Updated June 19, 2019
| Dataset date: Jun 19, 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 June 16, 2019
| Dataset date: Jan 1, 1999-Dec 31, 2017
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 June 11, 2019
| Dataset date: Jun 10, 2019
This dataset updates: Every six months
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.
OpenStreetMap exports for use in GIS applications.
This theme includes all OpenStreetMap features in this area matching:
building IS NOT NULL
Features may have these attributes:
name
building
building:levels
building:materials
addr:full
addr:housenumber
addr:street
addr:city
office
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
OpenStreetMap exports for use in GIS applications.
This theme includes all OpenStreetMap features in this area matching:
waterway IS NOT NULL OR water IS NOT NULL OR natural IN ('water','wetland','bay')
Features may have these attributes:
name
waterway
covered
width
depth
layer
blockage
tunnel
natural
water
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
OpenStreetMap exports for use in GIS applications.
This theme includes all OpenStreetMap features in this area matching:
highway IS NOT NULL
Features may have these attributes:
name
highway
surface
smoothness
width
lanes
oneway
bridge
layer
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
OpenStreetMap exports for use in GIS applications.
This theme includes all OpenStreetMap features in this area matching:
amenity IS NOT NULL OR man_made IS NOT NULL OR shop IS NOT NULL OR tourism IS NOT NULL
Features may have these attributes:
name
amenity
man_made
shop
tourism
opening_hours
beds
rooms
addr:full
addr:housenumber
addr:street
addr:city
This dataset is one of many OpenStreetMap exports on
HDX.
See the Humanitarian OpenStreetMap Team website for more
information.
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, 2017-Dec 31, 2017
This dataset updates: Every year
The health and survival of women and their new-born babies in low income countries is a key public health priority, but basic and consistent subnational data on the number of pregnancies to support decision making has been lacking. WorldPop integrates small area data on the distribution of women of childbearing age, age-specific fertility rates, still births and abortions to map the estimated distributions of pregnancies for each 1x1km grid square across all low and middle income countries. Further details on the methods can be found in Tatem et al and James et al..
WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton). 2017. Dominica 1km pregnancies. Version 2.0 2015 estimates of numbers of pregnancies per grid square, with national totals adjusted to match national estimates on numbers of pregnancies made by the Guttmacher Institute (http://www.guttmacher.org) DOI: 10.5258/SOTON/WP00522
Updated May 28, 2019
| Dataset date: Jan 1, 2017-Dec 31, 2017
This dataset updates: Every year
The health and survival of women and their new-born babies in low income countries is a key public health priority, but basic and consistent subnational data on the number of live births to support decision making has been lacking. WorldPop integrates small area data on the distribution of women of childbearing age and age-specific fertility rates to map the estimated distributions of births for each 1x1km grid square across all low and middle income countries. Further details on the methods can be found in Tatem et al. and James et al..
WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton). 2017. Dominica 1km births. Version 2.0 2015 estimates of numbers of live births per grid square, with national totals adjusted to match UN national estimates on numbers of live births (http://esa.un.org/wpp/). DOI: 10.5258/SOTON/WP00414
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
This map illustrates potentially damaged structures and buildings in the southeastern part of Dominica (St. Patrick & St. David Parishes) as detected by satellite image acquired after landfall of the Tropical Cyclone Maria-17 on 19 September 2017. The UNITAR-UNOSAT analysis combined with Copernicus analysis, identified 1304 potentially damaged structures within the extent of this map. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represents about 56 % of the total number of structures within the analysed cloud free areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
This map illustrates potentially damaged structures and buildings in the Southern part of Dominica (St. Luke, St. Mark, St. Georges & St. Patrick Parishes) as detected by satellite image acquired after landfall of the Tropical Cyclone Maria-17 on 19 September 2017. UNITAR-UNOSAT analysis used a Pleiades images acquired on 20 and 23 September 2017 as post imagery. Within the extent of this map UNITAR-UNOSAT identified in the cloud free zones about 5032 potentially damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represent about 75 % of the total number of structures within the analysed area. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
This map illustrates potentially damaged structures and buildings in the north-western part of Dominica (St. Andrew, St. Joseph & St. David Parishes) as detected by satellite image acquired after landfall of the Tropical Cyclone Maria-17 on 19 September 2017. UNITAR-UNOSAT analysis used a Pleiades image acquired on 23 September 2017 and a WorldView-3 image acquired on 1 October 2017 as post imagery. Within the extent of the analyzed areas, UNITAR-UNOSAT identified in the cloud free zones 5,609 potentially damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represents about 90 % of the total number of structures within the analysed cloud free areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
This map illustrates potentially damaged structures and buildings in the north-western part of Dominica (St. John, St. Andrew, St. Peter & St. Joseph Parishes) as detected by satellite images acquired after landfall of the Tropical Cyclone Maria-17 on 19 September 2017. UNITAR-UNOSAT analysis used a Pleiades images acquired on 20 and 23 September 2017 as post imagery. Within the extent of this map UNITAR-UNOSAT identified in the cloud free zones about 3723 potentially damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represents about 70 % of the total number of structures within the analysed cloud free areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
This map illustrates potentially damaged structures and buildings in the Central and Southern parts of Dominica (St. Patrick, St. Mak, St. George, St. Luke, St. David & St. Paul Parishes) as detected by satellite image acquired after landfall of the Tropical Cyclone Maria-17 on 19 September 2017. The UNITAR-UNOSAT analysis combined with Copernicus analysis, identified 12,873 potentially damaged structures in this zone. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represents about 70 % of the total number of structures within the analyzed areas. Please note that some areas could not be analyzed due to the cloud cover. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
This map illustrates potentially damaged structures and buildings in Pointe Michel (Saint Luke Parish) as detected by satellite image acquired after landfall of the Tropical Cyclone Maria-17 on 19 September 2017. UNITAR-UNOSAT analysis used a Pleiades image acquired on 23 September 2017 as post imagery. UNITAR-UNOSAT identified in the analysed area Pointe Michel (St. Luke Parish) 550 potentially damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represent about 70 % of the total number of structures within the analysed area. Evidences of floods and mudflow could be also observed along the two rivers that cross the town of Pointe Michel. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
This map illustrates potentially damaged structures and buildings in South Roseau (Saint Georges Parish) as detected by satellite image acquired after landfall of the Tropical Cyclone Maria-17 on 19 September 2017. UNITAR-UNOSAT analysis used a Pleiades image acquired on 21 September 2017 as post imagery. UNITAR-UNOSAT identified in the analysed area Roseau South (Castle Comfort, Citronnier and Loubiere) 863 potentially damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represents about 70 % of the total number of structures within the analysed area. Evidences of floods and mudflow could be also observed in the area of Wallhouse. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
This map illustrates potentially damaged structures and buildings in Marigot(Saint Andrew Parish) as detected by satellite image acquired after landfall of the Tropical Cyclone Maria-17 on 19 September 2017. UNITAR-UNOSAT analysis used a Pleiades image acquired on 23 September 2017 as post imagery. UNITAR-UNOSAT identified in the analysed area Marigot (Saint Andrew Parish) 1,345 potentially damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represent about 83 % of the total number of structures within the analysed area. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
This map illustrates potentially damaged structures and buildings in Portsmouth (Saint John Parish) as detected by satellite image acquired after landfall of the Tropical Cyclone Maria-17 on 19 September 2017. UNITAR-UNOSAT analysis used a Pleiades image acquired on 20 September 2017 as post imagery. UNITAR-UNOSAT identified in the analysed area (Portsmouth and surroundings) 782 potentially damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represents 55% of the total number of structures within the cloud free analysed area. The analysis could have been underestimated due to cloud cover. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
This map illustrates potentially damaged structures and buildings in Southern Dominica as detected by satellite image acquired after landfall of the Tropical Cyclone Maria-17 on 19 September 2017. UNITAR-UNOSAT analysis used a Pleiades image acquired on 23 September 2017 as post imagery. Within the extent of this map UNITAR-UNOSAT identified in the cloud free areas of southern Dominica 2482 potentially damaged structures. Taking into account the pre-building footprints provided by Humanitarian OpenStreetMap, this represent about 80 % of the total number of structures within the analysed area. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
Updated April 17, 2019
| Dataset date: Jan 1, 2019-Jan 1, 2020
This dataset updates: Live
Live list of active aid activities for Dominica 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
Updated April 2, 2019
| Dataset date: Sep 25, 2017
This data is by request only
This dataset contains links to 650+ geolocated and categorized images collated from social media sources depicting the damages on Dominica after Hurricane Maria. The images and videos are geolocated, and categorized according to severity of damage seen in the pictures.
Please contact Standby Task Force for access to the dataset. coreteam AT standbytaskforce.com
