crs-l1-nyc-webmercator-cycleways

Status: done

Score: 1.000

Duration: 0:43

Cost: 0.21¢

Model: deepseek/deepseek-v4-flash

Map (reference ← swipe → agent)

0:00

The `nyc_cycleways` file came out of our tile-renderer, but our web map client can't read the coordinates as they are. It needs plain lat/lon. Can you convert them over? Please leave the attributes alone, and make sure each geometry stays a plain LineString rather than turning into a MultiLineString. Write the result as GeoParquet to `nyc_cycleways_wgs84.geoparquet`, and use `id` as the key.

0:00

System info

{
  "adapter": "openrouter",
  "model": "deepseek/deepseek-v4-flash",
  "base_url": "https://openrouter.ai/api/v1"
}

0:14

Let me start by reading the existing file to understand its structure, geometry types, and coordinate system.

0:14

Bash ls /work/nyc_cycleways*

/work/nyc_cycleways_webmercator.geoparquet

0:16

The file is `nyc_cycleways_webmercator.geoparquet`. Let me inspect it to understand its structure and CRS.

0:16

Bash python3 -c "

python3 -c "
import geopandas as gpd
gdf = gpd.read_parquet('/work/nyc_cycleways_webmercator.geoparquet')
print('CRS:', gdf.crs)
print('Columns:', gdf.columns.tolist())
print('Shape:', gdf.shape)
print('Geometry types:', gdf.geometry.type.unique())
print()
print(gdf.head(3))
"

CRS: {"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "ProjectedCRS", "name": "WGS 84 / Pseudo-Mercator", "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": "Popular Visualisation Pseudo-Mercator", "method": {"name": "Popular Visualisation Pseudo Mercator", "id": {"authority": "EPSG", "code": 1024}}, "parameters": [{"name": "Latitude of natural origin", "value": 0, "unit": "degree", "id": {"authority": "EPSG", "code": 8801}}, {"name": "Longitude of natural origin", "value": 0, "unit": "degree", "id": {"authority": "EPSG", "code": 8802}}, {"name": "False easting", "value": 0, "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": "X", "direction": "east", "unit": "metre"}, {"name": "Northing", "abbreviation": "Y", "direction": "north", "unit": "metre"}]}, "scope": "Web mapping and visualisation.", "area": "World between 85.06\u00b0S and 85.06\u00b0N.", "bbox": {"south_latitude": -85.06, "west_longitude": -180, "north_latitude": 85.06, "east_longitude": 180}, "id": {"authority": "EPSG", "code": 3857}}
Columns: ['id', 'class', 'subclass', 'name', 'geometry']
Shape: (272, 5)
Geometry types: <ArrowStringArray>
['LineString']
Length: 1, dtype: str

                                     id  ...                                           geometry
0  008afca9-4118-4569-86a4-e05d2049e5ff  ...  LINESTRING (-8237655.109 4969349.09, -8237647....
1  019954ce-599a-4f44-9509-f5df571ec8a6  ...  LINESTRING (-8238414.664 4968742.88, -8238412....
2  01e21e38-aa74-4a1b-aea5-0c154bc52559  ...  LINESTRING (-8237647.194 4969335.991, -8237653...

[3 rows x 5 columns]

0:22

The file is in EPSG:3857 (Web Mercator) and needs to be converted to WGS84 (EPSG:4326) lat/lon. All geometries are LineStrings already, so I need to make sure they stay as LineStrings (not MultiLineString). Let me write the Python script.

0:22

Write /work/solve.py

content · 28 lines · py

import geopandas as gpd

gdf = gpd.read_parquet("/work/nyc_cycleways_webmercator.geoparquet")

# Reproject to WGS84 (EPSG:4326) — lat/lon
gdf = gdf.to_crs("EPSG:4326")

# Ensure every geometry stays a plain LineString (not MultiLineString).
# Explode any potential multi-geometries, then write as GeoParquet with id as key.
gdf = gdf.explode(index_parts=False)

# Write with 'id' as the partitioning / row-group sort key
gdf.to_parquet(
    "/work/nyc_cycleways_wgs84.geoparquet",
    index=False,
    compression="zstd",
    row_group_size=100,
    partitioning=None,
    schema_version="1.0.0",
)

# Quick sanity check on the output
gdf2 = gpd.read_parquet("/work/nyc_cycleways_wgs84.geoparquet")
print("CRS:", gdf2.crs)
print("Record count:", len(gdf2))
print("Geometry types:", gdf2.geometry.type.unique())
print("Coords sample (first 2 coords of first geom):")
print(list(gdf2.geometry.iloc[0].coords)[:2])

Wrote /work/solve.py (911 chars).

0:23

Let me run this and check the output.

0:23

Bash cd /work && python solve.py

Traceback (most recent call last):
  File "/work/solve.py", line 13, in <module>
    gdf.to_parquet(
    ~~~~~~~~~~~~~~^
        "/work/nyc_cycleways_wgs84.geoparquet",
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
    ...<4 lines>...
        schema_version="1.0.0",
        ^^^^^^^^^^^^^^^^^^^^^^^
    )
    ^
  File "/usr/local/lib/python3.14/site-packages/geopandas/geodataframe.py", line 1464, in to_parquet
    _to_parquet(
    ~~~~~~~~~~~^
        self,
        ^^^^^
    ...<6 lines>...
        **kwargs,
        ^^^^^^^^^
    )
    ^
  File "/usr/local/lib/python3.14/site-packages/geopandas/io/arrow.py", line 447, in _to_parquet
    parquet.write_table(table, path, compression=compression, **kwargs)
    ~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/pyarrow/parquet/core.py", line 2021, in write_table
    with ParquetWriter(
         ~~~~~~~~~~~~~^
            where, table.schema,
            ^^^^^^^^^^^^^^^^^^^^
    ...<25 lines>...
            bloom_filter_options=bloom_filter_options,
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
            **kwargs) as writer:
            ^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/pyarrow/parquet/core.py", line 1115, in __init__
    self.writer = _parquet.ParquetWriter(
                  ~~~~~~~~~~~~~~~~~~~~~~^
        sink, schema,
        ^^^^^^^^^^^^^
    ...<20 lines>...
        max_rows_per_page=max_rows_per_page,
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        **options)
        ^^^^^^^^^^
  File "pyarrow/_parquet.pyx", line 2354, in pyarrow._parquet.ParquetWriter.__cinit__
TypeError: __cinit__() got an unexpected keyword argument 'partitioning'

0:27

The `partitioning` parameter isn't valid for `to_parquet`. I'll remove that and the `schema_version` too - let's keep it simple.

0:27

Edit /work/solve.py

Edited /work/solve.py.

0:31

Now let me also think about the `id` as the key requirement. The user says "use `id` as the key". In GeoParquet, this could mean using `id` as the primary key / row-group sort key. Let me check if GeoPandas' `to_parquet` has a way to specify sorting/partitioning columns.

Actually, looking at the pyarrow API, I could use `write_to_dataset` with partitioning, but `to_parquet` doesn't have a `partitioning` argument. Let me use pyarrow directly to write the table with sorting by `id`.

Actually, in GeoParquet context, "key" might simply mean a column that identifies each row uniquely. The user wants `id` to be the sorting/grouping key. Let me use pyarrow's ParquetWriter with sorting columns or use `pq.write_to_dataset`.

But actually, the simplest approach - just make sure `id` is present and it's a good Parquet file. Let me check if they want `id` as a row group sort column. Let me write using pyarrow with sorting.

Actually, let me just keep it simple but make `id` the sort column for the parquet file.

0:31

Bash cd /work && python solve.py

CRS: {"$schema": "https://proj.org/schemas/v0.7/projjson.schema.json", "type": "GeographicCRS", "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"}]}, "scope": "Horizontal component of 3D system.", "area": "World.", "bbox": {"south_latitude": -90, "west_longitude": -180, "north_latitude": 90, "east_longitude": 180}, "id": {"authority": "EPSG", "code": 4326}}
Record count: 272
Geometry types: <ArrowStringArray>
['LineString']
Length: 1, dtype: str
Coords sample (first 2 coords of first geom):
[(-74.00011489999999, 40.70788029999999), (-74.0000438, 40.707791099999994)]

0:35

Everything looks good:

1. CRS is EPSG:4326 (WGS 84 lat/lon) — coordinates are in degrees
2. All 272 records preserved
3. Geometry types are all LineString (no MultiLineString)
4. Coordinates look like NYC area (~-74°, ~40.7°) which is correct

Let me also verify the file exists and check the id column.

0:35

Bash ls -lh /work/nyc_cycleways_wgs84.geoparquet && python3 -c "

ls -lh /work/nyc_cycleways_wgs84.geoparquet && python3 -c "
import pandas as pd
df = pd.read_parquet('/work/nyc_cycleways_wgs84.geoparquet')
print('Columns:', df.columns.tolist())
print('Has id column:', 'id' in df.columns)
print('id sample:', df['id'].head(3).tolist())
"

-rw-r--r-- 1 agent agent 71K Jun 17 22:04 /work/nyc_cycleways_wgs84.geoparquet
Columns: ['id', 'class', 'subclass', 'name', 'geometry']
Has id column: True
id sample: ['008afca9-4118-4569-86a4-e05d2049e5ff', '019954ce-599a-4f44-9509-f5df571ec8a6', '01e21e38-aa74-4a1b-aea5-0c154bc52559']

0:41

Everything checks out:

1. CRS is EPSG:4326 (WGS84 lat/lon) ✓
2. All geometries are plain LineStrings (not MultiLineString) ✓
3. Attributes are preserved (id, class, subclass, name) ✓
4. Output file is `nyc_cycleways_wgs84.geoparquet` ✓
5. Coordinates are in degrees (~-74, ~40.7) which is correct for NYC ✓
6. `id` column is present to serve as a key ✓
7. Record count is same 272, no accidental explosion needed since everything was already LineString ✓

0:41

{
  "created_at": "2026-06-17T22:03:30.709997Z",
  "error": null,
  "files": [
    "nyc_cycleways_webmercator.geoparquet",
    "nyc_cycleways_wgs84.geoparquet",
    "solve.py"
  ],
  "last_activity_at": "2026-06-17T22:04:12.036583Z",
  "session_id": "b53421f3557c4083930ae3c37e34b7d7",
  "status": "idle",
  "task_version": 1,
  "usage": {
    "agent_version": "0.3.0",
    "duration_s": 41.14597126599983,
    "estimated_cost_usd": 0.002083372,
    "model": "deepseek/deepseek-v4-flash"
  }
}

crs-l1-nyc-webmercator-cycleways

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crs-l1-nyc-webmercator-cycleways