Seattle Safety Data

Artikkel
08/28/2024

Seattle Fire Department 911 dispatches.

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's updated daily, and contains about 800,000 rows (20 MB) in 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. We recommend locating compute resources in East US for affinity.

Additional information

This dataset is sourced from city of Seattle government. For more information, see the city of Seattle website. View the Licensing and Attribution for the terms of using this dataset. Email open.data@seattle.gov if you have any questions about the data source.

Columns

Name	Data type	Unique	Values (sample)	Description
address	string	196,965	517 3rd Av 318 2nd Av Et S	Location of Incident.
category	string	232	Aid Response Medic Response	Response Type.
dataSubtype	string	1	911_Fire	“911_Fire”
dataType	string	1	Safety	“Safety”
dateTime	timestamp	1,533,401	2020-11-04 06:49:00 2019-06-19 13:49:00	The date and time of the call.
latitude	double	94,332	47.602172 47.600194	This is the latitude value. Lines of latitude are parallel to the equator.
longitude	double	79,492	-122.330863 -122.330541	This is the longitude value. Lines of longitude run perpendicular to lines of latitude, and all pass through both poles.

Preview

dataType	dataSubtype	dateTime	category	subcategory	status	address	latitude	longitude	source
Safety	911_Fire	4/28/2021 5:22:00 AM	Rubbish Fire	null	null	200 University St	47.607299	-122.337087	null
Safety	911_Fire	4/28/2021 5:15:00 AM	Triaged Incident	null	null	6th Ave / Olive Way	47.61313	-122.336282	null
Safety	911_Fire	4/28/2021 5:12:00 AM	Aid Response	null	null	4th Ave S / Seattle Blvd S	47.596486	-122.329046	null
Safety	911_Fire	4/28/2021 5:09:00 AM	Rubbish Fire	null	null	3rd Ave / University St	47.607763	-122.335976	null
Safety	911_Fire	4/28/2021 4:57:00 AM	Low Acuity Response	null	null	533 3rd Ave W	47.623717	-122.360635	null
Safety	911_Fire	4/28/2021 4:57:00 AM	Trans to AMR	null	null	4638 S Austin St	47.534702	-122.274812	null
Safety	911_Fire	4/28/2021 4:55:00 AM	Triaged Incident	null	null	8th Ave N / Harrison St	47.622051	-122.341066	null

Data access

Azure Notebooks

# 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()

safety.info()

# 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

# Azure storage access info
azure_storage_account_name = "azureopendatastorage"
azure_storage_sas_token = r""
container_name = "citydatacontainer"
folder_name = "Safety/Release/city=Seattle"

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)

# 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)

# you can add your filter at below
print('Loaded as a Pandas data frame: ')
df

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 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()

display(safety.limit(5))

# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "citydatacontainer"
blob_relative_path = "Safety/Release/city=Seattle"
blob_sas_token = r""

# 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)

# 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')

# Display top 10 rows
print('Displaying top 10 rows: ')
display(spark.sql('SELECT * FROM source LIMIT 10'))

Azure Synapse

# 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()

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

# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "citydatacontainer"
blob_relative_path = "Safety/Release/city=Seattle"
blob_sas_token = r""

# 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)

# 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')

# 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];

Examples

See the City Safety Analytics example on GitHub.

Next steps

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

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