dd-l1-london-parks-count

Status: done
Score: 1.000
Duration: 0:44
Cost: 0.13¢
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"
}
0:06 
Let me start by examining the input file to understand the data structure.
0:06
Bash ls /work/ 
london_parks.fgb
0:14 
The input file is `london_parks.fgb` (FlatGeobuf). Let me first inspect it to understand its schema and CRS.
0:14
Bash python3 -c " 
python3 -c "
import geopandas as gpd
gdf = gpd.read_file('/work/london_parks.fgb')
print('Columns:', gdf.columns.tolist())
print('CRS:', gdf.crs)
print('Shape:', gdf.shape)
print('---')
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]
0:30 
The data is in EPSG:27700 (British National Grid). I need to:

1. Calculate area for each park (already in metric CRS, so area will be in sq meters)
2. Filter parks with area >= 1 hectare (1 hectare = 10,000 sq meters)
3. Sum the areas for the filtered subset
4. Convert those parks' geometries to WGS84 (EPSG:4326) and compute the bounding box

Let me write the script.
0:30
Bash python3 -c " 
python3 -c "
import geopandas as gpd
gdf = gpd.read_file('/work/london_parks.fgb')
# area in sq m from the geometry
gdf['area_ha'] = gdf.geometry.area / 10000
print('Min area (ha):', gdf['area_ha'].min())
print('Max area (ha):', gdf['area_ha'].max())
print('Rows >= 1 ha:', (gdf['area_ha'] >= 1).sum())
"
Min area (ha): 0.0064354948284017
Max area (ha): 138.9197437430728
Rows >= 1 ha: 42
0:35 
Now I need to:
1. Filter to parks >= 1 ha
2. Compute combined area in hectares
3. Compute the bounding box in WGS84 (EPSG:4326)

Let me write a proper script.
0:35
Write /work/solve.py
content · 36 lines · py 
import geopandas as gpd
import json

# Read the parks
gdf = gpd.read_file('/work/london_parks.fgb')

# Already in EPSG:27700 (OSGB), good for area calculation
gdf['area_ha'] = gdf.geometry.area / 10000

# Filter to >= 1 ha
large = gdf[gdf['area_ha'] >= 1.0].copy()

count = len(large)
total_area_ha = large['area_ha'].sum()

# Compute the combined bounding box in WGS84
# Dissolve all geometries into one multi-polygon, then get bounds
# Alternatively compute bounds per-feature and union — simpler: dissolve
large_dissolved = large.dissolve()
large_dissolved = large_dissolved.set_crs('EPSG:27700')
large_dissolved_wgs84 = large_dissolved.to_crs('EPSG:4326')
bounds = large_dissolved_wgs84.total_bounds  # [xmin, ymin, xmax, ymax]

# Round to 6 decimal places (~0.1 m precision at equator)
bbox_wgs84 = [round(v, 6) for v in bounds.tolist()]

summary = {
    "count": count,
    "total_area_ha": round(total_area_ha, 4),
    "bbox_wgs84": bbox_wgs84
}

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

print(summary)
Wrote /work/solve.py (1054 chars).
0:36 
Let me run the script and verify the output.
0:36
Bash cd /work && python solve.py 
{'count': 42, 'total_area_ha': np.float64(519.1621), 'bbox_wgs84': [-0.199324, 51.491472, -0.082482, 51.547066]}
0:39 
The output looks good. The count is 42, total area ~519.16 ha, and the bbox is in WGS84 (decimal degrees) — xmin ≈ -0.199, ymin ≈ 51.49, xmax ≈ -0.082, ymax ≈ 51.55, which are sensible coordinates for inner London.

Let me check the JSON file output and verify it's valid.
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Read /work/parks_summary.json 
     1	{
     2	  "count": 42,
     3	  "total_area_ha": 519.1621,
     4	  "bbox_wgs84": [
     5	    -0.199324,
     6	    51.491472,
     7	    -0.082482,
     8	    51.547066
     9	  ]
    10	}
0:43 
The output looks good. Let me sanity-check the coordinate magnitudes. The bbox in WGS84 shows xmin=-0.199, ymin=51.491, xmax=-0.082, ymax=51.547 — that's perfectly within the expected range for inner London (degrees). The total_area_ha of 519.16 for 42 parks >= 1 ha seems reasonable for inner London parks.

However, I notice there's a potential issue: I used `large_dissolved.total_bounds` which gives the bounds of the dissolved multipolygon. But actually, `total_bounds` is a property of GeoDataFrame that gives the min/max across all individual geometries, which should work fine. Let me check if `.total_bounds` after dissolve gives the correct bbox of the dissolved geometry.

Actually, `total_bounds` on a dissolved GeoDataFrame with a single row should give the bounds of that single multipolygon. That's correct.

The values look right. Let me verify the file exists and is correct.
0:43