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
ls -F
capetown_landuse.fgb
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Write
/work/check_data.py
content · 10 lines · py
import geopandas as gpd
# Load the data
gdf = gpd.read_file('capetown_landuse.fgb')
# Display basic info to understand the schema
print("Columns:", gdf.columns.tolist())
print("CRS:", gdf.crs)
print(gdf.head())
Wrote /work/check_data.py (213 chars).
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Bash
python /work/check_data.py
python /work/check_data.py
Columns: ['id', 'class', 'subtype', 'geometry']
CRS: EPSG:32734
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 · 49 lines · py
import geopandas as gpd
# Load the data
gdf = gpd.read_file('capetown_landuse.fgb')
# Ensure we are using a projected CRS for area calculations in m2
# The current CRS is EPSG:32734 (UTM Zone 34S), which is appropriate for Cape Town.
# If it wasn't, we would reproject it here.
# Group by 'class'
# We want to dissolve geometries, sum area, and count parcels.
# Note: area of individual parcels should be summed.
# Calculate area of each parcel first
gdf['parcel_area'] = gdf.geometry.area
# Grouping and aggregating
summary = gdf.groupby('class').agg(
area_m2=('parcel_area', 'sum'),
parcel_count=('id', 'count'),
geometry=('geometry', 'dissolve') # This is not quite right in standard agg,
# 'dissolve' is a GeoPandas method.
)
# Let's do it more cleanly:
# 1. Dissolve geometry per class
dissolved_gdf = gdf.dissolve(by='class', aggfunc={'parcel_area': 'sum', 'id': 'count'})
# 2. Rename columns to match requirements
dissolved_gdf = dissolved_gdf.rename(columns={
'parcel_area': 'area_m2',
'id': 'parcel_count'
})
# 3. Reset index so 'class' becomes a column
dissolved_gdf = dissolved_gdf.reset_index()
# 4. Reorder/Select columns as requested: class, area_m2, parcel_count, geometry
# (The prompt asks for class, area_m2, and parcel_count, usually geometry is included in GeoParquet)
result = dissolved_gdf[['class', 'area_m2', 'parcel_count', 'geometry']]
# Check the result
print(result.head())
print(result.info())
# Save to GeoParquet
result.to_parquet('landuse_dissolved.geoparquet')
print("Successfully saved to landuse_dissolved.geoparquet")
Wrote /work/solve.py (1623 chars).
0:27
Bash
python /work/solve.py
python /work/solve.py
Traceback (most recent call last):
File "/work/solve.py", line 18, in <module>
summary = gdf.groupby('class').agg(
area_m2=('parcel_area', 'sum'),
...<2 lines>...
# 'dissolve' is a GeoPandas method.
)
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 'dissolve'
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Write
/work/solve.py
content · 40 lines · py
import geopandas as gpd
# Load the data
gdf = gpd.read_file('capetown_landuse.fgb')
# Calculate area of each parcel in m2 (CRS is EPSG:32734)
gdf['parcel_area'] = gdf.geometry.area
# We will use the dissolve method which handles the geometry union automatically.
# We pass a dictionary to aggfunc to specify which operation for each column.
# We use 'id' to count the parcels.
dissolved_gdf = gdf.dissolve(
by='class',
aggfunc={'parcel_area': 'sum', 'id': 'count'}
)
# Rename the columns to match the user's request
dissolved_gdf = dissolved_gdf.rename(columns={
'parcel_area': 'area_m2',
'id': 'parcel_count'
})
# Reset index to turn 'class' back into a column
dissolved_gdf = dissolved_gdf.reset_index()
# Final column selection and ordering
# The user wants: class, area_m2, parcel_count (and implicitly geometry)
result = dissolved_gdf[['class', 'area_m2', 'parcel_count', 'geometry']]
# Output verification
print("Result Summary:")
print(result.head())
print("\nData Types:")
print(result.dtypes)
# Save to GeoParquet
result.to_parquet('landuse_dissolved.geoparquet')
print("\nSuccessfully saved to landuse_dissolved.geoparquet")
Wrote /work/solve.py (1161 chars).
0:48
Bash
python /work/solve.py
python /work/solve.py
Result Summary:
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]
Data Types:
class str
area_m2 float64
parcel_count int64
geometry geometry
dtype: object
Successfully saved to landuse_dissolved.geoparquet
1:03
Bash
# Let's double check the output columns to ensure they match EXACTLY what was as...
# Let's double check the output columns to ensure they match EXACTLY what was asked.
# The user asked for: class, area_m2, parcel_count.
# My code produces: class, area_m2, parcel_count, geometry.
# In a GeoParquet, the geometry is essential.
python -c "import geopandas as gpd; gdf = gpd.read_parquet('landuse_dissolved.geoparquet'); print(gdf.columns.tolist())"
['class', 'area_m2', 'parcel_count', 'geometry']
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