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西雅图安全数据

2024-09-03

西雅图消防部门 911 调遣。

注意

Microsoft 按“原样”提供 Azure 开放数据集。 Microsoft 对数据集的使用不提供任何担保（明示或暗示）、保证或条件。在当地法律允许的范围内，Microsoft 对使用数据集而导致的任何损害或损失不承担任何责任，包括直接、必然、特殊、间接、偶发或惩罚性损害或损失。

此数据集是根据 Microsoft 接收源数据的原始条款提供的。数据集可能包含来自 Microsoft 的数据。

数量和保留期

此数据集以 Parquet 格式存储。它每日更新，在 2019 年包含大约 800,000 行 (20 MB)。

此数据集包含从 2010 年至今累积的历史记录。可使用我们的 SDK 中的参数设置来提取特定时间范围内的数据。

存储位置

此数据集存储在美国东部 Azure 区域。为确保相关性，建议将计算资源置于美国东部。

其他信息

此数据集来自西雅图市政府。有关详细信息，请参阅西雅图城市网站。请参阅许可和属性，获取与使用此数据集相关的条款。如果对数据源有任何疑问，请发送电子邮件至 open.data@seattle.gov。

列

名称	数据类型	唯一	值（示例）	说明
address	字符串	196,965	517 3rd Av 318 2nd Av Et S	事件位置。
category	字符串	232	帮助响应医疗响应	响应类型。
dataSubtype	字符串	1	911_Fire	“911_Fire”
dataType	字符串	1	安全	“安全”
dateTime	timestamp	1,533,401	2020 年 11 月 4 日 06:49:00 2019 年 6 月 19 日 13:49:00	呼叫的日期和时间。
latitude	Double	94,332	47.602172 47.600194	这是纬度值。纬线平行于赤道。
longitude	Double	79,492	-122.330863 -122.330541	这是经度值。经度线垂直于纬度线，并且都穿过两个极点。

预览

dataType	dataSubtype	dateTime	category	子类别	状态	address	latitude	longitude	source
安全	911_Fire	2021 年 4 月 28 日凌晨 5:22:00	垃圾着火	null	null	200 University St	47.607299	-122.337087	null
安全	911_Fire	2021 年 4 月 28 日凌晨 5:15:00	会审事件	null	null	6th Ave / Olive Way	47.61313	-122.336282	null
安全	911_Fire	2021 年 4 月 28 日凌晨 5:12:00	帮助响应	null	null	4th Ave S / Seattle Blvd S	47.596486	-122.329046	null
安全	911_Fire	2021 年 4 月 28 日凌晨 5:09:00	垃圾着火	null	null	3rd Ave / University St	47.607763	-122.335976	null
安全	911_Fire	2021 年 4 月 28 日凌晨 4:57:00	低灵敏度响应	null	null	533 3rd Ave W	47.623717	-122.360635	null
安全	911_Fire	2021 年 4 月 28 日凌晨 4:57:00	转向 AMR	null	null	4638 S Austin St	47.534702	-122.274812	null
安全	911_Fire	2021 年 4 月 28 日凌晨 4:55:00	会审事件	null	null	8th Ave N / Harrison St	47.622051	-122.341066	null

数据访问

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];

示例

请参阅 GitHub 上的城市安全分析示例。

后续步骤

查看开放数据集目录中的其余数据集。