dd-l1-london-parks-count

Status: done
Score: 1.000
Duration: 0:40
Cost: 0.11¢
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 data to understand what we're working with. I'll look for the london_parks file in the working directory.
0:05
Bash ls /work/ 
london_parks.fgb
0:08 
Let me examine the data first to understand the structure.
0:08
Bash cd /work && python3 -c " 
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('london_parks.fgb')
print(gdf.columns.tolist())
print(gdf.head())
print(gdf.crs)
"
['id', 'name', 'class', 'geometry']
                                     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]
EPSG:27700
0:16 
The data is in EPSG:27700 (British National Grid), which uses meters as units. So I can calculate area in square meters, convert to hectares (1 ha = 10,000 m²), filter those >= 1 ha, calculate total area, and get the bounding box in WGS84 (EPSG:4326).
0:16
Bash cd /work && python3 -c " 
cd /work && python3 -c "
import geopandas as gpd
import json

gdf = gpd.read_file('london_parks.fgb')

# Area in hectares
gdf['area_ha'] = gdf.geometry.area / 10000.0

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

count = len(subset)
total_area_ha = round(subset['area_ha'].sum(), 4)

# Get bounding box in the original CRS, then convert to WGS84
bounds = subset.total_bounds  # [xmin, ymin, xmax, ymax] in EPSG:27700
import pyproj
from shapely.geometry import box

bng_box = box(*bounds)
bng_to_wgs = pyproj.Transformer.from_crs('EPSG:27700', 'EPSG:4326', always_xy=True)
wgs_box = bng_to_wgs.transform(bng_box)
# wgs_box is a list of coords; get bbox
minx = min(p[0] for p in wgs_box[0])
miny = min(p[1] for p in wgs_box[0])
maxx = max(p[0] for p in wgs_box[0])
maxy = max(p[1] for p in wgs_box[0])
bbox_wgs84 = [round(minx, 6), round(miny, 6), round(maxx, 6), round(maxy, 6)]

result = {
    'count': count,
    'total_area_ha': total_area_ha,
    'bbox_wgs84': bbox_wgs84
}

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

print(json.dumps(result, indent=2))
"
Traceback (most recent call last):
  File "<string>", line 23, in <module>
    wgs_box = bng_to_wgs.transform(bng_box)
TypeError: Transformer.transform() missing 1 required positional argument: 'yy'
0:25 
The issue is that `transform` works differently with shapely geometries. I should use the `transform` method with the geometry directly, or use the `pyproj.Transformer.transform` with x, y coordinates. Let me fix this.

Actually, for shapely geometries, I should use `pyproj.Transformer.transform` with the geometry as the third argument (after x, y). Or better, I can use the `bounds` approach with `total_bounds` and transform the corners.

Let me use `geopandas` to reproject the subset to WGS84 and then get the total_bounds.
0:25
Bash cd /work && python3 -c " 
cd /work && python3 -c "
import geopandas as gpd
import json

gdf = gpd.read_file('london_parks.fgb')

# Area in hectares (in the native CRS, EPSG:27700)
gdf['area_ha'] = gdf.geometry.area / 10000.0

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

count = len(subset)
total_area_ha = round(subset['area_ha'].sum(), 4)

# Reproject to WGS84 and get total bounds
subset_wgs84 = subset.to_crs('EPSG:4326')
bounds = subset_wgs84.total_bounds  # [xmin, ymin, xmax, ymax]

bbox_wgs84 = [round(bounds[0], 6), round(bounds[1], 6), round(bounds[2], 6), round(bounds[3], 6)]

result = {
    'count': count,
    'total_area_ha': total_area_ha,
    'bbox_wgs84': bbox_wgs84
}

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

print(json.dumps(result, indent=2))
"
{
  "count": 42,
  "total_area_ha": 519.1621,
  "bbox_wgs84": [
    -0.199324,
    51.491472,
    -0.082482,
    51.547066
  ]
}
0:28 
Let me verify the output file was created correctly.
0:28
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	}
0:33 
The output looks good. Let me verify the results make sense - 42 parks of 1 hectare or more in inner London, total area ~519 hectares, and the bbox covers roughly central London (around -0.199 to -0.082 longitude, 51.491 to 51.547 latitude), which seems reasonable.

Let me confirm by checking a few more details.
0:33
Bash cd /work && python3 -c " 
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('london_parks.fgb')
gdf['area_ha'] = gdf.geometry.area / 10000.0
print('Total parks:', len(gdf))
print('Parks >= 1 ha:', (gdf['area_ha'] >= 1.0).sum())
print('Min area (ha):', gdf['area_ha'].min())
print('Max area (ha):', gdf['area_ha'].max())
print('Sum area >=1 ha:', gdf[gdf['area_ha'] >= 1.0]['area_ha'].sum())
"
Total parks: 317
Parks >= 1 ha: 42
Min area (ha): 0.0064354948284017
Max area (ha): 138.9197437430728
Sum area >=1 ha: 519.1620805430861
0:38 
The numbers look consistent. Let me verify the output file is correctly formatted.
0:38