spa-l1-paris-amenity-within
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).
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System info
{
"adapter": "openrouter",
"model": "google/gemma-4-26b-a4b-it",
"base_url": "https://openrouter.ai/api/v1"
}
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Bash
ls -F
paris_amenities.gpkg
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Bash
head -n 5 /work/paris_amenities.gpkg
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55�_tablegpkg_tile_matrix_setgpkg_tile_matrix_set CREATE TABLE gpkg_tile_matrix_set (table_name TEXT NOT NULL PRIMARY KEY,srs_id INTEGER NOT NULL,min_x DOUBLE NOT NULL,min_y DOUBLE NOT NULL,max_x DOUBLE NOT NULL,max_y DOUBLE NOT NULL,CONSTRAINT fk_gtms_table_name FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name),CONSTRAINT fk_gtms_srs FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys (srs_id))G [5 indexsqlite_autoindex_gpkg_tile_matrix_set_1gpkg_tile_matrix_set � 77� tablegpkg_geometry_columnsgpkg_geometry_columnsCREATE TABLE gpkg_geometry_columns (table_name TEXT NOT NULL,column_name TEXT NOT NULL,geometry_type_name TEXT NOT NULL,srs_id INTEGER NOT NULL,z TINYINT NOT NULL,m TINYINT NOT NULL,CONSTRAINT pk_geom_cols PRIMARY KEY (table_name, column_name),CONSTRAINT uk_gc_table_name UNIQUE (table_name),CONSTRAINT fk_gc_tn FOREIGN KEY (table_name) REFERENCES gpkg_contents(table_name),CONSTRAINT fk_gc_srs FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys (srs_id))I ]7 indexsqlite_autoindex_gpkg_geometry_columns_2gpkg_geometry_columns
I]7 indexsqlite_autoindex_gpkg_geometry_columns_1gpkg_geometry_columns �//�[tablegpkg_ogr_contentsgpkg_ogr_contentsCREATE TABLE gpkg_ogr_contents(table_name TEXT NOT NULL PRIMARY KEY,feature_count INTEGER DEFAULT NULL)AU/ indexsqlite_autoindex_gpkg_ogr_contents_1gpkg_ogr_contents� ''�wtablegpkg_contentsgpkg_contentsCREATE TABLE gpkg_contents (table_name TEXT NOT NULL PRIMARY KEY,data_type TEXT NOT NULL,identifier TEXT UNIQUE,description TEXT DEFAULT '',last_change DATETIME NOT NULL DEFAULT (strftime('%Y-%m-%dT%H:%M:%fZ','now')),min_x DOUBLE, min_y DOUBLE,max_x DOUBLE, max_y DOUBLE,srs_id INTEGER,CONSTRAINT fk_gc_r_srs_id FOREIGN KEY (srs_id) REFERENCES gpkg_spatial_ref_sys(srs_id))9M' indexsqlite_autoindex_gpkg_contents_2gpkg_contents9M' indexsqlite_autoindex_gpkg_contents_1gpkg_contents �55�)tablegpkg_spatial_ref_sysgpkg_spatial_ref_sysCREATE TABLE gpkg_spatial_ref_sys (srs_name TEXT NOT NULL,srs_id INTEGER NOT NULL PRIMARY KEY,organization TEXT NOT NULL,organization_coordsys_id INTEGER NOT NULL,definition TEXT NOT NULL,de )
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�h �L�j7 �_ RGF93 v1 / Lambert-93EPSGjPROJCS["RGF93 v1 / Lambert-93",GEOGCS["RGF93 v1",DATUM["Reseau_Geodesique_Francais_1993_v1",SPHEROID["GRS 1980",6378137,298.257222101,AUTHORITY["EPSG","7019"]],AUTHORITY["EPSG","6171"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4171"]],PROJECTION["Lambert_Conformal_Conic_2SP"],PARAMETER["latitude_of_origin",46.5],PARAMETER["central_meridian",3],PARAMETER["standard_parallel_1",49],PARAMETER["standard_parallel_2",44],PARAMETER["false_easting",700000],PARAMETER["false_northing",6600000],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","2154"]]\ = mUndefined geographic SRSNONEundefinedundefined geographic coordinate reference system[���������; kUndefined Cartesian SRSNONE�undefinedundefined Cartesian coordinate reference system��f + �i� WGS 84 geodeticEPSG�GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AXIS["Latitude",NORTH],AXIS["Longitude",EAST],AUTHORITY["EPSG","4326"]]longitude/latitude coordinates in decimal degrees on the WGS 84 spheroid
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=arrondissementsfeaturesarrondissements2026-05-07T18:35:10.066ZA#��(�(�AZ(��}U�A$,�
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Bash
python3 -c "import geopandas as gpd; print(gpd.read_file('/work/paris_amenities....
osm_id ... geometry 0 9000000000 ... POINT (652773.528 6864033.347) 1 9000000001 ... POINT (652322.91 6861774.142) 2 9000000002 ... POINT (653028.103 6863582.051) 3 9000000003 ... POINT (653087.577 6858190.655) 4 9000000004 ... POINT (650836.831 6863603.61) [5 rows x 4 columns]
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Bash
python3 -c "import geopandas as gpd; print(gpd.read_file('/work/paris_amenities....
Index(['id', 'name', 'geometry'], dtype='str')
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Bash
python3 -c "import geopandas as gpd; df = gpd.read_file('/work/paris_amenities.g...
id ... geometry 0 0cef9694-93f0-4acf-a44f-f54a01fce855 ... POLYGON ((651737.858 6858104.126, 651824.872 6... 1 0e2667a3-715f-45ef-8fd5-7cdba7344a12 ... POLYGON ((651390.128 6862244.403, 651327.017 6... 2 2747f137-3f7b-4ac4-b222-b4ac7acef256 ... POLYGON ((653870.967 6863517.544, 653863.816 6... 3 32fbdc6a-f89d-4d02-ac87-6d0e4fc98969 ... POLYGON ((653556.757 6862647.528, 653554.105 6... 4 366fc115-880d-47a0-90d8-95f28f275224 ... POLYGON ((651998.253 6863647.264, 651855.796 6... [5 rows x 3 columns]
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Bash
python3 -c "import geopandas as gpd; df = gpd.read_file('/work/paris_amenities.g...
Index(['id', 'name', 'geometry'], dtype='str')
id ... geometry
0 0cef9694-93f0-4acf-a44f-f54a01fce855 ... POLYGON ((651737.858 6858104.126, 651824.872 6...
1 0e2667a3-715f-45ef-8fd5-7cdba7344a12 ... POLYGON ((651390.128 6862244.403, 651327.017 6...
2 2747f137-3f7b-4ac4-b222-b4ac7acef256 ... POLYGON ((653870.967 6863517.544, 653863.816 6...
3 32fbdc6a-f89d-4d02-ac87-6d0e4fc98969 ... POLYGON ((653556.757 6862647.528, 653554.105 6...
4 366fc115-880d-47a0-90d8-95f28f275224 ... POLYGON ((651998.253 6863647.264, 651855.796 6...
[5 rows x 3 columns]
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Bash
python3 -c "import geopandas as gpd; df = gpd.read_file('/work/paris_amenities.g...
Index(['osm_id', 'amenity_class', 'name', 'geometry'], dtype='str')
osm_id ... geometry
0 9000000000 ... POINT (652773.528 6864033.347)
1 9000000001 ... POINT (652322.91 6861774.142)
2 9000000002 ... POINT (653028.103 6863582.051)
3 9000000003 ... POINT (653087.577 6858190.655)
4 9000000004 ... POINT (650836.831 6863603.61)
[5 rows x 4 columns]
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Bash
python3 -c "import geopandas as gpd; df = gpd.read_file('/work/paris_amenities.g...
id ... geometry 0 0cef9694-93f0-4acf-a44f-f54a01fce855 ... POLYGON ((651737.858 6858104.126, 651824.872 6... 1 0e2667a3-715f-45ef-8fd5-7cdba7344a12 ... POLYGON ((651390.128 6862244.403, 651327.017 6... 2 2747f137-3f7b-4ac4-b222-b4ac7acef256 ... POLYGON ((653870.967 6863517.544, 653863.816 6... 3 32fbdc6a-f89d-4d02-ac87-6d0e4fc98969 ... POLYGON ((653556.757 6862647.528, 653554.105 6... 4 366fc115-880d-47a0-90d8-95f28f275224 ... POLYGON ((651998.253 6863647.264, 651855.796 6... 5 39c0e434-e46e-4ac9-b35a-100f804ede99 ... POLYGON ((653563.877 6865098.936, 653561.716 6... 6 4687f7be-db17-41c3-a9d0-09afa5523060 ... POLYGON ((655893.476 6861249.807, 655915.942 6... 7 4e7c04fe-3c0d-4401-b79d-ce3e2395e5fd ... POLYGON ((650659.118 6865039.446, 650178.081 6... 8 5134a455-e91f-4fdb-8638-c33ee7cb1383 ... POLYGON ((655001.715 6867044.106, 654913.846 6... 9 5537e76e-52db-4342-938d-a91168f4e6a7 ... POLYGON ((651800.19 6861455.647, 651796.915 68... 10 571cb253-40c3-4761-b663-d30deedf713f ... POLYGON ((650838.564 6866750.339, 650841.844 6... 11 75e0a62a-117b-46e6-8782-a4b225ce37e4 ... POLYGON ((651929.446 6864969.291, 651739.736 6... 12 7caa32e9-d051-48f2-87ae-29d09104547e ... POLYGON ((647279.38 6864482.674, 647276.526 68... 13 818e7bcb-32e9-4567-919a-c44153254398 ... POLYGON ((649486.714 6859519.417, 649424.591 6... 14 8a6c934a-8534-4e30-a9e5-e827de7a9891 ... POLYGON ((653937.655 6860196.596, 653942.12 68... 15 98f4ac3a-fc4a-40de-b535-2ebfa6f4e202 ... POLYGON ((652370.492 6862619.641, 652335.937 6... 16 dcc8aaa3-2adb-424f-a745-b9a23163f7c0 ... POLYGON ((653827.727 6867030.321, 653828.777 6... 17 ea79b3d9-590d-450e-8da6-30cb1b6fcc66 ... POLYGON ((650807.203 6862456.265, 650721.566 6... 18 ed5166f4-7111-4e42-bcbe-0630271cc71e ... POLYGON ((652048.084 6861992.57, 652052.515 68... 19 f22f8b89-a6ec-449f-bc9a-cf3c7644d0e0 ... POLYGON ((647714.89 6861965.77, 647539.402 686... [20 rows x 3 columns]
1:02
Write
/work/solve.py
content · 103 lines · py
import geopandas as gpd
import pandas as pd
def solve():
# Load the data
print("Loading amenities...")
amenities = gpd.read_file('/work/paris_amenities.gpkg', layer='amenities')
print("Loading arrondissements...")
arrondissements = gpd.read_file('/work/paris_amenities.gpkg', layer='arrondissements')
# Ensure both are in the same CRS
if amenities.crs != arrondissements.crs:
print(f"Reprojecting amenities from {amenities.crs} to {arrondissements.crs}")
amenities = amenities.to_crs(arrondissements.crs)
# Perform spatial join
# 'within' means the amenity is inside the arrondissement
# We use 'left' join to keep all amenities if needed, but the prompt implies tagging them.
# Usually, a spatial join with 'within' or 'intersects' for points in polygons is standard.
print("Performing spatial join...")
joined = gpd.sjoin(amenities, arrondissements, predicate='within', how='left')
# The prompt asks for:
# osm_id, amenity_class, arrondissement_number (integer), arrondissement_name (string)
# Looking at the previous 'head' and 'print' outputs:
# amenities columns: ['osm_id', 'amenity_class', 'name', 'geometry']
# arrondissements columns: ['id', 'name', 'geometry']
# Wait, the 'arrondissements' table only has 'id' and 'name'.
# 'id' is a UUID. 'name' is likely the name (e.g., "1er Arrondissement" or "Paris 1er").
# I need to extract 'arrondissement_number' and 'arrondissement_name'.
# Let's examine the 'name' column of arrondissements more closely to see how to extract the number.
print("Inspecting arrondissement names...")
print(arrondissements['name'].head())
# If name is like "1er Arrondissement", I'll need to parse it.
# Let's assume for a moment the 'name' contains the number.
# If it's "1er Arrondissement", "2ème Arrondissement", etc.
def extract_number(name):
import re
match = re.search(r'(\d+)', name)
if match:
return int(match.group(1))
return None
# Let's try to create the required columns.
# The prompt says arrondissement_number (integer) and arrondissement_name (string).
# I'll use the 'name' column for arrondissement_name and extract the number for arrondissement_number.
# However, if the join results in NaN (amenity outside any arrondissement),
# I should decide how to handle it. The prompt says "each amenity tagged",
# implying they all fall inside.
arrondissements['arrondissement_number'] = arrondissements['name'].apply(extract_number)
# Now join again or just use the joined dataframe
# The sjoin added 'name_right' if there was a name collision, or it uses the columns from the right df.
# In geopandas sjoin, if both have 'name', it's usually 'name_left' and 'name_right'.
# Let's check the columns of 'joined'
print("Joined columns:", joined.columns)
# Redo join with explicit column selection to be safe or handle names
# Actually, let's just rename the columns after the join.
# The columns from 'arrondissements' will be 'id_right', 'name_right', etc.
# Re-running the join logic clearly:
# Join amenities with only the necessary columns from arrondissements
arr_subset = arrondissements[['name', 'geometry']].copy()
arr_subset['arrondissement_number'] = arr_subset['name'].apply(extract_number)
arr_subset = arr_subset.rename(columns={'name': 'arrondissement_name'})
# spatial join
final_joined = gpd.sjoin(amenities, arr_subset, predicate='within', how='left')
# Select and rename columns for the final output
# amenities: osm_id, amenity_class
# arr_subset: arrondissement_number, arrondissement_name
output = final_joined[['osm_id', 'amenity_class', 'arrondissement_number', 'arrondissement_name']].copy()
# Ensure arrondissement_number is integer (handle NaNs if any by filling with 0 or similar,
# but the prompt says integer. If there are NaNs, they won't be int unless we use Int64)
# Let's check if there are any NaNs.
if output['arrondissement_number'].isnull().any():
print("Warning: Some amenities are not in an arrondissement.")
# Use nullable integer type to allow NaNs if necessary, or drop them.
# But the prompt says "each amenity tagged", implying they are all inside.
# I'll use 'Int64' which is pandas' nullable integer type.
output['arrondissement_number'] = output['arrondissement_number'].astype('Int64')
else:
output['arrondissement_number'] = output['arrondissement_number'].astype(int)
# Convert to CSV
output.to_csv('/work/amenity_to_arrondissement.csv', index=False)
print(f"Successfully wrote {len(output)} rows to /work/amenity_to_arrondissement.csv")
if __name__ == "__main__":
solve()
Wrote /work/solve.py (4907 chars).
1:04
Bash
python3 /work/solve.py
Loading amenities...
Loading arrondissements...
Performing spatial join...
Inspecting arrondissement names...
0 Paris 13e Arrondissement
1 Paris 6e Arrondissement
2 Paris 11e Arrondissement
3 Paris 3e Arrondissement
4 Paris 2e Arrondissement
Name: name, dtype: str
Joined columns: Index(['osm_id', 'amenity_class', 'name_left', 'geometry', 'index_right', 'id',
'name_right'],
dtype='str')
Successfully wrote 85 rows to /work/amenity_to_arrondissement.csv
1:11
Bash
head -n 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
1:15
Bash
python3 -c "import pandas as pd; df = pd.read_csv('/work/amenity_to_arrondisseme...
osm_id int64 amenity_class str arrondissement_number int64 arrondissement_name str dtype: object [10 4 13 9 15 20 6 18 5 17 8 1 12 3 19 7 11 14 2 16]
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