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
Updated
24 November 2016
| Dataset date: July 15, 2015-July 15, 2015
The AfDB Statistics Department and the Fragile States Unit have compiled this data set from various sources (the World Bank, WHO, IMF, and many others)
Updated
31 October 2016
| Dataset date: October 26, 2016-October 26, 2016
This map illustrates satellite-detected areas of displaced persons shelters in the Sangaya settlement, Borno state, Nigeria, and in the surrounding town of Dikwa. UNITAR-UNOSAT analysis of satellite imagery acquired 29 September 2016 revealed a total of 433 shelters and 54 infrastructure and support buildings within the Sangaya compound and a total of 2,259 shelters scattered in the surrounding town. A density analysis has been performed to highlight the most dense shelters areas (Sangaya settlement included), ranging from 400 to 10,500 shelters per square kilometer. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
Updated
8 March 2016
| Dataset date: February 25, 2016-February 25, 2016
This map illustrates satellite-detected shelters and other buildings at the Minawao refugee settlement, Mayo-Tsanaga District, Far North Province in Cameroon as seen by the WorldView-2 satellite on 19 November 2015. UNOSAT analysed a total of 11,777 structures (9,390 tent shelters, 551 administrative buildings, 634 improvised shelters, and 1,202 semi-permanent shelters) within 502 hectares of the settlement area. Previous analysis from 10 March 2015 indicated 5, 220 shelters over 261 hectares and thus the updated analysis indicates an increase of approximately 126% on shelters and 93% in land occupied. Note that apparently adjoining, contiguous shelters were counted as a single shelter which may thus underestimate total number of shelters. This is a preliminary analysis & has not yet been validated in the field. Please send ground feedback to UNITAR/UNOSAT.
Updated
19 February 2016
| Dataset date: July 01, 2014-January 28, 2016
Since May 2013, Nigeria has seen an intensification of conflict due to Boko Haram attacks in its north east states of Adamawa, Bauchi, Borno, Gombe, Taraba and Yobe. Insurgency and counter-insurgency have inevitably resulted in the displacement of people across the troubled states. In response to the need for accurate information on internally displaced persons (IDPs), the International Organization for Migration (IOM) began implementing the Displacement Tracking Matrix (DTM) project in July 2014. The objective of the project is to support the Government of Nigeria in establishing a comprehensive system to collect and disseminate data on IDPs by strengthening the capacity of the National Emergency Management Agency (NEMA), the State Emergency Management Agencies (SEMAs) and other humanitarian actors to conduct assessments on IDPs in a unified and systematized manner. The activities of the DTM project, which consist of conducting baseline assessments and registration for IDPs living in camps and host communities, are currently being carried out in Adamawa, Bauchi, Borno, Gombe, Taraba and Yobe. The information collected will contribute to the provision of a comprehensive profile of the IDP population in Nigeria which will be shared with all relevant stakeholders and will contribute towards enabling the government of Nigeria and humanitarian partners identify the needs of Nigeria’s displaced population and develop interventions for providing IDPs necessary assistance. The project is funded by the Office of Disaster Assistance of the United States Agency for International Development and the European Commission's Humanitarian Aid and Civil Protection Department.
Updated
24 November 2015
| Dataset date: December 05, 2014-December 05, 2014
Who, What, Where (3W) dataset on the Ebola response effort. Some entries have a maximum level of desegregation up to administrative level 3. The dataset contains data from Guinea, Liberia, Sierra Leone, and Nigeria.
This dataset is updated weekly. Last Update 17 Nov. 2014
Note: If your humanitarian organization would like to make a correction or update the dataset, please contact the OCHA focal point for the respective country. Contacts can be found at https://wca.humanitarianresponse.info/fr/emergencies/virus-ebola
Updated
24 November 2015
| Dataset date: April 02, 2015-April 02, 2015
Ebola outbreak time series data at national and sub national levels since March 2014. Data compiled manually from a number of published reports. Updated by OCHA ROWCA every working day.
Updated
14 October 2015
| Dataset date: September 30, 2015-September 30, 2015
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:
westafrica201304_ML1 Most likely food security outcome for July-September 2015
westafrica201304_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
14 October 2015
| Dataset date: October 01, 2013-October 01, 2013
This Archive contains shapefiles for FEWS NET Food Security Outlook for West 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:
xx201304_ML1 Most likely food security outcome for October-December 2013
xx201304_ML2 Most likely food security outcome for January-March 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)
