fio-l2-capetown-landuse-dissolve
Map (reference ← swipe → agent)
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I'm prepping a transit-corridor study for the Cape Town metro, and I need a class-level summary of `landuse` so the team's spatial-SQL notebooks can join it against the bus-route table. Can you give me one row per landuse class with the geometry unified into a single MultiPolygon, the total area in m², and the count of source parcels that fed in? Please write the result to `landuse_dissolved.geoparquet` with `class`, `area_m2`, and `parcel_count` columns.
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System info
{
"adapter": "openrouter",
"model": "google/gemma-4-26b-a4b-it",
"base_url": "https://openrouter.ai/api/v1"
}
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Bash
ls -F
capetown_landuse.fgb
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Bash
pip install pyogrio geopandas
Defaulting to user installation because normal site-packages is not writeable Requirement already satisfied: pyogrio in /usr/local/lib/python3.14/site-packages (0.12.1) Requirement already satisfied: geopandas in /usr/local/lib/python3.14/site-packages (1.1.3) Requirement already satisfied: certifi in /usr/local/lib/python3.14/site-packages (from pyogrio) (2026.5.20) Requirement already satisfied: numpy in /usr/local/lib/python3.14/site-packages (from pyogrio) (2.4.6) Requirement already satisfied: packaging in /usr/local/lib/python3.14/site-packages (from pyogrio) (26.2) Requirement already satisfied: pandas>=2.0.0 in /usr/local/lib/python3.14/site-packages (from geopandas) (3.0.3) Requirement already satisfied: pyproj>=3.5.0 in /usr/local/lib/python3.14/site-packages (from geopandas) (3.7.2) Requirement already satisfied: shapely>=2.0.0 in /usr/local/lib/python3.14/site-packages (from geopandas) (2.1.2) Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.14/site-packages (from pandas>=2.0.0->geopandas) (2.9.0.post0) Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.14/site-packages (from python-dateutil>=2.8.2->pandas>=2.0.0->geopandas) (1.17.0)
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Write
/work/inspect_data.py
content · 36 lines · py
import geopandas as gpd
# Load the data
input_file = 'capetown_landuse.fgb'
gdf = gpd.read_file(input_file)
# Inspect the columns and CRS
print("Columns:", gdf.columns)
print("CRS:", gdf.crs)
print("First few rows:\n", gdf.head())
# Check if the CRS is projected. If it's geographic (degrees), we need to transform it to a metric CRS for area calculation.
# Cape Town is in South Africa. UTM Zone 34S (EPSG:32734) is a good candidate.
# Alternatively, we can use a more general one like World Equidistant Cylindrical or just check if it's already projected.
if gdf.crs.is_geographic:
print("CRS is geographic. Projecting to UTM Zone 34S (EPSG:32734) for area calculations.")
gdf_projected = gdf.to_crs(epsg=32734)
else:
print(f"CRS is already projected: {gdf.crs}")
gdf_projected = gdf
# Perform the aggregation
# We need:
# 1. Unified geometry (dissolved)
# 2. Total area in m2
# 3. Count of source parcels
# First, calculate the area for each individual parcel in the projected CRS
gdf_projected['temp_area'] = gdf_projected.geometry.area
# Now group by 'landuse' (Wait, the prompt says 'class', but the input might have 'landuse' or something else.
# I'll check the columns first in the next step of the script if I were writing it,
# but I'll assume 'landuse' or the column representing the class is the grouping key.
# Let's look at the printed head from the previous step to confirm the column name.)
Wrote /work/inspect_data.py (1432 chars).
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Bash
python /work/inspect_data.py
Columns: Index(['id', 'class', 'subtype', 'geometry'], dtype='str')
CRS: EPSG:32734
First few rows:
id ... geometry
0 f328c054-b040-3a7d-a015-53747d608fef ... MULTIPOLYGON (((872000.605 6252622.417, 871205...
1 f1943847-f785-390c-abb0-edc923f97997 ... MULTIPOLYGON (((253543.223 6283900.241, 254921...
2 c37e40aa-f202-3b15-b68d-89dafeeaa478 ... MULTIPOLYGON (((255047.308 6280773.284, 255024...
3 b9d39db7-5dcc-379e-bfeb-c994628df5a7 ... MULTIPOLYGON (((254978.723 6280842.179, 254992...
4 0033302c-c1d6-3624-b71e-0e300db64963 ... MULTIPOLYGON (((264759.63 6280552.85, 264825.5...
[5 rows x 4 columns]
CRS is already projected: EPSG:32734
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Write
/work/solve.py
content · 46 lines · py
import geopandas as gpd
# Load the data
input_file = 'capetown_landuse.fgb'
gdf = gpd.read_file(input_file)
# Confirm columns
# Columns are ['id', 'class', 'subtype', 'geometry']
# CRS is EPSG:32734 (already projected)
# 1. Calculate area for each parcel
gdf['area_m2'] = gdf.geometry.area
# 2. Group by 'class'
# We want:
# - class
# - area_m2 (sum of individual areas)
# - parcel_count (count of id/rows)
# - geometry (dissolved/union)
# Dissolving and aggregating
# We use groupby and apply to handle geometry union and other aggregates
# A more efficient way in GeoPandas:
dissolved = gdf.dissolve(by='class', aggfunc={'area_m2': 'sum', 'id': 'count'})
# Rename the 'id' column to 'parcel_count'
dissolved = dissolved.rename(columns={'id': 'parcel_count'})
# The 'dissolve' method already combines geometries into MultiPolygons.
# We just need to make sure the columns are in the correct order/named as requested.
# Requested columns: class, area_m2, parcel_count
# Reset index to bring 'class' back as a column
dissolved = dissolved.reset_index()
# Reorder columns to match instruction: class, area_m2, parcel_count (and geometry is implicit)
# Note: GeoPandas includes 'geometry' automatically.
output_gdf = dissolved[['class', 'area_m2', 'parcel_count', 'geometry']]
# Save to GeoParquet
output_file = 'landuse_dissolved.geoparquet'
output_gdf.to_parquet(output_file)
print(f"Successfully saved to {output_file}")
print(output_gdf.head())
Wrote /work/solve.py (1457 chars).
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Bash
python /work/solve.py
Successfully saved to landuse_dissolved.geoparquet
class ... geometry
0 airfield ... POLYGON ((267829.758 6244743.665, 267820.919 6...
1 allotments ... MULTIPOLYGON (((263870.512 6232477.907, 263865...
2 animal_keeping ... MULTIPOLYGON (((264114.249 6261265.896, 264220...
3 barracks ... MULTIPOLYGON (((262288.681 6215543.331, 262280...
4 base ... POLYGON ((272069.73 6245748.538, 272064.541 62...
[5 rows x 4 columns]
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Bash
python -c "import geopandas as gpd; gdf = gpd.read_file('landuse_dissolved.geopa...
Traceback (most recent call last):
File "<string>", line 1, in <module>
import geopandas as gpd; gdf = gpd.read_file('landuse_dissolved.geoparquet'); print(gdf.columns); print(gdf.head())
~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/geopandas/io/file.py", line 316, in _read_file
return _read_file_pyogrio(
filename, bbox=bbox, mask=mask, columns=columns, rows=rows, **kwargs
)
File "/usr/local/lib/python3.14/site-packages/geopandas/io/file.py", line 576, in _read_file_pyogrio
return pyogrio.read_dataframe(path_or_bytes, bbox=bbox, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pyogrio/geopandas.py", line 382, in read_dataframe
result = read_func(
path_or_buffer,
...<15 lines>...
**kwargs,
)
File "/usr/local/lib/python3.14/site-packages/pyogrio/raw.py", line 200, in read
return ogr_read(
get_vsi_path_or_buffer(path_or_buffer),
...<15 lines>...
datetime_as_string=datetime_as_string,
)
File "pyogrio/_io.pyx", line 1412, in pyogrio._io.ogr_read
File "pyogrio/_io.pyx", line 257, in pyogrio._io.ogr_open
pyogrio.errors.DataSourceError: 'landuse_dissolved.geoparquet' not recognized as being in a supported file format.; It might help to specify the correct driver explicitly by prefixing the file path with '<DRIVER>:', e.g. 'CSV:path'.
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Bash
python -c "import pandas as pd; df = pd.read_parquet('landuse_dissolved.geoparqu...
Index(['class', 'area_m2', 'parcel_count', 'geometry'], dtype='str')
class ... geometry
0 airfield ... b'\x01\x03\x00\x00\x00\x01\x00\x00\x00L\x00\x0...
1 allotments ... b'\x01\x06\x00\x00\x00\xd6\x00\x00\x00\x01\x03...
2 animal_keeping ... b'\x01\x06\x00\x00\x00~\x00\x00\x00\x01\x03\x0...
3 barracks ... b'\x01\x06\x00\x00\x00\x04\x00\x00\x00\x01\x03...
4 base ... b'\x01\x03\x00\x00\x00\x01\x00\x00\x00\x17\x00...
[5 rows x 4 columns]
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Bash
python -c "import geopandas as gpd; gdf = gpd.read_parquet('landuse_dissolved.ge...
Index(['class', 'area_m2', 'parcel_count', 'geometry'], dtype='str')
class ... geometry
0 airfield ... POLYGON ((267829.758 6244743.665, 267820.919 6...
1 allotments ... MULTIPOLYGON (((263870.512 6232477.907, 263865...
2 animal_keeping ... MULTIPOLYGON (((264114.249 6261265.896, 264220...
3 barracks ... MULTIPOLYGON (((262288.681 6215543.331, 262280...
4 base ... POLYGON ((272069.73 6245748.538, 272064.541 62...
[5 rows x 4 columns]
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