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Seattle Safety Data

Seattle 911 Fire Dispatch E911 SFD Mobile Public Safety

Servicios de los bomberos de Seattle por llamadas al 911.

Volumen y retención

Este conjunto de datos se almacena en formato Parquet. Se actualiza a diario y contiene unas 800 000 filas (20 MB) en total desde 2019.

Este conjunto de datos contiene registros históricos acumulados desde 2010 hasta la actualidad. Puede usar la configuración de parámetros de nuestro SDK para recuperar los datos de un intervalo de tiempo específico.

Ubicación de almacenamiento

Este conjunto de datos se almacena en la región Este de EE. UU. de Azure. Se recomienda asignar recursos de proceso de la misma región por afinidad.

Información adicional

Este conjunto de datos se alimenta con los datos de la administración pública de la ciudad de Seattle. El vínculo de origen se encuentra aquí. Consulte la información sobre licencias y atribuciones para conocer los términos de uso de este conjunto de datos. Envíe un correo electrónico a la dirección si tiene alguna duda sobre el origen de los datos.

Notificaciones

MICROSOFT PROPORCIONA AZURE OPEN DATASETS “TAL CUAL”. MICROSOFT NO OFRECE NINGUNA GARANTÍA, EXPRESA O IMPLÍCITA, NI CONDICIÓN CON RESPECTO AL USO QUE USTED HAGA DE LOS CONJUNTOS DE DATOS. EN LA MEDIDA EN LA QUE LO PERMITA SU LEGISLACIÓN LOCAL, MICROSOFT DECLINA TODA RESPONSABILIDAD POR POSIBLES DAÑOS O PÉRDIDAS, INCLUIDOS LOS DAÑOS DIRECTOS, CONSECUENCIALES, ESPECIALES, INDIRECTOS, INCIDENTALES O PUNITIVOS, QUE RESULTEN DE SU USO DE LOS CONJUNTOS DE DATOS.

Este conjunto de datos se proporciona bajo los términos originales con los que Microsoft recibió los datos de origen. El conjunto de datos puede incluir datos procedentes de 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 911_Fire 5/13/2021 12:07:00 PM Low Acuity Response null null 5146 S Morgan St 47.544278 -122.268606 null
Safety 911_Fire 5/13/2021 12:06:00 PM AFA4 - Auto Alarm 2 + 1 + 1 null null 4750 15th Ave Ne 47.663381 -122.311961 null
Safety 911_Fire 5/13/2021 12:03:00 PM Aid Response null null 1401 S Holgate St 47.586367 -122.314655 null
Safety 911_Fire 5/13/2021 12:02:00 PM Medic Response null null 5902 Lake Washington Blvd S 47.550605 -122.258063 null
Safety 911_Fire 5/13/2021 12:02:00 PM Aid Response null null 8124 Rainier Ave S 47.53025 -122.269989 null
Safety 911_Fire 5/13/2021 11:53:00 AM Trans to AMR null null 6042 Seaview Ave Nw 47.67296 -122.406358 null
Safety 911_Fire 5/13/2021 11:50:00 AM Water Job Minor null null 3015 Ne 120th St 47.715593 -122.296276 null
Safety 911_Fire 5/13/2021 11:44:00 AM Aid Response null null 1423 1st Ave 47.608051 -122.339309 null
Safety 911_Fire 5/13/2021 11:41:00 AM Aid Response null null 412 N 141st Ct 47.730869 -122.353525 null
Safety 911_Fire 5/13/2021 11:38:00 AM Aid Response null null 1522 E MADISON ST 47.614622 -122.312476 null
Name Data type Unique Values (sample) Description
address string 197,651 517 3rd Av
318 2nd Av Et S

Ubicación del incidente.

category string 231 Aid Response
Medic Response

Tipo de respuesta.

dataSubtype string 1 911_Fire

“911_Fire”

dataType string 1 Safety

“Safety”

dateTime timestamp 1,537,296 2020-11-04 06:49:00
2021-01-09 12:59:00

Fecha y hora de la llamada.

latitude double 94,418 47.602172
47.600194

Este es el valor de la latitud. Las líneas de la latitud son paralelas al ecuador.

longitude double 79,546 -122.330863
-122.330541

Este es el valor de la longitud. Las líneas de la longitud son perpendiculares a las líneas de la latitud y todas pasan por los dos polos.

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 SeattleSafety

from datetime import datetime
from dateutil import parser


end_date = parser.parse('2016-01-01')
start_date = parser.parse('2015-05-01')
safety = SeattleSafety(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=Seattle/part-00119-tid-845600952581210110-a4f62588-4996-42d1-bc79-23a9b4635c63-446962.c000.snappy.parquet under container citydatacontainer Done. ActivityCompleted: Activity=to_pandas_dataframe_in_worker, HowEnded=Success, Duration=6116.21 [ms] ActivityCompleted: Activity=to_pandas_dataframe, HowEnded=Success, Duration=6117.7 [ms]
In [2]:
safety.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 68346 entries, 14 to 1382908 Data columns (total 11 columns): dataType 68346 non-null object dataSubtype 68346 non-null object dateTime 68346 non-null datetime64[ns] category 68346 non-null object subcategory 0 non-null object status 0 non-null object address 68345 non-null object latitude 68346 non-null float64 longitude 68346 non-null float64 source 0 non-null object extendedProperties 68346 non-null object dtypes: datetime64[ns](1), float64(2), object(8) memory usage: 6.3+ 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=Seattle"
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 SeattleSafety

from datetime import datetime
from dateutil import parser


end_date = parser.parse('2016-01-01')
start_date = parser.parse('2015-05-01')
safety = SeattleSafety(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=2751.74 [ms] ActivityCompleted: Activity=to_spark_dataframe, HowEnded=Success, Duration=2753.86 [ms]
In [2]:
display(safety.limit(5))
dataTypedataSubtypedateTimecategorysubcategorystatusaddresslatitudelongitudesourceextendedProperties
Safety911_Fire2015-05-04T19:18:42.000+0000Medic Responsenullnull7101 38th Av S47.538872-122.284744nullincident_number:F150047883
Safety911_Fire2015-12-01T23:29:47.000+0000Aid Responsenullnull1011 S Weller St47.597509-122.319511nullincident_number:F150137603
Safety911_Fire2015-12-13T20:20:59.000+0000Aid Responsenullnull10049 College Way N47.701742-122.335029nullincident_number:F150142622
Safety911_Fire2015-11-23T00:19:21.000+0000Medic Responsenullnull9428 58th Av S47.518216-122.260497nullincident_number:F150134268
Safety911_Fire2015-05-19T16:25:55.000+0000Medic Responsenullnull10011 51st Av S47.510803-122.27006nullincident_number:F150054054
In [1]:
# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "citydatacontainer"
blob_relative_path = "Safety/Release/city=Seattle"
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 SQL
In [21]:
# This is a package in preview.
from azureml.opendatasets import SeattleSafety

from datetime import datetime
from dateutil import parser


end_date = parser.parse('2016-01-01')
start_date = parser.parse('2015-05-01')
safety = SeattleSafety(start_date=start_date, end_date=end_date)
safety = safety.to_spark_dataframe()
In [22]:
# Display top 5 rows
display(safety.limit(5))
Out[22]:
In [1]:
# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "citydatacontainer"
blob_relative_path = "Safety/Release/city=Seattle"
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'))
SELECT
    TOP 100 *
FROM
    OPENROWSET(
        BULK             'https://azureopendatastorage.blob.core.windows.net/citydatacontainer/Safety/Release/city=Seattle/*.parquet',
        FORMAT         = 'parquet'
    ) AS [r];

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.