Context

This database contains the data reported in the Annual Homeless Assessment Report to Congress (AHAR). It represents a point-In-time count (PIT) of homeless individuals, as well as a housing inventory count (HIC) conducted annually.

The data represent the most comprehensive national-level assessment of homelessness in America, including PIT and HIC estimates of homelessness, as well as estimates of chronically homeless persons, homeless veterans, and homeless children and youth.

These data can be trended over time and correlated with other metrics of housing availability and affordability, in order to better understand the particular type of housing resources that may be needed from a social determinants of health perspective.

HUD captures these data annually through the Continuum of Care (CoC) program. CoC-level reporting data have been crosswalked to county levels for purposes of analysis of this dataset.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.sdoh_hud_pit_homelessness

Sample Query

What has been the change in the number of homeless veterans in the state of New York’s CoC Regions since 2012? Determine how the patterns of homeless veterans have changes across the state of New York

homeless_2018 AS ( SELECT Homeless_Veterans AS Vet18, CoC_Name
FROM bigquery-public-data.sdoh_hud_pit_homelessness.hud_pit_by_coc WHERE SUBSTR(CoC_Number,0,2) = "NY" AND Count_Year = 2018 ),

veterans_change AS ( SELECT homeless_2012.COC_Name, Vet12, Vet18, Vet18 - Vet12 AS VetChange FROM homeless_2018 JOIN homeless_2012 ON homeless_2018.CoC_Name = homeless_2012.CoC_Name )

SELECT * FROM veterans_change

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.

INTRODUCTION: As California’s homeless population continues to grow at an alarming rate, large metropolitan regions like the San Francisco Bay Area face unique challenges in coordinating efforts to track and improve homelessness. As an interconnected region of nine counties with diverse community needs, identifying homeless population trends across San Francisco Bay Area counties can help direct efforts more effectively throughout the region, and inform initiatives to improve homelessness at the city, county, and metropolitan level. OBJECTIVES: The primary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness across San Francisco Bay Area counties between the years 2018-2022. The secondary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness among different age groups in each of the nine San Francisco Bay Area counties between the years 2018-2022. METHODS: Two datasets were used to conduct research. The first dataset (Dataset 1) contains Point-in-Time (PIT) homeless counts published by the U.S. Department of Housing and Urban Development. Dataset 1 was cleaned using Microsoft Excel and uploaded to Tableau Desktop Public Edition 2022.4.1 as a CSV file. The second dataset (Dataset 2) was published by Data SF and contains shapefiles of geographic boundaries of San Francisco Bay Area counties. Both datasets were joined in Tableau Desktop Public Edition 2022.4 and all data analysis was conducted using Tableau visualizations in the form of bar charts, highlight tables, and maps. RESULTS: Alameda, San Francisco, and Santa Clara counties consistently reported the highest annual count of people experiencing homelessness across all 5 years between 2018-2022. Alameda, Napa, and San Mateo counties showed the largest increase in homelessness between 2018 and 2022. Alameda County showed a significant increase in homeless individuals under the age of 18. CONCLUSIONS: Results from this research reveal both stark and fluctuating differences in homeless counts among San Francisco Bay Area Counties over time, suggesting that a regional approach that focuses on collaboration across counties and coordination of services could prove beneficial for improving homelessness throughout the region. Results suggest that more immediate efforts to improve homelessness should focus on the counties of Alameda, San Francisco, Santa Clara, and San Mateo. Changes in homelessness during the COVID-19 pandemic years of 2020-2022 point to an urgent need to support Contra Costa County.

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.

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 7/29/2025.

"Ratio of Homeless Population to General Population in major US Cities in 2012. *This represents a list of large U.S. cities for which DHS was able to confirm a recent estimate of the unsheltered population. Unsheltered estimates are from 2011 except for Seattle and New York City (2012) and Chicago (2009). All General Population figures are from the 2010 U.S. Census enumeration."

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.

Novel and comprehensive cross-sectional datasets were developed to document and measure city level homeless policies across issue area. The dataset is comprised of the 100 largest cities in the United States, including FIPS identifiers and matched Continuum of Care (CoC) level identifiers by CoC number associated with the city. The datasets include city-level homeless policies in the 100 largest cities across the issues of homeless plans; housing plans with mentions of homelessness; homeless outreach teams; and the role of sanitation in homeless policy. Each dataset includes sub-codes to evaluate the governance structure and policy goals of each type of city-level homeless policy. Primary data were collected in 2021 and 2022. Please see the attached publications for complete methodology and data collection procedures for each policy type.

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. Die soziale Situation von Obdachlosen in einem Kölner Vorort. Themen: Wichtigste Probleme in der Siedlung; Probleme im Verhältnis zwischen Siedlung und Umgebung; Auszugspläne; Wohndauer in der Siedlung und Jahr der ersten Inanspruchnahme einer städtischen Unterkunft; Grund für die Einweisung in eine städtische Unterkunft; Unterkunftstyp bei der ersten Einweisung und vor der Einweisung; Umzugshäufigkeit in solchen Unterkünften und Siedlungen; empfundene Verschlechterung durch den Umzug; Wohnraumzahl; Besitz langlebiger Wirtschaftsgüter; Schäden in der Wohnung; Kinderzahl und besuchte Schulen bzw. Kindergärten; Einstellung zur Einrichtung einer Sonderschule im Stadtteil; empfundene Diskriminierung der Kinder in der Schule; regelmäßiges Taschengeld für die Kinder; Freizeitort der Kinder; Kontakte der Kinder außerhalb der Siedlung; Erziehungsperson für die Kinder; empfundene Diskriminierung der Obdachlosen; Ausüben einer ehrenamtlichen Tätigkeit in der Siedlung; Einstellung zu einem Selbsthilfekomitee in der Siedlung; Interesse an der Beteiligung in einem solchen Komitee; vermutete Wirksamkeit einer Interessengemeinschaft der Obdachlosen; wichtigste Aufgaben einer solchen Interessengemeinschaft; wichtigste Institutionen als Ansprechpartner zur Verbesserung der Situation der Obdachlosen; Ortslage der Arbeitsstätte; Häufigkeit von Arbeitsplatzwechsel; Berufswechsel; Zufriedenheit mit der Arbeitsstelle; Einkaufsort; Besitz von Ersparnissen; Verwalter des Familieneinkommens; Entscheider über Ausgaben; Schulden; gemeinsame Einnahme der Hauptmahlzeit; Freizeitaktivitäten in der Siedlung; Kontaktpersonen in der Freizeit; Freizeitkontakte außerhalb der Siedlung; Nachbarschaftskontakte in der Siedlung; Kontakte zu Nichtobdachlosen; Aufnahme dieser Kontakte in der Freizeit oder durch die Arbeit; Identifikation als Kölner oder Bewohner des Stadtteils; Umzugswunsch in einen anderen Stadtteil; beliebtester Stadtteil in Köln; Intensität des Kontaktes zur Bevölkerung im Stadtteil; Kontakte zu Bewohnern einer anderen Siedlung; Beteiligung an Versammlungen des Poller Bürgervereins; vermutete Interessenvertretung der Obdachlosen durch diesen Verein; einflußreichste Persönlichkeiten im Stadtteil; Personen, die sich besonders für die Obdachlosen einsetzen; wichtigste Unterschiede zwischen den Bewohnern der eigenen Siedlung und einer weiteren Siedlung im Stadtteil; Kenntnis von Presseberichten und Fernsehberichten über die Obdachlosen und Beurteilung des Wahrheitsgehaltes; wichtigste Gründe für Obdachlosigkeit; wichtigste Vorbeugungsmaßnahmen zur Verhinderung von Obdachlosigkeit; perzipierte Unterschiede zwischen Obdachlosen; Beschwerdeführung gegen die Stadt zur Bereitstellung einer besseren Wohnung; Erfahrungen mit Behördenkontakten; Zufriedenheit mit dem Verwalter der Siedlung; wichtigste Aufgabe eines Verwalters; Anomie (Skala); Vergleich der eigenen Wohnsituation mit der der Eltern; soziale Herkunft; soziale Mobilität gegenüber dem Vater und dem Schwiegervater; Verwandtschaftskontakte; Urteil der Verwandtschaft über das Wohnen in dieser Siedlung; Verwandte, die ebenfalls in Notunterkünften leben; eigener Gesundheitszustand; Zahl der erkrankten Familienmitglieder und Art der Krankheiten; Vorschläge zur Behandlung von Obdachlosen; Gesellschaft oder Individuum als Verantwortlicher für die eigene Obdachlosigkeit; Wunsch nach Integration in eine normale Wohngegend; eigene Straffälligkeit und Verurteilung; Art der Delikte; empfundene Verbesserung der Lebensbedingungen in der Notunterkunft; Vergleich der Situation zwischen der Siedlung und einem Übergangshaus; Geburtsort; Wohndauer in Köln; wiederverheiratet; Religiosität; Vereinsmitgliedschaften; Umfang der Vereinstätigkeit; Parteipräferenz; vermutete Wirksamkeit dieser Befragung auf die Situation der Obdachlosen. Demographie: Alter; Geschlecht; Familienstand; Kirchgangshäufigkeit; Schulbildung; Berufstätigkeit; Einkommen. Interviewerrating: Namensschild an der Tür; Beschreibung der Wohnungseinrichtung bezüglich Familienbilder, sonstiger Bilder, Nippfiguren, religiöser Figuren und Bücherbesitz; Zustand der Fenster, Tapeten und Möbel; Interviewdauer; Anzahl der anwesenden Personen beim Interview; Erledigung von Haushaltsarbeiten der befragten Person während des Interviews; Verhalten der übrigen Anwesenden während des Gesprächs; Kooperationsbereitschaft des Befragten.

Tempe relies on data to inform and support decision making for the city’s Homeless Solutions strategy. This comprehensive effort ensures that the city has the most up-to-date information to meet needs, identify emerging trends and create solutions. In this hub site, you’ll find data related to:Outreach and engagementReporting homeless encampmentsVerifying and resolving encampmentsAnnual Point-in-Time homeless countSite is Google Translate enabled. DO NOT DELETE OR MODIFY THIS ITEM. This item is managed by the ArcGIS Hub application. To make changes to this page, please visit https://tempegov.hub.arcgis.com:/overview/edit.

This dataset represents the number of persons who are experiencing homelessness for the first time in a fiscal year (October 1 - September 30) in the Austin/Travis County Continuum of Care (CoC). Data Source: The data for this measure was reported to the City of Austin by the Ending Community Homelessness Coalition (ECHO). Each year, ECHO, as the homeless Continuum of Care Lead Agency (CoC Lead), aggregates and reports community wide data (including this measure) to the Department of Housing and Urban Development (HUD). This data is referred to as System Performance Measures as they are designed to examine how well a community is responding to homelessness at a system level. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/wk3t-h5qe

This dataset represents the number of persons who successfully exit from homelessness in a given fiscal year in the Austin/Travis County Continuum of Care (CoC). This measure is comprised of Metric 7b1 and 7b2 from the HUD System Performance Measures.

Data Source: The data for this measure was reported to the City of Austin by the Ending Community Homelessness Coalition (ECHO). Each year, ECHO, as the homeless Continuum of Care Lead Agency (CoC Lead), aggregates and reports community wide data (including this measure) to the Department of Housing and Urban Development (HUD). This data is referred to as System Performance Measures as they are designed to examine how well a community is responding to homelessness at a system level.

View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/xtip-he7k

Community integration of homeless in a Cologne suburb. Topics: Characterization of the suburb Poll; closeness with the suburb or with the city of Cologne; length of residence in the suburb; previous place of residence and moving frequency; rent costs; size of household and number of rooms; possession of durable economic goods; year of construction of building; satisfaction with residence; moving plans; possible destination of moving; particular advantages of the residential area in Poll; favorite part of town of Cologne; familial relations in the part of town or in the entire city; frequency of contact with parents, grandparents, children, siblings and the rest of the relatives; distribution of circle of friends about the part of town and the other parts of the city; contacts with neighbors and colleagues; location of place of work; frequency of change of place of work; occupational mobility; desire for remaining in the part of town given a change of occupation; shopping habits; frequency of trips downtown; leisure activities and place of these leisure activities; club membership; time extent of club activity; participation in activities of the Poll Buergerverein; significance of this organization; judgement on the moving of schools; most influencial personalities in the suburb; most important integration factors in the part of town; influence of the part of town on the entire city; anomy (scale); evaluation of despicability of selected crimes; most important reasons for development of so-called Rocker groups; most effective measures to reduce crime; perceived differences in the old and new part of town; identification of areas that belong together in the part of town and assignment of different social groups to the parts of town; assignment of social groups to the homeless settlement; significance of the homeless problem and preferred measures to eliminate it; measures to prevent homelessness; attitude to differential treatment of the homeless and the rest of the population; recommendations on treatment of the homeless; judgement on the proportion of homeless in the part of town; personal contacts with the homeless; intensity of contacts; fear of contact and social distance to the homeless; preferred measures in view of the two homeless settlements in Poll; perceived differences among the homeless; typical characteristics with which one can recognize the homeless; judgement on a media report about the homeless in Poll; judgement on the municipal facilities in the part of town; personal importance of the existence of such facilities; religiousness. Interviewer rating: residential building size and willingness of respondent to cooperate. Gemeindliche Integration von Obdachlosen in einem Kölner Vorort. Themen: Charakterisierung des Vororts Poll; Verbundenheit mit dem Vorort oder mit der Stadt Köln; Wohndauer im Vorort; vorheriger Wohnort und Umzugshäufigkeit; Mietkosten; Haushaltsgröße und Anzahl der Räume; Besitz langlebiger Wirtschaftsgüter; Baujahr des Hauses; Zufriedenheit mit der Wohnung; Umzugspläne; mögliches Umzugsziel; besondere Vorzüge der Wohnlage in Poll; beliebtester Stadtteil von Köln; verwandtschaftliche Beziehungen im Stadtteil bzw. in der gesamten Stadt; Kontakthäufigkeit mit den Eltern, Großeltern, Kindern, Geschwistern und den übrigen Verwandten; Verteilung des Bekanntenkreises über den Stadtteil und die übrigen Teile der Stadt; Kontakte zu Nachbarn und Arbeitskollegen; Ortslage der Arbeitsstätte; Häufigkeit des Wechselns der Arbeitsstätte; berufliche Mobilität; Wunsch nach Verbleiben im Stadtteil bei Berufswechsel; Einkaufsgewohnheiten; Besuchshäufigkeit in der City; Freizeitaktivitäten und Ort dieser Freizeitaktivitäten; Vereinsmitgliedschaft; zeitlicher Umfang von Vereinstätigkeit; Teilnahme an Aktivitäten des Poller Bürgervereins; Bedeutung dieses Vereins; Beurteilung der Verlegung von Schulen; einflußreichste Persönlichkeiten im Vorort; wichtigste Integrationsfaktoren im Stadtteil; Einfluß des Stadtteils auf die ganze Stadt; Anomie (Skala); Bewertung der Verwerflichkeit von ausgewählten Straftaten; wichtigste Ursachen für das Entstehen sogenannter Rockergruppen; wirksamste Maßnahmen zur Reduzierung der Kriminalität; perzipierte Unterschiede im alten und neuen Stadtteil; Identifizierung zusammengehörender Gebiete im Stadtteil und Zuordnung unterschiedlicher sozialer Gruppen zu den Stadtteilen; Zuordnung sozialer Gruppen zur Obdachlosensiedlung; Bedeutung des Obdachlosenproblems und präferierte Maßnahmen zur Beseitigung; vorbeugende Maßnahmen zur Verhinderung von Obdachlosigkeit; Einstellung zur differenzierten Behandlung von Obdachlosen und der übrigen Bevölkerung; Vorschläge zur Behandlung von Obdachlosen; Beurteilung des Obdachlosenanteils im Stadtteil; eigene Kontakte zu Obdachlosen; Intensität der Kontakte; Berührungsängste und soziale Distanz zu Obdachlosen; präferierte Maßnahmen im Hinblick auf die beiden Obdachlosensiedlungen in Poll; perzipierte Unterschiede bei den Obdachlosen; charakteristische Merkmale, an denen man Obdachlose erkennen kann; Beurteilung eines Medienberichts über die Obdachlosen in Poll; Beurteilung der kommunalen Einrichtungen im Stadtteil; persönliche Wichtigkeit der Existenz solcher Einrichtungen; Religiosität. Demographie: Alter; Familienstand; Kinderzahl; Kirchgangshäufigkeit; Schulbildung; Berufstätigkeit; Einkommen; Haushaltsgröße. Interviewerrating: Wohnhausgröße und Kooperationsbereitschaft des Befragten.

This dataset provides information on individuals experiencing sheltered or unsheltered homelessness in the Austin/Travis County Continuum of Care (CoC) on a single night in January when the Point in Time (PIT) Count occurs. "Sheltered" homelessness refers to individuals residing in emergency shelter, safe haven, or transitional housing project types. Unsheltered homelessness refers to individuals with a primary nighttime residence that is a public or private place not designed for or ordinarily used as a regular sleeping accommodation for human beings, including a car, park, abandoned building, bus or train station, airport, or camping ground on the night designated for the count. This measure overlaps, but is different from, the annual count of sheltered homelessness in HMIS (SD23 Measure EOA.E.1b). Data Source: The data for this measure was reported to the City of Austin by the Ending Community Homelessness Coalition (ECHO). Each year, ECHO, as the homeless Continuum of Care Lead Agency (CoC Lead), aggregates and reports community wide data (including this measure) to the Department of Housing and Urban Development (HUD). This data is referred to as System Performance Measures as they are designed to examine how well a community is responding to homelessness at a system level. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/hjiv-t2tm Last Updated December 2020 with data for 2020 PIT Count.

This dataset provides information on individuals who exit homelessness to permanent housing destinations and then return to homelessness within 2 years from their exit in the Austin/Travis County Continuum of Care (CoC) in a given fiscal year. Data Source: The data for this measure was reported to the City of Austin by the Ending Community Homelessness Coalition (ECHO). Each year, ECHO, as the homeless Continuum of Care Lead Agency (CoC Lead), aggregates and reports community wide data (including this measure) to the Department of Housing and Urban Development (HUD). This data is referred to as System Performance Measures as they are designed to examine how well a community is responding to homelessness at a system level. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/cutp-y8m4

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 po

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

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/

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. Any use of the information for commercial purposes is strictly prohibited. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://bit.ly/rk5Tpc.