311-oproepen die aan de stad Boston worden gemeld.
Volg deze koppeling voor meer informatie over BOS:311.
Volume en retentie
Deze gegevensset wordt opgeslagen in de Parquet-indeling. De gegevensset wordt dagelijks bijgewerkt en bevat sinds 2019 in totaal ongeveer 100.000 rijen (10 MB).
Deze gegevensset bevat historische records die vanaf 2011 tot heden zijn verzameld. U kunt in onze SDK gebruikmaken van parameterinstellingen om gegevens op te halen binnen een specifiek tijdsbereik.
Opslaglocatie
Deze gegevensset wordt opgeslagen in de Azure-regio US - oost. Het wordt aanbevolen om rekenresources in US - oost toe te wijzen voor affiniteit.
Aanvullende informatie
Deze gegevensset is afkomstig van het stadsbestuur van Boston. U vindt hier meer informatie. Raadpleeg Open Data Commons Public Domain Dedication and License (ODC PDDL) voor de licentie voor het gebruik van deze gegevensset.
Mededelingen
AZURE OPEN GEGEVENSSETS WORDEN DOOR MICROSOFT ONGEWIJZIGD GELEVERD. MICROSOFT GEEFT GEEN GARANTIES, EXPLICIET OF IMPLICIET, ZEKERHEDEN OF VOORWAARDEN MET BETREKKING TOT HET GEBRUIK VAN DE GEGEVENSSETS. VOOR ZOVER IS TOEGESTAAN ONDER HET TOEPASSELIJKE RECHT, WIJST MICROSOFT ALLE AANSPRAKELIJKHEID AF VOOR SCHADE OF VERLIEZEN, WAARONDER GEVOLGSCHADE OF DIRECTE, SPECIALE, INDIRECTE, INCIDENTELE OF PUNITIEVE SCHADE DIE VOORTVLOEIT UIT HET GEBRUIK VAN DE GEGEVENSSETS.
Deze gegevensset wordt geleverd onder de oorspronkelijke voorwaarden dat Microsoft de brongegevens heeft ontvangen. De gegevensset kan gegevens bevatten die afkomstig zijn van Microsoft.
Access
Available in | When 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 | 1/4/2021 11:35:57 PM | Enforcement & Abandoned Vehicles | Parking Enforcement | Open | 124 Boston St Dorchester MA 02125 | 42.3253 | -71.059 | Citizens Connect App | |
Safety | 311_All | 1/4/2021 11:32:16 PM | Street Lights | Street Light Outages | Open | 69 Coleman St Dorchester MA 02125 | 42.3078 | -71.0671 | Citizens Connect App | |
Safety | 311_All | 1/4/2021 11:24:08 PM | Graffiti | Graffiti Removal | Open | 161 Allston St Allston MA 02134 | 42.3481 | -71.1381 | Self Service | |
Safety | 311_All | 1/4/2021 11:18:30 PM | Highway Maintenance | Request for Pothole Repair | Open | 173 Alford St Charlestown MA 02129 | 42.3906 | -71.0702 | Citizens Connect App | |
Safety | 311_All | 1/4/2021 11:13:00 PM | Noise Disturbance | Automotive Noise Disturbance | Open | 122 Falcon St East Boston MA 02128 | 42.3821 | -71.0342 | Constituent Call | |
Safety | 311_All | 1/4/2021 11:06:01 PM | Enforcement & Abandoned Vehicles | Parking Enforcement | Closed | INTERSECTION of Brimmer St & Otis Pl Boston MA | 42.3594 | -71.0587 | Citizens Connect App | |
Safety | 311_All | 1/4/2021 10:59:37 PM | Code Enforcement | Poor Conditions of Property | Open | INTERSECTION of Blue Hill Ave & Dewey St Roxbury MA | 42.3594 | -71.0587 | Citizens Connect App | |
Safety | 311_All | 1/4/2021 10:58:11 PM | Enforcement & Abandoned Vehicles | Abandoned Vehicles | Open | 944 Dorchester Ave Dorchester MA 02125 | 42.3184 | -71.0563 | Citizens Connect App | |
Safety | 311_All | 1/4/2021 10:50:36 PM | Street Cleaning | Requests for Street Cleaning | Open | 154-158 Meridian St East Boston MA 02128 | 42.3741 | -71.0392 | Citizens Connect App | |
Safety | 311_All | 1/4/2021 10:50:28 PM | Code Enforcement | Poor Conditions of Property | Open | INTERSECTION of Blue Hill Ave & Dewey St Roxbury MA | 42.3594 | -71.0587 | Citizens Connect App |
Name | Data type | Unique | Values (sample) | Description |
---|---|---|---|---|
address | string | 138,798 | \" \" 1 City Hall Plz Boston MA 02108 |
Locatie. |
category | string | 54 | Street Cleaning Sanitation |
Reden van de serviceaanvraag. |
dataSubtype | string | 1 | 311_All | “311_All” |
dataType | string | 1 | Safety | ‘Safety’ |
dateTime | timestamp | 1,471,504 | 2015-07-23 10:51:00 2015-07-23 10:47:00 |
Datum en tijdstip waarop de serviceaanvraag is geopend. |
latitude | double | 1,622 | 42.3594 42.3603 |
Dit is de waarde voor de breedtegraad. Breedtegraden lopen parallel aan de evenaar. |
longitude | double | 1,806 | -71.0587 -71.0583 |
Dit is de waarde voor de lengtegraad. Lengtegraden staan haaks op breedtegraden en doorkruisen beide polen. |
source | string | 7 | Constituent Call Citizens Connect App |
Oorspronkelijke bron van de case. |
status | string | 2 | Closed Open |
Status van de case. |
subcategory | string | 208 | Parking Enforcement Requests for Street Cleaning |
Het type van de serviceaanvraag. |
Azure Notebooks
# This is a package in preview.
from azureml.opendatasets import BostonSafety
from datetime import datetime
from dateutil import parser
end_date = parser.parse('2016-01-01')
start_date = parser.parse('2015-05-01')
safety = BostonSafety(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=Boston"
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://docs.microsoft.com/en-us/azure/data-explorer/connect-from-databricks#install-the-python-library-on-your-azure-databricks-cluster
from azureml.opendatasets import BostonSafety
from datetime import datetime
from dateutil import parser
end_date = parser.parse('2016-01-01')
start_date = parser.parse('2015-05-01')
safety = BostonSafety(start_date=start_date, end_date=end_date)
safety = safety.to_spark_dataframe()
display(safety)
# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "citydatacontainer"
blob_relative_path = "Safety/Release/city=Boston"
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
from azureml.opendatasets import BostonSafety
from datetime import datetime
from dateutil import parser
end_date = parser.parse('2016-01-01')
start_date = parser.parse('2015-05-01')
safety = BostonSafety(start_date=start_date, end_date=end_date)
safety = safety.to_spark_dataframe()
display(safety)
# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "citydatacontainer"
blob_relative_path = "Safety/Release/city=Boston"
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'))

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.