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 · 17 lines · py
import geopandas as gpd
def main():
# Load the geojson file
gdf = gpd.read_file('london_admin.geojson')
print("Columns:", gdf.columns.tolist())
print("First few rows:")
print(gdf.head())
print("\nValue counts for 'type' (or similar identifying column):")
# Let's see what columns we have to identify county vs locality
# The prompt mentions 'county' and 'locality' features.
# Let's check if there is a 'type' or 'level' column.
if __name__ == "__main__":
main()
Wrote /work/inspect_data.py (515 chars).
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Bash
python /work/inspect_data.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]
Value counts for 'type' (or similar identifying column):
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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:", gdf.columns.tolist())
print("Subtype counts:")
print(gdf['subtype'].value_counts())
if __name__ == "__main__":
main()
Wrote /work/inspect_data.py (275 chars).
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Bash
python /work/inspect_data.py
Columns: ['id', 'name', 'subtype', 'country', 'geometry'] Subtype counts: subtype locality 199 county 33 Name: count, dtype: int64
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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')
# Ensure geometry is valid and handle potential errors
# Project to a metric system for accurate area calculation (UTM zone 30N for London)
# EPSG:32630 is WGS 84 / UTM zone 30N
gdf_metric = gdf.to_crs(epsg=32630)
# Calculate area in square meters, then convert to km2
gdf_metric['area_km2'] = gdf_metric.geometry.area / 10**6
# Select only the required columns
# The instruction asks for id, name, and area_km2
result = gdf[['id', 'name']].copy()
result['area_km2'] = gdf_metric['area_km2']
# Save to csv
result.to_csv('borough_areas.csv', index=False)
print("Successfully saved borough_areas.csv")
print(result.head())
if __name__ == "__main__":
main()
Wrote /work/solve.py (849 chars).
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Bash
python /work/solve.py
Successfully saved borough_areas.csv
id name area_km2
0 255163d2-43e6-4c60-af14-b2c29b57a97e Abbots Langley 18.545335
1 1f71e803-826e-4313-ab53-d8cc4c346743 Addington 3.347606
2 7f738dcf-eb8b-4c85-8141-9ef35825714d Addlestone 2.923439
3 62053957-e9df-444e-b63d-39a6c4dc5179 Aldenham 23.867017
4 5e1cd051-a913-4b44-8d51-9ed93670ffad Artington 4.923381
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Bash
ls -l borough_areas.csv
-rw-r--r-- 1 agent agent 15606 Jun 16 10:15 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,18.54533537567114 1f71e803-826e-4313-ab53-d8cc4c346743,Addington,3.347606043434471 7f738dcf-eb8b-4c85-8141-9ef35825714d,Addlestone,2.923439212549462 62053957-e9df-444e-b63d-39a6c4dc5179,Aldenham,23.86701706102138
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