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.

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.

In 2023, there were an estimated ******* white homeless people in the United States, the most out of any ethnicity. In comparison, there were around ******* 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.

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.

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.

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

Transitional Housing Services Market size was valued at USD 100 Billion in 2023 and is projected to reach USD 342.6 Billion by 2031, growing at a CAGR of 15.2% during the forecast period 2024-2031.

Global Transitional Housing Services Market Drivers

The market drivers for the Transitional Housing Services Market can be influenced by various factors. These may include:

Increasing Homelessness Rates: The rising rates of homelessness globally are a significant market driver for transitional housing services. Factors such as economic instability, lack of affordable housing, and social issues contribute to this increasing trend. Many cities report surges in homelessness, prompting governments and NGOs to seek robust solutions. Transitional housing serves as an intermediary step, offering individuals and families temporary support while they work towards permanent housing solutions.

Winter Shelter Overflow Monitoring Market Outlook

According to our latest research, the winter shelter overflow monitoring market size reached USD 1.12 billion globally in 2024, with a robust compound annual growth rate (CAGR) of 8.7% projected through to 2033. By the end of this forecast period, the market is expected to attain a value of USD 2.37 billion. The primary growth factor driving this market is the increasing demand for real-time occupancy tracking and resource allocation in winter shelters, particularly as urban homelessness rises and extreme weather events become more frequent and severe. As per our latest research, the market’s expansion is underpinned by technological advancements, heightened government focus on public welfare, and the proliferation of smart city initiatives that prioritize efficient management of emergency shelter resources.

The growth trajectory of the winter shelter overflow monitoring market is significantly influenced by the escalating prevalence of homelessness in urban centers across the globe. Cities are facing unprecedented challenges due to economic instability, housing shortages, and the impact of climate change, which has led to harsher and more unpredictable winter seasons. As a result, there is a heightened need for efficient, data-driven monitoring systems that can provide real-time insights into shelter occupancy, ensuring that no individual is left without access to safe, warm accommodations. This urgent demand is pushing municipalities, non-profit organizations, and social service providers to invest in advanced monitoring solutions that not only track shelter capacity but also optimize the allocation of resources, streamline communication, and enhance the overall responsiveness of emergency support networks.

Another pivotal growth factor for the winter shelter overflow monitoring market is the technological evolution of monitoring systems, particularly the integration of IoT sensors, AI-driven analytics, and cloud-based platforms. These innovations allow for seamless data collection and analysis, enabling shelter operators to anticipate overflow situations, coordinate with other facilities, and make informed decisions rapidly. The shift towards interoperable platforms that can connect with broader municipal emergency response systems is also facilitating a more holistic approach to managing shelter resources during peak winter periods. This technological shift is attracting increased investment from both public and private sectors, as stakeholders recognize the potential of these systems to save lives, reduce operational costs, and improve the overall effectiveness of winter shelter programs.

Furthermore, government initiatives and policy frameworks are playing a crucial role in accelerating the adoption of winter shelter overflow monitoring solutions. Many regional and national governments are now mandating the use of advanced monitoring technologies as part of their broader public safety and disaster preparedness strategies. These mandates are often accompanied by dedicated funding streams and technical support, which lower the barriers to entry for smaller organizations and foster a competitive market landscape. Additionally, public-private partnerships are emerging as an effective model for scaling up deployment, leveraging the expertise of technology providers while ensuring alignment with social welfare objectives. As regulatory standards continue to evolve, the market is expected to benefit from increased standardization, interoperability, and data security, further driving widespread adoption.

From a regional perspective, North America currently dominates the winter shelter overflow monitoring market, accounting for the largest share due to its advanced technological infrastructure, strong governmental support, and high incidence of urban homelessness. Europe follows closely, with significant investments in smart city projects and social welfare programs. The Asia Pacific region is witnessing rapid growth, fueled by urbanization, rising awareness about homelessness, and increasing government initiatives to address emergency shelter needs. Meanwhile, Latin America and the Middle East & Africa are gradually adopting these monitoring solutions, primarily in major urban centers and regions prone to extreme weather events. Each region presents unique challenges and opportunities, shaping the competitive dynamics and innovation trends within the global market.
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Older adults are increasingly becoming unhoused. Once homeless, older adults are forced to navigate the transition to housing, an area which requires further research. We conducted a secondary analysis of qualitative research data using the theory of ontological security to explore the question: How do older adults experience trauma across the transition to housing following homelessness? During the analysis, we created a central essence ‘When your entire world is upended you have to learn to live in a new reality’, with three underlying themes: 1) My life got “pulled out from under me” and I am struggling to regain even footing; 2) I see reality clearly, “the system is broken”; and 3) The importance of social connection in rebuilding ontological security. Findings indicate the transition to housing presents an opportunity for older adults to rebuild ontological security, however only when they have access to housing that is good quality, deeply affordable, and accessible.

Winter Shelter Overflow Monitoring Market Outlook

According to our latest research, the Global Winter Shelter Overflow Monitoring market size was valued at $415 million in 2024 and is projected to reach $1.18 billion by 2033, expanding at a CAGR of 12.2% during the forecast period of 2024–2033. One of the major factors fueling the growth of this market globally is the increasing demand for real-time capacity monitoring and intelligent resource allocation in homeless shelters and emergency response centers, especially during harsh winter months. As urban populations rise and climate change leads to more unpredictable and severe winter conditions, the need for advanced monitoring solutions that ensure the safety and well-being of vulnerable populations is more critical than ever. This has led to a surge in investments in digital infrastructure and smart monitoring platforms by municipalities, non-profit organizations, and government agencies worldwide, further propelling the market’s expansion.

Regional Outlook

North America currently holds the largest share of the Winter Shelter Overflow Monitoring market, accounting for over 38% of the global market value in 2024. The region’s dominance is attributed to its mature technological landscape, robust funding for social welfare programs, and stringent regulatory frameworks that mandate effective shelter management, especially during winter emergencies. The United States and Canada lead the adoption of advanced software and hardware solutions, leveraging IoT, cloud computing, and analytics for real-time occupancy tracking and resource optimization. The presence of numerous non-profit organizations, proactive municipal authorities, and significant federal investments in homelessness prevention further reinforce North America’s leadership in this sector. Ongoing public-private partnerships and integration of AI-driven analytics are expected to keep the region at the forefront of innovation and market growth through 2033.

Asia Pacific is identified as the fastest-growing region in the Winter Shelter Overflow Monitoring market, projected to register a remarkable CAGR of 15.7% from 2024 to 2033. This rapid growth is driven by increasing urbanization, rising incidences of extreme weather events, and heightened government focus on social welfare infrastructure across countries such as China, Japan, South Korea, and Australia. Investments in smart city initiatives and the proliferation of cloud-based monitoring solutions are enabling municipalities and non-profits to adopt scalable and cost-effective shelter overflow management systems. Additionally, regional governments are launching targeted policy reforms and incentives to improve the resilience of social services, which is fostering the adoption of advanced monitoring technologies. The market in Asia Pacific is also benefiting from collaborations with international humanitarian organizations and technology vendors, further accelerating the deployment of innovative solutions.

Emerging economies in Latin America, the Middle East, and Africa are gradually adopting Winter Shelter Overflow Monitoring solutions, although market penetration remains relatively low compared to developed regions. Challenges such as limited digital infrastructure, budgetary constraints, and varying policy frameworks often hinder the widespread implementation of advanced monitoring systems. However, localized demand is rising, particularly in urban centers facing increasing homelessness and unpredictable winter conditions. International aid, NGO partnerships, and localized pilot projects are playing a crucial role in bridging the technology gap and demonstrating the value of real-time monitoring for shelter management. Over the forecast period, as governments in these regions prioritize social protection and invest in digital transformation, the adoption rate of winter shelter monitoring solutions is expected to accelerate, albeit from a smaller base.

Report Scope

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...

Winter Weather Shelter Operations Market Outlook

According to the latest research, the global winter weather shelter operations market size reached USD 2.1 billion in 2024, reflecting a robust response to increasing climate volatility and the urgent need for humanitarian support during extreme cold weather events. The market is expected to expand at a CAGR of 8.4% from 2025 to 2033, with the total market size projected to reach USD 4.3 billion by 2033. This growth is primarily driven by rising incidences of severe winter storms, urban homelessness, and evolving emergency management protocols worldwide.

The growth trajectory of the winter weather shelter operations market is underpinned by several key factors, the foremost being the increasing frequency and severity of extreme winter weather events globally. As climate change accelerates, regions previously unaffected by harsh winters are now experiencing record-low temperatures, leading to a surge in demand for emergency shelters, warming centers, and related outreach services. This trend is further compounded by growing urbanization, which concentrates vulnerable populations in metropolitan areas, heightening the need for rapid deployment of shelter solutions. Governments and non-profit organizations are consequently allocating higher budgets for winter preparedness, fueling market expansion and innovation in shelter operations.

Another significant driver for the winter weather shelter operations market is the rising visibility and prioritization of homelessness and public health issues. Major cities across North America, Europe, and parts of Asia Pacific are witnessing increased advocacy for human rights and social welfare, prompting policymakers to invest in more robust shelter infrastructure. The COVID-19 pandemic also highlighted the intersection between public health and emergency sheltering, leading to the adoption of more stringent safety protocols, improved facility designs, and integration of health services within shelters. This holistic approach has not only improved operational standards but also expanded the scope of services offered, thereby increasing the market’s overall value.

Technological advancements and innovative service delivery models are also playing a pivotal role in shaping the winter weather shelter operations market. The adoption of modular and mobile shelter units, enhanced digital outreach through online platforms, and the use of data analytics for efficient resource allocation are transforming how shelters operate and respond to emergencies. These innovations enable rapid scalability, cost efficiency, and improved user experiences, attracting investments from both public and private sectors. Furthermore, partnerships between government agencies, non-profits, and community groups are fostering a more collaborative ecosystem, which is essential for addressing the complex challenges posed by winter weather emergencies.

From a regional perspective, North America remains the dominant market for winter weather shelter operations, accounting for over 38% of the global market share in 2024. This is attributed to the region’s high incidence of winter storms, robust government funding, and well-established non-profit networks. Europe follows closely, driven by strong social welfare policies and increasing urban homelessness. Meanwhile, the Asia Pacific region is emerging as a high-growth market, with a projected CAGR of 10.2% through 2033, due to rapid urbanization and growing awareness of climate resilience. Latin America and the Middle East & Africa, though smaller in market size, are witnessing steady growth as governments enhance disaster preparedness frameworks and invest in emergency shelter infrastructure.

Service Type Analysis

The service type segment of the winter weather shelter operations market encompasses emergency shelters, temporary warming centers, transitional housing, outreach services, and others. Emergency shelters repres

BackgroundSubstance use contributes to poor health and increases the risk of mortality in the homeless population. This study assessed the prevalence and risk levels of substance use and associated factors among adults experiencing homelessness in Accra, Ghana.Methods305 adults currently experiencing sheltered and unsheltered homelessness in Accra aged ≥ 18 years were recruited. The World Health Organization’s (WHO) Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST) was used to assess substance use risk levels. Association of high-risk substance use with sociodemographic, migration, homelessness, and health characteristics were assessed using logistic regression.ResultsNearly three-quarters (71%, n = 216) of the sample had ever used a substance, almost all of whom engaged in ASSIST-defined moderate-risk (55%) or high-risk (40%) use. Survivors of physical or emotional violence (AOR = 3.54; 95% confidence interval [CI] 1.89–6.65, p

Shelter Management Mobile Check-In Market Outlook

According to our latest research, the Shelter Management Mobile Check-In market size was valued at $1.2 billion in 2024 and is projected to reach $3.8 billion by 2033, expanding at a robust CAGR of 13.5% during the forecast period of 2025–2033. The primary factor fueling this market’s global growth is the increasing demand for real-time, data-driven solutions to streamline shelter operations, improve occupant safety, and ensure regulatory compliance. As governments and humanitarian organizations face mounting challenges in managing shelter populations due to natural disasters, homelessness, and public health emergencies, the adoption of mobile check-in technologies is rapidly accelerating. These solutions offer unparalleled efficiency in registration, occupancy tracking, and resource allocation, fundamentally transforming the way shelters operate worldwide.

Regional Outlook

North America currently holds the largest share of the global Shelter Management Mobile Check-In market, accounting for over 38% of total market value in 2024. This dominance is attributed to the region’s mature technological infrastructure, proactive government policies around disaster management, and a strong ecosystem of non-profit organizations. The United States, in particular, has seen widespread adoption of mobile check-in platforms across both government-run and privately managed shelters, driven by stringent regulatory requirements for data transparency and occupant safety. High-profile natural disasters and public health crises have further accelerated investments in digital shelter management solutions, making North America a bellwether for innovation and best practices in this sector.

In contrast, the Asia Pacific region is the fastest-growing market, projected to expand at a CAGR of 17.2% through 2033. Rapid urbanization, increasing frequency of natural disasters, and substantial government investment in digital public safety infrastructure are key drivers in countries such as China, India, and Japan. These nations are embracing cloud-based shelter management mobile check-in systems to address massive population displacements and improve emergency response capabilities. The region’s tech-savvy population and growing mobile penetration further support the adoption of these solutions, while international aid agencies and local governments collaborate to scale up digital shelter management initiatives.

Emerging economies in Latin America, Africa, and parts of Southeast Asia are also witnessing gradual adoption of shelter management mobile check-in technologies, albeit at a slower pace. Challenges such as limited digital infrastructure, inconsistent funding, and varying regulatory frameworks can impede rapid deployment. However, humanitarian crises and localized demand for efficient shelter management are prompting governments and NGOs to pilot mobile check-in platforms, often with support from international donors. As digital literacy improves and policy reforms are enacted, these regions are expected to contribute increasingly to the global market, although their aggregate market share remains modest compared to established and fast-growing regions.

Report Scope

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...

Locations and health center information was sourced from https://www.fhcsd.org/clinic-location-list/. This list was digitized into a spreadsheet and geolocated using ArcGIS World Geocoding Service. To be hosted by Homelessness Hub at UC San Diego. Data is current to April 2024.

The Pop-Up Housing Solutions market has emerged as a transformative force in the real estate and housing industry, providing innovative temporary housing options that address various needs ranging from disaster relief to urban homelessness. As the global population continues to rise and urban areas face increasing d

Poverty and social exclusion, social services, climate change, and the national economic situation and statistics.

Topics: 1. Poverty and social exclusion: own life satisfaction (scale); satisfaction with family life, health, job, and satisfaction with standard of living (scale); personal definition of poverty; incidence of poverty in the own country; estimated proportion of the poor in the total population; poor persons in the own residential area; estimated increase of poverty: in the residential area, in the own country, in the EU, and in the world; reasons for poverty in general; social and individual reasons for poverty; population group with the highest risk of poverty; things that are necessary to being able to afford to have a minimum acceptable standard of living (heating facility, adequate housing, a place to live with enough space and privacy, diversified meals, repairing or replacing a refrigerator or a washing machine, annual family holidays, medical care, dental care, access to banking services as well as to public transport, access to modern means of communication, to leisure and cultural activities, electricity, and running water); perceived deprivation through poverty in the own country regarding: access to decent housing, education, medical care, regular meals, bank services, modern means of communication, finding a job, starting up a business of one’s own, maintaining a network of friends and acquaintances; assessment of the financial situation of future generations and current generations compared to parent and grandparent generations; attitude towards poverty: necessity for the government to take action, too large income differences, national government should ensure the fair redistribution of wealth, higher taxes for the rich, economic growth reduces poverty automatically, poverty will always exist, income inequality is necessary for economic development; perceived tensions between population groups: rich and poor, management and workers, young and old, ethnic groups; general trust in people, in the national parliament, and the national government (scale); trust in institutions regarding poverty reduction: EU, national government, local authorities, NGOs, religious institutions, private companies, citizens; reasons for poverty in the own country: globalisation, low economic growth, pursuit of profit, global financial system, politics, immigration, inadequate national social protection system; primarily responsible body for poverty reduction; importance of the EU in the fight against poverty; prioritized policies of the national government to combat poverty; assessment of the effectiveness of public policies to reduce poverty; opinion on the amount of financial support for the poor; preference for governmental or private provision of jobs; attitude towards tuition fees; increase of taxes to support social spending; individual or governmental responsibility (welfare state) to ensure provision; attitude towards a minimum wage; optimism about the future; perceived own social exclusion; perceived difficulties to access to financial services: bank account, bank card, credit card, consumer loans, and mortgage; personal risk of over-indebtedness; attitude towards loans: interest free loans for the poor, stronger verification of borrowers by the credit institutions, easier access to start-up loans for the unemployed, free financial advice for the poor, possibility to open a basic bank account for everyone; affordable housing in the residential area; extent of homelessness in the residential area, and recent change; adequacy of the expenditures for the homeless by the national government, and the local authorities; assumed reasons for homelessness: unemployment, no affordable housing, destruction of the living space by a natural disaster, debt, illness, drug or alcohol addiction, family breakdown, loss of a close relative, mental health problems, lack of access to social services and support facilities, lack of identity papers, free choice of this life; probability to become homeless oneself; own support of homeless people: monetary donations to charities, volunteer work in a charity, help find access in emergency shelters and with job search, direct donations of clothes to homeless people, buying newspapers sold by homeless people, food donations; sufficient household income, or difficulties to make ends meet; ability to afford the heating costs, a week’s holiday once a year, and a meal with meat ever...