dd-l1-london-parks-count
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": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:03
Let me start by exploring the working directory to find the london_parks data.
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Bash
ls /work/
london_parks.fgb
0:08
Let me explore the data first to understand its structure.
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Bash
cd /work && python3 -c "
cd /work && python3 -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))
print('---')
print(gdf.dtypes)
"
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]
---
id str
name str
class str
geometry geometry
dtype: object
0:17
The data is in EPSG:27700 (British National Grid), which uses meters. I need to: 1. Filter parks with area >= 1 hectare (1 hectare = 10,000 m²) 2. Calculate their combined area in hectares 3. Get the bounding box in WGS84 (EPSG:4326) Let me write a Python script to do this.
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Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
import json
gdf = gpd.read_file('london_parks.fgb')
# Calculate area in hectares (CRS is 27700 which is meters)
gdf['area_ha'] = gdf.geometry.area / 10000 # 1 ha = 10,000 m²
# Filter parks >= 1 ha
subset = gdf[gdf['area_ha'] >= 1].copy()
print(f'Total parks: {len(gdf)}')
print(f'Parks >= 1 ha: {len(subset)}')
print(f'Combined area (ha): {subset[\"area_ha\"].sum():.2f}')
# Compute bounding box in original CRS, then reproject to WGS84
# We can use total_bounds to get the bbox
bounds = subset.total_bounds # [minx, miny, maxx, maxy] in EPSG:27700
print(f'Bounds in 27700: {bounds}')
# Create a single geometry to reproject
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')
wgs_bounds = bbox_wgs84.total_bounds
print(f'Bounds in WGS84: {wgs_bounds.tolist()}')
result = {
'count': int(len(subset)),
'total_area_ha': round(float(subset['area_ha'].sum()), 2),
'bbox_wgs84': [round(float(wgs_bounds[0]), 6), round(float(wgs_bounds[1]), 6), round(float(wgs_bounds[2]), 6), round(float(wgs_bounds[3]), 6)]
}
print(json.dumps(result, indent=2))
with open('parks_summary.json', 'w') as f:
json.dump(result, f, indent=2)
"
Total parks: 317
Parks >= 1 ha: 42
Combined area (ha): 519.16
Bounds in 27700: [524999.05820636 178637.28885445 533193.517188 184854.56805085]
Bounds in WGS84: [-0.20085078891494373, 51.491002026277656, -0.08053542602197632, 51.54874587242965]
{
"count": 42,
"total_area_ha": 519.16,
"bbox_wgs84": [
-0.200851,
51.491002,
-0.080535,
51.548746
]
}
0:20
The script ran successfully. Let me verify the output file was written correctly.
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Read
/work/parks_summary.json
1 {
2 "count": 42,
3 "total_area_ha": 519.16,
4 "bbox_wgs84": [
5 -0.200851,
6 51.491002,
7 -0.080535,
8 51.548746
9 ]
10 }
0:24
The summary JSON file has been created successfully. Let me provide a clear answer.
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