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  • 10+ Downloads
    Updated 27 July 2021 | Dataset date: June 20, 2020-May 28, 2022
    This dataset updates: Never
    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
  • 10+ Downloads
    Updated 27 July 2021 | Dataset date: June 20, 2020-May 28, 2022
    This dataset updates: Never
    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
  • 10+ Downloads
    Updated 27 July 2021 | Dataset date: June 20, 2020-May 28, 2022
    This dataset updates: Never
    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-May 28, 2022
    This dataset updates: Never
    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
  • 10+ Downloads
    Updated 27 July 2021 | Dataset date: June 20, 2020-May 28, 2022
    This dataset updates: Never
    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
  • 10+ Downloads
    Updated 4 May 2021 | Dataset date: February 11, 2014-February 11, 2014
    This dataset updates: Never
    This is "Flood vectors - TerraSAR-X (11 February 2014)" of the Flood analysis for Zimbabwe which began on 07 February 2014. It includes 3,064 satellite detected water bodies with a spatial extent of 183.82 square kilometers derived from the TerraSAR-X ima...
  • 30+ Downloads
    Updated 4 May 2021 | Dataset date: February 18, 2014-February 18, 2014
    This dataset updates: Never
    This is "Flood vectors - TerraSAR-X (18 February 2014)" of the Flood analysis for Zimbabwe which began on 07 February 2014. It includes 860 satellite detected water bodies with a spatial extent of 23.49 square kilometers derived from the TerraSAR-X image ...
  • 200+ Downloads
    Updated 10 November 2019 | Dataset date: January 01, 2006-December 31, 2015
    This dataset updates: Never
    Gender parity index in secondary - Indice de parité de genre au secondaire
  • 300+ Downloads
    Updated 10 November 2019 | Dataset date: December 31, 2015-December 31, 2015
    This dataset updates: Never
    EDUCATION Adult Illiteracy Rate ( % ) - Taux d'analphabetisme des adultes (% )
  • 600+ Downloads
    Updated 10 November 2019 | Dataset date: January 01, 2006-December 31, 2015
    This dataset updates: Never
    Total migrants in Africa.
  • 500+ Downloads
    Updated 17 January 2017 | Dataset date: August 06, 2015-August 06, 2015
    This dataset updates: Never
    African Development Bank, Bank Operations, 1967 January - 2012 December
  • 400+ Downloads
    Updated 24 November 2016 | Dataset date: December 08, 2011-December 08, 2011
    This dataset updates: Never
    Leadership, innovation and targeted investments in a number of social sectors have led to transformative interventions and in many cases revolutionized people’s lives, says an annual report produced jointly by the Economic Commission for Africa (ECA), the African Union (AU), the African Development Bank (AfDB) and the United Nations Development Programme (UNDP), called “Assessing Progress in Africa Toward the Millennium Development Goals”.
  • 400+ Downloads
    Updated 24 November 2016 | Dataset date: October 12, 2015-October 12, 2015
    This dataset updates: Never
    AFDB Market Trends, January 2011 - July 2015
  • 400+ Downloads
    Updated 24 November 2016 | Dataset date: August 22, 2013-August 22, 2013
    This dataset updates: Never
    Economic Community of Central African States Statistics, 2013
  • 300+ Downloads
    Updated 24 November 2016 | Dataset date: April 22, 2014-April 22, 2014
    This dataset updates: Never
    AfDB Country Policy and Institutional Assessment, 2013
  • 200+ Downloads
    Updated 24 November 2016 | Dataset date: December 08, 2011-December 08, 2011
    This dataset updates: Never
    African Development Bank, Food Security, January 1960 - December 2011
  • 200+ Downloads
    Updated 24 November 2016 | Dataset date: December 08, 2011-December 08, 2011
    This dataset updates: Never
    African Development Bank, Food Security, Prices, Monthly, January 1980 - December 2011
  • 500+ Downloads
    Updated 24 November 2016 | Dataset date: July 16, 2013-July 16, 2013
    This dataset updates: Never
    African Port Statistics, 2005-2009
  • 100+ Downloads
    Updated 29 January 2016 | Dataset date: December 31, 2015-December 31, 2015
    This dataset updates: Never
    The GAR15 global exposure database is based on a top-down approach where statistical information including socio-economic, building type, and capital stock at a national level are transposed onto the grids of 5x5 or 1x1 using geographic distribution of population data and gross domestic product (GDP) as proxies.
  • 20+ Downloads
    Updated 15 October 2015 | Dataset date: July 01, 2015-July 01, 2015
    This dataset updates: Never
    This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa. It was last updated on August 19, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: SA201304_ML1 Most likely food security outcome for July-September 2015 SA201304_ML2 Most likely food security outcome for October-December 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: 66 = water 88 = parks, forests, reserves 99 = missing data (usually urban centers)
  • 10+ Downloads
    Updated 15 October 2015 | Dataset date: June 01, 2015-June 01, 2015
    This dataset updates: Never
    This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa. It was last updated on June 01, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: southernafrica201304_ML1 Most likely food security outcome for April-June 2015 southernafrica201304_ML2 Most likely food security outcome for July-September 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: 66 = water 88 = parks, forests, reserves 99 = missing data (usually urban centers)
  • 10+ Downloads
    Updated 15 October 2015 | Dataset date: February 10, 2015-February 10, 2015
    This dataset updates: Never
    This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa. It was last updated on February 10, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: southernafrica201304_ML1 Most likely food security outcome for January-March 2015 southernafrica201304_ML2 Most likely food security outcome for April-June 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: 66 = water 88 = parks, forests, reserves 99 = missing data (usually urban centers).
  • Updated 15 October 2015 | Dataset date: October 01, 2014-October 01, 2014
    This dataset updates: Never
    This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa. It was last updated on November 13, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: southernafrica201304_ML1 Most likely food security outcome for October-December 2014 southernafrica201304_ML2 Most likely food security outcome for January-March 2015 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: 66 = water 88 = parks, forests, reserves 99 = missing data (usually urban centers).
  • 10+ Downloads
    Updated 15 October 2015 | Dataset date: July 01, 2014-July 01, 2014
    This dataset updates: Never
    This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa. It was last updated on September 26, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: southernafrica201307_ML1 Most likely food security outcome for July-September 2014 southernafrica201407_ML2 Most likely food security outcome for October-December 2014 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: 66 = water 88 = parks, forests, reserves 99 = missing data (usually urban centers).
  • 10+ Downloads
    Updated 15 October 2015 | Dataset date: April 01, 2014-April 01, 2014
    This dataset updates: Never
    This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa. It was last updated on July 17, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: southernafrica201304_ML1 Most likely food security outcome for April-June 2014 southernafrica201304_ML2 Most likely food security outcome for July-September 2014 Within the shapefiles, the food security outlook is contained in a field named as ML1 or ML2 according to the outlook period. The code itself is the IPC phase. Two additional codes are used: 66 = water 88 = parks, forests, reserves 99 = missing data (usually urban centers).