Data Grid Completeness defines
a set of core data that are essential for preparedness and emergency response.
For select countries, the HDX Team and trusted partners evaluate datasets available on HDX and add those meeting the definition of a core data category to the Data Grid Completeness board above. Please help us improve this feature by sending your feedback to
hdx@un.org.
Legend:
Presence, freshness, and quality of dataset
Dataset fully matches criteria and is up-to-date
Dataset partially matches criteria and/or is not up-to-date
Daily Covid-19 cases in african countries : daily infections, recoveries and deaths and cumulative cases of infections, recoveries and deaths since the beginning of the pandemic.
This no longer updated dataset contains Global Food Prices data from the World Food Programme covering foods such as maize, rice, beans, fish, and sugar for 76 countries and some 1,500 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.
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 Livelihoods 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. Consultations were held with governments, the private sector, field-based staff and civil society partners to devise a set of common, standardized measures rooted in global good practices.
Since 2017, a data collection (survey) has been rolled out globally, and the participating operations conducted a household surveys to a sample of beneficiaries of each livelihoods project implemented by UNHCR and its partner. The dataset consists of baseline and endline data from the same sample beneficiaries, in order to compare before and after the project implementation and thus to measure the impact.
More info is available on the official website: https://lis.unhcr.org
The UNHCR Livelihoods 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. Consultations were held with governments, the private sector, field-based staff and civil society partners to devise a set of common, standardized measures rooted in global good practices.
Since 2017, a data collection (survey) has been rolled out globally, and the participating operations conducted a household surveys to a sample of beneficiaries of each livelihoods project implemented by UNHCR and its partner. The dataset consists of baseline and endline data from the same sample beneficiaries, in order to compare before and after the project implementation and thus to measure the impact.
More info is available on the official website: https://lis.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
The COVID-19 pandemic has brought into stark focus the need for data and the value of models to inform response strategies. Since March, the Centre has been working with the Johns Hopkins University Applied Physics Laboratory (APL) to develop a COVID-19 model adapted for use in humanitarian contexts.
Access the - code repository , including all the source code scripts necessary to run the model.
View the - technical documentation and - FAQs explaining how to configure and run the source code in the repository.
Download the - methodology paper providing details on model assumptions and the main equations.
Access - [biweekly reports] (https://drive.google.com/drive/u/1/folders/16FR8owccpfIm-tspdAa4YTEwPoZKHtvI) for six countries.
Download the - OCHA-Bucky model card created according to the Centre’s Peer Review Framework.
The result is a model, named OCHA-Bucky, that forecasts the number of cases, hospitalizations, and deaths over two or four weeks, at the subnational and national levels.
Data on access constraints, aid workers security, % of affected CERF and CBPF projects combined with the status of Polio vaccination in the HRP countries.
The COVID-19 pandemic has brought into stark focus the need for data and the value of models to inform response strategies. Since March, the Centre has been working with the Johns Hopkins University Applied Physics Laboratory (APL) to develop a COVID-19 model adapted for use in humanitarian contexts.
Access the - code repository , including all the source code scripts necessary to run the model.
View the - technical documentation and - FAQs explaining how to configure and run the source code in the repository.
Download the - methodology paper providing details on model assumptions and the main equations.
Access - [biweekly reports] (https://drive.google.com/drive/u/1/folders/16FR8owccpfIm-tspdAa4YTEwPoZKHtvI) for six countries.
Download the - OCHA-Bucky model card created according to the Centre’s Peer Review Framework.
The result is a model, named OCHA-Bucky, that forecasts the number of cases, hospitalizations, and deaths over two or four weeks, at the subnational and national levels.
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
This dataset contains scores for humanitarian access constraints into country, constraints within country, impacts the constraints have led to as well as the mitigation strategies in place to limit the impact.
The scores have the following interpretations:
0 = NA,
1 = No or open,
2 = partially open/closed,
3 = Yes or closed
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