dd-l1-london-parks-count

Status: done
Score: 100
Duration: 0:38
Cost: 0.08¢
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"
}
0:01
Bash ls -F /work 
london_parks.fgb
0:24
Write /work/solve.py
content · 54 lines · py 
import geopandas as gpd
import json

def main():
    # Load the parks data
    gdf = gpd.read_file('london_parks.fgb')
    
    # Assuming the area is in square meters or similar depending on the CRS.
    # First, let's check the CRS and the geometry to be sure about the area calculation.
    print(f"CRS: {gdf.crs}")
    print(gdf.head())

    # To calculate area in hectares correctly, we should work in a projected CRS (like EPSG:27700 for UK)
    # if the data is in WGS84 or if it's not already in a metric system.
    
    # Let's re-project to British National Grid (EPSG:27700) for area calculation
    gdf_projected = gdf.to_crs(epsg=27700)
    
    # Calculate area in hectares (1 hectare = 10,000 square meters)
    # gdf_projected.area is in square meters
    gdf['area_ha'] = gdf_projected.area / 10000.0
    
    # Filter parks that are one hectare or more
    large_parks = gdf[gdf['area_ha'] >= 1.0].copy()
    
    # Metrics
    count = len(large_parks)
    total_area_ha = large_parks['area_ha'].sum()
    
    # Bounding box on WGS84
    # We need to take the subset and re-project it to WGS84 (EPSG:4326) to get the bbox
    if not large_parks.empty:
        large_parks_wgs84 = large_parks.to_crs(epsg=4326)
        bounds = large_parks_wgs84.total_bounds  # [xmin, ymin, xmax, ymax]
        bbox_wgs84 = bounds.tolist()
    else:
        bbox_wgs84 = []

    # Output results
    summary = {
        "count": int(count),
        "total_area_ha": float(total_area_ha),
        "bbox_wgs84": bbox_wgs84
    }
    
    with open('parks_summary.json', 'w') as f:
        json.dump(summary, f, indent=4)
    
    print("Summary created successfully.")
    print(summary)

if __name__ == "__main__":
    main()

Wrote /work/solve.py (1728 chars).
0:25
Bash python /work/solve.py 
CRS: EPSG:27700
                                     id  ...                                           geometry
0  2b746b97-b0ed-35cc-8eb2-28a64821d7d8  ...  MULTIPOLYGON (((532381.849 178719.026, 532391....
1  669c73cb-2314-3f8b-9b24-14b7db998247  ...  MULTIPOLYGON (((532435.728 178838.472, 532437....
2  bcdbe219-4769-3d9a-bec6-ba44b2f4301f  ...  MULTIPOLYGON (((532657.956 178707.481, 532664....
3  6cb3b89b-4f9b-3a9d-b011-504239533b00  ...  MULTIPOLYGON (((532863.795 178778.075, 532901....
4  7f18bab8-e6fc-34fa-a945-d784358559bd  ...  MULTIPOLYGON (((532724.97 178944.094, 532756.2...

[5 rows x 4 columns]
Summary created successfully.
{'count': 42, 'total_area_ha': 519.1620805430861, 'bbox_wgs84': [-0.19932378751294652, 51.49147199538401, -0.08248178747996572, 51.54706589542573]}
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Bash cat /work/parks_summary.json 
{
    "count": 42,
    "total_area_ha": 519.1620805430861,
    "bbox_wgs84": [
        -0.19932378751294652,
        51.49147199538401,
        -0.08248178747996572,
        51.54706589542573
    ]
}
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