2011 HDI Comparision

The dataset represents the 2011 Human Development Index of Mauritania.

Human Development Index trends

The INFORM Global Risk Index 2019 Mid Year, v0.3.7 data set identifies the countries at a high risk of humanitarian crisis that are more likely to require international assistance. The INFORM Global Risk Index (GRI) model is based on risk concepts published in the scientific literature and envisages three dimensions of risk: Hazard & Exposure, Vulnerability, and Lack of Coping Capacity. The INFORM GRI model is split into different levels to provide a quick overview of the underlying factors leading to humanitarian risk. The INFORM GRI model supports a proactive crisis management framework, and will be helpful for an objective allocation of resources for disaster management, as well as for coordinated actions focused on anticipating, mitigating, and preparing for humanitarian emergencies. Only the two main sections, Vulnerability and Lack of Coping Capacity, not the Hazard & Exposure section, were used in the IPCC AR6.

The INFORM Global Risk Index 2019 Mid Year, v0.3.7 data set identifies the countries at a high risk of humanitarian crisis that are more likely to require international assistance. The INFORM Global Risk Index (GRI) model is based on risk concepts published in the scientific literature and envisages three dimensions of risk: Hazard & Exposure, Vulnerability, and Lack of Coping Capacity. The INFORM GRI model is split into different levels to provide a quick overview of the underlying factors leading to humanitarian risk. The INFORM GRI model supports a proactive crisis management framework, and will be helpful for an objective allocation of resources for disaster management, as well as for coordinated actions focused on anticipating, mitigating, and preparing for humanitarian emergencies. Only the two main sections, Vulnerability and Lack of Coping Capacity, not the Hazard & Exposure section, were used in the IPCC AR6.

An index to target those in poverty and affected by cyclone Pam. Methodology and contact available on tab in spreadsheet.

Contains data crowdsourced monthly from Venezuelans through the Premise Data mobile application. The data tracks transport accessibility and mobility indicators at a monthly granularity.

More relevant information below:

• The booklet included HERE goes into more details on how Premise's crowdsourcing works.

The INFORM Global Crisis Severity Index (GCSI) is a regularly updated, and easily interpreted model for measuring the severity of humanitarian crisis globally.

It is a composite index, which brings together 31 core indicators, organised in three dimensions: impact, conditions of affected people, and complexity. All the indicators are scored on a scale of 1 to 5. These scores are then aggregated into components, the three dimensions (Impact, Conditions, Complexity), and the overall severity category based on the analytical framework. The three dimensions have been weighted according to their contribution to severity: impact of the crisis (20%); conditions of affected people (50%); complexity (30%). The weightings are currently a best estimate and will be refined using expert analysis and statistical methods. Each crisis will fall into 1 of 5 categories based on their score ranging from very low to high.

ACAPS – an INFORM technical partner – is responsible for collection, cleaning, analysis and input of data into the model and the production of the final results.

Read more on the GCSI methodology here: https://www.acaps.org/methodology/severity

A priority index created for use in the response to Typhoon Maysak using a combination of pre-disaster and disaster data

National Risk Index Version: March 2023 (1.19.0)A Heat Wave is a period of abnormally and uncomfortably hot and unusually humid weather typically lasting two or more days with temperatures outside the historical averages for a given area. Annualized frequency values for Heat Waves are in units of event-days per year.The National Risk Index is a dataset and online tool that helps to illustrate the communities most at risk for 18 natural hazards across the United States and territories: Avalanche, Coastal Flooding, Cold Wave, Drought, Earthquake, Hail, Heat Wave, Hurricane, Ice Storm, Landslide, Lightning, Riverine Flooding, Strong Wind, Tornado, Tsunami, Volcanic Activity, Wildfire, and Winter Weather. The National Risk Index provides Risk Index values, scores and ratings based on data for Expected Annual Loss due to natural hazards, Social Vulnerability, and Community Resilience. Separate values, scores and ratings are also provided for Expected Annual Loss, Social Vulnerability, and Community Resilience. For the Risk Index and Expected Annual Loss, values, scores and ratings can be viewed as a composite score for all hazards or individually for each of the 18 hazard types.Sources for Expected Annual Loss data include: Alaska Department of Natural Resources, Arizona State University’s (ASU) Center for Emergency Management and Homeland Security (CEMHS), California Department of Conservation, California Office of Emergency Services California Geological Survey, Colorado Avalanche Information Center, CoreLogic’s Flood Services, Federal Emergency Management Agency (FEMA) National Flood Insurance Program, Humanitarian Data Exchange (HDX), Iowa State University's Iowa Environmental Mesonet, Multi-Resolution Land Characteristics (MLRC) Consortium, National Aeronautics and Space Administration’s (NASA) Cooperative Open Online Landslide Repository (COOLR), National Earthquake Hazards Reduction Program (NEHRP), National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (NCEI), National Oceanic and Atmospheric Administration's National Hurricane Center, National Oceanic and Atmospheric Administration's National Weather Service (NWS), National Oceanic and Atmospheric Administration's Office for Coastal Management, National Oceanic and Atmospheric Administration's National Geophysical Data Center, National Oceanic and Atmospheric Administration's Storm Prediction Center, Oregon Department of Geology and Mineral Industries, Pacific Islands Ocean Observing System, Puerto Rico Seismic Network, Smithsonian Institution's Global Volcanism Program, State of Hawaii’s Office of Planning’s Statewide GIS Program, U.S. Army Corps of Engineers’ Cold Regions Research and Engineering Laboratory (CRREL), U.S. Census Bureau, U.S. Department of Agriculture's (USDA) National Agricultural Statistics Service (NASS), U.S. Forest Service's Fire Modeling Institute's Missoula Fire Sciences Lab, U.S. Forest Service's National Avalanche Center (NAC), U.S. Geological Survey (USGS), U.S. Geological Survey's Landslide Hazards Program, United Nations Office for Disaster Risk Reduction (UNDRR), University of Alaska – Fairbanks' Alaska Earthquake Center, University of Nebraska-Lincoln's National Drought Mitigation Center (NDMC), University of Southern California's Tsunami Research Center, and Washington State Department of Natural Resources.Data for Social Vulnerability are provided by the Centers for Disease Control (CDC) Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index, and data for Community Resilience are provided by University of South Carolina's Hazards and Vulnerability Research Institute’s (HVRI) 2020 Baseline Resilience Indicators for Communities.The source of the boundaries for counties and Census tracts are based on the U.S. Census Bureau’s 2021 TIGER/Line shapefiles. Building value and population exposures for communities are based on FEMA’s Hazus 6.0. Agriculture values are based on the USDA 2017 Census of Agriculture.

The landscape of climate change and extreme events will remain a wicked problem for equitable and forward-looking resource prioritisation. The question of how to couple climate and multi-risk information remains. IPCC has considered that multi-criteria decision analysis (MCDA) can help.

We use stochastic multi-attribute analysis (SMAA), a variant of MCDA, to compute prioritisations of climatic losses & damages (l&d) for fragile countries with a humanitarian response plan. SMAA is combined with the INFORM Severity index, measuring the status of crises and disasters, and preferences gathered from stakeholders (e.g., United Nations, European Union, World Bank, the research and public sector, civil society).

• Dataset S1. XLS-file with all the input data compiled from sources, concurrent data manipulation, and descriptions of steps taken until ready for the SMAA.
• Dataset S2. XLS-file with results of the SMAA for all weight schemes and concurrent analysis, such as sensitivity heat mapping, correlations, regressions, and Tukey mean-difference plot.

We use an anonymized snapshot of all active Facebook users and their friendship networks to measure the intensity of connectedness between locations. The Social Connectedness Index (SCI) is a measure of the social connectedness between different geographies. Specifically, it measures the relative probability that two individuals across two locations are friends with each other on Facebook.

Details on the underlying data and the construction of the index are provided in the “Facebook Social Connectedness Index - Data Notes.pdf” file. Please also see https://dataforgood.facebook.com/ as well as the associated research paper “Social Connectedness: Measurement, Determinants and Effects,” published in the Journal of Economic Perspectives (https://www.aeaweb.org/articles?id=10.1257/jep.32.3.259).

Region identifiers are taken from GADM v2.8 https://gadm.org/download_country_v2.html. Future versions will update IDs to be compatible with the newest GADM version.

The overall INFORM risk index identifies countries at risk from humanitarian crises and disasters that could overwhelm national response capacity. It is made up of three dimensions - hazards and exposure, vulnerability and lack of coping capacity.

High exposure, High sensitivity and low adaptive capacity leads to high vulnerability. The severity of vulnerability determines the likely impacts to a system that changing climate can have.

This data has been developed by RCMRD and Malawi Department of Disaster Management Affairs (DoDMA). SERVIR is a joint USAID-NASA project. For more information on SERVIR, visit http://www.servirglobal.net

Humanitarian Aid Parametric Insurance Market Outlook

According to our latest research, the global humanitarian aid parametric insurance market size reached USD 1.14 billion in 2024, underpinned by increasing climate-related disasters and the growing need for rapid financial response mechanisms in crisis situations. The market is expected to expand at a robust CAGR of 13.7% during the forecast period, with the market size projected to reach USD 3.38 billion by 2033. This strong growth trajectory is attributed to the rising adoption of data-driven insurance solutions by humanitarian organizations, governments, and international agencies seeking efficient risk transfer and faster payouts in the wake of emergencies.

One of the primary growth factors driving the humanitarian aid parametric insurance market is the increasing frequency and severity of natural disasters, such as hurricanes, floods, droughts, and wildfires. Traditional indemnity-based insurance models often face significant delays in claims processing and payouts, which can be detrimental in humanitarian crises where immediate funding is crucial. Parametric insurance, which relies on predefined triggers such as weather indices or seismic data, enables rapid disbursement of funds, ensuring that aid organizations can respond swiftly and effectively. This efficiency, combined with the growing impact of climate change, is pushing governments and NGOs to integrate parametric solutions into their disaster risk management strategies, further fueling market growth.

Another significant driver is the increasing emphasis on financial inclusion and resilience in vulnerable regions, particularly in low- and middle-income countries. Humanitarian aid parametric insurance products, such as crop and livestock insurance, are being leveraged to protect smallholder farmers and rural communities from climate-induced losses. By providing a safety net against income shocks, these products enhance food security and support sustainable livelihoods. International donors and multilateral organizations are also channeling funds into innovative insurance schemes, recognizing their role in building adaptive capacity and reducing the long-term costs of humanitarian interventions. This trend is expected to accelerate as more stakeholders prioritize proactive risk financing over reactive aid.

Technological advancements have also played a pivotal role in the expansion of the humanitarian aid parametric insurance market. The integration of satellite imagery, remote sensing, and advanced weather modeling has improved the accuracy and reliability of parametric triggers, reducing basis risk and increasing trust among end-users. Digital platforms have streamlined policy issuance, premium collection, and claims settlement, making insurance products more accessible to NGOs, government agencies, and affected communities. Furthermore, partnerships between insurance providers, technology firms, and humanitarian actors are fostering innovation in product design and distribution, enabling tailored solutions for diverse risk profiles and operational contexts.

From a regional perspective, the Asia Pacific region has emerged as a key market for humanitarian aid parametric insurance, driven by its high vulnerability to climate-related disasters and a large population exposed to food insecurity and health emergencies. North America and Europe are also witnessing increased adoption, particularly in the context of disaster relief and refugee assistance programs. Meanwhile, Latin America and the Middle East & Africa are experiencing steady growth, supported by international development initiatives and pilot projects aimed at strengthening disaster resilience. The regional outlook underscores the global relevance of parametric insurance as a critical tool for humanitarian risk management, with significant opportunities for market expansion across both developed and developing economies.

Product Type Analysis

The product type segment in the humanitarian aid parametric insurance market encompasses a diverse range of insurance solutions, including weather index insurance, catastrophe insurance, crop insurance, livestock insurance, and others. Weather index insurance has gained considerable traction due to its ability to provide rapid payouts based on measurable weather parameters such as rainfall, temperature, or wind speed. This product is especially valuable in regions prone to droughts or floods, w

Human Development Index (HDI) 2014

The International Federation of Red Cross and Red Crescent Societies (IFRC) is the world’s largest humanitarian network. Our secretariat supports local Red Cross and Red Crescent action in more than 192 countries, bringing together almost 15 million volunteers for the good of humanity.

We launch Emergency Appeals for big and complex disasters affecting lots of people who will need long-term support to recover. We also support Red Cross and Red Crescent Societies to respond to lots of small and medium-sized disasters worldwide—through our Disaster Response Emergency Fund (DREF) and in other ways.

There is also a global dataset.

National Risk Index Version: March 2023 (1.19.0)The National Risk Index Census Tracts feature layer contains Census tract-level data for the Risk Index, Expected Annual Loss, Social Vulnerability, and Community Resilience.The National Risk Index is a dataset and online tool that helps to illustrate the communities most at risk for 18 natural hazards across the United States and territories: Avalanche, Coastal Flooding, Cold Wave, Drought, Earthquake, Hail, Heat Wave, Hurricane, Ice Storm, Landslide, Lightning, Riverine Flooding, Strong Wind, Tornado, Tsunami, Volcanic Activity, Wildfire, and Winter Weather. The National Risk Index provides Risk Index values, scores and ratings based on data for Expected Annual Loss due to natural hazards, Social Vulnerability, and Community Resilience. Separate values, scores and ratings are also provided for Expected Annual Loss, Social Vulnerability, and Community Resilience. For the Risk Index and Expected Annual Loss, values, scores and ratings can be viewed as a composite score for all hazards or individually for each of the 18 hazard types.Sources for Expected Annual Loss data include: Alaska Department of Natural Resources, Arizona State University’s (ASU) Center for Emergency Management and Homeland Security (CEMHS), California Department of Conservation, California Office of Emergency Services California Geological Survey, Colorado Avalanche Information Center, CoreLogic’s Flood Services, Federal Emergency Management Agency (FEMA) National Flood Insurance Program, Humanitarian Data Exchange (HDX), Iowa State University's Iowa Environmental Mesonet, Multi-Resolution Land Characteristics (MLRC) Consortium, National Aeronautics and Space Administration’s (NASA) Cooperative Open Online Landslide Repository (COOLR), National Earthquake Hazards Reduction Program (NEHRP), National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (NCEI), National Oceanic and Atmospheric Administration's National Hurricane Center, National Oceanic and Atmospheric Administration's National Weather Service (NWS), National Oceanic and Atmospheric Administration's Office for Coastal Management, National Oceanic and Atmospheric Administration's National Geophysical Data Center, National Oceanic and Atmospheric Administration's Storm Prediction Center, Oregon Department of Geology and Mineral Industries, Pacific Islands Ocean Observing System, Puerto Rico Seismic Network, Smithsonian Institution's Global Volcanism Program, State of Hawaii’s Office of Planning’s Statewide GIS Program, U.S. Army Corps of Engineers’ Cold Regions Research and Engineering Laboratory (CRREL), U.S. Census Bureau, U.S. Department of Agriculture's (USDA) National Agricultural Statistics Service (NASS), U.S. Forest Service's Fire Modeling Institute's Missoula Fire Sciences Lab, U.S. Forest Service's National Avalanche Center (NAC), U.S. Geological Survey (USGS), U.S. Geological Survey's Landslide Hazards Program, United Nations Office for Disaster Risk Reduction (UNDRR), University of Alaska – Fairbanks' Alaska Earthquake Center, University of Nebraska-Lincoln's National Drought Mitigation Center (NDMC), University of Southern California's Tsunami Research Center, and Washington State Department of Natural Resources.Data for Social Vulnerability are provided by the Centers for Disease Control (CDC) Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index, and data for Community Resilience are provided by University of South Carolina's Hazards and Vulnerability Research Institute’s (HVRI) 2020 Baseline Resilience Indicators for Communities.The source of the boundaries for counties and Census tracts are based on the U.S. Census Bureau’s 2021 TIGER/Line shapefiles. Building value and population exposures for communities are based on FEMA’s Hazus 6.0. Agriculture values are based on the USDA 2017 Census of Agriculture.

The global Multidimensional Poverty Index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the acute deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. Critically, the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS), the Multi-Indicator Cluster Surveys (MICS) and in some cases, national surveys.

The subnational multidimensional poverty data from the data tables are published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. For the details of the global MPI methodology, please see the latest Methodological Notes found here.

Health Index

World Health Organization's Data for Sweden

Disease, Injury and Health System Indicators

By Humanitarian Data Exchange [source]

About this dataset

The World Health Organization's data portal offers a broad scope of health indicators on Mortality, Sustainable Development Goals, Millennium Development Goals (MDGs), Health Systems and beyond from more than 190 countries worldwide. This dataset contains essential information about Sweden that aims to help people comprehend the country’s current position in relation to other nations when it comes to public health and environment-related issues.

Explore various factors such as Disease Control, Demographics & Socioeconomics Statistics, Financial Protection, Mental Health and Substance Useand many more with this comprehensive collection of Swedish indicators covering both national and global data sources from WHO's portal! The data contains relevant information related to mortality rates; start and end year when the data was collected; display values for sex, region & age group codes as well as key URLs providing reference links to GHO indices or other external websites among others - everything that you need in order to evaluate the current state of health in Sweden. And not only can you access pre-aggregated datasets here on Kaggle but also have access sections of individual indicator metadata for further exploration using our distinct resource descriptions! With this interactive source at your fingertips make sure you seize this opportunity now!

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How to use the dataset

• Access the data – The WHO Data for Sweden can be accessed by clicking on the above link in this guide or through kaggle directly. To access the data once you have decided where to get it, download and save it to a suitable place on your device that is easy to recall later. These may include folders such as desktop, documents, or downloads folder so that you can access them easily when needed.

• Explore column names – Look at what are included in each of these columns in terms of information types like string, integer or float etc., details about indicators (like coded values for gender), object codes (like GHO code), URL’s related to countries and regions as applicable as per global places and standards used by organisations like WHO etc., feature types (like ‘display value’). Once you know what types of categories are available under each column name/ header, it would help understand which areas/ aspects one needs to further explore for insights through analysis using statistics tools such pandas & numpy etc; knowledge graphs & charts using libraries like matplotlib; keep track using a dashboard using Tableau online app & links etc.; fetch meaningful insights though machine learning libraries like Scikit-learn along with relevant algorithms depending upon need & loss function optimization techniques applied!

• Organize tabulation / visualisation format: Creating visuals such tables or charts helps one better understanding between various indexing columns (x-axis/ Y-axis) vs other columns mentioned in #2 above; further exploring categorisations of variables by agegroup(s); subcategorisation by sex , region wise vs national trends along with visibilities if any relationale validations among those metrics multiple category level combination generation comapre metric performance over time period frame set based upon start year , end year possible overlay competing validations across nations establish baseline metrics from already published / shared past reports if availble look at map view alternatives for visualisations based upon population patterns demography changes evaluated over years

• Utilize other sources : Online resources available which could be explored - datasets from working organisation linked with targets answered via google datastudio connected metric analysed versus potential forecast measures supported hyperlinks queries into specific KPIs gathered from forumlands managed indexed provided hints & clues attributed additional overlays hinted outcomes reflected intitiative related indices drawn creeed conclusions evidence deduction

Research Ideas

• Visualizing health trends in Sweden over time, by region, and by gender to identify areas of improvement or concentrated areas of vulnerability.
• Tracking poverty in Sweden and observing the correlation between poverty rate and the availability of public healthcare resources.
• Evaluating the impact of public health initiatives like immunization programs, substance abuse...