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

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.

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.

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.

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 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.

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

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.

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

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

In this study, a monitor was established to track infections among the homeless population during the first year of the COVID-19 pandemic in 8 street doctor practices in the Netherlands. Additionally, the impact of the pandemic on the lives of homeless individuals was documented through interviews with homeless individuals, healthcare and shelter professionals, as well as municipal/health department officials. The collected data has been summarized in 9 small reports in Dutch. English versions can be requested at the authors. Factsheet 1: More information about the study setup. Factsheet 2: Key results from the investigation into the implementation of municipal policies during the first two years of the pandemic. Factsheet 3: Key results regarding the impact of COVID-19 according to homeless individuals in the first year of the pandemic. Factsheet 4: Key results regarding the impact of COVID-19 according to healthcare and shelter staff in the first year of the pandemic. Factsheet 5: Outcomes of the COVID-19 monitor among homeless individuals in the first year of the pandemic. Factsheet 6: Key results of the impact of COVID-19 experienced by homeless individuals in the second year of the pandemic. Factsheet 7: Key results of the impact of COVID-19 on homeless individuals as experienced by healthcare and shelter staff in the second year of the pandemic. Factsheet 8: New initiatives for homeless individuals during COVID-19; inspiration and good examples. Factsheet 9: Insight into effective elements of a vaccination strategy for homeless individuals.

description: 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.; abstract: 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.

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 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. For more information about these data, please see here .

Analysis of ‘Strategic Measure_Number of persons who have not been served by the community’s homeless system in the two years prior to entry into the homeless system’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/41c1755f-0106-4be5-b5b9-c680de3252dd on 26 January 2022.

--- Dataset description provided by original source is as follows ---

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

--- Original source retains full ownership of the source dataset ---

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.

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

Homelessness Report May 2025. Published by Department of Housing, Local Government and Heritage. Available under the license Creative Commons Attribution Share-Alike 4.0 (CC-BY-SA-4.0).Homelessness data Official homelessness data is produced by local authorities through the Pathway Accommodation and Support System (PASS). PASS was rolled-out nationally during the course of 2013. The Department’s official homelessness statistics are published on a monthly basis and refer to the number of homeless persons accommodated in emergency accommodation funded and overseen by housing authorities during a specific count week, typically the last full week of the month. The reports are produced through the Pathway Accommodation & Support System (PASS), collated on a regional basis and compiled and published by the Department. Homelessness reporting commenced in this format in 2014. The format of the data may change or vary over time due to administrative and/or technology changes and improvements. The administration of homeless services is organised across nine administrative regions, with one local authority in each of the regions, “the lead authority”, having overall responsibility for the disbursement of Exchequer funding. In each region a Joint Homelessness Consultative Forum exists which includes representation from the relevant State and non-governmental organisations involved in the delivery of homeless services in a particular region. Delegated arrangements are governed by an annually agreed protocol between the Department and the lead authority in each region. These protocols set out the arrangements, responsibilities and financial/performance data reporting requirements for the delegation of funding from the Department. Under Sections 38 and 39 of the Housing (Miscellaneous Provisions) Act 2009 a statutory Management Group exists for each regional forum. This is comprised of representatives from the relevant housing authorities and the Health Service Executive, and it is the responsibility of the Management Group to consider issues around the need for homeless services and to plan for the implementation, funding and co-ordination of such services. In relation to the terms used in the report for the accommodation types see explanation below: PEA - Private Emergency Accommodation: this may include hotels, B&Bs and other residential facilities that are used on an emergency basis. Supports are provided to services users on a visiting supports basis. STA - Supported Temporary Accommodation: accommodation, including family hubs, hostels, with onsite professional support. TEA - Temporary Emergency Accommodation: emergency accommodation with no (or minimal) support....

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