3 datasets found
  1. Point-in-Time Homelessness Count

    • kaggle.com
    zip
    Updated May 6, 2020
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    Google BigQuery (2020). Point-in-Time Homelessness Count [Dataset]. https://www.kaggle.com/bigquery/sdoh-hud-pit-homelessness
    Explore at:
    zip(0 bytes)Available download formats
    Dataset updated
    May 6, 2020
    Dataset provided by
    BigQueryhttps://cloud.google.com/bigquery
    Authors
    Google BigQuery
    Description

    Context

    This database contains the data reported in the Annual Homeless Assessment Report to Congress (AHAR). It represents a point-In-time count (PIT) of homeless individuals, as well as a housing inventory count (HIC) conducted annually.

    The data represent the most comprehensive national-level assessment of homelessness in America, including PIT and HIC estimates of homelessness, as well as estimates of chronically homeless persons, homeless veterans, and homeless children and youth.

    These data can be trended over time and correlated with other metrics of housing availability and affordability, in order to better understand the particular type of housing resources that may be needed from a social determinants of health perspective.

    HUD captures these data annually through the Continuum of Care (CoC) program. CoC-level reporting data have been crosswalked to county levels for purposes of analysis of this dataset.

    Querying BigQuery tables

    You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.sdoh_hud_pit_homelessness

    Sample Query

    What has been the change in the number of homeless veterans in the state of New York’s CoC Regions since 2012? Determine how the patterns of homeless veterans have changes across the state of New York

    homeless_2018 AS ( SELECT Homeless_Veterans AS Vet18, CoC_Name
    FROM bigquery-public-data.sdoh_hud_pit_homelessness.hud_pit_by_coc WHERE SUBSTR(CoC_Number,0,2) = "NY" AND Count_Year = 2018 ),

    veterans_change AS ( SELECT homeless_2012.COC_Name, Vet12, Vet18, Vet18 - Vet12 AS VetChange FROM homeless_2018 JOIN homeless_2012 ON homeless_2018.CoC_Name = homeless_2012.CoC_Name )

    SELECT * FROM veterans_change

  2. US Continuums Of Care Records Based Homeless Population Statistics

    • johnsnowlabs.com
    csv
    Updated Jan 20, 2021
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    John Snow Labs (2021). US Continuums Of Care Records Based Homeless Population Statistics [Dataset]. https://www.johnsnowlabs.com/marketplace/us-continuums-of-care-records-based-homeless-population-statistics/
    Explore at:
    csvAvailable download formats
    Dataset updated
    Jan 20, 2021
    Dataset authored and provided by
    John Snow Labs
    Time period covered
    2007 - 2017
    Area covered
    United States
    Description

    This dataset contains estimates of homelessness, as well as estimates of chronically homeless persons, homeless veterans, and homeless children and youth provided by The U.S. Department of Housing and Urban Development. The estimates cover the period of years 2007-2017 and are at national, state and Continuums of Care (CoC) Point-In-Time (PIT) level.

  3. f

    Frequency of first administrative indicator of homelessness and receipt of...

    • plos.figshare.com
    xls
    Updated Jun 5, 2023
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    Rachel Peterson; Adi V. Gundlapalli; Stephen Metraux; Marjorie E. Carter; Miland Palmer; Andrew Redd; Matthew H. Samore; Jamison D. Fargo (2023). Frequency of first administrative indicator of homelessness and receipt of V60.0 and outpatient services in the 90 days immediately following the first administrative indicator among a cohort of OEF/OIF/OND Veterans during fiscal years 2001–2012. [Dataset]. http://doi.org/10.1371/journal.pone.0132664.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 5, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Rachel Peterson; Adi V. Gundlapalli; Stephen Metraux; Marjorie E. Carter; Miland Palmer; Andrew Redd; Matthew H. Samore; Jamison D. Fargo
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Note. ICD-9 codes V60.0 (lack of housing), V60.1 (inadequate housing), V60.89 (other specified housing or economic circumstance), V60.9 (unspecified housing or economic circumstances) and clinic stop codes 522 or 530 (Department of Housing and Urban Development VA shared housing program [HUD-VASH]), 528 (telephone/homeless mentally ill [HMI]), 529 (health care for homeless Veterans); 590 (community outreach to homeless Veterans), or inpatient homeless stays (28, homeless compensated mental health residential and rehab treatment program [(MH RRTP CWT/TR, treatment specialty]; 37, domiciliary care for homeless Veterans [DCHV, treatment specialty]).

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Share
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TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Google BigQuery (2020). Point-in-Time Homelessness Count [Dataset]. https://www.kaggle.com/bigquery/sdoh-hud-pit-homelessness
Organization logo

Point-in-Time Homelessness Count

Annual Homeless Assessment Report to Congress

Explore at:
161 scholarly articles cite this dataset (View in Google Scholar)
zip(0 bytes)Available download formats
Dataset updated
May 6, 2020
Dataset provided by
BigQueryhttps://cloud.google.com/bigquery
Authors
Google BigQuery
Description

Context

This database contains the data reported in the Annual Homeless Assessment Report to Congress (AHAR). It represents a point-In-time count (PIT) of homeless individuals, as well as a housing inventory count (HIC) conducted annually.

The data represent the most comprehensive national-level assessment of homelessness in America, including PIT and HIC estimates of homelessness, as well as estimates of chronically homeless persons, homeless veterans, and homeless children and youth.

These data can be trended over time and correlated with other metrics of housing availability and affordability, in order to better understand the particular type of housing resources that may be needed from a social determinants of health perspective.

HUD captures these data annually through the Continuum of Care (CoC) program. CoC-level reporting data have been crosswalked to county levels for purposes of analysis of this dataset.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.sdoh_hud_pit_homelessness

Sample Query

What has been the change in the number of homeless veterans in the state of New York’s CoC Regions since 2012? Determine how the patterns of homeless veterans have changes across the state of New York

homeless_2018 AS ( SELECT Homeless_Veterans AS Vet18, CoC_Name
FROM bigquery-public-data.sdoh_hud_pit_homelessness.hud_pit_by_coc WHERE SUBSTR(CoC_Number,0,2) = "NY" AND Count_Year = 2018 ),

veterans_change AS ( SELECT homeless_2012.COC_Name, Vet12, Vet18, Vet18 - Vet12 AS VetChange FROM homeless_2018 JOIN homeless_2012 ON homeless_2018.CoC_Name = homeless_2012.CoC_Name )

SELECT * FROM veterans_change

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