dd-l1-london-parks-count

Status: done
Score: 1.000
Duration: 0:18
Cost: 0.14¢
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"
}
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Bash ls -F 
london_parks.fgb
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Write /work/solve.py
content · 57 lines · py 
import geopandas as gpd

def analyze_parks(file_path):
    # Load the parks data
    gdf = gpd.read_file(file_path)
    
    # Print info to understand structure
    print("Columns:", gdf.columns.tolist())
    print("CRS:", gdf.crs)
    print("First few rows:\n", gdf.head())
    
    # Ensure we are working with a projected CRS to calculate area in meters
    # If it's WGS84, we need to reproject to a suitable metric CRS (e.g., EPSG:27700 for UK)
    if gdf.crs.is_geographic:
        print("Reprojecting to EPSG:27700 for area calculation...")
        gdf_projected = gdf.to_crs(epsg=27700)
    else:
        gdf_projected = gdf
    
    # Calculate area in square meters, then convert to hectares (1 ha = 10,000 m^2)
    gdf_projected['area_ha'] = gdf_projected.geometry.area / 10000.0
    
    # Filter for parks >= 1 hectare
    large_parks = gdf_projected[gdf_projected['area_ha'] >= 1.0].copy()
    
    # Count
    count = len(large_parks)
    
    # Total area
    total_area_ha = large_parks['area_ha'].sum()
    
    # Bounding box in WGS84
    # First, reproject the subset back to WGS84
    large_parks_wgs84 = large_parks.to_crs(epsg=4326)
    
    # Get bounds: (minx, miny, maxx, maxy)
    bounds = large_parks_wgs84.total_bounds
    bbox_wgs84 = [float(bounds[0]), float(bounds[1]), float(bounds[2]), float(bounds[3])]
    
    return count, total_area_ha, bbox_wgs84

if __name__ == "__main__":
    import json
    count, total_area, bbox = analyze_parks('london_parks.fgb')
    
    result = {
        "count": count,
        "total_area_ha": total_area,
        "bbox_wgs84": bbox
    }
    
    with open('parks_summary.json', 'w') as f:
        json.dump(result, f, indent=4)
    
    print("\nSummary:")
    print(json.dumps(result, indent=4))

Wrote /work/solve.py (1770 chars).
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Bash python solve.py 
Columns: ['id', 'name', 'class', 'geometry']
CRS: EPSG:27700
First few rows:
                                      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:
{
    "count": 42,
    "total_area_ha": 519.1620805430861,
    "bbox_wgs84": [
        -0.19932378751294652,
        51.49147199538401,
        -0.08248178747996572,
        51.54706589542573
    ]
}
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Bash ls -F 
london_parks.fgb
parks_summary.json
solve.py
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