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.

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.

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 youth living in California, the most out of any U.S. state. New York had the second-highest number of homeless youth in that year, at *****.

In 2023, there were an estimated ******* white homeless people in the United States, the most out of any ethnicity. In comparison, there were around ******* Black or African American homeless people in the U.S. How homelessness is counted The actual number of homeless individuals in the U.S. is difficult to measure. The Department of Housing and Urban Development uses point-in-time estimates, where employees and volunteers count both sheltered and unsheltered homeless people during the last 10 days of January. However, it is very likely that the actual number of homeless individuals is much higher than the estimates, which makes it difficult to say just how many homeless there are in the United States. Unsheltered homeless in the United States California is well-known in the U.S. for having a high homeless population, and Los Angeles, San Francisco, and San Diego all have high proportions of unsheltered homeless people. While in many states, the Department of Housing and Urban Development says that there are more sheltered homeless people than unsheltered, this estimate is most likely in relation to the method of estimation.

OverviewThese are the Homeless Counts for 2020 as provided by the Los Angeles Homeless Services Authority (LAHSA), and the cities of Glendale, Pasadena, and Long Beach. The majority of this data comes from LAHSA using tract-level counts; the cities of Glendale, Pasadena, and Long Beach did not have tract-level counts available. The purpose of this layer is to depict homeless density at a community scale. Please read the note from LAHSA below regarding the tract level counts. In this layer LAHSA's tract-level population count was rounded to the nearest whole number, and density was determined per square mile of each community. It should be noted that not all of the sub-populations captured from LAHSA (eg. people living in vans, unaccompanied minors, etc.) are not captured here; only sheltered, unsheltered, and total population. Data generated on 12/2/20.Countywide Statistical AreasLos Angeles County's 'Countywide Statistical Areas' layer was used to classify the city / community names. Since this is tract-level data there are several times where a tract is in more than one city/community. Whatever the majority of the coverage of a tract is, that is the community that got coded. The boundaries of these communities follow aggregated tract boundaries and will therefore often deviate from the 'Countywide Statistical Area' boundaries.Note from LAHSALAHSA does not recommend aggregating census tract-level data to calculate numbers for other geographic levels. Due to rounding, the census tract-level data may not add up to the total for Los Angeles City Council District, Supervisorial District, Service Planning Area, or the Los Angeles Continuum of Care.The Los Angeles Continuum of Care does not include the Cities of Long Beach, Glendale, and Pasadena and will not equal the countywide Homeless Count Total.Street Count Data include persons found outside, including persons found living in cars, vans, campers/RVs, tents, and makeshift shelters. A conversion factor list can be found at https://www.lahsa.org/homeless-count/Please visit https://www.lahsa.org/homeless-count/home to view and download data.Last updated 07/16/2020

BackgroundEnsuring effective access to vaccinations for people experiencing homelessness is crucial to protecting the health of a vulnerable, yet often overlooked population. Reaching this goal takes more than a one size fits all approach. This study evaluates how a dedicated health team collaborated with multiple agencies to register and deliver the COVID-19 vaccine to people experiencing homelessness.MethodsThis is a mixed methods study co-designed with the Adult Homeless Integrated Team, a multi-disciplinary team who work with local agencies to provide care to people experiencing homelessness in Cork, Ireland’s second largest city. Quantitative data collected at the point of vaccine registration described socio-demographics of the population. To explain the quantitative findings, eleven agencies involved in provision of homeless services were invited to participate in interviews. A manager in each of the agencies acted as a gatekeeper to clients. Interviews explored experiences with the pandemic and the delivery (staff) or receipt (clients) of the COVID-19 vaccine. Interviews were recorded and transcribed, transcriptions were thematically analysed.ResultsThere were 728 vaccine doses administered to people experiencing homelessness during the first roll-out of vaccines; 401 first doses and 325 second doses. Of those who received a vaccine, the majority were male (76%), and more than half were Irish (55%). Ten semi-structured interviews, seven staff members and three clients, were conducted. There were three themes that provided further insights into the quantitative findings: Adapting to unprecedented times, Misinformation causing vaccine hesitancy and The importance of building relationships.ConclusionsThis study provides valuable insights into how a multidisciplinary approach resulted in a successful well received vaccination programme among a traditionally hard to reach group.

This data set shows the location of Baltimore City's Tansitional and Emergency "Homeless" Shelter Facilities. However, this is not a complete list. It is the most recent update (2008), and is subjected to change. The purpose of this data set is to aid Baltimore City organizations to best identify facilities to aid the homeless population. The data is broken down into two categories: Emergency Shelter and Transitional Housing. Please find the two definitions below. The first is simply ��_��_��_shelter��_�� and the second is a more involved program that is typically a longer stay. Emergency Shelter: Any facility with overnight sleeping accommodations, the primary purpose of which is to provide temporary shelter for the homeless in general or for specific populations of homeless persons. The length of stay can range from one night up to as much as six months. Transitional Housing: a project that is designed to provide housing and appropriate support services to homeless persons to facilitate movement to independent living within 24 months. These data set was provided by Greg Sileo, Director of the Mayor's Office of Baltimore Homeless Services.

"Ratio of Homeless Population to General Population in major US Cities in 2009.

*This represents a list of large U.S. cities with a similar street count methodology for which DHS was able to confirm a recent Census; 2009 results are not yet available for LA, SF, and Chicago. All population figures are from the 2007 U.S. Census Bureau Population Estimate."

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

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.

This study examines the spatial patterns of homelessness and resources for the homeless population in Louisville, KY with the goal of identifying where homeless populations are located in relation to resources. Working with census data and some of the resources for the homeless, this study uncovers the realities that the homeless face in different parts of the city. This research research was made as a senior thesis for the University of Louisville's department of Geographic and Environmental Sciences. Table 1. Income and Poverty between the United States and Louisville/Jefferson County metro government, Kentucky in 2019 (United States Census Bureau 2021)Homeless people are thought of as less than full citizens. Whether the rest of the city's people agree or disagree, they are citizens, and should have rights to the city as much as everyone else. The opioid crisis, unmanaged mental illnesses, lack of employment, and other issues like limitations on affordable housing have increased the population of homeless people in Louisville in recent years (Reed 2021). More than 1.5 million children experience homelessness in the United States (Poverty USA 2019). The poverty rate in Louisville, Kentucky is 15.9%, and 1 in 10 renters were facing eviction as of 2019. The 2019 Point In Time Count shows that on a randomly picked night in Louisville, 1071 of the city's people are experiencing homelessness, which is an increase of 15% from the 2018 count (Coalition for the Homeless 2019). The previous data compared to the count for 2020 of 1102 people, shows a trend in increasing homeless population (Coalition for the Homeless 2020).

The County of Sonoma conducts an annual homeless count for the entire county. The survey data is derived from a sample of about 600 homeless persons countywide per year. The resulting information is statistically reliable only for the county as a whole, not for individual locations. The exception is the City of Santa Rosa, where the sample taken within the city is large enough to be predictive of the overall homeless population in that city.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

A survey of the homeless in two settlements of the part of town is archived under ZA Study No. 2579.

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.

ABSTRACT In this paper I present results of a project that, in the context of critical discourse studies and the interdiscursive analysis of public policies, focused on representations in online journalism regarding public policies aimed at the homeless population. The research project (CAPES 88881.172032/2018-01) was developed at the Pompeu Fabra University, Spain. Considering the main newspaper of the city of São Paulo, in its digital platform, we have compiled a comprehensive corpus of news about homeless situation published in a period of three years. The choice to specifically address data from Folha de S Paulo is justified because it is the city with the largest homeless population in Brazil. Also, because our previous study has shown that this is the vehicle, among those studied, that publishes more news related to territorial issues, our focus of interest to investigate via the discursive categories of metaphor and representation of social actors.

A. SUMMARY This archived dataset includes data for population characteristics that are no longer being reported publicly. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”. B. HOW THE DATASET IS CREATED Data on the population characteristics of COVID-19 cases are from:  * Case interviews  * Laboratories  * Medical providers    These multiple streams of data are merged, deduplicated, and undergo data verification processes.   Race/ethnicity * We include all race/ethnicity categories that are collected for COVID-19 cases. * The population estimates for the "Other" or “Multi-racial” groups should be considered with caution. The Census definition is likely not exactly aligned with how the City collects this data. For that reason, we do not recommend calculating population rates for these groups. Gender * The City collects information on gender identity using these guidelines. Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing Facility (SNF) is a type of long-term care facility that provides care to individuals, generally in their 60s and older, who need functional assistance in their daily lives.  * This dataset includes data for COVID-19 cases reported in Skilled Nursing Facilities (SNFs) through 12/31/2022, archived on 1/5/2023. These data were identified where “Characteristic_Type” = ‘Skilled Nursing Facility Occupancy’. Sexual orientation * The City began asking adults 18 years old or older for their sexual orientation identification during case interviews as of April 28, 2020. Sexual orientation data prior to this date is unavailable. * The City doesn’t collect or report information about sexual orientation for persons under 12 years of age. * Case investigation interviews transitioned to the California Department of Public Health, Virtual Assistant information gathering beginning December 2021. The Virtual Assistant is only sent to adults who are 18+ years old. Learn more about our data collection guidelines pertaining to sexual orientation. Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death. Homelessness Persons are identified as homeless based on several data sources: * self-reported living situation * the location at the time of testing * Department of Public Health homelessness and health databases * Residents in Single-Room Occupancy hotels are not included in these figures. These methods serve as an estimate of persons experiencing homelessness. They may not meet other homelessness definitions. Single Room Occupancy (SRO) tenancy * SRO buildings are defined by the San Francisco Housing Code as having six or more "residential guest rooms" which may be attached to shared bathrooms, kitchens, and living spaces. * The details of a person's living arrangements are verified during case interviews. Transmission Type * Information on transmission of COVID-19 is based on case interviews with individuals who have a confirmed positive test. Individuals are asked if they have been in close contact with a known COVID-19 case. If they answer yes, transmission category is recorded as contact with a known case. If they report no contact with a known case, transmission category is recorded as community transmission. If the case is not interviewed or was not asked the question, they are counted as unknown. C. UPDATE PROCESS This dataset has been archived and will no longer update as of 9/11/2023. D. HOW TO USE THIS DATASET Population estimates are only available for age groups and race/ethnicity categories. San Francisco po

In 2022, about ****** veterans living in California were homeless, the most out of all U.S. states.

The social situation of the homeless in a Cologne suburb. Topics: Most important problems in the settlement; problems in the relationship between the settlement and surroundings; plans to leave; length of residence in the settlement and year of first utilization of a city shelter; reason for admission into a city shelter; type of quarters on first admission and before admission; frequency of moving into such accomodations and settlements; perceived deterioration from the move; number of rooms; possession of durable economic goods; defects in residence; number of children and schools attended or kindergarten; attitude to establishment of a special school in the part of town; perceived discrimination of one´s children in school; regular pocket-money for the children; place of leisure time of one´s children; contacts of one´s children outside of the settlement; person raising the children; perceived discrimination of the homeless; exercise of an honorary activity in the settlement; attitude to a self-help committee in the settlement; interest in participation in such a committee; assumed effectiveness of a community of interests of the homeless; most important tasks of such a community of interests; most important institutions as contact to improve the situation of the homeless; location of place of work; frequency of change of job; change of occupation; satisfaction with place of work; shopping place; possession of savings; manager of family income; decision-maker for expenditures; debts; eating main meal together; leisure activities in the settlement; contact persons in leisure time; leisure contacts outside the settlement; neighborhood contacts in the settlement; contacts with non-homeless; establishing these contacts on leisure time or through work; identification as Cologne resident or resident of the part of town; desire to move to another part of town; favorite part of town in Cologne; intensity of contact with the population in the part of town; contacts with residents of another settlement; participation in meetings of the Poll Buergerverein; assumed representation of interests of the homeless through this organization; most influencial personalities in the part of town; persons making a particular effort for the homeless; most important differences between the residents of one´s own settlement and another settlement in the part of town; knowledge of press reports and television reports about the homeless and judgement on validity; most important reasons for homelessness; most important measures to prevent homelessness; perceived differences between the homeless; filing a complaint against the city to obtain better housing; experiences with contacts with authorities; satisfaction with the manager of the settlement; most important task of a manager; anomy (scale); comparison of personal housing situation with that of parents; social origins; social mobility compared with father and father-in-law; contacts with relatives; judgement of relatives about living in this settlement; relatives likewise living in emergency shelters; personal condition of health; number of sick family members and type of illnesses; recommendations on dealing with the homeless; society or the individual as responsible for one´s own homelessness; desire for integration in a normal residential area; personal extent of commiting crimes and conviction; type of offenses; perceived improvement in living conditions in the emergency shelter; comparison of the situation between the settlement and a temporary shelter; place of birth; length of residence in Cologne; re-married; religiousness; club memberships; extent of club activity; party preference; assumed effectiveness of this survey on the situation of the homeless. Interviewer rating: name sign on door; description of residential furnishings regarding family pictures, other pictures, knick-knacks, religious figures and possession of books; condition of windows, wallpaper and furniture; length of interview; number of persons present during interview; carrying out house work by the person interviewed during the interview; conduct of other persons present during the conversation; willingness of respondent to cooperate.