Afghanistan

Data Grid Completeness
90% 
18/20 Core Data 19 Datasets 10 Organisations Show legend
What is Data Grid Completeness?
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
  • No dataset found matching the criteria
Affected People
3 Datasets
50%  25%  25% 
Internally-Displaced Persons
International Organization for Migration (IOM)
Refugees & Persons of Concern
Returnees
Humanitarian Needs
Coordination & Context
5 Datasets
100% 
3w - Who is doing what where
Funding
OCHA Financial Tracking System (FTS)
Conflict Events
Armed Conflict Location & Event Data Project (ACLED)
Humanitarian Access
Food Security & Nutrition
3 Datasets
100% 
Food security
Integrated Food Security Phase Classification (IPC)
Acute Malnutrition
Food Prices
WFP - World Food Programme
Geography & Infrastructure
4 Datasets
100% 
Administrative Divisions
OCHA Field Information Services Section (FISS)
Populated Places
Humanitarian OpenStreetMap Team (HOT)
Roads
Humanitarian OpenStreetMap Team (HOT)
Airports
Humanitarian OpenStreetMap Team (HOT)
Health & Education
2 Datasets
100% 
Health Facilities
Education Facilities
OCHA Afghanistan
Population & Socio-economy
2 Datasets
100% 
Baseline Population
Poverty Rate
Oxford Poverty & Human Development Initiative
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  • 200+ Downloads
    Time Period of the Dataset [?]: January 01, 2019-December 31, 2019 ... More
    Modified [?]: 5 October 2020
    Dataset Added on HDX [?]: 5 October 2020
    This dataset updates: As needed
    This dataset is part of the data series [?]: OCHA Afghanistan - Natural Disaster Incidents
    1) Natural disaster events include avalanches, earthquakes, flooding, heavy rainfall & snowfall, and landslides & mudflows as recorded by OCHA field offices based on assessments in the field. 2) A natural disaster incident is defined as an event that has affected (i.e. impacted) people, who may or may not require humanitarian assistance. 3) The information includes assessment figures from OCHA, ANDMA, IOM, Red Crescent Societies, national NGOs, international NGOs, and ERM. 4) The number of affected people and houses damaged or destroyed are based on the reports received. These figures may change as updates are received.
  • 300+ Downloads
    Time Period of the Dataset [?]: January 01, 2000-December 31, 2020 ... More
    Modified [?]: 16 September 2020
    Dataset Added on HDX [?]: 20 July 2017
    This dataset updates: Every year
    This dataset is part of the data series [?]: World Pop - Population Counts
    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
  • 400+ Downloads
    Time Period of the Dataset [?]: August 31, 2020-August 31, 2020 ... More
    Modified [?]: 4 September 2020
    Dataset Added on HDX [?]: 26 May 2020
    This dataset updates: As needed
    The new and emerging access constraints that people are currently experiencing because of the COVID-19 outbreak.
  • 100+ Downloads
    Time Period of the Dataset [?]: August 31, 2020-August 31, 2020 ... More
    Modified [?]: 2 September 2020
    Dataset Added on HDX [?]: 22 July 2020
    This dataset updates: Every two weeks
    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
  • 100+ Downloads
    Time Period of the Dataset [?]: March 19, 2020-March 19, 2020 ... More
    Modified [?]: 29 July 2020
    Confirmed [?]: 12 October 2020
    Dataset Added on HDX [?]: 19 March 2020
    This dataset updates: Never
  • 433000+ Downloads
    Time Period of the Dataset [?]: January 22, 2020-March 09, 2023 ... More
    Modified [?]: 28 July 2020
    Dataset Added on HDX [?]: 7 February 2020
    This dataset updates: As needed
    JHU Has Stopped Collecting Data As Of 03/10/2023 After three years of around-the-clock tracking of COVID-19 data from around the world, Johns Hopkins has discontinued the Coronavirus Resource Center’s operations. The site’s two raw data repositories will remain accessible for information collected from 1/22/20 to 3/10/23 on cases, deaths, vaccines, testing and demographics. Novel Corona Virus (COVID-19) epidemiological data since 22 January 2020. The data is compiled by the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) from various sources including the World Health Organization (WHO), DXY.cn, BNO News, National Health Commission of the People’s Republic of China (NHC), China CDC (CCDC), Hong Kong Department of Health, Macau Government, Taiwan CDC, US CDC, Government of Canada, Australia Government Department of Health, European Centre for Disease Prevention and Control (ECDC), Ministry of Health Singapore (MOH), and others. JHU CCSE maintains the data on the 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository on Github. Fields available in the data include Province/State, Country/Region, Last Update, Confirmed, Suspected, Recovered, Deaths. On 23/03/2020, a new data structure was released. The current resources for the latest time series data are: time_series_covid19_confirmed_global.csv time_series_covid19_deaths_global.csv time_series_covid19_recovered_global.csv ---DEPRECATION WARNING--- The resources below ceased being updated on 22/03/2020 and were removed on 26/03/2020: time_series_19-covid-Confirmed.csv time_series_19-covid-Deaths.csv time_series_19-covid-Recovered.csv
  • 200+ Downloads
    Time Period of the Dataset [?]: January 01, 2000-December 31, 2020 ... More
    Modified [?]: 22 June 2020
    Dataset Added on HDX [?]: 23 November 2020
    This dataset updates: Every year
    This dataset is part of the data series [?]: WorldPop - Population Density
    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
  • 500+ Downloads
    Time Period of the Dataset [?]: January 01, 2020-December 31, 2020 ... More
    Modified [?]: 6 May 2020
    Confirmed [?]: 6 May 2020
    Dataset Added on HDX [?]: 6 May 2020
    This dataset updates: Every year
    This data contains the number of people in need, internally displaced persons (IDPs), returnees and refugees for 25 countries.
  • 300+ Downloads
    Time Period of the Dataset [?]: March 26, 2020-March 26, 2020 ... More
    Modified [?]: 6 May 2020
    Dataset Added on HDX [?]: 6 May 2020
    This dataset updates: As needed
    Data on unmitigated(no intervention) COVID-19 scenarios for OCHA HRP countries. Simulation done by Imperial College London.
  • 300+ Downloads
    Time Period of the Dataset [?]: May 05, 2020-May 05, 2020 ... More
    Modified [?]: 6 May 2020
    Dataset Added on HDX [?]: 6 May 2020
    This dataset updates: As needed
    This dataset contains simulation ­based estimates for COVID­-19 epidemic scenarios in OCHA HRP countries. Simulation is done by London School of Hygiene & Tropical Medicine(LSHTM).
  • 1300+ Downloads
    Time Period of the Dataset [?]: January 01, 2019-December 30, 2019 ... More
    Modified [?]: 3 March 2020
    Dataset Added on HDX [?]: 28 January 2019
    This dataset updates: Never
    This dataset is part of the data series [?]: OCHA Afghanistan - Conflict Induced Displacements 2016-2022
    Newly displaced population due to conflict between 01 January 2019 and 30 December 2019, compiled by OCHA sub offices based on inter-agency assessment results. This data is a snapshot as of 03 March 2020 and the numbers are expected to change as new assessment figures become available.
  • 1200+ Downloads
    Time Period of the Dataset [?]: October 01, 2019-December 31, 2019 ... More
    Modified [?]: 30 January 2020
    Dataset Added on HDX [?]: 30 January 2020
    This dataset updates: Every three months
    This dataset is part of the data series [?]: OCHA Afghanistan - Who does What Where
    The Who does What Where (3W) is a core humanitarian coordination dataset. It is critical to know where humanitarian organizations are working, what they are doing and their capability in order to identify gaps, avoid duplication of efforts, and plan for future humanitarian response (if needed). The data includes a list of humanitarian organizations by district and cluster, as well as a unique count of organizations. An interactive map of the 3W data can be accessed here.
  • 400+ Downloads
    Time Period of the Dataset [?]: January 01, 2018-December 31, 2018 ... More
    Modified [?]: 6 December 2019
    Dataset Added on HDX [?]: 18 March 2018
    This dataset updates: Every month
    This dataset is part of the data series [?]: OCHA Afghanistan - Natural Disaster Incidents
    1) Natural disaster events include avalanches, drought, earthquakes, flooding, heavy rainfall & snowfall, and landslides & mudflows as recorded by OCHA field offices based on assessments in the field. 2) A natural disaster incident is defined as an event that has affected (i.e. impacted) people, who may or may not require humanitarian assistance. 3) The information includes assessment figures from OCHA, ANDMA, IOM, Red Crescent Societies, national NGOs, international NGOs, and ERM. 4) The number of affected people and houses damaged or destroyed are based on the reports received. These figures may change as updates are received.
  • 10+ Downloads
    Time Period of the Dataset [?]: May 11, 2017-May 29, 2017 ... More
    Modified [?]: 5 December 2019
    Dataset Added on HDX [?]: 21 February 2021
    This dataset updates: Never
    Afghanistan hosts a protracted population of Pakistani refugees, who fled North Waziristan Agency in 2014 as a result of a joint military offensive by Pakistani government forces against non-state armed groups. As of May 2017, UNHCR has biometrically registered over 50,000 refugees in Khost province and 36,000 refugees in Paktika province, where access remains a challenge. Over 16,000 of these refugees receive shelter and essential services in the Gulan camp in Khost province, while most of the others live among the host population in various urban and rural locations. To better understand the needs of the refugees and the host communities, UNHCR and WFP agreed to conduct a joint assessment of Pakistani refugees in Khost and Paktika. The data collection commenced in May 2017 and covered 2,638 refugee households (2,198 in Khost and 440 in Paktika).
  • 30+ Downloads
    Time Period of the Dataset [?]: January 01, 2017-December 31, 2017 ... More
    Modified [?]: 5 December 2019
    Dataset Added on HDX [?]: 11 July 2021
    This dataset updates: Never
    This dataset is part of the data series [?]: UNHCR - Socioeconomic assessment of Refugees
    At the end of 2015, Herat Province was among the highest IDP hosting provinces in Afghanistan, accounting for approximately 10% of the country's IDP population. In order to obtain reliable information on the socio-economic conditions of IDPs and returnees in Herat Province, a comprehensive sample survey was carried out among 11,264 households in the 5 most populated IDP/returnee settlements (Shagofan, Jebraiel, Maslakh, Now Abad and Kahdistan) in 2017.
  • 90+ Downloads
    Time Period of the Dataset [?]: January 01, 2008-March 29, 2024 ... More
    Modified [?]: 4 December 2019
    Dataset Added on HDX [?]: 2 June 2014
    This dataset updates: Live
    This dataset is part of the data series [?]: Our Airports - Airports
    List of airports in Afghanistan, with latitude and longitude. Unverified community data from http://ourairports.com/countries/AF/
  • 300+ Downloads
    Time Period of the Dataset [?]: January 31, 2013-January 31, 2013 ... More
    Modified [?]: 10 November 2019
    Dataset Added on HDX [?]: 1 September 2015
    This dataset updates: Every year
    This spatial dataset of major rivers provides the delimitation of major water courses in Afghanistan. It comprises 24 water course features, which are attributed with a hydrological description and watercourse name (English). This dataset was last modified by the OCHA IMU in January 2013. The scales at which the original datasets were created are unknown, but it is likely that they were digitized at a scale of around 1:500,000. The purpose of this dataset is to show major rivers at a provincial scale or higher. The multiple sources of the data are as follows: South East Asia Rivers, USAID map of ongoing humanitarian assistance to Afghanistan; Foreign and Commonwealth Office Library map series 230 (2005); and an alternative country river line dataset (custodian unknown).
  • 200+ Downloads
    Time Period of the Dataset [?]: January 01, 2016-October 29, 2016 ... More
    Modified [?]: 10 November 2019
    Dataset Added on HDX [?]: 16 July 2017
    This dataset updates: Every year
    This dataset is part of the data series [?]: OCHA Afghanistan - Natural Disaster Incidents
    1) Natural disaster events include avalanches,earthquake, flooding, heavy rainfall & snowfall, and landslides & mudflows as recorded by OCHA field offices and IOM Afghanistan Humanitarian Assistance Database (HADB). 2) A natural disaster incident is defined as an event that has affected (i.e. impacted) people, who may or may not require humanitarian assistance. 3) HADB information is used as a main reference and supplemented by OCHA Field Office reports for those incidents where information is not available from the HADB. OCHA information includes assessment figures from OCHA, ANDMA, Red Crescent Societies, national NGOs, international NGOs, and ERM. 4) The number of affected people and houses damaged or destroyed are based on the reports received. These figures may change as updates are received.
  • 90+ Downloads
    Time Period of the Dataset [?]: March 18, 2018-May 03, 2018 ... More
    Modified [?]: 10 November 2019
    Dataset Added on HDX [?]: 24 June 2018
    This dataset updates: Every year
    The overall objective of the assessment was to inform humanitarian intervention planning through the identification of key protection-based needs and vulnerabilities of conflict-affected populations, specifically focusing on the impact conflict has on these needs and vulnerabilities.
  • 200+ Downloads
    Time Period of the Dataset [?]: January 01, 2017-March 31, 2017 ... More
    Modified [?]: 10 November 2019
    Confirmed [?]: 20 April 2021
    Dataset Added on HDX [?]: 12 April 2017
    This dataset updates: Never
    This dataset is part of the data series [?]: OCHA Afghanistan - Who does What Where
    The Who does What Where (3W) is a core humanitarian coordination dataset. It is critical to know where humanitarian organizations are working, what they are doing and their capability in order to identify gaps, avoid duplication of efforts, and plan for future humanitarian response (if needed). The data includes a list of humanitarian organizations by district and cluster, as well as a unique count of organizations. An interactive map of the 3W data can be accessed here.
  • 200+ Downloads
    Time Period of the Dataset [?]: October 01, 2016-December 31, 2016 ... More
    Modified [?]: 10 November 2019
    Dataset Added on HDX [?]: 23 January 2017
    This dataset updates: Every three months
    This dataset is part of the data series [?]: OCHA Afghanistan - Who does What Where
    The Who does What Where (3W) is a core humanitarian coordination dataset. It is critical to know where humanitarian organizations are working, what they are doing and their capability in order to identify gaps, avoid duplication of efforts, and plan for future humanitarian response (if needed). The data includes a list of humanitarian organizations by district and cluster, as well as a unique count of organizations. An interactive map of the 3W data can be accessed here.
  • 200+ Downloads
    Time Period of the Dataset [?]: April 01, 2016-June 30, 2016 ... More
    Modified [?]: 10 November 2019
    Confirmed [?]: 20 April 2021
    Dataset Added on HDX [?]: 10 July 2016
    This dataset updates: Never
    This dataset is part of the data series [?]: OCHA Afghanistan - Who does What Where
    The Who does What Where (3W) is a core humanitarian coordination dataset. It is critical to know where humanitarian organizations are working, what they are doing and their capability in order to identify gaps, avoid duplication of efforts, and plan for future humanitarian response (if needed). The data includes a list of humanitarian organizations by district and cluster, as well as a unique count of organizations. An interactive map of the 3W data can be accessed here.
  • 300+ Downloads
    Time Period of the Dataset [?]: July 01, 2016-September 30, 2016 ... More
    Modified [?]: 10 November 2019
    Dataset Added on HDX [?]: 4 October 2016
    This dataset updates: Every three months
    This dataset is part of the data series [?]: OCHA Afghanistan - Who does What Where
    The Who does What Where (3W) is a core humanitarian coordination dataset. It is critical to know where humanitarian organizations are working, what they are doing and their capability in order to identify gaps, avoid duplication of efforts, and plan for future humanitarian response (if needed). The data includes a list of humanitarian organizations by district and cluster, as well as a unique count of organizations. An interactive map of the 3W data can be accessed here.
  • 200+ Downloads
    Time Period of the Dataset [?]: July 01, 2015-September 30, 2015 ... More
    Modified [?]: 10 November 2019
    Dataset Added on HDX [?]: 6 September 2015
    This dataset updates: Every three months
    This dataset is part of the data series [?]: OCHA Afghanistan - Who does What Where
    The Who does What Where (3W) is a core humanitarian coordination dataset. It is critical to know where humanitarian organizations are working, what they are doing and their capability in order to identify gaps, avoid duplication of efforts, and plan for future humanitarian response (if needed). The data includes a list of humanitarian organizations by district and cluster, as well as a unique count of organizations. An interactive map of the 3W data can be accessed here.
  • 200+ Downloads
    Time Period of the Dataset [?]: April 01, 2017-June 30, 2017 ... More
    Modified [?]: 10 November 2019
    Confirmed [?]: 20 April 2021
    Dataset Added on HDX [?]: 23 April 2018
    This dataset updates: Never
    This dataset is part of the data series [?]: OCHA Afghanistan - Who does What Where
    The Who does What Where (3W) is a core humanitarian coordination dataset. It is critical to know where humanitarian organizations are working, what they are doing and their capability in order to identify gaps, avoid duplication of efforts, and plan for future humanitarian response (if needed). The data includes a list of humanitarian organizations by district and cluster, as well as a unique count of organizations. An interactive map of the 3W data can be accessed here.