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"
}
0:14
Let me start by reading the input file to understand the data structure.
0:14
Bash
ls /work/
london_parks.fgb
0:18
Let me look at the data first to understand its structure, CRS, and attributes.
0:18
Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
gdf = gpd.read_file('london_parks.fgb')
print('CRS:', gdf.crs)
print('Columns:', gdf.columns.tolist())
print('Shape:', gdf.shape)
print(gdf.head(3))
print(gdf.dtypes)
"
CRS: EPSG:27700
Columns: ['id', 'name', 'class', 'geometry']
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
0:26
Let me understand the data: - CRS: EPSG:27700 (British National Grid, metric) - 317 parks - Need to find parks >= 1 hectare (10000 m²) Let me compute the area, filter, get the count, total area in hectares, and bounding box in WGS84.
0:26
Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
import json
gdf = gpd.read_file('london_parks.fgb')
# Area is in CRS units (metres) since CRS is EPSG:27700
gdf['area_m2'] = gdf.geometry.area
# Filter >= 1 hectare (10000 m²)
big = gdf[gdf['area_m2'] >= 10000.0].copy()
print('Count >= 1 ha:', len(big))
# Total area in hectares
total_area_ha = big['area_m2'].sum() / 10000.0
print('Total area (ha):', total_area_ha)
# Bounding box in WGS84
big_wgs84 = big.to_crs('EPSG:4326')
xmin, ymin, xmax, ymax = big_wgs84.total_bounds
print(f'bbox: [{xmin}, {ymin}, {xmax}, {ymax}]')
"
Count >= 1 ha: 42 Total area (ha): 519.1620805430861 bbox: [-0.19932378751294652, 51.49147199538401, -0.08248178747996572, 51.54706589542573]
0:31
Good, I have the numbers. Let me write the JSON file now.
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Write
/work/parks_summary.json
content · 5 lines · json
{
"count": 42,
"total_area_ha": 519.1620805430861,
"bbox_wgs84": [-0.19932378751294652, 51.49147199538401, -0.08248178747996572, 51.54706589542573]
}Wrote /work/parks_summary.json (155 chars).
0:36
Let me verify the output file exists and is valid JSON.
0:36
Bash
cd /work && python -c "
cd /work && python -c "
import json
with open('parks_summary.json') as f:
d = json.load(f)
print(d)
print('Count:', d['count'])
print('Total area ha:', d['total_area_ha'])
print('Bbox:', d['bbox_wgs84'])
print('Bbox coords reasonable for London?',
-0.2 <= d['bbox_wgs84'][0] <= 0.1 and
51.4 <= d['bbox_wgs84'][1] <= 51.6 and
-0.2 <= d['bbox_wgs84'][2] <= 0.1 and
51.4 <= d['bbox_wgs84'][3] <= 51.6)
"
{'count': 42, 'total_area_ha': 519.1620805430861, 'bbox_wgs84': [-0.19932378751294652, 51.49147199538401, -0.08248178747996572, 51.54706589542573]}
Count: 42
Total area ha: 519.1620805430861
Bbox: [-0.19932378751294652, 51.49147199538401, -0.08248178747996572, 51.54706589542573]
Bbox coords reasonable for London? True
0:44
Everything checks out: 1. **count**: 42 parks ≥ 1 hectare 2. **total_area_ha**: 519.16 ha combined 3. **bbox_wgs84**: all coordinates are in ~-0.2 to -0.08 longitude and ~51.49 to 51.55 latitude — perfectly within inner London on a WGS84 map. The file `parks_summary.json` has been created and the sanity check passes.
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