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"
}
0:13
Let me start by exploring the working directory to understand what data is available.
0:13
Bash
ls -la /work/
total 15884 drwxr-xr-x 1 agent agent 40 Jun 16 02:07 . drwxr-xr-x 1 root root 14 Jun 16 02:07 .. -rw-r--r-- 1 agent agent 16264656 Jun 16 02:07 capetown_landuse.fgb
0:15
We have a FlatGeobuf file `capetown_landuse.fgb`. Let me explore it first to understand the schema and data.
0:15
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('Shape:', gdf.shape)
print('CRS:', gdf.crs)
print('Dtypes:')
print(gdf.dtypes)
print()
print('First few rows:')
print(gdf.head())
print()
print('Unique landuse classes:')
print(gdf['landuse'].unique())
"
Columns: ['id', 'class', 'subtype', 'geometry']
Shape: (30978, 4)
CRS: EPSG:32734
Dtypes:
id str
class str
subtype str
geometry geometry
dtype: object
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]
Unique landuse classes:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/pandas/core/indexes/base.py", line 3641, in get_loc
return self._engine.get_loc(casted_key)
~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
File "pandas/_libs/index.pyx", line 168, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/index.pyx", line 197, in pandas._libs.index.IndexEngine.get_loc
File "pandas/_libs/hashtable_class_helper.pxi", line 7668, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas/_libs/hashtable_class_helper.pxi", line 7676, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'landuse'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "<string>", line 14, in <module>
print(gdf['landuse'].unique())
~~~^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/geopandas/geodataframe.py", line 1891, in __getitem__
result = super().__getitem__(key)
File "/usr/local/lib/python3.14/site-packages/pandas/core/frame.py", line 4378, in __getitem__
indexer = self.columns.get_loc(key)
File "/usr/local/lib/python3.14/site-packages/pandas/core/indexes/base.py", line 3648, in get_loc
raise KeyError(key) from err
KeyError: 'landuse'
0:19
The column is `class`, not `landuse`. Let me check the unique classes.
0:19
Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
gdf = gpd.read_file('capetown_landuse.fgb')
print('Unique classes:')
print(gdf['class'].unique())
print()
print('Count per class:')
print(gdf['class'].value_counts())
"
Unique classes:
<ArrowStringArray>
[ 'nature_reserve', 'playground',
'pedestrian', 'construction',
'pitch', 'residential',
'religious', 'park',
'school', 'retail',
'grass', 'kindergarten',
'garden', 'recreation_ground',
'industrial', 'college',
'plant_nursery', 'farmyard',
'farmland', 'commercial',
'hospital', 'stadium',
'track', 'education',
'vineyard', 'quarry',
'orchard', 'species_management_area',
'animal_keeping', 'meadow',
'camp_site', 'golf_course',
'landfill', 'works',
'flowerbed', 'cemetery',
'wilderness_area', 'zoo',
'grave_yard', 'village_green',
'tee', 'bunker',
'green', 'water_hazard',
'fairway', 'lateral_water_hazard',
'rough', 'driving_range',
'clinic', 'beach_resort',
'dog_park', 'greenhouse_horticulture',
'water_park', 'resort',
'brownfield', 'university',
'railway', 'base',
'military', 'national_park',
'greenfield', 'airfield',
'marina', 'plaza',
'garages', 'music_school',
'allotments', 'doctors',
'training_area', 'naval_base',
'barracks', 'protected_landscape_seascape']
Length: 72, dtype: str
Count per class:
class
vineyard 8476
residential 3537
pitch 3393
farmland 2289
grass 2208
...
airfield 1
music_school 1
doctors 1
training_area 1
protected_landscape_seascape 1
Name: count, Length: 72, dtype: int64
0:25
Now I understand the data. The column is `class` (not `landuse`). I need to: 1. Group by `class` 2. Union/dissolve geometries into a single MultiPolygon per class 3. Calculate total area in m² (since CRS is EPSG:32734 which is UTM zone 34S, units are meters) 4. Count the number of source parcels per class 5. Save to `landuse_dissolved.geoparquet` with columns `class`, `area_m2`, `parcel_count` Let me write the script.
0:25
Write
/work/solve.py
content · 39 lines · py
import geopandas as gpd
import pandas as pd
from shapely.geometry import MultiPolygon
# Read the source data
gdf = gpd.read_file("/work/capetown_landuse.fgb")
print(f"CRS: {gdf.crs}")
print(f"Total parcels: {len(gdf)}")
# --- Dissolve by class ---
# Dissolve: union geometries, count parcels
dissolved = gdf.dissolve(by="class", aggfunc="count", numeric_only=False)
# 'id' was counted by aggfunc, let's rename
dissolved = dissolved.rename(columns={"id": "parcel_count"})
# Compute area in m² (CRS is EPSG:32734, units are meters)
dissolved["area_m2"] = dissolved.geometry.area
# Reset index so 'class' becomes a column
dissolved = dissolved.reset_index()
# Keep only the three required columns
result = dissolved[["class", "area_m2", "parcel_count"]].copy()
# Ensure geometries are MultiPolygon (some might be single Polygon)
result = result.set_geometry("geometry")
# Quick sanity check
print("\nResult summary:")
print(result.head())
print(f"\nTotal classes: {len(result)}")
print(f"Total area (km²): {result['area_m2'].sum() / 1e6:.2f}")
print(f"Column names: {result.columns.tolist()}")
# Write to GeoParquet
result.to_parquet("/work/landuse_dissolved.geoparquet")
print("\nWritten to /work/landuse_dissolved.geoparquet")Wrote /work/solve.py (1235 chars).
0:26
Let me run this to see if it works.
0:26
Bash
cd /work && python solve.py
CRS: EPSG:32734 Total parcels: 30978
Traceback (most recent call last):
File "/work/solve.py", line 13, in <module>
dissolved = gdf.dissolve(by="class", aggfunc="count", numeric_only=False)
File "/usr/local/lib/python3.14/site-packages/geopandas/geodataframe.py", line 2274, in dissolve
aggregated_data = data.groupby(**groupby_kwargs).agg(aggfunc, **kwargs)
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 291, in agg
return self.apply_str()
~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/apply.py", line 701, in apply_str
return self._apply_str(obj, func, *self.args, **self.kwargs)
~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/core/apply.py", line 792, in _apply_str
return f(*args, **kwargs)
TypeError: GroupBy.count() got an unexpected keyword argument 'numeric_only'
0:30
The `aggfunc="count"` with `numeric_only=False` doesn't work. Let me fix the approach - use `aggfunc="first"` or just use a different method. Actually, `dissolve` with `aggfunc="count"` will count all non-null values in each column. The issue is the `numeric_only=False` passed to `aggfunc`. Let me just use a simpler approach: dissolve with default aggregation (first), then compute parcel_count separately.
0:30
Edit
/work/solve.py
Edited /work/solve.py.
0:34
Wait, when we dissolve, the geometry column gets aggregated (union). The other columns like 'id' and 'subtype' would need aggregation too. Let me think about what `dissolve` does by default. `dissolve(by="class")` without aggfunc will use 'first' as the default aggfunc for non-geometry columns. That's fine - we don't really need the other columns. The key thing is that it unions the geometries. But there's a problem: after dissolve, the 'class' column becomes the index. Then we set parcel_count from the counts series. That should work. Let me also check - when we do `dissolved.reset_index()`, the 'class' becomes a regular column. Then we select `["class", "area_m2", "parcel_count"]` but we need geometry too for GeoParquet. Actually, the result should keep geometry as the active geometry column. Let me refine the script to be cleaner.
0:34
Read
/work/solve.py
1 import geopandas as gpd
2 import pandas as pd
3 from shapely.geometry import MultiPolygon
4
5 # Read the source data
6 gdf = gpd.read_file("/work/capetown_landuse.fgb")
7 print(f"CRS: {gdf.crs}")
8 print(f"Total parcels: {len(gdf)}")
9
10 # --- Dissolve by class ---
11
12 # Count parcels per class (before dissolve)
13 counts = gdf.groupby("class").size().rename("parcel_count")
14
15 # Dissolve: union geometries
16 dissolved = gdf.dissolve(by="class")
17
18 # Attach parcel counts
19 dissolved["parcel_count"] = counts
20
21 # Compute area in m² (CRS is EPSG:32734, units are meters)
22 dissolved["area_m2"] = dissolved.geometry.area
23
24 # Reset index so 'class' becomes a column
25 dissolved = dissolved.reset_index()
26
27 # Keep only the three required columns
28 result = dissolved[["class", "area_m2", "parcel_count"]].copy()
29
30 # Ensure geometries are MultiPolygon (some might be single Polygon)
31 result = result.set_geometry("geometry")
32
33 # Quick sanity check
34 print("\nResult summary:")
35 print(result.head())
36 print(f"\nTotal classes: {len(result)}")
37 print(f"Total area (km²): {result['area_m2'].sum() / 1e6:.2f}")
38 print(f"Column names: {result.columns.tolist()}")
39
40 # Write to GeoParquet
41 result.to_parquet("/work/landuse_dissolved.geoparquet")
42 print("\nWritten to /work/landuse_dissolved.geoparquet")
0:38
Wait, there's an issue. When I do `dissolved = gdf.dissolve(by="class")`, the result will have the geometry column. Then I reset_index() and select columns `["class", "area_m2", "parcel_count"]`. But I'm discarding the geometry column in that selection! I need to include the geometry. Let me fix this.
0:38
Edit
/work/solve.py
Edited /work/solve.py.
0:40
Bash
cd /work && python solve.py
CRS: EPSG:32734
Total parcels: 30978
Result summary:
class ... parcel_count
0 airfield ... 1
1 allotments ... 215
2 animal_keeping ... 302
3 barracks ... 4
4 base ... 1
[5 rows x 4 columns]
Total classes: 72
Total area (km²): 3876.57
Column names: ['class', 'geometry', 'area_m2', 'parcel_count']
Written to /work/landuse_dissolved.geoparquet
0:48
Let me verify the output file is correct by reading it back.
0:48
Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
gdf = gpd.read_file('landuse_dissolved.geoparquet')
print('Shape:', gdf.shape)
print('Columns:', gdf.columns.tolist())
print('CRS:', gdf.crs)
print()
print(gdf.head(10).to_string())
print()
print('Geometry types:')
print(gdf.geometry.geom_type.value_counts())
print()
# Verify a couple entries
print('vineyard:')
v = gdf[gdf['class'] == 'vineyard']
print(v[['class', 'area_m2', 'parcel_count']])
print()
print('residential:')
r = gdf[gdf['class'] == 'residential']
print(r[['class', 'area_m2', 'parcel_count']])
"
Traceback (most recent call last):
File "<string>", line 3, in <module>
gdf = gpd.read_file('landuse_dissolved.geoparquet')
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'.
0:52
It seems the GeoParquet file might not have been written with correct geometry encoding. Let me check the file and try to read it differently.
0:52
Bash
cd /work && python -c "
cd /work && python -c "
import pandas as pd
# Try reading with pandas to see what's in there
df = pd.read_parquet('landuse_dissolved.geoparquet')
print('Columns:', df.columns.tolist())
print('Dtypes:')
print(df.dtypes)
print()
print(df.head())
"
Columns: ['class', 'geometry', 'area_m2', 'parcel_count']
Dtypes:
class str
geometry object
area_m2 float64
parcel_count int64
dtype: object
class ... parcel_count
0 airfield ... 1
1 allotments ... 215
2 animal_keeping ... 302
3 barracks ... 4
4 base ... 1
[5 rows x 4 columns]
0:56
The geoparquet file was written, but when reading back with `geopandas.read_file()` it doesn't recognize the format. This is likely because `to_parquet()` writes a generic Parquet file (not the specialized GeoParquet format with geo metadata). Let me check the geo metadata and also try a different approach using `to_parquet` with the `geo` parameter or using `to_file` with the GeoParquet driver. Actually, `GeoDataFrame.to_parquet()` should write GeoParquet by default in newer versions of geopandas. Let me check the version and try again.
0:56
Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
print('GeoPandas version:', gpd.__version__)
import pyogrio
print('Pyogrio version:', pyogrio.__version__)
"
GeoPandas version: 1.1.3 Pyogrio version: 0.12.1
1:00
The GeoParquet file was written successfully, but reading it back with `geopandas.read_file()` fails because pyogrio doesn't recognize the file. Let me check if the file has proper GeoParquet metadata. Let me try reading it with GeoPandas using the `engine='pyarrow'` parameter.
1:00
Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
# Try reading with pyarrow engine
gdf = gpd.read_parquet('landuse_dissolved.geoparquet')
print('Shape:', gdf.shape)
print('Columns:', gdf.columns.tolist())
print('CRS:', gdf.crs)
print()
print(gdf.head(10).to_string())
print()
print('Geometry types:')
print(gdf.geometry.geom_type.value_counts())
print()
# Verify a couple entries
print('vineyard:')
v = gdf[gdf['class'] == 'vineyard']
print(v[['class', 'area_m2', 'parcel_count']])
print()
print('residential:')
r = gdf[gdf['class'] == 'residential']
print(r[['class', 'area_m2', 'parcel_count']])
"
Shape: (72, 4)
Columns: ['class', 'geometry', 'area_m2', 'parcel_count']
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}}
class
[... truncated ...]583, 272272.773 6244592.247, 272522.728 6244649.893, 272546.806 6244643.245, 272557.924 6244631.038, 272575.642 6244549.637, 272601.757 6244567.987, 272608.573 6244547.652, 272579.751 6244528.372, 272614.168 6244409.939, 272645.938 6244337.088, 272560.265 6244295.249, 272447.715 6244253.03, 272346.13 6244217.388, 272163.91 6244166.413, 272032.075 6244140.628, 271345.73 6244011.52, 270938.426 6243942.419, 270489.284 6243862.377, 270277.317 6243823.108, 270274.708 6243837.616, 270216.042 6243827.65, 270218.487 6243811.883, 270178.72 6243805.459, 269537.126 6243691.585, 269527.722 6243685.64, 269457.628 6243675.802, 269387.231 6243678.763, 269352.354 6243693.852, 269235.963 6243742.561, 269171.127 6243770.605, 269238.017 6243893.147, 269556.219 6243971.623, 269881.705 6244047.456, 270151.203 6244109.448, 270194.017 6244119.252, 270199.385 6244118.017, 270204.041 6244115.289)), ((260783.979 6244560.709, 260769.648 6244573.833, 260781.237 6244585.791, 260795.239 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Geometry types:
MultiPolygon 65
Polygon 7
Name: count, dtype: int64
vineyard:
class area_m2 parcel_count
66 vineyard 3.329834e+08 8476
residential:
class area_m2 parcel_count
54 residential 3.613121e+08 3537
1:07
The result looks good. We have 72 classes with dissolved geometries, area in m², and parcel counts. However, I notice that some geometries are Polygon (7 of them) rather than MultiPolygon. The specification asked for a MultiPolygon. Let me fix that by converting any single Polygon to MultiPolygon.
1:07
1:07
Read
/work/solve.py
1 import geopandas as gpd
2 import pandas as pd
3 from shapely.geometry import MultiPolygon
4
5 # Read the source data
6 gdf = gpd.read_file("/work/capetown_landuse.fgb")
7 print(f"CRS: {gdf.crs}")
8 print(f"Total parcels: {len(gdf)}")
9
10 # --- Dissolve by class ---
11
12 # Count parcels per class (before dissolve)
13 counts = gdf.groupby("class").size().rename("parcel_count")
14
15 # Dissolve: union geometries
16 dissolved = gdf.dissolve(by="class")
17
18 # Attach parcel counts
19 dissolved["parcel_count"] = counts
20
21 # Compute area in m² (CRS is EPSG:32734, units are meters)
22 dissolved["area_m2"] = dissolved.geometry.area
23
24 # Reset index so 'class' becomes a column
25 dissolved = dissolved.reset_index()
26
27 # Keep only the required columns (geometry included)
28 result = dissolved[["class", "geometry", "area_m2", "parcel_count"]].copy()
29
30 # Ensure geometries are MultiPolygon (some might be single Polygon)
31 result = result.set_geometry("geometry")
32
33 # Quick sanity check
34 print("\nResult summary:")
35 print(result.head())
36 print(f"\nTotal classes: {len(result)}")
37 print(f"Total area (km²): {result['area_m2'].sum() / 1e6:.2f}")
38 print(f"Column names: {result.columns.tolist()}")
39
40 # Write to GeoParquet
41 result.to_parquet("/work/landuse_dissolved.geoparquet")
42 print("\nWritten to /work/landuse_dissolved.geoparquet")
1:10
I need to convert single Polygons to MultiPolygons using shapely's `multi_polygon` function (or by checking geometry type). Let me update the script.
1:10
Edit
/work/solve.py
Edited /work/solve.py.
1:14
Also remove the duplicate import at the top, and remove the unused import of `MultiPolygon` that's already there at the top. Let me clean up. Actually the import at the top is `from shapely.geometry import MultiPolygon` and I added one later too. Let me remove the top one since the later one is inside the file. Actually it's fine to have it imported twice, no harm. But let me clean it up.
1:14
Edit
/work/solve.py
Edited /work/solve.py.
1:16
Bash
cd /work && python solve.py
CRS: EPSG:32734
Total parcels: 30978
Result summary:
class ... parcel_count
0 airfield ... 1
1 allotments ... 215
2 animal_keeping ... 302
3 barracks ... 4
4 base ... 1
[5 rows x 4 columns]
Total classes: 72
Total area (km²): 3876.57
Column names: ['class', 'geometry', 'area_m2', 'parcel_count']
Written to /work/landuse_dissolved.geoparquet
1:23
Now let me verify the output file is correct - all geometries are MultiPolygon now.
1:23
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()
print('All geometry types:')
print(gdf.geometry.geom_type.value_counts())
print()
print('Sample rows:')
print(gdf[['class', 'area_m2', 'parcel_count']].to_string())
"
Shape: (72, 4)
Columns: ['class', 'geometry', 'area_m2', 'parcel_count']
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}}
All geometry types:
MultiPolygon 72
Name: count, dtype: int64
Sample rows:
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
5 beach_resort 2.936896e+05 1
6 brownfield 1.418610e+05 13
7 bunker 6.712463e+04 638
8 camp_site 8.568784e+05 13
9 cemetery 4.515795e+06 65
10 clinic 9.552029e+04 19
11 college 1.178636e+07 37
12 commercial 7.190500e+06 327
13 construction 5.727714e+06 84
14 doctors 1.407718e+02 1
15 dog_park 1.021826e+05 11
16 driving_range 1.287389e+05 10
17 education 3.349939e+05 8
18 fairway 1.040348e+06 232
19 farmland 3.817290e+08 2289
20 farmyard 7.748543e+06 294
21 flowerbed 6.537666e+03 40
22 garages 6.635553e+02 3
23 garden 4.671889e+06 731
24 golf_course 1.353686e+07 50
25 grass 3.260907e+07 2208
26 grave_yard 1.920088e+05 17
27 green 1.316131e+05 310
28 greenfield 3.279033e+05 14
29 greenhouse_horticulture 8.850429e+04 5
30 hospital 4.673730e+06 55
31 industrial 6.496317e+07 321
32 kindergarten 1.540748e+05 65
33 landfill 4.550490e+06 18
34 lateral_water_hazard 6.541641e+04 12
35 marina 1.557197e+05 5
36 meadow 1.785986e+07 397
37 military 5.032478e+06 22
38 music_school 1.887399e+04 1
39 national_park 4.566999e+08 3
40 nature_reserve 1.700276e+09 42
41 naval_base 4.584897e+05 4
42 orchard 2.764034e+07 1052
43 park 2.179358e+07 1980
44 pedestrian 4.111786e+05 266
45 pitch 1.133306e+07 3393
46 plant_nursery 8.625895e+05 40
47 playground 5.245591e+05 506
48 plaza 2.755790e+04 3
49 protected_landscape_seascape 2.586956e+06 1
50 quarry 5.748884e+06 30
51 railway 4.382439e+06 19
52 recreation_ground 2.230580e+06 80
53 religious 1.284939e+06 358
54 residential 3.613121e+08 3537
55 resort 9.421589e+05 6
56 retail 8.692102e+06 414
57 rough 8.837839e+03 36
58 school 2.882922e+07 1046
59 species_management_area 1.758947e+07 3
60 stadium 4.357762e+05 12
61 tee 6.753886e+04 576
62 track 3.204643e+05 35
63 training_area 5.419677e+03 1
64 university 4.222409e+06 23
65 village_green 5.769054e+04 45
66 vineyard 3.329834e+08 8476
67 water_hazard 5.217910e+05 69
68 water_park 4.026974e+04 5
69 wilderness_area 3.064923e+08 5
70 works 2.503782e+06 68
71 zoo 4.314377e+05 5
1:30
The output looks exactly as requested. All 72 classes are present, every geometry is a MultiPolygon, areas are in m², and parcel counts are correct. Let me verify the file exists.
1:30