Data Completeness
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
1/24 Core Data 6 Datasets 6 Organisations Expand
Administration
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Physical Infrastructure and Geography
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Population and Socio-economic Indicators
1 Datasets
Health
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Education
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Food Security and Nutrition
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Humanitarian Profile
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Humanitarian Coordination
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  • 800+ Downloads
    Updated August 21, 2017 | 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.
  • 1000+ Downloads
    Updated July 7, 2017 | Dataset date: Oct 27, 2016
    This dataset updates: Every month
    This dataset contains data about the number of people reached with food assistance in emergency settings. The data is collected from external WFP situation reports and emergency dashboards.
  • 800+ Downloads
    Updated May 14, 2017 | Dataset date: Jan 1, 2017-Dec 31, 2017
    This dataset updates: Never
    This Excel file contains population projections for 2017 by the Yemen Central Statistical Organization. The projections are based off of 2004 Census data. The population figures are disaggregated by governorate and district levels, both containing p-codes. The data is further disaggregated by sex and age groups. Additional information on population figures that take into account internally displaced people and returnees in Yemen can be found here [https://www.humanitarianresponse.info/en/operations/yemen/protection] and is produced regularly by the Task Force on Population Movement.
  • 400+ Downloads
    Updated December 14, 2016 | Dataset date: Dec 5, 2016
    This dataset updates: Every year
    Global Humanitarian Overview 2017 Figures
  • 400+ Downloads
    Updated July 17, 2016 | Dataset date: Jan 1, 1975-Dec 1, 2012
    This dataset updates: Every year
    The UNHCR Refugee Population statistics are compiled and curated at headquarters-level and released yearly at the same time of the UNHCR Statistical Yearbooks. This is a subset of the data only with refugees from Yemen. The full dataset is available here. This dataset contains refugee population statistics from 1975 until 2012.
  • 100+ Downloads
    Updated April 12, 2016 | Dataset date: Apr 12, 2016
    This dataset updates: Never
    This Archive contains shapefiles for FEWS NET Food Security Outlook for East Africa. It was last updated on April 12, 2016. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: EA201304_ML1 Most likely food security outcome for January-March 2016 EA201304_ML2 Most likely food security outcome for April-June 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)
  • 20+ Downloads
    Updated March 21, 2016 | Dataset date: Oct 1, 2014
    This dataset updates: Never
    This Archive contains shapefiles for FEWS NET Food Security Outlook for East Africa. It was last updated on December 31, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: EA201304_ML1 Most likely food security outcome for October-December 2014 EA201304_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 March 21, 2016 | Dataset date: Jul 17, 2014
    This dataset updates: Never
    This Archive contains shapefiles for FEWS NET Food Security Outlook for East 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: eastafrica201304_ML1 Most likely food security outcome for April-June 2014 eastafrica201304_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 March 21, 2016 | Dataset date: Jul 1, 2014
    This dataset updates: Never
    This Archive contains shapefiles for FEWS NET Food Security Outlook for East 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: eastafrica201304_ML1 Most likely food security outcome for July-September 2014 eastafrica201304_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 March 21, 2016 | Dataset date: Jan 1, 2014
    This dataset updates: Never
    This Archive contains shapefiles for FEWS NET Food Security Outlook for East Africa. It was last updated on August 07, 2014. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: eastafrica201304_ML1 Most likely food security outcome for January-March 2014 eastafrica201304_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)
  • 10+ Downloads
    Updated March 21, 2016 | Dataset date: Nov 14, 2013
    This dataset updates: Never
    This Archive contains shapefiles for FEWS NET Food Security Outlook for East Africa. It was last updated on November 14, 2013. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: eastafrica201304_ML1 Most likely food security outcome for October-December 2013 eastafrica201304_ML2 Most likely food security outcome for January-March 2014 Where xx is one of the region codes listed above. 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 March 21, 2016 | Dataset date: Jul 14, 2013
    This dataset updates: Never
    This Archive contains shapefiles for FEWS NET Food Security Outlook for East 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: eastafrica201304_ML1 Most likely food security outcome for July-September 2013 eastafrica201304_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)
  • Updated March 21, 2016 | Dataset date: Jan 14, 2013
    This dataset updates: Never
    This Archive contains shapefiles for FEWS NET Food Security Outlook for East 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: eastafrica201304_ML1 Most likely food security outcome for January-March 2013 eastafrica201304_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)
  • 10+ Downloads
    Updated March 21, 2016 | Dataset date: Sep 30, 2015
    This dataset updates: Never
    This Archive contains shapefiles for FEWS NET Food Security Outlook for East Africa. It was last updated on September 30, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: easternafrica201507_ML1 Most likely food security outcome for July-September 2015 easternafrica201507_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)
  • This map illustrates satellite-detected potential damage following Cyclone Chapala in western Socotra Island, Socotra Governorate, Yemen. Using satellite imagery acquired 04 November 2015 compared with imagery from 27 October 2015, 10 and 23 September 2015, UNITAR-UNOSAT analyzed an area of approximately 2,157 square kilometers or roughly 59% of the island. A total of 81 potentially damaged structures were identified as of 04 November 2015. Many affected structures and boats were observed near the settlement of Qulansiyah. Detected damage likely reflects an underestimation due to significant cloud obstruction. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • 1400+ Downloads
    Updated November 25, 2015 | Dataset date: Nov 9, 2015
    This dataset updates: Never
    Population exposure estimates for 11 governorates based on the past 25 years flood hazard data for Yemen. Our source of data is Global Assessment Report on Disaster Risk Reduction 2015.
  • 20+ Downloads
    Updated November 25, 2015 | Dataset date: Nov 6, 2015
    This dataset updates: Never
    This map illustrates satellite-detected damage and destruction in Jilah village, Shabwah Governorate, Yemen. Using satellite imagery acquired 5 November 2015 and comparing with imagery collected on 22 August 2015, UNITAR - UNOSAT identified an area severely affected by flash flood resulting from rains during Cyclone Chapala. Imagery shows that the town of Jilah is partially covered by mud and a total of 150 structures appear damaged. Approximately 58 of these were destroyed, 57 severely damaged, and 35 moderately damaged. Primary road N4 is also highly affected by mud as a consequence of the flash floods and it is impassable across several sections. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • 10+ Downloads
    Updated November 25, 2015 | Dataset date: Nov 3, 2015
    This dataset updates: Never
    Contains previous and forecast positions and windspeeds in a Google Earth KMZ file. Updated every 6 hours.
  • 20+ Downloads
    Updated November 25, 2015 | Dataset date: Oct 15, 2015
    This dataset updates: Never
    This map illustrates satellite-detected damage and destruction in the city of Sana'a, Sana'a Governorate, Yemen. Using satellite imagery acquired 10 and 23 September 2015, as well as 15 May 2015, UNITAR-UNOSAT identified a total of 652 affected structures. Approximately 283 of these were impacted as of 10 and 23 September 2015, with 54 destroyed, 94 severely damaged, and 135 moderately damaged. Previously, using the 15 May 2015 satellite image, UNITAR-UNOSAT had located 369 affected structures, of which 60 were destroyed, 72 severely damaged, and 237 moderately damaged. Additionally, 8 impact craters and 16 areas with significant amounts of debris were observed in September 2015. A total of 7 medical facilities were identified within 100 meters of damaged and destroyed buildings, and it is possible that these facilities also sustained some damage. Notably, as of 10 and 23 September 2015, significant reconstruction of structures damaged as of 15 May 2015 was visible across the examined area. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
  • 10+ Downloads
    Updated November 25, 2015 | Dataset date: Jun 30, 2015
    This dataset updates: Never
    This Archive contains shapefiles for FEWS NET Food Security Outlook for East Africa. It was last updated on June 30, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: eastafrica201304_ML1 Most likely food security outcome for April-June 2015 eastafrica201304_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 November 25, 2015 | Dataset date: Apr 3, 2015
    This dataset updates: Never
    This Archive contains shapefiles for FEWS NET Food Security Outlook for East Africa. It was last updated on April 03, 2015. The classification used is IPC V2.0 Compatible, aimed to address acute food insecurity. The two shapefiles represent the two analytic periods: eastafrica201304_ML1 Most likely food security outcome for January-March 2015 eastafrica201304_ML2 Most likely food security outcome for April-June 2015 Where xx is one of the region codes listed above. 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 November 25, 2015 | Dataset date: Aug 28, 2015
    This dataset updates: Never
    This map illustrates satellite-detected damage and destruction in the city of Aden, Aden Governorate, Yemen. Using satellite imagery acquired 21 August 2015, 10 May 2015, and 31 December 2014, UNITAR-UNOSAT identified a total of 839 affected structures, a 30 percent increase from the previous 10 May 2015 analysis. Approximately 356 structures were destroyed, 202 severely damaged, and 270 moderately damaged. Additionally, 50 impact craters were found within the city, the majority of which were located in the vicinity of Aden International Airport. A total of 13 medical facilities were identified within 100 meters of damaged and destroyed buildings, and it is possible that these facilities also sustained some damage. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
  • 40+ Downloads
    Updated November 25, 2015 | Dataset date: Jul 3, 2015
    This dataset updates: Never
    This map illustrates satellite-detected areas of destruction in the majority of Taiz, Yemen, as seen by the WorldView-3 satellite on 26 June 2015. UNOSAT identified a total of 328 damaged buildings (54 destroyed, 66 severely damaged, 156 moderately damaged, 52 possibly damaged) as well as 410 areas with significant amounts of debris. A total of 11 health centers are possibly damaged as they are within 100 meters of other destroyed or damaged buildings. This is a preliminary analysis and has not yet been validated in the field. Note that satellite imagery analysis will not capture all damage to buildings and instead only detects significant or catastrophic amounts of structural damage. Please send ground feedback to UNITAR - UNOSAT.
  • 10+ Downloads
    Updated November 24, 2015 | Dataset date: Jun 3, 2015
    This dataset updates: Never
    This map illustrates satellite-detected damage and destruction at Sana'a International Airport, Sana'a Governorate, Yemen. Using satellite imagery acquired 15 May 2015 and 12 December 2014, UNITAR-UNOSAT identified a total of 70 affected structures and transportation vehicles. Approximately 18 of these were destroyed, 32 severely damaged, and 20 moderately damaged. Additionally, 32 impact craters were found. One medical facility was identified within 500 meters of impact craters. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
  • 30+ Downloads
    Updated November 24, 2015 | Dataset date: Jun 3, 2015
    This dataset updates: Never
    This map illustrates satellite-detected damage and destruction in the city of Sana'a, Sana'a Governorate, Yemen. Using satellite imagery acquired 15 May 2015, 12 and 31 December 2014, UNITAR-UNOSAT identified a total of 440 affected structures. Approximately 74 of these structures were destroyed, 106 severely damaged, and 260 moderately damaged. Additionally, 35 impact craters were found. A total of four medical facilities were identified within 100 meters of damaged and destroyed buildings, and it is possible that these facilities also sustained some damage. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.