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 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.

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 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 *****.

The dataset contains locations and attributes of organizations who deliver programs and services specifically targeted toward those experiencing homelessness. Although some organization listed in the dataset also provide housing resources, the inclusion of programs in this dataset indicates that the organization provides a non-housing resource or service which is available (by drop-in, referral, or appointment) during the daytime hours for people experiencing homelessness. The dataset was created by the Department of Human Services as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO).

Dataset contains n=115 facilities in the DC metropolitan area.

Data Dictionary:

OBJECTID ( type: esriFieldTypeOID, alias: OBJECTID ) PROGRAM_NAME ( type: esriFieldTypeString, alias: PROGRAM_NAME, length: 255 ) DESCRIPTION ( type: esriFieldTypeString, alias: DESCRIPTION, length: 1073741822 ) WEBSITE_URL ( type: esriFieldTypeString, alias: WEBSITE_URL, length: 255 ) CITY ( type: esriFieldTypeString, alias: CITY, length: 255 ) STATE ( type: esriFieldTypeString, alias: STATE, length: 255 ) ACCESSIBILITY_SERVICES ( type: esriFieldTypeString, alias: ACCESSIBILITY SERVICES, length: 255 ) ADULT_LITERACY ( type: esriFieldTypeString, alias: ADULT_LITERACY, length: 255 ) ART_THERAPY ( type: esriFieldTypeString, alias: ART_THERAPY, length: 255 ) ASSESSMENT ( type: esriFieldTypeString, alias: ASSESSMENT, length: 255 ) BORROW_MATERIALS ( type: esriFieldTypeString, alias: BORROW_MATERIALS, length: 255 ) CASE_MANAGEMENT ( type: esriFieldTypeString, alias: CASE_MANAGEMENT, length: 255 ) CHILD_CARE ( type: esriFieldTypeString, alias: CHILD_CARE, length: 255 ) CLOTHING ( type: esriFieldTypeString, alias: CLOTHING, length: 255 ) COMPUTERS ( type: esriFieldTypeString, alias: COMPUTERS, length: 255 ) DENTAL_SERVICES ( type: esriFieldTypeString, alias: DENTAL_SERVICES, length: 255 ) DOCUMENTATION_ASSISTANCE ( type: esriFieldTypeString, alias: DOCUMENTATION_ASSISTANCE, length: 255 ) DOMESTIC_VIOLENCE_SERVICES ( type: esriFieldTypeString, alias: DOMESTIC_VIOLENCE_SERVICES, length: 255 ) FOOD_GROCERIES ( type: esriFieldTypeString, alias: FOOD_GROCERIES, length: 255 ) GROUPS ( type: esriFieldTypeString, alias: GROUPS, length: 255 ) HAIRCUTS ( type: esriFieldTypeString, alias: HAIRCUTS, length: 255 ) HARM_REDUCTION ( type: esriFieldTypeString, alias: HARM_REDUCTION, length: 255 ) HIV_TESTING ( type: esriFieldTypeString, alias: HIV_TESTING, length: 255 ) HOUSING ( type: esriFieldTypeString, alias: HOUSING, length: 255 ) HOUSING_NAVIGATION ( type: esriFieldTypeString, alias: HOUSING_NAVIGATION, length: 255 ) INCOME_TAX_HELP ( type: esriFieldTypeString, alias: INCOME_TAX_HELP, length: 255 ) LAUNDRY ( type: esriFieldTypeString, alias: LAUNDRY, length: 255 ) LIBRARY_CARD ( type: esriFieldTypeString, alias: LIBRARY_CARD, length: 255 ) LEGAL_SERVICES ( type: esriFieldTypeString, alias: LEGAL_SERVICES, length: 255 ) MEDICAL_BENEFITS ( type: esriFieldTypeString, alias: MEDICAL_BENEFITS, length: 255 ) MEDICAL_SERVICES ( type: esriFieldTypeString, alias: MEDICAL_SERVICES, length: 255 ) MAIL ( type: esriFieldTypeString, alias: MAIL, length: 255 ) MEALS ( type: esriFieldTypeString, alias: MEALS, length: 255 ) MENTAL_HEALTH ( type: esriFieldTypeString, alias: MENTAL_HEALTH, length: 255 ) MINISTRY ( type: esriFieldTypeString, alias: MINISTRY, length: 255 ) PHONE ( type: esriFieldTypeString, alias: PHONE, length: 255 ) PUBLIC_RESTROOMS ( type: esriFieldTypeString, alias: PUBLIC_RESTROOMS, length: 255 ) REFRESHMENTS ( type: esriFieldTypeString, alias: REFRESHMENTS, length: 255 ) SHOWERS ( type: esriFieldTypeString, alias: SHOWERS, length: 255 ) SNAP_FOOD_STAMPS ( type: esriFieldTypeString, alias: SNAP_FOOD_STAMPS, length: 255 ) STORAGE ( type: esriFieldTypeString, alias: STORAGE, length: 255 ) SUBSTANCE_ABUSE_TREATMENT ( type: esriFieldTypeString, alias: SUBSTANCE_ABUSE_TREATMENT, length: 255 ) SUPPORTED_EMPLOYMENT ( type: esriFieldTypeString, alias: SUPPORTED_EMPLOYMENT, length: 255 ) TANF_FINANCIAL_ASSISTANCE ( type: esriFieldTypeString, alias: TANF_FINANCIAL_ASSISTANCE, length: 255 ) TRANSPORTATION ( type: esriFieldTypeString, alias: TRANSPORTATION, length: 255 ) VOCATIONAL_TRAINING ( type: esriFieldTypeString, alias: VOCATIONAL_TRAINING, length: 255 ) CLIENTS_SERVED_PER_DAY ( type: esriFieldTypeDouble, alias: CLIENTS_SERVED_PER_DAY ) TARGET ( type: esriFieldTypeString, alias: TARGET, length: 255 ) OPEN_TO_PUBLIC ( type: esriFieldTypeString, alias: OPEN_TO_PUBLIC, length: 255 ) HOURS_OF_OPERATION ( type: esriFieldTypeString, alias: HOURS_OF_OPERATION, length: 255 ) RECORD_LAST_UPDATED ( type: esriFieldTypeDate, alias: RECORD_LAST_UPDATED, length: 8 ) SHAPE ( type: esriFieldTypeGeometry, alias: SHAPE ) LATITUDE ( type: esriFieldTypeDouble, alias: LATITUDE ) LONGITUDE ( type: esriFieldTypeDouble, al...

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. https://www.sfdph.org/dph/files/PoliciesProcedures/COM9_SexualOrientationGuidelines.pdf">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 population estimates for race/ethnicity and age groups can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

This dataset includes many different types of characteristics. Filter the “Characteristic Type” column to explore a topic area. Then, the “Characteristic Group” column shows each group or category within that topic area and the number of cases on each date.

New cases are the count of cases within that characteristic group where the positive tests were collected on that specific specimen collection date. Cumulative cases are the running total of all San Francisco cases in that characteristic group up to the specimen collection date listed.

This data may not be immediately available for recently reported cases. Data updates as more information becomes available.

To explore data on the total number of cases, use the ARCHIVED: COVID-19 Cases Over Time dataset.

E. CHANGE LOG

• 9/11/2023 - data on COVID-19 cases by population characteristics over time are no longer being updated. The date on which each population characteristic type was archived can be found in the field “data_loaded_at”.
• 6/6/2023 - data on cases by transmission type have been removed. See section ARCHIVED DATA for more detail.
• 5/16/2023 - data on cases by sexual orientation, comorbidities, homelessness, and single room occupancy have been removed. See section ARCHIVED DATA for more detail.
• 4/6/2023 - the State implemented system updates to improve the integrity of historical data.
• 2/21/2023 - system updates to improve reliability and accuracy of cases data were implemented.
• 1/31/2023 - updated “population_estimate” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/5/2023 - data on SNF cases removed. See section ARCHIVED DATA for more detail.
• 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
• 1/22/2022 - system updates to improve timeliness and accuracy of cases and deaths data were implemented.
• 7/15/2022 - reinfections added to cases dataset. See section SUMMARY for more information on how reinfections are identified.