Updated
May 26, 2020
| Dataset date: September 26, 2017-September 26, 2017
This dataset updates: As needed
West and Central Africa Administrative boundaries, administrative level 0 to 2. Notice: The boundaries and names shown and the designations used on these shapefiles do not imply official endorsement or acceptance by the United Nations.
West and Central Africa settlements with administrative capitals
Updated
December 12, 2019
| Dataset date: September 04, 2019-October 16, 2019
This dataset updates: As needed
The datasets cover the period of 4 September - 16 October and includes the states mostly affected by displacement including Benue,Nasarawa, Kaduna, Kano, Katsina, Plateau, Sokoto and Zamfara.
Updated
December 12, 2019
| Dataset date: September 04, 2019-October 16, 2019
This dataset updates: As needed
The datasets cover the period of 4 September - 16 October and includes the states mostly affected by displacement including Benue,Nasarawa, Kaduna, Kano, Katsina, Plateau, Sokoto and Zamfara.
Updated
December 18, 2017
| Dataset date: February 01, 2016-October 31, 2016
This dataset updates: Never
From February to October 2016, the American Red Cross and its local Red Cross partners completed an effort to extensively map areas within a 15-kilometer distance of the shared borders between Guinea, Liberia, and Sierra Leone.
The goal of this work was to create an open and comprehensive dataset of communities for West Africa and to ensure that decision makers, humanitarian workers, and community stakeholders are better aware of water, sanitation, health, and community resources before and during the next crisis.
To complete this mapping, the American Red Cross launched a mapping center in Guéckédou, Guinea, and used it as both a base of operations and a community engagement facility. Over 100 volunteers helped to complete a rapid assessment of the region, visiting over 7,000 communities by motorbike to complete a vulnerability survey with the village leader. Next, over 100 communities were selected for a round of detailed mapping, focusing on collecting the location and information about every water point, health facility and other community resource in the area. In addition, we led technical skills trainings and mapping events both in Guéckédou and across the region.
ALL DATA EXCEPT FOR THE OpenStreetMap EXTRACTS ARE LICENSED AS CC-BY 4.0
Updated
July 25, 2017
| Dataset date: September 19, 2014-March 25, 2015
This dataset updates: Never
In 2014, RIWI Corp. launched an online survey in Nigeria, Liberia, and Sierra Leone capturing public perceptions data from over 4,000 respondents on the status of Ebola in those countries. Respondents were asked a series of questions related to their confidence in government and aid agencies to manage the Ebola outbreak, as well as their own behavioral response to the infection. The data was collected using RIWI's patented Random Domain Intercept Technology™ (RDIT).
Updated
June 20, 2017
| Dataset date: January 01, 1998-December 31, 2014
This dataset updates: Never
This dataset shows the extent of six climatic zones for West Africa.It is not intended for local studies but only for regional comparison. The dataset is one of the outputs of a complete GIS mapping study to calculate the hydropower potential of all West African rivers. The project was developed by ECREEE, the ECOWAS Regional Centre for Renewable Energy and Energy Efficiency, under the Small Scale Hydropower Program. For more information please go here: http://www.ecowrex.org/smallhydro
Updated
August 28, 2015
| Dataset date: January 01, 2013-April 01, 2014
This dataset updates: Never
Here we provide version 1 Flowminder (www.flowminder.org) human mobility models for West Africa, together with WorldPop population density data for the region, to support ongoing efforts to control the ebola outbreak. Before downloading any data, please read the documention carefully as it provides details on the datasets and models provided through the links below. The mobility data refer to estimated patterns before the Ebola outbreak and should be interpreted with caution for Ebola affected countries as mobility patters are known to have changed.
Additional discussion by the authors around the use of mobile operator data for epidemilogical research see: http://currents.plos.org/outbreaks/article/containing-the-ebola-outbreak-the-potential-and-challenge-of-mobile-network-data/
