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Seattle Safety Data

Seattle 911 Fire Dispatch E911 SFD Mobile Public Safety

A Seattle-i tűzoltóság segélyhívásra reagáló intézkedései.

Mennyiség és megőrzés

Az adatkészlet Parketta formátumban van tárolva. Naponta frissül, és 2019-ből összesen körülbelül 800 ezer sort (20 MB) tartalmaz.

Az adatkészlet a 2010-től napjainkig összegyűlt rekordokat tartalmazza. Adott időtartomány adatainak beolvasásához az általunk biztosított SDK paraméterbeállításait használhatja.

Tárolási hely

Az adatkészlet tárolási helye a Kelet-USA Azure-régió. Az affinitás érdekében a Kelet-USA régión belüli számítási erőforrások lefoglalását javasoljuk.

További információ

Az adatkészlet Seattle város közigazgatási szerveitől származik. A forrást itt tekintheti meg. Tekintse meg az adatkészlet használatára vonatkozó feltételek licencelési és terjesztési előírásait. Az adatforrással kapcsolatos kérdéseket e-mailben teheti fel ezen a címen: .

Értesítések

A MICROSOFT JELEN ÁLLAPOTUKBAN SZOLGÁLTATJA AZ AZURE NYÍLT ADATKÉSZLETEIT. A MICROSOFT NEM VÁLLAL SEMMINEMŰ KIFEJEZETT VAGY HALLGATÓLAGOS JÓTÁLLÁST AZ ADATKÉSZLETEK HASZNÁLATÁRA VONATKOZÓAN. A HELYI TÖRVÉNYEK ÁLTAL ENGEDETT MÉRTÉKBEN A MICROSOFT ELHÁRÍT MINDEN FELELŐSSÉGET AZ ADATKÉSZLETEK HASZNÁLATÁBÓL ADÓDÓ ESETLEGES KÁROKÉRT VAGY VESZTESÉGEKÉRT, BELEÉRTVE A KÖZVETLEN, KÖVETKEZMÉNYES, KÜLÖNLEGES, KÖZVETETT, VÉLETLEN VAGY BÜNTETÉSBŐL EREDŐ KÁROKAT.

Az adatkészletet a Microsoft forrásadataihoz tartozó eredeti feltételek szerint szolgáltatjuk. A készlet tartalmazhat Microsofttól származó adatokat.

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 1/7/2021 7:17:00 AM Medic Response null null 13716 Lake City Way Ne 47.72838 -122.292253 null
Safety 911_Fire 1/7/2021 7:08:00 AM Aid Response null null 6410 14th Av Nw 47.675337 -122.373572 null
Safety 911_Fire 1/7/2021 7:07:00 AM Aid Response null null 8316 Dibble Ave Nw 47.689227 -122.367271 null
Safety 911_Fire 1/7/2021 7:03:00 AM Low Acuity Response null null 2119 3rd Av 47.613308 -122.342432 null
Safety 911_Fire 1/7/2021 6:58:00 AM Aid Response null null 725 9th Av 47.606444 -122.325563 null
Safety 911_Fire 1/7/2021 6:41:00 AM Aid Response null null 2445 3rd Av S 47.581578 -122.329988 null
Safety 911_Fire 1/7/2021 6:35:00 AM Medic Response null null 7916 8th Av S 47.531837 -122.322826 null
Safety 911_Fire 1/7/2021 6:20:00 AM Auto Fire Alarm null null 2964 Sw Avalon Way 47.569061 -122.370737 null
Safety 911_Fire 1/7/2021 6:17:00 AM Water Job Minor null null 2600 Sw Holden St 47.533703 -122.365816 null
Safety 911_Fire 1/7/2021 6:05:00 AM Triaged Incident null null 1038 Ne 125th St 47.719477 -122.317223 null
Name Data type Unique Values (sample) Description
address string 191,230 517 3rd Av
318 2nd Av Et S

Az incidens helye.

category string 232 Aid Response
Medic Response

Válasz típusa.

dataSubtype string 1 911_Fire

“911_Fire”

dataType string 1 Safety

“Safety”

dateTime timestamp 1,507,181 2020-11-04 06:49:00
2020-08-07 12:44:00

A hívás dátuma és időpontja.

latitude double 93,711 47.602172
47.600194

Ez a földrajzi szélesség értéke. A földrajzi szélességi vonalak párhuzamosak az egyenlítővel.

longitude double 79,070 -122.330863
-122.330541

Ez a földrajzi hosszúság értéke. A hosszúsági körök merőlegesek a szélességi körökre, és mindkét sarkkört érintik.

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.