dd-l1-london-parks-count

Status: done
Score: 0.619
Duration: 2:51
Cost: 0.12¢
Model: deepseek/deepseek-v4-flash

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": "deepseek/deepseek-v4-flash",
  "base_url": "https://openrouter.ai/api/v1"
}
0:05 
Let me start by examining the input file to understand its structure.
0:05
Bash ls /work/ 
london_parks.fgb
0:11 
Let me first inspect the file to understand its structure, CRS, and attributes.
0:11
Bash cd /work && python -c " 
cd /work && python -c "
import geopandas as gpd
gdf = gpd.read_file('london_parks.fgb')
print('Columns:', gdf.columns.tolist())
print('CRS:', gdf.crs)
print('Shape:', gdf.shape)
print(gdf.head(3))
"
Columns: ['id', 'name', 'class', 'geometry']
CRS: EPSG:27700
Shape: (317, 4)
                                     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 rows x 4 columns]
0:31 
The data is in EPSG:27700 (British National Grid, meters). I need to:

1. Calculate area in hectares for each park (1 ha = 10,000 m²)
2. Filter to parks >= 1 hectare
3. Count them, sum their areas
4. Get the bounding box in WGS84 (EPSG:4326)

Let me write the script.
0:31
Write /work/solve.py
content · 46 lines · py 
import geopandas as gpd
import json

# Read the parks
gdf = gpd.read_file("/work/london_parks.fgb")
print(f"Original CRS: {gdf.crs}")
print(f"Total parks: {len(gdf)}")

# Calculate area in hectares (1 ha = 10 000 m²)
gdf["area_ha"] = gdf.geometry.area / 10_000

# Filter to parks >= 1 hectare
large = gdf[gdf["area_ha"] >= 1.0].copy()
print(f"Parks >= 1 ha: {len(large)}")

# Count and total area
count = len(large)
total_area_ha = round(float(large["area_ha"].sum()), 4)
print(f"Combined area (ha): {total_area_ha}")

# Get bounding box in the native CRS, then reproject to WGS84
# Use the unary_union envelope to get a single bbox
bounds = large.total_bounds  # [minx, miny, maxx, maxy] in native CRS (EPSG:27700)
print(f"Bounds in EPSG:27700: {bounds}")

# Create a polygon from the bounding box, reproject to WGS84
from shapely.geometry import box
bbox_poly = box(*bounds)
bbox_gdf = gpd.GeoDataFrame(geometry=[bbox_poly], crs="EPSG:27700")
bbox_wgs84 = bbox_gdf.to_crs("EPSG:4326")
bbox_coords = list(bbox_wgs84.total_bounds)  # [xmin, ymin, xmax, ymax]
# Round to reasonable precision
bbox_wgs84_arr = [round(v, 6) for v in bbox_coords]
print(f"Bounds in WGS84: {bbox_wgs84_arr}")

# Build output
result = {
    "count": count,
    "total_area_ha": total_area_ha,
    "bbox_wgs84": bbox_wgs84_arr
}

with open("/work/parks_summary.json", "w") as f:
    json.dump(result, f, indent=2)

print("Written /work/parks_summary.json")
Wrote /work/solve.py (1432 chars).
0:33 
Let me run the script.
0:33
Bash cd /work && python solve.py 
Original CRS: EPSG:27700
Total parks: 317
Parks >= 1 ha: 42
Combined area (ha): 519.1621
Bounds in EPSG:27700: [524999.05820636 178637.28885445 533193.517188   184854.56805085]
Bounds in WGS84: [np.float64(-0.200851), np.float64(51.491002), np.float64(-0.080535), np.float64(51.548746)]
Written /work/parks_summary.json
2:41 
The bbox has numpy float64 values, which aren't JSON serializable natively. Let me check if the json.dump worked correctly... It printed "Written" so it succeeded. But let me verify the output file to make sure it's valid JSON with plain float values.
2:41
Read /work/parks_summary.json 
     1	{
     2	  "count": 42,
     3	  "total_area_ha": 519.1621,
     4	  "bbox_wgs84": [
     5	    -0.200851,
     6	    51.491002,
     7	    -0.080535,
     8	    51.548746
     9	  ]
    10	}
2:46 
The output looks good. Let me verify the coordinate magnitudes are correct for WGS84 - they're around -0.2 to -0.08 longitude and 51.49 to 51.55 latitude, which is correct for inner London. The values are floats, not numpy types anymore since we used `round()` on Python floats. Let me double-check the JSON has proper native float types.

Actually, looking at the code: `bbox_wgs84_arr = [round(v, 6) for v in bbox_coords]` - bbox_coords comes from `list(bbox_wgs84.total_bounds)` which returns numpy floats. `round()` on numpy floats returns numpy floats. So the JSON will have numpy float64 values. Let me check if this is an issue.

The JSON file looks fine - it shows -0.200851, 51.491002 etc. as plain numbers. So json.dump is serializing them fine.

Let me just verify the file exists and sanity-check the output.
2:46
2:46
Bash cd /work && python -c " 
cd /work && python -c "
import json
with open('parks_summary.json') as f:
    d = json.load(f)
print('Type check:', type(d['count']), type(d['total_area_ha']), type(d['bbox_wgs84'][0]))
print('All values:', d)
# Sanity: lat 51.49-51.55, lon -0.20 to -0.08 are inner London
assert -1 < d['bbox_wgs84'][0] < 1, 'lon min out of range'
assert 50 < d['bbox_wgs84'][1] < 52, 'lat min out of range'
assert -1 < d['bbox_wgs84'][2] < 1, 'lon max out of range'
assert 50 < d['bbox_wgs84'][3] < 52, 'lat max out of range'
print('Coordinate sanity check passed')
"
Type check: <class 'int'> <class 'float'> <class 'float'>
All values: {'count': 42, 'total_area_ha': 519.1621, 'bbox_wgs84': [-0.200851, 51.491002, -0.080535, 51.548746]}
Coordinate sanity check passed
2:50 
All checks pass. Let me provide the summary to the user.
2:50