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Datasets:
EDK-Earth
/
sewer-issues

Tasks:
Tabular Classification
Other
Modalities:
Geospatial
Tabular
Languages:
English
Size:
10K<n<100K
Tags:
geospatial
open-data
tabular
parquet
geojson
csv
License:
Dataset card Files
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Dataset Viewer
Duplicate
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    TypeError
Message:      Couldn't cast array of type
string
to
{'name': Value('string'), 'url': Value('string')}
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2315, in cast_table_to_schema
                  cast_array_to_feature(
                  ~~~~~~~~~~~~~~~~~~~~~^
                      table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      feature,
                      ^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2161, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              string
              to
              {'name': Value('string'), 'url': Value('string')}

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Sewer Issues (311) - 30,000-record Geospatial Sample

A free, ready-to-use sample of 30,000 geolocated sewer issue service requests (calendar year 2025), published by EDK.

Sewer backups, blockages and related complaints reported through municipal 311 systems, with coordinates and the original city-specific fields preserved.

This is a 30,000-record sample of a much larger collection (about 1,138,000 source records). Need the full dataset, other places or years, or live search? See the EDK atlas.

At a glance

Records 30,000
Period Calendar year 2025
Geometry Point, WGS 84 (EPSG:4326)
Formats Parquet, GeoJSON, CSV
Publisher EDK

Quick start 

from datasets import load_dataset

ds = load_dataset("parquet", data_files="hf://datasets/EDK-Earth/sewer-issues/data.parquet", split="train")
print(ds)
print(ds[0])

import pandas as pd
import json

df = pd.read_parquet("hf://datasets/EDK-Earth/sewer-issues/data.parquet")
df["properties"] = df["properties"].apply(json.loads)
df[["latitude", "longitude"]].describe()

Search millions more geographic features

EDK provides developers and AI systems access to millions of geographic features and geolocated imagery spanning infrastructure, places, roads, buildings, nature and community issues.

Build with EDK

  • Geographic Feature List — browse geographic features available through EDK
  • EDK — programmable infrastructure for Earth
  • Developer Documentation — APIs, tools, guides, and examples
  • SDK — build geographic capabilities directly into applications
  • CLI — work with geographic search and spatial data from the terminal
  • MCP — connect geographic search and spatial tools to AI agents

Use cases

  • Finding areas with recurring sewer complaints
  • Infrastructure maintenance and capital planning research
  • Correlating complaints with rainfall, flooding or building age
  • Benchmarking city service response

Schema

Column Type Description
id string Record identifier from the source table.
latitude float64 Latitude in decimal degrees (WGS 84).
longitude float64 Longitude in decimal degrees (WGS 84).
description string Short plain-language summary of the record (category, type and status) taken from the source's metadescription field.
attribution string Original data provider and dataset the record comes from. Cite this when you reuse the record.
properties string JSON-encoded object with the original source fields for the record (keys vary by record).

The description column is a short plain-language summary of each record (category, type and status), ready to use for search, labelling or text models.

Properties

The properties column is a JSON string holding the original source attributes. The most common keys in this sample:

Key Example value
origin Phone Call
location (41.91179829672742, -87.71905485986424)
precinct 11
duplicate false
police_beat 2535
street_name WABANSIA
created_hour 15
x_coordinate 1151451.0394360537
y_coordinate 1911045.0555632855
created_month 2
legacy_record false
police_sector 3

Keys vary by record and source; not every field is populated for every row.

Files

Methodology

Records are exported directly from the EDK geographic index, ordered by identifier and capped at 30,000 rows. Coordinates are WGS 84. Source fields are preserved in properties without reinterpretation. Inclusion means a record appeared in the contributing public dataset; it was not independently verified.

Sources and attribution

Every record names its original provider in the attribution column. Contributing sources:

Prepared and distributed by EDK.

License

Records come from public municipal open-data portals whose terms vary by city. Credit the provider named in each record's attribution column and EDK when you reuse this data.
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