geo-l1-tokyo-busstop-buffer
Map (reference ← swipe → agent)
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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.
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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
tokyo_connectors.geojson
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Read
tokyo_connectors.geojson
offset and limit must be integers
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Bash
head -n 20 tokyo_connectors.geojson
{
"type": "FeatureCollection",
"name": "tokyo_connectors",
"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
"features": [
{ "type": "Feature", "properties": { "connector_id": "0096dba5-f5b5-4cd6-b10b-de96fac11d0c" }, "geometry": { "type": "Point", "coordinates": [ 139.7666758, 35.6816829 ] } },
{ "type": "Feature", "properties": { "connector_id": "00bfefa0-b287-48ac-acb4-9ee43e8ccc3e" }, "geometry": { "type": "Point", "coordinates": [ 139.767766599999987, 35.6817796 ] } },
{ "type": "Feature", "properties": { "connector_id": "00c74771-e2be-486d-8962-a411c4bdc882" }, "geometry": { "type": "Point", "coordinates": [ 139.766474100000011, 35.6817342 ] } },
{ "type": "Feature", "properties": { "connector_id": "012ea04f-85ec-417a-a7c4-c982b3de4436" }, "geometry": { "type": "Point", "coordinates": [ 139.766754, 35.6814118 ] } },
{ "type": "Feature", "properties": { "connector_id": "0365ffb5-6290-4cc7-899c-946d30e997bd" }, "geometry": { "type": "Point", "coordinates": [ 139.767668500000013, 35.6812699 ] } },
{ "type": "Feature", "properties": { "connector_id": "03aed995-97ff-4082-9ea4-461476e6fb59" }, "geometry": { "type": "Point", "coordinates": [ 139.766743, 35.6816612 ] } },
{ "type": "Feature", "properties": { "connector_id": "03bf62b2-8ef2-478b-ad8d-ee2d34dcb355" }, "geometry": { "type": "Point", "coordinates": [ 139.7680073, 35.68172 ] } },
{ "type": "Feature", "properties": { "connector_id": "041abf7f-8f32-431b-996d-36e9aaba89a7" }, "geometry": { "type": "Point", "coordinates": [ 139.7663958, 35.6814469 ] } },
{ "type": "Feature", "properties": { "connector_id": "0523ae99-9fed-4137-8345-2f8a155ed2b6" }, "geometry": { "type": "Point", "coordinates": [ 139.7662568, 35.68123940000001 ] } },
{ "type": "Feature", "properties": { "connector_id": "08b9b8e7-12fc-46b4-bd3a-7e94ee1d280b" }, "geometry": { "type": "Point", "coordinates": [ 139.7668761, 35.681728 ] } },
{ "type": "Feature", "properties": { "connector_id": "08cdb272-cd07-4501-b5b7-f41cf8ae0b24" }, "geometry": { "type": "Point", "coordinates": [ 139.7672097, 35.6826518 ] } },
{ "type": "Feature", "properties": { "connector_id": "0bfdf570-a7c6-44f8-9d7b-31203791523f" }, "geometry": { "type": "Point", "coordinates": [ 139.766253299999988, 35.6811865 ] } },
{ "type": "Feature", "properties": { "connector_id": "0dc5371d-3b64-4a73-90d7-e848a695a1f5" }, "geometry": { "type": "Point", "coordinates": [ 139.767078199999986, 35.6815685 ] } },
{ "type": "Feature", "properties": { "connector_id": "0eb0b1c6-28be-4dcd-aa29-9d2df97bdbaa" }, "geometry": { "type": "Point", "coordinates": [ 139.7665611, 35.6827282 ] } },
{ "type": "Feature", "properties": { "connector_id": "0ff6361e-d6db-4218-b7d0-c0c3a6213b67" }, "geometry": { "type": "Point", "coordinates": [ 139.7662075, 35.6826326 ] } },
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Write
solve.py
content · 47 lines · py
import geopandas as gpd
def solve():
# Load the connector data
input_file = 'tokyo_connectors.geojson'
output_file = 'tokyo_stop_catchments.geoparquet'
gdf = gpd.read_file(input_file)
# Check if connector_id exists
if 'connector_id' not in gdf.columns:
raise ValueError("Column 'connector_id' not found in input file.")
# Project to a metric CRS for accurate buffering.
# For Tokyo, EPSG:3857 (Web Mercator) is common, but EPSG:6677 (JGD2011 / Japan Plane Rectangular CS IX)
# or similar might be more accurate. However, 3857 is usually fine for 400m buffers
# if we just want a decent approximation, but let's use a local UTM or a common metric CRS.
# Tokyo is roughly in UTM zone 54N (EPSG:32654).
# Let's use EPSG:3857 for simplicity or try to find a better one.
# Actually, EPSG:32654 is better for Tokyo.
original_crs = gdf.crs
print(f"Original CRS: {original_crs}")
# Reproject to UTM zone 54N (EPSG:32654)
gdf_metric = gdf.to_crs(epsg=32654)
# Create 400m buffer
gdf_metric['geometry'] = gdf_metric.buffer(400)
# Reproject back to original CRS (WGS84)
gdf_final = gdf_metric.to_crs(original_crs)
# Ensure connector_id is preserved and only necessary columns are kept if requested,
# but the prompt says "keep connector_id on every row".
# It doesn't explicitly say to drop other columns, but usually, we want a clean layer.
# I'll keep all original columns to be safe, as long as connector_id is there.
# Save to GeoParquet
gdf_final.to_parquet(output_file)
print(f"Successfully saved buffer to {output_file}")
print(f"Shape: {gdf_final.shape}")
if __name__ == "__main__":
solve()
Wrote solve.py (1768 chars).
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Bash
python solve.py
Original CRS: EPSG:4326 Successfully saved buffer to tokyo_stop_catchments.geoparquet Shape: (300, 2)
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Bash
python -c "import geopandas as gpd; gdf = gpd.read_parquet('tokyo_stop_catchment...
connector_id geometry 0 0096dba5-f5b5-4cd6-b10b-de96fac11d0c POLYGON ((139.7711 35.68173, 139.77108 35.6813... 1 00bfefa0-b287-48ac-acb4-9ee43e8ccc3e POLYGON ((139.77219 35.68182, 139.77217 35.681... 2 00c74771-e2be-486d-8962-a411c4bdc882 POLYGON ((139.77089 35.68178, 139.77088 35.681... 3 012ea04f-85ec-417a-a7c4-c982b3de4436 POLYGON ((139.77117 35.68146, 139.77116 35.681... 4 0365ffb5-6290-4cc7-899c-946d30e997bd POLYGON ((139.77209 35.68132, 139.77207 35.680... Index(['connector_id', 'geometry'], dtype='str') Polygon 300 Name: count, dtype: int64
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