This statistic depicts the rate of homeless individuals in the United States in 2017, by metropolitan area. In 2017, the rate of homelessness per 10,000 individuals was highest in New York City, at ****.

The DC Metropolitan Area Drug Study (DCMADS) was conducted in 1991, and included special analyses of homeless and transient populations and of women delivering live births in the DC hospitals. DCMADS was undertaken to assess the full extent of the drug problem in one metropolitan area. The study was comprised of 16 separate studies that focused on different sub-groups, many of which are typically not included or are underrepresented in household surveys. The Homeless and Transient Population study examines the prevalence of illicit drug, alcohol, and tobacco use among members of the homeless and transient population aged 12 and older in the Washington, DC, Metropolitan Statistical Area (DC MSA). The sample frame included respondents from shelters, soup kitchens and food banks, major cluster encampments, and literally homeless people. Data from the questionnaires include history of homelessness, living arrangements and population movement, tobacco, drug, and alcohol use, consequences of use, treatment history, illegal behavior and arrest, emergency room treatment and hospital stays, physical and mental health, pregnancy, insurance, employment and finances, and demographics. Drug specific data include age at first use, route of administration, needle use, withdrawal symptoms, polysubstance use, and perceived risk.This study has 1 Data Set.

This statistic depicts the share of homeless individuals in the United States in 2017, by metropolitan area and sheltered status. In 2017, about ** percent of Los Angeles' homeless population were housed in permanent supportive housing.

This statistic depicts the number of homeless individuals in the United States in 2017, by metropolitan area. In 2017, there were an estimated 55,200 homeless individuals living in Los Angeles, California.

Aggregated and summarized information collected from the Point in Time count of the number of persons experiencing homelessness in the Phoenix-Mesa metro area as of the survey date. Detailed results for Mesa Only at https://data.mesaaz.gov/Community-Services/Unsheltered-Point-In-Time-PIT-Count-Details-Mesa-O/efjd-c5mi Due to the unprecedented COVID-19 pandemic, the US Department of Housing and Urban Development (HUD) approved the Maricopa Regional Continuum of Care to opt out of the unsheltered Point In Time (PIT) Homeless Count for 2021. Every January, volunteers and outreach teams from local communities collaborate to survey and count the number of homeless. persons in their respective locations. With the information provided by the PIT Count, the Maricopa Regional Continuum of Care and local communities can determine how best to address homelessness. For more information see https://www.azmag.gov/Programs/Homelessness/Point-In-Time-Homeless-Count. NOTE: The HUD definition of chronic homelessness is: (1) a person who lives in a place not meant for human habitation, Safe Haven, or Emergency Shelter, (2) has a disability, and (3) has been homeless continuously for one year OR four or more times homeless in the last three years, where the combined length of time homeless is at least 12 months.

INTRODUCTION: As California’s homeless population continues to grow at an alarming rate, large metropolitan regions like the San Francisco Bay Area face unique challenges in coordinating efforts to track and improve homelessness. As an interconnected region of nine counties with diverse community needs, identifying homeless population trends across San Francisco Bay Area counties can help direct efforts more effectively throughout the region, and inform initiatives to improve homelessness at the city, county, and metropolitan level. OBJECTIVES: The primary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness across San Francisco Bay Area counties between the years 2018-2022. The secondary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness among different age groups in each of the nine San Francisco Bay Area counties between the years 2018-2022. METHODS: Two datasets were used to conduct research. The first dataset (Dataset 1) contains Point-in-Time (PIT) homeless counts published by the U.S. Department of Housing and Urban Development. Dataset 1 was cleaned using Microsoft Excel and uploaded to Tableau Desktop Public Edition 2022.4.1 as a CSV file. The second dataset (Dataset 2) was published by Data SF and contains shapefiles of geographic boundaries of San Francisco Bay Area counties. Both datasets were joined in Tableau Desktop Public Edition 2022.4 and all data analysis was conducted using Tableau visualizations in the form of bar charts, highlight tables, and maps. RESULTS: Alameda, San Francisco, and Santa Clara counties consistently reported the highest annual count of people experiencing homelessness across all 5 years between 2018-2022. Alameda, Napa, and San Mateo counties showed the largest increase in homelessness between 2018 and 2022. Alameda County showed a significant increase in homeless individuals under the age of 18. CONCLUSIONS: Results from this research reveal both stark and fluctuating differences in homeless counts among San Francisco Bay Area Counties over time, suggesting that a regional approach that focuses on collaboration across counties and coordination of services could prove beneficial for improving homelessness throughout the region. Results suggest that more immediate efforts to improve homelessness should focus on the counties of Alameda, San Francisco, Santa Clara, and San Mateo. Changes in homelessness during the COVID-19 pandemic years of 2020-2022 point to an urgent need to support Contra Costa County.

In the United States in 2023, **** percent of the homeless population living in El Dorado County, California were unsheltered.

This dataset tracks the updates made on the dataset "Washington DC Metropolitan Area Drug Study Homeless and Transient Population (DC-MADST-1991)" as a repository for previous versions of the data and metadata.

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The Community Services division encompasses the client-based services including Neighborhood Place, Community Action Partnership, Self-Sufficiency Services, and Outreach & Advocacy.VisualData Dictionary:Index - Numeric identifier for each list item.Concern - High level issue related to homelessness in Louisville summarized briefly.Impacted population - Group most impacted by the listed concern.GAP in Local Services - Missing process / structure in the local system which leaves the concern unaddressed.Barriers to Progress - What issue/problem would need to be overcome to advance solutions to the listed concern.Possible FY22 Initiatives - What actions could be taken during FY22 to work toward fixing the listed concern.Post Fiscal Year Impact - Expected medium/long term result of proposed initiative.Financial Requirements - Brief statement about finances. i.e., what level of funding would be needed to accomplish the proposed initiative, identify potential source, explain use of funds, etc.Structural Changes Needed? - Y/N column indicating if capital changes (construction, renovation, purchase of land/structures, etc.) will be required to address concern.Contact:Ethan Lambertethan.lambert@louisvilleky.gov

In the United States in 2023, **** percent of the unaccompanied homeless youth in the Watsonville/Santa Cruz City and County, California were unsheltered.

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This data is no longer being actively updated. The dataset is deprecated and will be removed from the Portal within the next three months. If you have any questions, please reach out to the Open Data team by filling out the following Contact Us form: https://louisvilleky.wufoo.com/forms/open-data-contact-form/ The Community Services division encompasses the client-based services including Neighborhood Place, Community Action Partnership, Self-Sufficiency Services, and Outreach & Advocacy.VisualData Dictionary:Index - Numeric identifier for each list item.Concern - High level issue related to homelessness in Louisville summarized briefly.Impacted population - Group most impacted by the listed concern.GAP in Local Services - Missing process / structure in the local system which leaves the concern unaddressed.Barriers to Progress - What issue/problem would need to be overcome to advance solutions to the listed concern.Possible FY22 Initiatives - What actions could be taken during FY22 to work toward fixing the listed concern.Post Fiscal Year Impact - Expected medium/long term result of proposed initiative.Financial Requirements - Brief statement about finances. i.e., what level of funding would be needed to accomplish the proposed initiative, identify potential source, explain use of funds, etc.Structural Changes Needed? - Y/N column indicating if capital changes (construction, renovation, purchase of land/structures, etc.) will be required to address concern.Contact:Ethan Lambertethan.lambert@louisvilleky.gov

A Point-in-time homeless count has been conducted in Metro Vancouver since 2002. The data provided here was collected on the evening of March3rd and throughout the day/evening of March4th to give a snapshot of homelessness in the region. The count took place approximately two weeks before the Government of British Columbia declared a state of emergency due to the novel coronavirus. Although the pandemic did not markedly impact the implementation of the count, the state of homelessness in the region may have shifted significantly since March.

**No longer updated: see current data at https://data.mesaaz.gov/Community-Services/Unsheltered-Point-in-Time-PIT-Count-Phoenix-Metro-/jagk-fkkw ** Unsheltered Street Count by Municipality (2014‐2018), also known as Regional Homeless Point in Time Count. All communities participate in the unsheltered homeless count conducted during the last week of January. Numbers for all communities with the exception of Phoenix are a direct census of individuals interviewed by volunteers, law enforcement, and outreach workers. The City of Phoenix conducts a survey using an extrapolation method by which areas are designated “high density” or “low density” areas. Direct counts in those areas are then extrapolated to estimate the number of individuals experiencing homelessness in unsheltered situations within the City of Phoenix geographic boundaries.

GREATER VICTORIA’S FOURTH POINT IN TIME (PiT) Count and Homeless Needs Survey took place on March 7 and 8, 2023. PiT Counts are intended to provide a community-based measure, or snapshot, of individuals experiencing sheltered and unsheltered homelessness at a single point in time. The initiative is funded through the Reaching Home Program: Canada’s Homelessness Strategy and contributes to a national picture of homelessness. The 2023 PiT Count and Homeless Needs Survey was completed with the support of 138 community volunteers, in conjunction with local housing facilities and service providers. The PiT Count took place within the Victoria Census Metropolitan Area (CMA), commonly referred to as Greater Victoria, which contains 13 municipalities and spans the traditional territories of approximately 11 First Nations.

Detailed results from the 2022 Point-in-Time (PIT) count of unsheltered individuals identified and/or interviewed in Mesa on the morning of January 25, 2022. Aggregated and summarized regional data available at https://data.mesaaz.gov/Community-Services/Unsheltered-Point-in-Time-PIT-Count-Phoenix-Metro-/jagk-fkkw The PIT count is conducted annually in January. See the attached 2022 PIT Unsheltered Count Form for more information about questions asked. See also the Maricopa Association of Governments to learn more about the Point-in-Time Homeless Count program: https://azmag.gov/Programs/Homelessness/Point-In-Time-Homeless-Count

Interview guide used for homeless pregnant women or homeless women in the postpartum period.

Comprehensive dataset of 5 Homeless shelters in Metropolitan City of Genoa, Italy as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 2 Homeless shelters in Metropolitan City of Turin, Italy as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.