'Our World in Data' is compiling COVID-19 testing data over time for many countries around the world. They are adding further data in the coming days as more details become available for other countries. In some cases figures refer to the number of tests, in other cases to the number of individuals who have been tested. Refer to documentation provided here.
This dataset contains agency- and publicly-reported data for events in which an aid worker was killed, injured, kidnapped, or arrested (KIKA). Categorized by country.
Please get in touch if you are interested in curated datasets: info@insecurityinsight.org
Contains data from the DHS data portal. There is also a dataset containing Philippines - National Demographic and Health Data on HDX.
The DHS Program Application Programming Interface (API) provides software developers access to aggregated indicator data from The Demographic and Health Surveys (DHS) Program. The API can be used to create various applications to help analyze, visualize, explore and disseminate data on population, health, HIV, and nutrition from more than 90 countries.
Contains data from the DHS data portal. There is also a dataset containing Philippines - Subnational Demographic and Health Data on HDX.
The DHS Program Application Programming Interface (API) provides software developers access to aggregated indicator data from The Demographic and Health Surveys (DHS) Program. The API can be used to create various applications to help analyze, visualize, explore and disseminate data on population, health, HIV, and nutrition from more than 90 countries.
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.
90+ Downloads
This dataset updates: Every week
This dataset is part of the data series [?]: IFRC - Appeals
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'.
Thinking Machines Data Science is releasing TM Open Buildings, a dataset of manually-drawn building outlines covering 12 Philippine cities with detailed annotations on building and roof attributes as seen over satellite imagery. We contribute the buildings in OpenStreetMap and also made available for download in Kaggle. This is made possible with the support from the Lacuna Fund.
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 GHCU-IMU at 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.
ADAM ID: eq_us7000lfjr Magnitude 6.2 earthquake at 18.032 depth occurred on Dec 02 2023 in 46km E of Hinatuan. It impacted 68921 people. The epicentre was at latitude 8.4118 longitude 126.7464.
ADAM ID: eq_us7000lfmi Magnitude 6.0 earthquake at 49.093 depth occurred on Dec 02 2023 in 56km E of Hinatuan. It impacted 6315 people. The epicentre was at latitude 8.3525 longitude 126.8427.
ADAM ID: eq_us7000kv90 Magnitude 6.3 earthquake at 41.281 depth occurred on Sep 12 2023 in 29km W of Calayan. It impacted 11275 people. The epicentre was at latitude 19.2687 longitude 121.2178.
Philippines administrative level 0-4 shapefiles
Vetting and live service provision by Information Technology Outreach Services (ITOS) with funding from USAID.
OCHA acknowledges PSA and the National Mapping and Resource Information Authority (NAMRIA) as the sources. LMB is the source of official administrative boundaries of the Philippines. In the absence of available official administrative boundary, the IMTWG have agreed to clean and use the PSA administrative boundaries which are used to facilitate data collection of surveys and censuses. The dataset can only be considered as indicative boundaries and not official.
Its updated to reflect the new areas within BARMM; It uses the new 10-digit pcode consistent with government PSGC as of 2023.
These shapefiles are suitable for database or ArcGIS joins to the [Philippines - 2020 Census Admin4 Population Statistics] (https://data.humdata.org/dataset/popn-single-year-age-sex-and-barangay-census-2020) and [ Philippines - Admin 2 Population projections using 2015 population] (https://data.humdata.org/dataset/cod-ps-phl).
Philippines population density for 400m H3 hexagons.
Built from Kontur Population: Global Population Density for 400m H3 Hexagons Vector H3 hexagons with population counts at 400m resolution.
Fixed up fusion of GHSL, Facebook, Microsoft Buildings, Copernicus Global Land Service Land Cover, Land Information New Zealand, and OpenStreetMap data.
The COVID-19 preventative health survey is designed to help policymakers and health researchers better monitor and understand people’s knowledge, attitudes and practices about COVID-19 to improve communications and their response to the pandemic.
The ICA is a process of consultations supported by mapped-out data that produces a strategic plan describing where different combinations of programme themes are appropriate to achieve goals of reducing food insecurity and climate related shock risk.
The ICA combines multi-year food security trends with natural shock risk data to highlight sub-national areas where different programme strategies make sense. Food security trend maps shows areas where safety nets can address regular food insecurity, and others where shocks make recovery more important. Climate-related natural shock risk maps show where DRR, preparedness and early warning efforts can complement food-security objectives. Atop this core foundation, mapped data on subjects including nutrition, gender, livelihoods and resilience can enrich theme-level strategic planning in which all pieces work together. The full group of ICA partners discuss these analytical results to arrive at strategic programmatic directions.
Total Population by Single-Year Age, Sex, Region, Province, City/Municipality and Barangay (admin 4) Census 2020.
It uses the new 10-digit pcode consistent with [Philippines - Subnational Administrative Boundaries] (https://data.humdata.org/dataset/cod-ab-phl).
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.
"Internally displaced persons - IDPs" refers to the number of people living in displacement as of the end of each year.
"Internal displacements (New Displacements)" 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.
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 (2023).