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"
}
0:12
Let me start by examining the input file to understand its structure and contents.
0:12
Read
/work/london_parks
file does not exist: /work/london_parks
0:26
The file doesn't exist at that path. Let me check what files are in the working directory.
0:26
Bash
ls -la /work/
total 192 drwxr-xr-x 1 agent agent 32 Jun 16 17:30 . drwxr-xr-x 1 root root 14 Jun 16 17:30 .. -rw-r--r-- 1 agent agent 193504 Jun 16 17:30 london_parks.fgb
0:29
The file is `london_parks.fgb` (FlatGeobuf). Let me inspect it with geopandas to understand its structure.
0:29
Bash
cd /work && python3 -c "
cd /work && python3 -c "
import geopandas as gpd
import pandas as pd
gdf = gpd.read_file('london_parks.fgb')
print('Columns:', gdf.columns.tolist())
print('CRS:', gdf.crs)
print('Shape:', gdf.shape)
print('---')
print(gdf.head(3).to_string())
print('---')
print(gdf.dtypes)
"
Columns: ['id', 'name', 'class', 'geometry']
CRS: EPSG:27700
Shape: (317, 4)
---
id name class geometry
0 2b746b97-b0ed-35cc-8eb2-28a64821d7d8 Wansey Gardens park MULTIPOLYGON (((532381.849 178719.026, 532391.082 178704.822, 532400.509 178709.875, 532396.166 178718.475, 532415.114 178727.904, 532426.162 178732.331, 532422.81 178738.064, 532402.164 178730.093, 532379.828 178721.132, 532381.849 178719.026)))
1 669c73cb-2314-3f8b-9b24-14b7db998247 Victory Community Park park MULTIPOLYGON (((532435.728 178838.472, 532437.73 178836.544, 532501.094 178851.236, 532497.67 178865.902, 532549.25 178876.705, 532559.173 178833.109, 532561.694 178823.816, 532584.059 178835.36, 532581.44 178851.338, 532576.74 178880.026, 532573.799 178894.538, 532573.576 178895.633, 532537.045 178890.297, 532530.846 178890.936, 532516.122 178889.24, 532475.8 178884.127, 532428.046 178874.204, 532435.728 178838.472)))
2 bcdbe219-4769-3d9a-bec6-ba44b2f4301f park MULTIPOLYGON (((532657.956 178707.481, 532664.331 178691.59, 532674.048 178684.71, 532689.667 178680.355, 532684.405 178697.522, 532697.493 178704.673, 532688.581 178721.956, 532657.956 178707.481)))
---
id str
name str
class str
geometry geometry
dtype: object
0:38
The data is in EPSG:27700 (British National Grid, units are meters). Let me compute: 1. Area of each park in square meters (from geometry using the native CRS) 2. Filter parks with area >= 1 hectare (10,000 m²) 3. Compute combined area in hectares 4. Compute bounding box of the filtered subset in WGS84 (EPSG:4326) Let me write a Python script.
0:38
Write
/work/solve.py
content · 31 lines · py
import geopandas as gpd
import json
# Read the parks file
gdf = gpd.read_file('/work/london_parks.fgb')
# CRS is EPSG:27700 (British National Grid, metres)
# Compute area in square metres, convert to hectares
gdf['area_ha'] = gdf.geometry.area / 10_000
# Filter parks >= 1 hectare
subset = gdf[gdf['area_ha'] >= 1.0].copy()
count = len(subset)
total_area_ha = round(subset['area_ha'].sum(), 2)
# Reproject the subset to WGS84 for the bounding box
subset_wgs84 = subset.to_crs('EPSG:4326')
bbox = subset_wgs84.total_bounds # [xmin, ymin, xmax, ymax]
bbox_rounded = [round(v, 6) for v in bbox.tolist()]
# Build output
out = {
"count": count,
"total_area_ha": total_area_ha,
"bbox_wgs84": bbox_rounded
}
with open('/work/parks_summary.json', 'w') as f:
json.dump(out, f, indent=2)
print(json.dumps(out, indent=2))Wrote /work/solve.py (834 chars).
0:39
Let me run the script.
0:39
Bash
cd /work && python solve.py
{
"count": 42,
"total_area_ha": 519.16,
"bbox_wgs84": [
-0.199324,
51.491472,
-0.082482,
51.547066
]
}
0:43
Let me verify the output by reading it and also check the file exists.
0:43
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 }
0:43
Bash
ls -la /work/parks_summary.json
-rw-r--r-- 1 agent agent 126 Jun 16 17:30 /work/parks_summary.json
0:51
Wait, the bbox values in the file are slightly different from what was printed. Let me check. The output said:
```
"bbox_wgs84": [
-0.199324,
51.491472,
-0.082482,
51.547066
]
```
But earlier the print statement showed:
```
"bbox_wgs84": [
-0.199324,
51.491472, <- this is ymin (lat min)
-0.082482,
51.547066 <- this is ymax (lat max)
]
```
Wait, the print output said:
```
"bbox_wgs84": [
-0.199324,
51.491472,
-0.082482,
51.547066
]
```
Hmm, actually looking at the print statement output more carefully:
```
{
"count": 42,
"total_area_ha": 519.16,
"bbox_wgs84": [
-0.199324,
51.491472,
-0.082482,
51.547066
]
}
```
Wait, the print output showed 51.491472 for ymin but earlier I thought it was 51.499 something. Let me recheck...
The print output: `51.491472` - yes that's the ymin.
So the file content matches the print output. Both show ymin=51.491472. That's fine.
Let me sanity-check the coordinate magnitudes:
- xmin = -0.199324 (degrees, ~West London)
- ymin = 51.491472 (degrees, ~London latitude)
- xmax = -0.082482 (degrees, ~East London)
- ymax = 51.547066 (degrees, ~London latitude)
These look reasonable for inner London WGS84 coordinates.
The count and area seem reasonable too. 42 parks >= 1 ha with total area 519.16 ha seems plausible for inner London.
Everything checks out.
0:51