Yearly statewide and by-Continuum of Care total counts of individuals receiving homeless response services by age group, race, gender, veteran status, and disability status.

This data comes from the Homelessness Data Integration System (HDIS), a statewide data warehouse which compiles and processes data from all 44 California Continuums of Care (CoC)—regional homelessness service coordination and planning bodies. Each CoC collects data about the people it serves through its programs, such as homelessness prevention services, street outreach services, permanent housing interventions and a range of other strategies aligned with California’s Housing First objectives.

The dataset uploaded reflects the 2024 HUD Data Standard Changes. Previously, Race and Ethnicity were separate files but are now combined.

Information updated as of 11/13/2025.

The graph displays the top 15 states by an estimated number of homeless people in the United States for the year 2025. The x-axis represents U.S. states, while the y-axis shows the number of homeless individuals in each state. California has the highest homeless population with 187,084 individuals, followed by New York with 158,019, while Hawaii places last in this dataset with 11,637. This bar graph highlights significant differences across states, with some states like California and New York showing notably higher counts compared to others, indicating regional disparities in homelessness levels across the country.

Homeless Students in Arkansas (2024–25): What the data says

TL;DR: 10.9k Arkansas students experienced homelessness in 2024–25 (0.8% of enrollment). Most are “Doubled Up”, sharing housing because of loss of housing or economic hardship. Geography matters: large, fast-growing counties report the highest counts even when they aren’t the poorest, and poverty explains much but not all of variation in homelessness.

Data & Method

Sources: Arkansas Department of Education 2024–2025; NIH poverty estimates (see workbook notes).

Unit of analysis: county-level counts of students

Tools: Tableau Public dashboard + worksheets; regression overlay on county scatter.

What to look at in the dashboard

County Map – Homeless students by county. Use the map to spot hotspots, hover for counts and enrollment context.

Housing Type Breakdown – Statewide composition: Doubled-Up 89.3%, Awaiting Foster Care 4.9%, Hotels/Motels 3.9%, Unsheltered 1.9%. Hidden homelessness dominates the lived experience of students.

Poverty vs. Homeless Students (Scatter) – A clear positive relationship (R² ≈ 0.59, p < 0.0001) indicates poverty is a strong driver, but not the whole story—some populous counties sit above/below the line.

County Comparison Bars – For larger counties (e.g., Benton, Pulaski, Washington), most identified students are Doubled-Up, and that share typically ranges 80–92%, underscoring the need for family-stability interventions.

Key findings

Scale: ~10,872 students (≈0.8% of 1.46M enrollment) were identified as experiencing homelessness statewide.

Geography ≠ poverty alone: Benton County reports the highest count despite not being among the highest poverty counties, reflecting population growth and housing pressure.

Mechanism: “Doubled Up” is the dominant pathway into homelessness for students. It's far more common than shelters, motels, or unsheltered situations. Supports that keep families stably housed (rent/utility assistance, eviction prevention, rapid re-housing) are likely to reach the largest group.

How analysts can use this

Targeting: Combine county counts with local enrollment to compute rates and flag counties that are high count and high rate for prioritization.

Program design: Given the 89% Doubled Up share, expect needs around transportation, documentation, and quick stabilization rather than shelter capacity alone.

Further work: Add rental vacancy, eviction filings, and new construction permits to the model to explain outliers.

Caveats

Counts reflect identification, not true prevalence; under identification is common for Doubled Up students.

County differences may reflect district identification practices and local resources.

Exploration tips: Use the dashboard’s tooltips, legend toggles (to isolate housing types), and the regression line on the scatter to compare counties to the statewide trend.

Yearly statewide and by-Continuum of Care total counts of individuals receiving homeless response services by age group, race, and gender. This data comes from the Homelessness Data Integration System (HDIS), a statewide data warehouse which compiles and processes data from all 44 California Continuums of Care (CoC)—regional homelessness service coordination and planning bodies. Each CoC collects data about the people it serves through its programs, such as homelessness prevention services, street outreach services, permanent housing interventions and a range of other strategies aligned with California’s Housing First objectives. The dataset uploaded reflects the 2024 HUD Data Standard Changes. Previously, Race and Ethnicity are separate files but are now combined. Information updated as of 7/15/2024.

City staff and community partners work together to survey people experiencing homelessness in Ottawa. So far, the City has led two counts:April 2018October 2021Oct 2024The survey is conducted to gather information about people experiencing homelessness. The goal of this work is to guide new approaches to address homelessness at a local level and help in the planning and delivery of services.Date created: 28 April 2022Update frequency: As needed.Accuracy: Convenience sampling was used to recruit survey respondents. This method of recruiting respondents to answer the survey does not rely on a random selection process. Instead, surveyors approach potential respondents if they are close by at the time the surveyor is delivering the questionnaire. Many factors could determine participation in the survey including:Number of community partners involved in the PiT countLocation of surveyors and their physical proximity to potential respondentsNumber of engagement eventsSeason the survey was conductedDifferences in results between PiT count years may be due to changes within the homeless population and shifts in methodology. For comparisons of emergency shelter use over time, visit the Temporary Emergency Accommodations Dashboard. An analysis of factors related to housing and homelessness during COVID-19 provides context for unique housing market conditions during the pandemic.Results shown in the Survey results: Point-in-Time count dashboard are presented by sector. The name and definition of each sector are below:All: All respondents who answered the surveySingle adult: Respondents aged 25 years or older and not accompanied by anyoneUnaccompanied youth: Respondents under 25 years old and not accompanied by anyoneFamily: Respondents accompanied by children under 18 years oldAttributes:Question: The question that was asked in the surveyTopic: The classification of the survey question by themSector: Refers to the population (total, family, unaccompanied youth, single adults)Period: Month the Point-in-Time count was conductedResponse: Response category of the survey questionNumeratorDenominatorPercentage Author: Housing ServicesAuthor email: pitcount_denombrementponctuel@ottawa.ca

Project Overview This study used a community-based participatory approach to identify and investigate the needs of people experiencing homelessness in Dublin, Ireland. The project had several stages: A systematic review on health disparities amongst people experiencing homelessness in the Republic of Ireland; Observation and interviews with homeless attendees of a community health clinic; and Interviews with community experts (CEs) conducted from September 2022 to March 2023 on ongoing work and gaps in the research/health service response. This data deposit stems from stage 3, the community expert interview aspect of this project. Stage 1 of the project has been published (Ingram et al., 2023.) and associated data are available here. De-identified field note data from stage 2 of the project are planned for sharing upon completion of analysis, in January 2024. Data and Data Collection Overview A purposive, criterion-i sampling strategy (Palinkas et al., 2015) – where selected interviewees meet a predetermined criterion of importance – was used to identify professionals working in homeless health and/or addiction services in Dublin, stratified by occupation type. Potential CEs were identified through an internet search of homeless health and addiction services in Dublin. Interviewed CEs were invited to recommend colleagues they felt would have relevant perspectives on community health needs, expanding the sample via snowball strategy. Interview questions were based on World Health Organization Community Health Needs Assessment guidelines (Rowe at al., 2001). Semi-structured interviews were conducted between September 2022 and March 2023 utilising ZOOM™, the phone, or in person according to participant preference. Carolyn Ingram, who has formal qualitative research training, served as the interviewer. CEs were presented with an information sheet and gave audio recorded, informed oral consent – considered appropriate for remote research conducted with non-vulnerable adult participants – in the full knowledge that interviews would be audio recorded, transcribed, and de-identified, as approved by the researchers’ institutional Human Research Ethics Committee (LS-E-125-Ingram-Perrotta-Exemption). Interviewees also gave permission for de-identified transcripts to be shared in a qualitative data archive. Shared Data Organization 16 de-identified transcripts from the CE interviews are being published. Three participants from the total sample (N=19) did not consent to data archival. The transcript from each interviewee is named based on the type of work the interviewee performs, with individuals in the same type of work being differentiated by numbers. The full set of professional categories is as follows: Addiction Services Government Homeless Health Services Hospital Psychotherapist Researcher Social Care Any changes or removal of words or phrases for de-identification purposes are flagged by including [brackets] and italics. The documentation files included in this data project are the consent form and the interview guide used for the study, this data narrative and an administrative README file. References Ingram C, Buggy C, Elabbasy D, Perrotta C. (2023) “Homelessness and health-related outcomes in the Republic of Ireland: a systematic review, meta-analysis and evidence map.” Journal of Public Health (Berl). https://doi.org/10.1007/s10389-023-01934-0 Palinkas LA, Horwitz SM, Green CA, Wisdom JP, Duan N, Hoagwood K. (2015) “Purposeful sampling for qualitative data collection and analysis in mixed method implementation research.” Administration and Policy in Mental Health. Sep;42(5):533–44. https://doi.org/10.1007/s10488-013-0528-y Rowe A, McClelland A, Billingham K, Carey L. (2001) “Community health needs assessment: an introductory guide for the family health nurse in Europe” [Internet]. World Health Organization. Regional Office for Europe. Available at: https://apps.who.int/iris/handle/10665/108440

Information These published reports present information from the multi-agency database Combined Homelessness and Information Network (CHAIN), about people seen rough sleeping by outreach teams in London. CHAIN, which is commissioned and funded by the Greater London Authority (GLA) and managed by Homeless Link, represents one of the UK’s most detailed and comprehensive sources of information about rough sleeping. Services that record information on CHAIN include outreach teams, assessment centres, accommodation projects, day centres and other specialist projects. The system allows users to share information about work done with people sleeping rough and about their needs, ensuring that they receive the most appropriate support and that efforts are not duplicated. In these reports, people are counted as having been seen rough sleeping if they have been encountered by a commissioned outreach worker bedded down on the street, or in other open spaces or locations not designed for habitation, such as doorways, stairwells, parks or derelict buildings. The report does not include people from “hidden homeless” groups such as those “sofa surfing” or living in squats, unless they have also been seen bedded down in one of the settings outlined above. Separate reports are produced for London as a whole and for individual boroughs, and these are published each quarter. There are also annual reports that contain aggregated information for each full year. Interactive Visualisation Tool Quarterly Data Tool Annual Data Tool A suite of online interactive charts and maps based on CHAIN data is available by clicking the above links. The data available via these tools mirrors that presented in the published PDF documents, with the addition of filters and other enhancements to allow users to interrogate the data. The Quarterly Data Tool shows data from the last eight quarters, and the Annual Data Tool shows data from the last five years. Organisations Using CHAIN A list of the organisations which have signed the CHAIN Data Protection Agreement and are able to access the live CHAIN system is also available to download. PDF Reports & Data tables As of January 2024, published CHAIN PDF reports are accompanied by an OpenDocument Spreadsheet file providing the underlying data in an accessible aggregated tabular format. The file includes data at local authority level, and for London overall, including comparative data for previous periods. There is also an accompanying explanatory notes document, which provides important contextual information about the data. Please click the links below to download a zip file containing the PDF reports and OpenDocument Spreadsheet for the corresponding timeframe. Publication Schedule Reports are published 1 month after the end of each quarter and one quarter after the end of each year. The linked document below provides details of forthcoming publications Quarterly and Annual Report Schedule 2024/25 2024/25 Q3 2024/25 Greater London 2024/25 Q3 Borough Reports 2024/25 Q3 Quarterly Data Tables 2024/25 Q3 Q2 2024/25 Greater London 2024/25 Q2 Borough Reports 2024/25 Q2 Quarterly Data Tables 2024/25 Q2 Q1 2024/25 Greater London 2024/25 Q1 Borough Reports 2024/25 Q1 Quarterly Data Tables 2024/25 Q1 2023/24 Greater London Bulletin Greater London full report Borough Annual Reports Annual Data Tables Quarterly Reports and Data Tables (for Q3 and Q4 only) 2022/23 Greater London bulletin Greater London full report Borough Annual Reports Quarterly Reports 2021/22 Greater London bulletin Greater London full report Borough Annual Reports Quarterly Reports 2020/21 Greater London bulletin Greater London full report Borough Annual Reports Quarterly Reports 2019/20 Greater London bulletin Greater London full report Borough Annual Reports Quarterly Reports 2018/19 Greater London bulletin Greater London full report Borough Annual Reports Quarterly Reports 2017/18 Greater London bulletin Greater London full report Borough Annual Reports Quarterly Reports 2016/17 Greater London bulletin Greater London full report Borough Annual Reports Quarterly Reports 2015/16 Greater London bulletin Greater London full report Borough Annual Reports Quarterly Reports 2014/15 Greater London bulletin Greater London full report Borough Annual Reports Quarterly Reports Pre-2014/15 For earlier reports please see the end of this page. This dataset is one of the Greater London Authority's measures of Economic Fairness. Click here to find out more.