fio-l2-capetown-landuse-dissolve
Model error
Retrospective
assessed 2026-06-09T12:12:19Z
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
0:03
Write
/work/inspect_data.py
content · 11 lines · py
import geopandas as gpd
# Read the input file
input_file = 'capetown_landuse.fgb'
gdf = gpd.read_file(input_file)
# Inspect the column names and CRS
print("Columns:", gdf.columns.tolist())
print("CRS:", gdf.crs)
print("Head:\n", gdf.head())
Wrote /work/inspect_data.py (243 chars).
0:05
Bash
python /work/inspect_data.py
Columns: ['id', 'class', 'subtype', 'geometry']
CRS: EPSG:32734
Head:
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]
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Write
/work/solve.py
content · 40 lines · py
import geopandas as gpd
def solve():
input_file = 'capetown_landuse.fgb'
output_file = 'landuse_dissolved.geoparquet'
# Read the input file
gdf = gpd.read_file(input_file)
# Ensure we are working with a projected CRS for area calculation
# The input is EPSG:32734 (UTM zone 34S), which is appropriate for Cape Town
# area in m2 calculation.
# Group by 'class' and aggregate
# 1. geometry: unary_union (unifies into MultiPolygon)
# 2. area_m2: sum of area of each parcel
# 3. parcel_count: count of parcels
# We calculate area first on the individual parcels
gdf['parcel_area'] = gdf.geometry.area
# Grouping
summary = gdf.groupby('class').agg(
geometry=('geometry', 'unary_union'),
area_m2=('parcel_area', 'sum'),
parcel_count=('id', 'count')
).reset_index()
# Check if geometry is MultiPolygon or Polygon and ensure it's wrapped in a GeoDataFrame
# unary_union returns a single geometry object.
summary_gdf = gpd.GeoDataFrame(summary, geometry='geometry', crs=gdf.crs)
# Save to GeoParquet
summary_gdf.to_parquet(output_file)
print(f"Successfully saved summary to {output_file}")
print(summary_gdf)
if __name__ == "__main__":
solve()
Wrote /work/solve.py (1292 chars).
0:20
Bash
python /work/solve.py
Traceback (most recent call last):
File "/work/solve.py", line 39, in <module>
solve()
~~~~~^^
File "/work/solve.py", line 23, in solve
summary = gdf.groupby('class').agg(
~~~~~~~~~~~~~~~~~~~~~~~~^
geometry=('geometry', 'unary_union'),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
area_m2=('parcel_area', 'sum'),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
parcel_count=('id', 'count')
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
).reset_index()
^
File "/usr/local/lib/python3.14/site-packages/pandas/core/groupby/generic.py", line 2291, in aggregate
result = op.agg()
File "/usr/local/lib/python3.14/site-packages/pandas/core/apply.py", line 294, in agg
return self.agg_dict_like()
~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/apply.py", line 511, in agg_dict_like
return self.agg_or_apply_dict_like(op_name="agg")
~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/apply.py", line 1677, in agg_or_apply_dict_like
result_index, result_data = self.compute_dict_like(
~~~~~~~~~~~~~~~~~~~~~~^
op_name, selected_obj, selection, kwargs
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/pandas/core/apply.py", line 592, in compute_dict_like
results.append(getattr(series, op_name)(how, **kwargs))
~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/groupby/generic.py", line 464, in aggregate
ret = self._aggregate_multiple_funcs(func, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/pandas/core/groupby/generic.py", line 522, in _aggregate_multiple_funcs
results[key] = self.aggregate(func, *args, **kwargs)
~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/groupby/generic.py", line 456, in aggregate
return getattr(self, func)(*args, **kwargs)
~~~~~~~^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/groupby/groupby.py", line 1115, in __getattr__
raise AttributeError(
f"'{type(self).__name__}' object has no attribute '{attr}'"
)
AttributeError: 'SeriesGroupBy' object has no attribute 'unary_union'
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Write
/work/solve.py
content · 45 lines · py
import geopandas as gpd
def solve():
input_file = 'capetown_landuse.fgb'
output_file = 'landuse_dissolved.geoparquet'
# Read the input file
gdf = gpd.read_file(input_file)
# Calculate area of each parcel in m2 (CRS is already EPSG:32734)
gdf['parcel_area'] = gdf.geometry.area
# Initialize lists for aggregation
classes = []
geometries = []
areas = []
counts = []
# Grouping manually to avoid the groupby.agg issue with unary_union
for name, group in gdf.groupby('class'):
classes.append(name)
# unary_union returns a single geometry (often MultiPolygon)
geometries.append(group.geometry.unary_union)
areas.append(group['parcel_area'].sum())
counts.append(len(group))
# Create the summary GeoDataFrame
summary_gdf = gpd.GeoDataFrame({
'class': classes,
'area_m2': areas,
'parcel_count': counts,
'geometry': geometries
}, crs=gdf.crs)
# Reorder columns to match requirement
summary_gdf = summary_gdf[['class', 'area_m2', 'parcel_count', 'geometry']]
# Save to GeoParquet
summary_gdf.to_parquet(output_file)
print(f"Successfully saved summary to {output_file}")
print(summary_gdf)
if __name__ == "__main__":
solve()
Wrote /work/solve.py (1319 chars).
0:35
Bash
python /work/solve.py
Successfully saved summary 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...
.. ... ... ...
67 water_hazard ... MULTIPOLYGON (((262930.98 6220950.527, 262932....
68 water_park ... MULTIPOLYGON (((266899.54 6223130.289, 266886....
69 wilderness_area ... MULTIPOLYGON (((309148.258 6238338.064, 308966...
70 works ... MULTIPOLYGON (((267976.278 6236445.658, 267950...
71 zoo ... MULTIPOLYGON (((304737.546 6222677.112, 304798...
[72 rows x 4 columns]
/work/solve.py:23: DeprecationWarning: The 'unary_union' attribute is deprecated, use the 'union_all()' method instead. geometries.append(group.geometry.unary_union)
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Bash
ls -F /work/landuse_dissolved.geoparquet
/work/landuse_dissolved.geoparquet
0:57
Write
/work/solve.py
content · 47 lines · py
import geopandas as gpd
def solve():
input_file = 'capetown_landuse.fgb'
output_file = 'landuse_dissolved.geoparquet'
# Read the input file
gdf = gpd.read_file(input_file)
# Calculate area of each parcel in m2 (CRS is already EPSG:32734)
gdf['parcel_area'] = gdf.geometry.area
# Initialize lists for aggregation
classes = []
geometries = []
areas = []
counts = []
# Grouping manually to avoid the groupby.agg issue with unary_union
# Using union_all() instead of unary_union to avoid DeprecationWarning
for name, group in gdf.groupby('class'):
classes.append(name)
# union_all() is the preferred method in newer shapely/geopandas
geometries.append(group.geometry.union_all())
areas.append(group['parcel_area'].sum())
counts.append(len(group))
# Create the summary GeoDataFrame
summary_gdf = gpd.GeoDataFrame({
'class': classes,
'area_m2': areas,
'parcel_count': counts,
'geometry': geometries
}, crs=gdf.crs)
# Reorder columns to match requirement: class, area_m2, parcel_count
# The prompt asks for these columns. The geometry column is implicit in a GeoDataFrame.
summary_gdf = summary_gdf[['class', 'area_m2', 'parcel_count', 'geometry']]
# Save to GeoParquet
summary_gdf.to_parquet(output_file)
print(f"Successfully saved summary to {output_file}")
print(summary_gdf.head())
if __name__ == "__main__":
solve()
Wrote /work/solve.py (1527 chars).
1:02
Bash
python /work/solve.py
Successfully saved summary 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]
1:12
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.tolist()); 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'.
1:15
Bash
python -c "import pandas as pd; df = pd.read_parquet('landuse_dissolved.geoparqu...
['class', 'area_m2', 'parcel_count', 'geometry']
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Write
/work/solve.py
content · 47 lines · py
import geopandas as gpd
import pandas as pd
def solve():
input_file = 'capetown_landuse.fgb'
output_file = 'landuse_dissolved.geoparquet'
# Read the input file
gdf = gpd.read_file(input_file)
# Calculate area of each parcel in m2 (CRS is already EPSG:32734)
gdf['parcel_area'] = gdf.geometry.area
# Initialize lists for aggregation
classes = []
geometries = []
areas = []
counts = []
# Grouping manually to avoid the groupby.agg issue with unary_union
for name, group in gdf.groupby('class'):
classes.append(name)
# Use union_all() for efficiency and to avoid deprecation
geometries.append(group.geometry.union_all())
areas.append(group['parcel_area'].sum())
counts.append(len(group))
# Create the summary GeoDataFrame
# We use the standard GeoDataFrame constructor
summary_gdf = gpd.GeoDataFrame({
'class': classes,
'area_m2': areas,
'parcel_count': counts,
'geometry': geometries
}, crs=gdf.crs)
# Reorder columns to match requirement
summary_gdf = summary_gdf[['class', 'area_m2', 'parcel_count', 'geometry']]
# Save to GeoParquet using the GeoDataFrame's to_parquet method
# which correctly handles the geometry column for GeoParquet format
summary_gdf.to_parquet(output_file)
print(f"Successfully saved summary to {output_file}")
if __name__ == "__main__":
solve()
Wrote /work/solve.py (1479 chars).
1:29
Bash
python /work/solve.py
Successfully saved summary to landuse_dissolved.geoparquet
1:37
Bash
python -c "import pandas as pd; df = pd.read_parquet('landuse_dissolved.geoparqu...
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]
1:41
Bash
python -c "import geopandas as gpd; gdf = gpd.read_parquet('landuse_dissolved.ge...
['class', 'area_m2', 'parcel_count', 'geometry']
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]
1:50