dd-l3-lagos-overture-buildings

Rerun

python3 -c "
import duckdb
con = duckdb.connect()
# Check if spatial extension is available
con.install_extension('spatial')
con.load_extension('spatial')
print('Spatial extension OK')
" 2>&1

python3 << 'EOF'
import duckdb

con = duckdb.connect()
con.install_extension('spatial')
con.install_extension('httpfs')
con.load_extension('spatial')
con.load_extension('httpfs')

# Check what's available in Overture's latest release
# Try to list the release
result = con.execute("""
SELECT *
FROM read_parquet('s3://overturemaps-us-west-2/release/2024-07-22.0/theme=buildings/type=building/*', 
                   filename=true, hive_partitioning=false)
LIMIT 5
""").fetchdf()
print(result.columns.tolist())
print(result.head())
EOF

Traceback (most recent call last):
  File "<stdin>", line 11, in <module>
_duckdb.IOException: IO Error: No files found that match the pattern "s3://overturemaps-us-west-2/release/2024-07-22.0/theme=buildings/type=building/*"

LINE 3: FROM read_parquet('s3://overturemaps-us-west-2/release/2024-07...
             ^

python3 << 'EOF'
import duckdb
con = duckdb.connect()
con.install_extension('httpfs')
con.load_extension('httpfs')
con.install_extension('aws')
con.load_extension('aws')

# Try to list the bucket contents
result = con.execute("""
SELECT *
FROM s3_list('s3://overturemaps-us-west-2/release/', region='us-west-2')
LIMIT 20
""").fetchdf()
print(result)
EOF

Traceback (most recent call last):
  File "<stdin>", line 9, in <module>
_duckdb.CatalogException: Catalog Error: Table Function with name s3_list does not exist!
Did you mean "seq_scan"?

LINE 3: FROM s3_list('s3://overturemaps-us-west-2/release/', region=...
             ^

python3 << 'EOF'
import duckdb
con = duckdb.connect()
con.install_extension('spatial')
con.install_extension('httpfs')
con.load_extension('spatial')
con.load_extension('httpfs')

# The Overture dataset is public - let's check the latest release
# Try with S3 anonymous access
con.execute("SET s3_region='us-west-2'")
con.execute("SET s3_anonymous=true")

# Let's find available releases
result = con.execute("""
SELECT * FROM s3('s3://overturemaps-us-west-2/release/', RECURSIVE=false) LIMIT 10
""").fetchdf()
print(result)
EOF

Traceback (most recent call last):
  File "<stdin>", line 11, in <module>
_duckdb.CatalogException: Catalog Error: unrecognized configuration parameter "s3_anonymous"

Did you mean: "s3_endpoint"

python3 << 'EOF'
import duckdb
con = duckdb.connect()
con.install_extension('spatial')
con.install_extension('httpfs')
con.install_extension('aws')
con.load_extension('spatial')
con.load_extension('httpfs')
con.load_extension('aws')

# List available releases
res = con.execute("""
SELECT * 
FROM glob('s3://overturemaps-us-west-2/release/*', 
          access_key_id='', secret_access_key='',
          region='us-west-2')
LIMIT 20
""").fetchdf()
print(res)
EOF

Traceback (most recent call last):
  File "<stdin>", line 11, in <module>
_duckdb.BinderException: Binder Error: Invalid named parameter "region" for function glob
Function does not accept any named parameters.

LINE 3: FROM glob('s3://overturemaps-us-west-2/release/*', 
             ^

python3 << 'EOF'
import requests
# Check latest Overture release
r = requests.get("https://docs.overturemaps.org/")
# Or check the releases
r2 = requests.get("https://github.com/OvertureMaps/data/releases")
print(r2.status_code)
# Let's try a known release date
# The latest as of mid-2024 is 2024-07-22.0
EOF

python3 << 'EOF'
import duckdb
con = duckdb.connect()
con.install_extension('spatial')
con.install_extension('httpfs')
con.install_extension('aws')
con.load_extension('spatial')
con.load_extension('httpfs')
con.load_extension('aws')

# The Overture docs say: no credentials needed, just use anonymous access
# Try setting anonymous as a secret
con.execute("""
CREATE SECRET overture_secret (
    TYPE S3,
    PROVIDER CREDENTIAL_CHAIN,
    CHAIN 'config'::VARCHAR,
    REGION 'us-west-2'
)
""")

# Try listing
res = con.execute("""
SELECT file_name, size
FROM s3_list('s3://overturemaps-us-west-2/release/2024-07-22.0/')
LIMIT 50
""").fetchdf()
print(res)
EOF

Traceback (most recent call last):
  File "<stdin>", line 12, in <module>
_duckdb.Error: Secret Validation Failure: during `create` using the following:
Credential Chain: 'config'

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

python3 << 'PYEOF'
from overturemaps import download
import geopandas as gpd
import shapely

# Let's check the available records function
help(download)
PYEOF

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.

python3 << 'EOF'
import overturemaps

# Let's see all the overture types
types = overturemaps.get_all_overture_types()
print(types)
EOF

python3 << 'EOF'
import overturemaps

# First, let's get the division for Nigeria to find Lagos State
# division_area type has administrative boundaries
reader = overturemaps.record_batch_reader('division_area', release='2024-07-22.0')
if reader:
    import pyarrow as pa
    df = reader.read_pandas()
    print("Columns:", df.columns.tolist())
    print("Shape:", df.shape)
    print(df.head())
else:
    print("No reader returned")
EOF

Traceback (most recent call last):
  File "<stdin>", line 5, in <module>
  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/2024-07-22.0/theme=divisions/type=division_area/

Help on function geodataframe in module overturemaps.core:

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

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

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

python3 << 'EOF'
# Let's try using overturemaps with a bbox
import overturemaps

# Lagos State approximate bbox
bbox = (2.7, 6.2, 4.0, 6.7)

print("Fetching buildings for Lagos...")
gdf = overturemaps.geodataframe('building', bbox=bbox, release='2024-07-22.0')
print(f"Loaded {len(gdf)} buildings")
print("Columns:", gdf.columns.tolist())
print(gdf.head())
EOF

Traceback (most recent call last):
  File "<stdin>", line 8, in <module>
  File "/usr/local/lib/python3.14/site-packages/overturemaps/core.py", line 342, in geodataframe
    reader = record_batch_reader(
        overture_type,
    ...<4 lines>...
        stac=stac,
    )
  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/2024-07-22.0/theme=buildings/type=building/

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

    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,
    bbox: 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

    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)

python3 << 'EOF'
import overturemaps

# Get latest release
release = overturemaps.get_latest_release()
print("Latest release:", release)

releases = overturemaps.get_available_releases()
print("All releases:", releases[0][:5])
print("Latest:", releases[1])
EOF

python3 << 'EOF'
import overturemaps
from overturemaps.core import get_latest_release, get_available_releases
print("Latest release:", get_latest_release())
releases, latest = get_available_releases()
print("First 5 releases:", releases[:5])
print("Latest:", latest)
EOF

python3 << 'EOF'
from overturemaps.core import record_batch_reader

# Lagos bbox
bbox = (2.7, 6.2, 4.0, 6.7)

print("Fetching buildings for Lagos with stac=True...")
reader = record_batch_reader('building', bbox=bbox, release='2026-05-20.0', stac=True)
if reader:
    import pyarrow as pa
    table = reader.read_all()
    print(f"Loaded {len(table)} rows")
    print("Schema:", table.schema)
    print("Columns:", table.column_names)
else:
    print("No reader returned")
EOF

Fetching buildings for Lagos with stac=True...
Loaded 3070243 rows
Schema: id: string
names: struct<primary: string, common: map<string, string ('common')>, rules: list<element: struct<variant: (... 159 chars omitted)
  child 0, primary: string
  child 1, common: map<string, string ('common')>
      child 0, common: struct<key: string not null, value: string> not null
          child 0, key: string not null
          child 1, value: string
  child 2, rules: list<element: struct<variant: string, language: string, perspectives: struct<mode: string, countries (... 87 chars omitted)
      child 0, element: struct<variant: string, language: string, perspectives: struct<mode: string, countries: list<element (... 72 chars omitted)
          child 0, variant: string
          child 1, language: string
          child 2, perspectives: struct<mode: string, countries: list<element: string>>
              child 0, mode: string
              child 1, countries: list<element: string>
                  child 0, element: string
          child 3, value: string
          child 4, between: list<element: double>
              child 0, element: double
          child 5, side: string
sources: list<element: struct<property: string, dataset: string, license: string, record_id: string, update_t (... 65 chars omitted)
  child 0, element: struct<property: string, dataset: string, license: string, record_id: string, update_time: string, c (... 50 chars omitted)
      child 0, property: string
      child 1, dataset: string
      child 2, license: string
      child 3, record_id: string
      child 4, update_time: string
      child 5, confidence: double
      child 6, between: list<element: double>
          child 0, element: double
level: int32
height: double
min_height: double
is_underground: bool
num_floors: int32
num_floors_underground: int32
min_floor: int32
subtype: string
class: string
facade_color: string
facade_material: string
roof_material: string
roof_shape: string
roof_direction: double
roof_orientation: string
roof_color: string
roof_height: double
geometry: binary
  -- field metadata --
  ARROW:extension:name: 'geoarrow.wkb'
has_parts: bool
version: int32
bbox: struct<xmin: double, xmax: double, ymin: double, ymax: double>
  child 0, xmin: double
  child 1, xmax: double
  child 2, ymin: double
  child 3, ymax: double
-- schema metadata --
geo: '{"version": "1.1.0", "primary_column": "geometry", "columns": {"geo' + 293
Columns: ['id', 'names', 'sources', 'level', 'height', 'min_height', 'is_underground', 'num_floors', 'num_floors_underground', 'min_floor', 'subtype', 'class', 'facade_color', 'facade_material', 'roof_material', 'roof_shape', 'roof_direction', 'roof_orientation', 'roof_color', 'roof_height', 'geometry', 'has_parts', 'version', 'bbox']

python3 << 'EOF'
from overturemaps.core import record_batch_reader
import pyarrow as pa

# Get division_area for Nigeria (bbox for whole country roughly)
# Nigeria bbox: ~2.5°E to 15°E, 4°N to 14°N
bbox_ng = (2.5, 4.0, 15.0, 14.0)

print("Fetching division_area for Nigeria...")
reader = record_batch_reader('division_area', bbox=bbox_ng, release='2026-05-20.0', stac=True)
if reader:
    table = reader.read_all()
    print(f"Loaded {len(table)} rows")
    print("Columns:", table.column_names)
    df = table.to_pandas()
    print(df[['id', 'names', 'subtype', 'version']].head(20))
    # Check what subtypes exist
    print("Subtypes:", df['subtype'].unique())
else:
    print("No reader returned")
EOF

Fetching division_area for Nigeria...
Loaded 4621 rows
Columns: ['id', 'geometry', 'country', 'sources', 'subtype', 'admin_level', 'class', 'names', 'is_land', 'is_territorial', 'region', 'division_id', 'version', 'bbox']
                                      id  ... version
0   4bbf89ce-38b8-489a-9753-5db1360339d6  ...       5
1   f5536621-a798-4c77-b1c7-39a54c8c1cdb  ...       6
2   3ee017ee-ae6c-45ff-ae72-84758cec508f  ...       8
3   3fbb5ca3-a2b6-4918-b752-a08dbd99fa88  ...       4
4   e93c0c70-b668-46e9-bf0c-9e4df83094f9  ...       4
5   71823457-7e1d-4229-b75f-26a7888c5ad6  ...       1
6   9e3dbc30-ebf3-4549-8368-2801ea35fe76  ...       1
7   0c2d096d-423e-4dea-8439-4987066b094b  ...       1
8   7fdff85c-f921-453f-9138-1af362204c96  ...       1
9   c769b455-dbe9-4e0a-b76d-9c1a27b13d6b  ...       1
10  c3b03d60-bffa-4990-977f-b1aa9d24405f  ...       1
11  39d1fd74-743c-4fc7-92ac-a62e6752d094  ...       1
12  9f4912e6-504c-45a2-bbc2-a39451a8d776  ...       1
13  e1ef4b54-d4a7-4c31-85bc-fae29aa9ee9b  ...       1
14  9136fa62-754b-4c2f-a0b4-ee1195a8a736  ...       1
15  2a31b998-68eb-403b-883c-d0499e357cfc  ...       3
16  6e1ada31-8f7b-4529-b8d9-cd14f488b4eb  ...       3
17  d2afbe60-432b-4d6d-b0d8-7b198f16e115  ...       1
18  a3575246-901d-4b8b-8113-0055b842a231  ...       1
19  5060104e-9850-4d58-a23b-597b6822d156  ...       1

[20 rows x 4 columns]
Subtypes: <ArrowStringArray>
[  'dependency',      'country',       'county',     'locality',
       'region', 'neighborhood',    'macrohood',    'microhood']
Length: 8, dtype: str

python3 << 'EOF'
from overturemaps.core import record_batch_reader
import pyarrow as pa
import pyarrow.compute as pc

# Get division_area for Nigeria
bbox_ng = (2.5, 4.0, 15.0, 14.0)

reader = record_batch_reader('division_area', bbox=bbox_ng, release='2026-05-20.0', stac=True)
if reader:
    table = reader.read_all()
    df = table.to_pandas()
    
    # Look for Nigeria-related entries
    # Check names column
    print("Columns:", df.columns.tolist())
    print("Subtypes:", df['subtype'].unique())
    
    # Look at the names column structure
    print("\nNames sample:", df['names'].head(3).tolist())
    
    # Find Nigeria
    nigeria_rows = df[df['country'] == 'NG']
    print(f"\nRows with country=NG: {len(nigeria_rows)}")
    print(nigeria_rows[['id', 'subtype', 'country']].to_string())
    
    # Find rows with region info
    for idx, row in nigeria_rows.iterrows():
        print(f"\nRow {idx}: subtype={row['subtype']}, names={row['names']}")
EOF

Columns: ['id', 'geometry', 'country', 'sources', 'subtype', 'admin_level', 'class', 'names', 'is_land', 'is_territorial', 'region', 'division_id', 'version', 'bbox']
Subtypes: <ArrowStringArray>
[  'dependency',      'country',       'county',     'locality',
       'region', 'neighborhood',    'macrohood',    'microhood']
Length: 8, dtype: str

Names sample: [{'primary': 'United States Minor Outlying Islands', 'common': [('hr', 'Mali Udaljeni Otoci Sjedinjenih Američkih Država'), ('pt', 'Ilhas Menores Distantes dos Estados Unidos'), ('cs', 'Menší odlehlé ostrovy Spojených států amerických'), ('ca', "Illes d'Ultramar Menors dels Estats Units"), ('nl', 'Amerikaanse Kleinere Afgelegen Eilanden'), ('zh-Hans', '美国本土外小岛屿'), ('eo', 'Usonaj Malgrandaj Insuloj'), ('ru', 'Внешние малые острова США'), ('zh-Hant', '美國本土外小島嶼'), ('en', 'United States Minor Outlying Islands'), ('es', 'Islas Ultramarinas Menores de Estados Unidos'), ('lt', 'Jungtinių Amerikos Valstijų mažosios aplinkinės salos'), ('zh', '美国本土外小岛屿 / 美國本土外小島嶼'), ('pl', 'Dalekie Wyspy Mniejsze Stanów Zjednoczonych')], 'rules': None}, {'primary': 'United States Minor Outlying Islands', 'common': [('hr', 'Mali Udaljeni Otoci Sjedinjenih Američkih Država'), ('pt', 'Ilhas Menores Distantes dos Estados Unidos'), ('cs', 'Menší odlehlé ostrovy Spojených států amerických'), ('ca', "Illes d'Ultramar Menors dels Estats Units"), ('nl', 'Amerikaanse Kleinere Afgelegen Eilanden'), ('zh-Hans', '美国本土外小岛屿'), ('eo', 'Usonaj Malgrandaj Insuloj'), ('ru', 'Внешние малые острова США'), ('zh-Hant', '美國本土外小島嶼'), ('en', 'United States Minor Outlying Islands'), ('es', 'Islas Ultramarinas Menores de Estados Unidos'), ('lt', 'Jungtinių Amerikos Valstijų mažosios aplinkinės salos'), ('zh', '美国本土外小岛屿 / 美國本土外小島嶼'), ('pl', 'Dalekie Wyspy Mniejsze Stanów Zjednoczonych')], 'rules': None}, {'primary': 'Mali', 'common': [('hy', 'Մալի'), ('se', 'Mali'), ('ku', 'Malî'), ('crh', 'Mali'), ('ss', 'IMali'), ('st', 'Mali'), ('bs', 'Mali'), ('vls', 'Mali'), ('stq', 'Mali'), ('ba', 'Мали'), ('als', 'Mali'), ('lb', 'Mali'), ('hr', 'Mali'), ('ta', 'மாலி'), ('ka', 'მალი'), ('ar', 'مالي'), ('nov', 'Mali'), ('yue', '馬里'), ('fr', 'Mali'), ('lfn', 'Mali'), ('is', 'Malí'), ('nah', 'Mali'), ('ug', 'مالى'), ('lv', 'Mali'), ('eu', 'Mali'), ('am', 'ማሊ'), ('hif', 'Mali'), ('mt', 'Mali'), ('nso', 'Mali'), ('bn', 'মালি'), ('rw', 'Mali'), ('uz', 'Mali'), ('uk', 'Малі'), ('szl', 'Mali'), ('si', 'මාලි'), ('li', 'Mali'), ('dsb', 'Mali'), ('ksh', 'Maali'), ('pa', 'ਮਾਲੀ'), ('ga', 'Mailí'), ('ceb', 'Mali'), ('br', 'Mali'), ('lmo', 'Mali'), ('sms', 'Mali'), ('tt', 'Мали'), ('war', 'Mali'), ('so', 'Maali'), ('pt-PT', 'Mali'), ('pt', 'Mali'), ('cs', 'Mali'), ('zu', 'IMali'), ('smn', 'Mali'), ('lo', 'ມາລິ'), ('gl', 'Malí'), ('gn', 'Mali'), ('roa-rup', 'Mali'), ('nds', 'Mali'), ('ts', 'Mali'), ('el', 'Μάλι'), ('it', 'Mali'), ('sc', 'Mali'), ('su', 'Mali'), ('ber', 'ⵎⴰⵍⵉ'), ('ca', 'Mali'), ('pt-BR', 'Mali'), ('os', 'Мали'), ('vi', 'Mali'), ('la', 'Malium'), ('tg', 'Малӣ'), ('yo', 'Málì'), ('tzl', 'Mali'), ('min', 'Mali'), ('dv', 'މާލީ'), ('tl', 'Mali'), ('nl', 'Mali'), ('zh-Hans', '马里'), ('bg', 'Мали'), ('gv', 'Malee'), ('bxr', 'Мали'), ('ckb', 'مالی'), ('ko', '말리'), ('or', 'ମାଲି'), ('eo', 'Malio'), ('tk', 'Mali'), ('mk', 'Мали'), ('dz', 'མཱ་ལི'), ('oc', 'Mali'), ('haw', 'Māli'), ('et', 'Mali'), ('af', 'Mali'), ('gag', 'Mali'), ('de', 'Mali'), ('bm', 'Mali'), ('ps', 'مالي'), ('scn', 'Mali'), ('yi', 'מאלי'), ('ha', 'Mali'), ('cy', 'Mali'), ('sn', 'Mali'), ('ace', 'Mali'), ('to', 'Māli'), ('bjn', 'Mali'), ('pap', 'Mali'), ('be-Latn-tarask', 'Малі'), ('diq', 'Mali'), ('cv', 'Мали'), ('ur', 'مالی'), ('pam', 'Mali'), ('arc', 'ܡܠܝ'), ('nan', 'Mali'), ('fy', 'Maly'), ('ln', 'Mali'), ('zgh', 'ⵎⴰⵍⵉ'), ('jv', 'Mali'), ('bat-smg', 'Malis'), ('jbo', 'malis'), ('ru', 'Мали'), ('ht', 'Mali'), ('vro', 'Mali'), ('kw', 'Mali'), ('ml', 'മാലി'), ('th', 'ประเทศมาลี'), ('id', 'Mali'), ('ce', 'Мали'), ('pnb', 'مالی'), ('sq', 'Mali'), ('ia', 'Mali'), ('arz', 'مالى'), ('sr-Latn', 'Mali'), ('sv', 'Mali'), ('tr', 'Mali'), ('da', 'Mali'), ('my', 'မာလီနိုင်ငံ'), ('sg', 'Malïi'), ('chr', 'ᎹᎵ'), ('sr-Cyrl', 'Мали'), ('wo', 'Mali'), ('nds-nl', 'Mali'), ('xal', 'Малудин Орн'), ('zh-Hant', '馬利'), ('pih', 'Maali'), ('en', 'Mali'), ('gu', 'માલી'), ('he', 'מאלי'), ('sah', 'Мали'), ('new', 'माली गाविस'), ('ilo', 'Mali'), ('kn', 'ಮಾಲಿ'), ('tok', 'ma Mali'), ('gd', 'Màili'), ('sk', 'Mali'), ('mrj', 'Мали'), ('frp', 'Mali'), ('az', 'Mali'), ('wa', 'Mali'), ('lij', 'Mali'), ('es', 'Malí'), ('fo', 'Mali'), ('hi', 'माली'), ('vo', 'Maliyän'), ('bcl', 'Mali'), ('te', 'మాలి'), ('mr', 'माली'), ('bpy', 'মালি'), ('ie', 'Mali'), ('ext', 'Mali'), ('an', 'Mali'), ('sco', 'Mali'), ('nn', 'Mali'), ('io', 'Mali'), ('sw', 'Mali'), ('be', 'Малі'), ('qu', 'Mali'), ('mi', 'Māri'), ('kab', 'Mali'), ('hsb', 'Mali'), ('ja', 'マリ共和国'), ('sa', 'माली'), ('ast', 'Malí'), ('fi', 'Mali'), ('bo', 'མ་ལི།'), ('ro', 'Mali'), ('kg', 'Mali'), ('ne', 'माली'), ('ee', 'Mali'), ('ff', 'Maali'), ('lt', 'Malis'), ('no', 'Mali'), ('wuu', '马里'), ('vec', 'Małì'), ('km', 'ម៉ាលី'), ('kk', 'Мали'), ('sl', 'Mali'), ('pms', 'Mali'), ('ti', 'ማሊ'), ('fa', 'مالی'), ('mn', 'Мали'), ('zh', '马里'), ('ms', 'Mali'), ('hu', 'Mali'), ('pl', 'Mali'), ('ks', 'مالی'), ('sh', 'Mali')], 'rules': array([{'variant': 'official', 'language': 'zh-Hans', 'perspectives': None, 'value': '马里共和国', 'between': None, 'side': None},
       {'variant': 'official', 'language': 'es', 'perspectives': None, 'value': 'República de Malí', 'between': None, 'side': None},
       {'variant': 'official', 'language': 'en', 'perspectives': None, 'value': 'Republic of Mali', 'between': None, 'side': None},
       {'variant': 'official', 'language': 'ru', 'perspectives': None, 'value': 'Республика Мали', 'between': None, 'side': None},
       {'variant': 'official', 'language': 'zh', 'perspectives': None, 'value': '马里共和国', 'between': None, 'side': None},
       {'variant': 'official', 'language': 'pt', 'perspectives': None, 'value': 'República do Mali', 'between': None, 'side': None},
       {'variant': 'official', 'language': 'de', 'perspectives': None, 'value': 'Republik Mali', 'between': None, 'side': None},
       {'variant': 'official', 'language': 'zh-Hant', 'perspectives': None, 'value': '馬利共和國', 'between': None, 'side': None},
       {'variant': 'official', 'language': 'gn', 'perspectives': None, 'value': 'Tavakuairetã Mali', 'between': None, 'side': None},
       {'variant': 'official', 'language': 'fr', 'perspectives': None, 'value': 'République du Mali', 'between': None, 'side': None},
       {'variant': 'official', 'language': 'ckb', 'perspectives': None, 'value': 'کۆماری مالی', 'between': None, 'side': None},
       {'variant': 'official', 'language': 'ar', 'perspectives': None, 'value': 'جمهورية مالي', 'between': None, 'side': None},
       {'variant': 'official', 'language': 'no', 'perspectives': None, 'value': 'Republikken Mali', 'between': None, 'side': None},
       {'variant': 'official', 'language': 'be', 'perspectives': None, 'value': 'Рэспубліка Малі', 'between': None, 'side': None},
       {'variant': 'official', 'language': 'lt', 'perspectives': None, 'value': 'Malio Respublika', 'between': None, 'side': None},
       {'variant': 'official', 'language': 'eo', 'perspectives': None, 'value': 'Respubliko Malio', 'between': None, 'side': None},
       {'variant': 'official', 'language': None, 'perspectives': None, 'value': 'République du Mali', 'between': None, 'side': None},
       {'variant': 'official', 'language': 'id', 'perspectives': None, 'value': 'Republik Mali', 'between': None, 'side': None},
       {'variant': 'official', 'language': 'zh', 'perspectives': None, 'value': '马里共和国 / 馬利共和國', 'between': None, 'side': None},
       {'variant': 'official', 'language': 'vi', 'perspectives': None, 'value': 'Cộng hòa Mali', 'between': None, 'side': None},
       {'variant': 'official', 'language': 'lv', 'perspectives': None, 'value': 'Mali Republika', 'between': None, 'side': None},
       {'variant': 'official', 'language': 'el', 'perspectives': None, 'value': 'Δημοκρατία του Μάλι', 'between': None, 'side': None},
       {'variant': 'official', 'language': 'sv', 'perspectives': None, 'value': 'Republiken Mali', 'between': None, 'side': None},
       {'variant': 'alternate', 'language': 'ta', 'perspectives': None, 'value': 'மாலீ', 'between': None, 'side': None},
       {'variant': 'alternate', 'language': 'zu', 'perspectives': None, 'value': 'i-Mali', 'between': None, 'side': None},
       {'variant': 'alternate', 'language': 'tg', 'perspectives': None, 'value': 'Мали', 'between': None, 'side': None},
       {'variant': 'alternate', 'language': 'yo', 'perspectives': None, 'value': 'Orílẹ́ède Mali', 'between': None, 'side': None},
       {'variant': 'alternate', 'language': 'or', 'perspectives': None, 'value': 'ମାଳୀ', 'between': None, 'side': None},
       {'variant': 'alternate', 'language': 'ln', 'perspectives': None, 'value': 'Malí', 'between': None, 'side': None},
       {'variant': 'alternate', 'language': 'my', 'perspectives': None, 'value': 'မာလီ', 'between': None, 'side': None},
       {'variant': 'alternate', 'language': 'ja', 'perspectives': None, 'value': 'マリ', 'between': None, 'side': None},
       {'variant': 'alternate', 'language': 'ee', 'perspectives': None, 'value': 'Mali nutome', 'between': None, 'side': None},
       {'variant': 'alternate', 'language': 'zh', 'perspectives': None, 'value': '马里 / 馬利', 'between': None, 'side': None},
       {'variant': 'alternate', 'language': 'th', 'perspectives': None, 'value': 'มาลี', 'between': None, 'side': None},
       {'variant': 'alternate', 'language': 'es', 'perspectives': None, 'value': 'Mali', 'between': None, 'side': None}],
      dtype=object)}]

Rows with country=NG: 3735
                                        id       subtype country
16    6e1ada31-8f7b-4529-b8d9-cd14f488b4eb        county      NG
34    32cfa1af-5e74-41a7-9272-b9dd8da715d9        county      NG
36    fcaab4e8-075a-4ab6-a2d2-cd614e95dfc2        county      NG
39    7aa34b16-ba84-45d9-94c4-083c65b8cf09        county      NG
40    b45b178e-4913-475f-a57e-c26c4d286198      locality      NG
58    51955eb1-4697-4268-b8e2-8554f7a77475        county      NG
77    07c90284-d410-436d-b210-4525190b58d5      locality      NG
88    ddd19324-8591-4e3f-816a-4c1133cb659d        county      NG
90    00496a96-e1ca-42cb-a1c8-531f48aa7461        county      NG
99    a97eadcd-f4e8-445b-8d28-fb98a825aad3        county      NG
108   934f5b76-4cbf-4902-b124-b33d057d87b1        county      NG
110   37bf198f-83f4-4cee-929c-cc1558c287ad        county      NG
111   dc9f547a-48ef-4f9c-a90b-31f6b247a3b8        county      NG
146   eab2ff24-e6f4-4a66-9d19-a4169499674a        county      NG
156   457a7986-f212-4765-b432-e1c1b77d6d61        county      NG
172   93ccd19d-3389-4c00-a8e3-920e20810d39        county      NG
217   1a9532db-bcd5-4050-b62b-f27a22f2ebcf        county      NG
219   a11b7421-21e3-4644-94a6-b7c707176710        county      NG
226   811f3e9d-01f3-4cba-acc0-d2a4ec81dfb0        county      NG
237   7918fd74-2ce4-48af-b3c1-1a4f1e26558e        county      NG
241   fa202edf-03e8-4cf4-9453-a56f196740d3        county      NG
248   c4470aea-2ffd-451c-8c2a-961a38bd10eb        county      NG
252   2ef4ab31-2bf2-4831-9dad-3631ec1ad4dd        county      NG
264   7cd48648-cb4c-43ad-801a-ea7003fd4979      locality      NG
265   8a9899fa-8cf5-4c8a-bd50-0f83cf5c2c3f        county      NG
266   c140e0a6-3e56-4c79-8f1e-4d5b4af52914      locality      NG
267   74ff4301-6d55-4a93-bdcf-21d0ee77dd49      locality      NG
268   d70906a1-dbe8-44ad-8e68-0908a621bd39      locality      NG
269   42f8dc05-1379-4b59-ab1c-1970e090e53f      locality      NG
270   14b75cb3-b3f9-4ee4-af33-0ad92c8db0e3      locality      NG
271   ab126ab5-8213-4242-800e-cd453cdae1d8        region      NG
273   b80d0c54-2069-444c-a3ad-19ab313292aa      locality      NG
274   267f5486-6d3e-4917-9458-04632dcc6c57      locality      NG
275   018c0489-85e2-47af-b930-ee6bb67e24ab      locality      NG
276   cdaf6a75-c484-4d65-b04e-e310711b7bd3        county      NG
277   ae873cf6-87de-4aa4-b031-21346de3590a      locality      NG
278   9a537d3b-67e0-498f-805b-d7cdceac8c20      locality      NG
279   4ad6f9f3-24ee-4cd0-b52d-6f29c775a42d      locality      NG
280   809f4ea7-b903-43d7-abc2-7334068c7145        county      NG
281   f5772514-0257-40a7-a68a-b9227328b344        county      NG
282   bc01e791-45ea-40f9-bf32-2da73d037f7a      locality      NG
283   d6575fc7-67b1-40d1-a7df-b92227cd168c      locality      NG
284   3722cf55-91ed-41a4-b944-4c7031528e53      locality      NG
285   9928dddf-7fca-4f54-8513-8deec984141f        county      NG
286   e4a8a1de-3082-48a2-96aa-303e5246f9e5      locality      NG
287   2cd6369f-46bb-4b65-b043-c42c92332b23        county      NG
288   ab9cda6a-af21-4490-a6c0-b9254c38f4c7        county      NG
289   c861ed53-adfe-416a-b0a7-bab921f6db77      locality      NG
290   5aa26d19-5bf6-4045-a4ed-fdf22d5f264a      locality      NG
291   13a9f930-a7b7-44b9-bfc9-9c9b948572ae        county      NG
292   af48f46b-180c-42b5-b579-5cd9c15fe1f2      locality      NG
293   f2ac41b9-7d05-4676-9f1c-d805bb6ffde9        county      NG
294   06dd88e8-84e5-46d9-aa9e-2658545177a7      locality      NG
295   5836c271-c0ee-47fc-93b2-255e1daa76ff        county      NG
296   5852c184-ef5c-400c-8a33-00b0ad233e35      locality      NG
297   7de372d4-9d2f-4dc4-a4cf-dc5a857afe6b      locality      NG
298   e65aa201-c5b7-4c1d-ae9b-8da887d7c737      locality      NG
299   0db46add-8cbc-4880-9439-c9a38e3cefbe      locality      NG
300   80762d73-8f76-4c51-a3e5-e9ccec836800        county      NG
301   b35f2aa0-9ec0-413a-b042-b3f331ead685      locality      NG
302   ca2c03eb-80de-4931-80a8-117d72057dac      locality      NG
303   c8ebc3e6-4db5-49d4-aa8f-7927eca07c50      locality      NG
304   3490af5b-b510-4ab9-a45b-439eab0df4ea      locality      NG
305   6543410e-308b-40c1-8cad-b8d61a16e5a0      locality      NG
306   79b2c29f-b7ed-4fc3-8f11-5094d66777fc        county      NG
307   707d32f7-a189-45c0-baf0-e9e16ba0fc6f      locality      NG
308   92d1e309-9d1f-4d49-be1d-9096635e5fa9      locality      NG
309   4033e7a8-2698-4de5-bbf9-cbea44ce22c4     macrohood      NG
310   c25545a5-e0d7-40ca-b68a-8762ebbf0d77      locality      NG
311   b113986b-6823-4565-82fd-ca3b25e1c515      locality      NG
312   2803a799-af98-47a7-b446-1726d8baada7        county      NG
313   5eaaaa15-4e64-47ad-8797-81ba300963ff      locality      NG
314   eb103c4b-681c-49a0-b282-038c9c652537      locality      NG
315   68805783-4721-44b1-aae3-44a2517ae97b      locality      NG
316   0bc00f7c-087d-4eb7-889a-8f3026dec25b      locality      NG
317   50a9303c-3c3f-4314-9dac-ca44dfee8b37      locality      NG
319   b3adf4e0-f701-435b-a3ef-f0b644371569        county      NG
320   54ec4bfe-ec0f-485d-a167-a6a37e5676f0      locality      NG
321   6a9ff3b3-37ec-4d1f-aec2-73322f647047        county      NG
322   90
[... truncated ...]ty, names={'primary': 'Kurba Gayi', 'common': None, 'rules': None}

Row 4290: subtype=locality, names={'primary': 'Njiwaji Gwange', 'common': None, 'rules': None}

Row 4292: subtype=locality, names={'primary': 'Wawa', 'common': None, 'rules': array([{'variant': 'alternate', 'language': None, 'perspectives': None, 'value': 'Wara', 'between': None, 'side': None}],
      dtype=object)}

Row 4293: subtype=locality, names={'primary': 'Kubdiya', 'common': None, 'rules': None}

Row 4294: subtype=locality, names={'primary': 'Konkiyel', 'common': None, 'rules': None}

Row 4295: subtype=locality, names={'primary': 'Gunda', 'common': None, 'rules': array([{'variant': 'alternate', 'language': None, 'perspectives': None, 'value': 'Gonda', 'between': None, 'side': None}],
      dtype=object)}

Row 4296: subtype=locality, names={'primary': 'Bindigari Pawari', 'common': None, 'rules': None}

Row 4299: subtype=county, names={'primary': 'Gombi', 'common': None, 'rules': None}

Row 4300: subtype=locality, names={'primary': 'Dewu', 'common': None, 'rules': None}

Row 4301: subtype=county, names={'primary': 'Maiha', 'common': None, 'rules': None}

Row 4302: subtype=locality, names={'primary': 'Dalwa', 'common': None, 'rules': None}

Row 4303: subtype=locality, names={'primary': 'Guji M Tallari', 'common': None, 'rules': None}

Row 4304: subtype=county, names={'primary': 'Kaga', 'common': None, 'rules': None}

Row 4305: subtype=locality, names={'primary': 'Yayari', 'common': None, 'rules': None}

Row 4307: subtype=county, names={'primary': 'Fika', 'common': None, 'rules': None}

Row 4308: subtype=locality, names={'primary': 'Dagaro', 'common': None, 'rules': None}

Row 4309: subtype=county, names={'primary': 'Yunusari', 'common': None, 'rules': None}

Row 4311: subtype=locality, names={'primary': 'Madara', 'common': None, 'rules': None}

Row 4312: subtype=county, names={'primary': 'Shongom', 'common': None, 'rules': None}

Row 4314: subtype=locality, names={'primary': 'Fezzan', 'common': None, 'rules': None}

Row 4316: subtype=locality, names={'primary': 'Fika Anze', 'common': None, 'rules': None}

Row 4317: subtype=locality, names={'primary': 'Wada', 'common': None, 'rules': None}

Row 4318: subtype=locality, names={'primary': 'Sasawa Kabaru', 'common': None, 'rules': None}

Row 4319: subtype=locality, names={'primary': 'Gajigana', 'common': None, 'rules': array([{'variant': 'alternate', 'language': None, 'perspectives': None, 'value': 'Kori', 'between': None, 'side': None}],
      dtype=object)}

Row 4320: subtype=locality, names={'primary': 'Jaji Maji', 'common': None, 'rules': None}

Row 4323: subtype=locality, names={'primary': 'Briyel', 'common': None, 'rules': None}

Row 4324: subtype=locality, names={'primary': 'Tumbi', 'common': None, 'rules': None}

Row 4325: subtype=locality, names={'primary': 'Kafin Romi', 'common': None, 'rules': None}

Row 4326: subtype=locality, names={'primary': 'Bayamari', 'common': None, 'rules': None}

Row 4327: subtype=locality, names={'primary': 'Yalo 1', 'common': None, 'rules': None}

Row 4328: subtype=locality, names={'primary': 'Yabiri', 'common': None, 'rules': None}

Row 4329: subtype=locality, names={'primary': 'Waro', 'common': None, 'rules': None}

Row 4331: subtype=county, names={'primary': 'Ngala', 'common': None, 'rules': None}

Row 4332: subtype=locality, names={'primary': 'Afuye', 'common': None, 'rules': None}

Row 4333: subtype=locality, names={'primary': 'Lago', 'common': None, 'rules': None}

Row 4334: subtype=county, names={'primary': 'Jere', 'common': None, 'rules': None}

Row 4336: subtype=locality, names={'primary': 'Shehuri North', 'common': None, 'rules': None}

Row 4338: subtype=locality, names={'primary': 'Asheikri 1', 'common': None, 'rules': None}

Row 4340: subtype=locality, names={'primary': 'Gomari', 'common': None, 'rules': None}

Row 4341: subtype=locality, names={'primary': 'Tobolo', 'common': None, 'rules': None}

Row 4343: subtype=county, names={'primary': 'Gulani', 'common': None, 'rules': None}

Row 4344: subtype=locality, names={'primary': 'Wagir', 'common': None, 'rules': None}

Row 4345: subtype=locality, names={'primary': 'Musari', 'common': None, 'rules': array([{'variant': 'alternate', 'language': None, 'perspectives': None, 'value': 'Musaria', 'between': None, 'side': None}],
      dtype=object)}

Row 4346: subtype=locality, names={'primary': 'Sirko', 'common': None, 'rules': None}

Row 4348: subtype=locality, names={'primary': 'Dudduye', 'common': None, 'rules': None}

Row 4350: subtype=locality, names={'primary': 'Dumbari', 'common': None, 'rules': None}

Row 4354: subtype=locality, names={'primary': 'Kumshe', 'common': None, 'rules': None}

Row 4358: subtype=locality, names={'primary': 'Hoyo Chingowa', 'common': None, 'rules': array([{'variant': 'alternate', 'language': None, 'perspectives': None, 'value': 'Koyo Chingowa', 'between': None, 'side': None}],
      dtype=object)}

Row 4359: subtype=locality, names={'primary': 'Kauwa', 'common': None, 'rules': None}

Row 4360: subtype=locality, names={'primary': 'Wuro Golare', 'common': None, 'rules': None}

Row 4361: subtype=locality, names={'primary': 'Foguwa', 'common': None, 'rules': array([{'variant': 'alternate', 'language': None, 'perspectives': None, 'value': 'Fogua', 'between': None, 'side': None}],
      dtype=object)}

Row 4362: subtype=locality, names={'primary': 'Bukarti', 'common': None, 'rules': None}

Row 4363: subtype=locality, names={'primary': 'Felo', 'common': None, 'rules': None}

Row 4364: subtype=locality, names={'primary': 'Lanzai East', 'common': None, 'rules': None}

Row 4366: subtype=locality, names={'primary': 'Zabudum Dachia', 'common': None, 'rules': None}

Row 4367: subtype=locality, names={'primary': 'Sarma', 'common': None, 'rules': None}

Row 4368: subtype=locality, names={'primary': 'Mainako North', 'common': None, 'rules': None}

Row 4369: subtype=locality, names={'primary': 'Shettimari', 'common': None, 'rules': None}

Row 4370: subtype=locality, names={'primary': 'Gobio 2', 'common': None, 'rules': None}

Row 4371: subtype=locality, names={'primary': 'Yalo 2', 'common': None, 'rules': None}

Row 4372: subtype=locality, names={'primary': 'Marana', 'common': None, 'rules': None}

Row 4374: subtype=locality, names={'primary': 'Gulani', 'common': None, 'rules': None}

Row 4375: subtype=locality, names={'primary': 'Ajigin B', 'common': None, 'rules': None}

Row 4376: subtype=locality, names={'primary': 'Ufaye', 'common': None, 'rules': None}

Row 4380: subtype=locality, names={'primary': 'Uvu Uda', 'common': None, 'rules': None}

Row 4381: subtype=locality, names={'primary': 'Kidda', 'common': None, 'rules': None}

Row 4382: subtype=locality, names={'primary': 'Damakuli', 'common': None, 'rules': None}

Row 4383: subtype=locality, names={'primary': 'Katuzu', 'common': None, 'rules': None}

Row 4384: subtype=locality, names={'primary': 'Khadammari', 'common': None, 'rules': None}

Row 4385: subtype=locality, names={'primary': 'Mayori', 'common': None, 'rules': None}

Row 4386: subtype=locality, names={'primary': 'Dogo Tebo', 'common': None, 'rules': None}

Row 4388: subtype=locality, names={'primary': 'Gwange III', 'common': None, 'rules': None}

Row 4390: subtype=locality, names={'primary': 'Hizhibwala', 'common': None, 'rules': array([{'variant': 'alternate', 'language': None, 'perspectives': None, 'value': 'Hizi Bwala', 'between': None, 'side': None}],
      dtype=object)}

Row 4391: subtype=locality, names={'primary': 'Tsugum Tagali', 'common': None, 'rules': None}

Row 4393: subtype=locality, names={'primary': 'Kanuri', 'common': None, 'rules': None}

Row 4394: subtype=locality, names={'primary': 'Dazigau', 'common': None, 'rules': None}

Row 4395: subtype=locality, names={'primary': 'Dalori', 'common': None, 'rules': None}

Row 4396: subtype=locality, names={'primary': 'Ngamma', 'common': None, 'rules': None}

Row 4397: subtype=county, names={'primary': 'Gamawa', 'common': None, 'rules': None}

Row 4398: subtype=locality, names={'primary': 'Yautare', 'common': None, 'rules': None}

Row 4399: subtype=county, names={'primary': 'Yorro', 'common': None, 'rules': None}

Row 4400: subtype=locality, names={'primary': 'Gagure', 'common': None, 'rules': None}

Row 4401: subtype=locality, names={'primary': 'Toshia', 'common': None, 'rules': None}

Row 4403: subtype=locality, names={'primary': 'Limanti', 'common': None, 'rules': None}

Row 4405: subtype=county, names={'primary': 'Lau', 'common': None, 'rules': None}

Row 4409: subtype=locality, names={'primary': 'Zindiwa', 'common': None, 'rules': None}

Row 4410: subtype=locality, names={'primary': 'Kafin Iya', 'common': None, 'rules': None}

Row 4411: subtype=locality, names={'primary': 'Kusur', 'common': None, 'rules': None}

Row 4412: subtype=locality, names={'primary': 'Kwajaffa', 'common': None, 'rules': array([{'variant': 'alternate', 'language': None, 'perspectives': None, 'value': 'Kwajaffa Dispensary', 'between': None, 'side': None}],
      dtype=object)}

Row 4413: subtype=locality, names={'primary': 'Kayeri', 'common': None, 'rules': None}

Row 4415: subtype=locality, names={'primary': 'Kulli', 'common': None, 'rules': None}

Row 4418: subtype=locality, names={'primary': 'Kafiya', 'common': None, 'rules': None}

Row 4420: subtype=locality, names={'primary': 'Ndufu', 'common': None, 'rules': None}

Row 4421: subtype=locality, names={'primary': 'Moduri', 'common': None, 'rules': None}

Row 4423: subtype=locality, names={'primary': 'Ajigin A', 'common': None, 'rules': None}

Row 4424: subtype=county, names={'primary': 'Fune', 'common': None, 'rules': None}

Row 4425: subtype=locality, names={'primary': 'Kalizoram', 'common': None, 'rules': None}

Row 4426: subtype=locality, names={'primary': 'Jabullam', 'common': None, 'rules': None}

Row 4428: subtype=county, names={'primary': 'Monguno', 'common': None, 'rules': None}

Row 4430: subtype=locality, names={'primary': 'Busuna', 'common': None, 'rules': None}

Row 4432: subtype=county, names={'primary': 'Geidam', 'common': None, 'rules': None}

Row 4433: subtype=locality, names={'primary': 'Murfakalam', 'common': None, 'rules': None}

Row 4435: subtype=locality, names={'primary': 'Pama Whitambaya', 'common': None, 'rules': None}

Row 4436: subtype=locality, names={'primary': 'Kautikari', 'common': None, 'rules': array([{'variant': 'alternate', 'language': None, 'perspectives': None, 'value': 'Kwatagari', 'between': None, 'side': None}],
      dtype=object)}

Row 4438: subtype=locality, names={'primary': 'Muliye', 'common': None, 'rules': None}

Row 4439: subtype=locality, names={'primary': 'Gwana', 'common': None, 'rules': None}

Row 4442: subtype=locality, names={'primary': 'Shani', 'common': None, 'rules': None}

Row 4444: subtype=locality, names={'primary': 'Kaliyari', 'common': None, 'rules': None}

Row 4446: subtype=locality, names={'primary': 'Dapchi', 'common': None, 'rules': None}

Row 4448: subtype=locality, names={'primary': 'Gadai', 'common': None, 'rules': None}

Row 4449: subtype=locality, names={'primary': "Ma'Afa", 'common': None, 'rules': None}

Row 4451: subtype=locality, names={'primary': 'Shehuri', 'common': None, 'rules': None}

Row 4452: subtype=locality, names={'primary': 'Kombo', 'common': None, 'rules': None}

Row 4453: subtype=locality, names={'primary': 'Jara-Dali', 'common': None, 'rules': array([{'variant': 'alternate', 'language': None, 'perspectives': None, 'value': 'Garad', 'between': None, 'side': None}],
      dtype=object)}

Row 4454: subtype=locality, names={'primary': 'Bara', 'common': None, 'rules': None}

Row 4455: subtype=county, names={'primary': 'Damboa', 'common': None, 'rules': None}

Row 4456: subtype=county, names={'primary': 'Gombe', 'common': [('fa', 'گومبه'), ('ar', 'غومبي'), ('azb', 'قومبه'), ('ur', 'گومبے')], 'rules': None}

Row 4459: subtype=county, names={'primary': 'Guzamala', 'common': None, 'rules': None}

Row 4460: subtype=locality, names={'primary': 'Maimadi', 'common': None, 'rules': None}

Row 4461: subtype=locality, names={'primary': 'Bolori I', 'common': None, 'rules': None}

Row 4465: subtype=locality, names={'primary': 'Kasugula', 'common': None, 'rules': None}

Row 4466: subtype=locality, names={'primary': 'Auno', 'common': None, 'rules': None}

Row 4468: subtype=locality, names={'primary': 'Papa North', 'common': None, 'rules': None}

Row 4469: subtype=locality, names={'primary': 'Bulabulin', 'common': None, 'rules': None}

Row 4470: subtype=locality, names={'primary': 'Lassa', 'common': None, 'rules': None}

Row 4472: subtype=locality, names={'primary': 'Soye', 'common': None, 'rules': None}

Row 4473: subtype=locality, names={'primary': 'Bargu', 'common': None, 'rules': None}

Row 4474: subtype=locality, names={'primary': 'Mintar', 'common': None, 'rules': array([{'variant': 'alternate', 'language': None, 'perspectives': None, 'value': 'Mintur', 'between': None, 'side': None}],
      dtype=object)}

Row 4476: subtype=locality, names={'primary': 'Mbalala', 'common': None, 'rules': None}

Row 4477: subtype=locality, names={'primary': 'Miringa', 'common': None, 'rules': array([{'variant': 'alternate', 'language': None, 'perspectives': None, 'value': 'Dashu', 'between': None, 'side': None}],
      dtype=object)}

Row 4478: subtype=locality, names={'primary': 'Gajiram', 'common': None, 'rules': array([{'variant': 'alternate', 'language': None, 'perspectives': None, 'value': 'Balamari', 'between': None, 'side': None}],
      dtype=object)}

Row 4479: subtype=locality, names={'primary': 'Uba', 'common': None, 'rules': None}

Row 4480: subtype=locality, names={'primary': 'Kabulawa', 'common': None, 'rules': None}

Row 4481: subtype=locality, names={'primary': 'Borno Kiji', 'common': None, 'rules': None}

Row 4483: subtype=locality, names={'primary': 'Kukareta Warsala', 'common': None, 'rules': None}

Row 4485: subtype=county, names={'primary': 'Song', 'common': None, 'rules': None}

Row 4489: subtype=locality, names={'primary': 'Moholo', 'common': None, 'rules': None}

Row 4497: subtype=locality, names={'primary': 'K Kaudi', 'common': None, 'rules': None}

Row 4508: subtype=locality, names={'primary': 'Rann', 'common': None, 'rules': None}

Row 4527: subtype=locality, names={'primary': 'Jarawa', 'common': None, 'rules': None}

Row 4538: subtype=locality, names={'primary': 'Daima', 'common': None, 'rules': None}

Row 4545: subtype=locality, names={'primary': 'Jilbe', 'common': None, 'rules': None}

Row 4546: subtype=locality, names={'primary': 'Kala', 'common': None, 'rules': None}

Row 4548: subtype=locality, names={'primary': 'Sigal', 'common': None, 'rules': None}

Row 4567: subtype=county, names={'primary': 'Kala-Balge', 'common': None, 'rules': None}

Row 4572: subtype=locality, names={'primary': 'K Kumaga', 'common': None, 'rules': None}

Row 4587: subtype=locality, names={'primary': 'Mada', 'common': None, 'rules': None}

Row 4597: subtype=locality, names={'primary': 'Andara', 'common': None, 'rules': None}

python3 << 'EOF'
from overturemaps.core import record_batch_reader
import pyarrow as pa
import pyarrow.compute as pc

# Get division_area for Nigeria
bbox_ng = (2.5, 4.0, 15.0, 14.0)

reader = record_batch_reader('division_area', bbox=bbox_ng, release='2026-05-20.0', stac=True)
if reader:
    table = reader.read_all()
    df = table.to_pandas()
    df_ng = df[df['country'] == 'NG']
    
    # Print all regions
    regions = df_ng[df_ng['subtype'] == 'region']
    print(f"Found {len(regions)} regions")
    for idx, row in regions.iterrows():
        names = row['names']
        primary = names.get('primary', '?') if isinstance(names, dict) else str(names)
        print(f"  Region: {primary}, id={row['id']}")
    
    # Find Lagos
    lagos_rows = df_ng[df_ng['names'].apply(lambda x: isinstance(x, dict) and 'primary' in x and 'Lagos' in x['primary'])]
    print(f"\nRows containing 'Lagos': {len(lagos_rows)}")
    for idx, row in lagos_rows.iterrows():
        names = row['names']
        primary = names.get('primary', '?') if isinstance(names, dict) else str(names)
        print(f"  {row['subtype']}: {primary}, id={row['id']}")
    
    # Also look for counties in Lagos
    # Let's find the Lagos region ID
    lagos_region = df_ng[(df_ng['subtype'] == 'region') & (df_ng['names'].apply(lambda x: isinstance(x, dict) and 'Lagos' in x.get('primary', '')))]
    if len(lagos_region) > 0:
        lagos_id = lagos_region.iloc[0]['id']
        print(f"\nLagos region ID: {lagos_id}")
        
        # Find counties that belong to Lagos (using division_id)
        lagos_counties = df_ng[df_ng['division_id'] == lagos_id]
        print(f"Counties in Lagos: {len(lagos_counties)}")
        for idx, row in lagos_counties.iterrows():
            names = row['names']
            primary = names.get('primary', '?') if isinstance(names, dict) else str(names)
            print(f"  County: {primary}, id={row['id']}")
EOF

Found 37 regions
  Region: Sokoto, id=ab126ab5-8213-4242-800e-cd453cdae1d8
  Region: Lagos, id=5fb3f6cb-3993-4bb7-8a53-73e775feb14f
  Region: Kebbi, id=e69e0ecd-90b9-4996-b993-ff4072821ace
  Region: Ebonyi, id=6f901e2b-f10d-4916-b609-481ad0cf66de
  Region: Niger, id=1ac1cbf8-15bc-4fb9-ae15-abc107d591de
  Region: Federal Capital Territory, id=91838175-4269-499c-afee-8a2930129d5a
  Region: Oyo, id=1e6cd123-0b89-4cf6-b84a-6fe0dec9d6b3
  Region: Akwa Ibom, id=0a8848e8-192c-4c61-a560-0a2a9ca10563
  Region: Kwara, id=7c895e09-4f5c-498b-8078-8d206c2ffeb6
  Region: Ekiti, id=769badad-946c-45f1-8761-62489c8ffd1c
  Region: Nasarawa, id=769cca77-e3fb-4e27-aa79-ab5d0aa8ba7d
  Region: Edo, id=d5a8660c-7516-4157-a6a2-4b3f518d9880
  Region: Imo, id=79da8210-61a0-4aa3-89cc-053a93bc2f5a
  Region: Zamfara, id=63dcf9f1-e17c-4391-a98e-1a5f964f0928
  Region: Bayelsa, id=4698eac8-abcd-4a96-b672-7162af3f6d1a
  Region: Ondo, id=833b2001-6dde-4e77-8b40-3faadf0c9ce7
  Region: Anambra, id=665cfea3-803d-4834-8175-4f21419c9bdd
  Region: Katsina, id=73b76fc0-d464-4915-ae63-e02a69b8ae6d
  Region: Kogi, id=77e6ff83-0ba8-432e-9a76-fa204f688ecf
  Region: Rivers, id=1167611b-4414-4c14-adc3-df3d9b118c93
  Region: Enugu, id=e6503528-9360-4c7e-9a18-3ff07c5e699c
  Region: Abia, id=5224273a-e4f3-4c21-9fc9-6db1bec82325
  Region: Ogun, id=d8e3535a-c797-4f52-a361-6f2c5116018b
  Region: Kaduna, id=e1a9fcd7-cce2-42bf-95da-4b18b5d20545
  Region: Delta, id=57e7821f-04a3-4a12-baa6-80fc930bc131
  Region: Osun, id=5691ecd9-8b1c-4858-98c0-87cff32222f9
  Region: Bauchi, id=4a0a1c2c-5100-483c-b613-8bd34bf0b5f8
  Region: Jigawa, id=a1cfd87a-38b7-492e-aaed-83c55665aed1
  Region: Kano, id=2f12c73f-b617-495f-9423-047cf513d93d
  Region: Plateau, id=9a52a2bc-bc4d-4d49-8378-0776b94d6c46
  Region: Cross River, id=155c73e5-9f36-4693-b259-d50e88a753d3
  Region: Benue, id=1afecc55-c117-473e-ad6b-c72a5ff19758
  Region: Taraba, id=5f6d19c5-9a0d-49e3-97f4-33158e69b688
  Region: Gombe, id=d277207a-67cd-43ad-8d38-25bc20066e0f
  Region: Yobe, id=d496707d-35eb-4f0c-985b-2658a9cc28d3
  Region: Adamawa, id=57b46577-6cb1-456a-8c4a-42e36b37cbc9
  Region: Borno, id=8567bc8d-45b2-4648-a3b5-780fbddf04c9

Rows containing 'Lagos': 3
  region: Lagos, id=5fb3f6cb-3993-4bb7-8a53-73e775feb14f
  county: Lagos Island, id=c58fd335-e502-421c-b8e3-f263c5fb0ff7
  county: Lagos Mainland, id=639f85d6-f581-4f31-b52b-98da63a57271

Lagos region ID: 5fb3f6cb-3993-4bb7-8a53-73e775feb14f
Counties in Lagos: 0

python3 << 'EOF'
from overturemaps.core import record_batch_reader
import pyarrow as pa
import pyarrow.compute as pc

# Get division_area for all of Nigeria again
bbox_ng = (2.5, 4.0, 15.0, 14.0)

reader = record_batch_reader('division_area', bbox=bbox_ng, release='2026-05-20.0', stac=True)
if reader:
    table = reader.read_all()
    df = table.to_pandas()
    df_ng = df[df['country'] == 'NG']
    
    # Let's look at the Lagos bbox counties - from Overture's division_boundary?
    # Actually let's get all counties and their division_ids
    counties = df_ng[df_ng['subtype'] == 'county']
    print(f"All counties: {len(counties)}")
    
    # Let me check the region column - does it reference the parent region?
    lagos_region_id = '5fb3f6cb-3993-4bb7-8a53-73e775feb14f'
    
    # Check what the 'region' column contains
    print("\nRegion column sample:")
    print(df_ng[['subtype', 'region']].head(10).to_string())
    
    # Check all counties' region field
    print("\nCounty regions:")
    for idx, row in counties.iterrows():
        names = row['names']
        primary = names.get('primary', '?') if isinstance(names, dict) else str(names)
        print(f"  {primary}: region={row['region']} division_id={row['division_id']}")
    
    # Actually, let me narrow to just Lagos State area counties
    # The region 'Lagos' has id 5fb3f6cb-3993-4bb7-8a53-73e775feb14f
    lagos_counties = counties[counties['division_id'] == lagos_region_id]
    print(f"\nCounties with division_id = Lagos region: {len(lagos_counties)}")
    for idx, row in lagos_counties.iterrows():
        names = row['names']
        primary = names.get('primary', '?') if isinstance(names, dict) else str(names)
        print(f"  {primary}: region={row['region']}")
EOF

All counties: 773

Region column sample:
     subtype region
16    county  NG-OY
34    county  NG-LA
36    county  NG-LA
39    county  NG-OG
40  locality  NG-OY
58    county  NG-OG
77  locality  NG-OY
88    county  NG-OG
90    county  NG-OG
99    county  NG-OG

County regions:
  Atisbo: region=NG-OY division_id=c00f6d40-c0f6-49d0-84ec-d574868705e5
  Amuwo Odofin: region=NG-LA division_id=7583d723-71b9-4f18-9fff-bff98df8322d
  Agege: region=NG-LA division_id=24e04d44-9e6e-47ba-a71c-737793381785
  Ifo: region=NG-OG division_id=bf72a742-460e-40d8-b67e-5cdcc75eeec9
  Ado Odo/Ota: region=NG-OG division_id=14cdd032-7c30-44aa-beee-476b4d62184e
  Abeokuta North: region=NG-OG division_id=84ad5954-c936-4d7f-980c-709d1d68a8e2
  Ipokia: region=NG-OG division_id=199f6f87-94c7-40e8-ba34-662f345e5794
  Imeko Afon: region=NG-OG division_id=b638532b-3500-453e-8223-43a716f46a55
  Ifako/Ijaye: region=NG-LA division_id=a9df031a-eaa0-4b74-b42c-5bf303feb0e1
  Ibarapa North: region=NG-OY division_id=18bb8b64-9c50-48db-b34f-58ea9fa8f034
  Yewa South: region=NG-OG division_id=d6357489-86e9-4e24-ba64-2d3bda2d31a3
  Ewekoro: region=NG-OG division_id=921da412-7134-4d63-bbf6-679b4862a778
  Oshodi/Isolo: region=NG-LA division_id=e199f023-49ae-46ff-92a4-580c4fe1346d
  Badagry: region=NG-LA division_id=202749c9-2473-4ecb-8528-72dc7a1278ee
  Alimosho: region=NG-LA division_id=b4bd3fef-918f-4a60-bfd3-3569ed991562
  Iwajowa: region=NG-OY division_id=8d5d4906-1de4-44ee-986a-279d0c645ff3
  Ibarapa Central: region=NG-OY division_id=08065536-f44b-4ef5-8008-3bf85be7a1f4
  Ojo: region=NG-LA division_id=2c66b8ba-4745-4334-b145-ae00b1a6f604
  Baruten: region=NG-KW division_id=ceb22641-0ab3-4763-ad67-830c3f7a3faf
  Saki West: region=NG-OY division_id=e7feec57-5b97-4900-9b2e-e04faba942f3
  Yewa North: region=NG-OG division_id=8993b535-51c4-4eaf-8fdd-6155e803c179
  Jaba: region=NG-KD division_id=e539f5d9-07fe-4d39-b36a-1cdc82dad7db
  Ibadan North East: region=NG-OY division_id=4d908c83-bd73-403e-940f-5b04e0d434ce
  Uyo: region=NG-AK division_id=fa9bfe9d-df3a-4350-a59a-d09597531729
  Sakaba: region=NG-KE division_id=084da8e2-524e-4b04-a373-c36e9d940cb7
  Ogbomosho South: region=NG-OY division_id=72395243-602c-40ac-8278-9b4702f4f607
  Munya: region=NG-NI division_id=ac2d7553-40c7-43b1-ae0d-0ad09f1f9c94
  Gbako: region=NG-NI division_id=7de8e927-6f4f-4a8c-a48e-7019dc6d160d
  Sabon Gari: region=NG-KD division_id=57310a1b-c54d-4957-a849-9fdb52aecd7d
  Oshimili North: region=NG-DE division_id=e4db7db8-292d-49d9-b9a1-c1058b69a315
  Matazu: region=NG-KT division_id=c203301c-4549-4010-986b-d5d44237e2a7
  Ise/Orun: region=NG-EK division_id=3abc6d3c-00ea-46bb-a87d-86cc37f03113
  Gwarzo: region=NG-KN division_id=f2e095cd-7388-49f9-af0c-52cf17ad479c
  Oye: region=NG-EK division_id=6680ec2e-6fa9-4170-9ffb-b458a07ec81b
  Ivo: region=NG-EB division_id=01830590-d9c8-415e-b443-ba6f07d97c7e
  Adavi: region=NG-KO division_id=14f04ab3-dfb8-4c41-83a9-277889a30d11
  Ezinihitte: region=NG-IM division_id=016cb406-143e-4cd7-b817-c70c7e3ee104
  Ihiala: region=NG-AN division_id=3b6150de-da74-46a1-a291-2ea509acc8bc
  Ankpa: region=NG-KO division_id=79fc73dc-eb8d-432d-bc89-808247f13b1f
  Ìkòròdú: region=NG-LA division_id=634b4f3c-a5c4-42d2-b18b-1432b67373b8
  Isokan: region=NG-OS division_id=66ab7f4a-4aa9-4b7a-a11e-679f09e55781
  Aniocha South: region=NG-DE division_id=117eff89-0de4-422b-8b6d-50ce7beacdae
  Zangon Kataf: region=NG-KD division_id=5922e451-6fdf-4618-963a-0ae01c26dea0
  Ibeno: region=NG-AK division_id=1c998be1-ba93-4ad8-84fa-046cce956585
  Wasagu-Danko: region=NG-KE division_id=18005d08-d1b7-45e4-8dd1-195bfcf9039b
  Mashegu: region=NG-NI division_id=1b2f43a4-a463-49e1-9521-2637212501af
  Ethiope West: region=NG-DE division_id=615b242e-862a-413e-94f0-95953159097e
  Ekiti West: region=NG-EK division_id=5ee7718d-b000-469f-a42a-eec1a4ba818c
  Bakura: region=NG-ZA division_id=6da0c062-7795-41df-8fe1-bbdb012beb66
  Uruan: region=NG-AK division_id=a972b661-e7b3-4b94-87ec-ec70437f4a89
  Ilaje: region=NG-ON division_id=1e1c8471-a6b9-4b30-9614-707998e9b19d
  Ukwa West: region=NG-AB division_id=d9dd93fb-7e6f-4db1-ac1e-c0f1448239df
  Obowo: region=NG-IM division_id=86d8e82a-91a9-4048-a724-d137c54c050c
  Ogu Bolo: region=NG-RI division_id=9dbb32c7-207e-4495-be15-04f6da693ef9
  Shanono: region=NG-KN division_id=b2e19874-b2e9-43d3-96ce-e57121b727e3
  Gurara: region=NG-NI division_id=8b952d02-69f5-41c5-82cb-d0546c3151de
  Okene: region=NG-KO division_id=33d19640-3752-4a13-b4c2-0b2f4502aaec
  Pategi: region=NG-KW division_id=bae113a0-815b-4d08-b9ed-b484aa0ea99c
  Saki East: region=NG-OY division_id=5be629b2-53ed-41fc-8907-03ecddd2654b
  Enugu South: region=NG-EN division_id=52cc5be3-a17d-4ac7-ad1d-7631bdc96c52
  Dandi: region=NG-KE division_id=dd252446-b368-4c1c-8de3-da9c8b9cd86c
  Ikot Ekpene: region=NG-AK division_id=134b177a-a697-43c3-b059-71893ae00c54
  Ifelodun: region=NG-KW division_id=0f8d4938-4b79-47c4-b795-57915f2f5c02
  Isin: region=NG-KW division_id=f5eef957-e748-463e-ab59-ebb92905c775
  Lagelu: region=NG-OY division_id=8d1f5424-abda-4912-b5d7-868f605d45de
  Irele: region=NG-ON division_id=05905888-5b7f-471d-b96f-b8806474ff87
  Ijero: region=NG-EK division_id=332467c3-3a60-49b9-95cc-3591e682af36
  Oju: region=NG-BE division_id=b6f9a192-a02d-4761-abd3-97b4931fcb80
  Epe: region=NG-LA division_id=053c8979-6fdf-4078-a9d1-3b545a06d72d
  Ogbadibo: region=NG-BE division_id=205a6a4d-6444-4d90-ad29-b6c12109ea28
  Ajeromi/Ifelodun: region=NG-LA division_id=b9ecb255-fc97-4e0a-8bc7-9dd0588f7a21
  Etsako West: region=NG-ED division_id=3fb5e72a-9af6-4fea-bcbf-3244c1e22724
  Kusada: region=NG-KT division_id=501bf250-8846-450d-82ab-0c61e50e0a69
  Ifedore: region=NG-ON division_id=07f9cd8a-d300-4556-9dda-a80cccb1315b
  Offa: region=NG-KW division_id=832397f8-fb55-43d4-9b19-8d07fe183cd0
  Ibiono Ibom: region=NG-AK division_id=4dc9d657-27bb-49cd-9c1a-be77c51c716b
  Kosofe: region=NG-LA division_id=9713c2c3-7416-41b8-9912-6e1450d3347d
  Oruk Anam: region=NG-AK division_id=b6d90ba4-ab41-4b54-a7d9-3a9c6da8b083
  Port-Harcourt: region=NG-RI division_id=0b849f93-bf65-4e5c-a1b0-5e8ea9ceeaa9
  Ika: region=NG-AK division_id=9a0fbc4e-ec9d-4b46-92c6-fd3c6c67c6d8
  Iguegben: region=NG-ED division_id=aa19e0f9-553a-4d44-9a0e-6113223be3a5
  Nasarawa: region=NG-NA division_id=525ac9b2-793b-47e4-9574-d1a9ebd827b5
  Ugwunagbo: region=NG-AB division_id=bef653a4-a1e5-4fc8-9bd7-e8b96651102f
  Ughelli North: region=NG-DE division_id=a20e7246-16db-4645-83f6-4f4f6ed90e38
  Aleiro: region=NG-KE division_id=6b8e2588-f03e-4ac4-86f4-fa4209c4df9d
  Roni: region=NG-JI division_id=452a2982-943c-43f4-bff5-cc28e83830d2
  Nsit Ubium: region=NG-AK division_id=edb4e034-80f9-414d-9061-466959f78114
  Gwagwalada: region=NG-FC division_id=4eb7774b-25aa-4413-815d-158a636bdbbf
  Bassa: region=NG-KO division_id=6eb35917-cc53-48b0-8629-3f1df18d0980
  Oron: region=NG-AK division_id=d4d1d288-ee5e-4c1e-b9b1-ee9f3b7862cd
  Suru: region=NG-KE division_id=9df4a2ea-b192-4267-8d57-b6e680109ebd
  Esan North-East: region=NG-ED division_id=e461ab68-be6c-4397-8d8c-316c4c5ae507
  Katcha: region=NG-NI division_id=5a8207f5-d746-4c63-91c7-01d96473e478
  Ethiope East: region=NG-DE division_id=0345b583-8b6a-47fc-b647-cc1c40dbbea7
  Dawakin Tofa: region=NG-KN division_id=f121c938-d60e-49f4-919a-50a6566e93b5
  Ibadan South East: region=NG-OY division_id=4482346c-f58e-468a-aed0-b3348cb0894c
  Nnewi South: region=NG-AN division_id=324a5fa2-58d1-4a90-9602-1cfaa9a9fd0d
  Obio/Akpor: region=NG-RI division_id=4a977a5d-0029-4854-9423-8ae3245b63e9
  Talata Mafara: region=NG-ZA division_id=fa99fca7-ed66-444e-bdd9-ec57a35cc00a
  Okrika: region=NG-RI division_id=4f534dfe-cccf-4167-8417-d475e9860836
  Anambra West: region=NG-AN division_id=a8124cab-916c-4178-84d2-2425a2b0085b
  Ughelli South: region=NG-DE division_id=2b7200d6-4a0d-4eb8-8c05-b79ee642a7ad
  Mkpat Enin: region=NG-AK division_id=bda1f44d-c413-483d-ac73-bb343646ceaf
  Maiyama: region=NG-KE division_id=50374218-800a-4559-a6ee-dc04f1c98f12
  Etsako Central: region=NG-ED division_id=2e280d9d-9cf1-4c01-bb63-cb72847cc505
  Asari-Toru: region=NG-RI division_id=7194bfce-20e0-4d21-8e01-8bbf25b9a725
  Danja: region=NG-KT division_id=f99e0fd9-6a97-445e-8db2-20df56c3681d
  Odo Otin: region=NG-OS division_id=c2019fae-93d5-40dd-b543-1b8a36fae15c
  Kankia: region=NG-KT division_id=04d0671e-11b0-4664-b76c-4a5d16b3b782
  Ila: region=NG-OS division_id=bff82424-9d18-4df0-8eb4-88e97f3ff6e9
  Lagos Island: region=NG-LA division_id=65a83885-eae9-4a94-8663-f6917cc9a04e
  Nsit Atai: region=NG-AK division_id=297591f3-d7a4-4a50-88af-cf2d1b7d9f8a
  Unuimo: region=NG-IM division_id=fe469a29-597b-4f13-b0b2-686bfdccad07
  Warri North: region=NG-DE division_id=e10f2fa9-cd7a-4dbd-8e68-c6feefe16601
  Ukwuani: region=NG-DE division_id=3edcbb52-f56b-4ab7-bdaa-17aee74e168f
  Sandamu: region=NG-KT division_id=805ab571-6db8-4c0c-9098-1be31f1b1810
  Isu: region=NG-IM division_id=8294274b-3ddb-45fa-9aab-d54219bf0784
  Odeda: region=NG-OG division_id=a61b8220-89d0-4b3b-a3f2-9795056473ee
  Oredo: region=NG-ED division_id=8a3c4767-7687-4a4b-abd8-e14a608e6011
  Safana: region=NG-KT division_id=ff8bc87d-6463-41eb-babb-d596df98427e
  Akoko South East: region=NG-ON division_id=4ce9e8c2-605e-4273-9a96-c940e6e6db36
  Oyigbo: region=NG-RI division_id=3f79fec4-b791-4475-81f0-f50462f32604
  Tsafe: region=NG-ZA division_id=21bd0cdb-00d6-4c2d-a974-0ee85a095368
  Ondo East: region=NG-ON division_id=fbf72dad-fea2-4776-82ab-fc6332bdffba
  Apa: region=NG-BE division_id=b67ee817-8dac-41bc-bd7d-8f977881c329
  Nsukka: region=NG-EN division_id=fe32b968-8b49-40f9-8cb8-7dc26413cb30
  Akoko-Edo: region=NG-ED division_id=88050e55-f3a6-47ee-8d4e-928ff80b811d
  Rimi: region=NG-KT division_id=b05f145f-9a85-441d-9368-4c167c1b7c6e
  Anka: region=NG-ZA division_id=30f18b62-14f4-443e-9dc9-a7702032a5e7
  Obokun: region=NG-OS division_id=b12af933-0796-446f-937c-af258aa643af
  Aba South: region=NG-AB division_id=4154c70d-66e8-44bc-ae85-6a5e22aeb33e
  Ọ̀wọ̀: region=NG-ON division_id=77208746-90b6-4f87-9dad-a64959d69751
  Ogbaru: region=NG-AN division_id=dec485b2-420e-48a3-adf5-43a4b031c47b
  Esan Central: region=NG-ED division_id=fb316e62-e041-43dd-b554-c4d6bcd714b7
  Ika South: region=NG-DE division_id=4deb8c46-65dc-42ec-92c9-debbfcbf848c
  Kolokuma/Opokuma: region=NG-BY division_id=b9d9d4cb-fcff-45cb-aa16-dde9be020770
  Gbonyin: region=NG-EK division_id=cb7eac38-bdc9-4115-a2f7-74445335bfb0
  Igalamela-Odolu: region=NG-KO division_id=b2f25003-69f9-4b2f-b67a-1093f805d6e8
  Enugu North: region=NG-EN division_id=d643b396-a4e4-46ba-a8f7-3f93f129ec82
  Bakori: region=NG-KT division_id=488db47b-696a-4b48-bff3-5cb2c685504c
  Ikeja: region=NG-LA division_id=af88a8c6-f84f-4629-a25b-1e79a9ccbc3d
  Ajaokuta: region=NG-KO division_id=017cc30a-6ff5-4f90-8d63-021733ed4d88
  Ahoada East: region=NG-RI division_id=bfd242c5-aa11-48fe-8a59-e22aff9d91f4
  Kaduna South: region=NG-KD division_id=0ff2489b-97e4-4c1d-bef4-8b4cc90ed787
  Silame: region=NG-SO division_id=7d52b276-b75a-4cef-abbe-292ee7d50c63
  Oke Ero: region=NG-KW division_id=9a503431-cfa1-47f2-bf01-372810da58c5
  Bungudu: region=NG-ZA division_id=674f20e9-bb48-475c-b68f-02755d41d76b
  Ekwusigo: region=NG-AN division_id=9d9e509d-89db-48e7-8c87-d070a75a16eb
  Sagbama: region=NG-BY division_id=10999404-8c79-4e94-9dc3-5f2f0aa8efb5
  Oyo East: region=NG-OY division_id=34940a0c-ba65-4256-9424-2a49a0afd299
  Ika North East: region=NG-DE division_id=93259fc4-95ab-48a9-aad9-b5e720dea622
  Nkanu West: region=NG-EN division_id=57eeca97-48ed-44ff-bd49-020bbdb29062
  Birnin Kebbi: region=NG-KE division_id=c75076c3-c17b-442f-ba51-f51b56661edc
  Malumfashi: region=NG-KT division_id=d2093b23-7305-4bbf-98b7-7ec5251fc4b8
  Eleme: region=NG-RI division_id=fddea72e-d7b3-421b-a953-b659c9421ffb
  Brass: region=NG-BY division_id=9fd0143c-f385-49a5-8b38-83f60a39f041
  Akoko South West: region=NG-ON division_id=c461301c-7352-4e4c-838b-7d89c718e288
  Owerri North: region=NG-IM division_id=0e11039e-79e0-48a3-8aea-5e502ad0c123
  Paikoro: region=NG-NI division_id=98be1d25-9d0c-4e63-86e1-78de40536e80
  Esit Eket: region=NG-AK division_id=63c7cd0b-fe26-4a97-b5a1-215a68c99dea
  Efon: region=NG-EK division_id=82225f05-59a8-452d-9c14-2e9a5bed822c
  Wushishi: region=NG-NI division_id=b0361554-bf7f-453d-a5e5-817b3a7d7436
  Ona Ara: region=NG-OY division_id=374957d0-43bd-4c94-bfe8-30026b8bc99e
  Itu: region=NG-AK division_id=e9233eec-59ec-4aa6-94c5-11da670c370d
  Atakunmosa West: region=NG-OS division_id=c88d00c3-c6e7-4560-a977-6ea3405a7dc5
  Ohimini: region=NG-BE division_id=93e60d6a-798d-4649-ad3b-f56e2e2ce441
  Irepo: region=NG-OY division_id=654a4256-36f5-4626-b64f-db2508c25a04
  Batsari: region=NG-KT division_id=d7acb33d-f6f1-48b5-a530-81b12adc7335
  Kabba/Bunu: region=NG-KO division_id=a2e332e4-bd56-4c30-926c-9f80fe64ebb4
  Mokwa: region=NG-NI division_id=8f73793c-ae78-4ba5-8dc9-4a82ea8f56dd
  Patani: region=NG-DE division_id=6b99475d-d875-4f8c-8407-1a9bb03a0c2c
  Ikenne: region=NG-OG division_id=fb950c98-d3df-4213-b856-9db1e26a1d3e
  Shomolu: region=NG-LA division_id=1b64c604-707e-4a65-a114-75f0f1abb3c5
  Ideato North: region=NG-IM division_id=02a5e14d-6c98-4915-8bd1-05c2e3ef43c0
  Ibaji: region=NG-KO division_id=4879d68c-eb41-4ea1-9de0-a3c349dfbf0a
  Ijebu East: region=NG-OG division_id=5ce8fe65-2f8a-46d9-ad0b-178605615896
  Awgu: region=NG-EN division_id=33b74169-911a-4262-8d37-1b1249db4361
  Lagos Mainland: region=NG-LA division_id=4f1aabae-5a08-4179-bdfb-f0e9d71d2ca5
  Odigbo: region=NG-ON division_id=e43b759f-93c4-4d0a-bda2-45508853f26c
  Ishielu: region=NG-EB division_id=4be70afc-3049-4d4b-9425-1f84f69653d3
  Kokona: region=NG-NA division_id=822e40d0-def6-409d-923c-b142f96fc24c
  Ado: region=NG-BE division_id=b6570003-4714-4378-9595-0158b876206e
  Sanga: region=NG-KD division_id=bdc6fa96-3f34-497e-9f85-006c485182a3
  Abaji: region=NG-FC division_id=657006da-4e5c-400f-adbc-8a6c1a5d07a5
  Chanchaga: region=NG-NI division_id=2a560af9-f2fd-406d-b316-8623437b4f9a
  Okobo: region=NG-AK division_id=1603f4d7-8817-492a-a7a0-10e3d7fbdc5e
  Udung Uko: region=NG-AK division_id=29d934d1-670f-4d51-b073-4c869b7dd313
  Moro: region=NG-KW division_id=229a6ad5-308e-4641-8a3b-1369d282f5ae
  Ilorin South: region=NG-KW division_id=c2d4426a-e700-4f5c-8133-729610db69dd
  Kunchi: region=NG-KN division_id=d18735c6-794d-42da-8800-f4b69cf5742b
  Oyi: region=NG-AN division_id=86a6eb30-9f8e-4fb9-9aad-a41ee436d695
  Ede South: region=NG-OS division_id=2660c01e-6d34-444d-b525-d0b55b21a97d
  Agaie: region=NG-NI division_id=6cd68549-aa4e-4cd8-9237-19276e3eb92a
  Uzo-Uwani: region=NG-EN division_id=529d4797-74b8-4bc3-afb9-b5f47d75cccf
  Osisioma Ngwa: region=NG-AB division_id=cb26b576-a355-4e9f-821e-48d0c7267285
  Kaura Namoda: region=NG-ZA division_id=f1147088-68c1-48c6-b3dc-e60c1a904283
  Onitsha North: region=NG-AN division_id=25be7799-6906-43e5-81be-95550e0b307d
  Ikole: region=NG-EK division_id=ce57dd0d-4dfd-40a7-b85a-e27015efaaad
  Edu: region=NG-KW division_id=a1c1e934-bbe0-4825-a1c9-83563
[... truncated ...]de-787e-4e18-aad3-49cdbe66a820
  Biriniwa: region=NG-JI division_id=a8c1d53d-1eb0-4b3f-8147-35fbbc0f17e8
  Bogoro: region=NG-BA division_id=2705e836-9abf-4022-ab23-770a5c64db03
  Dawakin Kudu: region=NG-KN division_id=e9ace692-9714-4c99-9f52-a0b3f6c74fa2
  Kanam: region=NG-PL division_id=c878e101-8474-45c0-9fd3-60c31d0718ce
  Jos South: region=NG-PL division_id=6959a260-a682-4ae9-a2f0-6b53e2c70285
  Shendam: region=NG-PL division_id=f92b0167-2f73-406b-86e1-2e08a8c03609
  Baure: region=NG-KT division_id=e8db35f3-9e0e-4e7c-85fe-32804d9d97ae
  Akamkpa: region=NG-CR division_id=2e9299dc-4f8c-48ed-9b39-8661ae89a589
  Wukari: region=NG-TA division_id=c374e01a-d374-4aff-bc79-246baab16351
  Bunkure: region=NG-KN division_id=07353cb0-1f75-48b8-b3b7-e9decd176cad
  Kafin Hausa: region=NG-JI division_id=f379b371-4b74-4e95-9ba4-09eaa4fe297f
  Ussa: region=NG-TA division_id=88e5c39d-e18a-4db7-a1f8-43aa05cccae5
  Jos East: region=NG-PL division_id=7082a393-878e-445d-b6d7-42de78aa6592
  Ringim: region=NG-JI division_id=3b717fec-e7e2-4653-82d5-3d6e874829e9
  Kiri Kasama: region=NG-JI division_id=6ddf1205-9139-4164-9803-5a3ee5d552c4
  Yala: region=NG-CR division_id=a47f8140-a72b-461e-8b44-14b7cd51d025
  Garki: region=NG-JI division_id=4090ae73-362d-45ae-babb-d107c6437fdb
  Shira: region=NG-BA division_id=0c02f494-c50b-4fb2-9a6e-52472ab61e12
  Ganjuwa: region=NG-BA division_id=3bdc4e87-db8b-41f5-8d83-2cf9fc410143
  Keana: region=NG-NA division_id=aeae6d17-61fe-44df-a56f-9368910dd9c4
  Mikang: region=NG-PL division_id=6479080d-b989-494b-9ecd-ef64f7880041
  Buji: region=NG-JI division_id=447688ef-7351-422c-93ae-4b1b8bf087a6
  Etung: region=NG-CR division_id=326f533f-5b75-4d8a-b67c-0eabe0280954
  Lere: region=NG-KD division_id=4aa8f430-d9c0-4a00-9d26-a97a9daf9eee
  Dambatta: region=NG-KN division_id=ca4baec9-b3e6-41c6-b76d-1500f7865664
  Itas/Gadau: region=NG-BA division_id=35406bfb-1675-4394-b651-23e0c3c968c1
  Konshisha: region=NG-BE division_id=f403f559-9da2-4c03-be10-b6b8847e46ae
  Ibi: region=NG-TA division_id=42e3b439-117a-422f-98a1-eefd311e151c
  Machina: region=NG-YO division_id=b57d36db-8dcd-43c4-bd91-2297ca9b7184
  Auyo: region=NG-JI division_id=5e6a586a-876d-416f-b550-01930b5adca1
  Doguwa: region=NG-KN division_id=43c01372-ef43-4484-bc08-fd3496d5386d
  Boki: region=NG-CR division_id=f9335c8f-880b-49fe-a43b-10008904472e
  Sule Tankakar: region=NG-JI division_id=720988ea-735a-49fb-aebd-c5f82333abd1
  Ogoja: region=NG-CR division_id=6e75fb10-6c6d-4fe3-8b99-84324d15ebc7
  Garko: region=NG-KN division_id=d3beb21b-dd43-47f5-8ffc-22f9caf1d16c
  Kanke: region=NG-PL division_id=c2bf23fa-53ae-417e-9921-73408dfdcc6c
  Ungongo: region=NG-KN division_id=5729d43e-7ed9-41e7-93b3-58bea716022f
  Kumbotso: region=NG-KN division_id=bf229cc2-d6d6-4ad9-bc94-080e8b977d65
  Obudu: region=NG-CR division_id=d32c54fd-a4a7-4554-97ea-49d306b6f57c
  Donga: region=NG-TA division_id=9d2e5ef8-e0ce-4dad-b1c4-f4cf21147d52
  Ushongo: region=NG-BE division_id=f54bee6c-28cb-49da-a14e-313358635a3c
  Jahun: region=NG-JI division_id=43da9554-d0c6-49b6-9979-283e790a182b
  Bekwarra: region=NG-CR division_id=7b3dda46-1fd9-4696-8510-da00301f310f
  Warji: region=NG-BA division_id=099e482c-0742-4531-b2b8-78b8814e7b53
  Katagum: region=NG-BA division_id=f824afdf-a6a8-4b0e-8bc0-5a0e04f51431
  Hadejia: region=NG-JI division_id=e3c471db-cad7-4199-b354-736f6b7480c3
  Wase: region=NG-PL division_id=7cbd67fd-354f-4e2a-9c1c-a0af0209c8ef
  Takai: region=NG-KN division_id=86f6d1ef-61a1-47ba-a0d2-00979e35d46d
  Tudun Wada: region=NG-KN division_id=7cccdff8-e2ea-4269-b730-6b98d17aa622
  Bauchi: region=NG-BA division_id=7283f807-801f-442b-97b3-51127c6bdc86
  Kiyawa: region=NG-JI division_id=d0824078-8bc8-4392-aed9-f3b8a6620062
  Ikom: region=NG-CR division_id=ed59cfd5-624a-4071-a58c-790ed0e3edf8
  Wamba: region=NG-NA division_id=cd0d2313-bc5a-42f7-83c4-9f465331aa04
  Gwale: region=NG-KN division_id=9dea6337-b432-4ce4-99da-65080f7d91f0
  Ukum: region=NG-BE division_id=3ce19c99-ce0d-4335-b59f-c0bd45c2a30c
  Zango: region=NG-KT division_id=61252d69-15a9-45c8-8e53-e01e19002b64
  Bassa: region=NG-PL division_id=0dfe893b-a105-4ed5-84db-dac775478587
  Awe: region=NG-NA division_id=43d57d4e-f2b9-4481-99d4-11f490b99623
  Vandeikya: region=NG-BE division_id=d1fa14e4-45a1-45d1-a6f0-fa7dacf61d8f
  Taura: region=NG-JI division_id=1037a85d-2f56-4e14-b64d-0f93fff404d7
  Dass: region=NG-BA division_id=9b1bcbe7-daa7-4615-989e-da22aa5f8e03
  Akpabuyo: region=NG-CR division_id=c3466ebf-6dac-446e-bd5b-388a02434443
  Tarka: region=NG-BE division_id=1b3ac927-8eab-4e5b-a431-475eff7d9de9
  Langtang South: region=NG-PL division_id=6b599480-8120-404e-92a0-404ff02c6b10
  Lafia: region=NG-NA division_id=314dd7be-116e-446e-9078-e02af17daa1f
  Gaya: region=NG-KN division_id=86158423-bcb4-412f-9085-dcf7f658ea7d
  Kwande: region=NG-BE division_id=75c2bb89-7a32-4fe1-b446-146561356f55
  Mangu: region=NG-PL division_id=7ed87e2d-4be8-438c-bf41-17155c2b6005
  Toro: region=NG-BA division_id=d15041b7-9500-47ac-a169-6be14e4ad197
  Maigatari: region=NG-JI division_id=82d5fdef-97cc-4a6e-befa-2f0c3f3b16bd
  Katsina-Ala: region=NG-BE division_id=2a183668-ff8c-4b2f-ac92-2f4bf1638045
  Bokkos: region=NG-PL division_id=e4719625-7393-4b98-b592-a136a09c7a7f
  Birnin Kudu: region=NG-JI division_id=77997030-24f2-4e66-b13c-f2f6cc724619
  Dutse: region=NG-JI division_id=3283acb3-8ff0-4f7b-a012-4a874fc7a0a1
  Kaura: region=NG-KD division_id=529b289a-db8a-4abb-8511-909d2b2888cb
  Gwaram: region=NG-JI division_id=bc2dfbb6-e15a-405d-af9f-dafa90ca55ef
  Yankwashi: region=NG-JI division_id=6a39bcc1-e25f-45f0-aecb-8ae18de5b13b
  Fagge: region=NG-KN division_id=64e0545e-0189-4166-82fb-15ca3c0bfabe
  Riyom: region=NG-PL division_id=114c25d0-0384-4e20-8c6a-56d31af0dac4
  Obanliku: region=NG-CR division_id=2ecc4599-4e0c-4fdb-ad9b-0640b834f2a7
  Takum: region=NG-TA division_id=7428506c-3625-40d6-a4be-3e49ce4459fe
  Minjibir: region=NG-KN division_id=c8cdbc70-d99c-461a-a86b-9ffb8eff659b
  Guma: region=NG-BE division_id=fde9dbca-d954-444d-b0e5-4e5a0804d7ee
  Albasu: region=NG-KN division_id=a29f1135-d155-4138-91d1-e2ab750a4298
  Gabasawa: region=NG-KN division_id=372d36b8-a379-406c-851a-12d0f53d45a7
  Jama'Are: region=NG-BA division_id=64953db6-bafa-4652-87c5-b6862fe733e3
  Kazaure: region=NG-JI division_id=870de23e-2bd6-41e8-8b4e-c26294c0ee33
  Gumel: region=NG-JI division_id=9a3ab2bb-4954-4a9d-baa6-0635fd50f7c5
  Pankshin: region=NG-PL division_id=4539ea17-de02-4c7c-bc18-99ef438d8f8f
  Kura: region=NG-KN division_id=de50703b-2d4f-4601-8833-7a1d3b20abf5
  Kaugama: region=NG-JI division_id=4da914eb-183e-441d-9140-15f8b2eabc6e
  Rano: region=NG-KN division_id=789ab7ac-2e2f-47a1-a07f-d765b3921adf
  Warawa: region=NG-KN division_id=c7468171-10fe-4560-b8bb-a258f2e30314
  Kibiya: region=NG-KN division_id=9c7e3aa5-6eef-4b0c-95fb-2d00d89c5160
  Langtang North: region=NG-PL division_id=04ff4377-f718-4f7a-971d-5b597e16c833
  Logo: region=NG-BE division_id=a2f6de28-f05d-4945-91c3-12cc31c7b288
  Makoda: region=NG-KN division_id=a5651ad8-6b9a-48a0-adc3-60ce380ae491
  Ajingi: region=NG-KN division_id=30faeef2-8a2e-4d79-adb3-fe93d545bf88
  Giade: region=NG-BA division_id=50c8c980-7f00-442f-bbcf-18f7fd3e4081
  Dala: region=NG-KN division_id=52e9d13d-bca4-438d-8ca8-3a82eb698e71
  Babura: region=NG-JI division_id=ba43b6f1-91ac-48de-8cac-c6417a83fcf8
  Nassarawa: region=NG-KN division_id=f042e71a-a867-46b8-871e-176d83dd2c99
  Tafawa-Balewa: region=NG-BA division_id=65e9d08a-84c9-4376-8a74-fe2fc25a87b7
  Miga: region=NG-JI division_id=f89d1e5f-880d-4867-8644-01d9d9025a4d
  Tarauni: region=NG-KN division_id=31e48077-27e7-4abb-8c1d-726cd860abc3
  Qua'An Pan: region=NG-PL division_id=11f2b039-a36a-40f3-8acc-137655fa9473
  Gboko: region=NG-BE division_id=a542a6d1-110c-4ba6-be5d-24cc83ff6fc4
  Malam Maduri: region=NG-JI division_id=08f9d06f-582e-45cf-83b0-10d8dfc5ded4
  Sumaila: region=NG-KN division_id=e5822858-6450-402c-8100-2aec7c9693a6
  Jos North: region=NG-PL division_id=9c4d4c85-bc80-4ece-a179-b30e56d19f3e
  Buruku: region=NG-BE division_id=211cc823-c7a1-4319-b8c4-f9ce6da90fe6
  Markurdi: region=NG-BE division_id=efdcc082-e810-4ccb-89b1-47c41ef70c66
  Gubio: region=NG-BO division_id=cf6875dc-7559-47e1-8a20-47428a9ab2c6
  Yola North: region=NG-AD division_id=51f16d76-311d-4e7a-9522-bcf222c23650
  Guri: region=NG-JI division_id=9b4556b0-03ed-45b3-a728-98dd85a03f5e
  Akko: region=NG-GO division_id=3b475be8-7daa-4506-aedc-f11484ce4e8f
  Maiduguri: region=NG-BO division_id=0d7dcee3-685b-44ab-a898-c0753a0383ae
  Yola South: region=NG-AD division_id=c29be714-6e13-4265-bc64-ba49b8995283
  Misau: region=NG-BA division_id=fc3d48e8-b8e8-40a9-9f58-01884b7e4e12
  Abadam: region=NG-BO division_id=d97ab13a-28cf-45b9-b682-15c05e4c8b28
  Nangere: region=NG-YO division_id=20b75b68-8870-4094-8719-b4ef5b813e97
  Madagali: region=NG-AD division_id=16d3b5da-4414-4c8b-9480-86f6423d8e55
  Zing: region=NG-TA division_id=ef0642f2-4555-4d30-9043-7e32d7db6f37
  Mubi South: region=NG-AD division_id=1bbcdc34-beab-4f19-b3a1-f724ad6dddf0
  Karasuwa: region=NG-YO division_id=cad072a9-a6c7-49b4-8301-e96a19a5f979
  Balanga: region=NG-GO division_id=96cce6cf-13c2-4ed3-8747-422e75ca0cbe
  Mobbar: region=NG-BO division_id=9defcdca-e359-45e7-b84c-804a643d0a10
  Hawul: region=NG-BO division_id=e17b450d-a385-4359-9c84-b3d389065f07
  Bayo: region=NG-BO division_id=81436d89-75b5-4ce1-a385-f40aeb7d7081
  Magumeri: region=NG-BO division_id=7cdfaece-c78c-4a8d-ae32-2c6d854b77e8
  Numan: region=NG-AD division_id=b27d2aa1-0ae0-4bf9-98a2-ab6f75f9d634
  Girei: region=NG-AD division_id=1f983b6f-6f05-4016-8a81-8cefc6cbbeb4
  Karim Lamido: region=NG-TA division_id=dd2052c7-5dc6-4127-b92b-2f304e17d8da
  Zaki: region=NG-BA division_id=78d50a59-8967-4751-bf5c-9a6b1619af68
  Bama: region=NG-BO division_id=42710534-b311-491f-b5e7-7d48c438f601
  Gwoza: region=NG-BO division_id=c3641737-8962-4123-8d5c-bdc28cf7062f
  Nganzai: region=NG-BO division_id=e53db4e1-7835-4d5e-9752-58fb4a5b689b
  Kaltungo: region=NG-GO division_id=a6b5a97e-3939-4afc-a7ad-c1e932a5992d
  Jakusko: region=NG-YO division_id=a9c7f308-ff9f-40b5-9846-f743a7b485d3
  Guyuk: region=NG-AD division_id=147a1b26-4006-49f5-91ac-266807d6fd88
  Jada: region=NG-AD division_id=897e2cab-644b-463a-a5aa-d303d3eba439
  Gassol: region=NG-TA division_id=e3e921e8-885d-4921-894e-e71322d12986
  Barde: region=NG-YO division_id=b8336157-a1fd-40d4-82c4-a0dc124e80c3
  Alkaleri: region=NG-BA division_id=3224d46c-b438-47d0-9364-3db21e13444c
  Ardo-Kola: region=NG-TA division_id=1f307567-6b9a-42e4-aff5-310ea8181bb9
  Sardauna: region=NG-TA division_id=0203d973-144f-4e55-a1d4-5581b0baa94d
  Demsa: region=NG-AD division_id=30b0cec3-2a70-4777-bdbb-7c59331b2b99
  Damban: region=NG-BA division_id=b3d377f0-0c0a-49e8-902d-500931e235a7
  Ngelzarma: region=NG-YO division_id=d977d8c4-21fa-4e4d-9988-84fff073d24d
  Bali: region=NG-TA division_id=b79762fe-1fcc-4df3-b2fb-f5abbef02f17
  Larmurde: region=NG-AD division_id=60183b99-a5b5-4f87-9b40-d4701fbee0da
  Mubi North: region=NG-AD division_id=a0c3c656-0cb8-480e-a7b3-205c16f447fc
  Kukawa: region=NG-BO division_id=5c6034ff-1f9c-47b0-803a-75033442634e
  Dikwa: region=NG-BO division_id=46d36f3e-a13c-4a87-99ea-973aeccf68cd
  Funakaye: region=NG-GO division_id=f48afebb-91df-4015-b43c-c0d0f710180c
  Mafa: region=NG-BO division_id=dbf4ff93-a053-4678-b46d-7731fc982e46
  Kwami: region=NG-GO division_id=dfe5beaf-74d5-43b7-856a-88c2fb64e420
  Tarmua: region=NG-YO division_id=9622e1ea-6cb6-478c-ad70-80c494329030
  Borsari: region=NG-YO division_id=05b905a1-6dbd-4e82-8286-633340eee8bd
  Biu: region=NG-BO division_id=95e98ae6-69c5-48b0-830f-4bb69ab6be1f
  Michika: region=NG-AD division_id=f3bcdc91-ea85-4d14-975d-4f1956574369
  Mayo-Belwa: region=NG-AD division_id=1f11ade5-f459-47d4-94e8-8bc4c5205222
  Shelleng: region=NG-AD division_id=24974308-fd8c-425b-bc8b-d029996251f1
  Jalingo: region=NG-TA division_id=c7d99738-8e16-4c7c-b14b-95d2f565354e
  Billiri: region=NG-GO division_id=675fd5a5-8c7d-4fa5-85d9-e2d4c7fa2f51
  Marte: region=NG-BO division_id=4a12fc77-e470-4c1b-b924-0db21a50d16e
  Kurmi: region=NG-TA division_id=bc4b6ddb-7b00-4484-a279-66fbd83b35cc
  Darazo: region=NG-BA division_id=23322921-c931-4425-96e6-9d38da238eae
  Kirfi: region=NG-BA division_id=8caf9457-bb40-4023-9a45-f8ca65cff3a5
  Yamaltu/Deba: region=NG-GO division_id=3d8f81bd-89c0-48d1-9a5c-6d3cfd766158
  Nafada: region=NG-GO division_id=d962ca97-8c55-409f-9efe-30ee1fd9bcae
  Teungo: region=NG-AD division_id=126a2337-66b8-42a1-9607-1418f65737ef
  Kwaya Kusar: region=NG-BO division_id=6bf20f50-7ff5-438f-a882-3dd291c353dc
  Chibok: region=NG-BO division_id=8e05b2d5-a67f-46d6-a672-f132b53211d5
  Potiskum: region=NG-YO division_id=d019044a-7d3a-403a-932d-0f59aede9cd3
  Ganye: region=NG-AD division_id=857c73de-24d0-4578-81e1-62db64f1801f
  Askira-Uba: region=NG-BO division_id=378bd43b-4d8c-49c3-bc5f-403d37344422
  Konduga: region=NG-BO division_id=f30c03c5-5d6e-4217-ae27-9dad8c46d832
  Gashaka: region=NG-TA division_id=b50bfeef-3924-437d-b5e7-bb081d330d7a
  Gujba: region=NG-YO division_id=3dca6cda-f0e3-4617-bc8c-484542a1fd33
  Fufore: region=NG-AD division_id=b3cfa64d-8e79-422f-b45e-37fae7a77219
  Hong: region=NG-AD division_id=b9e831fe-e47f-4033-8050-421bc861dbf1
  Shani: region=NG-BO division_id=3a0b3f88-206d-45a1-b1f8-142270e37d04
  Yusufari: region=NG-YO division_id=03499a47-3556-48f6-9134-188d8a35c5d0
  Dukku: region=NG-GO division_id=60be1494-eb3c-4ac0-b455-ba34b7ee8afa
  Nguru: region=NG-YO division_id=01d13ce8-68c1-4205-b326-b58f17111453
  Gombi: region=NG-AD division_id=bebd88bc-bbaa-4385-aea7-dd38fe89ca26
  Maiha: region=NG-AD division_id=97519539-3712-42d2-b1c0-548906dd83df
  Kaga: region=NG-BO division_id=595dd75d-32b4-4e27-b1e0-d1724439bb83
  Fika: region=NG-YO division_id=5c4d8c5a-b855-4b50-a28d-1770803397d6
  Yunusari: region=NG-YO division_id=79e0bf45-e3e3-4327-81b6-5087f22dbbda
  Shongom: region=NG-GO division_id=bef4718e-751a-421e-b1a4-d04c7551923a
  Ngala: region=NG-BO division_id=2e57fa27-2e02-4bc4-be3e-0a521a4d8898
  Jere: region=NG-BO division_id=41e8342e-ede5-425b-beb1-f41df775b8b2
  Gulani: region=NG-YO division_id=6612e804-07e4-4375-a0fe-f510f5d062f7
  Gamawa: region=NG-BA division_id=2e372d75-bd10-48e3-b1f5-aeee6756f185
  Yorro: region=NG-TA division_id=cbf06a2d-cb8a-429f-8f91-58114e215762
  Lau: region=NG-TA division_id=9e3d754e-a337-4c13-a921-c65f06e331e6
  Fune: region=NG-YO division_id=d29ddaab-e1fd-44bb-96c0-7dca07f7e460
  Monguno: region=NG-BO division_id=f6b7d2fb-4c1b-45df-8e21-678ef29ef6d7
  Geidam: region=NG-YO division_id=0672f286-44be-4e02-9062-6cc7e678a435
  Damboa: region=NG-BO division_id=8e6f9de0-6db6-49ba-b215-ac67bde7d3ed
  Gombe: region=NG-GO division_id=a4659f33-4899-4b26-8749-2fb039a939c1
  Guzamala: region=NG-BO division_id=1d660e66-592b-4998-a5ae-5ee3d5e8495a
  Song: region=NG-AD division_id=04f2324b-8135-48e8-b826-ddc631d31294
  Kala-Balge: region=NG-BO division_id=5735706b-28b5-492f-abdb-bb4ecd182616

Counties with division_id = Lagos region: 0

python3 << 'EOF'
from overturemaps.core import record_batch_reader
import pyarrow as pa
import pyarrow.compute as pc

# Get division_area for all of Nigeria
bbox_ng = (2.5, 4.0, 15.0, 14.0)

reader = record_batch_reader('division_area', bbox=bbox_ng, release='2026-05-20.0', stac=True)
if reader:
    table = reader.read_all()
    df = table.to_pandas()
    df_ng = df[df['country'] == 'NG']
    
    # Get all counties (LGAs) with region=NG-LA (Lagos State)
    lagos_lgas = df_ng[(df_ng['subtype'] == 'county') & (df_ng['region'] == 'NG-LA')]
    print(f"Found {len(lagos_lgas)} LGAs in Lagos State")
    for idx, row in lagos_lgas.iterrows():
        names = row['names']
        primary = names.get('primary', '?') if isinstance(names, dict) else str(names)
        print(f"  {primary}: id={row['id']}")
    print()
    
    # Also check for Lagos State region geometry
    lagos_region = df_ng[(df_ng['subtype'] == 'region') & (df_ng['region'] == 'NG-LA')]
    print(f"Lagos region rows: {len(lagos_region)}")
    for idx, row in lagos_region.iterrows():
        names = row['names']
        primary = names.get('primary', '?') if isinstance(names, dict) else str(names)
        print(f"  {primary}: id={row['id']}")
EOF

Found 20 LGAs in Lagos State
  Amuwo Odofin: id=32cfa1af-5e74-41a7-9272-b9dd8da715d9
  Agege: id=fcaab4e8-075a-4ab6-a2d2-cd614e95dfc2
  Ifako/Ijaye: id=934f5b76-4cbf-4902-b124-b33d057d87b1
  Oshodi/Isolo: id=457a7986-f212-4765-b432-e1c1b77d6d61
  Badagry: id=93ccd19d-3389-4c00-a8e3-920e20810d39
  Alimosho: id=1a9532db-bcd5-4050-b62b-f27a22f2ebcf
  Ojo: id=7918fd74-2ce4-48af-b3c1-1a4f1e26558e
  Ìkòròdú: id=030cb2c1-697b-418e-b820-49393989a03b
  Epe: id=61530733-5992-41f4-9212-43f1b460319c
  Ajeromi/Ifelodun: id=e2af9375-5eed-46f8-a258-515f22297084
  Kosofe: id=cb527384-149e-41c1-b3c5-0070deab27d4
  Lagos Island: id=c58fd335-e502-421c-b8e3-f263c5fb0ff7
  Ikeja: id=c540f662-44c9-498d-8f3d-2a41a039a29f
  Shomolu: id=b098198f-55d1-46a6-ac1e-71ae35fdd913
  Lagos Mainland: id=639f85d6-f581-4f31-b52b-98da63a57271
  Ibeju Lekki: id=70bd9c0f-fcdd-4bd2-81df-34d9351c997e
  Apapa: id=4b715cfc-a9b3-4a05-b601-353b77619939
  Surulere: id=f0f8532f-4c5e-428a-981e-12a31b2a9044
  Eti Osa: id=0c5600b6-df25-4e75-a25e-ae9937299bad
  Mushin: id=ffe6c369-91e2-4984-97d1-bd24feff3732

Lagos region rows: 1
  Lagos: id=5fb3f6cb-3993-4bb7-8a53-73e775feb14f

python3 << 'EOF'
from overturemaps.core import record_batch_reader
import pyarrow as pa

# Get division_boundary for the Lagos region
# First let's check what division_boundary looks like
bbox_lagos = (2.5, 6.2, 4.2, 6.8)

reader = record_batch_reader('division_boundary', bbox=bbox_lagos, release='2026-05-20.0', stac=True)
if reader:
    table = reader.read_all()
    print(f"Rows: {len(table)}")
    print("Columns:", table.column_names)
    df = table.to_pandas()
    print(df.head())
    # Check for Lagos
    for idx, row in df.iterrows():
        names = row['names']
        primary = names.get('primary', '?') if isinstance(names, dict) else str(names)
        print(f"  subtype={row['subtype']}, primary={primary}, id={row['id']}")
EOF

Rows: 79
Columns: ['id', 'geometry', 'division_ids', 'subtype', 'admin_level', 'class', 'sources', 'perspectives', 'is_disputed', 'is_land', 'is_territorial', 'country', 'region', 'version', 'bbox']
                                     id  ...                                               bbox
0  32e3316f-5c7c-332d-955a-44ca333468db  ...  {'xmin': 2.706815004348755, 'xmax': 2.73794937...
1  a227cf32-7eed-3133-8194-cd6aef2d1d1f  ...  {'xmin': 3.2838950157165527, 'xmax': 3.3101251...
2  a77d2497-2bee-39f5-8185-cb8e137d0862  ...  {'xmin': 2.676931619644165, 'xmax': 3.84514546...
3  7e26cd12-79d9-3dad-b981-b17c09880764  ...  {'xmin': 2.494842290878296, 'xmax': 2.58074831...
4  a95e737b-a6be-37cf-b1d5-a79e38bccf7a  ...  {'xmin': 3.323601245880127, 'xmax': 3.35218429...

[5 rows x 15 columns]

Traceback (most recent call last):
  File "/usr/local/lib/python3.14/site-packages/pandas/core/indexes/base.py", line 3641, in get_loc
    return self._engine.get_loc(casted_key)
           ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
  File "pandas/_libs/index.pyx", line 168, in pandas._libs.index.IndexEngine.get_loc
  File "pandas/_libs/index.pyx", line 197, in pandas._libs.index.IndexEngine.get_loc
  File "pandas/_libs/hashtable_class_helper.pxi", line 7668, in pandas._libs.hashtable.PyObjectHashTable.get_item
  File "pandas/_libs/hashtable_class_helper.pxi", line 7676, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'names'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "<stdin>", line 17, in <module>
  File "/usr/local/lib/python3.14/site-packages/pandas/core/series.py", line 959, in __getitem__
    return self._get_value(key)
           ~~~~~~~~~~~~~~~^^^^^
  File "/usr/local/lib/python3.14/site-packages/pandas/core/series.py", line 1046, in _get_value
    loc = self.index.get_loc(label)
  File "/usr/local/lib/python3.14/site-packages/pandas/core/indexes/base.py", line 3648, in get_loc
    raise KeyError(key) from err
KeyError: 'names'

python3 << 'EOF'
from overturemaps.core import record_batch_reader
import pyarrow as pa

# Get division_boundary for Lagos region
bbox_lagos = (2.5, 6.2, 4.2, 6.8)

reader = record_batch_reader('division_boundary', bbox=bbox_lagos, release='2026-05-20.0', stac=True)
if reader:
    table = reader.read_all()
    df = table.to_pandas()
    
    # Check columns
    print("Columns:", df.columns.tolist())
    
    # Print all rows' info
    for idx, row in df.iterrows():
        subtypes = row['subtype']
        dtype = row['class'] if 'class' in df.columns else '?'
        div_ids = row['division_ids']
        bbox_data = row['bbox']
        print(f"  subtype={row['subtype']}, class={row['class']}, division_ids={div_ids}")
EOF

Columns: ['id', 'geometry', 'division_ids', 'subtype', 'admin_level', 'class', 'sources', 'perspectives', 'is_disputed', 'is_land', 'is_territorial', 'country', 'region', 'version', 'bbox']
  subtype=country, class=maritime, division_ids=['3264d7ab-fdb5-4956-ad97-6307200c9e0a'
 '4b6398a5-21d4-490a-9985-ac2bbdb96286']
  subtype=county, class=land, division_ids=['b4bd3fef-918f-4a60-bfd3-3569ed991562'
 'e199f023-49ae-46ff-92a4-580c4fe1346d']
  subtype=country, class=land, division_ids=['3264d7ab-fdb5-4956-ad97-6307200c9e0a'
 '4b6398a5-21d4-490a-9985-ac2bbdb96286']
  subtype=county, class=land, division_ids=['0aeb3167-073f-42ee-a0c1-53887ca39de8'
 'f36733bd-9e3a-411d-8bf9-00091d5d6724']
  subtype=county, class=land, division_ids=['e199f023-49ae-46ff-92a4-580c4fe1346d'
 'fe1ca044-b547-49be-9883-2c8ffa3f04d5']
  subtype=county, class=land, division_ids=['dc5547ff-cb10-4818-8e9b-363c14daa13b'
 'b9ecb255-fc97-4e0a-8bc7-9dd0588f7a21']
  subtype=county, class=land, division_ids=['741e1dc1-eaf1-4d64-935c-52eafd212b18'
 '2ac02887-fd6f-4d60-a947-99f5d35b95c4']
  subtype=county, class=land, division_ids=['111df6ec-3852-4479-aa07-90ed58908ed9'
 '16ebd81c-74f4-4a86-8a36-0ca2b90c02fc']
  subtype=county, class=land, division_ids=['0aeb3167-073f-42ee-a0c1-53887ca39de8'
 '111df6ec-3852-4479-aa07-90ed58908ed9']
  subtype=county, class=land, division_ids=['fd922dfa-0bc1-4bce-a445-d8627b500eca'
 '681a3626-3cab-4d51-94c3-78d64fa3694a']
  subtype=county, class=land, division_ids=['4d40b68b-a144-47cc-9345-4604adc7fb60'
 '053c8979-6fdf-4078-a9d1-3b545a06d72d']
  subtype=region, class=land, division_ids=['dde7b6fb-f45e-4418-a9c1-f155e34e03be'
 '78710781-930c-409a-9986-c11606ef20e0']
  subtype=county, class=land, division_ids=['0bcee6b4-e6c2-43ff-9be3-ea45ae04d436'
 '681a3626-3cab-4d51-94c3-78d64fa3694a']
  subtype=county, class=land, division_ids=['fefe156c-41ea-4369-94a7-57357f365423'
 '2ac02887-fd6f-4d60-a947-99f5d35b95c4']
  subtype=county, class=land, division_ids=['4f1aabae-5a08-4179-bdfb-f0e9d71d2ca5'
 '65a83885-eae9-4a94-8663-f6917cc9a04e']
  subtype=county, class=land, division_ids=['dc5547ff-cb10-4818-8e9b-363c14daa13b'
 '65a83885-eae9-4a94-8663-f6917cc9a04e']
  subtype=county, class=land, division_ids=['bf72a742-460e-40d8-b67e-5cdcc75eeec9'
 'd6357489-86e9-4e24-ba64-2d3bda2d31a3']
  subtype=county, class=land, division_ids=['f9636a90-699b-4fb7-91e9-7c12335a2a89'
 '16ebd81c-74f4-4a86-8a36-0ca2b90c02fc']
  subtype=county, class=land, division_ids=['fd922dfa-0bc1-4bce-a445-d8627b500eca'
 'bf72a742-460e-40d8-b67e-5cdcc75eeec9']
  subtype=county, class=land, division_ids=['dc5547ff-cb10-4818-8e9b-363c14daa13b'
 '4f1aabae-5a08-4179-bdfb-f0e9d71d2ca5']
  subtype=county, class=land, division_ids=['7583d723-71b9-4f18-9fff-bff98df8322d'
 'dc5547ff-cb10-4818-8e9b-363c14daa13b']
  subtype=county, class=land, division_ids=['02a5536f-3bd6-41ca-b2a4-4c2d31666dea'
 '111df6ec-3852-4479-aa07-90ed58908ed9']
  subtype=county, class=land, division_ids=['634b4f3c-a5c4-42d2-b18b-1432b67373b8'
 '65a83885-eae9-4a94-8663-f6917cc9a04e']
  subtype=county, class=land, division_ids=['741e1dc1-eaf1-4d64-935c-52eafd212b18'
 'f9636a90-699b-4fb7-91e9-7c12335a2a89']
  subtype=county, class=land, division_ids=['b4bd3fef-918f-4a60-bfd3-3569ed991562'
 '7583d723-71b9-4f18-9fff-bff98df8322d']
  subtype=county, class=land, division_ids=['b4bd3fef-918f-4a60-bfd3-3569ed991562'
 'af88a8c6-f84f-4629-a25b-1e79a9ccbc3d']
  subtype=county, class=land, division_ids=['e199f023-49ae-46ff-92a4-580c4fe1346d'
 'af88a8c6-f84f-4629-a25b-1e79a9ccbc3d']
  subtype=county, class=land, division_ids=['22ef6348-2be5-4543-8f1d-6e9faf24e126'
 'fe1ca044-b547-49be-9883-2c8ffa3f04d5']
  subtype=county, class=land, division_ids=['7583d723-71b9-4f18-9fff-bff98df8322d'
 'b9ecb255-fc97-4e0a-8bc7-9dd0588f7a21']
  subtype=county, class=land, division_ids=['7583d723-71b9-4f18-9fff-bff98df8322d'
 'e199f023-49ae-46ff-92a4-580c4fe1346d']
  subtype=county, class=land, division_ids=['741e1dc1-eaf1-4d64-935c-52eafd212b18'
 'f36733bd-9e3a-411d-8bf9-00091d5d6724']
  subtype=county, class=maritime, division_ids=['2c66b8ba-4745-4334-b145-ae00b1a6f604'
 '202749c9-2473-4ecb-8528-72dc7a1278ee']
  subtype=county, class=land, division_ids=['2c66b8ba-4745-4334-b145-ae00b1a6f604'
 '202749c9-2473-4ecb-8528-72dc7a1278ee']
  subtype=county, class=land, division_ids=['4d40b68b-a144-47cc-9345-4604adc7fb60'
 '65a83885-eae9-4a94-8663-f6917cc9a04e']
  subtype=county, class=land, division_ids=['af88a8c6-f84f-4629-a25b-1e79a9ccbc3d'
 '24e04d44-9e6e-47ba-a71c-737793381785']
  subtype=county, class=land, division_ids=['bf72a742-460e-40d8-b67e-5cdcc75eeec9'
 'b10e97c1-fb43-472b-8605-90fb881bfd21']
  subtype=county, class=land, division_ids=['634b4f3c-a5c4-42d2-b18b-1432b67373b8'
 '9713c2c3-7416-41b8-9912-6e1450d3347d']
  subtype=county, class=land, division_ids=['14cdd032-7c30-44aa-beee-476b4d62184e'
 'd6357489-86e9-4e24-ba64-2d3bda2d31a3']
  subtype=county, class=land, division_ids=['02a5536f-3bd6-41ca-b2a4-4c2d31666dea'
 '16ebd81c-74f4-4a86-8a36-0ca2b90c02fc']
  subtype=county, class=land, division_ids=['a9df031a-eaa0-4b74-b42c-5bf303feb0e1'
 'af88a8c6-f84f-4629-a25b-1e79a9ccbc3d']
  subtype=county, class=land, division_ids=['af88a8c6-f84f-4629-a25b-1e79a9ccbc3d'
 'fe1ca044-b547-49be-9883-2c8ffa3f04d5']
  subtype=county, class=land, division_ids=['e199f023-49ae-46ff-92a4-580c4fe1346d'
 '22ef6348-2be5-4543-8f1d-6e9faf24e126']
  subtype=county, class=land, division_ids=['b9ecb255-fc97-4e0a-8bc7-9dd0588f7a21'
 '22ef6348-2be5-4543-8f1d-6e9faf24e126']
  subtype=county, class=land, division_ids=['dc5547ff-cb10-4818-8e9b-363c14daa13b'
 '22ef6348-2be5-4543-8f1d-6e9faf24e126']
  subtype=county, class=land, division_ids=['741e1dc1-eaf1-4d64-935c-52eafd212b18'
 '111df6ec-3852-4479-aa07-90ed58908ed9']
  subtype=county, class=land, division_ids=['1b64c604-707e-4a65-a114-75f0f1abb3c5'
 '65a83885-eae9-4a94-8663-f6917cc9a04e']
  subtype=county, class=land, division_ids=['7583d723-71b9-4f18-9fff-bff98df8322d'
 '22ef6348-2be5-4543-8f1d-6e9faf24e126']
  subtype=county, class=maritime, division_ids=['053c8979-6fdf-4078-a9d1-3b545a06d72d'
 '343b4b84-9150-4f3b-937f-c58ca20db27f']
  subtype=county, class=land, division_ids=['053c8979-6fdf-4078-a9d1-3b545a06d72d'
 '343b4b84-9150-4f3b-937f-c58ca20db27f']
  subtype=county, class=land, division_ids=['053c8979-6fdf-4078-a9d1-3b545a06d72d'
 '65a83885-eae9-4a94-8663-f6917cc9a04e']
  subtype=county, class=land, division_ids=['2ac02887-fd6f-4d60-a947-99f5d35b95c4'
 'f36733bd-9e3a-411d-8bf9-00091d5d6724']
  subtype=county, class=land, division_ids=['b4bd3fef-918f-4a60-bfd3-3569ed991562'
 '24e04d44-9e6e-47ba-a71c-737793381785']
  subtype=county, class=land, division_ids=['4f1aabae-5a08-4179-bdfb-f0e9d71d2ca5'
 '1b64c604-707e-4a65-a114-75f0f1abb3c5']
  subtype=county, class=land, division_ids=['b4bd3fef-918f-4a60-bfd3-3569ed991562'
 'a9df031a-eaa0-4b74-b42c-5bf303feb0e1']
  subtype=county, class=land, division_ids=['199f6f87-94c7-40e8-ba34-662f345e5794'
 '14cdd032-7c30-44aa-beee-476b4d62184e']
  subtype=county, class=land, division_ids=['fd922dfa-0bc1-4bce-a445-d8627b500eca'
 'b10e97c1-fb43-472b-8605-90fb881bfd21']
  subtype=county, class=land, division_ids=['0aeb3167-073f-42ee-a0c1-53887ca39de8'
 '741e1dc1-eaf1-4d64-935c-52eafd212b18']
  subtype=county, class=land, division_ids=['02a5536f-3bd6-41ca-b2a4-4c2d31666dea'
 '0aeb3167-073f-42ee-a0c1-53887ca39de8']
  subtype=county, class=land, division_ids=['4da8d8b1-71cb-436e-969c-832ec71d53e5'
 '5cfe6158-91fc-4c3e-b290-cbd8db20f980']
  subtype=county, class=land, division_ids=['b4bd3fef-918f-4a60-bfd3-3569ed991562'
 '2c66b8ba-4745-4334-b145-ae00b1a6f604']
  subtype=county, class=land, division_ids=['14cdd032-7c30-44aa-beee-476b4d62184e'
 'bf72a742-460e-40d8-b67e-5cdcc75eeec9']
  subtype=county, class=land, division_ids=['634b4f3c-a5c4-42d2-b18b-1432b67373b8'
 '053c8979-6fdf-4078-a9d1-3b545a06d72d']
  subtype=county, class=land, division_ids=['af88a8c6-f84f-4629-a25b-1e79a9ccbc3d'
 '9713c2c3-7416-41b8-9912-6e1450d3347d']
  subtype=county, class=land, division_ids=['1b64c604-707e-4a65-a114-75f0f1abb3c5'
 '9713c2c3-7416-41b8-9912-6e1450d3347d']
  subtype=county, class=land, division_ids=['5ce8fe65-2f8a-46d9-ad0b-178605615896'
 '0bcee6b4-e6c2-43ff-9be3-ea45ae04d436']
  subtype=county, class=maritime, division_ids=['4d40b68b-a144-47cc-9345-4604adc7fb60'
 '343b4b84-9150-4f3b-937f-c58ca20db27f']
  subtype=county, class=land, division_ids=['4d40b68b-a144-47cc-9345-4604adc7fb60'
 '343b4b84-9150-4f3b-937f-c58ca20db27f']
  subtype=county, class=land, division_ids=['a9df031a-eaa0-4b74-b42c-5bf303feb0e1'
 '24e04d44-9e6e-47ba-a71c-737793381785']
  subtype=county, class=maritime, division_ids=['4d40b68b-a144-47cc-9345-4604adc7fb60'
 '7583d723-71b9-4f18-9fff-bff98df8322d']
  subtype=county, class=land, division_ids=['4d40b68b-a144-47cc-9345-4604adc7fb60'
 '7583d723-71b9-4f18-9fff-bff98df8322d']
  subtype=region, class=land, division_ids=['246ecf78-8622-4d7d-8d12-102e73a67cda'
 'cd9acba9-bca3-4c03-a710-9f7595c9ba79']
  subtype=county, class=land, division_ids=['199f6f87-94c7-40e8-ba34-662f345e5794'
 'd6357489-86e9-4e24-ba64-2d3bda2d31a3']
  subtype=county, class=land, division_ids=['4f1aabae-5a08-4179-bdfb-f0e9d71d2ca5'
 '22ef6348-2be5-4543-8f1d-6e9faf24e126']
  subtype=county, class=land, division_ids=['fd922dfa-0bc1-4bce-a445-d8627b500eca'
 'fb950c98-d3df-4213-b856-9db1e26a1d3e']
  subtype=county, class=maritime, division_ids=['7583d723-71b9-4f18-9fff-bff98df8322d'
 '2c66b8ba-4745-4334-b145-ae00b1a6f604']
  subtype=county, class=land, division_ids=['7583d723-71b9-4f18-9fff-bff98df8322d'
 '2c66b8ba-4745-4334-b145-ae00b1a6f604']
  subtype=county, class=land, division_ids=['65a83885-eae9-4a94-8663-f6917cc9a04e'
 '9713c2c3-7416-41b8-9912-6e1450d3347d']
  subtype=county, class=land, division_ids=['1b64c604-707e-4a65-a114-75f0f1abb3c5'
 'fe1ca044-b547-49be-9883-2c8ffa3f04d5']
  subtype=county, class=land, division_ids=['681a3626-3cab-4d51-94c3-78d64fa3694a'
 'fb950c98-d3df-4213-b856-9db1e26a1d3e']

python3 << 'EOF'
from overturemaps.core import record_batch_reader
import geopandas as gpd
import pyarrow as pa

# Get division_area for the Lagos region specifically
bbox_lagos = (2.5, 6.2, 4.2, 6.8)

# Get division_area (boundaries of administrative areas)
reader = record_batch_reader('division_area', bbox=bbox_lagos, release='2026-05-20.0', stac=True)
if reader:
    table = reader.read_all()
    df = table.to_pandas()
    print(f"Total rows in Lagos area: {len(df)}")
    print("Columns:", df.columns.tolist())
    
    # Convert to GeoDataFrame
    gdf = gpd.GeoDataFrame.from_arrow(table)
    print(f"\nCRS: {gdf.crs}")
    print(f"Types: {gdf['subtype'].value_counts()}")
    
    # Filter to counties in Lagos State (NG-LA)
    lagos_counties = gdf[(gdf['subtype'] == 'county') & (gdf['region'] == 'NG-LA')]
    print(f"\nLagos counties: {len(lagos_counties)}")
    for idx, row in lagos_counties.iterrows():
        names = row['names']
        primary = names.get('primary', '?') if isinstance(names, dict) else str(names)
        print(f"  {primary}")
EOF

Total rows in Lagos area: 59
Columns: ['id', 'geometry', 'country', 'sources', 'subtype', 'admin_level', 'class', 'names', 'is_land', 'is_territorial', 'region', 'division_id', 'version', 'bbox']

CRS: None
Types: subtype
county          42
region           4
country          4
locality         3
neighborhood     3
dependency       2
macrohood        1
Name: count, dtype: int64

Lagos counties: 20
  Amuwo Odofin
  Agege
  Ifako/Ijaye
  Oshodi/Isolo
  Badagry
  Alimosho
  Ojo
  Ìkòròdú
  Epe
  Ajeromi/Ifelodun
  Kosofe
  Lagos Island
  Ikeja
  Shomolu
  Lagos Mainland
  Ibeju Lekki
  Apapa
  Surulere
  Eti Osa
  Mushin

Fetching Lagos LGA boundaries ...
  Found 20 LGAs: ['Agege', 'Ajeromi/Ifelodun', 'Alimosho', 'Amuwo Odofin', 'Apapa', 'Badagry', 'Epe', 'Eti Osa', 'Ibeju Lekki', 'Ifako/Ijaye', 'Ikeja', 'Kosofe', 'Lagos Island', 'Lagos Mainland', 'Mushin', 'Ojo', 'Oshodi/Isolo', 'Shomolu', 'Surulere', 'Ìkòròdú']
Fetching building footprints for Lagos bbox (2.5, 6.2, 4.2, 6.8) ...
  Raw buildings loaded: 4401417
  Computing footprint areas ...
  Filtering to area > 1000 m² ...
  Buildings > 1000 m²: 10196
  Assigning LGAs via spatial join ...
  After spatial join: 10196 buildings
  Writing lagos_buildings.geoparquet (10196 rows) ...
Traceback (most recent call last):
  File "/work/solve.py", line 95, in <module>
    out_buildings.to_parquet(
    ~~~~~~~~~~~~~~~~~~~~~~~~^
        "/work/lagos_buildings.geoparquet",
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    ...<2 lines>...
        schema_version="1.0"
        ^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/usr/local/lib/python3.14/site-packages/geopandas/geodataframe.py", line 1464, in to_parquet
    _to_parquet(
    ~~~~~~~~~~~^
        self,
        ^^^^^
    ...<6 lines>...
        **kwargs,
        ^^^^^^^^^
    )
    ^
  File "/usr/local/lib/python3.14/site-packages/geopandas/io/arrow.py", line 440, in _to_parquet
    table = _geopandas_to_arrow(
        df,
    ...<3 lines>...
        write_covering_bbox=write_covering_bbox,
    )
  File "/usr/local/lib/python3.14/site-packages/geopandas/io/arrow.py", line 357, in _geopandas_to_arrow
    geo_metadata = _create_metadata(
        df,
    ...<2 lines>...
        write_covering_bbox=write_covering_bbox,
    )
  File "/usr/local/lib/python3.14/site-packages/geopandas/io/arrow.py", line 147, in _create_metadata
    raise ValueError(
        f"schema_version must be one of: {', '.join(SUPPORTED_VERSIONS)}"
    )
ValueError: schema_version must be one of: 0.1.0, 0.4.0, 1.0.0-beta.1, 1.0.0, 1.1.0

Fetching Lagos LGA boundaries ...
  Found 20 LGAs: ['Agege', 'Ajeromi/Ifelodun', 'Alimosho', 'Amuwo Odofin', 'Apapa', 'Badagry', 'Epe', 'Eti Osa', 'Ibeju Lekki', 'Ifako/Ijaye', 'Ikeja', 'Kosofe', 'Lagos Island', 'Lagos Mainland', 'Mushin', 'Ojo', 'Oshodi/Isolo', 'Shomolu', 'Surulere', 'Ìkòròdú']
Fetching building footprints for Lagos bbox (2.5, 6.2, 4.2, 6.8) ...
  Raw buildings loaded: 4401417
  Computing footprint areas ...
  Filtering to area > 1000 m² ...
  Buildings > 1000 m²: 10196
  Assigning LGAs via spatial join ...
  After spatial join: 10196 buildings
  Writing lagos_buildings.geoparquet (10196 rows) ...
  Done.
  Computing LGA summary ...
  Writing lagos_building_summary.parquet (21 LGAs) ...

── LGA Summary ──
             lga  n_buildings  total_footprint_m2  n_with_height  p50_height_m
           Agege           48        9.579810e+04              0           NaN
Ajeromi/Ifelodun           63        1.095248e+05              0           NaN
        Alimosho          359        6.589676e+05              0           NaN
    Amuwo Odofin          844        1.773502e+06              0           NaN
           Apapa          288        6.162378e+05              0           NaN
         Badagry          126        2.125274e+05              0           NaN
             Epe          341        8.165704e+05              0           NaN
         Eti Osa         1080        1.870387e+06            241          20.0
     Ibeju Lekki          603        1.813858e+06              0           NaN
     Ifako/Ijaye          117        2.453375e+05              0           NaN
           Ikeja          922        3.194643e+06             47          15.0
          Kosofe          166        3.508572e+05              0           NaN
    Lagos Island          121        2.322891e+05              9          20.0
  Lagos Mainland          123        2.315257e+05              0           NaN
          Mushin          466        1.062194e+06              1          35.0
             Ojo          237        4.177230e+05              0           NaN
    Oshodi/Isolo          426        8.076162e+05              3          27.0
         Shomolu          125        1.999903e+05              3          17.0
        Surulere          344        7.866885e+05              0           NaN
         Unknown         2855        9.110122e+06              0           NaN
         Ìkòròdú          542        1.879439e+06              2           8.0

All done.

python3 << 'EOF'
import pandas as pd
import geopandas as gpd

# Check lagos_buildings.geoparquet
buildings = gpd.read_parquet("/work/lagos_buildings.geoparquet")
print("=== lagos_buildings.geoparquet ===")
print(f"Rows: {len(buildings)}")
print(f"CRS: {buildings.crs}")
print(f"Columns: {buildings.columns.tolist()}")
print(f"dtypes:\n{buildings.dtypes}")
print(f"\nFirst 5 rows:")
print(buildings.head())
print(f"\nMin area: {buildings['footprint_area_m2'].min():.2f}")
print(f"Max area: {buildings['footprint_area_m2'].max():.2f}")
print(f"LGA values: {sorted(buildings['lga'].unique())}")

# Check lagos_building_summary.parquet
print("\n\n=== lagos_building_summary.parquet ===")
summary = pd.read_parquet("/work/lagos_building_summary.parquet")
print(f"Rows: {len(summary)}")
print(f"Columns: {summary.columns.tolist()}")
print(summary.to_string(index=False))
EOF

=== lagos_buildings.geoparquet ===
Rows: 10196
CRS: {"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "GeographicCRS", "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"}]}, "scope": "Horizontal component of 3D system.", "area": "World.", "bbox": {"south_latitude": -90, "west_longitude": -180, "north_latitude": 90, "east_longitude": 180}, "id": {"authority": "EPSG", "code": 4326}}
Columns: ['id', 'height', 'footprint_area_m2', 'lga', 'geometry']
dtypes:
id                        str
height                float64
footprint_area_m2     float64
lga                       str
geometry             geometry
dtype: object

First 5 rows:
                                     id  ...                                           geometry
0  b5d79574-bcc8-4b85-be16-1bb9e3a6b7e3  ...  POLYGON ((2.50037 6.36138, 2.50019 6.36156, 2....
1  a3ede8c1-7782-4a5f-a445-d26fe3ce9dc3  ...  POLYGON ((2.50334 6.36216, 2.50343 6.36216, 2....
2  4efa51f8-0c9a-4b45-ab74-c4b99dee853c  ...  POLYGON ((2.52468 6.36137, 2.52446 6.36138, 2....
3  aebce895-f92e-40b8-9217-0b9c8ac5cd7e  ...  POLYGON ((2.52654 6.36294, 2.52654 6.3633, 2.5...
4  5c8972c4-1cfc-4cf8-bea2-cb1eb24adde0  ...  POLYGON ((2.52592 6.36241, 2.52593 6.36279, 2....

[5 rows x 5 columns]

Min area: 1000.00
Max area: 491426.16
LGA values: ['Agege', 'Ajeromi/Ifelodun', 'Alimosho', 'Amuwo Odofin', 'Apapa', 'Badagry', 'Epe', 'Eti Osa', 'Ibeju Lekki', 'Ifako/Ijaye', 'Ikeja', 'Kosofe', 'Lagos Island', 'Lagos Mainland', 'Mushin', 'Ojo', 'Oshodi/Isolo', 'Shomolu', 'Surulere', 'Unknown', 'Ìkòròdú']


=== lagos_building_summary.parquet ===
Rows: 21
Columns: ['lga', 'n_buildings', 'total_footprint_m2', 'n_with_height', 'p50_height_m']
             lga  n_buildings  total_footprint_m2  n_with_height  p50_height_m
           Agege           48        9.579810e+04              0           NaN
Ajeromi/Ifelodun           63        1.095248e+05              0           NaN
        Alimosho          359        6.589676e+05              0           NaN
    Amuwo Odofin          844        1.773502e+06              0           NaN
           Apapa          288        6.162378e+05              0           NaN
         Badagry          126        2.125274e+05              0           NaN
             Epe          341        8.165704e+05              0           NaN
         Eti Osa         1080        1.870387e+06            241          20.0
     Ibeju Lekki          603        1.813858e+06              0           NaN
     Ifako/Ijaye          117        2.453375e+05              0           NaN
           Ikeja          922        3.194643e+06             47          15.0
          Kosofe          166        3.508572e+05              0           NaN
    Lagos Island          121        2.322891e+05              9          20.0
  Lagos Mainland          123        2.315257e+05              0           NaN
          Mushin          466        1.062194e+06              1          35.0
             Ojo          237        4.177230e+05              0           NaN
    Oshodi/Isolo          426        8.076162e+05              3          27.0
         Shomolu          125        1.999903e+05              3          17.0
        Surulere          344        7.866885e+05              0           NaN
         Unknown         2855        9.110122e+06              0           NaN
         Ìkòròdú          542        1.879439e+06              2           8.0

Step 1: Fetching Lagos administrative boundaries ...
  Found 20 LGAs: ['Agege', 'Ajeromi/Ifelodun', 'Alimosho', 'Amuwo Odofin', 'Apapa', 'Badagry', 'Epe', 'Eti Osa', 'Ibeju Lekki', 'Ifako/Ijaye', 'Ikeja', 'Kosofe', 'Lagos Island', 'Lagos Mainland', 'Mushin', 'Ojo', 'Oshodi/Isolo', 'Shomolu', 'Surulere', 'Ìkòròdú']
Step 2: Fetching building footprints (bbox=(2.5, 6.2, 4.2, 6.8)) ...
  Raw buildings loaded: 4401417
Step 3: Computing footprint areas (UTM 32N)...
Step 4: Filtering to area > 1000 m² ...
  Buildings > 1000 m²: 10196
Step 5: Clipping to Lagos State region boundary ...
  Buildings within Lagos region: 7364
Step 6: Spatial join to LGAs (intersects, max-overlap tiebreak) ...
  After spatial join: 7364 buildings assigned
  LGA distribution:
lga
Eti Osa             1080
Ikeja                930
Amuwo Odofin         845
Ibeju Lekki          603
Ìkòròdú              542
Mushin               468
Oshodi/Isolo         428
Alimosho             360
Surulere             345
Epe                  344
Apapa                288
Ojo                  237
Kosofe               168
Shomolu              127
Badagry              126
Lagos Mainland       124
Lagos Island         121
Ifako/Ijaye          117
Ajeromi/Ifelodun      63
Agege                 48

Step 7a: Writing lagos_buildings.geoparquet ...
  Written 7364 rows.
Step 7b: Computing LGA summary ...
  Written 20 LGA rows.

── LGA Building Summary ──
             lga  n_buildings  total_footprint_m2  n_with_height  p50_height_m
           Agege           48        9.579810e+04              0           NaN
Ajeromi/Ifelodun           63        1.095248e+05              0           NaN
        Alimosho          360        6.600176e+05              0           NaN
    Amuwo Odofin          845        1.775263e+06              0           NaN
           Apapa          288        6.162378e+05              0           NaN
         Badagry          126        2.125274e+05              0           NaN
             Epe          344        8.203201e+05              0           NaN
         Eti Osa         1080        1.870387e+06            241          20.0
     Ibeju Lekki          603        1.813858e+06              0           NaN
     Ifako/Ijaye          117        2.453375e+05              0           NaN
           Ikeja          930        3.218890e+06             47          15.0
          Kosofe          168        3.547048e+05              0           NaN
    Lagos Island          121        2.322891e+05              9          20.0
  Lagos Mainland          124        2.327481e+05              0           NaN
          Mushin          468        1.075540e+06              1          35.0
             Ojo          237        4.177230e+05              0           NaN
    Oshodi/Isolo          428        8.106392e+05              3          27.0
         Shomolu          127        2.055954e+05              3          17.0
        Surulere          345        7.901183e+05              0           NaN
         Ìkòròdú          542        1.879439e+06              2           8.0

All done.

python3 << 'EOF'
import geopandas as gpd
import pandas as pd

# Verify buildings file
b = gpd.read_parquet("/work/lagos_buildings.geoparquet")
print("=== lagos_buildings.geoparquet ===")
print(f"CRS: EPSG:4326 (verified)")
print(f"Rows: {len(b)}")
print(f"Columns: {b.columns.tolist()}")
print(f"No null geometry: {b.geometry.isna().sum() == 0}")
print(f"All area > 1000: {(b['footprint_area_m2'] > 1000.0).all()}")
print(f"No unknown LGA: {'Unknown' not in b['lga'].values}")
print(f"Sample rows:\n{b.head(3)}\n")

# Verify summary file
s = pd.read_parquet("/work/lagos_building_summary.parquet")
print("=== lagos_building_summary.parquet ===")
print(f"Rows: {len(s)}")
print(f"Columns: {s.columns.tolist()}")
print(f"Total buildings: {s['n_buildings'].sum()}")
print(f"Total footprint: {s['total_footprint_m2'].sum():.2f} m²")
print(f"Total with height: {s['n_with_height'].sum()}")
print(s.to_string(index=False))
EOF

=== lagos_buildings.geoparquet ===
CRS: EPSG:4326 (verified)
Rows: 7364
Columns: ['id', 'height', 'footprint_area_m2', 'lga', 'geometry']
No null geometry: True
All area > 1000: True
No unknown LGA: True
Sample rows:
                                     id  ...                                           geometry
0  000efc1c-4b84-4c2c-9168-eb308046b6c0  ...  POLYGON ((3.24629 6.46629, 3.24593 6.46628, 3....
1  000fecd4-9c10-415c-a1a7-8dc206edbf84  ...  POLYGON ((4.01899 6.45791, 4.01904 6.45792, 4....
2  00207e24-36c2-48a3-9e59-d9ec671147d9  ...  POLYGON ((3.2742 6.46932, 3.27404 6.46923, 3.2...

[3 rows x 5 columns]

=== lagos_building_summary.parquet ===
Rows: 20
Columns: ['lga', 'n_buildings', 'total_footprint_m2', 'n_with_height', 'p50_height_m']
Total buildings: 7364
Total footprint: 17436958.32 m²
Total with height: 306
             lga  n_buildings  total_footprint_m2  n_with_height  p50_height_m
           Agege           48        9.579810e+04              0           NaN
Ajeromi/Ifelodun           63        1.095248e+05              0           NaN
        Alimosho          360        6.600176e+05              0           NaN
    Amuwo Odofin          845        1.775263e+06              0           NaN
           Apapa          288        6.162378e+05              0           NaN
         Badagry          126        2.125274e+05              0           NaN
             Epe          344        8.203201e+05              0           NaN
         Eti Osa         1080        1.870387e+06            241          20.0
     Ibeju Lekki          603        1.813858e+06              0           NaN
     Ifako/Ijaye          117        2.453375e+05              0           NaN
           Ikeja          930        3.218890e+06             47          15.0
          Kosofe          168        3.547048e+05              0           NaN
    Lagos Island          121        2.322891e+05              9          20.0
  Lagos Mainland          124        2.327481e+05              0           NaN
          Mushin          468        1.075540e+06              1          35.0
             Ojo          237        4.177230e+05              0           NaN
    Oshodi/Isolo          428        8.106392e+05              3          27.0
         Shomolu          127        2.055954e+05              3          17.0
        Surulere          345        7.901183e+05              0           NaN
         Ìkòròdú          542        1.879439e+06              2           8.0