This dataset is extracted from https://en.wikipedia.org/wiki/List_of_countries_by_homeless_population. Context: There s a story behind every dataset and heres your opportunity to share yours.Content: What s inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Acknowledgements:We wouldn t be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.Inspiration: Your data will be in front of the world s largest data science community. What questions do you want to see answered?

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.

Content

Included in this data set are data elements that will help the public identify agencies that are certified to operate programs for runaway and homeless youth. These programs are available to assist runaway and homeless youth in emergency situation and provide independent living skills for youth in transition. Data elements include the agency name, agency business address, phone number, website and type of program offered.

Context

This is a dataset hosted by the State of New York. The state has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York State using Kaggle and all of the data sources available through the State of New York organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Zac Ong on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

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.

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...

Dear Partners,

This month, the Administration for Children and Families (ACF) observed World Day Against Child Labor by spotlighting and encouraging those, who could, to join the Within and Beyond Our Borders: Collective Action to Address Hazardous Child Labor organized by the U.S. Department of Labor (DOL) on June 12, 2023. If you missed it, or would like to rewatch it, you can find it here

.

Since 2018, the DOL has seen a 69 percent increase in children being employed illegally by companies. In the last fiscal year, the department found that 835 companies it investigated had employed more than 3,800 children in violation of labor laws. There has been a 26 percent increase in children employed in hazardous occupations. These numbers tell us that we have work to do as the human services sector to learn more and become engaged in preventing unlawful child labor and supporting youth.

As I have said before, child labor exploitation can disrupt a youth’s health, safety, education, and overall well-being, which are unacceptable consequences for any child. The Administration for Children and Families (ACF) supports a broad network of resources for vulnerable youth. We know that migrant and immigrant youth are especially vulnerable to exploitation, and it is often youth in or exiting the child welfare system who are targeted for various forms of exploitation. Child labor exploitation can impact children and youth across demographics.

On March 24, 2023, the DOL and the U.S. Department of Health and Human Services (HHS) announced a Memorandum of Agreement - PDF

to advance ongoing efforts to address child labor exploitation. In addition, DOL and HHS are collaborating on training and educational materials.

As we expand this work, we know how important our partners throughout the country are in this effort. The Administration for Children and Families (ACF) is committed to addressing the increased presence of child labor exploitation through a variety of actions including equipping partners with materials and educational resources to build knowledge about child labor laws and rights, and remedies. This information is important for our human services sector and the children and families who may be most at risk.

Please join ACF in increasing awareness and distributing resources to address child labor exploitation including the following:

ACF resources may be also useful when working with a youth who has concerns about their safety. This includes the Family and Youth Services Bureau (FYSB)’s program on Runaway and Homeless Youth which provides a hotline for youth, concerned adults, and providers to access resources. At, www.1800runaway.org

, their 24/7 crisis connection allows for calls, texts, live chat, and email to get information and resources.

In addition, ACF’s Office of Trafficking In-Persons (OTIP) is an important resource for identifying and supporting survivors of trafficking. The National Human Trafficking Hotline

provides a 24/7, confidential, multilingual hotline for victims, survivors, and witnesses of human trafficking. While labor exploitation should not be conflated with labor trafficking, in some cases labor exploitation may rise to meet the legal definitions of trafficking. The OTIP website

contains many resources for grantees and communities on labor trafficking.

Again, I hope you will continue to build awareness for yourself, your organization, or your community on child labor exploitation. It takes a whole community effort to support our children and youth.

Most sincerely,

January Contreras

Metadata-only record linking to the original dataset. Open original dataset below.

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

Houston Hurricane Harvey Shelter Availability and Needs Data

Aid and Resources for Displaced Residents

By Selene Arrazolo [source]

About this dataset

This dataset contains information on the shelter availability in Houston during Hurricane Harvey, when devastating floods and destruction left thousands homeless. Join the slack group to stay up-to-date on current emergency needs and view maps pinpointing available shelters in your immediate vicinity. With the help of charitable organizations, volunteers, and disaster responders across the globe—we’re helping those affected start their path to rebuilding a safe and stable home. Knowledge is power, so help us spread awareness to ensure that no one goes unaided in this time of need. View meaningful data points from this dataset including accommodation status, check-in times, address locations, contact info for established shelters – without bounds or borders! Let’s use our strengths together to make sure that every recovered household can continue life stronger than before

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How to use the dataset

How to Use This Dataset: Houston Hurricane Harvey Shelter Availability & Needs Info

This Dataset contains information on the availability of shelters in Houston during Hurricane Harvey that are actively being monitored by Sketch City. It includes data on shelter locations, availability, and needs for volunteers and donated items. Here's a quick guide on how to make the most of this dataset.

• Download and view the raw data from Houston-shelter-availability - LAST UPDATED September 2nd 2017.csv or view it directly at this Google doc. This contains detailed information on each shelter including its name, capacity, location, contact info, list of items needed (including food & water), and any other special notes or requests that have been provided by that particular shelter coordinator at the time of going live with this doc (Sept 2nd 2017)

2a) View an interactive map visualizing where all Houston shelters are located along with their current status (i.e., Open vs Closed): Visit this link. For example, if you would like to identify open shelters within a 5 mile radius from Downtown Houston you can utilize this map showing markers for them accordingly along with links to a full page profile containing more detailed information about each shelter plus needs list per location:

2b) Alternatively view an interactive map summarizing volunteer needs across all affected areas:visit this link This is ideal if you would like to summarize summary volunteer opportunities as well as donated materials needed across multiple locations at once — thus allowing visitors better understand situation holistically before viewing individual profiles per location specified in #1 above

• Last but not least; If you see something missing from any type of profile mentioned above or have any questions do not hesitate to get in touch with those who are running & curating efforts around these docs either via Hurricane Harvey Slack team #or Facebook page https:/facebook/​SketchCityHouston

Research Ideas

• Creating a map to help people in need to quickly find the nearest shelter with available space.
• Building an app that keeps people up-to-date about changing availability of shelters as well as needs for supplies and services at each location.
• Developing a chatbot that answers questions from potential volunteers or support staff about Harvey shelters and resources available in Houston during Hurricane Harvey

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

C...

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.

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.

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).

Context

The Canadian Disaster Database The Canadian Disaster Database (CDD) contains detailed disaster information on more than 1000 natural, technological and conflict events (excluding war) that have happened since 1900 at home or abroad and that have directly affected Canadians.

Content

Data description copied from: https://www.publicsafety.gc.ca/cnt/rsrcs/cndn-dsstr-dtbs/index-en.aspx

Dataset date range: 1900 - present

The CDD tracks "significant disaster events" which conform to the Emergency Management Framework for Canada definition of a "disaster" and meet one or more of the following criteria:

• 10 or more people killed
• 100 or more people affected/injured/infected/evacuated or homeless
• an appeal for national/international assistance
• historical significance
• significant damage/interruption of normal processes such that the community affected cannot recover on its own

The database describes where and when a disaster occurred, the number of injuries, evacuations, and fatalities, as well as a rough estimate of the costs. As much as possible, the CDD contains primary data that is valid, current and supported by reliable and traceable sources, including federal institutions, provincial/territorial governments, non-governmental organizations and media sources.

Data is updated and reviewed on a semi-annual basis.

Data Field Description

Disaster Type The type of disaster (e.g. flood, earthquake, etc.) that occurred.
Date of Event The date a specific event took place.
Specific Location The city, town or region where a specific event took place.
Description of Event A brief description of a specific event, including pertinent details that may not be captured in other data fields (e.g. amount of precipitation, temperatures, neighbourhoods, etc.)
Fatalities The number of people killed due to a specific event.
Injured/Infected The number of people injured or infected due to a specific event.
Evacuees The number of individuals evacuated by the government of Canada due to a specific event.
Latitude & Longitude The exact geographic location of a specific event.
Province/Territory The province or territory where a specific event took place.
Estimated Total Cost A roll-up of all the costs listed within the financial data fields for a specific event.
DFAA Payments The amount, in dollars, paid out by Disaster Financial Assistance Arrangements (Public Safety Canada) due to a specific event.
Insurance Payments The amount, in dollars, paid out by insurance companies due to a specific event.
Provincial/Territorial Costs/Payments The amount, in dollars, paid out by a Province or Territory due to a specific event.
Utility Costs/Losses The amount of people whose utility services (power, water, etc.) were interrupted/affected by a specific event.
Magnitude A measure of the size of an earthquake, related to the amount of energy released.
Other Federal Institution Costs The amount, in dollars, paid out by other federal institutions.

Acknowledgements

Data gathered from: http://cdd.publicsafety.gc.ca Terms of use for commercial and non-comerical reproduction: https://www.publicsafety.gc.ca/cnt/ntcs/trms-en.aspx

Inspiration

This dataset provides valuable insight to natural and non-natrual disasters which have affected Canada.

Possible explorations: * Where do different types of disasters occur more frequently? * Which Province / Location in Canada has been hit the hardest in terms of fatalities, number of injuries, estimated total cost, etc.?

Spatial-temporal correlations between natural/artifical distasters *

I think that this can be used to produce some interesting data visualizations. Some of the questions I look forward to answering include:

• Can any spatial-temporal correlations between disasters be found in this dataset?
• Which locations in Canada have been hit the hardest, in terms of people injured, fatalities, financial impact, etc.

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

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...

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

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.; ;

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 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.