Data Grid Completeness
16/27 Core Data 21 Datasets 13 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
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Affected People
1 Datasets
Internally-Displaced Persons
Refugees & Persons of Concern
Humanitarian Profile Locations
Humanitarian Needs
Casualties
Coordination & Context
4 Datasets
3w - Who is doing what where
International Aid Transparency Initiative
Affected Areas
Conflict Events
Humanitarian Access
Transportation Status
Damaged & Destroyed Buildings
Food Security & Nutrition
3 Datasets
Food security
Integrated Food Security Phase Classification (IPC)
Global Acute Malnutrition Rate
Severe Acute Malnutrition Rate
Food Prices
WFP - World Food Programme
Geography & Infrastructure
6 Datasets
Health & Education
4 Datasets
Health Facilities
Global Healthsites Mapping Project
Education Facilities
Affected Schools
Population & Socio-economy
3 Datasets
Baseline Population by Age & Sex
Poverty Rate
Oxford Poverty & Human Development Initiative
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  • 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 (% )
  • 500+ 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).
  • 10+ Downloads
    Updated 15 October 2015 | Dataset date: January 01, 2014-January 01, 2014
    This dataset updates: Never
    This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa. It was last updated on January 19, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: southernafrica201401_ML1 Most likely food security outcome for January-March 2014 southernafrica201401_ML2 Most likely food security outcome for April-June 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)
  • 30+ Downloads
    Updated 15 October 2015 | Dataset date: October 01, 2015-October 01, 2015
    This dataset updates: Never
    This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa. It was last updated on February 05, 2016. 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 October-December 2015 SA201304_ML2 Most likely food security outcome for January-March 2016 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: July 01, 2013-July 01, 2013
    This dataset updates: Never
    This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa. It was last updated on July 14, 2013. 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 2013 southernafrica201307_ML2 Most likely food security outcome for October-December 2013 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)
  • 20+ Downloads
    Updated 15 October 2015 | Dataset date: June 01, 2013-June 01, 2013
    This dataset updates: Never
    This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa. It was last updated on June 14, 2013. 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 2013 southernafrica201304_ML2 Most likely food security outcome for July-September 2013 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: January 01, 2013-January 01, 2013
    This dataset updates: Never
    This Archive contains shapefiles for FEWS NET Food Security Outlook for Southern Africa. It was last updated on January 14, 2013. 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 2013 southernafrica201304_ML2 Most likely food security outcome for April-June 2013 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)