Updated
4 August 2021
| Dataset date: January 01, 1970-December 31, 2019
Education indicators for Liberia.
Contains data from the UNESCO Institute for Statistics bulk data service covering the following categories: National Monitoring (made 2021 March), SDG 4 Global and Thematic (made 2021 March), Demographic and Socio-economic (made 2021 March)
Updated
27 July 2021
| Dataset date: June 20, 2020-November 28, 2021
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
Updated
27 July 2021
| Dataset date: June 20, 2020-November 28, 2021
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
Updated
27 July 2021
| Dataset date: June 20, 2020-November 28, 2021
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
Updated
27 July 2021
| Dataset date: June 20, 2020-November 28, 2021
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
Updated
27 July 2021
| Dataset date: June 20, 2020-November 28, 2021
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
Updated
23 June 2021
| Dataset date: June 20, 2020-November 28, 2021
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 needs of assistance
Updated
30 May 2021
| Dataset date: January 01, 2008-December 31, 2020
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.
Updated
18 May 2021
| Dataset date: January 01, 2014-November 28, 2021
The Cadre Harmonisé (CH) and Integrated Food Security Phase Classification (IPC) are analytical frameworks which synthesize indicators of food and nutrition security outcomes and the inference of contributing factors into scales and figures representing the nature and severity of crisis and implications for strategic response in food security and nutrition.
(Refer to the documents linked as showcases for more information).
Updated
4 May 2021
| Dataset date: January 01, 1990-December 31, 2030
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'.
Updated
15 April 2021
| Dataset date: March 01, 2020-December 31, 2020
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.
Updated
2 April 2021
| Dataset date: May 05, 2020-May 05, 2020
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
Updated
4 March 2021
| Dataset date: November 20, 2020-November 20, 2020
Data on access constraints, aid workers security, % of affected CERF and CBPF projects combined with the status of Polio vaccination in the HRP countries.
Updated
11 February 2021
| Dataset date: December 27, 2017-November 28, 2021
Liberia administrative level 0 (country), 1 (country), and 2 (district) boundaries
Vetting and live service provision by Information Technology Outreach Services (ITOS) with funding from USAID.
These boundaries are suitable for database or GIS linkage to the Liberia - Subnational Population Statistics tables.
Updated
20 January 2021
| Dataset date: May 17, 2020-November 28, 2021
Figures about the evolution of Covid19 in African countries, new infected, recovered and deceased per day and cumulative cases of infected, recovered and deceased.
Updated
24 November 2020
| Dataset date: January 01, 2000-December 31, 2020
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
Updated
24 November 2020
| Dataset date: January 01, 2000-December 31, 2020
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
Updated
10 September 2020
| Dataset date: July 08, 2020-July 10, 2021
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 2020 methodology is detailed in Alkire, Kanagaratnam & Suppa (2020).
Updated
9 September 2020
| Dataset date: January 01, 2018-December 31, 2018
This dataset is compiled from two categories of sources: (a) verified security events submitted to Insecurity Insight by 30 Aid in Danger partner agencies; and (b) publicly reported events identified by Insecurity Insight and published in the Aid in Danger Monthly News Brief. Events are categorised by date, country, type of organisation affected and event category, based on standard definitions.
