crs-l1-london-laea-areas

Status: done
Score: 1.000
Duration: 0:47
Cost: 0.19¢
Model: google/gemma-4-26b-a4b-it

Map (reference ← swipe → agent)

0:00
I need to create a land-area comparison for the Horizon report. Can you get me the area in km² for every administrative unit in `london_admin.geojson` (both the borough-level `county` features and the surrounding `locality` features)? Please write the result to `borough_areas.csv`, with one row per feature containing its id, name, and area_km2. Use the `id` field to identify each feature.
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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 
london_admin.geojson
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Write /work/inspect_geojson.py
content · 15 lines · py 
import geopandas as gpd

def main():
    # Load the geojson file
    gdf = gpd.read_file('london_admin.geojson')
    
    # Print info to see the structure
    print("Columns:", gdf.columns.tolist())
    print("First few rows:")
    print(gdf.head())
    print("CRS:", gdf.crs)

if __name__ == "__main__":
    main()

Wrote /work/inspect_geojson.py (317 chars).
0:08
Bash python /work/inspect_geojson.py 
Columns: ['id', 'name', 'subtype', 'country', 'geometry']
First few rows:
                                     id  ...                                           geometry
0  255163d2-43e6-4c60-af14-b2c29b57a97e  ...  POLYGON ((-0.42078 51.73969, -0.42094 51.73981...
1  1f71e803-826e-4313-ab53-d8cc4c346743  ...  POLYGON ((0.38861 51.30083, 0.38893 51.30079, ...
2  7f738dcf-eb8b-4c85-8141-9ef35825714d  ...  POLYGON ((-0.49709 51.378, -0.50158 51.3762, -...
3  62053957-e9df-444e-b63d-39a6c4dc5179  ...  POLYGON ((-0.36536 51.68926, -0.36671 51.68918...
4  5e1cd051-a913-4b44-8d51-9ed93670ffad  ...  POLYGON ((-0.60583 51.22804, -0.6059 51.22849,...

[5 rows x 5 columns]
CRS: EPSG:4326
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Write /work/inspect_subtypes.py
content · 12 lines · py 
import geopandas as gpd

def main():
    # Load the geojson file
    gdf = gpd.read_file('london_admin.geojson')
    
    # Check the subtypes present
    print("Subtypes present:", gdf['subtype'].unique())

if __name__ == "__main__":
    main()

Wrote /work/inspect_subtypes.py (246 chars).
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Bash python /work/inspect_subtypes.py 
Subtypes present: <ArrowStringArray>
['locality', 'county']
Length: 2, dtype: str
0:25
Write /work/solve.py
content · 28 lines · py 
import geopandas as gpd

def main():
    # Load the geojson file
    gdf = gpd.read_file('london_admin.geojson')
    
    # The CRS is EPSG:4326 (WGS 84), which uses degrees.
    # To calculate area in km2, we need to reproject to a projected CRS.
    # For London, EPSG:27700 (British National Grid) is appropriate.
    
    # Project to British National Grid
    gdf_projected = gdf.to_crs(epsg=27700)
    
    # Calculate area in square meters and then convert to km2
    # area() returns area in the units of the CRS (meters for EPSG:27700)
    gdf['area_km2'] = gdf_projected.geometry.area / 10**6
    
    # Select only the required columns: id, name, area_km2
    result = gdf[['id', 'name', 'area_km2']]
    
    # Save to csv
    result.to_csv('borough_areas.csv', index=False)
    print("Successfully created borough_areas.csv")
    print(result.head())

if __name__ == "__main__":
    main()

Wrote /work/solve.py (903 chars).
0:27
Bash python /work/solve.py 
Successfully created borough_areas.csv
                                     id            name   area_km2
0  255163d2-43e6-4c60-af14-b2c29b57a97e  Abbots Langley  18.537401
1  1f71e803-826e-4313-ab53-d8cc4c346743       Addington   3.345514
2  7f738dcf-eb8b-4c85-8141-9ef35825714d      Addlestone   2.922214
3  62053957-e9df-444e-b63d-39a6c4dc5179        Aldenham  23.856257
4  5e1cd051-a913-4b44-8d51-9ed93670ffad       Artington   4.921422
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Bash head -n 5 borough_areas.csv 
id,name,area_km2
255163d2-43e6-4c60-af14-b2c29b57a97e,Abbots Langley,18.537401200537325
1f71e803-826e-4313-ab53-d8cc4c346743,Addington,3.3455142086694245
7f738dcf-eb8b-4c85-8141-9ef35825714d,Addlestone,2.9222135486747507
62053957-e9df-444e-b63d-39a6c4dc5179,Aldenham,23.85625677462975
0:36
Write /work/solve.py
content · 27 lines · py 
import geopandas as gpd

def main():
    # Load the geojson file
    gdf = gpd.read_file('london_admin.geojson')
    
    # The CRS is EPSG:4326 (WGS 84), which uses degrees.
    # To calculate area in km2, we need to reproject to a projected CRS.
    # For London, EPSG:27700 (British National Grid) is appropriate.
    
    # Project to British National Grid
    gdf_projected = gdf.to_crs(epsg=27700)
    
    # Calculate area in square meters and then convert to km2
    # area() returns area in the units of the CRS (meters for EPSG:27700)
    gdf['area_km2'] = gdf_projected.geometry.area / 10**6
    
    # Select only the required columns: id, name, area_km2
    result = gdf[['id', 'name', 'area_km2']]
    
    # Save to csv
    result.to_csv('borough_areas.csv', index=False)
    print("Successfully created borough_areas.csv")

if __name__ == "__main__":
    main()

Wrote /work/solve.py (878 chars).
0:37
Bash python /work/solve.py 
Successfully created borough_areas.csv
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