Ignorar Navegação

Boston Safety Data

Boston 311 CRM Case Management City Services Public Safety

Chamadas para o 311 comunicadas pela câmara de Boston.

Siga esta ligação para saber mais sobre o BOS:311.

Volume e Retenção

Este conjunto de dados é armazenado no formato Parquet. É atualizado diariamente e contém cerca de 100 000 linhas (10 MB) no total à data de 2019.

Este conjunto de dados contém registos históricos acumulados desde 2011 até ao presente. Pode utilizar as definições de parâmetros no nosso SDK para obter os dados num intervalo de tempo específico.

Localização do Armazenamento

Este conjunto de dados é armazenado na região do Azure E.U.A. Leste. A alocação de recursos de computação nos E.U.A. Leste é recomendada por questões de afinidade.

Informações adicionais

Este conjunto de dados foi obtido junto da câmara de Boston. Pode encontrar mais detalhes aqui. Para obter a licença para utilizar este conjunto de dados, veja Open Data Commons Public Domain Dedication and License (ODC PDDL).

Avisos

A MICROSOFT DISPONIBILIZA OS CONJUNTOS DE DADOS ABERTOS DO AZURE TAL COMO ESTÃO. A MICROSOFT NÃO FAZ GARANTIAS, EXPRESSAS OU IMPLÍCITAS, NEM CONDIÇÕES RELATIVAMENTE À SUA UTILIZAÇÃO DOS CONJUNTOS DE DADOS. ATÉ AO LIMITE MÁXIMO PERMITIDO PELA LEGISLAÇÃO LOCAL, A MICROSOFT REJEITA QUALQUER RESPONSABILIDADE POR DANOS OU PERDAS, INCLUINDO DIRETOS, CONSEQUENCIAIS, ESPECIAIS, INDIRETOS, INCIDENTAIS OU PUNITIVOS, QUE RESULTEM DA SUA UTILIZAÇÃO DOS CONJUNTOS DE DADOS.

Este conjunto de dados é disponibilizado de acordo com os termos originais em que a Microsoft recebeu os dados de origem. O conjunto de dados pode incluir dados obtidos junto da 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 311_All 2/21/2021 12:03:17 AM Code Enforcement Unshoveled Sidewalk Open 10 Dawes St Dorchester MA 02125 42.3189 -71.0602 Citizens Connect App
Safety 311_All 2/21/2021 12:01:52 AM Enforcement & Abandoned Vehicles Parking Enforcement Open 195 E Cottage St Dorchester MA 02125 42.3197 -71.0608 Citizens Connect App
Safety 311_All 2/21/2021 12:01:00 AM Notification Notification Open 280 Friend St Boston MA 02114 42.3649 -71.0619 Constituent Call
Safety 311_All 2/21/2021 12:00:58 AM Code Enforcement Unshoveled Sidewalk Open 187 E Cottage St Dorchester MA 02125 42.3199 -71.0612 Citizens Connect App
Safety 311_All 2/21/2021 12:00:04 AM Code Enforcement Unshoveled Sidewalk Open 201 E Cottage St Dorchester MA 02125 42.3195 -71.0606 Citizens Connect App
Safety 311_All 2/20/2021 11:51:00 PM Signs & Signals Traffic Signal Inspection Open INTERSECTION of Massachusetts Ave & Beacon St Boston MA 42.3594 -71.0587 Constituent Call
Safety 311_All 2/20/2021 11:41:11 PM Street Cleaning Requests for Street Cleaning Open 81 Belnel Rd Mattapan MA 02126 42.2605 -71.1041 Citizens Connect App
Safety 311_All 2/20/2021 11:40:54 PM Street Cleaning Requests for Street Cleaning Open 81 Belnel Rd Mattapan MA 02126 42.2605 -71.1041 Citizens Connect App
Safety 311_All 2/20/2021 11:11:40 PM Street Cleaning Pick up Dead Animal Open 8 Beacon St Boston MA 02108 42.3582 -71.0616 Citizens Connect App
Safety 311_All 2/20/2021 11:05:00 PM Highway Maintenance Request for Pothole Repair Open 55 Church St West Roxbury MA 02132 42.2898 -71.1463 Citizens Connect App
Name Data type Unique Values (sample) Description
address string 142,046 \" \"
1 City Hall Plz Boston MA 02108

Localização.

category string 54 Street Cleaning
Sanitation

Motivo do pedido de serviço.

dataSubtype string 1 311_All

“311_All”

dataType string 1 Safety

“Segurança”

dateTime timestamp 1,723,318 2015-07-23 10:51:00
2015-07-23 10:47:00

Data e hora de abertura do pedido de emergência.

latitude double 1,622 42.3594
42.3603

Este é o valor da latitude. As linhas da latitude são paralelas ao equador.

longitude double 1,806 -71.0587
-71.0583

Este é o valor da longitude. As linhas da longitude são perpendiculares às linhas da latitude e todas passam por ambos os polos.

source string 7 Constituent Call
Citizens Connect App

Origem original do incidente.

status string 2 Closed
Open

Estado do caso.

subcategory string 208 Parking Enforcement
Requests for Street Cleaning

Tipo do pedido de serviço.

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 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()
ActivityStarted, to_pandas_dataframe ActivityStarted, to_pandas_dataframe_in_worker Looking for parquet files... Reading them into Pandas dataframe... Reading Safety/Release/city=Boston/part-00196-tid-845600952581210110-a4f62588-4996-42d1-bc79-23a9b4635c63-447039.c000.snappy.parquet under container citydatacontainer Done. ActivityCompleted: Activity=to_pandas_dataframe_in_worker, HowEnded=Success, Duration=2213.69 [ms] ActivityCompleted: Activity=to_pandas_dataframe, HowEnded=Success, Duration=2216.01 [ms]
In [2]:
safety.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1 entries, 56262 to 56262 Data columns (total 11 columns): dataType 1 non-null object dataSubtype 1 non-null object dateTime 1 non-null datetime64[ns] category 1 non-null object subcategory 1 non-null object status 1 non-null object address 1 non-null object latitude 1 non-null float64 longitude 1 non-null float64 source 1 non-null object extendedProperties 0 non-null object dtypes: datetime64[ns](1), float64(2), object(8) memory usage: 96.0+ bytes
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=Boston"
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 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()
ActivityStarted, to_spark_dataframe ActivityStarted, to_spark_dataframe_in_worker ActivityCompleted: Activity=to_spark_dataframe_in_worker, HowEnded=Success, Duration=2380.02 [ms] ActivityCompleted: Activity=to_spark_dataframe, HowEnded=Success, Duration=2381.75 [ms]
In [2]:
display(safety)
dataTypedataSubtypedateTimecategorysubcategorystatusaddresslatitudelongitudesourceextendedProperties
Safety311_All2015-07-24T12:48:24.000+0000Call InquiryOCR Front Desk InteractionsClosed 42.3594-71.0587Constituent Callnull
In [1]:
# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "citydatacontainer"
blob_relative_path = "Safety/Release/city=Boston"
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
In [1]:
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()
ActivityStarted, to_spark_dataframe ActivityStarted, to_spark_dataframe_in_worker ActivityCompleted: Activity=to_spark_dataframe_in_worker, HowEnded=Success, Duration=2380.02 [ms] ActivityCompleted: Activity=to_spark_dataframe, HowEnded=Success, Duration=2381.75 [ms]
In [2]:
display(safety)
dataTypedataSubtypedateTimecategorysubcategorystatusaddresslatitudelongitudesourceextendedProperties
Safety311_All2015-07-24T12:48:24.000+0000Call InquiryOCR Front Desk InteractionsClosed 42.3594-71.0587Constituent Callnull
In [1]:
# Azure storage access info
blob_account_name = "azureopendatastorage"
blob_container_name = "citydatacontainer"
blob_relative_path = "Safety/Release/city=Boston"
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'))

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.