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  • 2500+ Downloads
    Updated 10 March 2017 | Dataset date: September 01, 2016-September 01, 2016
    This dataset updates: Every year
    This dataset is produced by the United Nations for the Coordination of Humanitarian Affairs (OCHA) in collaboration with humanitarian partners. It covers the period from January to December 2017 and was issued on December 2016.
  • 300+ Downloads
    Updated 8 March 2017 | Dataset date: March 08, 2017-March 08, 2017
    This dataset updates: As needed
    This spatial dataset provides point data for education facilities of Bangladesh by LGED . The original source of the data is Local Government Engineering Department (LGDE) of Bangladesh. Dataset updated by WFP, Map Action and OCHA.
  • 300+ Downloads
    Updated 2 March 2017 | Dataset date: December 01, 2015-January 31, 2016
    This dataset updates: Never
    Iraq (Erbil) profiling of urban/out-of-camp IDPs, Syrian refugees, and host populations with data collected from Dec 2015 until January 2016 (report published June 2016). The exercise included a household survey administered to a sample of 1,163 households (403 IDP, 370 refugee and 390 host households). Data can also be explored through the DART: http://www.dart.jips.org/.
  • 400+ Downloads
    Updated 1 March 2017 | Dataset date: October 07, 2016-October 07, 2016
    This dataset updates: Never
    Partial Assessment and 3W matrix ,Hurricane Matthew. as of 07.10.2016
  • 700+ Downloads
    Updated 1 March 2017 | Dataset date: January 01, 2016-January 01, 2016
    This dataset updates: Every year
    This is Kenya Wards administrative. The dataset was digitized from a digital PDF maps by FAO Kenya
  • 1000+ Downloads
    Updated 28 February 2017 | Dataset date: November 30, 2016-November 30, 2016
    This dataset updates: Never
    Kenya’s commitment to close down Dadaab by mid-2017, the world’s largest refugee camp hosting over 300,000 Somalis, is putting increasing pressure on service delivery and infrastructure in Somalia. By October 2016, an estimate of 31,226 Somali people have returned from Kenya, the majority of which are heading to Kismayo, Baidoa, Luuq and Mogadishu. While the caseload of returnees is expected to continue growing over the coming months, there is no clear understanding of movement patterns of returnees or internally displaced persons which further complicates the attempts by humanitarian organisations to provide for required services. The absence of a CCCM cluster in Somalia means that information on humanitarian needs and gaps has been limited to ad-hoc and un-coordinated assessments. The Kismayo IDP settlement assessment was triggered as a result of the need for a multi-cluster, area-based and coordinated information approach for humanitarian planning and service delivery in informal IDP settlements in Somalia. The Kismayo IDP Settlement Assessment was carried out with financial support from USAID-OFDA and ECHO and in close collaboration with clusters (WASH, Shelter & NFIs, Education, Food Security, Health, Nutrition and Protection).
  • 800+ Downloads
    Updated 27 February 2017 | Dataset date: April 19, 2016-April 19, 2016
    This dataset updates: Never
    Contains data from OCHA's Financial Tracking Service on the financial requirements and current funding levels for appeals in the Lake Chad Basin crisis countries. Data is encoded as utf-8. The second row of the CSV contains HXL tags.
  • 1200+ Downloads
    Updated 24 February 2017 | Dataset date: March 23, 2016-March 23, 2016
    This dataset updates: Every three months
    Displacement figures for the Lake Chad Basin Crisis. Derived from http://ors.ocharowca.info/KeyFigures/KeyFiguresListingPublic.aspx. Data is encoded as utf-8. The second row of the CSV contains HXL tags.
  • 1100+ Downloads
    Updated 24 February 2017 | Dataset date: March 19, 2016-March 19, 2016
    This dataset updates: Every six months
    The data contains the latest estimated population of each administrative level 1 unit in the Lake Chad Basin. Estimation is based on input from UNFPA and the most recently available census for each country. Data is encoded as utf-8. The second row of the CSV contains HXL tags.
  • 400+ Downloads
    Updated 22 February 2017 | Dataset date: January 01, 2017-July 31, 2017
    This dataset updates: Never
    South Sudan Integrated Food Security Phase classification and population by state and County. The Integrated Food Security Phase Classification (IPC), also known as IPC scale, is a tool for improving food security analysis and decision-making. It is a standardized scale that integrates food security, nutrition and livelihood information into a statement about the nature and severity of a crisis and implications for strategic response. The IPC was originally developed for use in Somalia by the United Nations Food and Agriculture Organization's Food Security Analysis Unit (FSAU). Several national governments and international agencies, including CARE International, European Commission Joint Research Centre (EC JRC), Food and Agricultural Organization of the United Nations (FAO), USAID/FEWS NET, Oxfam GB, Save the Children UK/US, and United Nations World Food Programme (WFP), have been working together to adapt it to other food security contexts
  • 900+ Downloads
    Updated 22 February 2017 | Dataset date: January 01, 2016-December 31, 2017
    This dataset updates: Every three months
    Pakistan KP-FATA 4Ws 2016 with HXL Tags
  • 400+ Downloads
    Updated 10 February 2017 | Dataset date: February 01, 2015-January 01, 2016
    This dataset updates: Never
    Somalia (Hargeisa) profiling exercise of IDPs from Somaliland, IDPs from South-Central Somalia, economic migrants, host communities, and refugees and asylum-seekers with data collected between February and June 2015 (report published January 2016). The exercise included a household survey administered to a sample of 2,510 households. Data can also be explored through the DART.
  • 2300+ 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
  • 1200+ 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.
  • 2100+ 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
  • 5000+ 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.
  • 400+ Downloads
    Updated 30 December 2016 | Dataset date: January 02, 2014-October 31, 2016
    This dataset updates: Never
    A monthly report of Crop Farming in Kisumu County and their status from the year 2014 to 2016.
  • 700+ Downloads
    Updated 29 December 2016 | Dataset date: December 01, 2016-December 01, 2016
    This dataset updates: Never
    This dataset is about food security projects implemented in the Sahel region in 2015 and 2016.
  • 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.
  • 400+ Downloads
    Updated 24 November 2016 | Dataset date: August 20, 2016-August 20, 2016
    This dataset updates: Never
    AFDB Commodity Prices, Monthly January 1960 - July 2016
  • 500+ Downloads
    Updated 24 November 2016 | Dataset date: April 17, 2016-April 17, 2016
    This dataset updates: Never
    African Regional Energy Statistics, 2000 - 2014
  • 700+ Downloads
    Updated 24 November 2016 | Dataset date: July 15, 2015-July 15, 2015
    This dataset updates: Never
    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)
  • 4000+ 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.