Updated September 16, 2019
| Dataset date: Nov 22, 2018
This dataset updates: As needed
Data source: Simple Spatial Survey Method (S3M), 2013.
The main indicators used for the analysis were the overall prevalences of stunting and wasting, with thresholds consistent with the guidelines suggested by the World Health Organization (WHO).
Updated September 22, 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.
Updated September 16, 2019
| Dataset date: Nov 22, 2018
This dataset updates: As needed
This layer contains information about the drought risk - by second-level administrative area - estimated during the Integrated Context Analysis (ICA) run in Sudan in 2018.
Data source: HQ VAM Analysis of Chirps Rainfall Estimates (RFE), 1982-2017.
The main indicators used for the analysis were the number of poor growing seasons observed in the time window of interest and the inter-annual rainfall variability.
Updated September 22, 2019
| Dataset date: Jan 1, 1990-Sep 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.
Updated September 22, 2019
| Dataset date: Jan 15, 2000-Aug 15, 2019
This dataset updates: Every week
This dataset contains Food Prices data for Afghanistan. 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.
Updated September 22, 2019
| Dataset date: Sep 22, 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.
Updated September 21, 2019
| Dataset date: Aug 30, 2018
This dataset updates: Every year
Angola administrative level 0 (country), 1 (province), 2 (municipality | city council) and 3 (commune) boundary polygon, line, and point shapefiles, KMZ files, live services, and geodatabase; and gazetteer
Vetting and live service provision by Information Technology Outreach Services (ITOS) with funding from USAID.
The administrative level 0-2 shapefiles are suitable for database or ArcGIS linkage to the population statistics tables.
Updated September 20, 2019
| Dataset date: Sep 21, 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 September 21, 2019
| Dataset date: Sep 19, 2019
This dataset updates: Every six months
These high-resolution maps estimate not only the number of people living within 30-meter grid tiles, but also provide insights on demographics at unprecedentedly high resolutions. These maps aren’t built using Facebook data and instead rely on combining the power of machine vision AI with satellite imagery and census information.
Updated September 21, 2019
| Dataset date: Sep 19, 2019
This dataset updates: Every six months
These high-resolution maps estimate not only the number of people living within 30-meter grid tiles, but also provide insights on demographics at unprecedentedly high resolutions. These maps aren’t built using Facebook data and instead rely on combining the power of machine vision AI with satellite imagery and census information.
Updated September 21, 2019
| Dataset date: Sep 19, 2019
This dataset updates: Every six months
These high-resolution maps estimate not only the number of people living within 30-meter grid tiles, but also provide insights on demographics at unprecedentedly high resolutions. These maps aren’t built using Facebook data and instead rely on combining the power of machine vision AI with satellite imagery and census information.
Updated September 21, 2019
| Dataset date: Sep 19, 2019
This dataset updates: Every six months
These high-resolution maps estimate not only the number of people living within 30-meter grid tiles, but also provide insights on demographics at unprecedentedly high resolutions. These maps aren’t built using Facebook data and instead rely on combining the power of machine vision AI with satellite imagery and census information.
Updated September 21, 2019
| Dataset date: Sep 19, 2019
This dataset updates: Every six months
These high-resolution maps estimate not only the number of people living within 30-meter grid tiles, but also provide insights on demographics at unprecedentedly high resolutions. These maps aren’t built using Facebook data and instead rely on combining the power of machine vision AI with satellite imagery and census information.
Updated September 21, 2019
| Dataset date: Sep 19, 2019
This dataset updates: Every six months
These high-resolution maps estimate not only the number of people living within 30-meter grid tiles, but also provide insights on demographics at unprecedentedly high resolutions. These maps aren’t built using Facebook data and instead rely on combining the power of machine vision AI with satellite imagery and census information.
Updated September 21, 2019
| Dataset date: Sep 19, 2019
This dataset updates: Every six months
These high-resolution maps estimate not only the number of people living within 30-meter grid tiles, but also provide insights on demographics at unprecedentedly high resolutions. These maps aren’t built using Facebook data and instead rely on combining the power of machine vision AI with satellite imagery and census information.
Updated September 21, 2019
| Dataset date: Feb 1, 2019-Sep 20, 2019
This dataset updates: Every month
Es un ejercicio de la Cruz Roja Colombia para recoger información sobre asentamientos de migrantes y albergues en Bogotá cruzado con datos recolectados por IMMAP mediante el API de mercadeo de Facebook que representan valores estimados de usuarios que antes vivían en venezuela y ahora su lugar de residencia es Bogotá
Updated September 20, 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.
Updated September 20, 2019
| Dataset date: Sep 19, 2019
This dataset updates: Every six months
These high-resolution maps estimate not only the number of people living within 30-meter grid tiles, but also provide insights on demographics at unprecedentedly high resolutions. These maps aren’t built using Facebook data and instead rely on combining the power of machine vision AI with satellite imagery and census information.
Updated September 20, 2019
| Dataset date: Sep 11, 2019
This dataset updates: Never
UNOSAT code: FL20190905LAO This map illustrates satellite-detected surface water in Champasak province of Lao PDR as observed from Sentinel-1 imagery acquired on 10 September 2019. Within the analysed extent of about 14,400 km2, a total about 53 km2 of land appear to be flooded as of 10 September 2019. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT.
Important Note: Flood analysis from Sentinel-1 imagery acquired on 10 September 2019 may seriously underestimate presence of standing flood water in built up areas due to backscattering of the radar signal
