Key Figures
Data Completeness
4/26 Core Data 36 Datasets 13 Organisations Show legend
What is Data Completeness?
Data 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 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
4 Datasets
Coordination & Context
7 Datasets
Geography & Infrastructure
5 Datasets
Health & Education
2 Datasets
Population & Socio-economic Indicators
5 Datasets
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  • 900+ Downloads
    Updated November 24, 2015 | Dataset date: May 14, 2015
    This dataset updates: Never
    The Coping Strategy Index dataset measures the severity and frequency of the strategies that households use to cope with acute food insecurity. The strategies vary from borrowing food or money from neighbors to selling household assets. This data is available for 31 countries at a sub-national level.
  • 1100+ Downloads
    Updated November 24, 2015 | Dataset date: May 13, 2015
    This dataset updates: Every month
    The Food Consumption Score (FCS) dataset is based on the FCS indicator, which assigns a food security score based on food consumption and diets. This data is available sub-nationally for 38 countries, such as Nepal and Sierra Leone.
  • Updated October 15, 2015 | Dataset date: Jul 1, 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)
  • Updated October 15, 2015 | Dataset date: Jun 1, 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)
  • Updated October 15, 2015 | Dataset date: Feb 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 October 15, 2015 | Dataset date: Oct 1, 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 October 15, 2015 | Dataset date: Jul 1, 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).
  • Updated October 15, 2015 | Dataset date: Apr 1, 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 October 15, 2015 | Dataset date: Jan 1, 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 October 15, 2015 | Dataset date: Oct 1, 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 October 15, 2015 | Dataset date: Jul 1, 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)
  • 10+ Downloads
    Updated October 15, 2015 | Dataset date: Jun 1, 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)
  • Updated October 15, 2015 | Dataset date: Jan 1, 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)
  • 20+ Downloads
    Updated August 10, 2015 | Dataset date: Feb 16, 2015
    This dataset updates: Never
    This map illustrates satellite-detected flood waters in the Caia, Chemba, Mopeia and Mutarara and Morrumbala Districts of Mozambique and Nsanje District of southern Malawi along the Shire River as detected by Landsat-7 imagery acquired 07 February 2015. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks, and within built-up urban areas 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.
  • This map illustrates satellite-detected flood waters in Maganja Da Costa and Namacura District of Zambezia Province, Mozambique, as detected by Radarsat-2 imagery acquired 03 February 2015. Between 18 January and 03 February, flood waters slightly decreased and affected roughly 52,700 hectars of land. A total of 69 potentially affected towns were detected within the complete analyzed area.This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.
  • 40+ Downloads
    Updated August 10, 2015 | Dataset date: Feb 16, 2015
    This dataset updates: Never
    This map illustrates satellite-detected flood waters in the Chikwawa and Nsanje Districts of Southern Region of Malawi and in the Morrumbala District of Zambezia Province of Mozambique along the Shire River as detected by Landsat-7 imagery acquired 07 February 2015. It is likely that flood waters have been systematically underestimated along highly vegetated areas along main river banks, and within built-up urban areas 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.
  • This map illustrates satellite-detected flood waters in the Caia, Chemba, Mopeia and Mutarara and Morrumbala Districts of Mozambique and Nsanje District of southern Malawi along the Shire River as detected by Radarsat-2 imagery acquired 04 February 2015. Between 30 January 2015 and 04 February 2015 waters receded from approximatively 11% of the surface of lands detected as flooded the 30 January 2015. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.
  • 10+ Downloads
    Updated August 10, 2015 | Dataset date: Jan 21, 2015
    This dataset updates: Never
    This map illustrates satellite-detected flood waters in the Caia, Chemba, Mopeia and Mutarara and Morrumbala Districts of Mozambique and southern Malawi along the Chire River as detected by Radarsat-2 imagery acquired 21 January 2015. Between 11 December 2014 and 21 January 2015 flood waters affected roughly 55,000 hectares of lands in the five listed districts. About 31 villages are located within the flooded zone and according to the World Population database around 33,500 people are located within these potentially affected areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.
  • This map illustrates satellite-detected flood waters in the Caia, Chemba, Mopeia and Mutarara and Morrumbala Districts of Mozambique and Nsanje District of southern Malawi along the Shire River as detected by Radarsat-2 imagery acquired 30 January 2015. Between 21 January 2015 and 30 January 2015 waters receded from about 30,000 ha of lands but many areas along the Shire River remain affected. About 22 villages are located within the flooded zone as of 30 January 2015 and according to the World Population data base around 25,000 people are located within these potentially affected a rea s. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.
  • 20+ Downloads
    Updated August 10, 2015 | Dataset date: Jan 19, 2015
    This dataset updates: Never
    This map illustrates satellite-detected flood waters in the Maganka Da Costa, Namacurra and Mocuba Districts of Zambezia Province, Mozambique, as detected by Radarsat-2 imagery acquired 18 January 2015. Between 11 and 18 January 2015 flood waters affected roughly 85,000 hectares of land, with inundated areas increasing approximately 800% from pre-flood areas, particularly in the coastal part of Mangaja Da Costa District. About 41 villages are located within the flooded zone, and according to the World Population database around 73,000 people are located within these potentially affected areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.