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  • 3000+ Downloads
    Updated 10 February 2017 | Dataset date: February 10, 2017-February 10, 2017
    This dataset updates: Never
    Here we provide poverty data created using Bayesian model-based geostatistics in combination with high resolution gridded spatial covariates and aggregated mobile phone data applied to geolocated household survey data on poverty from the DHS wealth index (2011), the Progress out of Poverty Index (2014), and household income (2013). Citation: Steele, J. E. et al. Mapping poverty using mobile phone and satellite data. J. R. Soc. Interface 14, 20160690 (2017). Online here: http://rsif.royalsocietypublishing.org/content/14/127/20160690
  • 1000+ Downloads
    Updated 10 February 2017 | Dataset date: September 30, 2011-September 30, 2011
    This dataset updates: Every year
    Localités Les localités de la RDC sont issues au de la comparaison de deux bases pour lesquelles les doublons ont été supprimés. Des relevés GPS ainsi que des numérisation sur images satellites ont été réalisés par différentes acteurs présent en RDC et viennent compléter le fichier initial. Des ajouts se font régulièrement (2010). Sourced from Référentiel Géographique Commun
  • 300+ Downloads
    Updated 7 February 2017 | Dataset date: December 01, 2016-January 31, 2017
    This dataset updates: Every three months
    REACH Initiative support the Camp Coordination and Camp Management (CCCM) cluster by conducting Quarterly IDP Camp Profiling in order to comprehensively monitor the camps and to provide regular and updated information on developments, needs, and gaps in all accessible IDP camps across Iraq. To date, CCCM and REACH have conducted seven rounds of IDP Camp Profiling and mapping – in October 2014, January 2015, September/October 2015, December 2015, April 2016, August/September 2016 and December 2016/January 2017. This dataset contains findings from December 2016/January 2017. This exercise covered camps located in the governorates of Anbar, Baghdad, Dahuk, Diyala, Erbil, Kerbala, Kirkuk, Missan, Najaf, Ninewa, Salah al-Din and Sulaymaniyah.
  • 2000+ Downloads
    Updated 23 January 2017 | Dataset date: November 30, 2016-November 30, 2016
    This dataset updates: Every three months
    The Who does What Where (3W) is a core humanitarian coordination dataset. It is critical to know where humanitarian organizations are working, what they are doing and their capability in order to identify gaps, avoid duplication of efforts, and plan for future humanitarian response (if needed).
  • 600+ Downloads
    Updated 23 January 2017 | Dataset date: November 07, 2016-November 07, 2016
    This dataset updates: Every three months
    The data set contains the results of the 2016 Nutrition SMART Survey and the 2017 projections of the caseload for Mali
  • 5700+ Downloads
    Updated 3 January 2017 | Dataset date: January 03, 2017-January 03, 2017
    This dataset updates: Never
    Nigeria - LGA and Wards data for some of the states based on electoral registration points.
  • 1100+ Downloads
    Updated 14 December 2016 | Dataset date: September 13, 2016-September 13, 2016
    This dataset updates: Every two weeks
    This data is a global overview on Zika Virus.
  • 2400+ Downloads
    Updated 7 December 2016 | Dataset date: November 22, 2016-November 22, 2016
    This dataset updates: Never
    Blog post about this prediction can be found here: http://bit.ly/2fWF2jq The predicted priority index of Typhoon Haima is produced by a machine learning algorithm that was trained on four past typhoons: Haiyan, Melor, Hagupit and Rammasun. It uses base line data for the whole country, combined with impact data of windspeeds and rains, and trained on counts by the Philippine government on people affected and houses damaged. First run The Priority Index is a 1-5 classification that can be used to identify the worst hit areas: those that need to be visited for further assessments or support first. Second run The model now predicts two things: a weighted index between partially damaged and completely damaged, where partially damaged is counted as 25% of the completely damaged. This has proven to give he highest accuracy. the precentage of total damage (damaged houses versus all houses) The absolute number of houses damaged / people affected is insufficiently validated at the moment, and should just be used for further trainng and ground-truthing. Data sources: Administrative boundaries (P_Codes) - Philippines Government; Published by GADM and UN OCHA (HDX) Census 2015 (population) - Philippine Statistics Authority; received from UN OCHA (HDX) Avg. wind speed (km/h) - University College London Typhoon path - University College London Houses damaged - NDRRMC Rainfall - GPM Poverty - Pantawid pamilyang pilipino program (aggregated) For the second run of the algorithm we also included: Roof and wall materials New geographical features The result of different models can be found in the file 'Typhoon Haima - performance of different models - second run.csv' A note on how to interpret this. date running date alg_date same alg_model name of the algorithm used alg_predict_on name of the learning variable alg_use_log i s the learning variable transformed in log code_version version of the learn.py code All the columns with feat_ indicates the importance of that feature, if not present that feature was not used. learn_matrix name of the learning matrix with the 5 typhoons run_name unique run name (pickle files and csv files have this name for this model) typhoon_to_predict name of a new typhoon to predict val_accuracy accuracy based on 10 categories of damage 0% 10% 20% … val_perc_down perc of underpredicted categories val_perc_up perc of overpredicted categories Val_best_score best r2 score Val_stdev_best_score error on best score based on the CV Val_score_test r2 score on the test set (this should be around +- 5% of the previus number to not overfit Val_mean_error_num_houses average error on the number of houses val_median_error_num_houses median val_std_error_num_houses std deviation of the errors (lower is better) Algorithm developed by 510.global the data innovation initiative of the Netherlands Red Cross.
  • 200+ Downloads
    Updated 7 December 2016 | Dataset date: March 01, 2015-March 01, 2015
    This dataset updates: Every year
    According to information by the Ministry of Labour, Health and Social Affairs , 118 651 persons with disabilities are registered as recipients of state social assistance by 1 March, 2015 in Georgia that constitutes 3 percent of total population resided in Georgia. This dataset provides a breakdown of the number of persons with disabilities by first administrative level (region), and a detailed breakdown for the districts belonging to the capital city of Tbilisi. The provided information depicts the number of disabled persons receiving state social pension/allowance (beneficiaries) across the country. In light of this, state policy determines the total number of disabled persons by the sum of beneficiaries, which directly is connected to the actual number of disabled people living in Georgia. The actual number of disabled persons in Georgia is likely to be higher.
  • 4300+ Downloads
    Updated 18 November 2016 | Dataset date: November 01, 2016-November 01, 2016
    This dataset updates: Never
    Need comparison tools (NCT) used by the Eight clusters in Mali 2017.
  • 1200+ Downloads
    Updated 18 November 2016 | Dataset date: October 20, 2016-February 23, 2017
    This dataset updates: Never
    Borno State ward boundaries as developed by OCHA, E-Health and IOM for the Humanitarian Community. You can access the current version here.
  • 2400+ Downloads
    Updated 2 November 2016 | Dataset date: October 25, 2016-October 25, 2016
    This dataset updates: Every week
    This data is about damaged houses in the Philippines after Typhoon Haima (Lawin)
  • 200+ Downloads
    Updated 31 October 2016 | Dataset date: June 01, 2014-June 01, 2014
    This dataset updates: Every year
    The zipped shape-file contains point data on the location of nutrition feeding centers in South Kordofan State, Republic of the Sudan in 2014. The data was obtained from UNICEF Sudan, through UNDP Sudan. Updated data will be uploaded when it gets available.
  • 100+ Downloads
    Updated 30 October 2016 | Dataset date: June 01, 2014-June 01, 2014
    This dataset updates: Every year
    The zipped shape-file contains point data on the location of nutrition feeding centers in South Darfur State, Republic of the Sudan in 2014. The data was obtained from UNICEF Sudan, through UNDP Sudan. Updated data will be uploaded when it gets available.
  • 100+ Downloads
    Updated 30 October 2016 | Dataset date: June 01, 2014-June 01, 2014
    This dataset updates: Every year
    The zipped shape-file contains point data on the location of nutrition feeding centers in Gedaref State, Republic of the Sudan. The data was obtained from UNICEF Sudan, through UNDP Sudan. Updated data will be uploaded when it gets available.
  • 1000+ Downloads
    Updated 4 October 2016 | Dataset date: October 01, 2015-October 01, 2015
    This dataset updates: Every year
    Disaster loss and damage dataset for Sri Lanka
  • 4800+ Downloads
    Updated 30 September 2016 | Dataset date: August 22, 2016-August 22, 2016
    This dataset updates: Never
    Adm3 shapefile (District-level): For 31 districts affected by Nepal Earthquake 2015 Adm4 shapefile (VDC-level): For 31 districts affected by Nepal Earthquake 2015
  • 1600+ Downloads
    Updated 16 September 2016 | Dataset date: September 16, 2016-September 16, 2016
    This dataset updates: Never
    Burundi Admin bounderies. Admin 1 to 4
  • 1900+ Downloads
    Updated 15 September 2016 | Dataset date: May 23, 2016-May 23, 2016
    This dataset updates: Never
    Operational Presence for the Earthquake Response
  • 1200+ Downloads
    Updated 25 August 2016 | Dataset date: May 16, 2016-May 16, 2016
    This dataset updates: Never
    Who is doing what and where in Somalia, 2016
  • 1000+ Downloads
    Updated 26 July 2016 | Dataset date: May 30, 2016-May 30, 2016
    This dataset updates: Every year
    This datasets is roads shape file in Ethiopia. It's compiled by WFP and the last update is as of May 2016.
  • 2200+ Downloads
    Updated 16 July 2016 | Dataset date: July 07, 2016-July 07, 2016
    This dataset updates: Never
    Energy projects are being implemented in humanitarian contexts across the globe. These excel spreadsheets include all known past and present energy projects that have taken place in refugee camps, IDP communities, and other crisis-affected populations throughout the world, and were collected by the Global Alliance for Clean Cookstoves on behalf of the Safe Access to Fuel & Energy (SAFE) Humanitarian Working Group. To view full descriptions of the projects represented here, please visit www.safefuelandenergy.org/where-we-work. This project listing was created as part of an effort to enhance coordination of activities, encourage collaboration, and share knowledge between organizations working on Safe Access to Fuel and Energy (SAFE) in humanitarian settings. Projects included in this database are those that improve access to fuel or energy for cooking, lighting, heating, or powering among crisis-affected populations. By crisis-affected populations we mean refugees, internally displaced people (IDPs), or those affected by natural disaster or prolonged conflict. Examples of applicable energy interventions include providing solar lighting, manufacturing and/or distributing cookstoves and fuels, setting up mini grids for camp electrification, establishing and managing woodlots for fuel provision and environmental protection, improving protection mechanisms for women during firewood collection, and many others, provided they take place among crisis-affected populations. The SAFE Humanitarian Working Group is a consortium of partners including UNHCR, FAO, WFP, the Global Alliance for Clean Cookstoves, the Women's Refugee Commission, International Lifeline Fund, Mercy Corps, UNICEF, and other agencies. If you know of additional energy projects that are not shown here, please contact us at info@safefuelandenergy.org.
  • 1600+ Downloads
    Updated 12 July 2016 | Dataset date: July 12, 2016-July 12, 2016
    This dataset updates: Never
    Administrative Boundary dataset (shapefiles and spreadsheets) for Greece (levels 0,1,2 & Sea Areas)
  • 2600+ Downloads
    Updated 30 June 2016 | Dataset date: June 01, 2010-June 01, 2010
    This dataset updates: Every year
    Ecuador Educational Units at the National Level as reported in the CENSUS OF POPULATION AND DWELLING (2010)
  • 900+ Downloads
    Updated 10 May 2016 | Dataset date: February 01, 2014-December 01, 2014
    This dataset updates: Every three months
    Who What Where (3W) related to ongoing humanitarian activities in South Sudan Who - Agency What - Related Cluster of the given activity Where - Location of the activity (State and County)