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.

The incident locations represented are approximated and not the actual _location of the incident. Latitudinal and longitudinal coordinates have been truncate to 3 decimal points. The estimated _location lies within approximately a 1/4 mile radius. This approximated _location data is also shown on the dashboard.This feature layer supports the Opioid Abuse Probable EMS Call Dashboard. The following documents what data are collected and why they are being collected. Opioid Abuse ProbableA call may be coded as “opioid abuse probable” for many reasons, such asAre there are any medical symptoms indicative of opioid abuse?Are there physical indicators on scene (i.e. drug paraphernalia, pill bottles, etc.)?Are there witnesses or patient statements made that point to opioid abuse?Is there any other evidence that opioid abuse is probable with the patient?“Opioid abuse probable” is determined by Tempe Fire Medical Rescue Department’s Emergency medical technicians and paramedics on scene at the time of the incident. Narcan/Naloxone Given“Narcan/Naloxone Given” refers to whether the medication Narcan/Naloxone was given to patients who exhibited signs or symptoms of a potential opioid overdose or to patients who fall within treatment protocols that require Narcan/Naloxone to be given. Narcan/Naloxone are the same medication with Narcan being the trade name and Naloxone being the generic name for the medication. Narcan is the reversal medication used by medical providers for opioid overdoses.Groups“Groups” are used to determine if there are specific populations that have an increase in opioid abuse. The student population at ASU was being examined for other purposes to determine ASU's overall call volume impact in Tempe. Data collection with the university is consistent with Fire Departments who provide service to the other PAC 12 universities. Since this data set was already being evaluated, it was included in the opioid data collection as well.The Veteran and Homeless Groups were established as demographic tabs to identify trends and determine needs in conjunction with the City of Tempe’s Veterans and Homeless programs. Since these data sets were being evaluated already, they were included in the opioid data collection as well.The “unknown” group includes incidents where a patient is unable to answer or refuses to answer the demographic questions. GenderPatient gender is documented as male or female when crews are able to obtain this information from the patient. There are some circumstances where this information is not readily determined and the patient is unable to communicate with our crews. In these circumstances, crews may document unknown/unable to determine. Information about the data can be found at https://bit.ly/2xXbD20

Project Overview This study used a community-based participatory approach to identify and investigate the needs of people experiencing homelessness in Dublin, Ireland. The project had several stages: A systematic review on health disparities amongst people experiencing homelessness in the Republic of Ireland; Observation and interviews with homeless attendees of a community health clinic; and Interviews with community experts (CEs) conducted from September 2022 to March 2023 on ongoing work and gaps in the research/health service response. This data deposit stems from stage 3, the community expert interview aspect of this project. Stage 1 of the project has been published (Ingram et al., 2023.) and associated data are available here. De-identified field note data from stage 2 of the project are planned for sharing upon completion of analysis, in January 2024. Data and Data Collection Overview A purposive, criterion-i sampling strategy (Palinkas et al., 2015) – where selected interviewees meet a predetermined criterion of importance – was used to identify professionals working in homeless health and/or addiction services in Dublin, stratified by occupation type. Potential CEs were identified through an internet search of homeless health and addiction services in Dublin. Interviewed CEs were invited to recommend colleagues they felt would have relevant perspectives on community health needs, expanding the sample via snowball strategy. Interview questions were based on World Health Organization Community Health Needs Assessment guidelines (Rowe at al., 2001). Semi-structured interviews were conducted between September 2022 and March 2023 utilising ZOOM™, the phone, or in person according to participant preference. Carolyn Ingram, who has formal qualitative research training, served as the interviewer. CEs were presented with an information sheet and gave audio recorded, informed oral consent – considered appropriate for remote research conducted with non-vulnerable adult participants – in the full knowledge that interviews would be audio recorded, transcribed, and de-identified, as approved by the researchers’ institutional Human Research Ethics Committee (LS-E-125-Ingram-Perrotta-Exemption). Interviewees also gave permission for de-identified transcripts to be shared in a qualitative data archive. Shared Data Organization 16 de-identified transcripts from the CE interviews are being published. Three participants from the total sample (N=19) did not consent to data archival. The transcript from each interviewee is named based on the type of work the interviewee performs, with individuals in the same type of work being differentiated by numbers. The full set of professional categories is as follows: Addiction Services Government Homeless Health Services Hospital Psychotherapist Researcher Social Care Any changes or removal of words or phrases for de-identification purposes are flagged by including [brackets] and italics. The documentation files included in this data project are the consent form and the interview guide used for the study, this data narrative and an administrative README file. References Ingram C, Buggy C, Elabbasy D, Perrotta C. (2023) “Homelessness and health-related outcomes in the Republic of Ireland: a systematic review, meta-analysis and evidence map.” Journal of Public Health (Berl). https://doi.org/10.1007/s10389-023-01934-0 Palinkas LA, Horwitz SM, Green CA, Wisdom JP, Duan N, Hoagwood K. (2015) “Purposeful sampling for qualitative data collection and analysis in mixed method implementation research.” Administration and Policy in Mental Health. Sep;42(5):533–44. https://doi.org/10.1007/s10488-013-0528-y Rowe A, McClelland A, Billingham K, Carey L. (2001) “Community health needs assessment: an introductory guide for the family health nurse in Europe” [Internet]. World Health Organization. Regional Office for Europe. Available at: https://apps.who.int/iris/handle/10665/108440

Homeless Shelter Capacity in Canada, bed and shelter counts by target population and geographical location for emergency shelters, transitional housing, and domestic violence shelters.

This layer contains detailed Point in Time counts of homeless populations from 2019. This layer is modeled after a similar layer that contains data for 2018, 2013, and 2008.Layer is symbolized to show the count of the overall homeless population in 2019, with a pie chart of breakdown of type of shelter. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. The Point-in-Time (PIT) count is a count of sheltered and unsheltered homeless persons on a single night in January. HUD requires that Continuums of Care Areas (CoCs) conduct an annual count of homeless persons who are sheltered in emergency shelter, transitional housing, and Safe Havens on a single night. CoCs also must conduct a count of unsheltered homeless persons every other year (odd numbered years). Each count is planned, coordinated, and carried out locally.The Point-in-Time values were retrieved from HUD's Historical Data site. Original source is the 2019 sheet within the "2007 - 2019 PIT Counts by CoCs.xlsx" (downloaded on 3/10/2020) file. Key fields were kept and joined to the CoC boundaries available from HUD's Open Data site.Data note: MO-604 covers territory in both Missouri and Kansas. The record described in this file represents the CoC's total territory, the sum of the point-in-time estimates the CoC separately reported for the portions of its territory in MO and in KS.For more information and attributes on the CoC Areas themselves, including contact information, see this accompanying layer.Suggested Citation: U.S. Department of Housing and Urban Development (HUD)'s Point in Time (PIT) 2019 counts for Continuum of Care Grantee Areas, accessed via ArcGIS Living Atlas of the World on (date).

description: This data represents all emergency medical services calls related to possible opioid abuse. Opioid Abuse Probable A call may be coded as opioid abuse probable for many reasons, such as * Are there are any medical symptoms indicative of opioid abuse? * Are there physical indicators on scene (i.e. drug paraphernalia, pill bottles, etc.)? * Are there witnesses or patient statements made that point to opioid abuse? * Is there any other evidence that opioid abuse is probable with the patient? Opioid abuse probable is determined by Tempe Fire Medical Rescue Departments Emergency medical technicians and paramedics on scene at the time of the incident. Narcan/Naloxone Given Narcan/Naloxone Given refers to whether the medication Narcan/Naloxone was given to patients who exhibited signs or symptoms of a potential opioid overdose or to patients who fall within treatment protocols that require Narcan/Naloxone to be given. Narcan/Naloxone are the same medication with Narcan being the trade name and Naloxone being the generic name for the medication. Narcan is the reversal medication used by medical providers for opioid overdoses. Groups Groups are used to determine if there are specific populations that have an increase in opioid abuse. * The student population at ASU was being examined for other purposes to determine ASU's overall call volume impact in Tempe. Data collection with the university is consistent with Fire Departments who provide service to the other PAC 12 universities. Since this data set was already being evaluated, it was included in the opioid data collection as well. * The Veteran and Homeless Groups were established as demographic tabs to identify trends and determine needs in conjunction with the City of Tempes Veterans and Homeless programs. Since these data sets were being evaluated already, they were included in the opioid data collection as well. * The unknown group includes incidents where a patient is unable to answer or refuses to answer the demographic questions. Gender Patient gender is documented as male or female when crews are able to obtain this information from the patient. There are some circumstances where this information is not readily determined and the patient is unable to communicate with our crews. In these circumstances, crews may document unknown/unable to determine. Data Set History Data sets were evolving in 2017 due to software upgrades and identifying new parameters to focus data collection on. The 2018 data will be a more comprehensive set of data that includes all the fields identified throughout 2017. Data sets may continue to evolve based on the needs of the community and healthcare trends. Information about the data can be found at Data Documentation; abstract: This data represents all emergency medical services calls related to possible opioid abuse. Opioid Abuse Probable A call may be coded as opioid abuse probable for many reasons, such as * Are there are any medical symptoms indicative of opioid abuse? * Are there physical indicators on scene (i.e. drug paraphernalia, pill bottles, etc.)? * Are there witnesses or patient statements made that point to opioid abuse? * Is there any other evidence that opioid abuse is probable with the patient? Opioid abuse probable is determined by Tempe Fire Medical Rescue Departments Emergency medical technicians and paramedics on scene at the time of the incident. Narcan/Naloxone Given Narcan/Naloxone Given refers to whether the medication Narcan/Naloxone was given to patients who exhibited signs or symptoms of a potential opioid overdose or to patients who fall within treatment protocols that require Narcan/Naloxone to be given. Narcan/Naloxone are the same medication with Narcan being the trade name and Naloxone being the generic name for the medication. Narcan is the reversal medication used by medical providers for opioid overdoses. Groups Groups are used to determine if there are specific populations that have an increase in opioid abuse. * The student population at ASU was being examined for other purposes to determine ASU's overall call volume impact in Tempe. Data collection with the university is consistent with Fire Departments who provide service to the other PAC 12 universities. Since this data set was already being evaluated, it was included in the opioid data collection as well. * The Veteran and Homeless Groups were established as demographic tabs to identify trends and determine needs in conjunction with the City of Tempes Veterans and Homeless programs. Since these data sets were being evaluated already, they were included in the opioid data collection as well. * The unknown group includes incidents where a patient is unable to answer or refuses to answer the demographic questions. Gender Patient gender is documented as male or female when crews are able to obtain this information from the patient. There are some circumstances where this information is not readily determined and the patient is unable to communicate with our crews. In these circumstances, crews may document unknown/unable to determine. Data Set History Data sets were evolving in 2017 due to software upgrades and identifying new parameters to focus data collection on. The 2018 data will be a more comprehensive set of data that includes all the fields identified throughout 2017. Data sets may continue to evolve based on the needs of the community and healthcare trends. Information about the data can be found at Data Documentation

On May 22, 1960, a Mw 9.5 earthquake, the largest earthquake ever instrumentally recorded, occurred in southern Chile. The series of earthquakes that followed ravaged southern Chile and ruptured over a period of days a 1,000 km section of the fault, one of the longest ruptures ever reported. The number of fatalities associated with both the earthquake and tsunami has been estimated to be between 490 and 5,700. Reportedly there were 3,000 injured, and initially there were 717 missing in Chile. The Chilean government estimated 2,000,000 people were left homeless and 58,622 houses were completely destroyed. Damage (including tsunami damage) was more than $500 million U.S. dollars.

Source: The Emergency Events Database (EM-DAT) , Centre for Research on the Epidemiology of Disasters (CRED) / Université catholique de Louvain (UCLouvain), Brussels, Belgium – www.emdat.be.Category: Climate and WeatherData series: Climate related disasters frequency, Number of Disasters: TOTAL  Climate related disasters frequency, Number of Disasters: Drought  Climate related disasters frequency, Number of Disasters: Extreme temperature  Climate related disasters frequency, Number of Disasters: Flood  Climate related disasters frequency, Number of Disasters: Landslide  Climate related disasters frequency, Number of Disasters: Storm  Climate related disasters frequency, Number of Disasters: Wildfire Climate related disasters frequency, People Affected: Drought  Climate related disasters frequency, People Affected: Extreme temperature  Climate related disasters frequency, People Affected: Flood  Climate related disasters frequency, People Affected: Landslide  Climate related disasters frequency, People Affected: Storm  Climate related disasters frequency, People Affected: Wildfire Climate related disasters frequency, People Affected: TOTAL  Disaster IntensityMetadata:EM-DAT: The International Disasters Database - Centre for Research on the Epidemiology of Disasters (CRED), part of the University of Louvain (UCLouvain) www.emdat.be, Brussels, Belgium. Only climate related disasters (Wildfire, Storm, Landslide, Flood, Extreme Temperature, and Drought) are covered. See the CID Glossary for the definitions. EM-DAT records country level human and economic losses for disasters with at least one of the following criteria: i.          Killed ten (10) or more people  ii.         Affected hundred (100) or more people  iii.        Led to declaration of a state of emergency iv.        Led to call for international assistance   The reported total number of deaths “Total Deaths” includes confirmed fatalities directly imputed to the disaster plus missing people whose whereabouts since the disaster are unknown and so they are presumed dead based on official figures. “People Affected” is the total of injured, affected, and homeless people. Injured includes the number of people with physical injuries, trauma, or illness requiring immediate medical assistance due to the disaster. Affected includes the number of people requiring immediate assistance due to the disaster. Homeless includes the number of people requiring shelter due to their house being destroyed or heavily damaged during the disaster. Disaster intensity is calculated by summing “Total Deaths” and 30% of the “People Affected”, and then dividing the result by the total population. For each disaster and its corresponding sources, the population referred to in these statistics and the apportionment between injured, affected, homeless, and the total is checked by CRED staff members. Nonetheless, it is important to note that these are estimates based on certain assumptions, which have their limitations. For details on the criteria and underlying assumptions, please visit https://doc.emdat.be/docs/data-structure-and-content/impact-variables/human/. Methodology:Global climate related disasters are stacked to show the trends in climate related physical risk factors.

These published reports present information from the multi-agency database Combined Homelessness and Information Network (CHAIN), about people seen rough sleeping by outreach teams in London. CHAIN, which is commissioned and funded by the Greater London Authority (GLA) and managed by Homeless Link, represents one of the UK’s most detailed and comprehensive sources of information about rough sleeping. Services that record information on CHAIN include outreach teams, assessment centres, accommodation projects, day centres and other specialist projects. The system allows users to share information about work done with people sleeping rough and about their needs, ensuring that they receive the most appropriate support and that efforts are not duplicated. In these reports, people are counted as having been seen rough sleeping if they have been encountered by a commissioned outreach worker bedded down on the street, or in other open spaces or locations not designed for habitation, such as doorways, stairwells, parks or derelict buildings. The report does not include people from “hidden homeless” groups such as those “sofa surfing” or living in squats, unless they have also been seen bedded down in one of the settings outlined above. Separate reports are produced for London as a whole and for individual boroughs, and these are published each quarter. There are also annual reports that contain aggregated information for each full year. Interactive Visualisation Tool Quarterly Data Tool Annual Data Tool A suite of online interactive charts and maps based on CHAIN data is available by clicking the above links. The data available via these tools mirrors that presented in the published PDF documents, with the addition of filters and other enhancements to allow users to interrogate the data. The Quarterly Data Tool shows data from the last eight quarters, and the Annual Data Tool shows data from the last five years. Organisations Using CHAIN A list of the organisations which have signed the CHAIN Data Protection Agreement and are able to access the live CHAIN system is also available to download. PDF Reports & Data tables As of January 2024, published CHAIN PDF reports are accompanied by an OpenDocument Spreadsheet file providing the underlying data in an accessible aggregated tabular format. The file includes data at local authority level, and for London overall, including comparative data for previous periods. There is also an accompanying explanatory notes document, which provides important contextual information about the data. Please click the links below to download a zip file containing the PDF reports and OpenDocument Spreadsheet for the corresponding timeframe. Publication Schedule

This article analyzes the evictions faced by the homeless during the preparations of Rio de Janeiro for the 2014 World Cup and the 2016 Olympic Games, framed by social conflicts in favor of the right to the city, by juxtaposing urban security for the elites and disrespect for the rights of subaltern classes. The media’s and the official discourses classify the homeless as those who “live on the streets”, naturalizing their “home-less” condition and establishing the myth that, despite not having a home, that population inhabit somewhere. This process tends to empty the conflicting nature of the social relations that operate within the cities, such as the real reasons for the economic and social exclusion, thus accentuating opportunities for huge real estate investments in accelerated gentrification processes.

The incident locations represented are approximated and not the actual location of the incident (or individuals residence). A computer generated randomized distance adjustment is applied to each incident location to ensure data are anonymous. This approximated location data is also shown on the dashboard.The following documents what data are collected and why they are being collected. Additional variables will be added to the dashboard in the next phase.Opioid Abuse ProbableA call may be coded as “opioid abuse probable” for many reasons, such asAre there are any medical symptoms indicative of opioid abuse?Are there physical indicators on scene (i.e. drug paraphernalia, pill bottles, etc.)?Are there witnesses or patient statements made that point to opioid abuse?Is there any other evidence that opioid abuse is probable with the patient?“Opioid abuse probable” is determined by Tempe Fire Medical Rescue Department’s Emergency medical technicians and paramedics on scene at the time of the incident. Narcan/Naloxone Given“Narcan/Naloxone Given” refers to whether the medication Narcan/Naloxone was given to patients who exhibited signs or symptoms of a potential opioid overdose or to patients who fall within treatment protocols that require Narcan/Naloxone to be given. Narcan/Naloxone are the same medication with Narcan being the trade name and Naloxone being the generic name for the medication. Narcan is the reversal medication used by medical providers for opioid overdoses.Groups“Groups” are used to determine if there are specific populations that have an increase in opioid abuse. The student population at ASU was being examined for other purposes to determine ASU's overall call volume impact in Tempe. Data collection with the university is consistent with Fire Departments who provide service to the other PAC 12 universities. Since this data set was already being evaluated, it was included in the opioid data collection as well.The Veteran and Homeless Groups were established as demographic tabs to identify trends and determine needs in conjunction with the City of Tempe’s Veterans and Homeless programs. Since these data sets were being evaluated already, they were included in the opioid data collection as well.The “unknown” group includes incidents where a patient is unable to answer or refuses to answer the demographic questions. GenderPatient gender is documented as male or female when crews are able to obtain this information from the patient. There are some circumstances where this information is not readily determined and the patient is unable to communicate with our crews. In these circumstances, crews may document unknown/unable to determine.

This Map shows natural climatological drought disasters occurrence from 1900 to 2015. The data source is from the Centre for Research on the Epidemiology of Disasters, EM-DAT database.

EM-DAT is a global database on natural and technological disasters that contains essential core data on the occurrence and effects climatological disasters in the world from 1900 to present. EM-DAT is maintained by the Centre for Research on the Epidemiology of Disasters (CRED) at the School of Public Health of the Université catholique de Louvain located in Brussels, Belgium. The database is compiled from various sources, including UN agencies, non-governmental organisations, insurance companies, research institutes and press agencies. The main objective of the database is to serve the purposes of humanitarian action at national and international levels in order to rationalise decision making for disaster preparedness, as well as providing an objective base for vulnerability assessment and priority setting. In EM-DAT data are considered at the country level for two reasons: first, it is at this level that they are usually reported; and second, it allows the aggregation and disaggregation of data. In order to facilitate the comparison over time, the event start date has been used as the disaster reference date.

Affected people are the number of people requiring immediate assistance during a period of emergency; this may include displaced or evacuated people. Total affected are the sum of injured, homeless and affected. Total Deaths are the number of people who lost their life because the event happened (it includes also the missing people based on official figures). Homeless are the number of people whose house is destroyed or heavily damaged and therefore need shelter after an event.