crs-l1-nyc-webmercator-cycleways

Status: done
Score: 100
Duration: 1:21
Cost: 0.47¢
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"
}
0:13 
Let me start by examining the input file to understand its structure, coordinate system, and attributes.
0:13
Bash ls -la /work/nyc_cycleways* 2>/dev/null 
-rw-r--r-- 1 agent agent 86058 Jun 18 22:13 /work/nyc_cycleways_webmercator.geoparquet
0:15 
The file is `nyc_cycleways_webmercator.geoparquet`. Let me first inspect it to understand its structure.
0:15
Bash cd /work && python3 -c " 
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('nyc_cycleways_webmercator.geoparquet')
print('Columns:', gdf.columns.tolist())
print('CRS:', gdf.crs)
print('Num rows:', len(gdf))
print('Geometry types:', gdf.geometry.type.unique())
print('First few rows:')
print(gdf.head())
"
Traceback (most recent call last):
  File "<string>", line 3, in <module>
    gdf = gpd.read_file('nyc_cycleways_webmercator.geoparquet')
  File "/usr/local/lib/python3.14/site-packages/geopandas/io/file.py", line 316, in _read_file
    return _read_file_pyogrio(
        filename, bbox=bbox, mask=mask, columns=columns, rows=rows, **kwargs
    )
  File "/usr/local/lib/python3.14/site-packages/geopandas/io/file.py", line 576, in _read_file_pyogrio
    return pyogrio.read_dataframe(path_or_bytes, bbox=bbox, **kwargs)
           ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.14/site-packages/pyogrio/geopandas.py", line 382, in read_dataframe
    result = read_func(
        path_or_buffer,
    ...<15 lines>...
        **kwargs,
    )
  File "/usr/local/lib/python3.14/site-packages/pyogrio/raw.py", line 200, in read
    return ogr_read(
        get_vsi_path_or_buffer(path_or_buffer),
    ...<15 lines>...
        datetime_as_string=datetime_as_string,
    )
  File "pyogrio/_io.pyx", line 1412, in pyogrio._io.ogr_read
  File "pyogrio/_io.pyx", line 257, in pyogrio._io.ogr_open
pyogrio.errors.DataSourceError: 'nyc_cycleways_webmercator.geoparquet' not recognized as being in a supported file format.; It might help to specify the correct driver explicitly by prefixing the file path with '<DRIVER>:', e.g. 'CSV:path'.
0:18 
The file is named `nyc_cycleways_webmercator.geoparquet` but let's check what the actual file is.
0:18
Bash file /work/nyc_cycleways_webmercator.geoparquet 
bash: line 1: file: command not found
0:21 
Let me try to read this with a different approach - maybe it's in a different format despite the name.
0:21
Bash cd /work && python3 -c " 
cd /work && python3 -c "
import pyarrow.parquet as pq
table = pq.read_table('nyc_cycleways_webmercator.geoparquet')
print('Schema:', table.schema)
print('Num rows:', len(table))
print('Column names:', table.column_names)
"
Schema: id: string
class: string
subclass: string
name: string
geometry: binary
  -- field metadata --
  ARROW:extension:name: 'geoarrow.wkb'
  ARROW:extension:metadata: '{"crs": {"$schema": "https://proj.org/schema' + 2460
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 813
geo: '{"primary_column": "geometry", "columns": {"geometry": {"encoding":' + 2367
Num rows: 272
Column names: ['id', 'class', 'subclass', 'name', 'geometry']
0:24 
So the file is a GeoParquet with geoarrow.wkb extension. Let me read it properly using GeoPandas with explicit driver.
0:24
Bash cd /work && python3 -c " 
cd /work && python3 -c "
import geopandas as gpd
gdf = gpd.read_file('nyc_cycleways_webmercator.geoparquet', engine='pyarrow')
print('CRS:', gdf.crs)
print('Columns:', gdf.columns.tolist())
print('Num rows:', len(gdf))
print('Geometry types:', gdf.geometry.type.unique())
print()
print(gdf.head())
"
Traceback (most recent call last):
  File "<string>", line 3, in <module>
    gdf = gpd.read_file('nyc_cycleways_webmercator.geoparquet', engine='pyarrow')
  File "/usr/local/lib/python3.14/site-packages/geopandas/io/file.py", line 339, in _read_file
    raise ValueError(f"unknown engine '{engine}'")
ValueError: unknown engine 'pyarrow'
0:29 
Let me try reading it with the `fiona` engine or just use pyarrow directly and then convert to geopandas.
0:29
Bash cd /work && python3 -c " 
cd /work && python3 -c "
import pyarrow.parquet as pq
import geopandas as gpd
from shapely import wkb
import pyproj

table = pq.read_table('nyc_cycleways_webmercator.geoparquet')

# Read geometry as WKB and decode
wkb_bytes = table.column('geometry').to_pylist()
geoms = [wkb.loads(b) for b in wkb_bytes]

# Create GeoDataFrame with other columns
df = table.drop(['geometry']).to_pandas()
gdf = gpd.GeoDataFrame(df, geometry=geoms)

# Check CRS from metadata
import json
schema = table.schema
crs_info = None
for i in range(schema.num_fields):
    meta = schema.field(i).metadata
    if meta:
        for k, v in meta.items():
            if b'ARROW:extension:metadata' in k:
                import json as j
                try:
                    ext_meta = j.loads(v.decode('utf-8'))
                    if 'crs' in ext_meta:
                        crs_info = ext_meta['crs']
                except:
                    pass

print('CRS from metadata:', crs_info)
print('Shape:', gdf.shape)
print('Types:', gdf.geometry.type.unique())
print(gdf.head())
"
Traceback (most recent call last):
  File "<string>", line 21, in <module>
    for i in range(schema.num_fields):
                   ^^^^^^^^^^^^^^^^^
AttributeError: 'pyarrow.lib.Schema' object has no attribute 'num_fields'
0:34 
Let me fix the code and properly read this data.
0:34
Bash cd /work && python3 -c " 
cd /work && python3 -c "
import pyarrow.parquet as pq
import geopandas as gpd
from shapely import wkb
import json

table = pq.read_table('nyc_cycleways_webmercator.geoparquet')

# Read geometry as WKB and decode
wkb_bytes = table.column('geometry').to_pylist()
geoms = [wkb.loads(b) for b in wkb_bytes]

# Create GeoDataFrame with other columns
df = table.drop(['geometry']).to_pandas()
gdf = gpd.GeoDataFrame(df, geometry=geoms)

# Check CRS from metadata
schema_meta = table.schema.metadata
print('Schema metadata keys:', list(schema_meta.keys()))

# geo metadata
geo_meta = json.loads(schema_meta.get(b'geo', b'{}').decode())
print('geo meta:', json.dumps(geo_meta, indent=2)[:500])

# Check what crs is stored
print('gdf.crs:', gdf.crs)
print('Shape:', gdf.shape)
print('Types:', gdf.geometry.type.unique())
print(gdf.head())
"
Schema metadata keys: [b'pandas', b'geo']
geo meta: {
  "primary_column": "geometry",
  "columns": {
    "geometry": {
      "encoding": "WKB",
      "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)"
 
gdf.crs: None
Shape: (272, 5)
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  0241690c-de63-42c3-8049-dd993673823b  ...  LINESTRING (-8239417.709 4969903.63, -8239413....
4  03118a13-69c4-4dac-8677-6d9932e88536  ...  LINESTRING (-8233018.341 4976811.579, -8232985...

[5 rows x 5 columns]
0:39 
The data is in Web Mercator (EPSG:3857) projection using WKB-encoded geometries. I need to:

1. Read the file
2. Set the CRS to EPSG:3857
3. Convert to EPSG:4326 (WGS84 lat/lon)
4. Make sure each geometry stays as LineString (not MultiLineString)
5. Write to GeoParquet with `id` as the key (primary key / row group key)

Let me also check what the `id` column looks like to use as the key.

Let me write a script to do this properly.
0:39
Bash cd /work && python3 -c " 
cd /work && python3 -c "
import pyarrow.parquet as pq

table = pq.read_table('nyc_cycleways_webmercator.geoparquet')
print('Schema:', table.schema)
print()
print('Column names:', table.column_names)
print('id dtype:', table.column('id').type)
print('id sample:', table.column('id').to_pylist()[:3])
"
Schema: id: string
class: string
subclass: string
name: string
geometry: binary
  -- field metadata --
  ARROW:extension:name: 'geoarrow.wkb'
  ARROW:extension:metadata: '{"crs": {"$schema": "https://proj.org/schema' + 2460
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 813
geo: '{"primary_column": "geometry", "columns": {"geometry": {"encoding":' + 2367

Column names: ['id', 'class', 'subclass', 'name', 'geometry']
id dtype: string
id sample: ['008afca9-4118-4569-86a4-e05d2049e5ff', '019954ce-599a-4f44-9509-f5df571ec8a6', '01e21e38-aa74-4a1b-aea5-0c154bc52559']
0:47 
Now I understand the data. It's a GeoParquet file with Web Mercator coordinates (EPSG:3857). I need to:

1. Read it
2. Set the CRS to EPSG:3857 (Web Mercator)
3. Reproject to EPSG:4326 (WGS 84 lat/lon)
4. Make sure each geometry stays a LineString (not MultiLineString)
5. Use `id` as the partitioning/row-group key
6. Write to `nyc_cycleways_wgs84.geoparquet`

Let me write a proper script.
0:47
Write /work/solve.py
content · 49 lines · py 
import geopandas as gpd
from shapely import wkb
import pyarrow.parquet as pq
import json

# Read raw parquet
table = pq.read_table('/work/nyc_cycleways_webmercator.geoparquet')

# Decode WKB geometries
wkb_bytes = table.column('geometry').to_pylist()
geoms = [wkb.loads(b) for b in wkb_bytes]

# Build a GeoDataFrame from the attribute columns
df = table.drop(['geometry']).to_pandas()
gdf = gpd.GeoDataFrame(df, geometry=geoms, crs='EPSG:3857')

print(f"Original CRS: {gdf.crs}")
print(f"Original geometry types: {gdf.geometry.type.unique()}")
print(f"Number of rows: {len(gdf)}")

# Reproject to WGS84 (EPSG:4326)
gdf = gdf.to_crs('EPSG:4326')

print(f"New CRS: {gdf.crs}")
print(f"Geometry types after reprojection: {gdf.geometry.type.unique()}")

# Verify all are still LineStrings
non_ls = gdf[~gdf.geometry.type.isin(['LineString'])]
if len(non_ls) > 0:
    print(f"WARNING: {len(non_ls)} geometries are not LineStrings:")
    print(non_ls[['id', 'geometry']].head())
else:
    print("All geometries are LineStrings ✓")

# Write to GeoParquet with id as the key
gdf.to_parquet(
    '/work/nyc_cycleways_wgs84.geoparquet',
    index=False,
    compression='zstd',
    row_group_size=len(gdf)  # one row group for simplicity
)

print("Written to nyc_cycleways_wgs84.geoparquet")

# Verify round-trip
verify = gpd.read_parquet('/work/nyc_cycleways_wgs84.geoparquet')
print(f"\nVerification - rows: {len(verify)}, CRS: {verify.crs}")
print(f"Columns: {verify.columns.tolist()}")
print(verify.head(3))
Wrote /work/solve.py (1502 chars).
0:51 
Let me also check how to specify the "id" as the key in GeoParquet. Looking at the geopandas to_parquet docs, the 'key' parameter isn't directly available. But since the user wants to use `id` as the key, I think they might be referring to the GeoParquet specification's "primary_column" or a partition column. Actually, in GeoParquet, the "key" might refer to the primary key concept. Let me check what options exist.

Actually, looking at GeoParquet spec more carefully, there's no explicit "key" concept. But in the context of the tile-renderer/web map, `id` as the key likely means using `id` as the primary identifier column (which it already is). 

Let me re-read the instruction: "use `id` as the key". This could mean setting the `id` column as the partitioning column, or as the row group key. Let me just ensure it's part of the output.

Actually, looking at geoarrow/geoparquet, there's a concept of "key" in the geo metadata. But for geopandas' to_parquet, I can pass some geo metadata. Let me just make sure the `id` column is present and not dropped. It already is present.

Let me just run the script and see.
0:51
Bash cd /work && python solve.py 
Original CRS: EPSG:3857
Original geometry types: <ArrowStringArray>
['LineString']
Length: 1, dtype: str
Number of rows: 272
New CRS: EPSG:4326
Geometry types after reprojection: <ArrowStringArray>
['LineString']
Length: 1, dtype: str
All geometries are LineStrings ✓
Written to nyc_cycleways_wgs84.geoparquet

Verification - rows: 272, 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}}
Columns: ['id', 'class', 'subclass', 'name', 'geometry']
                                     id  ...                                           geometry
0  008afca9-4118-4569-86a4-e05d2049e5ff  ...  LINESTRING (-74.00011 40.70788, -74.00004 40.7...
1  019954ce-599a-4f44-9509-f5df571ec8a6  ...  LINESTRING (-74.00694 40.70375, -74.00692 40.7...
2  01e21e38-aa74-4a1b-aea5-0c154bc52559  ...  LINESTRING (-74.00004 40.70779, -74.0001 40.70...

[3 rows x 5 columns]
0:57 
The conversion was successful. Let me verify the output file exists and that `id` is properly handled. The user asked to use `id` as the key, which in GeoParquet terms means the primary key. Let me check if that's already handled or if I need to do something specific about it.

Actually, "use `id` as the key" likely means the GeoParquet's primary column index or the `geo` metadata's `primary_column`. Let me check the output file.
0:57
Bash cd /work && python3 -c " 
cd /work && python3 -c "
import pyarrow.parquet as pq
table = pq.read_table('nyc_cycleways_wgs84.geoparquet')
print('Schema:', table.schema)
print()
# Check geo metadata for primary_column
import json
meta = table.schema.metadata
if b'geo' in meta:
    geo = json.loads(meta[b'geo'].decode())
    print('Geo primary_column:', geo.get('primary_column'))
    print('Geo columns:', json.dumps(geo.get('columns'), indent=2))
print()
# Check pandas metadata for index columns
if b'pandas' in meta:
    pd_meta = json.loads(meta[b'pandas'].decode())
    print('Pandas index_columns:', pd_meta.get('index_columns'))
"
Schema: id: large_string
class: large_string
subclass: large_string
name: large_string
geometry: binary
  -- field metadata --
  ARROW:extension:name: 'geoarrow.wkb'
  ARROW:extension:metadata: '{"crs": {"$schema": "https://proj.org/schema' + 1498
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 629
geo: '{"primary_column": "geometry", "columns": {"geometry": {"encoding":' + 1384

Geo primary_column: geometry
Geo columns: {
  "geometry": {
    "encoding": "WKB",
    "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
      }
    },
    "geometry_types": [
      "LineString"
    ],
    "bbox": [
      -74.0177637,
      40.7004314,
      -73.931735,
      40.78877799999999
    ]
  }
}

Pandas index_columns: []
1:07 
The output looks good. Let me check: the user asked to use `id` as the key. In GeoParquet/GeoArrow context, a "key" typically refers to the primary key or row identifier. Let me check if there's a way to store that in the metadata. Looking at the pandas metadata, `index_columns` is empty. Since we already have `id` as a column but it's not being used as an index, the user may want `id` to be the row-group key / primary key in the GeoParquet metadata.

Actually, in Parquet, there's no standard "key" concept the way databases have. But GeoParquet 1.1+ has a `primary_column` metadata entry (which is the geometry column). The user says "use `id` as the key". This could mean:
1. Use `id` as the index (pandas index) when writing
2. Use `id` to order/sort the data

Let me think about what makes the most sense. In GeoParquet, "key" might refer to the partition key or the row key. Let me re-read: "Write the result as GeoParquet to `nyc_cycleways_wgs84.geoparquet`, and use `id` as the key."

In Parquet-land, there isn't a formal "key" concept, but in GeoParquet 1.0/1.1 spec, you can have a `primary_column` which is the geometry. Beyond that, some implementations might use pandas metadata to track an index column. So setting `id` as the index before writing to parquet would make it the index/key in the pandas metadata.

Let me re-run with `id` set as the index:
1:07
1:07
Write /work/solve.py
content · 40 lines · py 
import geopandas as gpd
from shapely import wkb
import pyarrow.parquet as pq

# Read raw parquet
table = pq.read_table('/work/nyc_cycleways_webmercator.geoparquet')

# Decode WKB geometries
wkb_bytes = table.column('geometry').to_pylist()
geoms = [wkb.loads(b) for b in wkb_bytes]

# Build a GeoDataFrame from the attribute columns
df = table.drop(['geometry']).to_pandas()
gdf = gpd.GeoDataFrame(df, geometry=geoms, crs='EPSG:3857')

# Reproject to WGS84 (EPSG:4326)
gdf = gdf.to_crs('EPSG:4326')

# Set 'id' as the index so it becomes the key in parquet metadata
gdf = gdf.set_index('id')

print(f"Index name: {gdf.index.name}")
print(f"Number of rows: {len(gdf)}")
print(f"CRS: {gdf.crs}")
print(f"Geometry types: {gdf.geometry.type.unique()}")

# Write to GeoParquet
gdf.to_parquet(
    '/work/nyc_cycleways_wgs84.geoparquet',
    compression='zstd',
)

print("Written to nyc_cycleways_wgs84.geoparquet")

# Verify round-trip
verify = gpd.read_parquet('/work/nyc_cycleways_wgs84.geoparquet')
print(f"\nVerification - rows: {len(verify)}, CRS: {verify.crs}")
print(f"Index name: {verify.index.name}")
print(f"Columns: {verify.columns.tolist()}")
print(verify.head(3))
Wrote /work/solve.py (1170 chars).
1:10 
Wait, looking at the original data more carefully — the original parquet had `id` as a regular column (string type), not an index. The user says "use `id` as the key" — that could mean:
1. Set it as the parquet row-group key (index)
2. Or just make it prominent

But looking at the file, I think setting it as the index is the right interpretation. Let me also check if the original had an index column set. The pandas metadata showed `"index_columns": []` for my first run, and for the original data it showed `{"kind": "range", "name": null, "start": 0}` which means it had a default range index.

Let me run the updated script.
1:10
Bash cd /work && python solve.py 
Index name: id
Number of rows: 272
CRS: EPSG:4326
Geometry types: <ArrowStringArray>
['LineString']
Length: 1, dtype: str
Written to nyc_cycleways_wgs84.geoparquet

Verification - rows: 272, 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}}
Index name: id
Columns: ['class', 'subclass', 'name', 'geometry']
                                         class  ...                                           geometry
id                                              ...                                                   
008afca9-4118-4569-86a4-e05d2049e5ff  cycleway  ...  LINESTRING (-74.00011 40.70788, -74.00004 40.7...
019954ce-599a-4f44-9509-f5df571ec8a6  cycleway  ...  LINESTRING (-74.00694 40.70375, -74.00692 40.7...
01e21e38-aa74-4a1b-aea5-0c154bc52559  cycleway  ...  LINESTRING (-74.00004 40.70779, -74.0001 40.70...

[3 rows x 4 columns]
1:14 
Let me verify that the `id` column is indeed set as the key (index) in the GeoParquet metadata.
1:14
Bash cd /work && python3 -c " 
cd /work && python3 -c "
import pyarrow.parquet as pq
import json

table = pq.read_table('nyc_cycleways_wgs84.geoparquet')
meta = table.schema.metadata
pd_meta = json.loads(meta[b'pandas'].decode())
print('Pandas index_columns:', pd_meta.get('index_columns'))
print()
# Also confirm geometry type
geo_meta = json.loads(meta[b'geo'].decode())
print('Geometry types:', geo_meta['columns']['geometry'].get('geometry_types'))
print('CRS type:', geo_meta['columns']['geometry']['crs']['type'])
"
Pandas index_columns: ['id']

Geometry types: ['LineString']
CRS type: GeographicCRS
1:18 
The file is correctly written with:
- Plain lat/lon (WGS84, EPSG:4326)
- All geometries are LineStrings (no MultiLineStrings)
- `id` is set as the key (index)
- All original attributes are preserved
- Written as GeoParquet to `/work/nyc_cycleways_wgs84.geoparquet`
1:18