dd-l1-london-parks-count

Status: done
Score: 1.000
Duration: 2:00
Cost: 0.29¢
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).
0:00
System info 
{
  "adapter": "openrouter",
  "model": "google/gemma-4-26b-a4b-it",
  "base_url": "https://openrouter.ai/api/v1"
}
0:00
Bash ls 
london_parks.fgb
0:41
Write /work/solve.py
content · 56 lines · py 
import geopandas as gpd
import json

def main():
    # Load the data
    gdf = gpd.read_file('london_parks.fgb')
    
    # Ensure we have an area column. 
    # If not, we calculate it. 
    # First, we need a projected CRS for accurate area calculation.
    # EPSG:27700 is the standard for UK (OSGB36 / British National Grid)
    
    # Check if 'area' or similar exists, but it's safer to calculate from geometry 
    # in a projected CRS to ensure accuracy.
    
    # Create a projected copy for area calculation
    gdf_projected = gdf.to_crs(epsg=27700)
    
    # 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 in hectares
    total_area_ha = large_parks['area_ha'].sum()
    
    # Bounding box in WGS84
    # First, convert the subset back to WGS84 (EPSG:4326)
    large_parks_wgs84 = large_parks.to_crs(epsg=4326)
    
    # Get the bounding box [xmin, ymin, xmax, ymax]
    # total_bounds returns (minx, miny, maxx, maxy)
    bbox = list(large_parks_wgs84.total_bounds)
    
    # Prepare output dictionary
    output = {
        "count": int(count),
        "total_area_ha": float(total_area_ha),
        "bbox_wgs84": bbox
    }
    
    # Write to json
    with open('parks_summary.json', 'w') as f:
        json.dump(output, f, indent=4)
    
    print(f"Processed {count} parks.")
    print(f"Total area: {total_area_ha:.2f} ha")
    print(f"BBox: {bbox}")

if __name__ == "__main__":
    main()

Wrote /work/solve.py (1695 chars).
0:55
Bash python solve.py 
Processed 42 parks.
Total area: 519.16 ha
BBox: [np.float64(-0.19932378751294652), np.float64(51.49147199538401), np.float64(-0.08248178747996572), np.float64(51.54706589542573)]
1:06
Edit /work/solve.py 
Edited /work/solve.py.
1:07
Bash python solve.py 
Processed 42 parks.
Total area: 519.16 ha
BBox: [np.float64(-0.19932378751294652), np.float64(51.49147199538401), np.float64(-0.08248178747996572), np.float64(51.54706589542573)]
1:22
Edit /work/solve.py 
old_string not found in /work/solve.py
1:39
Write /work/solve.py
content · 49 lines · py 
import geopandas as gpd
import json

def main():
    # Load the data
    gdf = gpd.read_file('london_parks.fgb')
    
    # Create a projected copy for area calculation
    # EPSG:27700 is the standard for UK (OSGB36 / British National Grid)
    gdf_projected = gdf.to_crs(epsg=27700)
    
    # 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 in hectares
    total_area_ha = float(large_parks['area_ha'].sum())
    
    # Bounding box in WGS84
    # First, convert the subset back to WGS84 (EPSG:4326)
    large_parks_wgs84 = large_parks.to_crs(epsg=4326)
    
    # Get the bounding box [xmin, ymin, xmax, ymax]
    # total_bounds returns (minx, miny, maxx, maxy)
    bbox = list(large_parks_wgs84.total_bounds)
    
    # Prepare output dictionary
    output = {
        "count": int(count),
        "total_area_ha": total_area_ha,
        "bbox_wgs84": [float(coord) for coord in bbox]
    }
    
    # Write to json
    with open('parks_summary.json', 'w') as f:
        json.dump(output, f, indent=4)
    
    print(f"Processed {count} parks.")
    print(f"Total area: {total_area_ha:.2f} ha")
    print(f"BBox: {output['bbox_wgs84']}")

if __name__ == "__main__":
    main()

Wrote /work/solve.py (1462 chars).
1:41
Bash python solve.py 
Processed 42 parks.
Total area: 519.16 ha
BBox: [-0.19932378751294652, 51.49147199538401, -0.08248178747996572, 51.54706589542573]
1:45
Bash ls -l parks_summary.json 
-rw-r--r-- 1 agent agent 199 Jun 17 20:23 parks_summary.json
1:47
Read parks_summary.json 
     1	{
     2	    "count": 42,
     3	    "total_area_ha": 519.1620805430861,
     4	    "bbox_wgs84": [
     5	        -0.19932378751294652,
     6	        51.49147199538401,
     7	        -0.08248178747996572,
     8	        51.54706589542573
     9	    ]
    10	}
2:00