crs-l1-london-laea-areas
Map (reference ← swipe → agent)
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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_data.py
content · 13 lines · py
import geopandas as gpd
def main():
# Load the geojson file
gdf = gpd.read_file('london_admin.geojson')
print("Columns in gdf:", gdf.columns.tolist())
print("First few rows:\n", gdf.head())
print("CRS:", gdf.crs)
if __name__ == "__main__":
main()
Wrote /work/inspect_data.py (278 chars).
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Bash
python /work/inspect_data.py
Columns in gdf: ['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_data.py
content · 11 lines · py
import geopandas as gpd
def main():
# Load the geojson file
gdf = gpd.read_file('london_admin.geojson')
print("Subtypes found:", gdf['subtype'].unique())
if __name__ == "__main__":
main()
Wrote /work/inspect_data.py (211 chars).
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Bash
python /work/inspect_data.py
Subtypes found: <ArrowStringArray> ['locality', 'county'] Length: 2, dtype: str
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Write
/work/solve.py
content · 25 lines · py
import geopandas as gpd
def main():
# Load the geojson file
gdf = gpd.read_file('london_admin.geojson')
# Ensure we work with a projected CRS for accurate area calculation
# EPSG:3857 is a common web mercator, but for UK/London,
# EPSG:27700 (British National Grid) is much more accurate.
gdf_projected = gdf.to_crs(epsg=27700)
# Calculate area in square meters, then convert to square kilometers
gdf['area_km2'] = gdf_projected.geometry.area / 10**6
# Select only the required columns: id, name, area_km2
result = gdf[['id', 'name', 'area_km2']]
# Write to CSV
result.to_csv('borough_areas.csv', index=False)
print("Successfully wrote borough_areas.csv")
print(result.head())
if __name__ == "__main__":
main()
Wrote /work/solve.py (794 chars).
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Bash
python /work/solve.py
Successfully wrote 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
ls -l borough_areas.csv
-rw-r--r-- 1 agent agent 15608 Jun 17 19:48 borough_areas.csv
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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
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