27 datasets found
  1. Rate of homelessness in the U.S. 2023, by state

    • statista.com
    Updated Sep 5, 2024
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    Statista (2024). Rate of homelessness in the U.S. 2023, by state [Dataset]. https://www.statista.com/statistics/727847/homelessness-rate-in-the-us-by-state/
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    Dataset updated
    Sep 5, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States
    Description

    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 73 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 653,104 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 243,000. 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.

  2. Estimated number of homeless people in the U.S. 2007-2023

    • statista.com
    Updated Sep 5, 2024
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    Statista (2024). Estimated number of homeless people in the U.S. 2007-2023 [Dataset]. https://www.statista.com/statistics/555795/estimated-number-of-homeless-people-in-the-us/
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    Dataset updated
    Sep 5, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    In 2023, there were about 653,104 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 647,258. 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.

  3. Rate of homeless individuals by metro area in the U.S. 2017

    • statista.com
    Updated Aug 12, 2024
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    Statista (2024). Rate of homeless individuals by metro area in the U.S. 2017 [Dataset]. https://www.statista.com/statistics/1007757/rate-homeless-individuals-metro-area-us/
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    Dataset updated
    Aug 12, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2017
    Area covered
    United States
    Description

    This statistic depicts the rate of homeless individuals in the United States in 2017, by metropolitan area. In 2017, the rate of homelessness per 10,000 individuals was highest in New York City, at 88.7.

  4. Number of homeless people in the U.S. 2023, by race

    • statista.com
    Updated Sep 5, 2024
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    Statista (2024). Number of homeless people in the U.S. 2023, by race [Dataset]. https://www.statista.com/statistics/555855/number-of-homeless-people-in-the-us-by-race/
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    Dataset updated
    Sep 5, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States
    Description

    In 2023, there were an estimated 324,854 white homeless people in the United States, the most out of any ethnicity. In comparison, there were around 243,624 Black or African American homeless people in the U.S. How homelessness is counted The actual number of homeless individuals in the U.S. is difficult to measure. The Department of Housing and Urban Development uses point-in-time estimates, where employees and volunteers count both sheltered and unsheltered homeless people during the last 10 days of January. However, it is very likely that the actual number of homeless individuals is much higher than the estimates, which makes it difficult to say just how many homeless there are in the United States. Unsheltered homeless in the United States California is well-known in the U.S. for having a high homeless population, and Los Angeles, San Francisco, and San Diego all have high proportions of unsheltered homeless people. While in many states, the Department of Housing and Urban Development says that there are more sheltered homeless people than unsheltered, this estimate is most likely in relation to the method of estimation.

  5. d

    Directory Of Unsheltered Street Homeless To General Population Ratio 2012

    • catalog.data.gov
    • data.cityofnewyork.us
    • +3more
    Updated Sep 2, 2023
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    data.cityofnewyork.us (2023). Directory Of Unsheltered Street Homeless To General Population Ratio 2012 [Dataset]. https://catalog.data.gov/dataset/directory-of-unsheltered-street-homeless-to-general-population-ratio-2012
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    Dataset updated
    Sep 2, 2023
    Dataset provided by
    data.cityofnewyork.us
    Description

    "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."

  6. d

    Data from: Homeless Shelters.

    • datadiscoverystudio.org
    csv, json, rdf, xml
    Updated Feb 3, 2018
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    (2018). Homeless Shelters. [Dataset]. http://datadiscoverystudio.org/geoportal/rest/metadata/item/158d5f3bd2d2412fb41f40979779951c/html
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    rdf, xml, json, csvAvailable download formats
    Dataset updated
    Feb 3, 2018
    Description

    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.

  7. f

    Themes, sub-themes and quotes.

    • plos.figshare.com
    xls
    Updated Jan 8, 2025
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    Grace Phillips; Emmy Racine; Anna Marie Naughton; Julieann Lane; Patricia M. Kearney (2025). Themes, sub-themes and quotes. [Dataset]. http://doi.org/10.1371/journal.pone.0312617.t002
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    xlsAvailable download formats
    Dataset updated
    Jan 8, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Grace Phillips; Emmy Racine; Anna Marie Naughton; Julieann Lane; Patricia M. Kearney
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

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

  8. d

    Homelessness (Survey of Homeless) - Dataset - B2FIND

    • b2find.dkrz.de
    Updated Oct 22, 2023
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    (2023). Homelessness (Survey of Homeless) - Dataset - B2FIND [Dataset]. https://b2find.dkrz.de/dataset/86195dfd-f8fb-5f0b-855f-c976e15da1cc
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    Dataset updated
    Oct 22, 2023
    Description

    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.

  9. a

    Persons Experiencing Homelessness

    • hub.arcgis.com
    • data.lacounty.gov
    • +2more
    Updated Dec 19, 2023
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    County of Los Angeles (2023). Persons Experiencing Homelessness [Dataset]. https://hub.arcgis.com/datasets/c772c0bb9df54a21aabe8ebaa3eb2c0a
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    Dataset updated
    Dec 19, 2023
    Dataset authored and provided by
    County of Los Angeles
    Area covered
    Description

    According to U.S. Department of Housing and Urban Development's definition, homelessness includes individuals and families who lack a fixed, regular, and adequate nighttime residence. A homeless count provides a "snapshot in time" to quantify the size of the homeless population at a specific point during the year. Regardless of how successful outreach efforts are, an undercount of people experiencing homelessness is possible. Counts includes persons experiencing unsheltered and sheltered homelessness. Greater Los Angeles Homeless Count occurred in the nights of February 22, 23 and 24, 2022. Glendale's count occurred in the morning and evening of February 25, 2022. Long Beach's count occurred in the early morning of February 24, 2022. Pasadena's count occurred in the evening of February 22, 2022 and morning of February 23, 2022. Data not available for Los Angeles City neighborhoods and unincorporated Los Angeles County; LAHSA does not recommend aggregating census tract-level data to calculate numbers for other geographic levels.Housing affordability is a major concern for many Los Angeles County residents. Housing burden can increase the risk for homelessness. Individuals experiencing homelessness experience disproportionately higher rates of certain health conditions, such as tuberculosis, HIV infection, alcohol and drug abuse, and mental illness. Barriers to accessing care and limited access to resources contribute greatly to these observed disparities.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

  10. Tables on homelessness

    • gov.uk
    Updated Feb 27, 2025
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    Tables on homelessness [Dataset]. https://www.gov.uk/government/statistical-data-sets/live-tables-on-homelessness
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    Dataset updated
    Feb 27, 2025
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Ministry of Housing, Communities and Local Government
    Description

    Statutory homelessness live tables

    Statutory homelessness England Level Time Series

    https://assets.publishing.service.gov.uk/media/67bdd6bc44ceb49381213c61/StatHomeless_202409.ods">Statutory homelessness England level time series "live tables"

     <p class="gem-c-attachment_metadata"><span class="gem-c-attachment_attribute"><abbr title="OpenDocument Spreadsheet" class="gem-c-attachment_abbr">ODS</abbr></span>, <span class="gem-c-attachment_attribute">306 KB</span></p>
    
    
    
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    Detailed local authority-level tables

    For quarterly local authority-level tables prior to the latest financial year, see the Statutory homelessness release pages.

    https://assets.publishing.service.gov.uk/media/67bdd57b89b4a58925ac6d17/Detailed_LA_202409.xlsx">Statutory homelessness in England: July to September 2024

     <p class="gem-c-attachment_metadata"><span class="gem-c-attachment_attribute">MS Excel Spreadsheet</span>, <span class="gem-c-attachment_attribute">2.24 MB</span></p>
    
    
    
    
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  11. U.S. poverty rate of the top 25 most populated cities 2021

    • statista.com
    Updated Jul 5, 2024
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    Statista (2024). U.S. poverty rate of the top 25 most populated cities 2021 [Dataset]. https://www.statista.com/statistics/205637/percentage-of-poor-people-in-the-top-20-most-populated-cities-in-the-us/
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    Dataset updated
    Jul 5, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2021
    Area covered
    United States
    Description

    In 2021, Philadelphia, Pennsylvania was the city with the highest poverty rate of the United States' most populated cities. In this statistic, the cities are sorted by poverty rate, not population. The most populated city in 2021 according to the source was New York city - which had a poverty rate of 18 percent.

  12. c

    Homelessness (Investigation in Part of Town)

    • datacatalogue.cessda.eu
    • dbk.gesis.org
    • +3more
    Updated Mar 14, 2023
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    Sack, Fritz; Höhmann, Peter (2023). Homelessness (Investigation in Part of Town) [Dataset]. http://doi.org/10.4232/1.2578
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    Dataset updated
    Mar 14, 2023
    Dataset provided by
    Forschungsinstitut für Soziologie, Universität zu Köln
    Authors
    Sack, Fritz; Höhmann, Peter
    Time period covered
    1969 - 1970
    Measurement technique
    Oral survey with standardized questionnaire
    Description

    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.

  13. A

    ‘COVID-19 Cases by Population Characteristics Over Time’ analyzed by...

    • analyst-2.ai
    Updated Jul 23, 2021
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2021). ‘COVID-19 Cases by Population Characteristics Over Time’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/data-gov-covid-19-cases-by-population-characteristics-over-time-097d/latest
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    Dataset updated
    Jul 23, 2021
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Analysis of ‘COVID-19 Cases by Population Characteristics Over Time’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/a3291d85-0076-43c5-a59c-df49480cdc6d on 13 February 2022.

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

    Note: On January 22, 2022, system updates to improve the timeliness and accuracy of San Francisco COVID-19 cases and deaths data were implemented. You might see some fluctuations in historic data as a result of this change. Due to the changes, starting on January 22, 2022, the number of new cases reported daily will be higher than under the old system as cases that would have taken longer to process will be reported earlier.

    A. SUMMARY This dataset shows San Francisco COVID-19 cases by population characteristics and by specimen collection date. Cases are included on the date the positive test was collected.

    Population characteristics are subgroups, or demographic cross-sections, like age, race, or gender. The City tracks how cases have been distributed among different subgroups. This information can reveal trends and disparities among groups.

    Data is lagged by five days, meaning the most recent specimen collection date included is 5 days prior to today. Tests take time to process and report, so more recent data is less reliable.

    B. HOW THE DATASET IS CREATED Data on the population characteristics of COVID-19 cases and deaths are from: * Case interviews * Laboratories * Medical providers

    These multiple streams of data are merged, deduplicated, and undergo data verification processes. This data may not be immediately available for recently reported cases because of the time needed to process tests and validate cases. Daily case totals on previous days may increase or decrease. Learn more.

    Data are continually updated to maximize completeness of information and reporting on San Francisco residents with COVID-19.

    Data notes on each population characteristic type is listed below.

    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.

    Sexual orientation * Sexual orientation data is collected from individuals who are 18 years old or older. These individuals can choose whether to provide this information during case interviews. Learn more about our data collection guidelines. * The City began asking for this information on April 28, 2020.

    Gender * The City collects information on gender identity using these guidelines.

    Comorbidities * Underlying conditions are reported when a person has one or more underlying health conditions at the time of diagnosis or death.

    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.

    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.

    Skilled Nursing Facility (SNF) occupancy * A Skilled Nursing

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

  14. Number of homeless veterans in the U.S., by state 2022

    • statista.com
    Updated Sep 5, 2024
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    Statista (2024). Number of homeless veterans in the U.S., by state 2022 [Dataset]. https://www.statista.com/statistics/727819/number-of-homeless-veterans-in-the-us-by-state/
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    Dataset updated
    Sep 5, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2022
    Area covered
    United States
    Description

    This statistic shows the estimated number of homeless veterans in the United States in 2022, by state. In 2022, about 10,395 veterans living in California were homeless.

  15. Number of homeless youth U.S. 2023, by state

    • statista.com
    Updated Dec 5, 2024
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    Statista (2024). Number of homeless youth U.S. 2023, by state [Dataset]. https://www.statista.com/statistics/727835/number-of-homeless-young-people-in-the-us-by-state/
    Explore at:
    Dataset updated
    Dec 5, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States
    Description

    In 2023, there were about 10,173 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 4,468.

  16. f

    Disease course per group between March 2020 and March 2021.

    • plos.figshare.com
    xls
    Updated Feb 5, 2024
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    Eline Mennis; Michelle Hobus; Maria van den Muijsenbergh; Tessa van Loenen (2024). Disease course per group between March 2020 and March 2021. [Dataset]. http://doi.org/10.1371/journal.pone.0296754.t004
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Feb 5, 2024
    Dataset provided by
    PLOS ONE
    Authors
    Eline Mennis; Michelle Hobus; Maria van den Muijsenbergh; Tessa van Loenen
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Disease course per group between March 2020 and March 2021.

  17. i

    Household Living Conditions Survey 2008 - Ukraine

    • catalog.ihsn.org
    Updated Mar 29, 2019
    + more versions
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    State Statistics Committee of Ukraine (2019). Household Living Conditions Survey 2008 - Ukraine [Dataset]. https://catalog.ihsn.org/index.php/catalog/3689
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    Dataset updated
    Mar 29, 2019
    Dataset authored and provided by
    State Statistics Committee of Ukraine
    Time period covered
    2008
    Area covered
    Ukraine
    Description

    Abstract

    The Household Living Conditions Survey has been carried out annually since 1999 by the State Statistics Service of Ukraine (formerly the State Statistics Committee of Ukraine). The survey is based on generally accepted international standards and depicts social and demographic situation in Ukraine.

    From 2002, items of consumer money and aggregate expenditures have been developed in line with the International Classification of Individual Consumption of Goods and Services (COICOP-HBS), recommended by Eurostat.

    From 2004, the State Statistics Service of Ukraine has been implementing a new system of household sample survey organization and delivery. A unified interviewer network was established to run simultaneously three household surveys: Household Living Conditions Survey, households' economic activity survey and the survey of household farming in rural areas. A new national territorial probability sampling was introduced to deliver the three sampling surveys in 2004-2008.

    Geographic coverage

    National, except some settlements within the territories suffered from the Chernobyl disaster.

    Analysis unit

    • Households,
    • Individuals.

    A household is a totality of persons who jointly live in the same residential facilities of part of those, satisfy all their essential needs, jointly keep the house, pool and spend all their money or portion of it. These persons may be relatives by blood, relatives by law or both, or have no kinship relations. A household may consist of one person (Law of Ukraine "On Ukraine National Census of Population," Article 1). As only 0.50% households have members with no kinship relations (0.65% total households if bachelors are excluded), the contemporary concepts "household" and "family" are very close.

    Universe

    Whole country, all private households. The survey does not cover collective households, foreigners temporarily living in Ukraine as well as the homeless.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    12,977 households representing all regions of Ukraine (including 8,975 in urban areas and 4,002 in rural areas) are selected for this survey. Grossing up sample survey results to all households of Ukraine is done by the statistic weighting method.

    Building a territorial sample, researchers excluded settlements located in the excluded zone (Zone 1) and unconditional (forced) resettlement zone (Zone 2) within the territories suffered from the Chernobyl disaster.

    Computing the number of population subject to surveying, from the number of resident population researchers excluded institutional population - army conscripts, persons in places of confinement, residents of boarding schools and nursing homes, - and marginal population (homeless, etc).

    The parent population was stratified so that the sample could adequately represent basic specifics of the administrative and territorial division and ensure more homogeneous household populations. To achieve this objective, the parent population was divided into strata against the regions of Ukraine. In each stratum three smaller substrata were formed: urban settlements (city councils) having 100,000 or more inhabitants (big cities), urban settlements (city councils) having less than 100,000 inhabitants (small towns) and all districts (except city districts), i.e. administrative districts in rural areas. Sample size was distributed among strata and substrata in proportion to their non-institutional resident population.

    Detailed information about selecting primary territorial units of sampling (PTUS) and households is available in the document "Household Living Conditions Survey Methodological Comments" (p. 4-7).

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The HLCS uses the following survey tools:

    1) Main interviews

    Main interview questionnaires collect general data on households, such as household composition, housing facilities, availability and use of land plots, cattle and poultry, characteristics of household members: anthropometric data, education, employment status. Interviewing of households takes place at the survey commencement stage. In addition, while interviewing, the interviewer completes a household composition check card to trace any changes during the entire survey period.

    2) Observation of household expenditures and incomes

    For the observation, two tools are used: - Weekly diary of current expenditures. It is completed directly by a household twice a quarter. In the diary respondents (households) record all daily expenditures in details (e.g. for purchased foodstuffs - product description, its weight and value, and place of purchase). In addition, a household puts into the diary information on consumption of products produced in private subsidiary farming or received as a gift.

    Households are evenly distributed among rotation groups, who complete diaries in different week days of every quarter. Assuming that the two weeks data are intrinsic for the entire quarter, the single time period of data processing (quarter) is formed by means of multiplying diary data by ratio 6.5 (number of weeks in a quarter divided on the number of weeks when diary records were made). Inclusion of foodstuffs for long-time consumption is done based on quarterly interview data.

    • Quarterly questionnaire. It is used to interview households in the first month following the reporting quarter. Researchers collect data on large and irregular expenditures, in particular those relating to the purchase of foodstuffs for long-time consumption (e.g. sacks, etc.), and also data on household incomes. Since recalling all incomes and expenditures made in a quarter is uneasy, households make records during a quarter in a special quarterly expenditures log.

    The major areas for quarterly observation are the following: - structure of consumer financial expenditures for goods and services; - structure of other expenditures (material aid to other households, expenditures for private subsidiary farming, purchase of real estate, construction and major repair of housing facilities and outbuildings, accumulating savings, etc); - importance of private subsidiary farming for household welfare level (receipt and use of products from private subsidiary farming for own consumption, financial income from sales of such products, etc.); - structure of income and other financial sources of a household. We separately study the income of every individual household member (remuneration of labor, pension, scholarship, welfare, etc.) and the income in form payments to a household as a whole (subsidies for children, aid of relatives and other persons, income from - sales of real estate and property, housing and utility subsidies, use of savings, etc.).

    3) Single-time topical interviews

    Single-time topical interviews questionnaires are used quarterly and cover the following topics: - household expenditures for construction and repair of housing facilities and outbuilding - availability of durable goods in a household - assessment by households members of own health and accessibility of selected medical services - self-assessment by a household of adequacy of its income - household's access to Internet

    Response rate

    10,622 households took part in the 2008 survey (83.3% sampled addresses excluding nonresidential buildings). The response rate of rural households (95.5%) was higher than the similar parameter in urban areas (77.5%).

    The highest response rate of the 2008 survey was in Trans-Carpathians (98.4%), Chernivtsi (98.3%), Rivno (97.6%), Sumy (96.3%), Volyn (96.0%), Cherkassy (95.4%), Ternopil and Chernivtsi (95.1% in each) regions, the lowest rate - in Odessa region (59.8%), Kiev City (61.1%) and Donetsk region (63.5%). In most regions this parameter fluctuated from 73.4% to 94.7%.

  18. Share of unsheltered homeless youth population by county of residence U.S....

    • statista.com
    Updated Dec 4, 2024
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    Statista (2024). Share of unsheltered homeless youth population by county of residence U.S. 2023 [Dataset]. https://www.statista.com/statistics/964748/share-unsheltered-homeless-youth-population-us-metropolitan-area-residence/
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    Dataset updated
    Dec 4, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States
    Description

    In the United States in 2023, 98.5 percent of the unaccompanied homeless youth in the Watsonville/Santa Cruz City and County, California were unsheltered.

  19. Number of homeless people in the Netherlands 2009-2022, by location

    • statista.com
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    Statista, Number of homeless people in the Netherlands 2009-2022, by location [Dataset]. https://www.statista.com/statistics/522768/netherlands-number-of-homeless-people-by-location/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Netherlands
    Description

    This statistic shows the total number of homeless people in the Netherlands from 2009 to 2022, by location (in thousands). It reveals that between 2009 and 2022, the majority of the homeless people lived outside the four major cities Amsterdam, Rotterdam, The Hague and Utrecht.

  20. Number of homeless people in London 2024, by borough

    • statista.com
    Updated Dec 16, 2024
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    Statista (2024). Number of homeless people in London 2024, by borough [Dataset]. https://www.statista.com/statistics/381365/london-homelessness-rough-sleepers-by-london-borough/
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    Dataset updated
    Dec 16, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Apr 1, 2023 - Mar 31, 2024
    Area covered
    United Kingdom (England), London
    Description

    In 2023/24, there were 2,102 rough sleepers reported in Westminster, making it the London borough with the highest number of rough sleepers in that year. Other boroughs which also had a high number of homeless people included, Camden, Ealing, and Lambeth.

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Statista (2024). Rate of homelessness in the U.S. 2023, by state [Dataset]. https://www.statista.com/statistics/727847/homelessness-rate-in-the-us-by-state/
Organization logo

Rate of homelessness in the U.S. 2023, by state

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4 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
Sep 5, 2024
Dataset authored and provided by
Statistahttp://statista.com/
Time period covered
2023
Area covered
United States
Description

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 73 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 653,104 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 243,000. 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.

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