Hurricane Maria peak 3-sec gust footprints in kml format (through 9UT 21st Sept 2017) Credit - Tropical Storm Risk/UCL

This resource is comprised of Twitter data collected and processed by the AIDR system during the 2017 hurricane Maria. The data contains information about number of people affected, injured, dead, reports of damages, missing people and so on. Please contact us if you need full dataset with tweets content.

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.

.A large scale landslide inventory was carried out by a team from the University of Twente, use of 5 scenes of Pléiades satellite imageries with resolution of 0.5m, which were obtained in September 23 and October 5 after the hurricane, made available through UNITAR-UNOSAT. Apart from these also a series of Digital Globe Images were used that were collected for the Google Crisis Response through a KML layer. The images were visually interpreted by image interpretation experts, and landslides were mapped as polygons, separating scarp, transport and accumulation areas, and classifying the landslides in types. A total of 9,960 landslides were identified, which include 8,576 debris slides, 1,010 debris flows and 374 rock falls, with area of 7.30km2, 2.50km2, and 0.50 km2 respectively. The whole area of landslide is 10.30 km2, which covers 1.37 percent of the island. The source of landslides is 3.30km2, and the other 7.0 km2 is transportation and deposition area. Almost all of the rivers flooded due to intensive precipitation. The flooded area is 13.03km2, which covers 1.74% of the island. Dominica will face some new problems for mountain hazards in the coming years, as many of the fresh scarps may produce more debris, and many tree trunks are still on the slopes or in the river channels. With so many fresh landslides in the upper catchments, it is likely that debris flows will be triggered with rainfall thresholds that are substantially lower than before the hurricane. Hurricane Maria damaged the forest cover dramatically, which changed the conditions for hazard initiation. Without the protection of vegetation, more new shallow landslides could happen in the near future. A series of cascading hazards may happen, for example landslides or debris flow blocking rivers and resulting in outburst floods. Therefore more detailed evaluation of the post-Maria hazard and risk situation is very important.

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 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 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 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 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.

Displacement Tracking Matrix (DTM) Dominica- Hurricane Maria Response

Third Round, Displacement Tracking Matrix (DTM) Dominica- Hurricane Maria Response

Locations for responding NetHope NGO members in Puerto Rico, following Hurricanes Irma and Maria. Represented organizations include: American Red Cross, Americares, DirectRelief, Feeding America, Global Communities, Habitat for Humanity, International Medical Corps, Mercy Corps, Oxfam America, Samaritan's Purse, Save the Children, and World Vision.

The Database of Government Actions on COVID-19 in Developing Countries collates and tracks national policies and actions in response to the pandemic, with a focus on developing countries. The database provides information for 20 Global South countries – plus 6 Global North countries for reference – that Dalberg staff are either based in or know well. The database content is drawn from publicly available information combined, crucially, with on-the-ground knowledge of Dalberg staff. The database contains a comprehensive set of 100 non-pharmaceutical interventions – organized in a framework intended to make it easy to observe common variations between countries in the scope and extent of major interventions. Interventions we are tracking include: • Health-related: strengthening of healthcare systems, detection and isolation of actual / possible cases, quarantines • Policy-related: government coordination and legal authorization, public communications and education, movement restrictions • Distancing and hygiene: social distancing measures, movement restrictions, decontamination of physical spaces • Economic measures: economic and social measures, logistics / supply chains and security. We hope the database will be a useful resource for several groups of users: (i) governments and policymakers looking for a quick guide to actions taken by different countries—including a range of low- and middle-income countries, (ii) policy analysts and researchers studying the data to identify patterns of actions taken and compare the effectiveness of different interventions in curbing the pandemic, and (iii) media and others seeking to quickly access facts about the actions taken by governments in the countries covered in the database. Comments on the data can be submitted to covid.database.comments@dalberg.com Questions can be submitted to covid.database.questions@dalberg.com www.dalberg.com

This map illustrates satellite-detected, potentially damaged structures in some affected colonies located in Xochimilco and Tlahuac Municipalities, Federal District, Mexico. The analysis was performed by Faculty of Geography of the Autonomous University of the State of Mexico (UAEMex) using as post-event WorldView-2 satellite imagery acquired 20 September 2017. UAEMex identified 87 potentially damaged structures from which 63 are located in Santa Maria Nativitas colony and surroundings. Please do not hesitate to send feedback to UNITAR - UNOSAT.

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 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 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.

These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the United Nations Children's Fund ( UNICEF ) - Population Modelling for use in Routine Health Planning and Monitoring project (contract no. 43335861). Projects partners included the Kenya Unicef Regional and Country Offices, WorldPop research group at the University of Southampton and the Center for International Earth Science Information Network in the Columbia Climate School at Columbia University. Assane Gadiaga (WorldPop) led the input processing and the modelling work following the Random Forest (RF)-based dasymetric mapping approach developed by Stevens et al. (2015). Thomas Abbott supported the covariates processing work. In-country engagements were done by David Kyalo, Olena Borkovska ( GRID3 , Maria Muniz (Unicef). Using the 2009 and 2019 census data from the Kenya’s National Bureau of Statistics (KNBS), the US Census Bureau released the census-based total population projections, population by age and sex and digital sub-counties boundaries. Duygu Cihan helped in the preparation of these input population data. Attila N Lazar, Edith Darin and Heather Chamberlain advised on the modelling procedure. The work was overseen by Attila N Lazar and Andy J Tatem. Recommended citations Gadiaga A. N., Abbott T. J., Chamberlain H., Lazar A. N., Darin E., Tatem A. J. 2023. Census disaggregated gridded population estimates for Kenya (2022), version 2.0. University of Southampton. doi:10.5258/SOTON/WP00762 License These data may be distributed using a Creative Commons Attribution 4.0 International (CC BY 4.0) License, specified in legal code. Contact release[at]worldpop.org for more information. The authors followed rigorous procedures designed to ensure that the used data, the applied method and thus the results are appropriate and of reasonable quality. If users encounter apparent errors or misstatements, they should contact WorldPop at release[at]worldpop.org. WorldPop, University of Southampton, and their sponsors offer these data on a "where is, as is" basis; do not offer an express or implied warranty of any kind; do not guarantee the quality, applicability, accuracy, reliability or completeness of any data provided; and shall not be liable for incidental, consequential, or special damages arising out of the use of any data that they offer.

These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the United Nations Children's Fund ( UNICEF ) - Population Modelling for use in Routine Health Planning and Monitoring project (contract no. 43335861). Projects partners included the Kenya Unicef Regional and Country Offices, WorldPop research group at the University of Southampton and the Center for International Earth Science Information Network CIESIN in the Columbia Climate School at Columbia University. Assane Gadiaga (WorldPop) led the input processing and the modelling work following the Random Forest (RF)-based dasymetric mapping approach developed by Stevens et al. (2015). Thomas Abbott supported the covariates processing work, as well as Christopher Lloyd, particularly for the processing of residential/non-residential building footprints. In-country engagements were done by Benard Mitto, Justine Dowden (CIESIN) and Maria Muniz (Unicef). Using the 2009 and 2019 census data from the Kenya’s National Bureau of Statistics (KNBS), the US Census Bureau released the census-based total population projections, population by age and sex and digital sub-counties boundaries. Duygu Cihan helped in the preparation of these input population data. Attila N Lazar, Edith Darin and Heather Chamberlain advised on the modelling procedure. The work was overseen by Attila N Lazar and Andy J Tatem. For further details, please, read the Release Statement. Release content KEN_population_v1_0_gridded.tif KEN_population_v1_0_agesex.zip KEN_population_v1_0_mastergrid.tif Recommended citations Gadiaga A. N., Abbott T. J., Chamberlain H., Lloyd C. T., Lazar A. N., Darin E., Tatem A. J. 2022. Census disaggregated gridded population estimates for Kenya (2021), version 1.0 University of Southampton. doi:10.5258/SOTON/WP00747 License These data may be distributed using a Creative Commons Attribution 4.0 International (CC BY 4.0) License, specified in legal code. Contact release[at]worldpop.org for more information. The authors followed rigorous procedures designed to ensure that the used data, the applied method and thus the results are appropriate and of reasonable quality. If users encounter apparent errors or misstatements, they should contact WorldPop at release[at]worldpop.org. WorldPop, University of Southampton, and their sponsors offer these data on a "where is, as is" basis; do not offer an express or implied warranty of any kind; do not guarantee the quality, applicability, accuracy, reliability or completeness of any data provided; and shall not be liable for incidental, consequential, or special damages arising out of the use of any data that they offer.

The Ministry of Education announced the first phase of school reopening on 16 October 2017. Therefore, IOM prioritized schools currently housing the displaced population in the first round of DTM assessment. 43 of the pre-identified collective centers were schools and 33 of these were housing displaced individuals on 6 October. IOM aims to provide basic information on these targeted schools to inform the government and general humanitarian community of the situation in these collective centers and support provision of assistance. This report presents the results of assessments carried out from 11-14 October in 19 schools that are scheduled to reopen in the coming days.