In 2023, there were about ******* homeless people estimated to be living in the United States, the highest number of homeless people recorded within the provided time period. In comparison, the second-highest number of homeless people living in the U.S. within this time period was in 2007, at *******. How is homelessness calculated? Calculating homelessness is complicated for several different reasons. For one, it is challenging to determine how many people are homeless as there is no direct definition for homelessness. Additionally, it is difficult to try and find every single homeless person that exists. Sometimes they cannot be reached, leaving people unaccounted for. In the United States, the Department of Housing and Urban Development calculates the homeless population by counting the number of people on the streets and the number of people in homeless shelters on one night each year. According to this count, Los Angeles City and New York City are the cities with the most homeless people in the United States. Homelessness in the United States Between 2022 and 2023, New Hampshire saw the highest increase in the number of homeless people. However, California was the state with the highest number of homeless people, followed by New York and Florida. The vast amount of homelessness in California is a result of multiple factors, one of them being the extreme high cost of living, as well as opposition to mandatory mental health counseling and drug addiction. However, the District of Columbia had the highest estimated rate of homelessness per 10,000 people in 2023. This was followed by New York, Vermont, and Oregon.

The graph displays the estimated number of homeless people in the United States from 2007 to 2024. The x-axis represents the years, ranging from 2007 to 2023, while the y-axis indicates the number of homeless individuals. The estimated homeless population varies over this period, ranging from a low of 57,645 in 2014 to a high of 771,000 in 2024. From 2007 to 2013, there is a general decline in numbers from 647,258 to 590,364. In 2014, the number drops significantly to 57,645, followed by an increase to 564,708 in 2015. The data shows fluctuations in subsequent years, with another notable low of 55,283 in 2018. From 2019 onwards, the estimated number of homeless people generally increases, reaching its peak in 2024. This data highlights fluctuations in homelessness estimates over the years, with a recent upward trend in the homeless population.

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.

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.

BackgroundHomelessness is one of the most disabling and precarious living conditions. The objective of this Delphi consensus study was to identify priority needs and at-risk population subgroups among homeless and vulnerably housed people to guide the development of a more responsive and person-centred clinical practice guideline.MethodsWe used a literature review and expert working group to produce an initial list of needs and at-risk subgroups of homeless and vulnerably housed populations. We then followed a modified Delphi consensus method, asking expert health professionals, using electronic surveys, and persons with lived experience of homelessness, using oral surveys, to prioritize needs and at-risk sub-populations across Canada. Criteria for ranking included potential for impact, extent of inequities and burden of illness. We set ratings of ≥ 60% to determine consensus over three rounds of surveys.FindingsEighty four health professionals and 76 persons with lived experience of homelessness participated from across Canada, achieving an overall 73% response rate. The participants identified priority needs including mental health and addiction care, facilitating access to permanent housing, facilitating access to income support and case management/care coordination. Participants also ranked specific homeless sub-populations in need of additional research including: Indigenous Peoples (First Nations, Métis, and Inuit); youth, women and families; people with acquired brain injury, intellectual or physical disabilities; and refugees and other migrants.InterpretationThe inclusion of the perspectives of both expert health professionals and people with lived experience of homelessness provided validity in identifying real-world needs to guide systematic reviews in four key areas according to priority needs, as well as launch a number of working groups to explore how to adapt interventions for specific at-risk populations, to create evidence-based guidelines.

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

Context

Disasters include all geophysical, meteorological and climate events including earthquakes, volcanic activity, landslides, drought, wildfires, storms, and flooding. Decadal figures are measured as the annual average over the subsequent ten-year period.

Content

Thanks to Our World in Data, you can explore death from natural disasters by country and by date.

Acknowledgements

https://www.acacamps.org/sites/default/files/resource_library_images/naturaldisaster4.jpg" alt="Natural Disasters">

Inspiration

List of variables for inspiration: Number of deaths from drought Number of people injured from drought Number of people affected from drought Number of people left homeless from drought Number of total people affected by drought Reconstruction costs from drought Insured damages against drought Total economic damages from drought Death rates from drought Injury rates from drought Number of people affected by drought per 100,000 Homelessness rate from drought Total number of people affected by drought per 100,000 Number of deaths from earthquakes Number of people injured from earthquakes Number of people affected by earthquakes Number of people left homeless from earthquakes Number of total people affected by earthquakes Reconstruction costs from earthquakes Insured damages against earthquakes Total economic damages from earthquakes Death rates from earthquakes Injury rates from earthquakes Number of people affected by earthquakes per 100,000 Homelessness rate from earthquakes Total number of people affected by earthquakes per 100,000 Number of deaths from disasters Number of people injured from disasters Number of people affected by disasters Number of people left homeless from disasters Number of total people affected by disasters Reconstruction costs from disasters Insured damages against disasters Total economic damages from disasters Death rates from disasters Injury rates from disasters Number of people affected by disasters per 100,000 Homelessness rate from disasters Total number of people affected by disasters per 100,000 Number of deaths from volcanic activity Number of people injured from volcanic activity Number of people affected by volcanic activity Number of people left homeless from volcanic activity Number of total people affected by volcanic activity Reconstruction costs from volcanic activity Insured damages against volcanic activity Total economic damages from volcanic activity Death rates from volcanic activity Injury rates from volcanic activity Number of people affected by volcanic activity per 100,000 Homelessness rate from volcanic activity Total number of people affected by volcanic activity per 100,000 Number of deaths from floods Number of people injured from floods Number of people affected by floods Number of people left homeless from floods Number of total people affected by floods Reconstruction costs from floods Insured damages against floods Total economic damages from floods Death rates from floods Injury rates from floods Number of people affected by floods per 100,000 Homelessness rate from floods Total number of people affected by floods per 100,000 Number of deaths from mass movements Number of people injured from mass movements Number of people affected by mass movements Number of people left homeless from mass movements Number of total people affected by mass movements Reconstruction costs from mass movements Insured damages against mass movements Total economic damages from mass movements Death rates from mass movements Injury rates from mass movements Number of people affected by mass movements per 100,000 Homelessness rate from mass movements Total number of people affected by mass movements per 100,000 Number of deaths from storms Number of people injured from storms Number of people affected by storms Number of people left homeless from storms Number of total people affected by storms Reconstruction costs from storms Insured damages against storms Total economic damages from storms Death rates from storms Injury rates from storms Number of people affected by storms per 100,000 Homelessness rate from storms Total number of people affected by storms per 100,000 Number of deaths from landslides Number of people injured from landslides Number of people affected by landslides Number of people left homeless from landslides Number of total people affected by landslides Reconstruction costs from landslides Insured damages against landslides Total economic damages from landslides Death rates from landslides Injury rates from landslides Number of people affected by landslides per 100,000 Homelessness rate from landslides Total number of people affected by landslides per 100,000 Number of deaths from fog Number of people injured from fog Number of people affected by fog Number of people left homel...

Social Housing Provision by County and Method

Annual Breakdown of Social Housing Provisions by County and Method from 2016

By Ian Baldwin [source]

About this dataset

This structured dataset encompasses a wealth of specifics such as the local government authority's name that is responsible for social housing provisions within each county. It reveals the year when the data was recorded and also offers information on period time – whether it was during a specific quarter within that year (Q1, Q2, Q3, Q4), or an annual data record.

One of its most notable aspects covers details about delivery type - showing various methods via which Social Housing Provisions were delivered. This could range from new construction builds to property acquisitions or leasing agreements among others.

Another enriching detail it provides is on categories of social housing. It exhibits how these provisions were categorized based on certain criteria such as general needs houses, specialized residences for older people or homes specifically made available for alleviating homelessness etc.

Equally important is information about who delivered these housing provisions- be it different entities like local authority itself directly involved , approved external bodies related with housing authorities processor private developers who have played their role in this regard Moreover amount denotes units provided during specified period thus enabling readers understand scale operation .

Last but not least , file provides full annual target - showcasing total units planned provisioned during defined year enhancing comprehension around planning effectiveness implementation aforementioned activities . All diverse specifics uniquely consolidated forming invaluable resource diverse stakeholders particularly those involved urban development planning population management research analysis purposes

How to use the dataset

This dataset can serve as an excellent resource for anyone looking to explore the specifics of social housing provision in different counties. It provides a comprehensive view of how social housing has been delivered, who delivers it, what types of houses have been constructed and how many were planned versus the actual amount delivered.

Here are some ways you could use this dataset:

• Benchmarking and Comparative Analysis: One can compare the overall performance of different local authorities in delivering their full annual targets for social housing. This will help stakeholders understand which areas are performing well or poorly.

• Trend Analysis over years: Analyse trends in social housing provision over multiple years to identify patterns and predict future needs or shortfalls.

• Category Wise Study: Break down the data by category (general needs, older persons, homeless) and gain insights on specific demands catered by the local authorities across counties.

• Provider profiles: The Delivered By column can give information about particular entities involved in providing social houses as well as their contributions towards meeting demand for such housings over several years.

• Policy Feedback: The data from this dataset could provide significant inputs into policymaking processes related to infrastructure, public housing projects etc., allowing you to measure impact or suggest improvements based on empirical evidence collected over time.

• Housing Provision Method Overview: Compare effectiveness of various delivery methods like new builds, acquisitions or leasing etc In terms of fulfilling requirements set under full annual target - providing insight into which methods might be most efficient for achieving goals

To get started with analyzing this dataset: - You may want to start by cleaning up any missing values. - Create aggregate statistics per year/per quarter/per category - Calculate ratios between delivery targets and actual number provided. - Use visualization tools like bar graphs or pie charts for better understanding

Remember that correlation does not imply causation – so make sure context is considered when interpreting your data. Always use a critical eye and consider all possible factors. Happy analyzing!

Research Ideas

• Policy Decision-making: Policy makers at the local and national level could use this data to understand the effectiveness of different methods of housing provision in meeting annual targets. This can be crucial for future planning and deciding which method or entity to prioritise.
• Housing Research: Researchers studying housing policy, homelessness, or urban development could use this dataset to explore correlations be...

BackgroundIndividuals who are homeless or vulnerably housed are at an increased risk for mental illness, other morbidities and premature death. Standard case management interventions as well as more intensive models with practitioner support, such as assertive community treatment, critical time interventions, and intensive case management, may improve healthcare navigation and outcomes. However, the definitions of these models as well as the fidelity and adaptations in real world interventions are highly variable. We conducted a systematic review to examine the effectiveness and cost-effectiveness of case management interventions on health and social outcomes for homeless populations.Methods and findingsWe searched Medline, Embase and 7 other electronic databases for trials on case management or care coordination, from the inception of these databases to July 2019. We sought outcomes on housing stability, mental health, quality of life, substance use, hospitalization, income and employment, and cost-effectiveness. We calculated pooled random effects estimates and assessed the certainty of the evidence using the GRADE approach. Our search identified 13,811 citations; and 56 primary studies met our full inclusion criteria. Standard case management had both limited and short-term effects on substance use and housing outcomes and showed potential to increase hostility and depression. Intensive case management substantially reduced the number of days spent homeless (SMD -0.22 95% CI -0.40 to -0.03), as well as substance and alcohol use. Critical time interventions and assertive community treatment were found to have a protective effect in terms of rehospitalizations and a promising effect on housing stability. Assertive community treatment was found to be cost-effective compared to standard case management.ConclusionsCase management approaches were found to improve some if not all of the health and social outcomes that were examined in this study. The important factors were likely delivery intensity, the number and type of caseloads, hospital versus community programs and varying levels of participant needs. More research is needed to fully understand how to continue to obtain the increased benefits inherent in intensive case management, even in community settings where feasibility considerations lead to larger caseloads and less-intensive follow-up.

Data published by Our World in Data based on EM-DAT, CRED / UCLouvain, Brussels, Belgium – www.emdat.be (D. Guha-Sapir)

Variable time span 1900 – 2010

This dataset has been calculated and compiled by Our World in Data based on raw disaster data published by EM-DAT, CRED / UCLouvain, Brussels, Belgium – www.emdat.be (D. Guha-Sapir). EM-DAT publishes comprehensive, global data on each individual disaster event – estimating the number of deaths; people affected; and economic damages, from UN reports; government records; expert opinion; and additional sources. Our World in Data has calculated annual aggregates, and decadal averages, for each country based on this raw event-by-event dataset. Decadal figures are measured as the annual average over the subsequent ten-year period. This means figures for ‘1900’ represent the average from 1900 to 1909; ‘1910’ is the average from 1910 to 1919 etc. We have calculated per capita rates using population figures from Gapminder (gapminder.org) and the UN World Population Prospects (https://population.un.org/wpp/). Economic damages data is provided by EM-DAT in concurrent US$. We have calculated this as a share of gross domestic product (GDP) using the World Bank’s GDP figures (also in current US$) (https://data.worldbank.org/indicator). Definitions of specific metrics are as follows: – ‘All disasters’ includes all geophysical, meteorological, and climate events including earthquakes, volcanic activity, landslides, drought, wildfires, storms, and flooding. – People affected are those requiring immediate assistance during an emergency situation. – The total number of people affected is the sum of injured, affected, and homeless.Link www.emdat.be

This dataset "Global hotspots of climate related disasters" shows the number of people impacted by climate-related disasters recorded in the EM-DAT database between 2000 and 2020. This dataset was used to prepare the maps and the analysis of the paper Donatti C.I., Nicholas K., Fedele G., Delforge D., Speybroeck N., Moraga P., Blatter J., Below R., Zvoleff A. 2024. Global hotspots of climate-related disasters. International Journal of Disaster Risk Reduction. https://doi.org/10.1016/j.ijdrr.2024.104488. This dataset includes information on people impacted by Drought, tropical cyclones, flash flood, riverine flood, forest fire, land fire, heat wave, landslide and mudslide. Data on coastal flood was not included because the database only had recordings until 2013. Data on disaster sub-types “landslides” and “mudslides” as presented in the EM-DAT were further combined as one single climate-related disaster (“land and mudslides”) for the analyses. Likewise, data on disaster sub-types “forest fire” and “land fire” were further combined as one climate-related disaster (“wildfire”). The data was accessed directly from the EM-DAT database and then summarized as show in the dataset. We used this database, downloaded on June 2nd 2021, to access data on “total affected” people and the “total deaths” per disaster event impacting a country (i.e., an entry in the EM-DAT), which were combined in this study to create the variable “total people impacted”. In the EM-DAT database, “total affected” represents the sum of people “injured,” “affected,” and “homeless” resulting from a particular event. “Injured” were considered those that have suffered from physical injuries, trauma, or an illness requiring immediate medical assistance, including people hospitalized, as a direct result of a disaster, “affected” were considered people requiring immediate assistance during an emergency and “homeless” were considered those whose homes were destroyed or heavily damaged and therefore needed shelter after an event. “Total deaths” include people that have died or were considered missing, those whose whereabouts since the disaster were unknown and presumed dead based on official figures. More details can be found under “documentation, data structure and content description” at emdat.be. In the dataset, "ADM-CODE" refers to the code used to identify each administrative area, which refers to the code of FAO's Global Administrative Unit Layer, GAUL.

The research, entitled Homelessness during COVID-19: Homeless Migrants in a Global Crisis, took a biographical life story approach to understand the experiences of 43 non-UK nationals who experienced homelessness during the COVID-19 pandemic. In the first phase of the project, and in order to gain insight into the homelessness sector, we conducted semi-structured interviews with 37 people across nine homelessness organisations. The focus of the interviews was on migrant homelessness before and during the pandemic. Due to ethical reasons, we are not able to upload data from the life story interviews that we conducted with migrants experiencing homelessness. However, the data from the semi-structured interviews with staff in the homelessness sector that we have submitted to the UK Data Service helped us to frame our research and provided much-needed contextual information during the pandemic.

People experiencing homelessness are disproportionately impacted by coronavirus. Despite government efforts to place rough sleepers in hotels to contain the spread of the disease, many migrants sleeping rough with No Recourse to Public Funds (NRPF) have been left behind at the height of a global pandemic. This project, involving researchers from University of Portsmouth, University of Sussex and St Mungo's, the homeless charity, will produce an 18-month qualitative-based study of migrant homelessness framed by the wider global and national context. Working with two of St Mungo's migrant services, Street Legal, St Mungo's legal team and Routes Home, a service supporting people sleeping rough from outside of the UK, a particular focus of the study will be the experience of non-UK nationals and their attempts, during the crisis, to resolve their immigration status. Many of these migrants are at the sharpest end of homelessness: almost 1,000 rough sleepers housed in emergency accommodation in London have NRPF (Heath, 2020).

Most migrant homeless clients are faced with multiple everyday challenges; they experience the hostility and aggression directed toward homeless people, compounded with often intense experiences of racism. Migrant homeless clients are also likely to be afraid of 'authorities' for various reasons including fear of deportation by the Home Office and personal histories of violent persecution by state actors in their original countries of belonging. During the pandemic, increased numbers of police on the streets have created high anxiety for refugees/asylum seekers and destitute migrants who report being retriggered with PTSD symptoms, with no access to NHS mental health services that are now delivered primarily remotely and are restricted access except to those patients who have access to free or cheap wifi, or unlimited phone credit (Munt 2020). A cultural miasma of fear and anxiety due to pandemic can affect such vulnerable minority groups particularly forcefully, with public attitudes generating direct aggression toward perceived 'outsiders' as harbingers of disease. Historically, the discourse of the 'stranger' (Ahmed 1991) or foreigner as bringer of disease has been well recognised within cultural sociology (Munt 2007), and as cultural suspicion grows under such conditions, feelings of alienation and estrangement amongst vulnerable groups intensifies.

The project will innovate by examining the biographical and life history narratives of St Mungo's clients in London in relation to their experiences of homelessness during the coronavirus crisis. Alongside semi-structured interviews, we will use participatory research methods including peer research, autoethnographic diaries, mobile phone photo-ethnographies and life history narratives in order to capture the rich and emotive narratives of those experiencing crisis. In doing so, we will examine the intersection of personal histories, complex global processes and the dynamics of the particular situation (Stewart, 2012, 2013). Researching vulnerable groups requires ethical sensitivity. It carries the danger of risking more disappointment among the respondents and exacerbating intense feelings of loneliness and isolation. To avoid this, and to make a positive intervention, we will seek to engage clients with services and support as part of the research project. Based on its findings, and working with St Mungo's partners, the project will make recommendations for measures that can be taken across the UK and elsewhere to support the homeless, particularly those most vulnerable, during times of crisis.

The re-emergence of tuberculosis (TB) in the mid-1980s in many parts of the world, including the United States, is often attributed to the emergence and rapid spread of human immunodeficiency virus (HIV) and acquired immunodeficiency syndrome (AIDS). Although it is well established that TB transmission is particularly amplified in populations with high HIV prevalence, the epidemiology of interaction between TB and HIV is not well understood. This is partly due to the scarcity of HIV-related data, a consequence of the voluntary nature of HIV status reporting and testing, and partly due to current practices of screening high risk populations through separate surveillance programs for HIV and TB. The San Francisco Department of Public Health, TB Control Program, has been conducting active surveillance among the San Francisco high-risk populations since the early 1990s. We present extensive TB surveillance data on HIV and TB infection among the San Francisco homeless to investigate the association between the TB cases and their HIV+ contacts. We applied wavelet coherence and phase analyses to the TB surveillance data from January 1993 through December 2005, to establish and quantify statistical association and synchrony in the highly non-stationary and ostensibly non-periodic waves of TB cases and their HIV+ contacts in San Francisco. When stratified by homelessness, we found that the evolution of TB cases and their HIV+ contacts is highly coherent over time and locked in phase at a specific periodic scale among the San Francisco homeless, but no significant association was observed for the non-homeless. This study confirms the hypothesis that the dynamics of HIV and TB are significantly intertwined and that HIV is likely a key factor in the sustenance of TB transmission among the San Francisco homeless. The findings of this study underscore the importance of contact tracing in detection of HIV+ individuals that may otherwise remain undetected, and thus highlights the ever-increasing need for HIV-related data and an integrative approach to monitoring high-risk populations with respect to HIV and TB transmission.

Analysis unit

Persons, households, and dwellings

UNITS IDENTIFIED: - Dwellings: yes - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: yes

UNIT DESCRIPTIONS: - Dwellings: Not available - Households: An individual or group of people who inhabit part or all of the physical or census building, usually live together, who eat from one kitchen or organize daily needs together as one unit. - Group quarters: A special household includes people living in dormitories, barracks, or institutions in which daily needs are under the responsibility of a foundation or other organization. Also includes groups of people in lodging houses or buildings, where the total number of lodgers is ten or more.

Universe

All population residing in the geographic area of Indonesia regardless of residence status. Diplomats and their families residing in Indonesia were excluded.

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: Central Bureau of Statistics

SAMPLE SIZE (person records): 20112539.

SAMPLE DESIGN: Geographically stratified systematic sample (drawn by IPUMS).

Mode of data collection

Face-to-face [f2f]

Research instrument

L1 questionnaire for buildings and households; L2 questionnaire for permanent residents; and L3 questionnaire for non-permanent residents (boat people, homeless persons, etc).

Adjusted odds ratios (and 95% confidence intervals) from binary logistic regression of ever experiencing housing loss due to "housing/financial loss", "health issues", and "interpersonal/family issues" by selected characteristics among homeless individuals (N = 207), Nipissing District, Ontario 2021.

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.

Sri Lanka LK: Droughts, Floods, Extreme Temperatures: Average 1990-2009: % of Population data was reported at 2.160 % in 2009. Sri Lanka LK: Droughts, Floods, Extreme Temperatures: Average 1990-2009: % of Population data is updated yearly, averaging 2.160 % from Dec 2009 (Median) to 2009, with 1 observations. Sri Lanka LK: Droughts, Floods, Extreme Temperatures: Average 1990-2009: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sri Lanka – Table LK.World Bank: Land Use, Protected Areas and National Wealth. Droughts, floods and extreme temperatures is the annual average percentage of the population that is affected by natural disasters classified as either droughts, floods, or extreme temperature events. A drought is an extended period of time characterized by a deficiency in a region's water supply that is the result of constantly below average precipitation. A drought can lead to losses to agriculture, affect inland navigation and hydropower plants, and cause a lack of drinking water and famine. A flood is a significant rise of water level in a stream, lake, reservoir or coastal region. Extreme temperature events are either cold waves or heat waves. A cold wave can be both a prolonged period of excessively cold weather and the sudden invasion of very cold air over a large area. Along with frost it can cause damage to agriculture, infrastructure, and property. A heat wave is a prolonged period of excessively hot and sometimes also humid weather relative to normal climate patterns of a certain region. Population affected is the number of people injured, left homeless or requiring immediate assistance during a period of emergency resulting from a natural disaster; it can also include displaced or evacuated people. Average percentage of population affected is calculated by dividing the sum of total affected for the period stated by the sum of the annual population figures for the period stated.; ; EM-DAT: The OFDA/CRED International Disaster Database: www.emdat.be, Université Catholique de Louvain, Brussels (Belgium), World Bank.; ;