dd-l1-london-parks-count

Status: done
Score: 1.000
Duration: 1:12
Cost: 0.10¢
Model: google/gemma-4-26b-a4b-it

Map (reference ← swipe → agent)

0:00
I've got inner-London parks (london_parks) and I'm sizing the corpus before commissioning a green-space accessibility study. How many of those parks come in at one hectare or more, what's their combined area in hectares, and what bounding box would I draw around that subset on a WGS84 map? Output parks_summary.json with keys count, total_area_ha, and bbox_wgs84 (as a [xmin, ymin, xmax, ymax] array).
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System info 
{
  "adapter": "openrouter",
  "model": "google/gemma-4-26b-a4b-it",
  "base_url": "https://openrouter.ai/api/v1"
}
0:07
Bash ls -F 
london_parks.fgb
0:22
Write /work/solve.py
content · 48 lines · py 
import geopandas as gpd
import json

def main():
    # Load the parks data
    gdf = gpd.read_file('london_parks.fgb')
    
    # Ensure we are working with an area-friendly CRS for calculations
    # EPSG:27700 is the British National Grid, good for London area measurements
    original_crs = gdf.crs
    gdf_projected = gdf.to_crs(epsg=27700)
    
    # Calculate area in hectares (1 hectare = 10,000 m^2)
    gdf_projected['area_ha'] = gdf_projected.geometry.area / 10000.0
    
    # Filter parks that are 1 hectare or more
    large_parks = gdf_projected[gdf_projected['area_ha'] >= 1.0].copy()
    
    # Calculate count and total area
    count = len(large_parks)
    total_area_ha = large_parks['area_ha'].sum()
    
    # Calculate bounding box in WGS84
    # Convert large_parks back to WGS84
    large_parks_wgs84 = large_parks.to_crs(epsg=4326)
    
    if not large_parks_wgs84.empty:
        bounds = large_parks_wgs84.total_bounds  # [xmin, ymin, xmax, ymax]
    else:
        bounds = [0.0, 0.0, 0.0, 0.0]
        
    # Prepare the output dictionary
    summary = {
        "count": int(count),
        "total_area_ha": float(total_area_ha),
        "bbox_wgs84": [float(bounds[0]), float(bounds[1]), float(bounds[2]), float(bounds[3])]
    }
    
    # Write to JSON
    with open('parks_summary.json', 'w') as f:
        json.dump(summary, f, indent=4)
    
    print("Summary generated successfully.")
    print(summary)

if __name__ == "__main__":
    main()

Wrote /work/solve.py (1481 chars).
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Bash python solve.py 
Summary generated successfully.
{'count': 42, 'total_area_ha': 519.1620805430861, 'bbox_wgs84': [-0.19932378751294652, 51.49147199538401, -0.08248178747996572, 51.54706589542573]}
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Bash cat parks_summary.json 
{
    "count": 42,
    "total_area_ha": 519.1620805430861,
    "bbox_wgs84": [
        -0.19932378751294652,
        51.49147199538401,
        -0.08248178747996572,
        51.54706589542573
    ]
}
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