geo-l1-tokyo-busstop-buffer
Map (reference ← swipe → agent)
0:00
I'm refreshing the 400 m walkable-catchment layer for `tokyo_connectors.geojson` so I can join the result to our ridership table. Can you draw a 400 m buffer around every connector and save the result as `tokyo_stop_catchments.geoparquet` in GeoParquet, with one Polygon or MultiPolygon row per input connector? Please keep `connector_id` on every row with the original value preserved, since that's the join key.
0:00
System info
{
"adapter": "openrouter",
"model": "deepseek/deepseek-v4-flash",
"base_url": "https://openrouter.ai/api/v1"
}
0:03
Let me start by reading the input file to understand its structure.
0:03
Read
/work/tokyo_connectors.geojson
1 {
2 "type": "FeatureCollection",
3 "name": "tokyo_connectors",
4 "crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
5 "features": [
6 { "type": "Feature", "properties": { "connector_id": "0096dba5-f5b5-4cd6-b10b-de96fac11d0c" }, "geometry": { "type": "Point", "coordinates": [ 139.7666758, 35.6816829 ] } },
7 { "type": "Feature", "properties": { "connector_id": "00bfefa0-b287-48ac-acb4-9ee43e8ccc3e" }, "geometry": { "type": "Point", "coordinates": [ 139.767766599999987, 35.6817796 ] } },
8 { "type": "Feature", "properties": { "connector_id": "00c74771-e2be-486d-8962-a411c4bdc882" }, "geometry": { "type": "Point", "coordinates": [ 139.766474100000011, 35.6817342 ] } },
9 { "type": "Feature", "properties": { "connector_id": "012ea04f-85ec-417a-a7c4-c982b3de4436" }, "geometry": { "type": "Point", "coordinates": [ 139.766754, 35.6814118 ] } },
10 { "type": "Feature", "properties": { "connector_id": "0365ffb5-6290-4cc7-899c-946d30e997bd" }, "geometry": { "type": "Point", "coordinates": [ 139.767668500000013, 35.6812699 ] } },
11 { "type": "Feature", "properties": { "connector_id": "03aed995-97ff-4082-9ea4-461476e6fb59" }, "geometry": { "type": "Point", "coordinates": [ 139.766743, 35.6816612 ] } },
12 { "type": "Feature", "properties": { "connector_id": "03bf62b2-8ef2-478b-ad8d-ee2d34dcb355" }, "geometry": { "type": "Point", "coordinates": [ 139.7680073, 35.68172 ] } },
13 { "type": "Feature", "properties": { "connector_id": "041abf7f-8f32-431b-996d-36e9aaba89a7" }, "geometry": { "type": "Point", "coordinates": [ 139.7663958, 35.6814469 ] } },
14 { "type": "Feature", "properties": { "connector_id": "0523ae99-9fed-4137-8345-2f8a155ed2b6" }, "geometry": { "type": "Point", "coordinates": [ 139.7662568, 35.68123940000001 ] } },
15 { "type": "Feature", "properties": { "connector_id": "08b9b8e7-12fc-46b4-bd3a-7e94ee1d280b" }, "geometry": { "type": "Point", "coordinates": [ 139.7668761, 35.681728 ] } },
16 { "type": "Feature", "properties": { "connector_id": "08cdb272-cd07-4501-b5b7-f41cf8ae0b24" }, "geometry": { "type": "Point", "coordinates": [ 139.7672097, 35.6826518 ] } },
17 { "type": "Feature", "properties": { "connector_id": "0bfdf570-a7c6-44f8-9d7b-31203791523f" }, "geometry": { "type": "Point", "coordinates": [ 139.766253299999988, 35.6811865 ] } },
18 { "type": "Feature", "properties": { "connector_id": "0dc5371d-3b64-4a73-90d7-e848a695a1f5" }, "geometry": { "type": "Point", "coordinates": [ 139.767078199999986, 35.6815685 ] } },
19 { "type": "Feature", "properties": { "connector_id": "0eb0b1c6-28be-4dcd-aa29-9d2df97bdbaa" }, "geometry": { "type": "Point", "coordinates": [ 139.7665611, 35.6827282 ] } },
20 { "type": "Feature", "properties": { "connector_id": "0ff6361e-d6db-4218-b7d0-c0c3a6213b67" }, "geometry": { "type": "Point", "coordinates": [ 139.7662075, 35.6826326 ] } },
21 { "type": "Feature", "properties": { "connector_id": "1042b660-bd6c-44d8-aa57-5ae5b2c0b021" }, "geometry": { "type": "Point", "coordinates": [ 139.7685234, 35.6820894 ] } },
22 { "type": "Feature", "properties": { "connector_id": "107754ac-b30f-42d9-bd08-8bceaab5ef3d" }, "geometry": { "type": "Point", "coordinates": [ 139.7662904, 35.6815201 ] } },
23 { "type": "Feature", "properties": { "connector_id": "14bf8787-bcd0-484e-bbec-26671548b522" }, "geometry": { "type": "Point", "coordinates": [ 139.766840699999989, 35.681635 ] } },
24 { "type": "Feature", "properties": { "connector_id": "15aa80a7-d9d0-4b7b-9880-8b7730988919" }, "geometry": { "type": "Point", "coordinates": [ 139.7668243, 35.6821165 ] } },
25 { "type": "Feature", "properties": { "connector_id": "1755e3d3-cbe6-4505-a84a-2db671cf160d" }, "geometry": { "type": "Point", "coordinates": [ 139.7674389, 35.6814538 ] } },
26 { "type": "Feature", "properties": { "connector_id": "17bf825e-fbfb-4a35-801b-a7d2a8050c37" }, "geometry": { "type": "Point", "coordinates": [ 139.7686438, 35.6820768 ] } },
27 { "type": "Feature", "properties": { "connector_id": "17e28e53-c665-420b-b42d-4134b56a0cb0" }, "geometry": { "type": "Point", "coordinates": [ 139.767269, 35.6823914 ] } },
28 { "type": "Feature", "properties": { "connector_id": "1a0b85d5-879d-4853-9706-dbb7a8e3d0c5" }, "geometry": { "type": "Point", "coordinates": [ 139.7665403, 35.6814005 ] } },
29 { "type": "Feature", "properties": { "connector_id": "1a0e6944-6916-4dfa-87f5-a79da26cf3e7" }, "geometry": { "type": "Point", "coordinates": [ 139.7661871, 35.6823068 ] } },
30 { "type": "Feature", "properties": { "connector_id": "1bad9944-9b32-4dc9-92a9-da39f6444b99" }, "geometry": { "type": "Point", "coordinates": [ 139.7672446, 35.6822196 ] } },
31 { "type": "Feature", "properties": { "connector_id": "1bdef3a6-743f-4043-9418-7eb5862db3c3" }, "geometry": { "type": "Point", "coordinates": [ 139.767980099999988, 35.6817346 ] } },
32 { "type": "Feature", "properties": { "connector_id": "1c1b79be-15f4-4840-9ee9-66a0056c63a9" }, "geometry": { "type": "Point", "coordinates": [ 139.7669065, 35.68209550000001 ] } },
33 { "type": "Feature", "properties": { "connector_id": "1df978d7-2473-4eb7-a84f-6ba106ddb2ba" }, "geometry": { "type": "Point", "coordinates": [ 139.7664861, 35.6826852 ] } },
34 { "type": "Feature", "properties": { "connector_id": "1e4592ad-97a1-4b0e-a3a9-2a88786e245b" }, "geometry": { "type": "Point", "coordinates": [ 139.7661904, 35.6815162 ] } },
35 { "type": "Feature", "properties": { "connector_id": "1e47d210-5226-43a2-b8d3-7fa552046671" }, "geometry": { "type": "Point", "coordinates": [ 139.767438700000014, 35.682777 ] } },
36 { "type": "Feature", "properties": { "connector_id": "1e9a60f3-8d86-4a4a-a8f1-da5444d25409" }, "geometry": { "type": "Point", "coordinates": [ 139.7667501, 35.6819392 ] } },
37 { "type": "Feature", "properties": { "connector_id": "21d629e8-6d1a-4db7-9a2c-55a3a32d7119" }, "geometry": { "type": "Point", "coordinates": [ 139.766539599999987, 35.6817969 ] } },
38 { "type": "Feature", "properties": { "connector_id": "227c0b52-ebc1-4ce8-bf3c-555b540a055a" }, "geometry": { "type": "Point", "coordinates": [ 139.7670965, 35.6822246 ] } },
39 { "type": "Feature", "properties": { "connector_id": "22a27d4c-0ed4-4539-a333-f0e3fc58a98c" }, "geometry": { "type": "Point", "coordinates": [ 139.7674276, 35.6814886 ] } },
40 { "type": "Feature", "properties": { "connector_id": "22e84fd0-f1c1-4df9-baf5-cb09d96d566f" }, "geometry": { "type": "Point", "coordinates": [ 139.7671445, 35.6813867 ] } },
41 { "type": "Feature", "properties": { "connector_id": "239cc324-c0e4-4ea6-9965-03eef604c48b" }, "geometry": { "type": "Point", "coordinates": [ 139.7673543, 35.6815024 ] } },
42 { "type": "Feature", "properties": { "connector_id": "23c1c9f4-096b-4c37-bedb-ab9d6c78b2a1" }, "geometry": { "type": "Point", "coordinates": [ 139.7676047, 35.6810607 ] } },
43 { "type": "Feature", "properties": { "connector_id": "240eff61-3cd4-4ded-8c46-719e5a4540c0" }, "geometry": { "type": "Point", "coordinates": [ 139.768252, 35.6816658 ] } },
44 { "type": "Feature", "properties": { "connector_id": "245322de-48e8-4dfb-9d41-46bd8dc09cf3" }, "geometry": { "type": "Point", "coordinates": [ 139.7685602, 35.6821874 ] } },
45 { "type": "Feature", "properties": { "connector_id": "2487f82a-33b3-4832-b23c-bd5e11e21caa" }, "geometry": { "type": "Point", "coordinates": [ 139.76663640000001, 35.681672 ] } },
46 { "type": "Feature", "properties": { "connector_id": "268e303a-0b68-4c21-9f97-ffb31168c140" }, "geometry": { "type": "Point", "coordinates": [ 139.7664135, 35.6827351 ] } },
47 { "type": "Feature", "properties": { "connector_id": "2695c023-fe63-4b9d-9859-508c957c361b" }, "geometry": { "type": "Point", "coordinates": [ 139.766538700000012, 35.6827287 ] } },
48 { "type": "Feature", "properties": { "connector_id": "275d38d4-0e32-46d4-b598-4a1dae03a9af" }, "geometry": { "type": "Point", "coordinates": [ 139.7672059, 35.6813719 ] } },
49 { "type": "Feature", "properties": { "connector_id": "2924096b-4277-4761-a7f8-21dd647db870" }, "geometry": { "type": "Point", "coordinates": [ 139.7677969, 35.6817624 ] } },
50 { "type": "Feature", "properties": { "connector_id": "2a92e2bd-0d74-40a1-b9b8-6420103a5401" }, "geometry": { "type": "Point", "coordinates": [ 139.7682812, 35.6821648 ] } },
<system-reminder>Showed lines 1-50 of 307. Re-call Read with offset=51 to continue, or raise limit.</system-reminder>
0:13
I can see the data is a GeoJSON FeatureCollection of points in EPSG:4326 (CRS84 — longitude/latitude). Each feature has a `connector_id` property. I need to: 1. Read the file with GeoPandas 2. Buffer each point by 400 meters — but since the data is in degrees, I need to first project to a suitable metric CRS for Tokyo area. UTM zone 54N (EPSG:32654) covers Tokyo. Then buffer by 400m, then optionally project back to WGS84 or keep it in the metric CRS. 3. Keep the `connector_id` column 4. Save as GeoParquet Let me write the script.
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Write
/work/solve.py
content · 22 lines · py
import geopandas as gpd
import pyproj
# 1. Read the input GeoJSON
gdf = gpd.read_file("/work/tokyo_connectors.geojson")
print(f"Read {len(gdf)} connectors, CRS: {gdf.crs}")
# 2. The data is in WGS84 (EPSG:4326). Tokyo falls in UTM zone 54N (EPSG:32654).
# Project, buffer 400 m, then keep the geometry in the same metric CRS.
gdf = gdf.to_crs("EPSG:32654")
gdf["geometry"] = gdf.geometry.buffer(400.0)
print(f"Buffered, CRS: {gdf.crs}")
# 3. Keep only connector_id and geometry
gdf_out = gdf[["connector_id", "geometry"]].copy()
print(f"Output columns: {list(gdf_out.columns)}")
print(f"Row count: {len(gdf_out)}")
# 4. Write GeoParquet
gdf_out.to_parquet("/work/tokyo_stop_catchments.geoparquet")
print("Saved tokyo_stop_catchments.geoparquet")
Wrote /work/solve.py (753 chars).
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Let me run the script to see if it works.
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Bash
cd /work && python solve.py
Read 300 connectors, CRS: EPSG:4326 Buffered, CRS: EPSG:32654 Output columns: ['connector_id', 'geometry'] Row count: 300 Saved tokyo_stop_catchments.geoparquet
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Let me verify the output file exists and do a quick sanity check.
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Bash
cd /work && python -c "
cd /work && python -c "
import geopandas as gpd
gdf = gpd.read_parquet('tokyo_stop_catchments.geoparquet')
print(f'Rows: {len(gdf)}')
print(f'Columns: {list(gdf.columns)}')
print(f'CRS: {gdf.crs}')
print(f'Geometry types: {gdf.geometry.type.value_counts().to_dict()}')
print()
print('First 5 rows:')
print(gdf.head())
print()
# Sanity check: area of a circle with r=400 is ~502,655 m², buffers should be close
areas = gdf.geometry.area
print(f'Area stats (m²): min={areas.min():.1f}, max={areas.max():.1f}, median={areas.median():.1f}')
"
Rows: 300
Columns: ['connector_id', 'geometry']
CRS: {"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "ProjectedCRS", "name": "WGS 84 / UTM zone 54N", "base_crs": {"name": "WGS 84", "datum_ensemble": {"name": "World Geodetic System 1984 ensemble", "members": [{"name": "World Geodetic System 1984 (Transit)"}, {"name": "World Geodetic System 1984 (G730)"}, {"name": "World Geodetic System 1984 (G873)"}, {"name": "World Geodetic System 1984 (G1150)"}, {"name": "World Geodetic System 1984 (G1674)"}, {"name": "World Geodetic System 1984 (G1762)"}, {"name": "World Geodetic System 1984 (G2139)"}, {"name": "World Geodetic System 1984 (G2296)"}], "ellipsoid": {"name": "WGS 84", "semi_major_axis": 6378137, "inverse_flattening": 298.257223563}, "accuracy": "2.0", "id": {"authority": "EPSG", "code": 6326}}, "coordinate_system": {"subtype": "ellipsoidal", "axis": [{"name": "Geodetic latitude", "abbreviation": "Lat", "direction": "north", "unit": "degree"}, {"name": "Geodetic longitude", "abbreviation": "Lon", "direction": "east", "unit": "degree"}]}, "id": {"authority": "EPSG", "code": 4326}}, "conversion": {"name": "UTM zone 54N", "method": {"name": "Transverse Mercator", "id": {"authority": "EPSG", "code": 9807}}, "parameters": [{"name": "Latitude of natural origin", "value": 0, "unit": "degree", "id": {"authority": "EPSG", "code": 8801}}, {"name": "Longitude of natural origin", "value": 141, "unit": "degree", "id": {"authority": "EPSG", "code": 8802}}, {"name": "Scale factor at natural origin", "value": 0.9996, "unit": "unity", "id": {"authority": "EPSG", "code": 8805}}, {"name": "False easting", "value": 500000, "unit": "metre", "id": {"authority": "EPSG", "code": 8806}}, {"name": "False northing", "value": 0, "unit": "metre", "id": {"authority": "EPSG", "code": 8807}}]}, "coordinate_system": {"subtype": "Cartesian", "axis": [{"name": "Easting", "abbreviation": "E", "direction": "east", "unit": "metre"}, {"name": "Northing", "abbreviation": "N", "direction": "north", "unit": "metre"}]}, "scope": "Navigation and medium accuracy spatial referencing.", "area": "Between 138\u00b0E and 144\u00b0E, northern hemisphere between equator and 84\u00b0N, onshore and offshore. Japan. Russian Federation.", "bbox": {"south_latitude": 0, "west_longitude": 138, "north_latitude": 84, "east_longitude": 144}, "id": {"authority": "EPSG", "code": 32654}}
Geometry types: {'Polygon': 300}
First 5 rows:
connector_id geometry
0 0096dba5-f5b5-4cd6-b10b-de96fac11d0c POLYGON ((388795.659 3949344.057, 388793.733 3...
1 00bfefa0-b287-48ac-acb4-9ee43e8ccc3e POLYGON ((388894.506 3949353.544, 388892.58 39...
2 00c74771-e2be-486d-8962-a411c4bdc882 POLYGON ((388777.478 3949349.977, 388775.551 3...
3 012ea04f-85ec-417a-a7c4-c982b3de4436 POLYGON ((388802.358 3949313.899, 388800.432 3...
4 0365ffb5-6290-4cc7-899c-946d30e997bd POLYGON ((388884.919 3949297.121, 388882.993 3...
Area stats (m²): min=501847.8, max=501847.8, median=501847.8
0:30
The area values are all the same (~501,848 m²), which is very close to the expected ~502,655 m² for a 400m-radius circle (π × 400² = 502,654.8). The slight difference (501,848 vs 502,655) is because UTM 54N has a scale factor of 0.9996 at the central meridian, but the actual buffers are all near-perfect circles so the area is consistent across all rows. This is expected. Let me verify: - Every row: connector_id preserved, geometry as Polygon or MultiPolygon (all are Polygon in this case) - 300 rows, matching the 300 input connectors - CRS is EPSG:32654 (UTM zone 54N) - GeoParquet format Everything looks correct.
0:30