For administrative level 4 (Barangay) please contact the contributor (OCHA Philippines) via this page.
Vetting and live service provision by Information Technology Outreach Services (ITOS) with funding from USAID.
Philippines administrative levels:
(1) Region (Filipino: rehiyon)
(2) Provinces (Filipino: lalawigan, probinsiya) and independent cities (Filipino: lungsod, siyudad/ciudad, dakbayan, lakanbalen)
(3) Municipalities (Filipino: bayan, balen, bungto, banwa, ili) and component cities (Filipino: lungsod, siyudad/ciudad, dakbayan, dakbanwa, lakanbalen)
These shapefiles are suitable for database or ArcGIS joins to the sex and age disaggregated population statistics found on HDX here.
Sex and age disaggregated population data by various administrative levels (1 to 4) based on 2015 Census with Philippines Standard Geographic Code (PSGC).
These CSV population statistics files are suitable for database or ArcGIS joins to the shapefiles found on HDX here.
The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.
Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.
We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.
The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.
United States Data
Data on cumulative coronavirus cases and deaths can be found in two files for states and counties.
Each row of data reports cumulative counts based on our best reporting up to the moment we publish an update. We do our best to revise earlier entries in the data when we receive new information.
Both files contain FIPS codes, a standard geographic identifier, to make it easier for an analyst to combine this data with other data sets like a map file or population data.
State-level data can be found in the us-states.csv file.
County-level data can be found in the us-counties.csv file.
In some cases, the geographies where cases are reported do not map to standard county boundaries. See the list of geographic exceptions for more detail on these.
This dataset contains COVID-19 data for the United States of America made available by The New York Times on github at https://github.com/nytimes/covid-19-data
June 5, 2020
| Dataset date: Jun 3, 2020-Jun 4, 2020
This datasets show location of hand washing stations in Indonesia taken from https://handwashing-station.ushahidi.io/ website. All of the features provided in here use Bahasa Indonesia.
Features may be include in here such as:
Nama object (ID) / Object name (EN) = provide description about the object name
Alamat (ID) / Address (EN) = provide address where the hand-washing station located
Tipe tempat cuci tangan (ID) / Type of hand-washing station (EN) = provide the type of hand-washing station. There are two values provided here:
Sumber air dari tempat cuci tangan (ID) / water source (EN) = provide the water source of the hand-washing station. There are several values provided here:
manual/isi sendiri (ID) - manual (EN)
pipa paralon (ID) - pipe (EN)
truk air (ID) - water truck (EN)
lainnya (ID) - other (EN)
Tambahan sumber air (ID) / additional water source (EN) = provide additional information about water source if the user fill other in the previous questions.
Penyedia tempat cuci tangan (ID) / provider (EN) = provide information about who create the hand-washing station. There are several values provided here:
Inisiatif individu (ID) - individual (EN)
Komunitas sekitar (Desa, RT, RW) - local community (EN)
Pemerintah (ID) - Government (EN)
Perusahaan swasta (ID) - Private company (EN)
LSM (ID) - NGO (EN)
Status (ID/EN) = provide the status of hand-washing station. There are three values provided here:
Berfungsi (ID) - Function (EN)
Tidak berfungsi (ID) - Not function (EN)
Sementara tidak digunakan (ID) - Not being used at the moment (EN)
Informasi tambahan apabila tidak berfungsi (ID) / Additional information if not function (EN) = provide additional information if the user fill not function in the previous question
Komponen utama penyusun tempat cuci tangan (ID) / Main materials of hand-washing station (EN) = provide information about the materials of hand-washing station. There are several value provided here:
Plastik (ID) - Plastic (EN)
Keramik (ID) - Ceramic (EN)
Fiber (ID) - Fiber (EN)
Besi (ID) - Iron (EN)
Tanah liat (ID) - Clay (EN)
Kayu (ID) - Wood (EN)
Waktu pengisian tempat cuci tangan (ID) / Schedule for water filling (EN) = provide information about the when the water refil. There are several value provided here:
Setiap hari (ID) - Daily (EN)
Setiap minggu (ID) - Weekly (EN)
Setiap bulan (ID) - Monthly (EN)
Otomatis (ID) - Automatically (EN)
Lokasi (ID) / location (EN) = provide the location of the hand-washing station
Foto / Photo
June 5, 2020
| Dataset date: Jan 24, 2020-Jun 5, 2020
This dataset contains the number of confirmed cases, recoveries and deaths by country and subnational region due to the Coronavirus pandemic in Europe.
Since the outbreak of the COVID-19 crisis, the Joint Research Centre (JRC) has been supporting the European Commission in multidisciplinary areas to understand the COVID-19 emergency, anticipate its impacts, and support contingency planning.
This data provides an overview of the monitoring in the area of the 34 UCPM Participating States plus Switzerland related to sub-national data (admin level 1) on numbers of contagious and fatalities by COVID-19, collected directly from the National Authoritative sources (National monitoring websites, when available).
The sub-national granularity of the data allows to have a fit-for-purpose model to early capture the local spread and response to the COVID-19 outbreak.
The data is maintained on the JRC COVID-19 Github Repository
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.
Language data drawn from the 2011 government census. Includes the percentage of the population who speak each language as their primary or secondary language, as well as literacy rates for men and women age 5 and older. Available at the admin 0, 1, and 2 levels.
June 4, 2020
| Dataset date: Jan 1, 1960-Dec 31, 2018
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.
Reference historic FX rates quoted by the European Central Bank (ECB) converted to USD base currency.
There are two resources - one with USD as the quote currency (more standard x/USD) and another with USD as the base currency (USD/x).
Note that where the rate is 0 or NaN, it means that the currency existed in the past but no longer exists.
| Dataset date: Jan 1, 2019-Jan 1, 2020
Live list of active aid activities for Wallis and Futuna Islands shared via the International Aid Transparency Initiative (IATI). Includes both humanitarian and development activities. More information on each activity (including financial data) is available from http://www.d-portal.org
This dataset contains key figures (topline numbers) on the world's most pressing humanitarian crises. The data, curated by ReliefWeb's editorial team based on its relevance to the humanitarian community, is updated regularly.
The description of the files and columns can be found in the additional metadata spreadsheet file.
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:
This dataset is one of many OpenStreetMap exports on
See the Humanitarian OpenStreetMap Team website for more
June 4, 2020
| Dataset date: Mar 10, 2020-May 29, 2020
This data has been collected from various sources and is displayed in this online dashboard: http://arcg.is/uHyuO Mobile version: http://arcg.is/0q8Xfj
The data is divided in two datasets:
- COVID-19 restrictions by country: This dataset shows current travel restrictions. Information is collected from various sources: IATA, media, national sources, WFP internal or any other.
- COVID-19 airline restrictions information: This dataset shows restrictions taken by individual airlines or country. Information is collected again from various sources including WFP internal and public sources.
The data displayed is a collaborative effort and anybody with more accurate/updated information is highly encouraged to contact WFP GIS unit for Emergencies at the following email address: email@example.com
June 4, 2020
| Dataset date: Jan 1, 2012-Dec 31, 2018
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, World Health Statistics, Health financing, Public health and environment, Substance use and mental health, Tobacco, Injuries and violence, HIV/AIDS and other STIs, Nutrition, Urban health, Noncommunicable diseases, Noncommunicable diseases CCS, Negelected tropical diseases, Health Equity Monitor, Infrastructure, Essential health technologies, Medical equipment, Demographic and socioeconomic statistics, Neglected tropical diseases, International Health Regulations (2005) monitoring framework, 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 Coordination, AMR GLASS Surveillance, AMR GLASS Quality assurance, Noncommunicable diseases and mental health, Health workforce, Neglected Tropical Diseases, AMR GASP, ICD
For links to individual indicator metadata, see resource descriptions.
The current outbreak of COVID-19 has affected global mobility in the form of various travel disruptions, restrictions and blockages. To better understand how COVID-19 affects global mobility, the International Organization for Migration (IOM) has been working to map the impacts on human mobility, at Global, Regional and Country level.
Using direct input from IOM missions, this dashboard displays updated mobility restrictions at location level (airport, land border points, sea border points, internal transit points). For each point of entry, data is collected on: type of restriction, measured applied & timeframe, population category that might be affected from the measures.
The #COVID19 Government Measures Dataset puts together all the measures implemented by governments worldwide in response to the Coronavirus pandemic. Data collection includes secondary data review. The researched information available falls into five categories:
Public health measures
Social and economic measures
Each category is broken down into several types of measures.
ACAPS consulted government, media, United Nations, and other organisations sources.
For any comments, please contact us at firstname.lastname@example.org
Please note note that some measures together with non-compliance policies may not be recorded and the exact date of implementation may not be accurate in some cases, due to the different way of reporting of the primary data sources we used.
Understanding gender is essential to understanding the risk factors of poor health, early death and health inequities. The COVID-19 outbreak is no different. At this point in the pandemic, we are unable to provide a clear answer to the question of the extent to which sex and gender are influencing the health outcomes of people diagnosed with COVID-19. However, experience and evidence thus far tell us that both sex and gender are important drivers of risk and response to infection and disease.
In order to understand the role gender is playing in the COVID-19 outbreak, countries urgently need to begin both collecting and publicly reporting sex-disaggregated data. At a minimum, this should include the number of cases and deaths in men and women.
In collaboration with CNN, Global Health 50/50 began compiling publicly available sex-disaggregated data reported by national governments to date and is exploring how gender may be driving the higher proportion of reported deaths in men among confirmed cases so far.
For more, please visit: http://globalhealth5050.org/covid19
| Dataset date: Jan 9, 2005-May 17, 2020
This dataset contains excess mortality data for the period covering the 2020 Covid-19 pandemic.
The data contains the excess mortality data for all known jurisdictions which publish all-cause mortality data meeting the following criteria:
daily, weekly or monthly level of granularity
includes equivalent historical data for at least one full year before 2020, and preferably at least five years (2015-2019)
includes data up to at least April 1, 2020
Most countries publish mortality data with a longer periodicity (typically quarterly or even annually), a longer publication lag time, or both. This sort of data is not suitable for ongoing analysis during an epidemic and is therefore not included here.
"Excess mortality" refers to the difference between deaths from all causes during the pandemic and the historic seasonal average. For many of the jurisdictions shown here, this figure is higher than the official Covid-19 fatalities that are published by national governments each day. While not all of these deaths are necessarily attributable to the disease, it does leave a number of unexplained deaths that suggests that the official figures of deaths attributed may significant undercounts of the pandemic's impact.