FTS publishes data on humanitarian funding flows as reported by donors and recipient organizations. It presents all humanitarian funding to a country and funding that is specifically reported or that can be specifically mapped against funding requirements stated in humanitarian response plans. The data comes from OCHA's Financial Tracking Service, is encoded as utf-8 and the second row of the CSV contains HXL tags.

This dataset contains rainfall indicators computed from Climate Hazards Group InfraRed Precipitation satellite imagery with insitu Station data (CHIRPS) at level-2 administrative units. Included indicators are (for each dekad): 10 day rainfall [mm] (rfh) rainfall 1-month rolling aggregation [mm] (r1h) rainfall 3-month rolling aggregation [mm] (r3h) rainfall long term average [mm] (rfh_lta) rainfall 1-month rolling aggregation long term average [mm] (r1h_lta) rainfall 3-month rolling aggregation long term average [mm] (r3h_lta) rainfall anomaly [%] (rfq) rainfall 1-month anomaly [%] (r1q) rainfall 3-month anomaly [%] (r3q)

This dataset brings together data from a range of sources to provide a greater overall and comparative understanding of the current situation and context inside each district. The core indicators consist of key drivers (conflict, basic commodity prices, exclusion and marginalization, and disrupted access to life-saving services and income sources) and their major expected humanitarian impacts (food insecurity, cholera).

This bi-weekly dataset provides an overview of the latest areas of control in Yemen and includes a map, excel spreadsheet and GIS layer. All datasets are fully PCODED to Admin 2 district level, enabling interoperability with other datasets. Through understanding the latest areas of control, it is possible to enhance analysis in areas such as political-economy through adjusting tools to take into account the current geographic situation. This dataset forms part of the Yemen CrisisInsight product suite which includes the Yemen Core Dataset, Yemen: Crisis Impact Overview and Yemen Risk Overview.

This dataset contains Food Prices data for Yemen, sourced from the World Food Programme Price Database. The World Food Programme Price Database covers foods such as maize, rice, beans, fish, and sugar for 98 countries and some 3000 markets. It is updated weekly but contains to a large extent monthly data. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.

Contains data from World Health Organization's data portal covering the following categories: Mortality and global health estimates, Sustainable development goals, Millennium Development Goals (MDGs), Health systems, Malaria, Tuberculosis, Child health, Infectious diseases, Neglected Tropical Diseases, World Health Statistics, Health financing, Tobacco, Substance use and mental health, Injuries and violence, HIV/AIDS and other STIs, Public health and environment, Nutrition, Urban health, Child mortality, Noncommunicable diseases, Noncommunicable diseases CCS, Negelected tropical diseases, Infrastructure, Essential health technologies, Medical equipment, Demographic and socioeconomic statistics, Health inequality monitor, Health Equity Monitor, Child malnutrition, TOBACCO, Neglected tropical diseases, International Health Regulations (2005) monitoring framework, 0, Insecticide resistance, Oral health, Universal Health Coverage, Global Observatory for eHealth (GOe), RSUD: GOVERNANCE, POLICY AND FINANCING : PREVENTION, RSUD: GOVERNANCE, POLICY AND FINANCING: TREATMENT, RSUD: GOVERNANCE, POLICY AND FINANCING: FINANCING, RSUD: SERVICE ORGANIZATION AND DELIVERY: TREATMENT SECTORS AND PROVIDERS, RSUD: SERVICE ORGANIZATION AND DELIVERY: TREATMENT CAPACITY AND TREATMENT COVERAGE, RSUD: SERVICE ORGANIZATION AND DELIVERY: PHARMACOLOGICAL TREATMENT, RSUD: SERVICE ORGANIZATION AND DELIVERY: SCREENING AND BRIEF INTERVENTIONS, RSUD: SERVICE ORGANIZATION AND DELIVERY: PREVENTION PROGRAMS AND PROVIDERS, RSUD: SERVICE ORGANIZATION AND DELIVERY: SPECIAL PROGRAMMES AND SERVICES, RSUD: HUMAN RESOURCES, RSUD: INFORMATION SYSTEMS, RSUD: YOUTH, FINANCIAL PROTECTION, AMR GLASS, Noncommunicable diseases and mental health, Health workforce, AMR GASP, ICD, SEXUAL AND REPRODUCTIVE HEALTH, Immunization, NLIS, AMC GLASS For links to individual indicator metadata, see resource descriptions.

This dataset is UCDP's most disaggregated dataset, covering individual events of organized violence (phenomena of lethal violence occurring at a given time and place). These events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single, individual days. Sundberg, Ralph, and Erik Melander, 2013, “Introducing the UCDP Georeferenced Event Dataset”, Journal of Peace Research, vol.50, no.4, 523-532 Högbladh Stina, 2019, “UCDP GED Codebook version 19.1”, Department of Peace and Conflict Research, Uppsala University

The IPC Acute Food Insecurity (IPC AFI) classification provides strategically relevant information to decision makers that focuses on short-term objectives to prevent, mitigate or decrease severe food insecurity that threatens lives or livelihoods. This data has been produced by the National IPC Technical Working Groups for IPC population estimates since 2017. All national population figures are based on official country population estimates. IPC estimates are those published in country IPC reports.

This dataset contains COVID-19 vaccine dose availability forecasts as well as actual deliveries for countries with Humanitarian Response Plans. The data on vaccine availability forecasts was manually extracted from the COVAX Facility Interim Distribution Forecast as announced by COVAX on 3 February 2021. Figures for actual deliveries through channels other than COVAX are compiled by OCHA from press reports. The source(s) press releases, official announcements or articles for each such vaccine delivery are included in the dataset.

Contains data from the World Bank's data portal covering the following topics which also exist as individual datasets on HDX: Agriculture and Rural Development, Aid Effectiveness, Economy and Growth, Education, Energy and Mining, Environment, Financial Sector, Health, Infrastructure, Social Protection and Labor, Poverty, Private Sector, Public Sector, Science and Technology, Social Development, Urban Development, Gender, Climate Change, External Debt, Trade.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. Trade is a key means to fight poverty and achieve the Millennium Development Goals, specifically by improving developing country access to markets, and supporting a rules based, predictable trading system. In cooperation with other international development partners, the World Bank launched the Transparency in Trade Initiative to provide free and easy access to data on country-specific trade policies.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. Debt statistics provide a detailed picture of debt stocks and flows of developing countries. Data presented as part of the Quarterly External Debt Statistics takes a closer look at the external debt of high-income countries and emerging markets to enable a more complete understanding of global financial flows. The Quarterly Public Sector Debt database provides further data on public sector valuation methods, debt instruments, and clearly defined tiers of debt for central, state and local government, as well as extra-budgetary agencies and funds. Data are gathered from national statistical organizations and central banks as well as by various major multilateral institutions and World Bank staff.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. Climate change is expected to hit developing countries the hardest. Its effects—higher temperatures, changes in precipitation patterns, rising sea levels, and more frequent weather-related disasters—pose risks for agriculture, food, and water supplies. At stake are recent gains in the fight against poverty, hunger and disease, and the lives and livelihoods of billions of people in developing countries. Addressing climate change requires unprecedented global cooperation across borders. The World Bank Group is helping support developing countries and contributing to a global solution, while tailoring our approach to the differing needs of developing country partners. Data here cover climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use. Other indicators relevant to climate change are found under other data pages, particularly Environment, Agriculture & Rural Development, Energy & Mining, Health, Infrastructure, Poverty, and Urban Development.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. Gender equality is a core development objective in its own right. It is also smart development policy and sound business practice. It is integral to economic growth, business growth and good development outcomes. Gender equality can boost productivity, enhance prospects for the next generation, build resilience, and make institutions more representative and effective. In December 2015, the World Bank Group Board discussed our new Gender Equality Strategy 2016-2023, which aims to address persistent gaps and proposed a sharpened focus on more and better gender data. The Bank Group is continually scaling up commitments and expanding partnerships to fill significant gaps in gender data. The database hosts the latest sex-disaggregated data and gender statistics covering demography, education, health, access to economic opportunities, public life and decision-making, and agency.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. Cities can be tremendously efficient. It is easier to provide water and sanitation to people living closer together, while access to health, education, and other social and cultural services is also much more readily available. However, as cities grow, the cost of meeting basic needs increases, as does the strain on the environment and natural resources. Data on urbanization, traffic and congestion, and air pollution are from the United Nations Population Division, World Health Organization, International Road Federation, World Resources Institute, and other sources.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. Data here cover child labor, gender issues, refugees, and asylum seekers. Children in many countries work long hours, often combining studying with work for pay. The data on their paid work are from household surveys conducted by the International Labour Organization (ILO), the United Nations Children's Fund (UNICEF), the World Bank, and national statistical offices. Gender disparities are measured using a compilation of data on key topics such as education, health, labor force participation, and political participation. Data on refugees are from the United Nations High Commissioner for Refugees complemented by statistics on Palestinian refugees under the mandate of the United Nations Relief and Works Agency.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. Technological innovation, often fueled by governments, drives industrial growth and helps raise living standards. Data here aims to shed light on countries technology base: research and development, scientific and technical journal articles, high-technology exports, royalty and license fees, and patents and trademarks. Sources include the UNESCO Institute for Statistics, the U.S. National Science Board, the UN Statistics Division, the International Monetary Fund, and the World Intellectual Property Organization.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. Effective governments improve people's standard of living by ensuring access to essential services – health, education, water and sanitation, electricity, transport – and the opportunity to live and work in peace and security. Data here includes World Bank staff assessments of country performance in economic management, structural policies, policies for social inclusion and equity, and public sector management and institutions for the poorest countries. Also included are indicators on revenues and expenses from the International Monetary Fund's Government Finance Statistics, and on tax policies from various sources.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. Private markets drive economic growth, tapping initiative and investment to create productive jobs and raise incomes. Trade is also a driver of economic growth as it integrates developing countries into the world economy and generates benefits for their people. Data on the private sector and trade are from the World Bank Group's Private Participation in Infrastructure Project Database, Enterprise Surveys, and Doing Business Indicators, as well as from the International Monetary Fund's Balance of Payments database and International Financial Statistics, the UN Commission on Trade and Development, the World Trade Organization, and various other sources.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. For countries with an active poverty monitoring program, the World Bank—in collaboration with national institutions, other development agencies, and civil society—regularly conducts analytical work to assess the extent and causes of poverty and inequality, examine the impact of growth and public policy, and review household survey data and measurement methods. Data here includes poverty and inequality measures generated from analytical reports, from national poverty monitoring programs, and from the World Bank’s Development Research Group which has been producing internationally comparable and global poverty estimates and lines since 1990.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. The supply of labor available in an economy includes people who are employed, those who are unemployed but seeking work, and first-time job-seekers. Not everyone who works is included: unpaid workers, family workers, and students are often omitted, while some countries do not count members of the armed forces. Data on labor and employment are compiled by the International Labour Organization (ILO) from labor force surveys, censuses, establishment censuses and surveys, and administrative records such as employment exchange registers and unemployment insurance schemes.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. Infrastructure helps determine the success of manufacturing and agricultural activities. Investments in water, sanitation, energy, housing, and transport also improve lives and help reduce poverty. And new information and communication technologies promote growth, improve delivery of health and other services, expand the reach of education, and support social and cultural advances. Data here are compiled from such sources as the International Road Federation, Containerisation International, the International Civil Aviation Organization, the International Energy Association, and the International Telecommunications Union.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. Improving health is central to the Millennium Development Goals, and the public sector is the main provider of health care in developing countries. To reduce inequities, many countries have emphasized primary health care, including immunization, sanitation, access to safe drinking water, and safe motherhood initiatives. Data here cover health systems, disease prevention, reproductive health, nutrition, and population dynamics. Data are from the United Nations Population Division, World Health Organization, United Nations Children's Fund, the Joint United Nations Programme on HIV/AIDS, and various other sources.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. An economy's financial markets are critical to its overall development. Banking systems and stock markets enhance growth, the main factor in poverty reduction. Strong financial systems provide reliable and accessible information that lowers transaction costs, which in turn bolsters resource allocation and economic growth. Indicators here include the size and liquidity of stock markets; the accessibility, stability, and efficiency of financial systems; and international migration and workers\ remittances, which affect growth and social welfare in both sending and receiving countries.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. Natural and man-made environmental resources – fresh water, clean air, forests, grasslands, marine resources, and agro-ecosystems – provide sustenance and a foundation for social and economic development. The need to safeguard these resources crosses all borders. Today, the World Bank is one of the key promoters and financiers of environmental upgrading in the developing world. Data here cover forests, biodiversity, emissions, and pollution. Other indicators relevant to the environment are found under data pages for Agriculture & Rural Development, Energy & Mining, Infrastructure, and Urban Development.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. The world economy needs ever-increasing amounts of energy to sustain economic growth, raise living standards, and reduce poverty. But today's trends in energy use are not sustainable. As the world's population grows and economies become more industrialized, nonrenewable energy sources will become scarcer and more costly. Data here on energy production, use, dependency, and efficiency are compiled by the World Bank from the International Energy Agency and the Carbon Dioxide Information Analysis Center.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. Education is one of the most powerful instruments for reducing poverty and inequality and lays a foundation for sustained economic growth. The World Bank compiles data on education inputs, participation, efficiency, and outcomes. Data on education are compiled by the United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics from official responses to surveys and from reports provided by education authorities in each country.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. Economic growth is central to economic development. When national income grows, real people benefit. While there is no known formula for stimulating economic growth, data can help policy-makers better understand their countries' economic situations and guide any work toward improvement. Data here covers measures of economic growth, such as gross domestic product (GDP) and gross national income (GNI). It also includes indicators representing factors known to be relevant to economic growth, such as capital stock, employment, investment, savings, consumption, government spending, imports, and exports.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. Aid effectiveness is the impact that aid has in reducing poverty and inequality, increasing growth, building capacity, and accelerating achievement of the Millennium Development Goals set by the international community. Indicators here cover aid received as well as progress in reducing poverty and improving education, health, and other measures of human welfare.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX. For the 70 percent of the world's poor who live in rural areas, agriculture is the main source of income and employment. But depletion and degradation of land and water pose serious challenges to producing enough food and other agricultural products to sustain livelihoods here and meet the needs of urban populations. Data presented here include measures of agricultural inputs, outputs, and productivity compiled by the UN's Food and Agriculture Organization.

This dataset was compiled by the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) on behalf of the Humanitarian Country Team and partners. It provides the Humanitarian Country Team’s shared understanding of the crisis, including the most pressing humanitarian need and the estimated number of people who need assistance, and represents a consolidated evidence base and helps inform joint strategic response planning. Intersectoral People in Need 2022: For the 2022 Humanitarian Needs Overview, Yemen applied the enhanced HPC approach and the corresponding IASC Joint Inter-sector Analysis Framework (JIAF) global guidance. More details in the methodology of the HNO 2022 in the below link. https://reliefweb.int/sites/reliefweb.int/files/resources/Yemen_HNO_2022%20-%20Final%20Version%20%281%29.pdf Cluster People in Need 2022: The data file contains people in need for 2022 per cluster. The data approved for use by the Humanitarian Country Team and it’s based on the Humanitarian Needs Overview for 2022 in Yemen. The tables are suitable for database or GIS linkages to the Yemen – Administrative Boundaries. The full Humanitarian Needs Overview available in the below link: https://reliefweb.int/sites/reliefweb.int/files/resources/Yemen_HNO_2022%20-%20Final%20Version%20%281%29.pdf Yemen Population estimates for 2022: The data contains population estimates for 2022. The projections are based on 2004 Census data. The population figures are dis-aggregated by governorate and district levels, both containing p-codes. The data is further dis-aggregated by sex and age groups. The data approved for use by the Humanitarian Country Team and used in Humanitarian Needs Overview (HNO) for Yemen in 2022. These tables are suitable for database or GIS linkage to the Yemen - Administrative Boundaries boundaries. Intersectoral People in Need 2021: For the 2021 Humanitarian Needs Overview, Yemen applied the enhanced HPC approach and the corresponding IASC Joint Inter-sector Analysis Framework (JIAF) global guidance. More details in the methodology of the HNO 2021 in the below link. https://reliefweb.int/sites/reliefweb.int/files/resources/Yemen_HNO_2021_Final.pdf Cluster People in Need 2021: The data file contains people in need for 2021 per cluster. The data approved for use by the Humanitarian Country Team and it’s based on the Humanitarian Needs Overview for 2021 in Yemen. The tables are suitable for database or GIS linkages to the Yemen – Administrative Boundaries. The full Humanitarian Needs Overview available in the below link: https://reliefweb.int/sites/reliefweb.int/files/resources/Yemen_HNO_2021_Final.pdf Yemen Population estimates for 2021: The data contains population estimates for 2021. The projections are based on 2004 Census data. The population figures are dis-aggregated by governorate and district levels, both containing p-codes. The data is further dis-aggregated by sex and age groups. The data approved for use by the Humanitarian Country Team and used in Humanitarian Needs Overview (HNO) for Yemen in 2021. These tables are suitable for database or GIS linkage to the Yemen - Administrative Boundaries boundaries.

This dataset contains agency- and open source events in which aid access, education and healthcare services were impacted by explosive weapons. Categorized by country. Insecurity Insight collaborates with the International Network on Explosive Weapons (INEW) in producing research and analysis on the harm and use of explosive weapons for the Explosive Weapons Monitor by documenting their effects on health care, education, and aid access.

These document are compiled by the Information Management team in the Global Health Cluster Unit GHCU, and aims to compile the monitoring framework of the Humanitarian Response Plans in Activated Health Clusteres. The data comes from humanitarianaction For any ideas, updates, or corrections please contact Alberto Castillo Aroca castilloalb@who.int GHCU-IM Assessment and Analysis IMO or the GHC healthcluster@who.int. The data used as populations, names, and other designations are used only as a reference and do not imply any endorsement. The compilation is made by the Global Health Cluster IM team and it is expected to be updated. Not all the fields are available in the reviewed documents and it is expected to be complemented. Please see the version control table in the document

This document is compiled by the Information Management team in the Global Health Cluster Unit GHCU, and aims to gather the figures relevant to Humanitarian Health response at global levels. The information is collected from the last available data from publicly validated sources. See detailed info below. The data is mainly compiled from HRP and follows the structure of the Global Humanitarian Overview. For any ideas, updates, or corrections please contact Luis Hernando AGUILAR R aguilarl@who.int GHCU-IM team-lead or the GHC healthcluster@who.int. The data used as populations, names, and other designations are used only as a reference and do not imply any endorsement. The compilation is expected to be updated. Not all the fields are available in the reviewed documents, and it is expected to be complemented.

This table contains subnational multidimensional poverty data from the data tables published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. 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 severe 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. The global MPI methodology is detailed in Alkire, Kanagaratnam & Suppa (2022).

This dataset is a standardized and enhanced Common Operational Dataset. Administrative boundary datasets for levels 0, 1, 2 and 3 (international, governorate, district and sub-district) for Yemen approved for use by Humanitarian Country Team in October 2019. Vetting and live service provision by Information Technology Outreach Services (ITOS) with funding from USAID. Admin Level 1= Governorate = Mohafadha Admin Level 2 = District = Modeeriyyah Admin Level 3 = Sub-district = Ozlah P-codes are those used by Yemen Central Statistical Organization ( CSO ). "YE" is added as a prefix for the codes. Point layers are created by the calculating the centroids of the polygons.

View the column descriptions here: https://hqfao.maps.arcgis.com/sharing/rest/content/items/5d16cc7cc65d4245b58cb0cf07f1d129/data The Office of Emergency and Resilience (OER) of the Food and Agriculture Organization (FAO) is piloting a monitoring system to better understand the impacts of COVID-19 and other shocks on food supply, agricultural livelihoods and food security in a number of food crisis countries. This project is supported by the United States Agency for International Development (USAID). The monitoring system consists of primary data collected from households and key informants (including agricultural inputs vendors, food traders and agriculture extension officers) on a periodic basis (more or less every 3 months). Data are mainly collected through Computer-Assisted Telephone Interviews (CATI). In-person surveys are conducted where the circumstances allow for field access. During each round of the system, more than 40,000 interviews have been completed in more than 20 countries. In order to associate each round of data collection with the dates it was performed, refer to the calendar available here: https://covid-19-data-hqfao.hub.arcgis.com/pages/rounds_calendar Data are used to guide strategic decisions, to design programmes and to inform analytical processes such as the IPC. The present layer contains data aggregated on Admin1 level, from Afghanistan, Colombia, DRC, Liberia, Mali, Niger, Sierra Leone, Somalia, Yemen and Zimbabwe. Indicator: Percentage of households reporting drop in crop production

View the column descriptions here: https://hqfao.maps.arcgis.com/sharing/rest/content/items/d4969616de2348e388164b0a7909d352/data The Office of Emergency and Resilience (OER) of the Food and Agriculture Organization (FAO) is piloting a monitoring system to better understand the impacts of COVID-19 and other shocks on food supply, agricultural livelihoods and food security in a number of food crisis countries. This project is supported by the United States Agency for International Development (USAID). The monitoring system consists of primary data collected from households and key informants (including agricultural inputs vendors, food traders and agriculture extension officers) on a periodic basis (more or less every 3 months). Data are mainly collected through Computer-Assisted Telephone Interviews (CATI). In-person surveys are conducted where the circumstances allow for field access. During each round of the system, more than 40,000 interviews have been completed in more than 20 countries. In order to associate each round of data collection with the dates it was performed, refer to the calendar available here: https://covid-19-data-hqfao.hub.arcgis.com/pages/rounds_calendar Data are used to guide strategic decisions, to design programmes and to inform analytical processes such as the IPC. The present layer contains data aggregated on Admin1 level, from Afghanistan, Colombia, DRC, Liberia, Mali, Niger, Sierra Leone, Somalia, Yemen and Zimbabwe. Indicator: Percentage of households reporting income change

View the column descriptions here: https://hqfao.maps.arcgis.com/sharing/rest/content/items/db6576af73174f6db30b6d6b4702537d/data The Office of Emergency and Resilience (OER) of the Food and Agriculture Organization (FAO) is piloting a monitoring system to better understand the impacts of COVID-19 and other shocks on food supply, agricultural livelihoods and food security in a number of food crisis countries. This project is supported by the United States Agency for International Development (USAID). The monitoring system consists of primary data collected from households and key informants (including agricultural inputs vendors, food traders and agriculture extension officers) on a periodic basis (more or less every 3 months). Data are mainly collected through Computer-Assisted Telephone Interviews (CATI). In-person surveys are conducted where the circumstances allow for field access. During each round of the system, more than 40,000 interviews have been completed in more than 20 countries. In order to associate each round of data collection with the dates it was performed, refer to the calendar available here: https://covid-19-data-hqfao.hub.arcgis.com/pages/rounds_calendar Data are used to guide strategic decisions, to design programmes and to inform analytical processes such as the IPC. The present layer contains data aggregated on Admin1 level, from Afghanistan, Colombia, DRC, Liberia, Mali, Niger, Sierra Leone, Somalia, Yemen and Zimbabwe. Indicator: Percentage of households reporting herd size change

View the column descriptions here: https://hqfao.maps.arcgis.com/sharing/rest/content/items/9d44fdf00fde4213950e8da771163ea7/data The Office of Emergency and Resilience (OER) of the Food and Agriculture Organization (FAO) is piloting a monitoring system to better understand the impacts of COVID-19 and other shocks on food supply, agricultural livelihoods and food security in a number of food crisis countries. This project is supported by the United States Agency for International Development (USAID). The monitoring system consists of primary data collected from households and key informants (including agricultural inputs vendors, food traders and agriculture extension officers) on a periodic basis (more or less every 3 months). Data are mainly collected through Computer-Assisted Telephone Interviews (CATI). In-person surveys are conducted where the circumstances allow for field access. During each round of the system, more than 40,000 interviews have been completed in more than 20 countries. In order to associate each round of data collection with the dates it was performed, refer to the calendar available here: https://covid-19-data-hqfao.hub.arcgis.com/pages/rounds_calendar Data are used to guide strategic decisions, to design programmes and to inform analytical processes such as the IPC. The present layer contains data aggregated on Admin1 level, from Afghanistan, Colombia, DRC, Liberia, Mali, Niger, Sierra Leone, Somalia, Yemen and Zimbabwe. Indicator: Percentage of households reporting shocks directly or indirectly related to COVID-19

View the column descriptions here: https://hqfao.maps.arcgis.com/sharing/rest/content/items/f767b5c05c694ae4b891d87016c611b4/data The Office of Emergency and Resilience (OER) of the Food and Agriculture Organization (FAO) is piloting a monitoring system to better understand the impacts of COVID-19 and other shocks on food supply, agricultural livelihoods and food security in a number of food crisis countries. This project is supported by the United States Agency for International Development (USAID). The monitoring system consists of primary data collected from households and key informants (including agricultural inputs vendors, food traders and agriculture extension officers) on a periodic basis (more or less every 3 months). Data are mainly collected through Computer-Assisted Telephone Interviews (CATI). In-person surveys are conducted where the circumstances allow for field access. During each round of the system, more than 40,000 interviews have been completed in more than 20 countries. In order to associate each round of data collection with the dates it was performed, refer to the calendar available here: https://covid-19-data-hqfao.hub.arcgis.com/pages/rounds_calendar Data are used to guide strategic decisions, to design programmes and to inform analytical processes such as the IPC. The present layer contains data aggregated on Admin1 level, from Afghanistan, Colombia, DRC, Liberia, Mali, Niger, Sierra Leone, Somalia, Yemen and Zimbabwe. Indicator: Percentage of households reporting sales change

The IPC Acute Food Insecurity (IPC AFI) classification provides strategically relevant information to decision makers that focuses on short-term objectives to prevent, mitigate or decrease severe food insecurity that threatens lives or livelihoods. This data has been produced by the National IPC Technical Working Groups for IPC population estimates since 2017. All national population figures are based on official country population estimates. IPC estimates are those published in country IPC reports.

Data collated by UNHCR, containing information about forcibly displaced populations and stateless persons, spanning across more than 70 years of statistical activities. The data includes the countries / territories of asylum and origin. Specific resources are available for end-year population totals, demographics, asylum applications, decisions, and solutions availed by refugees and IDPs (resettlement, naturalisation or returns).

The UNHCR Energy Monitoring Framework takes a program-based approach to monitoring, with the aim of tracking both outputs and the impact of UNHCR dollars spent on programming (either via partners or through direct implementation). The process for developing the indicators began in 2015 with a review of existing tools and approaches and consultations with Government, Private Sector, field-based staff and NGO partners to devise a set of common, standardized measures rooted in global good practices. More info is available on the official website: https://eis.unhcr.org/

The geodatabase contains boundaries for the national and first- and second-order administrative divisions, aligned to the Large Scale International Boundaries dataset from the U.S. Department of State. The feature classes are suitable for linking to the attribute data provided. The tabular data contain total population for 2004 (census) and 2016 (population estimates), as well as five-year age group and sex, and information relating to IDPs and refugees, households, housing, income and poverty, information and communication technology, and health.

In partnership with Yale, Meta launched a climate change opinion survey that explores public climate change knowledge, attitudes, policy preferences, and behaviors. The 2022 survey includes respondents from nearly 200 countries and territories. We are sharing country level data from this survey, providing policymakers, research institutions, and nonprofits with an international view of public climate change opinion. For more information please see https://dataforgood.facebook.com/dfg/tools/climate-change-opinion-survey If you're interested in becoming a research partner and accessing record level data, please email dataforgood@fb.com.

The Food and Agriculture Organization of the United Nations (FAO) has developed a monitoring system in 25 food crisis countries to better understand the impacts of various shocks on agricultural livelihoods, food security and local value chains. The Monitoring System consists of primary data collected from households and key informants (including agricultural inputs vendors, food traders and agriculture extension officers) on a periodic basis (more or less every four months, depending on seasonality). Data are mainly collected through Computer-Assisted Telephone Interviews (CATI). In-person surveys are conducted where the circumstances allow for field access. As the system is developed, the information collected and analyzed is being used to guide strategic decisions, to design programmes and to inform analytical processes such as the Integrated Phase Classification (IPC) and the Humanitarian Needs Overview (HNO). At the core of the system is a standardized household questionnaire administered to around 100,000 households across the 25 countries. Standardization permits comparisons across time and space, considerably enhancing the utility of the data for decision makers. At minimum the household data are representative at Admin 1 level (e.g. province, or region) and in some cases at Admin 2 level (e.g. district). Core funding for this initiative comes from the United States Agency for International Development (USAID). The initiative also benefits from support from the European Union and FAO’s Special Fund for Emergency and Rehabilitation (SFERA). The contents of the work are the sole responsibility of FAO and do not necessarily reflect the views of USAID, the United States Government or the European Union. On the Hub Homepage ( https://data-in-emergencies.fao.org/ ) it is possible to create an account and explore/download aggregated data related to the following thematic areas: 1) Incomes and shocks 2) Crop Production 3) Livestock Production 4) Food Security 5) Needs Aggregated datasets are generated from household interviews performed after August 2021. At every new survey data release, after cleaning and validation phases, aggregated data is appended to the present datasets. In each aggregated field, the values indicate the frequencies of the different responses, expressed as a weighted percentage of the total sample.

Internally displaced persons are defined according to the 1998 Guiding Principles (http://www.internal-displacement.org/publications/1998/ocha-guiding-principles-on-internal-displacement) as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border. "People Displaced" refers to the number of people living in displacement as of the end of each year. "New Displacement" refers to the number of new cases or incidents of displacement recorded, rather than the number of people displaced. This is done because people may have been displaced more than once. Contains data from IDMC's Global Internal Displacement Database.

UNOSAT code: FL20210729YEM This map illustrates satellite-detected surface waters over Sirwah and Ma'rib City districts of Yemen as observed from a Sentinel-2 image acquired on 5 August 2021. From the previous preliminary flood assessment conducted on 2 August 2021, an additional 1.45 km2. Based on Worldpop population data and the detected surface waters, about 90 additional people may be potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR- UNOSAT.

UNOSAT code: FL20210729YEM This map illustrates the estimated total precipitation accumulated over Yemen. The total estimate was derived from the NASA Precipitation Processing System (PPS) dataset at a spatial resolution of approximately 10km and covers the period from 20th July to 1st August 2021. Western Governorates have been predominantly affected by precipitation, with some values greater than 600mm over the 13-day period. It is possible that precipitation levels may have been underestimated for local areas and are not a substitute for ground station measurements. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

UNOSAT code: FL20210729YEM This map illustrates satellite-detected surface waters over Sirwah, Bani Dabyan, and Al Jubah districts of Yemen as observed from a Sentinel-1 radar image acquired on 26 July 2021. Within the analyzed area of about 3,800 km2, a total of about 6.5 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 1,800 people may be potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR- UNOSAT.

DTM in Yemen includes the monitoring of key migrant and return locations on Yemen's northern border with Saudi Arabia and southern coastal border. DTM monitors the arrivals of migrants and Yemeni nationals in order to identify different patterns and types of migration. IOM estimates that 6,589 migrants entered Yemen in addition to 2,819 Yemeni returns from Saudia Arabia during November 2019 bringing the number of migrants who have arrived in Yemen in 2019 to ‭127,275‬‬ and the number of Yemeni returnees to 44,777

The aim of the Sub-Area Assessment is to track and monitor IDP and returnee populations in Yemen. Information is collected on population size, area of origin, current location, duration of displacement, shelter types, priority needs and movement trends. Among the main outputs of the Sub-Area Assessment, is a list of locations where IDPs and/or returnees are present that can be used to inform more detailed assessments at the location level, including the annual Multi-Cluster Location Assessment (MCLA). DTM field staff, along with KIs, use the Sub-Area Assessment tool to capture locations, which are matched to the identified locations in the OCHA Common Operational Dataset (P-Codes).

The UNHCR Energy Monitoring Framework takes a program-based approach to monitoring, with the aim of tracking both outputs and the impact of UNHCR dollars spent on programming (either via partners or through direct implementation). The process for developing the indicators began in 2015 with a review of existing tools and approaches and consultations with Government, Private Sector, field-based staff and NGO partners to devise a set of common, standardized measures rooted in global good practices. More info is available on the official website: https://eis.unhcr.org/

The Civilian Impact Monitoring Project (CIMP) is a monitoring mechanism for real-time collection, analysis and dissemination of open source data on the civilian impact from armed violence in Yemen, with the purpose of informing and complementing protection programming. CIMP is a service under the United Nations Protection Cluster for Yemen and, since going live on 01 August 2018, has reported in real-time on the impact of incidents of armed violence on civilians at the national level, divided into 5 hubs: Al-Hudaydah, Sa’ada, Sana’a, Aden and Ibb. The dataset shows the number of incidents to have impacted upon civilian houses, farms, vehicles, businesses and markets and the number of incidents to have impacted upon civilian infrastructure sites since December 2017.

The Civilian Impact Monitoring Project (CIMP) is a monitoring mechanism for real-time collection, analysis and dissemination of open source data on the civilian impact from armed violence in Yemen, with the purpose of informing and complementing protection programming. CIMP is a service under the United Nations Protection Cluster for Yemen and, since going live on 01 August 2018, has reported in real-time on the impact of incidents of armed violence on civilians at the national level, divided into 5 hubs: Al-Hudaydah, Sa’ada, Sana’a, Aden and Ibb. The dataset contains the total number of incidents of armed violence reported to have impacted upon civilians each month since December 2017, and the total number of civilian casualties reported each month and per hub.

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities. The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions. The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

Under the leadership of UNDP and DCO, an inter-agency task team developed the UN framework for the immediate socio-economic response to COVID-19 (adopted in April 2020) to govern its response over 12 to 18 months. To measure the UN’s support to the socio-economic response and recovery, UN entities developed a simple monitoring framework with 18 programmatic indicators (endorsed by the UNSDG in July 2020). Lead entities – based on their mandate and comparative advantage – were nominated to lead the development of methodological notes for each indicator and lead the collection of data at the country level. These lead entities reported through the Office of the Resident Coordinators the collective UN results on a quarterly basis through UN Info. All 2020 data was reported by March 2021. This is the UN development system’s first comprehensive attempt at measuring its collective programming contribution and results. These programmatic indicators enabled the UN system to monitor the progress and achievements of UNCT’s collective actions in socio-economic response. In support of the Secretary-General’s call for a "… single, consolidated dashboard to provide up-to-date visibility on [COVID-19] activities and progress across all pillars” all data was published in real time on the COVID-19 data portal, hosted by DCO. The data is disaggregated by geography (rural/urban), sex, age group and at-risk populations -- to measure system-wide results on the socio-economic response to the pandemic, in order to ensure UNDS accountability and transparency for results.

The UNHCR Energy Monitoring Framework takes a program-based approach to monitoring, with the aim of tracking both outputs and the impact of UNHCR dollars spent on programming (either via partners or through direct implementation). The process for developing the indicators began in 2015 with a review of existing tools and approaches and consultations with Government, Private Sector, field-based staff and NGO partners to devise a set of common, standardized measures rooted in global good practices. More info is available on the official website: https://eis.unhcr.org/

This data was developed as part of the Modelling Exposure Through Earth Observation Routines (METEOR) project and is a Level 1, or a global-quality exposure data set. Minimal country-specific data was collected. The data is intended for CAT modeling and loss estimation. Repurposing this data for any reason other than assessing risk is not recommended. The data presents the estimated number of buildings, building area, and rebuilding value at a 15-arcsecond grid resolution (approximately 500 meters at the equator). This data set is in point shapefile format where the points represent the centroids of the 15-arcsecond grid. The results were created through a process of spreading the number of buildings to the 15-arcsecond level by a statistical assessment of moderate resolution EO data, which is described in more detail in the dasymetric mapping lineage processing step. The estimated building count at any given area is a result of statistical processes and should not be mistaken as a building count. The structural classes of buildings used for risk assessment are estimated given the building wall, floor, and roof material classes surveyed through 2002 Population and Housing Census - Volume 1. Analytical report. Additionally, the data is provided in Open Exposure Data (OED) import format, as a pair of CSV files. One CSV file contains the location details, and the other is an "account" file that is filled with default information to satisfy OED format requirements. The OED input files are set to use "All perils" (i.e. "AA1"). All required OED account-related fields are populated with "1" by default (such as PortNumber, AccNumber, PolNumber). If you find this data useful please provide feedback via our questionnaire; it should take only a few minutes: https://forms.gle/DQjhE89CRegNKB3X8 Please see the METEOR project page for information about the METEOR Project: http://meteor-project.org/ Please see the METEOR map portal for interactive maps: https://maps.meteor-project.org/ For more information about the Open Exposure Data (OED) standard, please see https://github.com/OasisLMF/OpenDataStandards

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset. Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc-seconds (approximately 1km at the equator) -Unconstrained individual countries 2000-2020: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population count datasets by dividing the number of people in each pixel by the pixel surface area. These are produced using the unconstrained top-down modelling method. -Unconstrained individual countries 2000-2020 UN adjusted: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population UN adjusted count datasets by dividing the number of people in each pixel, adjusted to match the country total from the official United Nations population estimates (UN 2019), by the pixel surface area. These are produced using the unconstrained top-down modelling method. Data for earlier dates is available directly from WorldPop. WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00674

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset. Bespoke methods used to produce datasets for specific individual countries are available through the WorldPop Open Population Repository (WOPR) link below. These are 100m resolution gridded population estimates using customized methods ("bottom-up" and/or "top-down") developed for the latest data available from each country. They can also be visualised and explored through the woprVision App. The remaining datasets in the links below are produced using the "top-down" method, with either the unconstrained or constrained top-down disaggregation method used. Please make sure you read the Top-down estimation modelling overview page to decide on which datasets best meet your needs. Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 3 and 30 arc-seconds (approximately 100m and 1km at the equator, respectively): - Unconstrained individual countries 2000-2020 ( 1km resolution ): Consistent 1km resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020. - Unconstrained individual countries 2000-2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020. - Unconstrained individual countries 2000-2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019) -Unconstrained individual countries 2000-2020 UN adjusted ( 1km resolution ): Consistent 1km resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019). -Unconstrained global mosaics 2000-2020 ( 1km resolution ): Mosaiced 1km resolution versions of the "Unconstrained individual countries 2000-2020" datasets. -Constrained individual countries 2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using constrained top-down methods for all countries of the World for 2020. -Constrained individual countries 2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using constrained top-down methods for all countries of the World for 2020 and adjusted to match United Nations national population estimates (UN 2019). Older datasets produced for specific individual countries and continents, using a set of tailored geospatial inputs and differing "top-down" methods and time periods are still available for download here: Individual countries and Whole Continent. Data for earlier dates is available directly from WorldPop. WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

The World Food Programme (WFP) launched the mobile Vulnerability Analysis and Mapping (mVAM) project in 2013, beginning in DRC and Somalia. mVAM uses mobile technology to track food security trends in real-time, providing high-frequency data that supports humanitarian decision-making. Data collection methods are tailored to the needs of each country that mVAM operates in. This dataset contains data from the mVAM databank covering various indicators (one per resource).

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: amenity = 'ferry_terminal' OR building = 'ferry_terminal' OR port IS NOT NULL Features may have these attributes: amenity port name operator:type addr:full building source addr:city This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: amenity IN ('mobile_money_agent','bureau_de_change','bank','microfinance','atm','sacco','money_transfer','post_office') Features may have these attributes: amenity operator name network addr:full source addr:city This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: amenity IN ('kindergarten','school','college','university') OR building IN ('kindergarten','school','college','university') Features may have these attributes: amenity name operator:type addr:full building source addr:city capacity:persons This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: aeroway IS NOT NULL OR building = 'aerodrome' OR emergency:helipad IS NOT NULL OR emergency = 'landing_site' Features may have these attributes: emergency name operator:type emergency:helipad addr:full aeroway building source addr:city capacity:persons This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: healthcare IS NOT NULL OR amenity IN ('doctors','dentist','clinic','hospital','pharmacy') Features may have these attributes: amenity healthcare name operator:type addr:full building source healthcare:speciality addr:city capacity:persons This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: waterway IS NOT NULL OR water IS NOT NULL OR natural IN ('water','wetland','bay') Features may have these attributes: covered name width natural waterway layer water source tunnel depth blockage This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: amenity IS NOT NULL OR man_made IS NOT NULL OR shop IS NOT NULL OR tourism IS NOT NULL Features may have these attributes: amenity addr:street name beds man_made addr:full rooms addr:housenumber source addr:city shop opening_hours tourism This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: building IS NOT NULL Features may have these attributes: addr:street name addr:city building:materials office addr:full addr:housenumber building source building:levels This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.