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).
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:08
Let me start by finding the London parks data file and understanding its structure.
0:08
Bash
find /work -type f -name "*.gpkg" -o -name "*.geojson" -o -name "*.shp" -o -name...
/work/london_parks.fgb
0:08
Bash
find /work -type f -name "*park*" -o -name "*london*" 2>/dev/null | head -30
/work/london_parks.fgb
0:20
Let's examine the file to understand its structure.
0:20
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('london_parks.fgb')
print(gdf.head())
print()
print('Columns:', gdf.columns.tolist())
print('CRS:', gdf.crs)
print('Shape:', gdf.shape)
print()
print('Dtypes:')
print(gdf.dtypes)
"
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] Columns: ['id', 'name', 'class', 'geometry'] CRS: EPSG:27700 Shape: (317, 4) Dtypes: id str name str class str geometry geometry dtype: object
0:34
The data is in EPSG:27700 (British National Grid, meters). I need to: 1. Calculate area in hectares for each park (in EPSG:27700 which uses meters - so area in m², divide by 10,000 to get hectares) 2. Filter those with area >= 1 hectare 3. Count them 4. Sum their areas in hectares 5. Compute the bounding box in WGS84 (EPSG:4326) for the filtered subset Let me write a Python script to do this.
0:34
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
import json
# Read the parks
gdf = gpd.read_file('london_parks.fgb')
# Calculate area in hectares (CRS is EPSG:27700, units are meters)
gdf['area_ha'] = gdf.geometry.area / 10_000
# Filter to parks >= 1 hectare
subset = gdf[gdf['area_ha'] >= 1.0].copy()
print('Total parks:', len(gdf))
print('Parks >= 1 ha:', len(subset))
print('Combined area (ha):', subset['area_ha'].sum())
# Get bounding box in original CRS then reproject to WGS84
# Or reproject the whole subset and get total_bounds
subset_wgs84 = subset.to_crs('EPSG:4326')
bounds = subset_wgs84.total_bounds # [xmin, ymin, xmax, ymax]
print('BBox WGS84:', bounds.tolist())
result = {
'count': int(len(subset)),
'total_area_ha': round(float(subset['area_ha'].sum()), 2),
'bbox_wgs84': [round(float(bounds[0]), 6),
round(float(bounds[1]), 6),
round(float(bounds[2]), 6),
round(float(bounds[3]), 6)]
}
with open('parks_summary.json', 'w') as f:
json.dump(result, f, indent=2)
print()
print(json.dumps(result, indent=2))
"
Total parks: 317
Parks >= 1 ha: 42
Combined area (ha): 519.1620805430861
BBox WGS84: [-0.19932378751294652, 51.49147199538401, -0.08248178747996572, 51.54706589542573]
{
"count": 42,
"total_area_ha": 519.16,
"bbox_wgs84": [
-0.199324,
51.491472,
-0.082482,
51.547066
]
}
0:44
The results look good. Let me verify the output file was created correctly.
0:44
Read
/work/parks_summary.json
1 {
2 "count": 42,
3 "total_area_ha": 519.16,
4 "bbox_wgs84": [
5 -0.199324,
6 51.491472,
7 -0.082482,
8 51.547066
9 ]
10 }
0:50
The output looks good. Let me summarize the results for the user.
0:50