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  • Updated November 18, 2019 | Dataset date: Oct 23, 2019
    This data is by request only
    Caracterización de las necesidades humanitarias multisectoriales de la población afectada por la dinámica de flujos migratorios mixtos de Venezuela en Colombia, en términos de necesidades, respuesta y brechas, realizada en los departamentos de Santander y Norte de Santander
  • Updated November 18, 2019 | Dataset date: Mar 1, 2017-Aug 31, 2019
    This dataset updates: As needed
    Base que contiene datos por departamento y municipio de variables e indicadores como, número de atención en abortos, partos y nacimientos. Causas externas de mortalidad, que incluye homicidios, suicidios y accidentes. Número de atenciones por planificación familiar y personas con VIH/SIDA; y costo de atención de partos. Todos los datos son de población tanto colombiana como de venezolanos residentes en Colombia.
  • 200+ Downloads
    Updated November 18, 2019 | Dataset date: Aug 8, 2019-Oct 10, 2019
    This dataset updates: Every three months
    NPM Bangladesh has produced a number of tools based on its regular data collection activities. The package of November 2019 is based on NPM Site Assessment 16 (during the months of 21st August – 10th October) and NPM most updated drone imagery (as of 23 January 2019). Here below, the complete package by camp: SW Map package KMZ file Drone image The full image and shapefiles are available at this link.
  • 100+ Downloads
    Updated November 18, 2019 | Dataset date: Nov 18, 2019
    This dataset updates: Every month
    Monthly humanitarian update on disasters event in Indonesia (period September - January 2019)
  • 900+ Downloads
    Updated November 18, 2019 | Dataset date: Jan 1, 2019-Nov 17, 2019
    This dataset updates: Every week
    Number of Refugees returning to Afghanistan for the period of 01 January 2019 - 17 November 2019 by district of destination and origin.
  • 2700+ Downloads
    Updated November 18, 2019 | Dataset date: Nov 18, 2019
    This dataset updates: Every day
    FTS publishes data on humanitarian funding flows as reported by donors and recipient organizations. It presents all humanitarian funding to a country and funding that is specifically reported or that can be specifically mapped against funding requirements stated in humanitarian response plans. The data comes from OCHA's Financial Tracking Service, is encoded as utf-8 and the second row of the CSV contains HXL tags.
  • 2300+ Downloads
    Updated November 17, 2019 | Dataset date: Aug 20, 2018
    This dataset updates: Every month
    Market Monitoring monthly dataset with Survival Minimum Expenditure Basket (SMEB) prices. To inform humanitarian actors’ cash and voucher programming, REACH and the Cash-Based Responses Technical Working Group (CBR–TWG) conduct monthly monitoring of key markets throughout Syria to assess the availability and affordability of basic commodities (Market Monitoring Exercise).
  • 90+ Downloads
    Updated November 17, 2019 | Dataset date: Jan 1, 1999-Dec 31, 2018
    This dataset updates: Every year
    FAO statistics collates and disseminates food and agricultural statistics globally. The division develops methodologies and standards for data collection, and holds regular meetings and workshops to support member countries develop statistical systems. We produce publications, working papers and statistical yearbooks that cover food security, prices, production and trade and agri-environmental statistics.
  • 5100+ Downloads
    Updated November 17, 2019 | Dataset date: Jan 1, 1990-Nov 15, 2019
    This dataset updates: Every week
    This dataset contains Global Food Prices data from the World Food Programme covering foods such as maize, rice, beans, fish, and sugar for 76 countries and some 1,500 markets. It is updated weekly but contains to a large extent monthly data. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.
  • 1400+ Downloads
    Updated November 17, 2019 | Dataset date: Mar 15, 2009-Sep 15, 2019
    This dataset updates: Every week
    This dataset contains Food Prices data for Yemen. Food prices data comes from the World Food Programme and covers foods such as maize, rice, beans, fish, and sugar for 76 countries and some 1,500 markets. It is updated weekly but contains to a large extent monthly data. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.
  • 1200+ Downloads
    Updated November 17, 2019 | Dataset date: Jan 1, 2019-Nov 6, 2019
    This dataset updates: Every week
    Newly displaced population due to conflict between 01 January 2019 and 06 November 2019, compiled by OCHA sub offices based on inter-agency assessment results. This data is a snapshot as of 17 November 2019 and the numbers are expected to change as new assessment figures become available.
  • 20+ Downloads
    Updated November 16, 2019 | Dataset date: May 29, 2019
    This dataset updates: Every day
    Reference historic FX rates quoted by the European Central Bank (ECB) converted to USD base currency. There are two resources - one with USD as the quote currency (more standard x/USD) and another with USD as the base currency (USD/x). Note that where the rate is 0 or NaN, it means that the currency existed in the past but no longer exists.
  • 80+ Downloads
    Updated November 16, 2019 | Dataset date: Nov 18, 2019
    This dataset updates: Every week
    This dataset lists all contributions made by donors to the Central Emergency Response Fund (CERF). CERF receives broad support from United Nations Member States, observers, regional governments and international organizations, and the private sector, including corporations, non-governmental organizations and individuals.
  • 2300+ Downloads
    Updated November 16, 2019 | Dataset date: Nov 18, 2019
    This dataset updates: Every day
    This dataset contains key figures (topline numbers) on the world's most pressing humanitarian crises. The data, curated by ReliefWeb's editorial team based on its relevance to the humanitarian community, is updated regularly. The description of the files and columns can be found in the additional metadata spreadsheet file.
  • Updated November 15, 2019 | Dataset date: Nov 15, 2019
    This dataset updates: Live
    This dataset shows reports of attacks on education in Africa and the Middle East from the Armed Conflict Location & Event Data Project (ACLED) as well as social media posts from Twitter about education insecurity identified by the Artificial Intelligence for Digital Response (AIDR) platform.
  • 400+ Downloads
    Updated November 15, 2019 | Dataset date: Nov 6, 2019
    This dataset updates: Every week
    This dataset contains the data used by the Humanitarian Country Team in Somalia to monitor the evolution of the drought situation in Somalia. The data covers the following topics: Internal displacement by cause (drought related, conflict/insecurity, other cause) River levels in the Shabelle and Juba rivers Water prices by Region Cumulative annual rainfall Disease burden (acute watery diarrhea (AWD)/cholera, bloody diarrhea and measles) Monthly response monitoring by region for the following indicators: CCCM: Number of people benefiting from site improvement projects EDUCATION: Number of children with access to safe drinking water FOOD SECURITY: Number of people reached through activities geared towards improving access to food and safety nets HEALTH: Number of medical consultations NUTRITION: Number of acute malnutrition admissions PROTECTION: Number of girls and boys, women and men participating in community-based psycho-social activities SHELTER: Number of people in need of emergency assistance receiving appropriate NFIs through in-kind distribution, vouchers or cash WASH: Number of people reached with access to sustainable safe water services The sources for the data are as follows: IDP data (PRMN/UNHCR); Rainfall and Rivers (SWALIM); Diseases (Health Cluster/WHO); Monthly Response (Humanitarian Clusters), Water Prices (FAO)
  • 500+ Downloads
    Updated November 15, 2019 | Dataset date: Sep 13, 2018
    This dataset updates: Never
    Update 15/09 (POST-EVENT) Now that the typhoon has passed the country, the model is not run with forecasted wind speeds and typhoon track any more, but with actual estimated wind speeds and typhoon track. They come from the same source (Tropical Storm Risk - UCL), and are of the exact same format. All output (map in PDF, data in Excel and in Shapefile) is of the exact same format and interpretation. Full methodology 1. Based on existing Priority Index model: 510 has previously developed the Priority Index model for typhoons in the Philippines One day after a typhoon has passed the Philippines .. .. the model predicts ‘% of completely damaged houses’ per municipality Based on 12 large typhoons in the last 5 years in the Philippines, for which detailed damage reports were available through NDRRMC (https://www.ndrrmc.gov.ph/) For these same events, we also collected possible explanatory indicators, such as wind speed (event-specific) and building materials of houses (PH national census). We built a statistical model, which could explain differences in damage on the basis of differences in wind speed and building materials (etc.) When dividing all municipalities in 5 equal damage classes (class 1 being the 20% municipalities with lowest damage; class 5 the 20% with highest damage) .. .. we found that in 73% of the cases we are at most 1 class off. 2. Mangkhut methodology: In the case of typhoon Mangkhut, we are dealing with an upcoming typhoon, which is still awaiting landfall on Saturday 15/09. This is a new situation, which requires the following noteworthy changes in methodology. Our wind speed source (UCL Tropical Storm Risk) has – in addition to post-event wind speed data as used above – also forecast wind speed data for 5 days ahead. This forecasted wind speed (and typhoon track) are plugged as input into the above-mentioned prediction model, which - still in combination with building materials - lead to the predicted damage class per municipality that can be seen in the map. Note that the results are strongly dependent on the input of windspeed, which is itself still an unknown. (see accuracy below). 3. How to use this product: The map contains damage classes (1-5) per municipality. As such, we advise to put priority on municipalities in damage class 5, and depending on available resources continue with class 4, etc. This damage class is based on ‘% of houses that are completely damaged’. As priority might also be based on exposure and vulnerability, we have added to the Excel a couple of relevant indicators, from the Community Risk Assessment dashboard. PRC can decide if and how to combine these various features. If needed, 510 can be asked for assistance of course. 4. Important notes: ACCURACY: it should be realized that during the course of the coming 3 days, the typhoon might change course, or increase/decrease in terms of strength. This will affect the quality of these predictions. The accuracy figure of 73% that is mentioned in the post-event case should be seen as an upper bound. Given the added inaccuracy of wind speed, the overall accuracy will be lower. This damage prediction is only about completely damaged houses, not about partially damaged houses. We only included municipalities that are within 100km of the forecasted typhoon track, as we have seen from previous typhoons (with comparable wind speeds) that damage figures outside of this area are low. 5. Sources The wind speed is provided by Tropical Storm Risk (University College London). It is the ‘maximum 1-minute sustained wind speed’. An average of this is calculated per municipality. (Latest forecast date: 2018-09-14 00:00 UT >> 7:00AM Manila time) Typhoon track (from which ‘distance to typhoon track’ per municipality is calculated), is provided by UCL as well. (Latest forecast date: 2018-09-14 00:00 UT >> 7:00AM Manila time) Additionally, various wall and roof type categories from the Philippines national census. The model uses 2010 census data, as it was developed using this data (2015 census data on municipality level only became available in 2018). The 2015 census data could not be easily plugged in, because of some differences in roof/wall categories. We believe that this would not change the result much though, as even if there are large differences from 2010 to 2015, these would still be dominated by wind speed effects in the model. All additional indicators, that are added to the Excel table (population, poverty) are derived from the Community Risk Assessment dashboard (Go to this link and click ‘Export to CSV’ on top-right.) The sources for these indicators can be found in the dashboard itself.
  • Updated November 15, 2019 | Dataset date: Oct 4, 2019
    This dataset updates: As needed
    This data is about the population projections using the results of the 2015 Census of Population.
  • 600+ Downloads
    Updated November 14, 2019 | Dataset date: Jan 1, 2019-Oct 5, 2019
    This dataset updates: As needed
    1) Natural disaster events include avalanches, earthquakes, flooding, heavy rainfall & snowfall, and landslides & mudflows as recorded by OCHA field offices based on assessments in the field. 2) A natural disaster incident is defined as an event that has affected (i.e. impacted) people, who may or may not require humanitarian assistance. 3) The information includes assessment figures from OCHA, ANDMA, IOM, Red Crescent Societies, national NGOs, international NGOs, and ERM. 4) The number of affected people and houses damaged or destroyed are based on the reports received. These figures may change as updates are received.
  • 1300+ Downloads
    Updated November 14, 2019 | Dataset date: Nov 14, 2019
    This dataset updates: Every month
    This set includes data on fatalities in UN peacekeeping operations. It includes a unique casualty identifier, the incident date, the mission acronym, the type of casualty, the ISO code associated with the country of origin of the personnel, the relevant M49 DESA code, the type pf personnel involved, and the type of incident.
  • UNOSAT code: FL20191030SOM This map illustrates the extent of surface waters detected over Hiraan, Middle Shabelle and Lower Shabelle Region in Somalia as detect by VIIRS-NOAA satellite between 2 & 6 November 2019. In the analysed area, a total of about 830 km2 are likely flooded and about 74,000 people might be exposed by taking into account WorldPop population estimates. About 10 km of the roads seem to be affected. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
  • UNOSAT code: FL20191030SOM This map illustrates the satellite-detected flood water extent and IDP distribution within the town of Belet Weyne in Belet Weyne District, Hiiran Region, Somalia. The analysis was conducted by analyzing WorldView-1 images acquired on the 1 November 2019. As observed from the satellite image, the town of Belet Weyne is heavily affected by floods. Around 60% of the vicinity of the town is completely inundated; the districts of Hawa tako, Kutimbo, and the Lamagalay Regional Millitary Base completely submerged in water. More than 110 IDP sites are located inside of the town and 40% of them are located within completely flooded areas. This is a preliminary analysis and has not been validated in the field yet. Please send ground feedback to UNITAR-UNOSAT.
  • 80+ Downloads
    Updated November 14, 2019 | Dataset date: Aug 31, 2019
    This dataset updates: Every six months
    According to the IPC Acute Food Insecurity analysis update conducted in May - August 2019, over 1.8 million people in Central African Republic are in severe acute food insecurity (IPC Phases 3+), including more than 465,000 people in emergency conditions during the lean season, latest data shows. Find more information here: https://bit.ly/36XG8VU.
  • Updated November 14, 2019 | Dataset date: May 1, 2018
    This dataset updates: As needed
    Ukraine education facilities in Donetska and Luhanska
  • 100+ Downloads
    Updated November 14, 2019 | Dataset date: Feb 28, 2019
    This dataset updates: Every year
    As of September 2018, 9.8 million people (43.6% of the rural population) were estimated to be in Food Crisis and Emergency (IPC Phase 3 and Phase 4). An estimated 2.6 million are classified in IPC Phase 4 nationwide: these people require urgent action to reduce their food deficits and to protect their livelihoods. The current Phase 3 and 4 estimates correspond to a 17.4% increase (from 26.2% to 43.6%) compared to the previous analysis for the same time period last year (2017). Projections suggest that from November 2018 to February 2019, the total population in IPC Phase 3 and IPC Phase 4 is expected to increase to 10.6 million (47.1% of the rural population). For more visit: ipcinfo.org