The graph displays the estimated number of homeless people in the United States from 2007 to 2024. The x-axis represents the years, ranging from 2007 to 2023, while the y-axis indicates the number of homeless individuals. The estimated homeless population varies over this period, ranging from a low of 57,645 in 2014 to a high of 771,000 in 2024. From 2007 to 2013, there is a general decline in numbers from 647,258 to 590,364. In 2014, the number drops significantly to 57,645, followed by an increase to 564,708 in 2015. The data shows fluctuations in subsequent years, with another notable low of 55,283 in 2018. From 2019 onwards, the estimated number of homeless people generally increases, reaching its peak in 2024. This data highlights fluctuations in homelessness estimates over the years, with a recent upward trend in the homeless population.

In 2023, there were about ******* homeless people estimated to be living in the United States, the highest number of homeless people recorded within the provided time period. In comparison, the second-highest number of homeless people living in the U.S. within this time period was in 2007, at *******. How is homelessness calculated? Calculating homelessness is complicated for several different reasons. For one, it is challenging to determine how many people are homeless as there is no direct definition for homelessness. Additionally, it is difficult to try and find every single homeless person that exists. Sometimes they cannot be reached, leaving people unaccounted for. In the United States, the Department of Housing and Urban Development calculates the homeless population by counting the number of people on the streets and the number of people in homeless shelters on one night each year. According to this count, Los Angeles City and New York City are the cities with the most homeless people in the United States. Homelessness in the United States Between 2022 and 2023, New Hampshire saw the highest increase in the number of homeless people. However, California was the state with the highest number of homeless people, followed by New York and Florida. The vast amount of homelessness in California is a result of multiple factors, one of them being the extreme high cost of living, as well as opposition to mandatory mental health counseling and drug addiction. However, the District of Columbia had the highest estimated rate of homelessness per 10,000 people in 2023. This was followed by New York, Vermont, and Oregon.

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.

This dataset is extracted from https://en.wikipedia.org/wiki/List_of_countries_by_homeless_population. Context: There s a story behind every dataset and heres your opportunity to share yours.Content: What s inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Acknowledgements:We wouldn t be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.Inspiration: Your data will be in front of the world s largest data science community. What questions do you want to see answered?

Nearly 11.3 thousand people in Russia were homeless, based on the population census data from 2021. The number of homeless residents decreased by 82 percent compared to 2010. The largest share of homeless people in the country lived in urban areas, at around 95 percent in 2021.

BackgroundAddressing Citizen’s perspectives on homelessness is crucial for the design of effective and durable policy responses, and available research in Europe is not yet substantive. We aim to explore citizens’ opinions about homelessness and to explain the differences in attitudes within the general population of eight European countries: France, Ireland, Italy, the Netherlands, Poland, Portugal, Spain, and Sweden.MethodsA nationally representative telephone survey of European citizens was conducted in 2017. Three domains were investigated: Knowledge, Attitudes, and Practices about homelessness. Based on a multiple correspondence analysis (MCA), a generalized linear model for clustered and weighted samples was used to probe the associations between groups with opposing attitudes.ResultsResponse rates ranged from 30.4% to 33.5% (N = 5,295). Most respondents (57%) had poor knowledge about homelessness. Respondents who thought the government spent too much on homelessness, people who are homeless should be responsible for housing, people remain homeless by choice, or homelessness keeps capabilities/empowerment intact (regarding meals, family contact, and access to work) clustered together (negative attitudes, 30%). Respondents who were willing to pay taxes, welcomed a shelter, or acknowledged people who are homeless may lack some capabilities (i.e. agreed on discrimination in hiring) made another cluster (positive attitudes, 58%). Respondents living in semi-urban or urban areas (ORs 1.33 and 1.34) and those engaged in practices to support people who are homeless (ORs > 1.4; p<0.005) were more likely to report positive attitudes, whereas those from France and Poland (p<0.001) were less likely to report positive attitudes.ConclusionThe majority of European citizens hold positive attitudes towards people who are homeless, however there remain significant differences between and within countries. Although it is clear that there is strong support for increased government action and more effective solutions for Europe’s growing homelessness crisis, there also remain public opinion barriers rooted in enduring negative perceptions.

When analyzing the ratio of homelessness to state population, New York, Vermont, and Oregon had the highest rates in 2023. However, Washington, D.C. had an estimated ** homeless individuals per 10,000 people, which was significantly higher than any of the 50 states. Homeless people by race The U.S. Department of Housing and Urban Development performs homeless counts at the end of January each year, which includes people in both sheltered and unsheltered locations. The estimated number of homeless people increased to ******* in 2023 – the highest level since 2007. However, the true figure is likely to be much higher, as some individuals prefer to stay with family or friends - making it challenging to count the actual number of homeless people living in the country. In 2023, nearly half of the people experiencing homelessness were white, while the number of Black homeless people exceeded *******. How many veterans are homeless in America? The  number of homeless veterans in the United States has halved since 2010. The state of California, which is currently suffering a homeless crisis, accounted for the highest number of homeless veterans in 2022. There are many causes of homelessness among veterans of the U.S. military, including post-traumatic stress disorder (PTSD), substance abuse problems, and a lack of affordable housing.

This indicator presents available data at national level on the number of people reported by public authorities as homeless. Data are drawn from the OECD Questionnaire on Affordable and Social Housing (QuASH 2021, QuASH 2019, QuASH 2016) and other available sources. Overall, homelessness data are available for 36 countries (Table HC 3.1.1 in Annex I). Further discussion of homelessness can be found in the 2020 OECD Policy Brief, “Better data and policies to fight homelessness in the OECD”, available online (and in French). Discussion of national strategies to combat homelessness can be found in indicator HC3.2 National Strategies for combating homelessness. Comparing homeless estimates across countries is difficult, as countries do not define or count the homeless population in the same way. There is no internationally agreed definition of homelessness. Therefore, this indicator presents a collection of available statistics on homelessness in OECD, EU and key partner countries in line with definitions used in national surveys (comparability issues on the data are discussed below). Even within countries, different definitions of homelessness may co-exist. In this indicator, we refer only to the statistical definition used for data collection purposes. Detail on who is included in the number of homeless in each country, i.e. the definition used for statistical purposes, is presented in Table HC 3.1.2 at the end of this indicator. To facilitate comparison of the content of homeless statistics across countries, it is also indicated whether the definition includes the categories outlined in Box HC3.1, based on “ETHOS Light” (FEANTSA, 2018). Homelessness data from 2020, which are available for a handful of countries and cover at least part of the COVID-19 pandemic, add an additional layer of complexity to cross-country comparison. The homeless population estimate in this case depends heavily on the point in time at which the count took place in the year, the method to estimate the homeless (through a point-in-time count or administrative data, as discussed below), the existence, extent and duration of emergency supports introduced in different countries to provide shelter to the homeless and/or to prevent vulnerable households from becoming homeless (such as eviction bans). Where they are available, homeless data for 2020 are thus compared to data from the previous year in order to facilitate comparison with other countries.

Content

Included in this data set are data elements that will help the public identify agencies that are certified to operate programs for runaway and homeless youth. These programs are available to assist runaway and homeless youth in emergency situation and provide independent living skills for youth in transition. Data elements include the agency name, agency business address, phone number, website and type of program offered.

Context

This is a dataset hosted by the State of New York. The state has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York State using Kaggle and all of the data sources available through the State of New York organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Zac Ong on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

BackgroundHomelessness is one of the most disabling and precarious living conditions. The objective of this Delphi consensus study was to identify priority needs and at-risk population subgroups among homeless and vulnerably housed people to guide the development of a more responsive and person-centred clinical practice guideline.MethodsWe used a literature review and expert working group to produce an initial list of needs and at-risk subgroups of homeless and vulnerably housed populations. We then followed a modified Delphi consensus method, asking expert health professionals, using electronic surveys, and persons with lived experience of homelessness, using oral surveys, to prioritize needs and at-risk sub-populations across Canada. Criteria for ranking included potential for impact, extent of inequities and burden of illness. We set ratings of ≥ 60% to determine consensus over three rounds of surveys.FindingsEighty four health professionals and 76 persons with lived experience of homelessness participated from across Canada, achieving an overall 73% response rate. The participants identified priority needs including mental health and addiction care, facilitating access to permanent housing, facilitating access to income support and case management/care coordination. Participants also ranked specific homeless sub-populations in need of additional research including: Indigenous Peoples (First Nations, Métis, and Inuit); youth, women and families; people with acquired brain injury, intellectual or physical disabilities; and refugees and other migrants.InterpretationThe inclusion of the perspectives of both expert health professionals and people with lived experience of homelessness provided validity in identifying real-world needs to guide systematic reviews in four key areas according to priority needs, as well as launch a number of working groups to explore how to adapt interventions for specific at-risk populations, to create evidence-based guidelines.

In 2022, there were an estimated 196,000 Germans who were homeless. There were significantly more non-Germans who were homeless, at 411,000.

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

Background: Homeless people are a socially excluded group whose health reflects exposures to intersecting social determinants of health. The aim of this study was to describe and compare the demographic composition, certain social determinants of health, and self-reported health among homeless people in Stockholm, Sweden, in 2006 and 2018.Methods: Analysis of data from face-to-face interviews with homeless people in Stockholm 2006 (n = 155) and 2018 (n = 148), based on a public health survey questionnaire adapted to the group, including the EQ-5D-3L instrument. The chi-squared test was employed to test for statistical significance between groups and the independent t-test for comparison of mean scores and values. Ordinary Least Squares (OLS) regression, with Robust Standard Errors (RSE) was performed on merged 2006 and 2018 data with mean observed EQ VAS score as outcome variable.Results: In 2018 more homeless people originated from countries outside Europe, had temporary social assistance than long-term social insurance, compared to in 2006. In 2018 more respondents reported lack of social support, exposure to violence, and refrained from seeking health care because of economic reasons. Daily smoking, binge drinking, and use of narcotic drugs was lower 2018 than 2006. In 2018 a higher proportion reported problems in the EQ-5D-3L dimensions, the mean TTO index value and the VAS index value was significantly lower than in 2006. In the regression analysis of merged data there was no significant difference between the years.Conclusions: Homeless people are an extremely disadvantaged group, have high rates of illness and disease and report poor health in all EQ-5D-3L dimensions. The EQ VAS score among the homeless people in 2018 is comparable to the score among persons aged 95–104 years in the general Swedish population 2017. The EQ-5D-3L instrument was easily administered to this group, its use allows comparison with larger population groups. Efforts are needed regarding housing, but also intensified collaboration by public authorities with responsibilities for homeless people's health and social welfare. Further studies should evaluate the impact of such efforts by health and social care services on the health and well-being of homeless people.

In 2024/25, there were ***** rough sleepers reported in Westminster, making it the London borough with the highest number of rough sleepers in that year. Other boroughs which also had a high number of homeless people included, Camden, Ealing, and Southwark.

There are over 400 service requests types that are reported in the 311 system that affect the quality of life of our citizens, neighborhoods, and communities. The most popular service requests include but are not limited to animal services requests, high weeds, junk motor vehicles, and a number of other code compliance-related issues. Requests that deal with streets and mobility such as street and pot hole repair are also very common. 311 also receives requests to address environmental issues such as water conservation and air quality complaints.

This dataset represents all Service Requests for the fiscal year time period of October 1, 2016 to September 30, 2017.

BackgroundIndividuals who are homeless or vulnerably housed are at an increased risk for mental illness, other morbidities and premature death. Standard case management interventions as well as more intensive models with practitioner support, such as assertive community treatment, critical time interventions, and intensive case management, may improve healthcare navigation and outcomes. However, the definitions of these models as well as the fidelity and adaptations in real world interventions are highly variable. We conducted a systematic review to examine the effectiveness and cost-effectiveness of case management interventions on health and social outcomes for homeless populations.Methods and findingsWe searched Medline, Embase and 7 other electronic databases for trials on case management or care coordination, from the inception of these databases to July 2019. We sought outcomes on housing stability, mental health, quality of life, substance use, hospitalization, income and employment, and cost-effectiveness. We calculated pooled random effects estimates and assessed the certainty of the evidence using the GRADE approach. Our search identified 13,811 citations; and 56 primary studies met our full inclusion criteria. Standard case management had both limited and short-term effects on substance use and housing outcomes and showed potential to increase hostility and depression. Intensive case management substantially reduced the number of days spent homeless (SMD -0.22 95% CI -0.40 to -0.03), as well as substance and alcohol use. Critical time interventions and assertive community treatment were found to have a protective effect in terms of rehospitalizations and a promising effect on housing stability. Assertive community treatment was found to be cost-effective compared to standard case management.ConclusionsCase management approaches were found to improve some if not all of the health and social outcomes that were examined in this study. The important factors were likely delivery intensity, the number and type of caseloads, hospital versus community programs and varying levels of participant needs. More research is needed to fully understand how to continue to obtain the increased benefits inherent in intensive case management, even in community settings where feasibility considerations lead to larger caseloads and less-intensive follow-up.

Social Housing Provision by County and Method

Annual Breakdown of Social Housing Provisions by County and Method from 2016

By Ian Baldwin [source]

About this dataset

This structured dataset encompasses a wealth of specifics such as the local government authority's name that is responsible for social housing provisions within each county. It reveals the year when the data was recorded and also offers information on period time – whether it was during a specific quarter within that year (Q1, Q2, Q3, Q4), or an annual data record.

One of its most notable aspects covers details about delivery type - showing various methods via which Social Housing Provisions were delivered. This could range from new construction builds to property acquisitions or leasing agreements among others.

Another enriching detail it provides is on categories of social housing. It exhibits how these provisions were categorized based on certain criteria such as general needs houses, specialized residences for older people or homes specifically made available for alleviating homelessness etc.

Equally important is information about who delivered these housing provisions- be it different entities like local authority itself directly involved , approved external bodies related with housing authorities processor private developers who have played their role in this regard Moreover amount denotes units provided during specified period thus enabling readers understand scale operation .

Last but not least , file provides full annual target - showcasing total units planned provisioned during defined year enhancing comprehension around planning effectiveness implementation aforementioned activities . All diverse specifics uniquely consolidated forming invaluable resource diverse stakeholders particularly those involved urban development planning population management research analysis purposes

How to use the dataset

This dataset can serve as an excellent resource for anyone looking to explore the specifics of social housing provision in different counties. It provides a comprehensive view of how social housing has been delivered, who delivers it, what types of houses have been constructed and how many were planned versus the actual amount delivered.

Here are some ways you could use this dataset:

• Benchmarking and Comparative Analysis: One can compare the overall performance of different local authorities in delivering their full annual targets for social housing. This will help stakeholders understand which areas are performing well or poorly.

• Trend Analysis over years: Analyse trends in social housing provision over multiple years to identify patterns and predict future needs or shortfalls.

• Category Wise Study: Break down the data by category (general needs, older persons, homeless) and gain insights on specific demands catered by the local authorities across counties.

• Provider profiles: The Delivered By column can give information about particular entities involved in providing social houses as well as their contributions towards meeting demand for such housings over several years.

• Policy Feedback: The data from this dataset could provide significant inputs into policymaking processes related to infrastructure, public housing projects etc., allowing you to measure impact or suggest improvements based on empirical evidence collected over time.

• Housing Provision Method Overview: Compare effectiveness of various delivery methods like new builds, acquisitions or leasing etc In terms of fulfilling requirements set under full annual target - providing insight into which methods might be most efficient for achieving goals

To get started with analyzing this dataset: - You may want to start by cleaning up any missing values. - Create aggregate statistics per year/per quarter/per category - Calculate ratios between delivery targets and actual number provided. - Use visualization tools like bar graphs or pie charts for better understanding

Remember that correlation does not imply causation – so make sure context is considered when interpreting your data. Always use a critical eye and consider all possible factors. Happy analyzing!

Research Ideas

• Policy Decision-making: Policy makers at the local and national level could use this data to understand the effectiveness of different methods of housing provision in meeting annual targets. This can be crucial for future planning and deciding which method or entity to prioritise.
• Housing Research: Researchers studying housing policy, homelessness, or urban development could use this dataset to explore correlations be...

Context

Disasters include all geophysical, meteorological and climate events including earthquakes, volcanic activity, landslides, drought, wildfires, storms, and flooding. Decadal figures are measured as the annual average over the subsequent ten-year period.

Content

Thanks to Our World in Data, you can explore death from natural disasters by country and by date.

Acknowledgements

https://www.acacamps.org/sites/default/files/resource_library_images/naturaldisaster4.jpg" alt="Natural Disasters">

Inspiration

List of variables for inspiration: Number of deaths from drought Number of people injured from drought Number of people affected from drought Number of people left homeless from drought Number of total people affected by drought Reconstruction costs from drought Insured damages against drought Total economic damages from drought Death rates from drought Injury rates from drought Number of people affected by drought per 100,000 Homelessness rate from drought Total number of people affected by drought per 100,000 Number of deaths from earthquakes Number of people injured from earthquakes Number of people affected by earthquakes Number of people left homeless from earthquakes Number of total people affected by earthquakes Reconstruction costs from earthquakes Insured damages against earthquakes Total economic damages from earthquakes Death rates from earthquakes Injury rates from earthquakes Number of people affected by earthquakes per 100,000 Homelessness rate from earthquakes Total number of people affected by earthquakes per 100,000 Number of deaths from disasters Number of people injured from disasters Number of people affected by disasters Number of people left homeless from disasters Number of total people affected by disasters Reconstruction costs from disasters Insured damages against disasters Total economic damages from disasters Death rates from disasters Injury rates from disasters Number of people affected by disasters per 100,000 Homelessness rate from disasters Total number of people affected by disasters per 100,000 Number of deaths from volcanic activity Number of people injured from volcanic activity Number of people affected by volcanic activity Number of people left homeless from volcanic activity Number of total people affected by volcanic activity Reconstruction costs from volcanic activity Insured damages against volcanic activity Total economic damages from volcanic activity Death rates from volcanic activity Injury rates from volcanic activity Number of people affected by volcanic activity per 100,000 Homelessness rate from volcanic activity Total number of people affected by volcanic activity per 100,000 Number of deaths from floods Number of people injured from floods Number of people affected by floods Number of people left homeless from floods Number of total people affected by floods Reconstruction costs from floods Insured damages against floods Total economic damages from floods Death rates from floods Injury rates from floods Number of people affected by floods per 100,000 Homelessness rate from floods Total number of people affected by floods per 100,000 Number of deaths from mass movements Number of people injured from mass movements Number of people affected by mass movements Number of people left homeless from mass movements Number of total people affected by mass movements Reconstruction costs from mass movements Insured damages against mass movements Total economic damages from mass movements Death rates from mass movements Injury rates from mass movements Number of people affected by mass movements per 100,000 Homelessness rate from mass movements Total number of people affected by mass movements per 100,000 Number of deaths from storms Number of people injured from storms Number of people affected by storms Number of people left homeless from storms Number of total people affected by storms Reconstruction costs from storms Insured damages against storms Total economic damages from storms Death rates from storms Injury rates from storms Number of people affected by storms per 100,000 Homelessness rate from storms Total number of people affected by storms per 100,000 Number of deaths from landslides Number of people injured from landslides Number of people affected by landslides Number of people left homeless from landslides Number of total people affected by landslides Reconstruction costs from landslides Insured damages against landslides Total economic damages from landslides Death rates from landslides Injury rates from landslides Number of people affected by landslides per 100,000 Homelessness rate from landslides Total number of people affected by landslides per 100,000 Number of deaths from fog Number of people injured from fog Number of people affected by fog Number of people left homel...

This layer contains detailed Point in Time counts of homeless populations from 2019. This layer is modeled after a similar layer that contains data for 2018, 2013, and 2008.Layer is symbolized to show the count of the overall homeless population in 2019, with a pie chart of breakdown of type of shelter. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. The Point-in-Time (PIT) count is a count of sheltered and unsheltered homeless persons on a single night in January. HUD requires that Continuums of Care Areas (CoCs) conduct an annual count of homeless persons who are sheltered in emergency shelter, transitional housing, and Safe Havens on a single night. CoCs also must conduct a count of unsheltered homeless persons every other year (odd numbered years). Each count is planned, coordinated, and carried out locally.The Point-in-Time values were retrieved from HUD's Historical Data site. Original source is the 2019 sheet within the "2007 - 2019 PIT Counts by CoCs.xlsx" (downloaded on 3/10/2020) file. Key fields were kept and joined to the CoC boundaries available from HUD's Open Data site.Data note: MO-604 covers territory in both Missouri and Kansas. The record described in this file represents the CoC's total territory, the sum of the point-in-time estimates the CoC separately reported for the portions of its territory in MO and in KS.For more information and attributes on the CoC Areas themselves, including contact information, see this accompanying layer.Suggested Citation: U.S. Department of Housing and Urban Development (HUD)'s Point in Time (PIT) 2019 counts for Continuum of Care Grantee Areas, accessed via ArcGIS Living Atlas of the World on (date).