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New York City Safety Data

New York City Social Services 311 Service Requests City Government Public Safety

Alle New York City 311-serviceanmodninger fra 2010 og frem til i dag.

Mængde og opbevaring

Dette datasæt gemmes i Parquet-formatet. Det opdateres dagligt og indeholder omkring 12.000.000 rækker (500 MB) i alt fra og med 2019.

Dette datasæt indeholder historiske poster, der er akkumuleret fra 2010 og frem til i dag. Du kan bruge parameterindstillingerne i vores SDK til at hente data inden for en bestemt tidsperiode.

Lagerplacering

Dette datasæt er gemt i Azure-området Det østlige USA. Tildeling af beregningsressourcer i det østlige USA anbefales af tilhørsmæssige årsager.

Yderligere oplysninger

Dette datasæt kommer fra bystyret i New York City. Du kan få flere oplysninger her. Se vilkårene for brug af dette datasæt her.

Meddelelser

MICROSOFT STILLER AZURE OPEN DATASETS TIL RÅDIGHED, SOM DE ER OG FOREFINDES. MICROSOFT FRASKRIVER SIG ETHVERT ANSVAR, UDTRYKKELIGT ELLER STILTIENDE, OG GARANTIER ELLER BETINGELSER MED HENSYN TIL BRUGEN AF DATASÆTTENE. I DET OMFANG DET ER TILLADT I HENHOLD TIL GÆLDENDE LOVGIVNING FRASKRIVER MICROSOFT SIG ETHVERT ANSVAR FOR SKADER ELLER TAB, INKLUSIVE DIREKTE, FØLGESKADER, SÆRLIGE SKADER, INDIREKTE SKADER, HÆNDELIGE SKADER ELLER PONALE SKADER, DER MÅTTE OPSTÅ I FORBINDELSE MED BRUG AF DATASÆTTENE.

Dette datasæt stilles til rådighed under de oprindelige vilkår, som Microsoft modtog kildedataene under. Datasættet kan indeholde data fra Microsoft.

Access

Available inWhen to use
Azure Notebooks

Quickly explore the dataset with Jupyter notebooks hosted on Azure or your local machine.

Azure Databricks

Use this when you need the scale of an Azure managed Spark cluster to process the dataset.

Azure Synapse

Use this when you need the scale of an Azure managed Spark cluster to process the dataset.

Preview

dataType dataSubtype dateTime category subcategory status address latitude longitude source extendedProperties
Safety 311_All 10/21/2020 2:03:37 AM Noise - Street/Sidewalk Loud Talking In Progress 54 BRISTOL STREET 40.6701111766495 -73.9127917960956 null
Safety 311_All 10/21/2020 2:02:45 AM Noise - Street/Sidewalk Loud Music/Party In Progress 2371 EXTERIOR STREET 40.8652797014419 -73.9105112249567 null
Safety 311_All 10/21/2020 2:02:42 AM Homeless Person Assistance N/A Assigned 677 FIFTH AVENUE 40.7606336684385 -73.975659556277 null
Safety 311_All 10/21/2020 2:01:53 AM Illegal Parking Blocked Hydrant In Progress 104-35 121 STREET 40.6865826902044 -73.8222844441511 null
Safety 311_All 10/21/2020 2:01:30 AM Blocked Driveway No Access In Progress 3664 BARNES AVENUE 40.8799756553691 -73.8613868262772 null
Safety 311_All 10/21/2020 2:00:55 AM Noise - Residential Loud Talking In Progress 2605 GRAND CONCOURSE 40.8647262928713 -73.895406880958 null
Safety 311_All 10/21/2020 2:00:53 AM Noise - Residential Loud Television In Progress 1465 WASHINGTON AVENUE 40.8369406629242 -73.903744412981 null
Safety 311_All 10/21/2020 1:59:53 AM Noise - Residential Banging/Pounding In Progress 51-01 39 AVENUE 40.7488089613434 -73.9126707302604 null
Safety 311_All 10/21/2020 1:59:46 AM Noise - Vehicle Car/Truck Music In Progress 37-52 104 STREET 40.7520925510192 -73.8628584274433 null
Safety 311_All 10/21/2020 1:57:45 AM Noise - Residential Loud Music/Party In Progress 60 WEST 104 STREET 40.7976101667666 -73.9631241324697 null
Name Data type Unique Values (sample) Description
address string 1,466,414 655 EAST 230 STREET
89-21 ELMHURST AVENUE

Husnummer for adressen for den hændelse, som indsenderen har angivet.

category string 444 Noise - Residential
HEAT/HOT WATER

Dette er det første niveau i et hierarki, der identificerer emnet for hændelsen eller forholdet (klagetype). Det kan have en tilsvarende underkategori (beskrivelse) eller også stå alene.

dataSubtype string 1 311_All

“311_All”

dataType string 1 Safety

“Sikkerhed”

dateTime timestamp 16,375,396 2013-01-24 00:00:00
2015-01-08 00:00:00

Den dato, hvor serviceanmodningen blev oprettet.

latitude double 1,469,095 40.89187241649303
40.1123853

Geobaseret breddegrad for placeringen af hændelsen.

longitude double 1,490,963 -73.86016845296459
-77.5195844

Geobaseret længdegrad for placeringen af hændelsen.

status string 12 Closed
Pending

Status for den indsendte serviceanmodning.

subcategory string 1,692 Loud Music/Party
HEAT

Den er knyttet til kategorien (Klagetype) og giver yderligere oplysninger om hændelsen eller forholdet. Dens værdier er afhængige af klagetypen og er ikke altid påkrævet i serviceanmodningen.

Select your preferred service:

Azure Notebooks

Azure Databricks

Azure Synapse

Azure Notebooks

Package: Language: Python Python
In [1]:
# This is a package in preview.
from azureml.opendatasets import NycSafety

from datetime import datetime
from dateutil import parser


end_date = parser.parse('2016-01-01')
start_date = parser.parse('2015-05-01')
safety = NycSafety(start_date=start_date, end_date=end_date)
safety = safety.to_pandas_dataframe()
ActivityStarted, to_pandas_dataframe ActivityStarted, to_pandas_dataframe_in_worker Looking for parquet files... Reading them into Pandas dataframe... Reading Safety/Release/city=NewYorkCity/part-00026-tid-845600952581210110-a4f62588-4996-42d1-bc79-23a9b4635c63-446869.c000.snappy.parquet under container citydatacontainer Done. ActivityCompleted: Activity=to_pandas_dataframe_in_worker, HowEnded=Success, Duration=106593.46 [ms] ActivityCompleted: Activity=to_pandas_dataframe, HowEnded=Success, Duration=106687.96 [ms]
In [2]:
safety.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1204035 entries, 7 to 12307252 Data columns (total 11 columns): dataType 1204035 non-null object dataSubtype 1204035 non-null object dateTime 1204035 non-null datetime64[ns] category 1204035 non-null object subcategory 1203974 non-null object status 1204035 non-null object address 1010833 non-null object latitude 1169358 non-null float64 longitude 1169358 non-null float64 source 0 non-null object extendedProperties 0 non-null object dtypes: datetime64[ns](1), float64(2), object(8) memory usage: 110.2+ MB
In [1]:
# Pip install packages
import os, sys

!{sys.executable} -m pip install azure-storage-blob
!{sys.executable} -m pip install pyarrow
!{sys.executable} -m pip install pandas
In [2]:
# Azure storage access info
azure_storage_account_name = "azureopendatastorage"
azure_storage_sas_token = r""
container_name = "citydatacontainer"
folder_name = "Safety/Release/city=NewYorkCity"
In [3]:
from azure.storage.blob import BlockBlobServicefrom azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient

if azure_storage_account_name is None or azure_storage_sas_token is None:
    raise Exception(
        "Provide your specific name and key for your Azure Storage account--see the Prerequisites section earlier.")

print('Looking for the first parquet under the folder ' +
      folder_name + ' in container "' + container_name + '"...')
container_url = f"https://{azure_storage_account_name}.blob.core.windows.net/"
blob_service_client = BlobServiceClient(
    container_url, azure_storage_sas_token if azure_storage_sas_token else None)

container_client = blob_service_client.get_container_client(container_name)
blobs = container_client.list_blobs(folder_name)
sorted_blobs = sorted(list(blobs), key=lambda e: e.name, reverse=True)
targetBlobName = ''
for blob in sorted_blobs:
    if blob.name.startswith(folder_name) and blob.name.endswith('.parquet'):
        targetBlobName = blob.name
        break

print('Target blob to download: ' + targetBlobName)
_, filename = os.path.split(targetBlobName)
blob_client = container_client.get_blob_client(targetBlobName)
with open(filename, 'wb') as local_file:
    blob_client.download_blob().download_to_stream(local_file)
In [4]:
# Read the parquet file into Pandas data frame
import pandas as pd

print('Reading the parquet file into Pandas data frame')
df = pd.read_parquet(filename)
In [5]:
# you can add your filter at below
print('Loaded as a Pandas data frame: ')
df
In [6]:
 

Azure Databricks

Package: Language: Python Python
In [1]:
# This is a package in preview.
# You need to pip install azureml-opendatasets in Databricks cluster. https://docs.microsoft.com/en-us/azure/data-explorer/connect-from-databricks#install-the-python-library-on-your-azure-databricks-cluster
from azureml.opendatasets import NycSafety

from datetime import datetime
from dateutil import parser


end_date = parser.parse('2016-01-01')
start_date = parser.parse('2015-05-01')
safety = NycSafety(start_date=start_date, end_date=end_date)
safety = safety.to_spark_dataframe()
ActivityStarted, to_spark_dataframe ActivityStarted, to_spark_dataframe_in_worker ActivityCompleted: Activity=to_spark_dataframe_in_worker, HowEnded=Success, Duration=4392.11 [ms] ActivityCompleted: Activity=to_spark_dataframe, HowEnded=Success, Duration=4395.98 [ms]
In [2]:
display(safety.limit(5))
dataTypedataSubtypedateTimecategorysubcategorystatusaddresslatitudelongitudesourceextendedProperties
Safety311_All2015-12-28T13:58:58.000+0000HEAT/HOT WATERENTIRE BUILDINGClosed548 11 STREET40.664924841709606-73.98101480555805nullnull
Safety311_All2015-06-14T01:11:08.000+0000Noise - ResidentialLoud Music/PartyClosednull40.86969422534882-73.86620623861982nullnull
Safety311_All2015-06-14T04:47:37.000+0000Noise - ResidentialLoud TalkingClosednull40.858744389082254-73.93011726711445nullnull
Safety311_All2015-06-16T16:56:00.000+0000SewerCatch Basin Clogged/Flooding (Use Comments) (SC)Closed82 JEWETT AVENUE40.63510898432114-74.12886658384302nullnull
Safety311_All2015-06-22T14:03:05.000+0000ELECTRICLIGHTINGClosed2170 BATHGATE AVENUE40.852335329676464-73.89389734164266nullnull
In [1]:
# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "citydatacontainer"
blob_relative_path = "Safety/Release/city=NewYorkCity"
blob_sas_token = r""
In [2]:
# Allow SPARK to read from Blob remotely
wasbs_path = 'wasbs://%s@%s.blob.core.windows.net/%s' % (blob_container_name, blob_account_name, blob_relative_path)
spark.conf.set(
  'fs.azure.sas.%s.%s.blob.core.windows.net' % (blob_container_name, blob_account_name),
  blob_sas_token)
print('Remote blob path: ' + wasbs_path)
In [3]:
# SPARK read parquet, note that it won't load any data yet by now
df = spark.read.parquet(wasbs_path)
print('Register the DataFrame as a SQL temporary view: source')
df.createOrReplaceTempView('source')
In [4]:
# Display top 10 rows
print('Displaying top 10 rows: ')
display(spark.sql('SELECT * FROM source LIMIT 10'))

Azure Synapse

Package: Language: Python Python
In [15]:
# This is a package in preview.
from azureml.opendatasets import NycSafety

from datetime import datetime
from dateutil import parser


end_date = parser.parse('2016-01-01')
start_date = parser.parse('2015-05-01')
safety = NycSafety(start_date=start_date, end_date=end_date)
safety = safety.to_spark_dataframe()
In [16]:
# Display top 5 rows
display(safety.limit(5))
Out[16]:
In [1]:
# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "citydatacontainer"
blob_relative_path = "Safety/Release/city=NewYorkCity"
blob_sas_token = r""
In [2]:
# Allow SPARK to read from Blob remotely
wasbs_path = 'wasbs://%s@%s.blob.core.windows.net/%s' % (blob_container_name, blob_account_name, blob_relative_path)
spark.conf.set(
  'fs.azure.sas.%s.%s.blob.core.windows.net' % (blob_container_name, blob_account_name),
  blob_sas_token)
print('Remote blob path: ' + wasbs_path)
In [3]:
# SPARK read parquet, note that it won't load any data yet by now
df = spark.read.parquet(wasbs_path)
print('Register the DataFrame as a SQL temporary view: source')
df.createOrReplaceTempView('source')
In [4]:
# Display top 10 rows
print('Displaying top 10 rows: ')
display(spark.sql('SELECT * FROM source LIMIT 10'))

City Safety

From the Urban Innovation Initiative at Microsoft Research, databricks notebook for analytics with safety data (311 and 911 call data) from major U.S. cities. Analyses show frequency distributions and geographic clustering of safety issues within cities.