spa-l1-paris-amenity-within

Status: done
Score: 1.000
Duration: 2:12
Cost: 0.35¢
Model: deepseek/deepseek-v4-flash

Map (reference ← swipe → agent)

0:00
I'm putting together a neighbourhood demographic crosswalk for the INSEE census team and need each amenity tagged with the Paris arrondissement it falls inside. Can you join `amenities` to `arrondissements` and write the result to `amenity_to_arrondissement.csv` with one row per amenity? The columns are `osm_id`, `amenity_class`, `arrondissement_number` (integer), and `arrondissement_name` (string).
0:00
System info 
{
  "adapter": "openrouter",
  "model": "deepseek/deepseek-v4-flash",
  "base_url": "https://openrouter.ai/api/v1"
}
0:13 
Let me start by reading the input files to understand their structure.
0:13
Bash ls /work/ 
paris_amenities.gpkg
0:28 
There's only one file visible: `paris_amenities.gpkg`. Let me check what layers are in it, and also look for the arrondissements file. It might be a separate layer in the GPKG, or maybe there's another file I'm not seeing.

Wait, the user mentioned `amenities` and `arrondissements` - both could be layers within the same GPKG. Let me explore.
0:28
Bash ls -la /work/ 
total 208
drwxr-xr-x 1 agent agent     40 Jun 16 20:11 .
drwxr-xr-x 1 root  root      14 Jun 16 20:11 ..
-rw-r--r-- 1 agent agent 212992 Jun 16 20:11 paris_amenities.gpkg
0:42 
The user mentions joining `amenities` to `arrondissements`. I see `paris_amenities.gpkg` in the work directory. But I don't see an arrondissements file. Let me explore what's in the GPKG file to understand the layers and data.
0:42
Bash python3 -c " 
python3 -c "
import fiona
# List layers in the GPKG
layers = fiona.listlayers('/work/paris_amenities.gpkg')
print('Layers:', layers)
"
Layers: ['amenities', 'arrondissements']
0:46 
Great, the GPKG contains two layers: `amenities` and `arrondissements`. Let me inspect their schemas and a few rows.
0:46
Bash python3 -c " 
python3 -c "
import fiona
with fiona.open('/work/paris_amenities.gpkg', layer='amenities') as src:
    print('CRS:', src.crs)
    print('Schema:', src.schema)
    for i, feat in enumerate(src):
        if i >= 3:
            break
        print(feat)
"
CRS: EPSG:2154
Schema: {'properties': {'osm_id': 'int', 'amenity_class': 'str', 'name': 'str'}, 'geometry': 'Point'}
fiona.Feature(geometry=fiona.Geometry(coordinates=(652773.5282945887, 6864033.346917884), type='Point'), id='1', properties=fiona.Properties(osm_id=9000000000, amenity_class='restaurant', name='Le Grand Amour Restaurant'))
fiona.Feature(geometry=fiona.Geometry(coordinates=(652322.9098378047, 6861774.142344695), type='Point'), id='2', properties=fiona.Properties(osm_id=9000000001, amenity_class='restaurant', name='Talego'))
fiona.Feature(geometry=fiona.Geometry(coordinates=(653028.1032193318, 6863582.050945236), type='Point'), id='3', properties=fiona.Properties(osm_id=9000000002, amenity_class='bakery', name='Mamiche'))
0:52 
Let me also look at the arrondissements layer to see its schema.
0:52
Bash python3 -c " 
python3 -c "
import fiona
with fiona.open('/work/paris_amenities.gpkg', layer='arrondissements') as src:
    print('CRS:', src.crs)
    print('Schema:', src.schema)
    for i, feat in enumerate(src):
        if i >= 3:
            break
        print(feat)
    print('Total features:', len(src))
"
CRS: EPSG:2154
Schema: {'properties': {'id': 'str', 'name': 'str'}, 'geometry': 'Polygon'}
fiona.Feature(geometry=fiona.Geometry(coordinates=[[(651737.8579509392, 6858104.126012336), ...]], type='Polygon'), id='1', properties=fiona.Properties(id='0cef9694-93f0-4acf-a44f-f54a01fce855', name='Paris 13e Arrondissement'))
fiona.Feature(geometry=fiona.Geometry(coordinates=[[(651390.1277230554, 6862244.402818555), ...]], type='Polygon'), id='2', properties=fiona.Properties(id='0e2667a3-715f-45ef-8fd5-7cdba7344a12', name='Paris 6e Arrondissement'))
fiona.Feature(geometry=fiona.Geometry(coordinates=[[(653870.9668014996, 6863517.544094685), ...]], type='Polygon'), id='3', properties=fiona.Properties(id='2747f137-3f7b-4ac4-b222-b4ac7acef256', name='Paris 11e Arrondissement'))
Total features: 20
0:58 
Let me understand the data:

1. **amenities** layer: Points with `osm_id` (int), `amenity_class` (str), `name` (str). CRS: EPSG:2154 (French Lambert-93).
2. **arrondissements** layer: Polygons with `id` (str) and `name` (str). CRS: EPSG:2154. They have names like "Paris 13e Arrondissement". There are 20 features (Paris has 20 arrondissements).

I need to:
- Do a spatial join: for each amenity point, find which arrondissement polygon it falls inside.
- Extract the arrondissement number from the name (e.g., "Paris 13e Arrondissement" → 13).
- Output columns: `osm_id`, `amenity_class`, `arrondissement_number` (integer), `arrondissement_name` (string).

Let me write a Python script using geopandas.
0:58
Read /work/paris_amenities.gpkg 
binary file: /work/paris_amenities.gpkg contains NUL bytes. Use a domain tool to inspect it (duckdb, geopandas, pyogrio, sqlite3, ...).
1:13 
Let me inspect the data more carefully. The arrondissements layer has `id` (uuid string) and `name` (e.g., "Paris 13e Arrondissement"). I need to parse the arrondissement number from the name. The amenity has `osm_id`, `amenity_class`, and geometry. I need to join by spatial intersection (point-in-polygon).

Let me write a Python script to do the spatial join and output the CSV.
1:13
Bash python3 -c " 
python3 -c "
import fiona
with fiona.open('/work/paris_amenities.gpkg', layer='arrondissements') as src:
    for feat in src:
        print(feat['properties'])
"
fiona.Properties(id='0cef9694-93f0-4acf-a44f-f54a01fce855', name='Paris 13e Arrondissement')
fiona.Properties(id='0e2667a3-715f-45ef-8fd5-7cdba7344a12', name='Paris 6e Arrondissement')
fiona.Properties(id='2747f137-3f7b-4ac4-b222-b4ac7acef256', name='Paris 11e Arrondissement')
fiona.Properties(id='32fbdc6a-f89d-4d02-ac87-6d0e4fc98969', name='Paris 3e Arrondissement')
fiona.Properties(id='366fc115-880d-47a0-90d8-95f28f275224', name='Paris 2e Arrondissement')
fiona.Properties(id='39c0e434-e46e-4ac9-b35a-100f804ede99', name='Paris 10e Arrondissement')
fiona.Properties(id='4687f7be-db17-41c3-a9d0-09afa5523060', name='Paris 20e Arrondissement')
fiona.Properties(id='4e7c04fe-3c0d-4401-b79d-ce3e2395e5fd', name='Paris 8e Arrondissement')
fiona.Properties(id='5134a455-e91f-4fdb-8638-c33ee7cb1383', name='Paris 19e Arrondissement')
fiona.Properties(id='5537e76e-52db-4342-938d-a91168f4e6a7', name='Paris 5e Arrondissement')
fiona.Properties(id='571cb253-40c3-4761-b663-d30deedf713f', name='Paris 17e Arrondissement')
fiona.Properties(id='75e0a62a-117b-46e6-8782-a4b225ce37e4', name='Paris 9e Arrondissement')
fiona.Properties(id='7caa32e9-d051-48f2-87ae-29d09104547e', name='Paris 16e Arrondissement')
fiona.Properties(id='818e7bcb-32e9-4567-919a-c44153254398', name='Paris 14e Arrondissement')
fiona.Properties(id='8a6c934a-8534-4e30-a9e5-e827de7a9891', name='Paris 12e Arrondissement')
fiona.Properties(id='98f4ac3a-fc4a-40de-b535-2ebfa6f4e202', name='Paris 4e Arrondissement')
fiona.Properties(id='dcc8aaa3-2adb-424f-a745-b9a23163f7c0', name='Paris 18e Arrondissement')
fiona.Properties(id='ea79b3d9-590d-450e-8da6-30cb1b6fcc66', name='Paris 7e Arrondissement')
fiona.Properties(id='ed5166f4-7111-4e42-bcbe-0630271cc71e', name='Paris 1er Arrondissement')
fiona.Properties(id='f22f8b89-a6ec-449f-bc9a-cf3c7644d0e0', name='Paris 15e Arrondissement')
1:20 
Now I understand the data:

**amenities** layer: Points with EPSG:2154 (Lambert-93, French projection). Properties: `osm_id` (int), `amenity_class` (str), `name` (str).

**arrondissements** layer: Polygons with EPSG:2154. Properties: `id` (str, UUID), `name` (str) — e.g. "Paris 13e Arrondissement", "Paris 1er Arrondissement".

I need to join them spatially (point-in-polygon) and output:
- `osm_id`
- `amenity_class`
- `arrondissement_number` (integer) — parsed from the name like 13 from "Paris 13e Arrondissement" or 1 from "Paris 1er Arrondissement"
- `arrondissement_name` (string) — the full name

Let me write a Python script using geopandas to do the spatial join.
1:20
1:20
Write /work/solve.py
content · 45 lines · py 
import geopandas as gpd
import re
import pandas as pd

# Read both layers (already in EPSG:2154)
amenities = gpd.read_file("/work/paris_amenities.gpkg", layer="amenities")
arrondissements = gpd.read_file("/work/paris_amenities.gpkg", layer="arrondissements")

print(f"Amenities: {len(amenities)} rows")
print(f"Arrondissements: {len(arrondissements)} rows")

# Spatial join: each amenity to the arrondissement it falls inside
joined = gpd.sjoin(
    amenities,
    arrondissements,
    how="left",
    predicate="within",
)

# Parse arrondissement number from the name field, e.g.
# "Paris 13e Arrondissement" → 13
# "Paris 1er Arrondissement" → 1
def parse_arrondissement_number(name):
    if pd.isna(name):
        return None
    # Match the first number in the string
    m = re.search(r"(\d+)", name)
    if m:
        return int(m.group(1))
    return None

joined["arrondissement_number"] = joined["name_right"].apply(parse_arrondissement_number)
joined["arrondissement_name"] = joined["name_right"]

# Build output table
result = joined[["osm_id", "amenity_class", "arrondissement_number", "arrondissement_name"]].copy()
result = result.sort_values(["osm_id", "arrondissement_number"]).reset_index(drop=True)

# Write CSV
result.to_csv("/work/amenity_to_arrondissement.csv", index=False)

print(f"\nOutput rows: {len(result)}")
print(result.head(10))
print("\nArrondissement coverage:")
print(result["arrondissement_number"].value_counts().sort_index())
Wrote /work/solve.py (1461 chars).
1:30 
Let me run the script and check the output.
1:30
Bash cd /work && python solve.py 
Amenities: 85 rows
Arrondissements: 20 rows

Output rows: 85
       osm_id amenity_class  arrondissement_number       arrondissement_name
0  9000000000    restaurant                     10  Paris 10e Arrondissement
1  9000000001    restaurant                      4   Paris 4e Arrondissement
2  9000000002        bakery                     10  Paris 10e Arrondissement
3  9000000003        bakery                     13  Paris 13e Arrondissement
4  9000000004    restaurant                      9   Paris 9e Arrondissement
5  9000000005    restaurant                     15  Paris 15e Arrondissement
6  9000000006    restaurant                     20  Paris 20e Arrondissement
7  9000000007          cafe                      6   Paris 6e Arrondissement
8  9000000008    restaurant                     18  Paris 18e Arrondissement
9  9000000009          cafe                      5   Paris 5e Arrondissement

Arrondissement coverage:
arrondissement_number
1      5
2      1
3      2
4      2
5      5
6      4
7      5
8      4
9      6
10     7
11     1
12     3
13     6
14     3
15    13
16     3
17     3
18     4
19     7
20     1
Name: count, dtype: int64
1:34 
The output looks good. Let me verify the CSV file was created and check the first few lines.
1:34
Bash head -5 /work/amenity_to_arrondissement.csv 
osm_id,amenity_class,arrondissement_number,arrondissement_name
9000000000,restaurant,10,Paris 10e Arrondissement
9000000001,restaurant,4,Paris 4e Arrondissement
9000000002,bakery,10,Paris 10e Arrondissement
9000000003,bakery,13,Paris 13e Arrondissement
1:34
Bash wc -l /work/amenity_to_arrondissement.csv 
86 /work/amenity_to_arrondissement.csv
2:02 
The output looks correct:

- 85 amenities + 1 header = 86 lines ✓
- Columns: `osm_id`, `amenity_class`, `arrondissement_number` (integer), `arrondissement_name` (string) ✓
- All 20 arrondissements are represented ✓
- The numbers look right (Paris has 20 arrondissements numbered 1-20) ✓
- CRS was EPSG:2154 (Lambert 93, metric) which is the correct French projection and the spatial join was done correctly ✓

Let me also verify a few more rows and check there are no NaN values in the critical columns.
2:02
2:02
Bash python3 -c " 
python3 -c "
import pandas as pd
df = pd.read_csv('/work/amenity_to_arrondissement.csv')
print('Shape:', df.shape)
print('Null counts:')
print(df.isnull().sum())
print()
print('dtypes:')
print(df.dtypes)
print()
print('All 20 arrondissements present:', df['arrondissement_number'].nunique() == 20)
print('All amenities joined:', df['osm_id'].nunique() == 85)
"
Shape: (85, 4)
Null counts:
osm_id                   0
amenity_class            0
arrondissement_number    0
arrondissement_name      0
dtype: int64

dtypes:
osm_id                   int64
amenity_class              str
arrondissement_number    int64
arrondissement_name        str
dtype: object

All 20 arrondissements present: True
All amenities joined: True
2:11 
Everything looks good. Let me verify the final file one more time.
2:11