Datasets:
The dataset viewer is not available for this split.
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 147, 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 127, 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 483, 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 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, 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 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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 343, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, 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 2158, 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')}Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Garage Permits - 22,184-record Geospatial Sample
A free, ready-to-use sample of 22,184 geolocated garage permit records (calendar year 2025), published by Outerview.
Building permits for garage construction, conversion and repair compiled from municipal open-data portals, with coordinates, permit lifecycle fields and the original city-specific attributes preserved.
This is a 22,184-record sample of a much larger collection (about 839,000 source records). Need the full dataset, other places or years, or live search? See the Outerview atlas.
At a glance
| Records | 22,184 |
| Period | Calendar year 2025 |
| Geometry | Point, WGS 84 (EPSG:4326) |
| Formats | Parquet, GeoJSON, CSV |
| Publisher | Outerview |
Quick start
from datasets import load_dataset
ds = load_dataset("parquet", data_files="hf://datasets/Outerview/garage-permit/data.parquet", split="train")
print(ds)
print(ds[0])
import pandas as pd
import json
df = pd.read_parquet("hf://datasets/Outerview/garage-permit/data.parquet")
df["properties"] = df["properties"].apply(json.loads)
df[["latitude", "longitude"]].describe()
Search millions more geographic features
Outerview 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 Outerview
- Geographic Feature List — browse geographic features available through Outerview
- Outerview — 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
- Tracking residential construction and renovation activity
- Studying housing density and accessory-structure trends
- Neighbourhood change and development pipeline analysis
- Geospatial machine learning on permit data
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 |
|---|---|
street_name |
Flora |
location |
POINT (-87.639186876149 41.70198681153) |
pin_list |
2516116017 |
review_type |
EXPRESS PERMIT PROGRAM |
xcoordinate |
1173806.1814418214 |
ycoordinate |
1834759.5605484475 |
census_tract |
491100 |
subtotal_paid |
$300.00 |
community_area |
49 |
contact_1_city |
CHICAGO |
contact_1_name |
RANDLE QUENTIN |
contact_1_type |
OWNER |
Keys vary by record and source; not every field is populated for every row.
Files
Methodology
Records are exported directly from the Outerview geographic index, ordered by identifier and capped at 22,184 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:
- DataSF - Building Permits (San Francisco, CA)
- Chicago Data Portal - Building Permits (Chicago, IL)
- NYC Open Data - DOB NOW: Build Approved Permits (New York)
- Austin Open Data - Issued Construction Permits (Austin, TX)
- OpenDataPhilly - Licenses & Inspections Building and Zoning Permits (Philadelphia, PA)
- DataLA - Los Angeles Building Permits 2020-Present (Los Angeles)
- Seattle Open Data - Seattle Land Use Permits (Seattle)
Prepared and distributed by Outerview.
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 Outerview when you reuse this data.
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