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New York City Safety Data

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All New York City 311 service requests from 2010 to the present.

Note

Microsoft provides Azure Open Datasets on an “as is” basis. Microsoft makes no warranties, express or implied, guarantees or conditions with respect to your use of the datasets. To the extent permitted under your local law, Microsoft disclaims all liability for any damages or losses, including direct, consequential, special, indirect, incidental or punitive, resulting from your use of the datasets.

This dataset is provided under the original terms that Microsoft received source data. The dataset may include data sourced from Microsoft.

Volume and retention

This dataset is stored in Parquet format. It is updated daily, and contains about 12M rows (500 MB) in total as of 2019.

This dataset contains historical records accumulated from 2010 to the present. You can use parameter settings in our SDK to fetch data within a specific time range.

Storage location

This dataset is stored in the East US Azure region. Allocating compute resources in East US is recommended for affinity.

Additional information

This dataset is sourced from New York City government, for more information, see the City of New York website. See the terms of this dataset.

Columns

Name Data type Unique Values (sample) Description
address string 1,536,593 655 EAST 230 STREET 78-15 PARSONS BOULEVARD House number of incident address provided by submitter.
category string 446 Noise - Residential HEAT/HOT WATER This is the first level of a hierarchy identifying the topic of the incident or condition (Complaint Type). It may have a corresponding subcategory (Descriptor) or may stand alone.
dataSubtype string 1 311_All “311_All”
dataType string 1 Safety “Safety”
dateTime timestamp 17,332,609 2013-01-24 00:00:00 2015-01-08 00:00:00 Date Service Request was created.
latitude double 1,513,691 40.89187241649303 40.72195913199264 Geo based Latitude of the incident location.
longitude double 1,513,713 -73.86016845296459 -73.80969682426189 Geo based Longitude of the incident location.
status string 13 Closed Pending Status of Service Request submitted.
subcategory string 1,716 Loud Music/Party ENTIRE BUILDING This is associated to the category (Complaint Type), and provides further detail on the incident or condition. Its values are dependent on the Complaint Type, and are not always required in Service Request.

Preview

dataType dataSubtype dateTime category subcategory status address latitude longitude source extendedProperties
Safety 311_All 4/25/2021 2:05:05 AM Noise - Street/Sidewalk Loud Music/Party In Progress 2766 BATH AVENUE 40.5906129741766 -73.9847949011337 null
Safety 311_All 4/25/2021 2:04:33 AM Noise - Commercial Loud Music/Party In Progress 1033 WEBSTER AVENUE 40.8285784533256 -73.9117746958432 null
Safety 311_All 4/25/2021 2:04:27 AM Noise - Residential Loud Music/Party In Progress 620 WEST 141 STREET 40.8241726554395 -73.9530069547366 null
Safety 311_All 4/25/2021 2:04:04 AM Noise - Residential Loud Music/Party In Progress 1647 64 STREET 40.6218907202382 -73.9931125332078 null
Safety 311_All 4/25/2021 2:04:01 AM Noise - Residential Loud Music/Party In Progress 30 LENOX AVENUE 40.7991622274945 -73.9517496365803 null
Safety 311_All 4/25/2021 2:03:40 AM Illegal Parking Double Parked Blocking Traffic In Progress 304 WEST 148 STREET 40.8248229687124 -73.940696262361 null
Safety 311_All 4/25/2021 2:03:31 AM Noise - Street/Sidewalk Loud Music/Party In Progress ADEE AVENUE 40.8708386263454 -73.8382363208686 null
Safety 311_All 4/25/2021 2:03:18 AM Noise - Residential Loud Music/Party In Progress 340 EVERGREEN AVENUE 40.6947512704197 -73.9248330229197 null
Safety 311_All 4/25/2021 2:03:13 AM Noise - Residential Banging/Pounding In Progress 25 REMSEN STREET 40.6948938116483 -73.9973494607802 null

Data access

Azure Notebooks

# This is a package in preview.
from azureml.opendatasets import SanFranciscoSafety

from datetime import datetime
from dateutil import parser


end_date = parser.parse('2016-01-01')
start_date = parser.parse('2015-05-01')
safety = SanFranciscoSafety(start_date=start_date, end_date=end_date)
safety = safety.to_pandas_dataframe()

safety.info()

Azure Databricks

# This is a package in preview.
# You need to pip install azureml-opendatasets in Databricks cluster. https://learn.microsoft.com/azure/data-explorer/connect-from-databricks#install-the-python-library-on-your-azure-databricks-cluster
from azureml.opendatasets import SanFranciscoSafety

from datetime import datetime
from dateutil import parser


end_date = parser.parse('2016-01-01')
start_date = parser.parse('2015-05-01')
safety = SanFranciscoSafety(start_date=start_date, end_date=end_date)
safety = safety.to_spark_dataframe()

display(safety.limit(5))

Azure Synapse

# This is a package in preview.
from azureml.opendatasets import SanFranciscoSafety

from datetime import datetime
from dateutil import parser


end_date = parser.parse('2016-01-01')
start_date = parser.parse('2015-05-01')
safety = SanFranciscoSafety(start_date=start_date, end_date=end_date)
safety = safety.to_spark_dataframe()

# Display top 5 rows
display(safety.limit(5))

Examples

Next steps

View the rest of the datasets in the Open Datasets catalog.