Data Grid Completeness
7/27 Core Data 16 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.
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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
Returnees
Humanitarian Profile Locations
Humanitarian Needs
Casualties
Armed Conflict Location & Event Data Project (ACLED)
Coordination & Context
4 Datasets
3w - Who is doing what where
International Aid Transparency Initiative
Affected Areas
Conflict Events
Armed Conflict Location & Event Data Project (ACLED)
Humanitarian Access
Transportation Status
Damaged & Destroyed Buildings
Food Security & Nutrition
2 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
5 Datasets
Health & Education
3 Datasets
Health Facilities
Global Healthsites Mapping Project
Education Facilities
Affected Schools
Population & Socio-economy
2 Datasets
Baseline Population
Baseline Population by Age & Sex
Poverty Rate
Oxford Poverty & Human Development Initiative
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  • 30+ Downloads
    Updated August 14, 2015 | 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 ...
  • 20+ Downloads
    Updated August 10, 2015 | Dataset date: February 19, 2014-February 19, 2014
    This dataset updates: Never
    This map illustrates satellite-detected water bodies at the Tokwe Mukorsi Dam, Masvingo Province, Zimbabwe, as imaged by TerraSAR-X on 18 February 2014. The flooded area above the dam has decreased slightly since the previous analysis using an image from 11 February 2014 and currently encompasses about 2,278 ha. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks because of the special characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.
  • 10+ Downloads
    Updated August 10, 2015 | Dataset date: February 13, 2014-February 13, 2014
    This dataset updates: Never
    This map illustrates water bodies at the Tokwe Mukorsi Dam, Masvingo Province, Zimbabwe, as detected by TerraSAR-X on 11 February 2014. The flooded area above the dam has greatly increased due to recent heavy rains and currently encompasses about 2,300 ha. Using a WorldView-1 image acquired on 2 January 2012, UNOSAT located a total of 751 structures in 143 homestead locations that would be submerged by the current flood water extent. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks because of the special characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.
  • 10+ Downloads
    Updated August 10, 2015 | Dataset date: February 11, 2014-February 11, 2014
    This dataset updates: Never
    This map illustrates satellite-detected water bodies at the Tokwe Mukorsi Dam, Masvingo Province, Zimbabwe, as detected by TerraSAR-X on 11 February 2014. The flooded area above the dam has greatly increased due to recent heavy rains and currently encompasses about 2,300 ha. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks because of the special characteristics of the satellite data used. This analysis has not yet been validated in the field. Please send ground feedback to UNITAR /UNOSAT.