Updated July 11, 2019
| Dataset date: Jul 11, 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.
The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery.
Updated May 28, 2019
| Dataset date: Jan 1, 2019-Dec 31, 2019
This dataset updates: Every year
Age and sex structures: WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. An overview of the data can be found in Tatem et al, and a description of the modelling methods used found in Tatem et al and Pezzulo et al. The 'Global per country 2000-2020' datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World for each year 2000-2020 structured by male/female and 5-year age classes (plus a <1 year class). These efforts necessarily involved some shortcuts for consistency. The 'individual countries' datasets represent older efforts to map population age and sex counts for each country separately, using a set of tailored geospatial inputs and differing methods and time periods. The 'whole continent' datasets are mosaics of the individual countries datasets.
WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076).
Updated May 28, 2019
| Dataset date: Jan 1, 2000-Dec 31, 2020
This dataset updates: Every year
WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. An overview of the data can be found in Tatem et al, and a description of the modelling methods used found in Stevens et al. The 'Global per country 2000-2020' datasets represent the outputs from a project focused on construction of consistent 100m resolution population count datasets for all countries of the World for each year 2000-2020. These efforts necessarily involved some shortcuts for consistency. The 'individual countries' datasets represent older efforts to map populations for each country separately, using a set of tailored geospatial inputs and differing methods and time periods. The 'whole continent' datasets are mosaics of the individual countries datasets
WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645
Updated April 5, 2019
| Dataset date: Jan 1, 2019-Jan 1, 2020
This dataset updates: Live
Live list of active aid activities for Guam shared via the International Aid Transparency Initiative (IATI). Includes both humanitarian and development activities. More information on each activity (including financial data) is available from http://www.d-portal.org
This is a Twitter dataset collected during the typhoon Mangkhut 2018 in the Philippines. The data was collected, processed, and analyzed by the AIDR (http://aidr.qcri.org) platform using state of the art machine learning techniques. The data includes the reports of number of injured and dead people, infrastructure damage reports, missing or found people, urgent needs and donation offers for each hour. Due to Twitter TOS, we do not share full tweets content on HDX. Please contact us via HDX or on aidr.qcri@gmail.com to get tweet ids of the dataset along with a tool which can be used to rehydrate tweets from tweet ids.
