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.

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

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).

Problem statement Having a pet is one of life’s most fulfilling experiences. Your pets spoil you with their love, compassion, and loyalty. And dare anyone lay a finger on you in your pet’s presence, they are in for a lot of trouble. Thanks to social media, videos of clumsy and fussy (yet adorable) pets from across the globe entertain you all day long. Their love is pure and infinite. So, in return, all pets deserve a warm and loving family, indeed. And occasional boops, of course.

Numerous organizations across the world provide shelter to all homeless animals until they are adopted into a new home. However, finding a loving family for them can be a daunting task at times. This International Homeless Animals Day, we present a Machine Learning challenge to you: Adopt a buddy.

The brighter side of the pandemic is an increase in animal adoption and fostering. To ensure that their customers stay indoors, a leading pet adoption agency plans on creating a virtual-tour experience, showcasing all animals available in their shelter. To enable that, you have been tasked to build a Machine Learning model that determines the type and breed of the animal-based on its physical attributes and other factors.

Dataset The dataset consists of parameters such as a unique ID assigned to each animal that is up for adoption, the date on which they arrived at the shelter, their physical attributes such as color, length, and height, among other factors.

The benefits of practicing this problem by using Machine Learning techniques are as follows:

This challenge will help you to actively enhance your knowledge of multi-label classification. It is one of the basic building blocks of Machine Learning We challenge you to build a predictive model that detects the type and breed of an animal-based on its condition, appearance, and other factors.

Prizes Considering these unprecedented times that the world is facing due to the Coronavirus pandemic, we wish to do our bit and contribute the prize money for the welfare of society.

Overview Machine Learning is an application of Artificial Intelligence (AI) that provides systems with the ability to automatically learn and improve from experiences without being explicitly programmed. Machine Learning is a Science that determines patterns in data. These patterns provide a deeper meaning to problems. First, it helps you understand the problems better and then solve the same with elegance.

Here is the new HackerEarth Machine Learning Challenge—Adopt a buddy

This challenge is designed to help you improve your Machine Learning skills by competing and learning from fellow participants.

Why should you participate? To analyze and implement multiple algorithms, and determine which is more appropriate for a problem. To get hands-on experience of Machine Learning problems.

Who should participate? Working professionals. Data Science or Machine Learning enthusiasts. College students (if you understand the basics of predictive modeling).

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.

< a href="https://www.paris.fr/services-et-infos-pratiques/social-et-solidarites/personnes-en-situation-de-precarite/s-inserer-129">https://www .paris.fr/services-et-infos-pratiques/social-et-solidarites/personnes-en-situation-de-precarite/s-inserer-129

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.

The data are part of the Jyväskylä Longitudinal Study of Personality and Social Development (JYLS), in which the same individuals have been followed over 40 years. At this research stage, the lives of 50-year-olds were surveyed in terms of family, work, health, and leisure. This dataset contains the responses to the life history calendar, which was filled in during the interviews, where the respondents told about their life from the age of 15 onwards with the help of a table describing life events. The respondents' life events between the ages of 15 and 42 were previously charted when they were 42 years old (FSD2124). This dataset contains both these earlier responses and the responses collected from the respondents at the age of 50 charting life events from the age of 43 to 50. The life history calendar was used to examine what kind of events had occurred in the lives of the respondents and when. The respondents were asked where they had lived each year, when they had moved away from their parents' home, and whether they had ever been homeless. Cohabitations, marriages and childbirths were also marked in the calendars. The respondents were also asked about their education, full-time and part-time jobs, and periods of unemployment. The respondents indicated whether they had been homemakers, conscripted, on leave of absence, or retired and when. Relating to employment, the respondents were asked whether they had learned new skills/tasks after the age of 42 and if they had, when this had taken place and whether learning new skills/tasks had been associated with changing jobs. In addition, they were asked to tell about the deaths and serious accidents of their friends and close relatives, as well as whether they had ever been victims of a crime or caught for committing an illegal act, such as speeding, vandalism, theft, or violence and whether they had been convicted of these acts. The respondents who had not filled out the life history calendar in the previous collection round at the age of 42 were presented additional questions about the same themes to chart the life events of previous years. The respondents asked to fill out the additional question form were asked, for instance, about their marriages and cohabiting relationships, places of residence, education, and being caught for committing offences prior to the age of 43.