file=sys.stderr)
            return None

        # Read the specific registry file with filter (predicate pushdown)
        # This only reads the relevant row groups instead of the entire file
        registry_path = f"overturemaps-us-west-2/registry/{registry_file}"
        filesystem = fs.S3FileSystem(anonymous=True, region="us-west-2")

        # Use filters parameter for predicate pushdown
        filtered_table = pq.read_table(
            registry_path, filesystem=filesystem, filters=[("id", "=", gers_id_lower)]
        )

        if filtered_table.num_rows == 0:
            print(f"{gers_id} does not exist in the GERS Registry.", file=sys.stderr)
            return None

        # Get the first (should be only) result
        row = filtered_table.to_pylist()[0]
        path = row["path"]
        bbox_struct = row.get("bbox")
        version = row.get("version")
        first_seen = row.get("first_seen")
        last_seen = row.get("last_seen")
        last_changed = row.get("last_changed")

        # Check if path is NULL - means feature is not present in current release
        if path is None:
            print(
                f"GERS ID '{gers_id}' found in registry but not present in release {release}",
                file=sys.stderr,
            )
            print(f"  Version: {version}", file=sys.stderr)
            print(f"  First seen: {first_seen}", file=sys.stderr)
            print(f"  Last seen: {last_seen}", file=sys.stderr)
            if last_changed:
                print(f"  Last changed: {last_changed}", file=sys.stderr)
            return None

        # Construct full filepath
        if not path.startswith("/"):
            path = "/" + path
        filepath = f"{release_path}{path}"

        # Extract bbox values if available
        if bbox_struct is not None:
            bbox = BBox(
                xmin=bbox_struct["xmin"],
                ymin=bbox_struct["ymin"],
                xmax=bbox_struct["xmax"],
                ymax=bbox_struct["ymax"],
            )
        else:
            bbox = None

        # Write registry information to stderr
        print(f"Found GERS ID '{gers_id}' in release {release}", file=sys.stderr)
        print(f"  Version: {version}", file=sys.stderr)
        print(f"  Filepath: s3://{filepath}", file=sys.stderr)
        if bbox is not None:
            print(
                f"  Bbox: [{bbox.xmin:.6f}, {bbox.ymin:.6f}, {bbox.xmax:.6f}, {bbox.ymax:.6f}]",
                file=sys.stderr,
            )
        else:
            print(f"  Bbox: None", file=sys.stderr)
        print(f"  First seen: {first_seen}", file=sys.stderr)
        print(f"  Last seen: {last_seen}", file=sys.stderr)
        if last_changed:
            print(f"  Last changed: {last_changed}", file=sys.stderr)

        return (filepath, bbox)

    except Exception as e:
        print(f"Error querying GERS registry: {e}", file=sys.stderr)
        return None


def 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.

    The registry always uses the latest release.

    Parameters
    ----------
    gers_id: The GERS ID to look up
    connect_timeout: Optional connection timeout in seconds
    request_timeout: Optional request timeout in seconds
    registry_result: Optional pre-fetched registry result (filepath, bbox)
                    to avoid duplicate registry queries

    Returns
    -------
    RecordBatchReader with the feature data, or None if not found
    """
    # Use pre-fetched result if provided, otherwise query the registry
    if registry_result is None:
        result = query_gers_registry(gers_id)
        if result is None:
            return None
    else:
        result = registry_result

    filepath, bbox = result

    # Build filter expression based on ID and bbox (if available)
    filter_expr = pc.field("id") == gers_id.lower()

    if bbox is not None:
        xmin, ymin, xmax, ymax = bbox.as_tuple()
        bbox_filter = (
            (pc.field("bbox", "xmin") == xmin)
            & (pc.field("bbox", "ymin") == ymin)
            & (pc.field("bbox", "xmax") == xmax)
            & (pc.field("bbox", "ymax") == ymax)
        )
        filter_expr = filter_expr & bbox_filter

    try:
        dataset = ds.dataset(
            filepath,
            filesystem=fs.S3FileSystem(
                anonymous=True,
                region="us-west-2",
                connect_timeout=connect_timeout,
                request_timeout=request_timeout,
            ),
        )
    except Exception as e:
        print(f"Error opening dataset for GERS ID '{gers_id}': {e}", file=sys.stderr)
        return None
    return _record_batch_reader_from_dataset(dataset, filter_expr=filter_expr)

python -c "
import overturemaps
print('Latest release:', overturemaps.core.get_latest_release())
print('All releases:', overturemaps.core.get_available_releases())
" 2>&1

python -c "
import overturemaps
# Check the place schema - latest release
reader = overturemaps.record_batch_reader('place', release='2026-06-17.0')
if reader:
    schema = reader.schema
    print('Place schema:')
    for f in schema:
        print(f'  {f.name}: {f.type}')
" 2>&1

python -c "
import overturemaps
# Check the land schema
reader = overturemaps.record_batch_reader('land', release='2026-06-17.0')
if reader:
    schema = reader.schema
    print('Land schema:')
    for f in schema:
        print(f'  {f.name}: {f.type}')
" 2>&1

python -c "
import overturemaps
# Check the water schema
reader = overturemaps.record_batch_reader('water', release='2026-06-17.0')
if reader:
    schema = reader.schema
    print('Water schema:')
    for f in schema:
        print(f'  {f.name}: {f.type}')
" 2>&1

python -c "
import overturemaps
# Check the bathymetry schema
reader = overturemaps.record_batch_reader('bathymetry', release='2026-06-17.0')
if reader:
    schema = reader.schema
    print('Bathymetry schema:')
    for f in schema:
        print(f'  {f.name}: {f.type}')
" 2>&1

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

python -c "
import overturemaps
# Check water schema - with bbox for Antarctica
reader = overturemaps.record_batch_reader('water', bbox=(-180, -90, 180, -60), release='2026-06-17.0', stac=True)
if reader:
    schema = reader.schema
    print('Water schema:')
    for f in schema:
        print(f'  {f.name}: {f.type}')
else:
    print('No reader returned')
" 2>&1

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

python -c "
import overturemaps
# Check place schema with bbox
reader = overturemaps.record_batch_reader('place', bbox=(-180, -90, 180, -60), release='2026-06-17.0', stac=True)
if reader:
    schema = reader.schema
    print('Place schema:')
    for f in schema:
        print(f'  {f.name}: {f.type}')
else:
    print('No reader returned for place')
" 2>&1

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

python -c "
import overturemaps
# Check land schema with bbox
reader = overturemaps.record_batch_reader('land', bbox=(-180, -90, 180, -60), release='2026-06-17.0', stac=True)
if reader:
    schema = reader.schema
    print('Land schema:')
    for f in schema:
        print(f'  {f.name}: {f.type}')
else:
    print('No reader returned for land')
" 2>&1

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

python -c "
import overturemaps
# Look at place data for Antarctica - get categories
reader = overturemaps.record_batch_reader('place', bbox=(-180, -90, 180, -60), release='2026-06-17.0', stac=True)
import pyarrow.parquet as pq
import pyarrow as pa
# Read first few rows to see categories
table = reader.read_all()
print('Total rows:', len(table))
# Look at categories and names
cats = table.column('categories').to_pylist()
names = table.column('names').to_pylist()
for i in range(min(50, len(table))):
    primary = cats[i]['primary']
    alt = cats[i].get('alternate', [])
    name = names[i]['primary']
    print(f'{i}: name={name}, primary_cat={primary}, alt={alt}')
" 2>&1

Total rows: 246
0: name=Poeanuanua, primary_cat=retail, alt=None
1: name=Muebles Marispal Algeciras, primary_cat=furniture_store, alt=None
2: name=Lamoon Cafe & Restaurant - สระบุรี, primary_cat=cafe, alt=['coffee_shop', 'restaurant']
3: name=Cap College Mariveles Bataan, primary_cat=college_university, alt=['education']
4: name=מעדני מניה Maadaney Mania, primary_cat=delicatessen, alt=['supermarket', 'liquor_store']
5: name=Te Fare Informatique, primary_cat=hardware_store, alt=['computer_hardware_company']
6: name=Samira's Hair Fashion +599 98693311, primary_cat=beauty_salon, alt=None
Traceback (most recent call last):
  File "<string>", line 14, in <module>
    primary = cats[i]['primary']
              ~~~~~~~^^^^^^^^^^^
TypeError: 'NoneType' object is not subscriptable

python -c "
import overturemaps
import pyarrow.compute as pc

reader = overturemaps.record_batch_reader('place', bbox=(-180, -90, 180, -60), release='2026-06-17.0', stac=True)
import pyarrow as pa
table = reader.read_all()

# Filter by geometry bbox - points south of -60
# Overture places have point geometry, so filter by latitude
# The bbox struct has ymin/ymax for the row group - not individual rows
# Let's just iterate through and check names/categories of everything

cats = table.column('categories').to_pylist()
names = table.column('names').to_pylist()
ids = table.column('id').to_pylist()

for i in range(len(table)):
    if cats[i] is None:
        continue
    primary = cats[i]['primary']
    name = names[i]['primary'] if names[i] else None
    print(f'id={ids[i]}, name={name}, cat={primary}')
" 2>&1

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

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

# Get water data for Antarctica
reader = overturemaps.record_batch_reader('water', bbox=(-180, -90, 180, -60), release='2026-06-17.0', stac=True)
if reader:
    import geopandas as gpd
    gdf = gpd.GeoDataFrame.from_arrow(reader)
    print('Water columns:', list(gdf.columns))
    print('Water CRS:', gdf.crs)
    print('Water shape:', gdf.shape)
    print('Subtype values:', gdf['subtype'].value_counts().head(20))
    print('Class values:', gdf['class'].value_counts().head(20))
    print('Sample names:')
    for i, row in gdf.head(10).iterrows():
        names = row.get('names', {})
        name = names.get('primary', '') if names else ''
        print(f'  {name} | subtype={row[\"subtype\"]} | class={row[\"class\"]}')
else:
    print('No water reader')
" 2>&1

Water columns: ['id', 'names', 'subtype', 'class', 'sources', 'source_tags', 'level', 'wikidata', 'is_intermittent', 'is_salt', 'geometry', 'version', 'bbox']
Water CRS: None
Water shape: (13500, 13)
Subtype values: subtype
stream        5374
ocean         4978
lake          1746
physical       808
pond           372
water          180
river           34
canal            5
human_made       2
reservoir        1
Name: count, dtype: int64
Class values: class
stream           5374
ocean            4978
lake             1745
cape              411
pond              372
bay               349
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
Name: count, dtype: int64
Sample names:
   | subtype=lake | class=lake
  Thanksgiving Point | subtype=physical | class=cape
   | subtype=lake | class=lake
   | subtype=lake | class=lake
   | subtype=stream | class=stream
   | subtype=lake | class=lake
   | subtype=lake | class=lake
   | subtype=stream | class=stream
   | subtype=stream | class=stream
   | subtype=lake | class=lake

python -c "
import overturemaps
import pyarrow as pa

# Check the place schema - look at all columns
reader = overturemaps.record_batch_reader('place', bbox=(-180, -90, 180, -60), release='2026-06-17.0', stac=True)
table = reader.read_all()

# Filter to find research stations - look for names with 'station', 'base', 'antarctic'
import pyarrow.compute as pc
names = table.column('names').to_pylist()
ids = table.column('id').to_pylist()
geoms = table.column('geometry').to_pylist()

# Let's check the geometry columns to see what coordinate system
print('Geometry sample (first 5):')
for i in range(min(5, len(geoms))):
    if geoms[i]:
        print(f'  {geoms[i][:100]}...')
print('Type of geometry:', type(geoms[0]))

# Check if we can filter by station-like names
station_keywords = ['station', 'base', 'antarctic', 'research', 'mcmurdo', 'vostok', 'amundsen', 'scott']
for i in range(len(table)):
    if names[i]:
        name_lower = names[i]['primary'].lower() if names[i]['primary'] else ''
        if any(kw in name_lower for kw in station_keywords):
            print(f'  id={ids[i]}, name={names[i][\"primary\"]}')
" 2>&1

Geometry sample (first 5):
  b"\x01\x01\x00\x00\x00\x80ZA\xce\xe6\x9be\xc0\xd1<\xabM 'P\xc0"...
  b'\x01\x01\x00\x00\x00\x00\x00\x00\x00\x80\x99c\xc01\xc3\xa7\xbe\\\x96S\xc0'...
  b'\x01\x01\x00\x00\x00\xa5\x00\x00\xa0\x90\x86c\xc0Ho3\t\xbaaS\xc0'...
  b'\x01\x01\x00\x00\x00\x00\x00\x00\x00\x00\xeea\xc0\xf4|\xa70\xab\x9fT\xc0'...
  b'\x01\x01\x00\x00\x00\x00\x00\x00\x00\x00\x1bb\xc0KkO\xe9\xad0T\xc0'...
Type of geometry: <class 'bytes'>
  id=2a100ade-be10-41c0-b6c2-a49e921a24e7, name=Port Lockroy, Antarctic Peninsula
  id=f73848f3-abaa-432b-b3ed-8092a6b39b9e, name=Palmer Station, Antarctica
  id=eb858253-acd6-4231-9ce9-e0e4c799d7a0, name=Brown Station
  id=ba9d8ef6-32f3-4098-a010-b5b485e4e75f, name=González Videla Antarctic Base
  id=9d364bf8-eaaf-4f79-ad45-195f74f49a93, name=Antarctica, Antarctic Circle
  id=3bf8e968-6050-4ee6-8a60-f046db54cc02, name=Esperanto Island, Antarctica
  id=6240b821-30cb-4fe4-a08a-6fddb4fe2978, name=Bellingshausen Russian Antarctic Station
  id=18414d58-4e4c-480f-bfab-ee3e224c5b5e, name=Base Marambio, Antartida Argentina
  id=015df87e-1265-4d80-844b-97a4c1af44a5, name=Marambio Base
  id=4775fc70-d68b-48b4-af40-377af7e9a457, name=Chapel of the Blessed Virgin of Lujan, Antarctica
  id=11037d14-337b-4a53-a23b-85566484f016, name=Base Antártica Marambio
  id=b1662153-d852-4a90-b8a7-5d5e113439bb, name=Base Esperanza, Antartida Argentina
  id=8536af5a-82ba-46ed-bd6c-069082cbfc5d, name=Esperanza Base, Antarctica
  id=e3417e97-f283-4ecc-a32b-ae164e69903f, name=Antarctica/Troll
  id=63b91770-28a5-4a5c-bea8-0e5842150734, name=Lake Vostok
  id=bfd01a42-92a7-43f9-a7d3-0cc7fb3157b4, name=Vostok İstasyonu
  id=04d8258a-e765-4a4d-96c0-20cd3b11a9c3, name=Base antarctique Dumont-d'Urville
  id=26f0564a-e108-46da-b7aa-a4f3aca0daa2, name=Antarctic Plateau
  id=fcdd73f9-5cb8-4812-be88-099727bf3936, name=Mario-Zucchelli-Station
  id=bb48b222-57c4-4c85-8743-752df4bba2ca, name=Polární stanice McMurdo
  id=d5882de0-07ff-46b9-a3c0-bbfb0cc9552c, name=Base McMurdo
  id=d74e5a73-042c-4694-9e16-fa9622f203b3, name=Base antarctique McMurdo
  id=82b40435-6b61-4d8e-9caa-7ff1d248b8a1, name=McMurdo General Hospital

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

reader = overturemaps.record_batch_reader('place', bbox=(-180, -90, 180, -60), release='2026-06-17.0', stac=True)
gdf = gpd.GeoDataFrame.from_arrow(reader)
print('CRS:', gdf.crs)
print('Total rows:', len(gdf))

# Convert geometry from WKB bytes to shapely
gdf['geometry'] = gdf['geometry'].apply(lambda x: wkb.loads(x) if isinstance(x, bytes) else x)
gdf = gdf.set_geometry('geometry')
print('After conversion:')
print(gdf.head())

# Check coordinates for Antarctic places
for i, row in gdf.iterrows():
    if row['names'] and row['names']['primary']:
        name = row['names']['primary']
        if any(kw in name.lower() for kw in ['station', 'base', 'antarctic', 'antarctica', 'mcmurdo', 'vostok', 'dumont', 'esperanza', 'marambio', 'bellingshausen', 'brown', 'gonzalez', 'videla', 'troll', 'zhuchelli', 'mario']):
            pt = row['geometry']
            if pt:
                print(f'{name}: ({pt.x:.4f}, {pt.y:.4f})')
" 2>&1

CRS: None
Total rows: 246
After conversion:
                                     id  ...                                               bbox
0  ff93e6a3-b7fc-4691-980a-c36820a301c4  ...  {'xmin': -172.8719482421875, 'xmax': -172.8719...
1  e4cd796b-b4c6-47e1-927b-79edfc50c48c  ...  {'xmin': -156.796875, 'xmax': -156.796875, 'ym...
2  52dd18ca-aaae-4f06-aa53-06349b302c66  ...  {'xmin': -156.20516967773438, 'xmax': -156.205...
3  b01d6401-437f-4be8-bb25-21055d6881fa  ...  {'xmin': -143.4375, 'xmax': -143.4375, 'ymin':...
4  d61f38d3-a73b-44d6-ae69-1120a1518aaf  ...  {'xmin': -144.84375, 'xmax': -144.84375, 'ymin...

[5 rows x 17 columns]
Port Lockroy, Antarctic Peninsula: (-63.4910, -64.8247)
Palmer Station, Antarctica: (-64.0531, -64.7743)
Brown Station: (-62.8700, -64.8950)
González Videla Antarctic Base: (-62.8575, -64.8234)
Antarctica, Antarctic Circle: (-60.9322, -64.0536)
Esperanto Island, Antarctica: (-60.3647, -62.6414)
Bellingshausen Russian Antarctic Station: (-58.9624, -62.1998)
Base Marambio, Antartida Argentina: (-56.6198, -64.2413)
Marambio Base: (-56.6268, -64.2410)
Chapel of the Blessed Virgin of Lujan, Antarctica: (-56.6284, -64.2407)
Base Antártica Marambio: (-56.6235, -64.2374)
Base Esperanza, Antartida Argentina: (-56.9974, -63.3980)
Esperanza Base, Antarctica: (-56.9961, -63.3977)
Antarctica/Troll: (2.5350, -72.0114)
Lake Vostok: (106.0000, -77.5000)
Vostok İstasyonu: (106.8373, -78.4644)
Base antarctique Dumont-d'Urville: (140.0013, -66.6628)
Antarctic Plateau: (150.0000, -77.0000)
Mario-Zucchelli-Station: (164.1125, -74.6943)
Polární stanice McMurdo: (166.6667, -77.8500)
Base McMurdo: (166.6667, -77.8500)
Base antarctique McMurdo: (166.6667, -77.8500)
McMurdo General Hospital: (166.6693, -77.8468)

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

reader = overturemaps.record_batch_reader('place', bbox=(-180, -90, 180, -60), release='2026-06-17.0', stac=True)
gdf = gpd.GeoDataFrame.from_arrow(reader)

# Parse geometry
gdf['geometry'] = gdf['geometry'].apply(lambda x: wkb.loads(x) if isinstance(x, bytes) else x)
gdf = gdf.set_geometry('geometry')
gdf = gdf.set_crs('EPSG:4326')

# Filter to south of -60
gdf = gdf[gdf.geometry.y < -60].copy()
print(f'Rows south of -60: {len(gdf)}')

# Extract names and categories
for i, row in gdf.iterrows():
    name = row['names']['primary'] if row['names'] else ''
    cats = row['categories']
    primary_cat = cats['primary'] if cats else ''
    print(f'{name:50s} | cat={primary_cat:40s} | ({row.geometry.x:.4f}, {row.geometry.y:.4f})')
" 2>&1

Rows south of -60: 246
Poeanuanua                                         | cat=retail                                   | (-172.8719, -64.6113)
Muebles Marispal Algeciras                         | cat=furniture_store                          | (-156.7969, -78.3494)
Lamoon Cafe & Restaurant - สระบุรี                 | cat=cafe                                     | (-156.2052, -77.5270)
Cap College Mariveles Bataan                       | cat=college_university                       | (-143.4375, -82.4948)
מעדני מניה Maadaney Mania                          | cat=delicatessen                             | (-144.8438, -80.7606)
Te Fare Informatique                               | cat=hardware_store                           | (-146.8762, -65.7560)
Samira's Hair Fashion +599 98693311                | cat=beauty_salon                             | (-136.4062, -74.7758)
CAMBO Cleaner                                      | cat=                                         | (-137.6954, -75.4252)
Iriga City Public Library                          | cat=public_and_government_association        | (-136.5469, -81.0823)
Dinastia Nortena                                   | cat=                                         | (-137.8125, -81.9232)
Ser Esencia Restaurant                             | cat=restaurant                               | (-127.4854, -84.8382)
مدينة رشيد Rosetta city                            | cat=                                         | (-125.5001, -82.7274)
Blessed Souls Photography & Film                   | cat=professional_services                    | (-127.2313, -74.5874)
黎齊妙中醫診所                                            | cat=hospital                                 | (-126.5625, -74.0195)
dieukhaclienvu.com - phù điêu,điêu khắc đá nhân tạo composite,cảnh quan đẹp | cat=arts_and_crafts                          | (-124.4531, -78.6300)
R-AGE Nation Apparel                               | cat=womens_clothing_store                    | (-118.1250, -81.0932)
Basketball Forever                                 | cat=mass_media                               | (-113.9062, -81.9232)
Marc Silber                                        | cat=professional_services                    | (-113.9062, -81.7232)
Tama Manège Import                                 | cat=park                                     | (-118.8281, -64.1681)
Irantha Fonseka                                    | cat=                                         | (-106.1719, -80.5321)
Ozhousestudio                                      | cat=topic_concert_venue                      | (-109.7113, -82.0814)
Jamela Payne                                       | cat=panamanian_restaurant                    | (-96.1109, -80.8255)
In Aeternum Historia                               | cat=print_media                              | (-94.9229, -84.1257)
Destiny Monique                                    | cat=arts_and_entertainment                   | (-93.5156, -78.2066)
Frente al Mar                                      | cat=eat_and_drink                            | (-92.9534, -74.4494)
SaiJai ใส่ใจ เครื่องปรุงและอาหารสุขภาพ             | cat=restaurant                               | (-88.5938, -72.8161)
Tay Noel’s Kalan-an                                | cat=fast_food_restaurant                     | (-87.9330, -82.3097)
גלית קאשי עיצוב תכשיטים                            | cat=jewelry_store                            | (-87.1875, -73.2267)
GypsyMaal                                          | cat=fashion_accessories_store                | (-85.0781, -83.1111)
Prasad Art Gallery                                 | cat=art_gallery                              | (-77.7802, -76.7630)
Tahlia Marie                                       | cat=                                         | (-77.3463, -84.8039)
Barbaro Negocios Inmobiliarios                     | cat=real_estate                              | (-73.1250, -83.6769)
VALKUR Perú                                        | cat=lawyer                                   | (-71.0156, -71.9654)
Experience Rarotonga                               | cat=tours                                    | (-70.3124, -83.7539)
Caprichos Accesorios                               | cat=fashion_accessories_store                | (-69.1699, -77.4495)
Emmy J                                             | cat=                                         | (-68.1980, -78.2417)
Asra Derm Official                                 | cat=                                         | (-66.0226, -75.4181)
Mobilio Suksawat โมบิลิโอ้ สาขาสุขสวัสดิ์          | cat=shopping                                 | (-64.0955, -78.9683)
ال سلوع لتجاره السيراميك والادوات الصحيه           | cat=real_estate                              | (-63.8436, -64.7752)
Port Lockroy, Antarctic Peninsula                  | cat=landmark_and_historical_building         | (-63.4910, -64.8247)
Palmer Station, Antarctica                         | cat=home_developer                           | (-64.0531, -64.7743)
BellaVista Voronet                                 | cat=holiday_rental_home                      | (-65.1975, -66.6002)
Sachet Imprimé                                     | cat=                                         | (-61.8750, -66.5133)
Brown Station                                      | cat=landmark_and_historical_building         | (-62.8700, -64.8950)
González Videla Antarctic Base                     | cat=airport                                  | (-62.8575, -64.8234)
Cuverville Island                                  | cat=landmark_and_historical_building         | (-62.6333, -64.6833)
Madison Beer                                       | cat=beer_bar                                 | (-63.2812, -79.9359)
Dj daya                                            | cat=music_production                         | (-62.8125, -84.9283)
Matrioska Laços                                    | cat=childrens_clothing_store                 | (-62.5831, -83.8696)
Antarctica, Antarctic Circle                       | cat=landmark_and_historical_building         | (-60.9322, -64.0536)
Esperanto Island, Antarctica                       | cat=landmark_and_historical_building         | (-60.3647, -62.6414)
Yankee Harbor                                      | cat=landmark_and_historical_building         | (-59.7822, -62.5333)
Bellingshausen Russian Antarctic Station           | cat=educational_research_institute           | (-58.9624, -62.1998)
Bahia Fildes (Antartica)                           | cat=structure_and_geography                  | (-58.9619, -62.2013)
Crystal Power                                      | cat=jewelry_store                            | (-58.3208, -75.5940)
Bestiario Moderno                                  | cat=print_media                              | (-58.0165, -78.1542)
Base Marambio, Antartida Argentina                 | cat=central_government_office                | (-56.6198, -64.2413)
Marambio Base                                      | cat=airport                                  | (-56.6268, -64.2410)
Chapel of the Blessed Virgin of Lujan, Antarctica  | cat=church_cathedral                         | (-56.6284, -64.2407)
Base Antártica Marambio                            | cat=public_and_government_association        | (-56.6235, -64.2374)
Base Esperanza, Antartida Argentina                | cat=airport                                  | (-56.9974, -63.3980)
Esperanza Base, Antarctica                         | cat=landmark_and_historical_building         | (-56.9961, -63.3977)
Backwoods Bouquet                                  | cat=                                         | (-55.1167, -61.1333)
Shop Thời Trang Nam - Nữ Thanh Bình                | cat=fashion                                  | (-54.5217, -76.4785)
Lucas Fontoura                                     | cat=arts_and_entertainment                   | (-44.4562, -82.5946)
Descobrincar  - Musicalização infantil             | cat=school                                   | (-41.4844, -78.6300)
Hurricane On Saturn                                | cat=                                         | (-40.7299, -78.1487)
Paradise Eventi                                    | cat=event_planning                           | (-43.6577, -79.6643)
Psikolog ve Aile Danışmanı Esra Erciyas            | cat=                                         | (-34.4531, -79.7499)
Proexc Engenharia                                  | cat=engineering_services                     | (-39.3750, -79.3026)
Villa's Caldos                                     | cat=fast_food_restaurant                     | (-36.5625, -84.6735)
Gabbi Garcia                                       | cat=                                         | (-31.0029, -82.1136)
Bendita CR                                         | cat=                                         | (-33.0469, -79.3026)
Behr Productions                                   | cat=music_production                         | (-33.0469, -77.3125)
Max land  MX Raceway Park                          | cat=race_track                               | (-28.1250, -78.6300)
DRK Kreisverband Gelnhausen-Schlüchtern e.V.       | cat=community_services_non_profits           | (-28.8171, -83.1649)
Muhammad Hasri Videography                         | cat=professional_services                    | (-25.3125, -84.8025)
JJ Niceley                                         | cat=                                         | (-23.2090, -81.8270)
Jankari Kendra जानकारी केन्द्र                     | cat=mass_media                               | (-26.8022, -80.0572)
Reggae Village House of Jerk                       | cat=caterer                                  | (-25.3152, -75.1276)
Công Ty TNHH Nam Hưng Thái Nguyên                  | cat=b2b_textiles                             | (-25.5440, -76.3527)
美妍美容教育中心-Beauty in                                 | cat=beauty_and_spa                           | (-28.5938, -61.2702)
Foul Point                                         | cat=landmark_and_historical_building         | (-45.4830, -60.5330)
Malou Madi                                         | cat=                                         | (-10.5469, -72.3957)
Institut für Emotionspädagogik                     | cat=educational_services                     | (-9.1423, -72.1828)
Animations enfants Tahiti, Le PitiMotu             | cat=arts_and_entertainment                   | (-18.9844, -74.0195)
Dregg Ackies                                       | cat=record_label                             | (-15.2241, -77.7364)
Sunsoli                                            | cat=                                         | (-19.6875, -77.4660)
Flocreazionipereventi                              | cat=wedding_planning                         | (-18.9844, -79.5605)
Martin Kráľ - Skladateľ                            | cat=                                         | (-21.0938, -81.9232)
Babul Yaman Store                                  | cat=shopping                                 | (-17.7061, -82.0149)
SMDC Property Investment                           | cat=real_estate_agent                        | (-11.0905, -80.5299)
WM Case - Profesjonalne skrzynie transportowe      | cat=                                         | (-12.0066, -78.3460)
Dwill Burguer                                      | cat=burger_restaurant                        | (-10.1604, -77.7070)
Sund med Mia                                       | cat=professional_services                    | (-11.2500, -83.6769)
Deli Repostería                                    | cat=desserts                                 | (-9.1406, -82.9834)
Buli Makhubo                                       | cat=                                         | (-3.5156, -83.6769)
Стиль и мода. Израиль                              | cat=image_consultant                         | (-4.2184, -81.4142)
Bedlam Studios                                     | cat=graphic_designer                         | (-4.2188, -78.2066)
رضا الحديثي - صفحتي الرسمية                        | cat=fitness_trainer                          | (-4.2188, -77.3125)
Friis Boliger                                      | cat=real_estate                              | (-6.3281, -77.7676)
রঙ-Israt's Art                                     | cat=arts_and_crafts                          | (-6.6440, -77.4370)
DirtySnatcha                                       | cat=                                         | (-6.9668, -77.8510)
Calbero                                            | cat=                                         | (-8.0376, -77.8665)
Jobswagon                                          | cat=                                         | (-1.3121, -75.4876)
هاوار                                              | cat=education                                | (-1.1250, -73.0636)
Antarctica/Troll                                   | cat=landmark_and_historical_building         | (2.5350, -72.0114)
Ventamark                                          | cat=electronics                              | (1.8698, -73.8298)
De Todo                                            | cat=electronics                              | (2.0158, -77.1183)
Inglés Personal                                    | cat=language_school                          | (3.5156, -75.4972)
João Gomes                                         | cat=                                         | (3.5276, -76.2323)
RA CARS PH                                         | cat=car_dealer                               | (3.4277, -76.4173)
אריאל שרם - צילום                                  | cat=professional_services                    | (0.5625, -77.8270)
Eden's Essential Elements                          | cat=flowers_and_gifts_shop                   | (-0.9375, -81.6554)
Laviva Cosmetics                                   | cat=beauty_and_spa                           | (-0.9375, -81.5871)
Elblesk e-mobility                                 | cat=automotive                               | (-0.7031, -83.9793)
ليو - Leo                                          | cat=social_media_company                     | (1.4062, -82.3089)
Otago Potters Group                                | cat=                                         | (2.1094, -83.9793)
Johanna Oedin                                      | cat=                                         | (4.9219, -70.3779)
Alchemist Craftworks                               | cat=metal_fabricator                         | (6.3446, -84.2951)
Panaderia Doña Tere                                | cat=bakery                                   | (6.6907, -84.5583)
廸康醫療中心 Madison Medical Centre                      | cat=acupuncture                              | (7.9102, -79.3677)
A Tale of Four Mages                               | cat=print_media                              | (9.1406, -84.7384)
JP Ranch Grass Valley                              | cat=farm                                     | (9.1406, -79.9359)
Wolf’s Fang Runway                                 | cat=                                         | (8.8064, -71.5246)
Fishing 411 TV                                     | cat=active_life                              | (10.5469, -77.4660)
Pro Massage & Pro PT                               | cat=                                         | (12.6562, -80.0580)
St. Isidore Catholic Learning Centre               | cat=                                         | (12.6562, -84.2672)
Cajurine.                                          | cat=tattoo_and_piercing                      | (13.3594, -80.9837)
Higi + Higienização e Impermeabilização de Estofados | cat=home_cleaning                            | (13.0162, -79.7517)
Organica Mart                                      | cat=food_delivery_service                    | (13.0508, -72.1964)
ซัมไทม์ วิว รีสอร์ท                                | cat=hostel                                   | (13.7109, -77.1620)
چارەسەر لە سروشتەوە                                | cat=beauty_and_spa                           | (14.0778, -83.2068)
Pro Punjab Tv                                      | cat=media_news_company                       | (16.3101, -74.3358)
elevatedxconscience                                | cat=                                         | (16.8750, -79.6872)
B.olivia - ps                                      | cat=beauty_and_spa                           | (15.4688, -62.2424)
Twory i Stwory                                     | cat=                                         | (19.6875, -78.4906)
Geraint Jones 4x4                                  | cat=automotive_dealer                        | (20.3906, -78.7378)
2 Nice                                             | cat=shoe_store                               | (19.7107, -81.1986)
Stress Relief Massage                              | cat=                                         | (21.0984, -77.7677)
Amrinder Bobby                                     | cat=                                         | (22.3903, -80.4665)
Make Noise Pro Audio LTD                           | cat=audio_visual_equipment_store             | (23.2031, -84.5414)
Morjane Ténéré                                     | cat=                                         | (23.2031, -78.9039)
Southern Ocean                                     | cat=landmark_and_historical_building         | (25.3125, -80.7606)
ليلى kids                                          | cat=childrens_clothing_store                 | (26.1029, -65.4201)
鉢伏山荘冬期営業                                           | cat=lodge                                    | (26.8581, -83.5918)
البروج للمواد الكهربائية                           | cat=shopping_center                          | (30.2344, -75.3200)
Beauty & Care                                      | cat=beauty_and_spa                           | (31.2106, -76.8967)
Béjaïa béni ksila vacances                         | cat=                                         | (31.2592, -79.1299)
In Love Furniture - ศูนย์รวมเฟอร์นิเจอร์ชุดห้องนอน และ เฟอร์นิเจอร์สำนักงาน | cat=furniture_store                          | (32.3438, -72.8161)
Woodstock Dentistry                                | cat=dentist                                  | (34.1059, -84.5392)
Paubril’s Beauty                                   | cat=beauty_salon                             | (33.7167, -60.9196)
This Esme                                          | cat=                                         | (35.1562, -82.6763)
Alex Sá                                            | cat=                                         | (38.3168, -70.4805)
MOFT                                               | cat=fashion_accessories_store                | (45.2344, -77.9157)
I-narin Beauty : เครื่องนวดหน้า สไตล์เกาหลี        | cat=beauty_and_spa                           | (45.0000, -75.1408)
Abracadabra Technologie                            | cat=telecommunications_company               | (46.4062, -84.4741)
Cafés Ellouze                                      | cat=coffee_shop                              | (49.2188, -76.6798)
หมามะเร็ง Dogs Cancer                              | cat=community_services_non_profits           | (52.0312, -78.4906)
Catiline Kindergarten - Whampoa カティライン幼稚園・インターナショナルプレスクール | cat=education                                | (54.5632, -73.6669)
厚興瑜記 WegoMall                                      | cat=restaurant                               | (54.0834, -78.0670)
Americano Cafe' Happy Moment with Coffee and Drink | cat=cafe                                     | (55.6006, -75.3202)
Japfa Experience                                   | cat=restaurant                               | (58.4581, -84.4264)
Celest Jewelry                                     | cat=                                         | (58.7188, -72.7876)
My湯 - 茘枝角                                          | cat=desserts                                 | (60.2448, -80.8134)
Bobby Layal                                        | cat=computer_museum                          | (60.5255, -83.0705)
Janeth E. Sarona                                   | cat=professional_services                    | (64.1526, -80.7189)
PD.Kannel                                          | cat=                                         | (63.6065, -70.0510)
แพวันวาน จังหวัดกาญจนบุรี                          | cat=hotel                                    | (66.0938, -68.1389)
Dalbe Réunion                                      | cat=                                         | (67.6686, -65.2935)
สมุทรสงครามรีวิว                                   | cat=                                         | (68.6079, -70.2800)
مركز آفنيو الطبي                                   | cat=hospital                                 | (69.6094, -69.9001)
ครูเปิ้ล สอนคอมฯ : ปวช. สกร.เมืองนครราชสีมา        | cat=shopping                                 | (70.5122, -72.7265)
Cat & Calmell                                      | cat=                                         | (69.2670, -83.2209)
Mette MÆRsk                                        | cat=professional_services                    | (70.6641, -80.6470)
อึ้งกุ่ยเฮง มอเตอร์ไซค์ ฮอนด้า ยามาฮ่า รถมือสอง ร้อยเอ็ด | cat=motorcycle_dealer                        | (72.8648, -84.9799)
World Auto Glass Inc.                              | cat=                                         | (73.2903, -63.4646)
Noskill Sensi                                      | cat=video_game_store                         | (74.5228, -70.1805)
Ingrid Christensen Coast                           | cat=landmark_and_historical_building         | (77.0000, -69.5000)
Tutti Bambino                                      | cat=toy_store                                | (77.3438, -70.6126)
Ellis Fjord                                        | cat=landmark_and_historical_building         | (78.0833, -68.6000)
Lake Jabs                                          | cat=landmark_and_historical_building         | (78.2500, -68.5500)
Langnes Fjord                                      | cat=landmark_and_historical_building         | (78.2500, -68.5000)
Krok Lake                                          | cat=lake                                     | (78.4000, -68.6167)
Lied Bluff                                         | cat=landmark_and_historical_building         | (78.2667, -68.5167)
Heidemann Bay                                      | cat=landmark_and_historical_building         | (77.9667, -68.5833)
Filla Island                                       | cat=landmark_and_historical_building         | (77.8333, -68.8167)
Lake Zvezda                                        | cat=landmark_and_historical_building         | (78.4500, -68.5333)
Dr. Cláudio Gurgel Magalhães - Clínica Belle Esthetique | cat=doctor                                   | (78.6741, -78.3909)
Ynnez Group                                        | cat=non_governmental_association             | (78.8864, -81.8986)
芸臻命理諮詢                                             | cat=psychic                                  | (79.6289, -82.5634)
Miya Élégance                                      | cat=womens_clothing_store                    | (82.9688, -67.8755)
แม่อี่ ผักดองตำหรับยูนนาน                          | cat=                                         | (88.2482, -78.9079)
NPS Institutions - Bidar                           | cat=school                                   | (92.1094, -81.9232)
Sozialkontakt                                      | cat=                                         | (93.5156, -78.7678)
albertopiernas                                     | cat=                                         | (96.3082, -65.3010)
Sekolah Islam Terpadu Green Bhakti Insani          | cat=middle_school                            | (98.4375, -76.5168)
Motel Mozaique Concerts                            | cat=hotel                                    | (98.1134, -75.5395)
CT Trampoline Fitness 沙田 石門 彈床班 Jump Fitness 健身 親子彈床班 | cat=gym                                      | (97.1123, -75.9774)
Casa de Adoración Yahweh                           | cat=religious_organization                   | (97.0059, -78.9339)
Sydney Amigurumi Corner 手勾公仔                       | cat=arts_and_crafts                          | (99.1406, -81.9232)
Shanaya Hotel Borobudur                            | cat=hotel                                    | (103.7109, -83.3595)
The Cumberland River Project                       | cat=river                                    | (105.4722, -80.9844)
Lake Vostok                                        | cat=landmark_and_historical_building         | (106.0000, -77.5000)
ร้านแน็ตโมบายโฟน                                   | cat=                                         | (106.5234, -70.4110)
Vostok İstasyonu                                   | cat=landmark_and_historical_building         | (106.8373, -78.4644)
Tiệm ảnh Phúc- Lạng Sơn                            | cat=professional_services                    | (107.5781, -73.6278)
مدرسة أحمد نمر حمدان الأساسية للبنين               | cat=school                                   | (107.5953, -76.8258)
ইলমুল কুরআন মাদ্রাসা লিল্লাহ বোর্ডিং ও ইয়াতিম খানা | cat=                                         | (108.9844, -69.7384)
سنتر بغداد                                         | cat=shopping                                 | (109.6875, -71.4388)
住商不動產樹林后站加盟店                                       | cat=professional_services                    | (110.3906, -83.3595)
塩彩 ～shiosai～                                       | cat=shopping                                 | (110.3906, -73.8248)
Kurd Sport HD                                      | cat=sports_club_and_league                   | (110.6866, -76.6303)
Sofiya Designs Gh                                  | cat=                                         | (113.2214, -70.0754)
水草館                                                | cat=museum                                   | (114.2895, -79.7468)
Compartilhagem                                     | cat=arts_and_crafts                          | (117.4219, -74.7758)
Kimstore                                           | cat=retail                                   | (118.8281, -68.9110)
Masjid Terapung Tanjung Bungah, Pulau Pinang       | cat=mosque                                   | (120.2344, -83.4403)
Angel Rising                                       | cat=                                         | (123.7500, -84.1250)
Lailo Farm Sanctuary                               | cat=community_services_non_profits           | (122.3438, -73.8248)
Dr Henri Bauer                                     | cat=                                         | (123.3330, -75.1000)
Academia Nacional de Sommeliers y Gastrónomos      | cat=school                                   | (128.9037, -83.2655)
ทำประกันกับอาอิซะห์                                | cat=beauty_and_spa                           | (133.6411, -80.3639)
Base antarctique Dumont-d'Urville                  | cat=landmark_and_historical_building         | (140.0013, -66.6628)
شركة الأركان لقطع غيار وزيوت السيارات\nAlarkan Auto Parts & Oils | cat=shopping                                 | (136.0460, -70.7250)
Seaoil Dangcagan                                   | cat=gas_station                              | (135.7801, -80.4574)
The Seasoning Outlet                               | cat=shopping                                 | (144.1406, -79.6872)
MAX Reprise                                        | cat=automotive_repair                        | (149.2263, -74.4441)
Antarctic Plateau                                  | cat=landmark_and_historical_building         | (150.0000, -77.0000)
Warning Five จำหน่ายสายนาฬิกาหนังแท้ สายนาฬิกา smartwatch | cat=                                         | (154.4062, -74.4635)
海港青年商會 JCI Harbour                                 | cat=community_services_non_profits           | (153.2633, -83.7114)
মুক্তি হারবাল চিকিৎসালয়-Mukti Herbal Chikitsalay   | cat=family_practice                          | (155.7297, -69.4263)
Herrlich Beauty By Cloriss                         | cat=beauty_salon                             | (150.4688, -68.9110)
Mario-Zucchelli-Station                            | cat=                                         | (164.1125, -74.6943)
Psicóloga Serly C. de Souza                        | cat=psychotherapist                          | (164.4135, -77.8789)
Polární stanice McMurdo                            | cat=landmark_and_historical_building         | (166.6667, -77.8500)
Base McMurdo                                       | cat=landmark_and_historical_building         | (166.6667, -77.8500)
Base antarctique McMurdo                           | cat=landmark_and_historical_building         | (166.6667, -77.8500)
Gallagher Pub                                      | cat=bar                                      | (166.6700, -77.8454)
Southern Exposure                                  | cat=bar                                      | (166.6700, -77.8454)
ZiroZiro                                           | cat=religious_organization                   | (166.6700, -77.8454)
McMurdo General Hospital                           | cat=hospital                                 | (166.6693, -77.8468)
Coffee House                                       | cat=coffee_shop                              | (166.6643, -77.8469)
Chapel of the Snows                                | cat=church_cathedral                         | (166.6620, -77.8470)
Pegasus Field                                      | cat=airport                                  | (166.5244, -77.9633)
悦聖翡翠                                               | cat=fashion                                  | (176.0276, -82.2987)

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

reader = overturemaps.record_batch_reader('place', bbox=(-180, -90, 180, -60), release='2026-06-17.0', stac=True)
gdf = gpd.GeoDataFrame.from_arrow(reader)

# Parse geometry and set CRS
gdf['geometry'] = gdf['geometry'].apply(lambda x: wkb.loads(x) if isinstance(x, bytes) else x)
gdf = gdf.set_geometry('geometry')
gdf = gdf.set_crs('EPSG:4326')

# Filter to south of -60 by actual geometry
gdf = gdf[gdf.geometry.y < -60].copy()
print(f'Places south of -60: {len(gdf)}')

# Now find research stations
station_keywords = [
    'station', 'base', 'research', 'antarctic', 'mcmurdo', 'vostok', 
    'amundsen-scott', 'scott base', 'dumont', 'esperanza', 'marambio',
    'bellingshausen', 'brown', 'gonzalez', 'videla', 'troll', 'zucchelli',
    'mario zucchelli', 'palmer', 'casey', 'davis', 'mawson', 'mirny',
    'novolazarevskaya', 'progress', 'south pole', 'amundsen', 'Scott',
    'syowa', 'halley', 'rothera', 'san martin', 'orcad', 'carlini',
    'petrel', 'deception', 'matienzo', 'primavera', 'cámara',
    'comandante ferraz', 'king sejong', 'jang bogo', 'zhongshan',
    'great wall', 'taishan', 'kunlun', 'bharati', 'maitri', 'dakshin gangotri',
    'concordia', 'domingo f. sarmiento', 'teniente luis candelaria',
    'profesor julio escudero', 'general bernardo o higgins',
    'presidente eduardo frei', 'capitan arturo prat', 'gabriel gonzalez videla',
    'risopatron', 'ohiggins', 'frei', 'escudero', 'prat', 'marsh',
    'vernadsky', 'faraday', 'akademik vernadsky', 'artigas',
    'machu picchu', 'neumayer', 'kohnen', 'princess elisabeth',
    'law-racovita', 'zavidovsky', 'leningradskaya', 'russian',
    'vladivostok', 'molodyozhnaya', 'soyuz', 'druzhnaya',
]

# Names I'm specifically looking for from the output above
interesting = []
for i, row in gdf.iterrows():
    name = row['names']['primary'] if row['names'] and row['names']['primary'] else ''
    name_lower = name.lower()
    if any(kw.lower() in name_lower for kw in station_keywords):
        interesting.append((name, row.geometry.x, row.geometry.y, row['id']))

print(f'\\nPotential stations ({len(interesting)}):')
for n, x, y, i in interesting:
    print(f'  {n:50s} | ({x:.4f}, {y:.4f}) | {i}')
" 2>&1

Places south of -60: 246

Potential stations (23):
  Port Lockroy, Antarctic Peninsula                  | (-63.4910, -64.8247) | 2a100ade-be10-41c0-b6c2-a49e921a24e7
  Palmer Station, Antarctica                         | (-64.0531, -64.7743) | f73848f3-abaa-432b-b3ed-8092a6b39b9e
  Brown Station                                      | (-62.8700, -64.8950) | eb858253-acd6-4231-9ce9-e0e4c799d7a0
  González Videla Antarctic Base                     | (-62.8575, -64.8234) | ba9d8ef6-32f3-4098-a010-b5b485e4e75f
  Antarctica, Antarctic Circle                       | (-60.9322, -64.0536) | 9d364bf8-eaaf-4f79-ad45-195f74f49a93
  Esperanto Island, Antarctica                       | (-60.3647, -62.6414) | 3bf8e968-6050-4ee6-8a60-f046db54cc02
  Bellingshausen Russian Antarctic Station           | (-58.9624, -62.1998) | 6240b821-30cb-4fe4-a08a-6fddb4fe2978
  Base Marambio, Antartida Argentina                 | (-56.6198, -64.2413) | 18414d58-4e4c-480f-bfab-ee3e224c5b5e
  Marambio Base                                      | (-56.6268, -64.2410) | 015df87e-1265-4d80-844b-97a4c1af44a5
  Chapel of the Blessed Virgin of Lujan, Antarctica  | (-56.6284, -64.2407) | 4775fc70-d68b-48b4-af40-377af7e9a457
  Base Antártica Marambio                            | (-56.6235, -64.2374) | 11037d14-337b-4a53-a23b-85566484f016
  Base Esperanza, Antartida Argentina                | (-56.9974, -63.3980) | b1662153-d852-4a90-b8a7-5d5e113439bb
  Esperanza Base, Antarctica                         | (-56.9961, -63.3977) | 8536af5a-82ba-46ed-bd6c-069082cbfc5d
  Antarctica/Troll                                   | (2.5350, -72.0114) | e3417e97-f283-4ecc-a32b-ae164e69903f
  Lake Vostok                                        | (106.0000, -77.5000) | 63b91770-28a5-4a5c-bea8-0e5842150734
  Vostok İstasyonu                                   | (106.8373, -78.4644) | bfd01a42-92a7-43f9-a7d3-0cc7fb3157b4
  Base antarctique Dumont-d'Urville                  | (140.0013, -66.6628) | 04d8258a-e765-4a4d-96c0-20cd3b11a9c3
  Antarctic Plateau                                  | (150.0000, -77.0000) | 26f0564a-e108-46da-b7aa-a4f3aca0daa2
  Mario-Zucchelli-Station                            | (164.1125, -74.6943) | fcdd73f9-5cb8-4812-be88-099727bf3936
  Polární stanice McMurdo                            | (166.6667, -77.8500) | bb48b222-57c4-4c85-8743-752df4bba2ca
  Base McMurdo                                       | (166.6667, -77.8500) | d5882de0-07ff-46b9-a3c0-bbfb0cc9552c
  Base antarctique McMurdo                           | (166.6667, -77.8500) | d74e5a73-042c-4694-9e16-fa9622f203b3
  McMurdo General Hospital                           | (166.6693, -77.8468) | 82b40435-6b61-4d8e-9caa-7ff1d248b8a1

Step 1: Fetching places...
  Total places fetched: 246
  Places south of -60°: 246
  Identified research stations: 17
    Port Lockroy                              (-63.4910, -64.8247)
    Palmer Station                            (-64.0531, -64.7743)
    Brown Station                             (-62.8700, -64.8950)
    González Videla Antarctic Base            (-62.8575, -64.8234)
    Bellingshausen Station                    (-58.9624, -62.1998)
    Marambio Base                             (-56.6198, -64.2413)
    Marambio Base                             (-56.6268, -64.2410)
    Marambio Base                             (-56.6235, -64.2374)
    Esperanza Base                            (-56.9974, -63.3980)
    Esperanza Base                            (-56.9961, -63.3977)
    Troll Station                             (2.5350, -72.0114)
    Vostok Station                            (106.8373, -78.4644)
    Dumont d'Urville Station                  (140.0013, -66.6628)
    Mario Zucchelli Station                   (164.1125, -74.6943)
    McMurdo Station                           (166.6667, -77.8500)
    McMurdo Station                           (166.6667, -77.8500)
    McMurdo Station                           (166.6667, -77.8500)
  Total stations after fallback: 17

Step 2: Fetching Antarctic landmass...
  Land features: 93369
  Land features intersecting Antarctic region: 93369
  Unexpected land geometry type: GeometryCollection
  Antarctic land area (approx sq deg): 6662

Step 3: Creating buffered zones clipped to land...
  Land-clipped zones: 17

Step 4: Forming coalitions from overlapping zones...
  Number of coalitions: 5
  Coalition assignments:
    Coalition 1: Port Lockroy, Palmer Station, Brown Station, González Videla Antarctic Base, Bellingshausen Station, Marambio Base, Marambio Base, Marambio Base, Esperanza Base, Esperanza Base
    Coalition 2: Troll Station
    Coalition 3: Vostok Station
    Coalition 4: Dumont d'Urville Station
    Coalition 5: Mario Zucchelli Station, McMurdo Station, McMurdo Station, McMurdo Station

  Output spheres count: 17
  Coalitions: [np.int64(1), np.int64(2), np.int64(3), np.int64(4), np.int64(5)]

Step 5: Fetching water features...
  Water features: 13500
  Water features in Antarctic region: 13500

Step 6: Fetching bathymetry features...
  Bathymetry features: 3096
  Bathymetry features in Antarctic region: 3096

Step 7: Computing over-water intersections...
  Troll Station: No over-water portion (landlocked)
  Vostok Station: No over-water portion (landlocked)
  Over-water intersection rows: 1980

Step 8: Writing output files...
  Wrote station_spheres.geoparquet: 17 rows
  Wrote station_water_overlap.geoparquet: 1980 rows

Step 9: Verification...

  station_spheres columns: ['station_id', 'station_name', 'coalition', 'geometry']
  station_water_overlap columns: ['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source', 'geometry']
  Distinct coalition values in spheres: [np.int64(1), np.int64(2), np.int64(3), np.int64(4), np.int64(5)]
  Number of distinct coalition values: 5
  spheres CRS: EPSG:3031
  water_overlap CRS: EPSG:3031
  spheres geometry types: <ArrowStringArray>
['MultiPolygon']
Length: 1, dtype: str
  water_overlap geometry types: <ArrowStringArray>
['MultiPolygon']
Length: 1, dtype: str

Done!

Step 1: Fetching places...
  Total places fetched: 246
  Places south of -60°: 246
  Identified research stations: 12
    Port Lockroy                              (-63.4910, -64.8247)
    Palmer Station                            (-64.0531, -64.7743)
    Brown Station                             (-62.8700, -64.8950)
    González Videla Antarctic Base            (-62.8575, -64.8234)
    Bellingshausen Station                    (-58.9624, -62.1998)
    Marambio Base                             (-56.6233, -64.2399)
    Esperanza Base                            (-56.9968, -63.3978)
    Troll Station                             (2.5350, -72.0114)
    Vostok Station                            (106.8373, -78.4644)
    Dumont d'Urville Station                  (140.0013, -66.6628)
    Mario Zucchelli Station                   (164.1125, -74.6943)
    McMurdo Station                           (166.6667, -77.8500)
  Total stations after fallback: 12

Step 2: Fetching Antarctic landmass...
  Land features: 93369
  Land features intersecting Antarctic region: 93369
  Unexpected land geometry type: GeometryCollection
  Antarctic land area (approx sq deg): 6662

Step 3: Creating buffered zones clipped to land...
  Land-clipped zones: 12

Step 4: Forming coalitions from overlapping zones...
  Number of coalitions: 5
  Coalition assignments:
    Coalition 1: Port Lockroy, Palmer Station, Brown Station, González Videla Antarctic Base, Bellingshausen Station, Marambio Base, Esperanza Base
    Coalition 2: Troll Station
    Coalition 3: Vostok Station
    Coalition 4: Dumont d'Urville Station
    Coalition 5: Mario Zucchelli Station, McMurdo Station

  Output spheres count: 12
  Coalitions: [np.int64(1), np.int64(2), np.int64(3), np.int64(4), np.int64(5)]

Step 5: Fetching water features...
  Water features: 13500
  Water features in Antarctic region: 13500

Step 6: Fetching bathymetry features...
  Bathymetry features: 3096
  Bathymetry features in Antarctic region: 3096

Step 7: Computing over-water intersections...
  Troll Station: No over-water portion (landlocked)
  Vostok Station: No over-water portion (landlocked)
  Over-water intersection rows: 1372

Step 8: Writing output files...
  Wrote station_spheres.geoparquet: 12 rows
  Wrote station_water_overlap.geoparquet: 1372 rows

Step 9: Verification...

  station_spheres columns: ['station_id', 'station_name', 'coalition', 'geometry']
  station_water_overlap columns: ['station_id', 'station_name', 'water_id', 'water_name', 'water_subtype', 'water_source', 'geometry']
  Distinct coalition values in spheres: [np.int64(1), np.int64(2), np.int64(3), np.int64(4), np.int64(5)]
  Number of distinct coalition values: 5
  spheres CRS: EPSG:3031
  water_overlap CRS: EPSG:3031
  spheres geometry types: <ArrowStringArray>
['MultiPolygon']
Length: 1, dtype: str
  water_overlap geometry types: <ArrowStringArray>
['MultiPolygon']
Length: 1, dtype: str

Done!

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

# Check spheres
spheres = gpd.read_parquet('station_spheres.geoparquet')
print('=== station_spheres.geoparquet ===')
print(f'Rows: {len(spheres)}')
print(f'Columns: {list(spheres.columns)}')
print(f'CRS: {spheres.crs}')
print(f'Geometry types: {spheres.geom_type.unique()}')
print(f'Coalition values: {sorted(spheres.coalition.unique())}')
print(f'Distinct coalitions: {spheres.coalition.nunique()}')
print()
print('Sample data:')
for _, row in spheres.iterrows():
    print(f'  Coalition {row.coalition}: {row.station_name} ({row.station_id[:20]}...)')
    # Check coordinate magnitude
    bounds = row.geometry.bounds
    print(f'    bounds: minx={bounds[0]:.1f}, miny={bounds[1]:.1f}, maxx={bounds[2]:.1f}, maxy={bounds[3]:.1f}')
print()

# Check water overlap
water = gpd.read_parquet('station_water_overlap.geoparquet')
print('=== station_water_overlap.geoparquet ===')
print(f'Rows: {len(water)}')
print(f'Columns: {list(water.columns)}')
print(f'CRS: {water.crs}')
print(f'Geometry types: {water.geom_type.unique()}')
print()
print('Water source values:')
print(water.water_source.value_counts())
print()
print('Water subtype values:')
print(water.water_subtype.value_counts().head(10))
print()
print('Sample rows:')
for _, row in water.head(5).iterrows():
    print(f'  Station: {row.station_name} | water_id={row.water_id[:20]}... | name={row.water_name} | subtype={row.water_subtype} | source={row.water_source}')
" 2>&1

=== station_spheres.geoparquet ===
Rows: 12
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: <ArrowStringArray>
['MultiPolygon']
Length: 1, dtype: str
Coalition values: [np.int64(1), np.int64(2), np.int64(3), np.int64(4), np.int64(5)]
Distinct coalitions: 5

Sample data:
  Coalition 1: Port Lockroy (2a100ade-be10-41c0-b...)
    bounds: minx=-2619603.0, miny=1067421.5, maxx=-2286339.8, maxy=1440129.9
  Coalition 1: Palmer Station (f73848f3-abaa-432b-b...)
    bounds: minx=-2619183.5, miny=1037237.1, maxx=-2303542.8, maxy=1418138.9
  Coalition 1: Brown Station (eb858253-acd6-4231-9...)
    bounds: minx=-2619603.0, miny=1067421.5, maxx=-2265630.3, maxy=1463356.3
  Coalition 1: González Videla Antarctic Base (ba9d8ef6-32f3-4098-a...)
    bounds: minx=-2646173.7, miny=1072116.0, maxx=-2272604.3, maxy=1467610.1
  Coalition 1: Bellingshausen Station (6240b821-30cb-4fe4-a...)
    bounds: minx=-2662909.1, miny=1388152.8, maxx=-2438335.0, maxy=1785500.6
  Coalition 1: Marambio Base (18414d58-4e4c-480f-b...)
    bounds: minx=-2538565.0, miny=1365667.9, maxx=-2350922.8, maxy=1704487.5
  Coalition 1: Esperanza Base (b1662153-d852-4a90-b...)
    bounds: minx=-2653126.6, miny=1402118.2, maxx=-2360880.3, maxy=1704487.5
  Coalition 2: Troll Station (e3417e97-f283-4ecc-a...)
    bounds: minx=-112864.4, miny=1768144.4, maxx=287135.6, maxy=2168144.4
  Coalition 3: Vostok Station (bfd01a42-92a7-43f9-a...)
    bounds: minx=1003543.0, miny=-564226.8, maxx=1403543.0, maxy=-164226.8
  Coalition 4: Dumont d'Urville Station (04d8258a-e765-4a4d-9...)
    bounds: minx=1451835.3, miny=-2059055.8, maxx=1832042.4, maxy=-1768673.6
  Coalition 5: Mario Zucchelli Station (fcdd73f9-5cb8-4812-b...)
    bounds: minx=305410.0, miny=-1808677.1, maxx=657864.9, maxy=-1408677.1
  Coalition 5: McMurdo Station (bb48b222-57c4-4c85-8...)
    bounds: minx=105542.2, miny=-1489174.7, maxx=505542.2, maxy=-1089183.8

=== station_water_overlap.geoparquet ===
Rows: 1372
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: <ArrowStringArray>
['MultiPolygon']
Length: 1, dtype: str

Water source values:
water_source
base.bathymetry    701
base.water         671
Name: count, dtype: int64

Water subtype values:
water_subtype
            701
ocean       505
physical    161
lake          5
Name: count, dtype: int64

Sample rows:
  Station: Port Lockroy | water_id=91cdb906-a4a0-3f2c-a... | name= | subtype=ocean | source=base.water
  Station: Port Lockroy | water_id=f4566628-63e7-3fa1-b... | name= | subtype=ocean | source=base.water
  Station: Port Lockroy | water_id=7d304f2e-2455-3e8f-8... | name= | subtype=ocean | source=base.water
  Station: Port Lockroy | water_id=f5ebe726-9c35-36c9-b... | name= | subtype=ocean | source=base.water
  Station: Port Lockroy | water_id=24544cd9-2a2d-324f-8... | name= | subtype=ocean | source=base.water

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

water = gpd.read_parquet('station_water_overlap.geoparquet')
print('=== Checking water_name values ===')
named = water[water['water_name'] != '']
print(f'Rows with water_name: {len(named)} out of {len(water)}')
if len(named) > 0:
    print('Sample named water features:')
    for _, row in named.head(10).iterrows():
        print(f'  {row.station_name} | name={row.water_name} | subtype={row.water_subtype} | source={row.water_source}')
print()

# Also check no weird values
print('=== Checking for any nulls ===')
print(f'water_id nulls: {water.water_id.isna().sum()}')
print(f'water_name nulls: {water.water_name.isna().sum()}')
print(f'water_subtype nulls: {water.water_subtype.isna().sum()}')
print(f'water_source nulls: {water.water_source.isna().sum()}')
print(f'water_source values: {water.water_source.unique()}')
" 2>&1

=== Checking water_name values ===
Rows with water_name: 161 out of 1372
Sample named water features:
  Port Lockroy | name=Port-Charcot | subtype=physical | source=base.water
  Port Lockroy | name=Flanders Bay | subtype=physical | source=base.water
  Port Lockroy | name=Gerritsz Bay | subtype=physical | source=base.water
  Port Lockroy | name=Oscar Pinochet Strait | subtype=physical | source=base.water
  Port Lockroy | name=Neko Harbour | subtype=physical | source=base.water
  Port Lockroy | name=Canal Almirante Merino | subtype=physical | source=base.water
  Port Lockroy | name=Buls Bay | subtype=physical | source=base.water
  Port Lockroy | name=Larsen Inlet | subtype=physical | source=base.water
  Palmer Station | name=Port-Charcot | subtype=physical | source=base.water
  Palmer Station | name=Flanders Bay | subtype=physical | source=base.water

=== Checking for any nulls ===
water_id nulls: 0
water_name nulls: 0
water_subtype nulls: 0
water_source nulls: 0
water_source values: <ArrowStringArray>
['base.water', 'base.bathymetry']
Length: 2, dtype: str