dd-l1-london-parks-count

Status: done
Score: 100
Duration: 0:58
Cost: 0.18¢
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"
}
0:06
Bash ls -F 
ls -F

london_parks.fgb
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Write /work/inspect_data.py
content · 10 lines · py 
import geopandas as gpd

# Load the data
gdf = gpd.read_file('london_parks.fgb')

# Print first few rows and info to understand the structure
print(gdf.head())
print(gdf.info())
print(gdf.crs)

Wrote /work/inspect_data.py (193 chars).
0:17
Bash python /work/inspect_data.py 
python /work/inspect_data.py

                                     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]
<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 317 entries, 0 to 316
Data columns (total 4 columns):
 #   Column    Non-Null Count  Dtype   
---  ------    --------------  -----   
 0   id        317 non-null    str     
 1   name      317 non-null    str     
 2   class     317 non-null    str     
 3   geometry  317 non-null    geometry
dtypes: geometry(1), str(3)
memory usage: 26.5 KB
None
EPSG:27700
0:32
Write /work/solve.py
content · 44 lines · py 
import geopandas as gpd
import json

# Load the data
gdf = gpd.read_file('london_parks.fgb')

# The current CRS is EPSG:27700 (British National Grid)
# Area in EPSG:27700 is in square meters.
# 1 hectare = 10,000 square meters.

# Calculate area in square meters
gdf['area_m2'] = gdf.geometry.area

# Filter parks that are 1 hectare or more
subset = gdf[gdf['area_m2'] >= 10000].copy()

# Count of such parks
count = len(subset)

# Total area in hectares
total_area_ha = subset['area_m2'].sum() / 10000.0

# Bounding box in WGS84 (EPSG:4326)
if not subset.empty:
    # Reproject to WGS84
    subset_wgs84 = subset.to_crs(epsg=4326)
    bbox = subset_wgs84.total_bounds # returns [xmin, ymin, xmax, ymax]
else:
    bbox = [0.0, 0.0, 0.0, 0.0]

# Prepare the output dictionary
result = {
    "count": int(count),
    "total_area_ha": float(total_area_ha),
    "bbox_wgs84": [float(x) for x in bbox]
}

# Write to JSON file
with open('parks_summary.json', 'w') as f:
    json.dump(result, f, indent=4)

print("Summary created successfully.")
print(result)

Wrote /work/solve.py (1053 chars).
0:38
Bash python /work/solve.py 
python /work/solve.py

Summary created successfully.
{'count': 42, 'total_area_ha': 519.1620805430861, 'bbox_wgs84': [-0.19932378751294652, 51.49147199538401, -0.08248178747996572, 51.54706589542573]}
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Bash cat parks_summary.json 
cat parks_summary.json

{
    "count": 42,
    "total_area_ha": 519.1620805430861,
    "bbox_wgs84": [
        -0.19932378751294652,
        51.49147199538401,
        -0.08248178747996572,
        51.54706589542573
    ]
}
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