The dataset contains locations and attributes of Homeless Shelters, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. A database provided by the Department of Human Services identified Homeless Shelter locations.

Location analysis for homeless services within the City of Phoenix.

The purpose of the San Mateo County Homeless Census and Survey is to gather and analyze information to help us understand who is homeless in our community, why they are homeless and what interventions they need to end their homelessness. This data forms the basis for effective planning to solve this complex and longstanding problem. The San Mateo County Human Services Agency’s Center on Homelessness the San Mateo County Continuum of Care Steering Committee were responsible for overseeing this data collection effort, with assistance from a broad group of community partners, including non-profit social service providers, city and town governments, and homeless and formerly homeless individuals. The Census and Survey was designed to meet two related sets of data needs. The first is the requirement of the U.S. Department of Housing and Urban Development (HUD) that communities applying for McKinney-Vento Homelessness Assistance funds (also known as Continuum of Care or “CoC” funds) must conduct a point-in-time count of homeless people a minimum of every two years. These counts are required to take place in the last ten days of January.

City expenditures related to Homelessness efforts in the City of LA starting with fiscal year 2024. Data populated by City departments who code and map expenses to homeless project categories. (Not all departments are included due to the way the department maps homeless expenditures)

The web map shows map about homelessness service requests over the 30 days and it has three main layers.One of the layers contains service requests for both open and closed status and the other two contain open and closed status respectively.The web map also contains base maps.This Map feeds this dashboard: https://dallasgis.maps.arcgis.com/home/item.html?id=ccd41f0d795f407a94ae17e2c27bf073

The dashboard app shows homeless service requests in the city of Dallas with closed and open/in progress status in 30 days roll back.Click here to view the dashboard:

https://arcg.is/XSmLS

This application is fed by this map: https://dallasgis.maps.arcgis.com/home/item.html?id=8e18143be28a43959d34f8037afffeb9The dashboard application provides a comprehensive overview of homeless service requests within the city of Dallas. It visualizes key data, such as the type and volume of requests, their geographic distribution across the city, and timelines for when they were submitted. This tool enables city officials, organizations, and stakeholders to track trends, identify hotspots or underserved areas, and allocate resources efficiently to address homelessness. Additionally, the dashboard may include filters and interactive features, allowing users to analyze specific timeframes, neighborhoods, or request types for a deeper understanding of the challenges and needs faced by the community.

The dataset contains routes, stops, and estimated arrival/departure times for the Access Hotline Vans operated by the United Planning Organization (UPO) on the District’s behalf. The District provides this transportation services to individuals experiencing homelessness as both a daily routed service and as a door-to-door service when necessary. The routes in this dataset are specific to hypothermia season, which operates from November 1st through March 31st each year. The estimated times in this dataset are subject to change based on the changing needs of individuals experiencing homelessness. The dataset was created by the Department of Human Services, in partnership with the Interagency Council on Homelessness (ICH) and UPO as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO).

The most recent rate of homelessness is calculated using ACS population estimates from the previous year, unless otherwise noted.

Data Source: HUD's Annual Homeless Assessment Report (AHAR) Point-in-Time (PIT) Estimates by State and American Community Survey (ACS) 1-Year Estimates

Why this MattersSafe, adequate, and stable housing is a human right and essential for the health and well-being of individuals, families, and communities.People who experience homelessness also struggle to maintain access to healthcare, employment, education, healthy relationships, and other basic necessities in life, according to the DC Interagency Council on Homelessness Strategic Plan.BIPOC populations are disproportionately affected by homelessness due to housing discrimination, mass incarceration, and other policies that have limited socioeconomic opportunities for Black, Latino, and other people of color.

The District's Response Strategic investments in proven strategies for driving down homelessness, including the Career Mobility Action Plan (Career MAP) program, operation of non-congregate housing, and expansion of the District’s shelter capacity.Homelessness prevention programs for at-risk individuals and families, such as emergency rental assistance, targeted affordable housing, and permanent supporting housing.Programs and services to enhance resident’s economic and employment security and ensure access to affordable housing.

Map of homeless percentages by state in the United States

Data Prepared by Los Angeles Homeless Services AuthorityRevised July 29, 2019Homeless Count 2019 Dashboard MethodologyTotal number of people experiencing homelessness is the sum of (1) the sheltered population (the total number of people staying in emergency shelter, transitional housing, or safe haven programs on the night of the point-in-time count) and (2) the unsheltered population (the total number of people counted by volunteers and the estimated number of people sleeping in the dwellings counted by volunteers).

(1) The total number of people experiencing homelessness who slept in an emergency shelter, transitional housing, or safe haven program was reported to LAHSA by each provider and assigned to a census tract. For shelter programs with multiple scattered sites in the LA CoC, an administrative address is used for locating the sheltered population in this dashboard. Shelters that serve persons fleeing domestic or intimate partner violence are excluded due to confidentiality concerns. Persons receiving motel vouchers are excluded in this dashboard because the location of the motel is unknown.

(2) The total number of people experiencing homelessness who slept on the street or in a dwelling not meant for human habitation were counted by volunteers on January 22nd, 23rd, or 24th. 3,873 demographic survey interviews were conducted with persons experiencing unsheltered homelessness from December 2018 to March 2019 to describe the population’s demographics and approximate the number of people in each dwelling. The total persons in uninhabitable dwellings was estimated for each type (car, van, camper/RV, tent, or makeshift shelter) and was estimated at the SPA-level for individual and for family households and can be found on our website. Estimates of the people inside these dwellings was rounded to whole numbers for the purposes of this dashboard. Please visit our website for further information at www.lahsa.org.

In 2013, homeless populations were concentrated primarily in states with large urban areas, such as California, New York and Florida. The rural and sparsely-populated central and western states, including North Dakota, had comparably fewer homeless in terms of count alone. While this map indicates that North Dakota does not constitute a homeless problem on a national scale, it does not show the struggles the state is facing on its own, smaller scale.

The By-Name List was created through Built for Zero Canada (https://bfzcanada.ca/by-name-lists/) and is powered by the Canadian Alliance to End Homelessness (CAEH). The By-Name List is a collaborative effort through various local agencies to use real-time data to reduce and prevent homelessness to provide streamlines access to available support. Although data is collected in real-time this data set provides an aggregated monthly update.

The Street Needs Assessment (SNA) is a survey and point-in-time count of people experiencing homelessness in Toronto on April 26, 2018. The results provide a snapshot of the scope and profile of the City's homeless population. The results also give people experiencing homelessness a voice in the services they need to find and keep housing. The 2018 SNA is the City's fourth homeless count and survey and was part of a coordinated point-in-time count conducted by communities across Canada and Ontario. The results of the 2018 Street Needs Assessment were summarized in a report and key highlights slide deck. During the course of the night, a 23 core question survey was completed with 2,019 individuals experiencing homelessness staying in shelters (including provincially-administered Violence Against Women shelters), 24-hour respite sites (including 24-hour women's drop-ins and the Out of the Cold overnight program open on April 26, 2018), and outdoors. The SNA includes individuals experiencing absolute homelessness but does not capture hidden homelessness (i.e., people couch surfing or staying temporarily with others who do not have the means to secure permanent housing). This dataset includes the SNA survey results; it does not include the count of people experiencing homelessness in Toronto. The SNA employs a point-in-time methodology for enumerating homelessness that is now the standard for most major US and Canadian urban centres. While a consistent methodology and approach has been used each year in Toronto, changes were made in 2018, in part, as a result of participation in the national and provincial coordinated point-in-time count. As a result, caution should be made in comparing these results to previous SNA survey results. Key changes included: administering the survey in a representative sample (rather than census) of shelters; administering the survey in all 24-hour respite sites and a sample of refugee motel programs added to the homelessness service system since the 2013 SNA; and a standard set of core survey questions that communities were required to follow to ensure comparability. In addition, in 2018, surveys were not conducted in provincially-administered health and treatment facilities and correctional facilities as was done in 2013. The 2018 survey results provide a valuable source of information about the service needs of people experiencing homelessness in Toronto. This information is used to improve the housing and homelessness programs provided by the City of Toronto and its partners to better serve our clients and more effectively address homelessness. Visit https://www.toronto.calcity-government/data-research-maps/research-reports/housing-and-homelessness-research-and-reports/

There are several forms, regulations and data associated with the Emergency Assistance (EA) Family Shelter Program for our business partners and constituents.

Dataset for the maps accompanying the Housing in Aotearoa New Zealand: 2025 report. This dataset contains data for severe housing deprivation from the 2018 and 2023 Censuses.

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Data is available by health district.

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Severe housing deprivation has data for the census usually resident population from the 2018 and 2023 Censuses, including:

• estimated prevalence rate of severe housing deprivation (per 10,000 people)
• estimated rate for those; without shelter, in temporary accommodation, sharing someone else’s private dwelling, in uninhabitable housing, for whom it could not be determined whether they were severely housing deprived or not.

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Map shows the estimated prevalence rate of severe housing deprivation (per 10,000 people) for the census usually resident population for the 2023 Census.

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Download lookup file from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

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Footnotes

Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city. 

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Population counts

Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts. 

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

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Severe housing deprivation time series

The 2018 estimates of severe housing deprivation have been updated using the 2023 methodology for estimating severe housing deprivation. Severe housing deprivation (homelessness) estimates – updated methodology: 2023 Census has more information.

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Severe housing deprivation

Figures in this map and geospatial file exclude Women’s refuge data, as well as estimates for children living in non-private dwellings. Severe housing deprivation (homelessness) estimates – updated methodology: 2023 Census has more information.

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About the 2023 Census dataset

For information on the 2023 Census dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

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Census usually resident population count concept quality rating

The census usually resident population count is rated as very high quality.

Census usually resident population count – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Quality of severe housing deprivation data

Severe housing deprivation (homelessness) estimates – updated methodology: 2023 Census has more information on the data quality of this variable.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Inconsistencies in definitions

Please note that there may be differences in definitions between census classifications and those used for other data collections.

A map used in the Homelessness Point-in-Time Count site to visualize where volunteers are needed in the community.

BackgroundHomelessness staff often experience high job demands, limited resources, and significant emotional strains; with high levels of burnout, stress, and trauma being common within the workforce. Despite growing recognition of these issues, limited literature exists on interventions to address them. This study aims to conduct a systematic scoping review to map and identify interventions aimed at improving well-being and reducing burnout among homelessness staff.MethodsAll eligible studies needed to include an intervention addressing burnout and/or well-being in homelessness staff, published in English with primary data. Evidence sources were left open with no data restrictions. Following protocol registration, a systematic search of five electronic databases (Medline, APA PsychInfo, Global Health, ASSIA, CINAHL) and Google Scholar was conducted. Studies were double-screened for inclusion. Methodological quality was assessed using the Mixed Methods Appraisal Tool.ResultsOf the 5,775 screened studies, six met the inclusion criteria: two peer-reviewed and four non-peer-reviewed publications. No studies were retrieved from Google Scholar. The included studies comprised four quantitative non-randomised designs, one randomised controlled trial, and one mixed-methods study. All included studies were complex interventions. Three were therapy-based, two included supervision, and two were one-time educational sessions. Most were conducted in the United States (n = 4), with two in the United Kingdom. The total pooled sample was 347 participants, though four studies were missing demographic data (age and gender). The studies used heterogenous measures and outcomes. Limitations included restrictions to English-only publications, potential gaps in capturing well-being measures, and a limited grey literature scope.ConclusionThere is a lack of research on well-being and burnout interventions in frontline homelessness staff. Identified studies were generally low quality, using heterogenous measures and outcomes to assess well-being and burnout, limiting the generalisability of findings. Future research should employ more robust study designs with standardised measures and outcomes.

A. SUMMARY Geographic zones of the priority areas in the Tenderloin neighborhood used in the COVID-19 assessment and Tenderloin Neighborhood Plan. See more details on the plan here: https://sf.gov/news/san-francisco-releases-tenderloin-neighborhood-safety-assessment-and-plan-covid-19 B. HOW THE DATASET IS CREATED A team of representative City departments from the Healthy Streets Operation Center (Department of Emergency Management, Department of Public Health, Department of Homelessness and Supportive Housing, Human Rights Commission, San Francisco Police Department, San Francisco Fire Department, and Department of Public Works), SF Homeless Outreach Team, Felton Institute, and community groups and stakeholders was assembled to design and implement a robust Tenderloin Neighborhood Needs Assessment. This assessment was conducted on the morning of April 28, 2020 and consisted of multi-disciplinary teams walking each block of an area of the Tenderloin broken into six geographic zones. These zone locations are shown in the plan, and are mapped in this dataset. C. UPDATE PROCESS This is a reference map that will not be updated. D. HOW TO USE THIS DATASET These zones can be used with other datasets to track trends by zone. Note that these zones are the priority zones for the Tenderloin Plan and do not represent the entire Tenderloin Neighborhood boundary. For a boundary of the entire Tenderloin, use the analysis neighborhood boundary: https://data.sfgov.org/Geographic-Locations-and-Boundaries/Analysis-Neighborhoods/p5b7-5n3h

A feature layer joined view used in the Point-in-Time Count Dashboard map to monitor the results of a point-in-time count of sheltered and unsheltered persons experiencing homelessness.