dd-l3-lagos-overture-buildings
Map (reference ← swipe → agent)
0:00
Updating the flood-risk model before rainy season. Need every building footprint exceeding 1000 m² across Lagos State (Nigeria) from Overture.
Also need a per-LGA roll-up: for each Lagos Local Government Area, the building count, total footprint area, count of buildings with a non-null Overture height, and the median height where available (null otherwise).
Write two output files. `lagos_buildings.geoparquet` should be EPSG:4326 GeoParquet with columns `id`, `height`, `footprint_area_m2`, `lga`, and `geometry`. `lagos_building_summary.parquet` should be a plain Parquet with no geometry column and columns `lga`, `n_buildings`, `total_footprint_m2`, `n_with_height`, and `p50_height_m`.
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:08
The user wants me to: 1. Get building footprints from Overture Maps for Lagos State, Nigeria, focusing on buildings > 1000 m² 2. Create two output files: - `lagos_buildings.geoparquet` - EPSG:4326 GeoParquet with columns: id, height, footprint_area_m2, lga, geometry - `lagos_building_summary.parquet` - plain Parquet with columns: lga, n_buildings, total_footprint_m2, n_with_height, p50_height_m Let me start by understanding the Overture Maps data structure. Overture Maps data is available as GeoParquet files on S3 or via DuckDB with the overture extension. Let me use DuckDB to query Overture data. First, let me check what's available and plan the approach. Lagos State, Nigeria - I need to get the boundary or at least query by location. Let me use DuckDB with the overture extension to query the buildings data. Overture provides data in S3 buckets. Let me start by installing the needed extensions and querying the data. First, let me check if we have DuckDB and what version.
0:08
Bash
cd /work && python -c "import duckdb; print(duckdb.__version__)"
1.5.3
0:08
Bash
cd /work && python -c "import geopandas; print(geopandas.__version__)"
1.1.3
0:29
Let me plan this out. I need to: 1. Query Overture Maps data for buildings in Lagos State, Nigeria 2. Filter to footprints > 1000 m² 3. Get LGA (Local Government Area) boundaries to do a spatial join 4. Output lagos_buildings.geoparquet with columns: id, height, footprint_area_m2, lga, geometry (EPSG:4326) 5. Compute per-LGA summary and output lagos_building_summary.parquet with columns: lga, n_buildings, total_footprint_m2, n_with_height, p50_height_m Let me start by exploring what's available. I'll first check the overture datasets available through DuckDB (Overture uses Parquet files on S3).
0:29
Bash
cd /work && python << 'EOF'
cd /work && python << 'EOF'
import duckdb
# Check what tables/views are available in the overture schema
con = duckdb.connect()
# List the available tables in the main schema
result = con.execute("SELECT * FROM information_schema.tables WHERE table_schema='main'").fetchdf()
print(result)
EOF
Empty DataFrame Columns: [table_catalog, table_schema, table_name, table_type, self_referencing_column_name, reference_generation, user_defined_type_catalog, user_defined_type_schema, user_defined_type_name, is_insertable_into, is_typed, commit_action, TABLE_COMMENT] Index: []
0:35
Let me explore how to access Overture data via DuckDB. The Overture dataset is typically available via the `read_parquet` function from the Overture S3 bucket or via the Overture extension in DuckDB.
0:35
Bash
cd /work && python << 'EOF'
cd /work && python << 'EOF'
import duckdb
# Check if overture extension exists
con = duckdb.connect()
try:
result = con.execute("SELECT current_setting('extensions_directory')").fetchone()
print("extensions_directory:", result)
except Exception as e:
print("Error:", e)
# List available extensions
result = con.execute("SELECT * FROM duckdb_extensions()").fetchdf()
print(result[['extension_name', 'installed', 'loaded']])
EOF
Error: Catalog Error: unrecognized configuration parameter "extensions_directory"
Did you mean: "extension_directory", "extension_directories"
extension_name installed loaded
0 autocomplete False False
1 aws False False
2 azure False False
3 core_functions True True
4 delta False False
5 ducklake False False
6 encodings False False
7 excel False False
8 fts False False
9 httpfs False False
10 iceberg False False
11 icu True True
12 inet False False
13 json True True
14 lance False False
15 motherduck False False
16 mysql_scanner False False
17 odbc_scanner False False
18 parquet True True
19 postgres_scanner False False
20 quack False False
21 spatial False False
22 sqlite_scanner False False
23 tpcds False False
24 tpch False False
25 ui False False
26 vortex False False
27 vss False False
0:41
I need to install the spatial extension and the overture extension for DuckDB. Let me check what's available and install what I need.
0:41
Bash
cd /work && python << 'EOF'
cd /work && python << 'EOF'
import duckdb
con = duckdb.connect()
# Install spatial extension
con.execute("INSTALL spatial;")
con.execute("LOAD spatial;")
print("Spatial installed")
# Check if overture extension is available
try:
con.execute("INSTALL overture;")
print("Overture installed")
except Exception as e:
print("Overture install error:", e)
EOF
Spatial installed Overture install error: HTTP Error: Failed to download extension "overture" at URL "http://extensions.duckdb.org/v1.5.3/linux_amd64/overture.duckdb_extension.gz" (HTTP 404) Candidate extensions: "vortex", "motherduck", "inet", "postgres", "postgres_scanner" For more info, visit https://duckdb.org/docs/stable/extensions/troubleshooting?version=v1.5.3&platform=linux_amd64&extension=overture
0:48
The overture extension is not available for DuckDB 1.5.3. Let me check if there's a newer version of DuckDB available or use a different approach. I can use the Overture Maps Python library or directly query the Overture data from S3 using DuckDB with spatial and httpfs extensions. Let me first check the DuckDB version and see if I can upgrade, or use the Overture Maps Python library directly. Actually, Overture Maps data is available as Parquet files on AWS S3. I can use DuckDB with httpfs and spatial extensions to query them directly. Let me explore the Overture data structure.
0:48
Bash
cd /work && pip list 2>/dev/null | grep -i overture
overturemaps 1.0.0
0:49
Bash
cd /work && python -c "import overturemaps; print(overturemaps.__version__)" 2>&...
Traceback (most recent call last):
File "<string>", line 1, in <module>
import overturemaps; print(overturemaps.__version__)
^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: module 'overturemaps' has no attribute '__version__'
Not installed
0:53
The `overturemaps` Python package is installed. Let me check how to use it to download building data for Lagos State, Nigeria.
0:53
Bash
cd /work && python -c "import overturemaps; help(overturemaps)" 2>&1 | head -60
Help on package overturemaps:
NAME
overturemaps
PACKAGE CONTENTS
__main__
changelog
cli
core
models
releases
state
writers
FILE
/usr/local/lib/python3.14/site-packages/overturemaps/__init__.py
0:57
Let me explore the overturemaps library to understand how to query Overture data.
0:57
Bash
cd /work && python -c "import overturemaps.core; help(overturemaps.core)" 2>&1 |...
Help on module overturemaps.core in overturemaps:
NAME
overturemaps.core
FUNCTIONS
count_rows(
overture_type,
bbox=None,
release=None,
connect_timeout=None,
request_timeout=None,
stac=False
) -> int
Return the number of rows matching the given parameters.
geoarrow_schema_adapter(schema: pa.Schema) -> pa.Schema
Convert a geoarrow-compatible schema to a proper geoarrow schema
This assumes there is a single "geometry" column with WKB formatting
Parameters
----------
schema: pa.Schema
Returns
-------
pa.Schema
A copy of the input schema with the geometry field replaced with
a new one with the proper geoarrow ARROW:extension metadata
geodataframe(
overture_type: str,
bbox: BBox | tuple[float, float, float, float] | list[float] | None = None,
release: str = None,
connect_timeout: int = None,
request_timeout: int = None,
stac: bool = False
) -> GeoDataFrame
Loads geoparquet for specified type into a geopandas dataframe
Parameters
----------
overture_type: type to load
bbox: optional bounding box for data fetch (xmin, ymin, xmax, ymax)
connect_timeout: optional connection timeout in seconds
request_timeout: optional request timeout in seconds
Returns
-------
GeoDataFrame with the optionally filtered theme data
get_all_overture_types() -> List[str]
get_available_releases() -> Tuple[List[str], str]
Fetch available releases from the STAC catalog.
Returns
-------
Tuple of (all_releases, latest_release) where:
- all_releases is a list of release version strings
- latest_release is the latest release version string
get_latest_release() -> str
Get the latest release version.
Returns
-------
str: The latest release version
query_gers_registry(gers_id: str) -> Optional[Tuple[str, BBox | None]]
Query the GERS registry to get the filepath and bbox for a given GERS ID.
The registry always uses the latest release.
Parameters
----------
gers_id: The GERS ID to look up
Returns
-------
Tuple of (filepath, bbox) where bbox is a BBox, or None if not found
record_batch_reader(
overture_type,
bbox=None,
release=None,
connect_timeout=None,
request_timeout=None,
stac=False
) -> Optional[pa.RecordBatchReader]
Return a pyarrow RecordBatchReader for the desired bounding box and s3 path, or None on error.
record_batch_reader_from_gers(
gers_id: str,
connect_timeout: int = None,
request_timeout: int = None,
registry_result: Optional[Tuple[str, BBox | None]] = None
) -> Optional[pa.RecordBatchReader]
Return a pyarrow RecordBatchReader for a specific GERS ID.
1:01
The `overturemaps` library has a nice `geodataframe` function that can load Overture data into a GeoDataFrame. Let me explore how to use it for building footprints. Let me first check what types are available and understand the structure.
1:01
Bash
cd /work && python << 'EOF'
cd /work && python << 'EOF' import overturemaps.core as oc types = oc.get_all_overture_types() print(types) EOF
['address', 'bathymetry', 'building', 'building_part', 'division', 'division_area', 'division_boundary', 'place', 'segment', 'connector', 'infrastructure', 'land', 'land_cover', 'land_use', 'water']
1:07
So the building type is available. Now I need to get data for Lagos State, Nigeria. I need to find the bounding box for Lagos State or query by administrative boundary. Let me look up Lagos State's bounding box and get the buildings. I can use the `division` or `division_area` type to get Lagos State boundaries. Let me first find the Lagos State area.
1:07
Bash
cd /work && python << 'EOF'
cd /work && python << 'EOF'
import overturemaps.core as oc
# Get divisions for Nigeria - Lagos State
# division_area contains polygons of administrative areas
df = oc.geodataframe("division_area", release="2024-09-18.0")
print("Columns:", df.columns.tolist())
print("Subtypes:", df.subtype.unique() if hasattr(df, 'subtype') else "N/A")
print("Shape:", df.shape)
print(df.head(2))
EOF
Traceback (most recent call last):
File "<stdin>", line 5, 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/2024-09-18.0/theme=divisions/type=division_area/
1:13
The default release might not exist for all themes. Let me find the latest release and use it. Also, I need to get Lagos State boundary data. Let me first understand the data schema better.
1:13
Bash
cd /work && python << 'EOF'
cd /work && python << 'EOF'
import overturemaps.core as oc
releases, latest = oc.get_available_releases()
print("Latest:", latest)
print("All:", releases[:10])
EOF
Latest: 2026-05-20.0 All: ['2026-05-20.0']
1:18
Let me use the latest release. Let me check what the building schema looks like.
1:18
Bash
cd /work && python << 'EOF'
cd /work && python << 'EOF'
import overturemaps.core as oc
# Use latest release
df = oc.geodataframe("division_area")
print("Columns:", df.columns.tolist())
print("Shape:", df.shape)
print(df.head(2))
EOF
<exit_code>-1</exit_code> <error>Command timed out after 120000ms</error>
3:26
The command timed out because downloading all division_area data globally is huge. I need to use a bounding box for Lagos State, Nigeria. Let me first find the bounding box for Lagos State, then query with a bbox.
3:26
Bash
cd /work && python << 'EOF'
cd /work && python << 'EOF'
# First get the bounding box for Lagos State, Nigeria
# Using a known approximate bbox for Lagos State
# Lagos State roughly: lon 2.68 to 4.5, lat 6.2 to 6.6
# Let's be a bit generous
bbox = (2.5, 6.2, 4.5, 6.7)
import overturemaps.core as oc
# Try to get building data first with bbox
df = oc.geodataframe("building", bbox=bbox)
print("Building columns:", df.columns.tolist())
print("Shape:", df.shape)
print(df.head(2))
EOF
Building 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']
Shape: (3608755, 24)
id ... bbox
0 1395d28c-839c-4845-a969-5b96ea47e4bf ... {'xmin': 4.499796390533447, 'xmax': 4.49994945...
1 00cc2ce2-803f-4c72-b4e2-88d5fc099bcd ... {'xmin': 4.499771595001221, 'xmax': 4.49990940...
[2 rows x 24 columns]
6:38
We have 3.6M buildings within the Lagos bbox. But the bbox is generous - it likely covers more than Lagos State. We need to filter to only buildings within Lagos State boundaries. Let me get the division_area for Nigeria/Lagos State to get the exact boundary, then clip.
6:38
Bash
cd /work && python << 'EOF'
cd /work && python << 'EOF'
import overturemaps.core as oc
# Get division_area for Nigeria to find Lagos State boundary
# First check what columns are available for division_area
df = oc.geodataframe("division_area", bbox=(2.5, 4.0, 15.0, 14.0))
print("Division columns:", df.columns.tolist())
print("Shape:", df.shape)
# Check what regions are available
print(df.region.unique() if 'region' in df.columns else "no region col")
print(df.country.unique() if 'country' in df.columns else "no country col")
print(df.subtype.unique() if 'subtype' in df.columns else "no subtype col")
# Print all column names and first few values
print(df.head(3).to_string())
EOF
Division columns: ['id', 'geometry', 'country', 'sources', 'subtype', 'admin_level', 'class', 'names', 'is_land', 'is_territorial', 'region', 'division_id', 'version', 'bbox']
Shape: (4621, 14)
<ArrowStringArray>
[ nan, 'BJ-OU', 'BJ-BO', 'NG-OY', 'BJ-PL', 'BJ-ZO', 'NG-LA', 'BJ-CO',
'NG-OG', 'NE-6', 'BJ-AL', 'NE-3', 'NG-KW', 'NG-KD', 'NG-KN', 'NG-SO',
'NG-KT', 'NG-AK', 'NG-KE', 'NG-ZA', 'NG-NI', 'NG-DE', 'NG-EK', 'NG-JI',
'NG-EN', 'NG-EB', 'NG-KO', 'NG-OS', 'NG-IM', 'NG-AN', 'NG-ON', 'NG-AB',
'NG-RI', 'NG-FC', 'NG-BE', 'NG-ED', 'NG-NA', 'NG-BY', 'NG-CR', 'NE-4',
'NE-5', 'NE-7', 'CM-SW', 'CM-LT', 'NG-BA', 'NG-YO', 'CM-OU', 'CM-NW',
'NG-PL', 'NG-TA', 'CM-NO', 'NG-BO', 'CM-AD', 'CM-EN', 'NG-AD', 'NE-2',
'NG-GO', 'CM-CE', 'CM-ES', 'TD-MO', 'TD-LC', 'TD-ND', 'TD-KA', 'CF-OP',
'TD-HL', 'CF-HS', 'CF-NM', 'TD-ME']
Length: 68, dtype: str
<ArrowStringArray>
['UM', 'ML', 'BJ', 'NG', 'NE', 'KI', 'CM', 'TD', 'CF', 'CD']
Length: 10, dtype: str
<ArrowStringArray>
[ 'dependency', 'country', 'county', 'locality',
'region', 'neighborhood', 'macrohood', 'microhood']
Length: 8, dtype: str
id
[... truncated ...].35843, -5.87094 10.35847, -5.87077 10.35857, -5.87065 10.35871, -5.8705 10.35883, -5.87015 10.35898, -5.87004 10.35919, -5.86992 10.35957, -5.86999 10.36145, -5.87013 10.36261, -5.87027 10.36339, -5.87146 10.36563, -5.87156 10.3662, -5.87198 10.3672, -5.87192 10.36752, -5.87169 10.36802, -5.8711 10.36877, -5.87042 10.36941, -5.87016 10.36973, -5.87 10.37061, -5.86925 10.37086, -5.86904 10.37114, -5.86883 10.37166, -5.86896 10.3723, -5.86888 10.37271, -5.86826 10.37405, -5.8681 10.37495, -5.86768 10.37585, -5.86749 10.37614, -5.86729 10.37687, -5.86681 10.37765, -5.86678 10.37792, -5.86637 10.37793, -5.86603 10.37798, -5.86546 10.37826, -5.86334 10.37798, -5.86214 10.37751, -5.86106 10.37726, -5.85954 10.37664, -5.85881 10.37665, -5.85736 10.37591, -5.8572 10.37578, -5.85684 10.37591, -5.85649 10.37583, -5.85584 10.37538, -5.85478 10.37468, -5.85399 10.37446, -5.85347 10.37428)) ML [{'property': '', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'r192785@164', 'update_time': '2026-02-24T17:55:57Z', 'confidence': None, 'between': None}, {'property': '/properties/names/common/lfn', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/common/ksh', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/common/lo', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/common/dz', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/common/haw', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/common/to', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/common/chr', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/common/gu', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/common/tok', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/common/te', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/common/ne', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/common/km', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/common/ks', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/rules/13', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/rules/14', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/rules/15', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/rules/16', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/rules/17', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/rules/18', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/rules/19', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/rules/20', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/rules/21', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/rules/22', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/rules/23', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/rules/24', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/rules/25', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/rules/26', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/rules/27', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/rules/28', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/rules/29', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/rules/30', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/rules/31', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/rules/32', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/rules/33', 'dataset': 'OpenStreetMap', 'license': 'ODbL-1.0', 'record_id': 'n424297816@58', 'update_time': '2023-08-18T22:28:26Z', 'confidence': None, 'between': None}, {'property': '/properties/names/common/pt-PT', 'dataset': 'Esri Community Maps', 'license': 'CC0-1.0', 'record_id': 'R192785', 'update_time': None, 'confidence': None, 'between': None}, {'property': '/properties/names/common/pt-BR', 'dataset': 'Esri Community Maps', 'license': 'CC0-1.0', 'record_id': 'R192785', 'update_time': None, 'confidence': None, 'between': None}, {'property': '/properties/names/common/sr-Latn', 'dataset': 'Esri Community Maps', 'license': 'CC0-1.0', 'record_id': 'R192785', 'update_time': None, 'confidence': None, 'between': None}, {'property': '/properties/names/rules/34', 'dataset': 'Esri Community Maps', 'license': 'CC0-1.0', 'record_id': 'R192785', 'update_time': None, 'confidence': None, 'between': None}, {'property': '/properties/names/rules/35', 'dataset': 'Esri Community Maps', 'license': 'CC0-1.0', 'record_id': 'R192785', 'update_time': None, 'confidence': None, 'between': None}] country 0.0 land {'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'), ...], 'rules': [{'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}]} True True NaN 4d81e101-5ed5-4e4d-b3a1-0878f70f254b 8 {'xmin': -12.240283966064453, 'xmax': 4.267383098602295, 'ymin': 10.147810935974121, 'ymax': 25.001087188720703}
7:10
I can see that the division_area data has `region` column with values like 'NG-LA' for Lagos State. Let me find the Lagos State boundary and use it to clip the buildings. Let me also look for Nigeria sub-regions to find the Lagos LGA boundaries.
7:10
Bash
cd /work && python << 'EOF'
cd /work && python << 'EOF'
import overturemaps.core as oc
# Get division_area for Nigeria to find Lagos State
df = oc.geodataframe("division_area", bbox=(2.5, 4.0, 15.0, 14.0))
print(df.columns.tolist())
# Filter for Nigeria (country='NG') and region='NG-LA' (Lagos State)
ng_lagos = df[df['region'] == 'NG-LA']
print(f"Lagos State rows: {len(ng_lagos)}")
print("Subtypes:", ng_lagos.subtype.unique())
print("Classes:", ng_lagos['class'].unique())
print(ng_lagos[['id', 'subtype', 'class', 'admin_level']].to_string())
EOF
['id', 'geometry', 'country', 'sources', 'subtype', 'admin_level', 'class', 'names', 'is_land', 'is_territorial', 'region', 'division_id', 'version', 'bbox']
Lagos State rows: 27
Subtypes: <ArrowStringArray>
['county', 'region', 'locality', 'macrohood', 'neighborhood']
Length: 5, dtype: str
Classes: <ArrowStringArray>
['land']
Length: 1, dtype: str
id subtype class admin_level
34 32cfa1af-5e74-41a7-9272-b9dd8da715d9 county land 2.0
36 fcaab4e8-075a-4ab6-a2d2-cd614e95dfc2 county land 2.0
108 934f5b76-4cbf-4902-b124-b33d057d87b1 county land 2.0
156 457a7986-f212-4765-b432-e1c1b77d6d61 county land 2.0
172 93ccd19d-3389-4c00-a8e3-920e20810d39 county land 2.0
217 1a9532db-bcd5-4050-b62b-f27a22f2ebcf county land 2.0
237 7918fd74-2ce4-48af-b3c1-1a4f1e26558e county land 2.0
326 5fb3f6cb-3993-4bb7-8a53-73e775feb14f region land 1.0
330 030cb2c1-697b-418e-b820-49393989a03b county land 2.0
424 61530733-5992-41f4-9212-43f1b460319c county land 2.0
428 e2af9375-5eed-46f8-a258-515f22297084 county land 2.0
444 cb527384-149e-41c1-b3c5-0070deab27d4 county land 2.0
593 11d7010b-4feb-41a8-8fd6-2062a2bc075c locality land NaN
598 c58fd335-e502-421c-b8e3-f263c5fb0ff7 county land 2.0
710 c540f662-44c9-498d-8f3d-2a41a039a29f county land 2.0
796 b098198f-55d1-46a6-ac1e-71ae35fdd913 county land 2.0
811 639f85d6-f581-4f31-b52b-98da63a57271 county land 2.0
818 439f3b37-5d1a-42f2-9c4f-45d2aab88423 macrohood land NaN
826 365d8234-4821-4229-9a4e-6a0a6325bd0b neighborhood land NaN
1115 4b748354-7b73-4c51-b5c3-8dc811d48714 locality land NaN
1192 70bd9c0f-fcdd-4bd2-81df-34d9351c997e county land 2.0
1292 4b715cfc-a9b3-4a05-b601-353b77619939 county land 2.0
1296 f0f8532f-4c5e-428a-981e-12a31b2a9044 county land 2.0
1647 0c5600b6-df25-4e75-a25e-ae9937299bad county land 2.0
1689 bab8ae3c-3858-4fa7-921f-9b8d71c07827 neighborhood land NaN
1691 e4636d6f-0327-49fd-b5fc-24076c5fd19d neighborhood land NaN
1891 ffe6c369-91e2-4984-97d1-bd24feff3732 county land 2.0
7:56
So I can see that for Lagos State (NG-LA), we have: - 1 region (admin_level=1) - this is the state level - Many counties (admin_level=2) - these are the Local Government Areas (LGAs) - Some localities, macrohoods, neighborhoods Let me get the LGA boundaries (counties) for Lagos State. I need to filter by region='NG-LA' and subtype='county' to get the LGA boundaries. Then I need to get the building footprints that exceed 1000 m² within Lagos State. Let me look at the names column to see if I can extract LGA names from there.
7:56
Bash
cd /work && python << 'EOF'
cd /work && python << 'EOF'
import overturemaps.core as oc
df = oc.geodataframe("division_area", bbox=(2.5, 4.0, 15.0, 14.0))
# Get Lagos counties (LGAs)
lagos_counties = df[(df['region'] == 'NG-LA') & (df['subtype'] == 'county')]
print(f"Number of LGAs: {len(lagos_counties)}")
# Check names
for idx, row in lagos_counties.iterrows():
names = row['names']
primary = names.get('primary', 'N/A')
common = names.get('common', [])
if isinstance(common, list) and len(common) > 0:
# Find English name
en = [c for c in common if isinstance(c, tuple) and c[0] == 'en']
if en:
print(f"{row['id'][:8]}... primary={primary}, en={en[0][1]}")
else:
print(f"{row['id'][:8]}... primary={primary}, common={common[:2]}")
else:
print(f"{row['id'][:8]}... primary={primary}")
EOF
Number of LGAs: 20
32cfa1af... primary=Amuwo Odofin
fcaab4e8... primary=Agege
934f5b76... primary=Ifako/Ijaye
457a7986... primary=Oshodi/Isolo
93ccd19d... primary=Badagry
1a9532db... primary=Alimosho
7918fd74... primary=Ojo
030cb2c1... primary=Ìkòròdú, en=Ikorodu
61530733... primary=Epe, common=[('ru', 'Эпе')]
e2af9375... primary=Ajeromi/Ifelodun
cb527384... primary=Kosofe
c58fd335... primary=Lagos Island, common=[('ar', 'جزيرة لاغوس')]
c540f662... primary=Ikeja, common=[('ru', 'Икеджа')]
b098198f... primary=Shomolu
639f85d6... primary=Lagos Mainland
70bd9c0f... primary=Ibeju Lekki
4b715cfc... primary=Apapa
f0f8532f... primary=Surulere
0c5600b6... primary=Eti Osa, common=[('yo', 'Etí Ọ̀sà')]
ffe6c369... primary=Mushin, common=[('ru', 'Мушин')]
8:16
There are 20 LGAs (counties) in Lagos State. Let me get the geometry for the Lagos State region (admin_level 1) and also the LGA boundaries. Now let me plan the approach: 1. Get all buildings in the Lagos State bbox (2.5, 6.2, 4.5, 6.7) 2. Get the Lagos State region boundary 3. Spatially filter buildings to those within Lagos State 4. For each building, calculate footprint area 5. Intersect with LGA boundaries to assign LGA names 6. Filter buildings > 1000 m² 7. Output results Let me write a comprehensive script.
8:16
Bash
cd /work && python << 'EOF'
cd /work && python << 'EOF'
# Get the Lagos State boundary geometry (region subtype, admin_level=1)
import overturemaps.core as oc
import geopandas as gpd
df = oc.geodataframe("division_area", bbox=(2.5, 4.0, 15.0, 14.0))
# Get the Lagos region (admin_level=1)
lagos_region = df[(df['region'] == 'NG-LA') & (df['subtype'] == 'region')]
print(f"Lagos region rows: {len(lagos_region)}")
print(lagos_region[['id', 'subtype', 'admin_level']])
# Get its geometry
geom = lagos_region.iloc[0].geometry
print(f"Geometry type: {geom.geom_type}")
print(f"Bounds: {geom.bounds}")
EOF
Lagos region rows: 1
id subtype admin_level
326 5fb3f6cb-3993-4bb7-8a53-73e775feb14f region 1.0
Geometry type: Polygon
Bounds: (2.7059846, 6.366684, 4.3509056, 6.6967964)
8:45
Now I have: 1. The Lagos State boundary (bbox: 2.7059846, 6.366684, 4.3509056, 6.6967964) 2. 20 LGAs (counties) with their names 3. Building data from Overture with columns including id, height, geometry Now I need to: 1. Get all buildings in Lagos State (from Overture using the bbox) 2. Filter to those with footprint area > 1000 m² 3. Spatially join with LGAs to assign each building to an LGA 4. Create the two output files Let me first check the CRS of the building data.
8:45
Bash
cd /work && python << 'EOF'
cd /work && python << 'EOF'
import overturemaps.core as oc
# Get a smaller sample to check CRS and geometry types
bbox_small = (3.3, 6.38, 3.45, 6.5)
df = oc.geodataframe("building", bbox=bbox_small)
print("CRS:", df.crs)
print("Geometry types:", df.geometry.geom_type.unique())
print("Columns:", df.columns.tolist())
print("Height stats:")
print(df.height.describe())
print("Null height:", df.height.isna().sum())
EOF
CRS: None Geometry types: <ArrowStringArray> ['Polygon', 'MultiPolygon'] Length: 2, dtype: str 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'] Height stats: count 1498.000000 mean 15.467637 std 18.753797 min 3.000000 25% 5.000000 50% 9.000000 75% 18.000000 max 227.000000 Name: height, dtype: float64 Null height: 193557
10:38
Now I have a good understanding of the data. Let me plan the approach: 1. Get Lagos State boundary polygon (region subtype) 2. Get all buildings within the Lagos State bbox 3. Calculate footprint area for each building using shapely 4. Filter those with area > 1000 m² 5. Get LGAs (county subtype) from division_area 6. Spatial join buildings to LGAs to assign LGA name 7. Compute per-LGA summary statistics 8. Write output files Let me write the full script.
10:38
Write
/work/solve.py
content · 179 lines · py
#!/usr/bin/env python3
"""
Extract Lagos State buildings >1000 m² from Overture and produce:
- lagos_buildings.geoparquet (EPSG:4326, columns: id, height, footprint_area_m2, lga, geometry)
- lagos_building_summary.parquet (no geometry, columns: lga, n_buildings, total_footprint_m2, n_with_height, p50_height_m)
"""
import geopandas as gpd
import pandas as pd
import numpy as np
import overturemaps.core as oc
from shapely import area as shapely_area
import pyarrow as pa
import pyarrow.parquet as pq
# ---------------------------------------------------------------------------
# 1. Fetch Lagos State boundary
# ---------------------------------------------------------------------------
print("[1] Fetching Lagos State boundary...")
div_area = oc.geodataframe("division_area", bbox=(2.5, 4.0, 15.0, 14.0))
lagos_region = div_area[(div_area['region'] == 'NG-LA') & (div_area['subtype'] == 'region')]
if len(lagos_region) == 0:
raise RuntimeError("Could not find Lagos State region")
lagos_boundary = lagos_region.iloc[0].geometry
print(f" Lagos boundary bounds: {lagos_boundary.bounds}")
# Also get LGAs (counties)
lagos_lgas = div_area[(div_area['region'] == 'NG-LA') & (div_area['subtype'] == 'county')].copy()
print(f" Found {len(lagos_lgas)} LGAs")
# Extract LGA names from the names column
def get_lga_name(names_dict):
"""Get the primary or English name from Overture names dict."""
if names_dict is None or not isinstance(names_dict, dict):
return None
primary = names_dict.get('primary')
if primary:
return primary
common = names_dict.get('common', [])
if isinstance(common, list):
for c in common:
if isinstance(c, tuple) and c[0] == 'en':
return c[1]
return None
lagos_lgas['lga'] = lagos_lgas['names'].apply(get_lga_name)
print(f" LGA names: {lagos_lgas['lga'].tolist()}")
# ---------------------------------------------------------------------------
# 2. Fetch all buildings within Lagos bbox
# ---------------------------------------------------------------------------
# Use Lagos bounds with a small buffer
lagos_bbox = lagos_boundary.bounds # (minx, miny, maxx, maxy)
print(f"[2] Fetching buildings within bbox {lagos_bbox}...")
buildings = oc.geodataframe("building", bbox=lagos_bbox)
print(f" Total buildings fetched: {len(buildings)}")
print(f" CRS: {buildings.crs}")
# ---------------------------------------------------------------------------
# 3. Spatial filter: keep only buildings within Lagos State boundary
# ---------------------------------------------------------------------------
print("[3] Filtering buildings within Lagos State boundary...")
# The building geometry is in EPSG:4326 (lon/lat). We need to set CRS.
buildings.set_geometry('geometry', inplace=True)
buildings = buildings.set_crs("EPSG:4326")
# Spatial join / intersect
lagos_boundary_gdf = gpd.GeoDataFrame({'geometry': [lagos_boundary]}, crs="EPSG:4326")
# Use overlay or spatial predicate
buildings_in_lagos = buildings[buildings.intersects(lagos_boundary)]
print(f" Buildings within Lagos: {len(buildings_in_lagos)}")
# ---------------------------------------------------------------------------
# 4. Calculate footprint area in m²
# ---------------------------------------------------------------------------
print("[4] Calculating footprint areas...")
# Project to UTM zone 32N (EPSG:32632) which covers Lagos for area calculation
buildings_utm = buildings_in_lagos.to_crs("EPSG:32632")
buildings_in_lagos['footprint_area_m2'] = shapely_area(buildings_utm.geometry.values)
# ---------------------------------------------------------------------------
# 5. Filter >1000 m²
# ---------------------------------------------------------------------------
print("[5] Filtering buildings > 1000 m²...")
buildings_large = buildings_in_lagos[buildings_in_lagos['footprint_area_m2'] > 1000.0].copy()
print(f" Large buildings: {len(buildings_large)}")
# ---------------------------------------------------------------------------
# 6. Spatial join with LGAs
# ---------------------------------------------------------------------------
print("[6] Assigning LGA via spatial join...")
# Ensure LGA geometries are valid and in same CRS
lagos_lgas = lagos_lgas.set_crs("EPSG:4326")
lagos_lgas = lagos_lgas[lagos_lgas.geometry.is_valid]
# Spatial join: for each building, find which LGA it falls in
buildings_lga = gpd.sjoin(
buildings_large,
lagos_lgas[['lga', 'geometry']],
how='left',
predicate='within'
)
# Some buildings might be on boundaries; use 'intersects' as fallback
# For ones that didn't get an LGA, try intersects
no_lga = buildings_lga[buildings_lga['lga'].isna()]
if len(no_lga) > 0:
print(f" {len(no_lga)} buildings not within any LGA polygon, trying intersects...")
intersects_join = gpd.sjoin(
no_lga.drop(columns=['index_right', 'lga']),
lagos_lgas[['lga', 'geometry']],
how='left',
predicate='intersects'
)
# Update the main dataframe
buildings_lga.loc[buildings_lga['lga'].isna(), 'lga'] = intersects_join['lga'].values
if intersects_join['lga'].isna().any():
print(f" Still {intersects_join['lga'].isna().sum()} without LGA — assigning 'Unknown'")
buildings_lga['lga'] = buildings_lga['lga'].fillna('Unknown')
print(f" LGA distribution:\n{buildings_lga['lga'].value_counts()}")
# ---------------------------------------------------------------------------
# 7. Build final output dataframe
# ---------------------------------------------------------------------------
print("[7] Building output dataframe...")
out_buildings = buildings_lga[['id', 'height', 'footprint_area_m2', 'lga', 'geometry']].copy()
out_buildings = out_buildings.reset_index(drop=True)
# ---------------------------------------------------------------------------
# 8. Compute per-LGA summary
# ---------------------------------------------------------------------------
print("[8] Computing per-LGA summary...")
summary_rows = []
for lga_name, group in out_buildings.groupby('lga'):
n_buildings = len(group)
total_footprint = group['footprint_area_m2'].sum()
has_h = group['height'].notna()
n_with_height = has_h.sum()
heights_with_data = group.loc[has_h, 'height']
if len(heights_with_data) > 0:
p50 = float(heights_with_data.median())
else:
p50 = None # null
summary_rows.append({
'lga': lga_name,
'n_buildings': n_buildings,
'total_footprint_m2': total_footprint,
'n_with_height': n_with_height,
'p50_height_m': p50
})
summary_df = pd.DataFrame(summary_rows)
summary_df = summary_df.sort_values('lga').reset_index(drop=True)
print(f"Summary:\n{summary_df.to_string()}")
# ---------------------------------------------------------------------------
# 9. Write outputs
# ---------------------------------------------------------------------------
print("[9] Writing output files...")
# Write buildings GeoParquet
out_buildings.to_parquet(
'/work/lagos_buildings.geoparquet',
index=False,
compression='zstd',
geometry_encoding='geoarrow'
)
# Write summary Parquet (no geometry)
summary_df.to_parquet(
'/work/lagos_building_summary.parquet',
index=False,
compression='zstd'
)
print("Done! Files written:")
print(" /work/lagos_buildings.geoparquet")
print(" /work/lagos_building_summary.parquet")Wrote /work/solve.py (7429 chars).
10:44
Let me run this script. It might take a while since it needs to download building data for the entire Lagos bbox.
10:44
Bash
cd /work && timeout 600 python solve.py 2>&1
[1] Fetching Lagos State boundary...
Lagos boundary bounds: (2.7059846, 6.366684, 4.3509056, 6.6967964)
Found 20 LGAs
LGA names: ['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']
[2] Fetching buildings within bbox (2.7059846, 6.366684, 4.3509056, 6.6967964)...
Total buildings fetched: 3057423
CRS: None
[3] Filtering buildings within Lagos State boundary...
Buildings within Lagos: 2245422
[4] Calculating footprint areas...
[5] Filtering buildings > 1000 m²...
Large buildings: 7374
[6] Assigning LGA via spatial join...
29 buildings not within any LGA polygon, trying intersects...
Traceback (most recent call last):
File "/work/solve.py", line 116, in <module>
buildings_lga.loc[buildings_lga['lga'].isna(), 'lga'] = intersects_join['lga'].values
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/indexing.py", line 938, in __setitem__
iloc._setitem_with_indexer(indexer, value, self.name)
~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/indexing.py", line 1953, in _setitem_with_indexer
self._setitem_with_indexer_split_path(indexer, value, name)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/indexing.py", line 2009, in _setitem_with_indexer_split_path
raise ValueError(
"Must have equal len keys and value when setting with an iterable"
)
ValueError: Must have equal len keys and value when setting with an iterable
13:47
The issue is that the intersects_join may return more rows than the no_lga set due to multiple LGA matches, or the index alignment is wrong. Let me fix the LGA assignment logic.
13:47
Read
/work/solve.py
1 #!/usr/bin/env python3
2 """
3 Extract Lagos State buildings >1000 m² from Overture and produce:
4 - lagos_buildings.geoparquet (EPSG:4326, columns: id, height, footprint_area_m2, lga, geometry)
5 - lagos_building_summary.parquet (no geometry, columns: lga, n_buildings, total_footprint_m2, n_with_height, p50_height_m)
6 """
7
8 import geopandas as gpd
9 import pandas as pd
10 import numpy as np
11 import overturemaps.core as oc
12 from shapely import area as shapely_area
13 import pyarrow as pa
14 import pyarrow.parquet as pq
15
16 # ---------------------------------------------------------------------------
17 # 1. Fetch Lagos State boundary
18 # ---------------------------------------------------------------------------
19 print("[1] Fetching Lagos State boundary...")
20 div_area = oc.geodataframe("division_area", bbox=(2.5, 4.0, 15.0, 14.0))
21 lagos_region = div_area[(div_area['region'] == 'NG-LA') & (div_area['subtype'] == 'region')]
22 if len(lagos_region) == 0:
23 raise RuntimeError("Could not find Lagos State region")
24 lagos_boundary = lagos_region.iloc[0].geometry
25 print(f" Lagos boundary bounds: {lagos_boundary.bounds}")
26
27 # Also get LGAs (counties)
28 lagos_lgas = div_area[(div_area['region'] == 'NG-LA') & (div_area['subtype'] == 'county')].copy()
29 print(f" Found {len(lagos_lgas)} LGAs")
30
31 # Extract LGA names from the names column
32 def get_lga_name(names_dict):
33 """Get the primary or English name from Overture names dict."""
34 if names_dict is None or not isinstance(names_dict, dict):
35 return None
36 primary = names_dict.get('primary')
37 if primary:
38 return primary
39 common = names_dict.get('common', [])
40 if isinstance(common, list):
41 for c in common:
42 if isinstance(c, tuple) and c[0] == 'en':
43 return c[1]
44 return None
45
46 lagos_lgas['lga'] = lagos_lgas['names'].apply(get_lga_name)
47 print(f" LGA names: {lagos_lgas['lga'].tolist()}")
48
49 # ---------------------------------------------------------------------------
50 # 2. Fetch all buildings within Lagos bbox
51 # ---------------------------------------------------------------------------
52 # Use Lagos bounds with a small buffer
53 lagos_bbox = lagos_boundary.bounds # (minx, miny, maxx, maxy)
54 print(f"[2] Fetching buildings within bbox {lagos_bbox}...")
55 buildings = oc.geodataframe("building", bbox=lagos_bbox)
56 print(f" Total buildings fetched: {len(buildings)}")
57 print(f" CRS: {buildings.crs}")
58
59 # ---------------------------------------------------------------------------
60 # 3. Spatial filter: keep only buildings within Lagos State boundary
61 # ---------------------------------------------------------------------------
62 print("[3] Filtering buildings within Lagos State boundary...")
63 # The building geometry is in EPSG:4326 (lon/lat). We need to set CRS.
64 buildings.set_geometry('geometry', inplace=True)
65 buildings = buildings.set_crs("EPSG:4326")
66
67 # Spatial join / intersect
68 lagos_boundary_gdf = gpd.GeoDataFrame({'geometry': [lagos_boundary]}, crs="EPSG:4326")
69 # Use overlay or spatial predicate
70 buildings_in_lagos = buildings[buildings.intersects(lagos_boundary)]
71 print(f" Buildings within Lagos: {len(buildings_in_lagos)}")
72
73 # ---------------------------------------------------------------------------
74 # 4. Calculate footprint area in m²
75 # ---------------------------------------------------------------------------
76 print("[4] Calculating footprint areas...")
77
78 # Project to UTM zone 32N (EPSG:32632) which covers Lagos for area calculation
79 buildings_utm = buildings_in_lagos.to_crs("EPSG:32632")
80 buildings_in_lagos['footprint_area_m2'] = shapely_area(buildings_utm.geometry.values)
81
82 # ---------------------------------------------------------------------------
83 # 5. Filter >1000 m²
84 # ---------------------------------------------------------------------------
85 print("[5] Filtering buildings > 1000 m²...")
86 buildings_large = buildings_in_lagos[buildings_in_lagos['footprint_area_m2'] > 1000.0].copy()
87 print(f" Large buildings: {len(buildings_large)}")
88
89 # ---------------------------------------------------------------------------
90 # 6. Spatial join with LGAs
91 # ---------------------------------------------------------------------------
92 print("[6] Assigning LGA via spatial join...")
93 # Ensure LGA geometries are valid and in same CRS
94 lagos_lgas = lagos_lgas.set_crs("EPSG:4326")
95 lagos_lgas = lagos_lgas[lagos_lgas.geometry.is_valid]
96
97 # Spatial join: for each building, find which LGA it falls in
98 buildings_lga = gpd.sjoin(
99 buildings_large,
100 lagos_lgas[['lga', 'geometry']],
101 how='left',
102 predicate='within'
103 )
104 # Some buildings might be on boundaries; use 'intersects' as fallback
105 # For ones that didn't get an LGA, try intersects
106 no_lga = buildings_lga[buildings_lga['lga'].isna()]
107 if len(no_lga) > 0:
108 print(f" {len(no_lga)} buildings not within any LGA polygon, trying intersects...")
109 intersects_join = gpd.sjoin(
110 no_lga.drop(columns=['index_right', 'lga']),
111 lagos_lgas[['lga', 'geometry']],
112 how='left',
113 predicate='intersects'
114 )
115 # Update the main dataframe
116 buildings_lga.loc[buildings_lga['lga'].isna(), 'lga'] = intersects_join['lga'].values
117 if intersects_join['lga'].isna().any():
118 print(f" Still {intersects_join['lga'].isna().sum()} without LGA — assigning 'Unknown'")
119 buildings_lga['lga'] = buildings_lga['lga'].fillna('Unknown')
120
121 print(f" LGA distribution:\n{buildings_lga['lga'].value_counts()}")
122
123 # ---------------------------------------------------------------------------
124 # 7. Build final output dataframe
125 # ---------------------------------------------------------------------------
126 print("[7] Building output dataframe...")
127 out_buildings = buildings_lga[['id', 'height', 'footprint_area_m2', 'lga', 'geometry']].copy()
128 out_buildings = out_buildings.reset_index(drop=True)
129
130 # ---------------------------------------------------------------------------
131 # 8. Compute per-LGA summary
132 # ---------------------------------------------------------------------------
133 print("[8] Computing per-LGA summary...")
134 summary_rows = []
135 for lga_name, group in out_buildings.groupby('lga'):
136 n_buildings = len(group)
137 total_footprint = group['footprint_area_m2'].sum()
138 has_h = group['height'].notna()
139 n_with_height = has_h.sum()
140 heights_with_data = group.loc[has_h, 'height']
141 if len(heights_with_data) > 0:
142 p50 = float(heights_with_data.median())
143 else:
144 p50 = None # null
145 summary_rows.append({
146 'lga': lga_name,
147 'n_buildings': n_buildings,
148 'total_footprint_m2': total_footprint,
149 'n_with_height': n_with_height,
150 'p50_height_m': p50
151 })
152
153 summary_df = pd.DataFrame(summary_rows)
154 summary_df = summary_df.sort_values('lga').reset_index(drop=True)
155 print(f"Summary:\n{summary_df.to_string()}")
156
157 # ---------------------------------------------------------------------------
158 # 9. Write outputs
159 # ---------------------------------------------------------------------------
160 print("[9] Writing output files...")
161
162 # Write buildings GeoParquet
163 out_buildings.to_parquet(
164 '/work/lagos_buildings.geoparquet',
165 index=False,
166 compression='zstd',
167 geometry_encoding='geoarrow'
168 )
169
170 # Write summary Parquet (no geometry)
171 summary_df.to_parquet(
172 '/work/lagos_building_summary.parquet',
173 index=False,
174 compression='zstd'
175 )
176
177 print("Done! Files written:")
178 print(" /work/lagos_buildings.geoparquet")
179 print(" /work/lagos_building_summary.parquet")
14:03
The issue is on line 116. When we do `buildings_lga.loc[buildings_lga['lga'].isna(), 'lga']`, the number of True values might not match the length of `intersects_join['lga'].values`. Let me fix this logic. The problem is that `intersects_join` might have a different index after the sjoin. Let me use a different approach - just drop the 'lga' and 'index_right' columns from `no_lga` and then merge back. Actually, a simpler fix: after the intersects join, the resulting dataframe has the same index as `no_lga` (since sjoin preserves the left index). So we should be able to update using index-based assignment.
14:03
Edit
/work/solve.py
Edited /work/solve.py.
14:07
Bash
cd /work && timeout 600 python solve.py 2>&1
[1] Fetching Lagos State boundary...
Lagos boundary bounds: (2.7059846, 6.366684, 4.3509056, 6.6967964)
Found 20 LGAs
LGA names: ['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']
[2] Fetching buildings within bbox (2.7059846, 6.366684, 4.3509056, 6.6967964)...
Total buildings fetched: 3057423
CRS: None
[3] Filtering buildings within Lagos State boundary...
Buildings within Lagos: 2245422
[4] Calculating footprint areas...
[5] Filtering buildings > 1000 m²...
Large buildings: 7374
[6] Assigning LGA via spatial join...
29 buildings not within any LGA polygon, trying intersects...
Traceback (most recent call last):
File "/work/solve.py", line 116, in <module>
buildings_lga.update(intersects_join[['lga']])
~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/frame.py", line 10574, in update
raise ValueError("Update not allowed with duplicate indexes on other.")
ValueError: Update not allowed with duplicate indexes on other.
17:08
The issue is that `no_lga` has a duplicated index (since it's a subset of `buildings_lga`), and `intersects_join` also has a duplicate index from the sjoin. Let me simplify this approach - instead of trying to update, I'll just use `predicate='intersects'` directly for all buildings.
17:08
Edit
/work/solve.py
Edited /work/solve.py.
17:13
The sjoin with 'intersects' should find all LGA matches directly. But there may be buildings that genuinely don't intersect any LGA polygon (e.g., they're in water areas not covered by any LGA boundary). Let me also make the code more robust.
17:13
Bash
cd /work && timeout 600 python solve.py 2>&1
[1] Fetching Lagos State boundary...
Lagos boundary bounds: (2.7059846, 6.366684, 4.3509056, 6.6967964)
Found 20 LGAs
LGA names: ['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']
[2] Fetching buildings within bbox (2.7059846, 6.366684, 4.3509056, 6.6967964)...
Total buildings fetched: 3057423
CRS: None
[3] Filtering buildings within Lagos State boundary...
Buildings within Lagos: 2245422
[4] Calculating footprint areas...
[5] Filtering buildings > 1000 m²...
Large buildings: 7374
[6] Assigning LGA via spatial join...
LGA distribution:
lga
Eti Osa 1080
Ikeja 931
Amuwo Odofin 845
Ibeju Lekki 606
Ìkòròdú 542
Mushin 468
Oshodi/Isolo 429
Alimosho 359
Surulere 346
Epe 345
Apapa 288
Ojo 237
Kosofe 168
Badagry 128
Shomolu 127
Lagos Mainland 123
Lagos Island 121
Ifako/Ijaye 120
Ajeromi/Ifelodun 63
Agege 48
Name: count, dtype: int64
[7] Building output dataframe...
[8] Computing per-LGA summary...
Summary:
lga n_buildings total_footprint_m2 n_with_height p50_height_m
0 Agege 48 9.579810e+04 0 NaN
1 Ajeromi/Ifelodun 63 1.095248e+05 0 NaN
2 Alimosho 359 6.589676e+05 0 NaN
3 Amuwo Odofin 845 1.775263e+06 0 NaN
4 Apapa 288 6.162378e+05 0 NaN
5 Badagry 128 2.325200e+05 0 NaN
6 Epe 345 8.209697e+05 0 NaN
7 Eti Osa 1080 1.870387e+06 241 20.0
8 Ibeju Lekki 606 1.817608e+06 0 NaN
9 Ifako/Ijaye 120 2.509820e+05 0 NaN
10 Ikeja 931 3.220139e+06 47 15.0
11 Kosofe 168 3.547048e+05 0 NaN
12 Lagos Island 121 2.322891e+05 9 20.0
13 Lagos Mainland 123 2.315257e+05 0 NaN
14 Mushin 468 1.075540e+06 1 35.0
15 Ojo 237 4.177230e+05 0 NaN
16 Oshodi/Isolo 429 8.116892e+05 3 27.0
17 Shomolu 127 2.055954e+05 3 17.0
18 Surulere 346 7.913406e+05 0 NaN
19 Ìkòròdú 542 1.879439e+06 2 8.0
[9] Writing output files...
Done! Files written:
/work/lagos_buildings.geoparquet
/work/lagos_building_summary.parquet
20:22
The script ran successfully. Let me verify the output files to make sure they're correct.
20:22
Bash
cd /work && python << 'EOF'
cd /work && python << 'EOF'
import geopandas as gpd
import pandas as pd
# Check buildings geoparquet
buildings = gpd.read_parquet('/work/lagos_buildings.geoparquet')
print("=== lagos_buildings.geoparquet ===")
print(f"Shape: {buildings.shape}")
print(f"Columns: {buildings.columns.tolist()}")
print(f"CRS: {buildings.crs}")
print(f"dtypes:\n{buildings.dtypes}")
print(f"\nHead:\n{buildings.head(3)}")
print(f"\nMin area: {buildings['footprint_area_m2'].min():.2f}")
print(f"Max area: {buildings['footprint_area_m2'].max():.2f}")
# Check summary parquet
summary = pd.read_parquet('/work/lagos_building_summary.parquet')
print("\n=== lagos_building_summary.parquet ===")
print(f"Shape: {summary.shape}")
print(f"Columns: {summary.columns.tolist()}")
print(f"dtypes:\n{summary.dtypes}")
print(f"\nFull table:\n{summary.to_string()}")
print(f"\nGeometry column present: {'geometry' in summary.columns}")
EOF
=== lagos_buildings.geoparquet ===
Shape: (7374, 5)
Columns: ['id', 'height', 'footprint_area_m2', 'lga', 'geometry']
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}}
dtypes:
id str
height float64
footprint_area_m2 float64
lga str
geometry geometry
dtype: object
Head:
id ... geometry
0 e24f1bf5-e957-4867-859b-a6a578fd9b06 ... MULTIPOLYGON (((2.70836 6.37804, 2.70816 6.378...
1 5df57f70-f431-495f-98cb-eff96e1ca151 ... MULTIPOLYGON (((2.72675 6.37881, 2.72672 6.379...
2 cfb03953-1851-4ec2-a087-3283cd510753 ... MULTIPOLYGON (((2.7137 6.37984, 2.7136 6.37984...
[3 rows x 5 columns]
Min area: 1000.00
Max area: 182684.79
=== lagos_building_summary.parquet ===
Shape: (20, 5)
Columns: ['lga', 'n_buildings', 'total_footprint_m2', 'n_with_height', 'p50_height_m']
dtypes:
lga str
n_buildings int64
total_footprint_m2 float64
n_with_height int64
p50_height_m float64
dtype: object
Full table:
lga n_buildings total_footprint_m2 n_with_height p50_height_m
0 Agege 48 9.579810e+04 0 NaN
1 Ajeromi/Ifelodun 63 1.095248e+05 0 NaN
2 Alimosho 359 6.589676e+05 0 NaN
3 Amuwo Odofin 845 1.775263e+06 0 NaN
4 Apapa 288 6.162378e+05 0 NaN
5 Badagry 128 2.325200e+05 0 NaN
6 Epe 345 8.209697e+05 0 NaN
7 Eti Osa 1080 1.870387e+06 241 20.0
8 Ibeju Lekki 606 1.817608e+06 0 NaN
9 Ifako/Ijaye 120 2.509820e+05 0 NaN
10 Ikeja 931 3.220139e+06 47 15.0
11 Kosofe 168 3.547048e+05 0 NaN
12 Lagos Island 121 2.322891e+05 9 20.0
13 Lagos Mainland 123 2.315257e+05 0 NaN
14 Mushin 468 1.075540e+06 1 35.0
15 Ojo 237 4.177230e+05 0 NaN
16 Oshodi/Isolo 429 8.116892e+05 3 27.0
17 Shomolu 127 2.055954e+05 3 17.0
18 Surulere 346 7.913406e+05 0 NaN
19 Ìkòròdú 542 1.879439e+06 2 8.0
Geometry column present: False
20:38
The data looks good! Let me verify everything meets the requirements: 1. **lagos_buildings.geoparquet**: - EPSG:4326 ✓ (the CRS is WGS 84/EPSG:4326) - GeoParquet format ✓ - Columns: id, height, footprint_area_m2, lga, geometry ✓ - Buildings exceeding 1000 m² across Lagos State ✓ (min area is 1000.00, all > 1000) - 7374 buildings 2. **lagos_building_summary.parquet**: - Plain Parquet ✓ - No geometry column ✓ - Columns: lga, n_buildings, total_footprint_m2, n_with_height, p50_height_m ✓ - 20 LGAs ✓ Both files are correctly produced. Let me just double check that the `Ìkòròdú` name is the correct spelling/rendering of Ikorodu LGA - that's the primary name from Overture data. It's fine. Let me verify the files exist on disk.
20:38
20:38
Bash
ls -lh /work/lagos_buildings.geoparquet /work/lagos_building_summary.parquet
-rw-r--r-- 1 agent agent 4.1K Jun 16 23:15 /work/lagos_building_summary.parquet -rw-r--r-- 1 agent agent 1.4M Jun 16 23:15 /work/lagos_buildings.geoparquet
20:47
Both files have been successfully created. Let me do a final review of what was produced: 1. `lagos_buildings.geoparquet` - EPSG:4326 GeoParquet with columns: `id`, `height`, `footprint_area_m2`, `lga`, `geometry`. Contains 7,374 building footprints each exceeding 1000 m² across Lagos State. 2. `lagos_building_summary.parquet` - Plain Parquet (no geometry) with columns: `lga`, `n_buildings`, `total_footprint_m2`, `n_with_height`, `p50_height_m`. Contains 20 rows (one per LGA). Everything looks correct.
20:47