fio-l2-capetown-landuse-dissolve
Map (reference ← swipe → agent)
0:00
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.
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:02
Let me start by exploring what data is available in the working directory.
0:02
Bash
ls /work/
capetown_landuse.fgb
0:08
Let me first explore the input file to understand its structure.
0:08
Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
gdf = gpd.read_file('capetown_landuse.fgb')
print('Columns:', gdf.columns.tolist())
print('CRS:', gdf.crs)
print('Geometry types:', gdf.geometry.type.unique())
print('Shape:', gdf.shape)
print('---')
print(gdf.head(3))
"
Columns: ['id', 'class', 'subtype', 'geometry']
CRS: EPSG:32734
Geometry types: <ArrowStringArray>
['MultiPolygon']
Length: 1, dtype: str
Shape: (30978, 4)
---
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 rows x 4 columns]
0:16
We have: - 30978 rows (parcels) - Columns: id, class, subtype, geometry - CRS: EPSG:32734 (UTM zone 34S, which is appropriate for Cape Town) - All geometries are MultiPolygon - We need to dissolve by `class`: unify all geometries into one MultiPolygon per class, compute total area in m², and count how many source parcels fed in. Let me write a script to do this.
0:16
Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
gdf = gpd.read_file('capetown_landuse.fgb')
print('Unique classes:', gdf['class'].nunique())
print(gdf['class'].value_counts().head(20))
"
Unique classes: 72 class vineyard 8476 residential 3537 pitch 3393 farmland 2289 grass 2208 park 1980 orchard 1052 school 1046 garden 731 bunker 638 tee 576 playground 506 retail 414 meadow 397 religious 358 commercial 327 industrial 321 green 310 animal_keeping 302 farmyard 294 Name: count, dtype: int64
0:24
Now I understand the data. I need to: 1. Dissolve (unify) geometries by `class` 2. Compute area in m² for each unified geometry 3. Count the number of source parcels that fed into each class 4. Write to GeoParquet with columns `class`, `area_m2`, `parcel_count` Since the CRS is EPSG:32734 (UTM zone 34S, meter-based), area in m² will be correct. Let me write a script.
0:24
Write
/work/solve.py
content · 25 lines · py
import geopandas as gpd
# Read the input data
gdf = gpd.read_file('capetown_landuse.fgb')
# Dissolve by class: unify geometries, count parcels
dissolved = gdf.dissolve(by='class', aggfunc='count')
# Keep only the columns we need
dissolved = dissolved[['id']].rename(columns={'id': 'parcel_count'})
# Compute area in m² (CRS is EPSG:32734, metre-based)
dissolved['area_m2'] = dissolved.geometry.area
# Reset index so 'class' becomes a column
result = dissolved.reset_index()
# Select and order columns
result = result[['class', 'area_m2', 'parcel_count']]
# Write to GeoParquet
result.to_parquet('landuse_dissolved.geoparquet', index=False)
print(f"Written {len(result)} rows to landuse_dissolved.geoparquet")
print(result.head())Wrote /work/solve.py (737 chars).
0:27
Let me run the script.
0:27
Bash
cd /work && python solve.py
Traceback (most recent call last):
File "/work/solve.py", line 13, in <module>
dissolved['area_m2'] = dissolved.geometry.area
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/generic.py", line 6206, in __getattr__
return object.__getattribute__(self, name)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'geometry'
0:37
The dissolve operation returned a non-geodataframe? Let me debug this. Actually, the `dissolve` method on a GeoDataFrame should return a GeoDataFrame. But perhaps after renaming and selecting columns, the geometry column got lost? Let me check. Actually, when I do `dissolved = dissolved[['id']]`, the geometry column is preserved. But `rename` might cause issues. Let me debug further. Actually the issue is that `dissolve` with `aggfunc='count'` returns a GeoDataFrame but the geometry column should still be there. Let me check by running a simpler script to see the type.
0:37
Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
gdf = gpd.read_file('capetown_landuse.fgb')
dissolved = gdf.dissolve(by='class', aggfunc='count')
print(type(dissolved))
print(dissolved.columns.tolist())
print(dissolved.geometry.name)
"
<class 'geopandas.geodataframe.GeoDataFrame'> ['geometry', 'id', 'subtype'] geometry
0:52
The issue is that after `dissolve` with `aggfunc='count'`, the columns that get counted are all non-groupby columns. But the geometry column should still be there. The error says no 'geometry' attribute - let me check what happened. Actually, the issue might be that `dissolved[['id']]` selects only the 'id' column, which returns a DataFrame, not a GeoDataFrame. So the geometry is lost. Let me fix this by selecting columns differently.
0:52
Edit
/work/solve.py
Edited /work/solve.py.
0:56
Actually, the issue is that when you select `['id']` after dissolve, you lose the geometry column because you're selecting only the 'id' column, not preserving the geometry. Let me fix this properly - I should select both 'id' and the geometry column explicitly, or better yet, just rename and work with the dissolved GeoDataFrame properly. Let me rewrite the script more carefully.
0:56
Write
/work/solve.py
content · 30 lines · py
import geopandas as gpd
# Read the input data
gdf = gpd.read_file('capetown_landuse.fgb')
# Dissolve by class: unify geometries, count parcels (using 'id' as the count proxy)
dissolved = gdf.dissolve(by='class', aggfunc='count')
# Rename 'id' column to 'parcel_count'
dissolved.rename(columns={'id': 'parcel_count'}, inplace=True)
# Compute area in m² (CRS is EPSG:32734, metre-based)
dissolved['area_m2'] = dissolved.geometry.area
# Keep only required columns (geometry is auto-preserved in GeoDataFrame)
result = dissolved[['parcel_count', 'area_m2']]
# Reset index so 'class' becomes a column
result = result.reset_index()
# Reorder columns
result = result[['class', 'area_m2', 'parcel_count']]
# Write to GeoParquet
result.to_parquet('landuse_dissolved.geoparquet', index=False)
print(f"Written {len(result)} rows to landuse_dissolved.geoparquet")
print(result.head())
print('---')
print('CRS:', result.crs)Wrote /work/solve.py (920 chars).
0:59
Let me run this clean version.
0:59
Bash
cd /work && python solve.py
Written 72 rows to landuse_dissolved.geoparquet
class area_m2 parcel_count
0 airfield 2.444056e+06 1
1 allotments 1.715652e+04 215
2 animal_keeping 6.636804e+05 302
3 barracks 1.873189e+03 4
4 base 9.244053e+05 1
---
Traceback (most recent call last):
File "/work/solve.py", line 30, in <module>
print('CRS:', result.crs)
^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/generic.py", line 6206, in __getattr__
return object.__getattribute__(self, name)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'crs'
1:09
The script successfully wrote the file - the error is just from the debug print at the end. Let me fix that and verify the output file.
1:09
1:09
Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
gdf = gpd.read_parquet('landuse_dissolved.geoparquet')
print('Shape:', gdf.shape)
print('Columns:', gdf.columns.tolist())
print('CRS:', gdf.crs)
print('Geometry type:', gdf.geometry.type.unique())
print()
print(gdf.head(10).to_string())
print()
print('Total classes:', len(gdf))
# Quick sanity: sum of parcel_count should equal original row count
print('Sum parcel_count:', gdf['parcel_count'].sum())
"
Traceback (most recent call last):
File "<string>", line 3, in <module>
gdf = gpd.read_parquet('landuse_dissolved.geoparquet')
File "/usr/local/lib/python3.14/site-packages/geopandas/io/arrow.py", line 774, in _read_parquet
geo_metadata = _validate_and_decode_metadata(metadata)
File "/usr/local/lib/python3.14/site-packages/geopandas/io/arrow.py", line 623, in _validate_and_decode_metadata
raise ValueError(
...<2 lines>...
)
ValueError: Missing geo metadata in Parquet/Feather file.
Use pandas.read_parquet/read_feather() instead.
1:19
The GeoParquet file didn't preserve the geo metadata properly. This is because `to_parquet` from a GeoDataFrame should work, but maybe the `.reset_index()` or column selection stripped it? Let me check the intermediate result. Actually, looking at my code: `result = dissolved[['parcel_count', 'area_m2']]` should still be a GeoDataFrame since it's selecting from a GeoDataFrame that has a geometry column. But then `result.reset_index()` — let me check if that retains the geometry. Actually, the problem might be that when I do `result = dissolved[['parcel_count', 'area_m2']]`, the geometry column is dropped because I didn't include it in the selection list. In GeoPandas, if you select columns from a GeoDataFrame without including the geometry column name, you get a plain DataFrame. Let me fix this by always including the geometry column in selections.
1:19
Write
/work/solve.py
content · 29 lines · py
import geopandas as gpd
# Read the input data
gdf = gpd.read_file('capetown_landuse.fgb')
# Dissolve by class: unify geometries, count parcels (using 'id' as the count proxy)
dissolved = gdf.dissolve(by='class', aggfunc='count')
# Rename 'id' column to 'parcel_count'
dissolved.rename(columns={'id': 'parcel_count'}, inplace=True)
# Compute area in m² (CRS is EPSG:32734, metre-based)
dissolved['area_m2'] = dissolved.geometry.area
# Keep only required columns — always include geometry to stay a GeoDataFrame
result = dissolved[['parcel_count', 'area_m2', 'geometry']]
# Reset index so 'class' becomes a column
result = result.reset_index()
# Reorder columns
result = result[['class', 'area_m2', 'parcel_count', 'geometry']]
# Write to GeoParquet
result.to_parquet('landuse_dissolved.geoparquet', index=False)
print(f"Written {len(result)} rows to landuse_dissolved.geoparquet")
print(result.head())
print('CRS:', result.crs)Wrote /work/solve.py (935 chars).
1:26
The issue is that `to_parquet` from a GeoDataFrame should include geo metadata. But maybe the issue is that when you select columns from a GeoDataFrame, the geometry column might not be properly recognized. Let me check by running again and also check the result before writing.
1:26
Bash
cd /work && python solve.py
Written 72 rows 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]
CRS: EPSG:32734
1:36
Now let me verify the output file is valid GeoParquet with the expected columns.
1:36
Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
gdf = gpd.read_parquet('landuse_dissolved.geoparquet')
print('Shape:', gdf.shape)
print('Columns:', gdf.columns.tolist())
print('CRS:', gdf.crs)
print('Geometry types:', gdf.geometry.type.unique())
print()
print(gdf.head(10).to_string())
print()
print('Total classes:', len(gdf))
print('Sum parcel_count:', gdf['parcel_count'].sum(), '(original rows: 30978)')
print('Total area (km²):', gdf['area_m2'].sum() / 1e6)
"
Shape: (72, 4)
Columns: ['class', 'area_m2', 'parcel_count', 'geometry']
CRS: {"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "ProjectedCRS", "name": "WGS 84 / UTM zone 34S", "base_crs": {"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"}]}, "id": {"authority": "EPSG", "code": 4326}}, "conversion": {"name": "UTM zone 34S", "method": {"name": "Transverse Mercator", "id": {"authority": "EPSG", "code": 9807}}, "parameters": [{"name": "Latitude of natural origin", "value": 0, "unit": "degree", "id": {"authority": "EPSG", "code": 8801}}, {"name": "Longitude of natural origin", "value": 21, "unit": "degree", "id": {"authority": "EPSG", "code": 8802}}, {"name": "Scale factor at natural origin", "value": 0.9996, "unit": "unity", "id": {"authority": "EPSG", "code": 8805}}, {"name": "False easting", "value": 500000, "unit": "metre", "id": {"authority": "EPSG", "code": 8806}}, {"name": "False northing", "value": 10000000, "unit": "metre", "id": {"authority": "EPSG", "code": 8807}}]}, "coordinate_system": {"subtype": "Cartesian", "axis": [{"name": "Easting", "abbreviation": "E", "direction": "east", "unit": "metre"}, {"name": "Northing", "abbreviation": "N", "direction": "north", "unit": "metre"}]}, "scope": "Navigation and medium accuracy spatial referencing.", "area": "Between 18\u00b0E and 24\u00b0E, southern hemisphere between 80\u00b0S and equator, onshore and offshore. Angola. Botswana. Democratic Republic of the Congo (Zaire). Namibia. South Africa. Zambia.", "bbox": {"south_latitude": -80, "west_longitude": 18, "north_latitude": 0, "east_longitude": 24}, "id": {"authority": "EPSG", "code": 32734}}
Geometry types: <ArrowStringArray>
['Polygon', 'MultiPolygon']
Length: 2, dtype: str
class area_m2 parcel_count
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Total classes: 72
Sum parcel_count: 30978 (original rows: 30978)
Total area (km²): 3876.5664726364703
1:50
The output file is ready. Let me verify the columns are exactly as requested: `class`, `area_m2`, `parcel_count` (plus `geometry` which is expected in a GeoParquet). The sum of parcel_count (30978) matches the original row count, so the dissolve worked correctly.
1:50