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  • 300+ Downloads
    Updated December 11, 2019 | Dataset date: Jan 1, 2019-Nov 30, 2019
    This dataset updates: Every month
    DTM’s Displacement Tracking tool collects and reports on displaced numbers of households on a daily basis, allowing for regular reporting of new displacements in terms of numbers, geography and needs. More than 3.6 million people are displaced as per August 2018 assessment.
  • 20+ Downloads
    Updated December 6, 2019 | Dataset date: Nov 5, 2019
    This dataset updates: Every three months
    This data was collected through 66 key informant interviews across 23 settlements in the affected areas of between the 30th of October and 5th of November. At the time of completion, 5,704 individuals were identified as living in the affected settlements in Greater & Little Abaco. This multi-sectoral location assessment has been designed with input from the various Emergency Support Functions (ESFs) coordinating the Hurricane Dorian response in the Bahamas. It provides an overview of the population distribution, needs, and access to services, of returnees, remainees, and evacuees across Greater and Little Abaco. This document is part of a series of ongoing efforts conducted by IOM to inform service providers, humanitarian actors and donors on main needs while reducing survey fatigue of key informants by streamlining assessment activities.
  • 10+ Downloads
    Updated November 29, 2019 | Dataset date: Nov 8, 2019
    This dataset updates: Every three months
    The dataset contains IDPs at sub national level displaced by Cyclone Kenneth
  • 200+ Downloads
    Updated November 26, 2019 | Dataset date: Oct 2, 2019-Nov 9, 2019
    This dataset updates: Every three months
    Tropical cyclone Idai, on March 15th 2019, brought torrential rains and winds affecting mostly the provinces of Manica, Sofala and Zambézia, In Mozambique, causing flash flooding and subsequent destruction. This dataset contains DTM assessments containing number of affected people, their needs, geographic locations etc.
  • 30+ Downloads
    Updated November 21, 2019 | Dataset date: Nov 21, 2019
    This dataset updates: As needed
    Draft Subdivisions for Grand Bahama, showing draft subdivisions by jurisdiction. The boundaries, designations and names used on this map do not imply official endorsement or acceptance by the Shelter Cluster. This is a draft produced for programmatic and planning purposes, it will be updated as soon as possible. The boundaries for Grand Bahama Port Authority (GBPA) on this shapefile are approved. Other boundaries are draft boundaries. Any areas labelled "private" are partially under jurisdiction of GBPA depending on the activity.
  • 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.
  • 2000+ Downloads
    Updated November 13, 2019 | Dataset date: Nov 8, 2019
    This dataset updates: Every month
    DTM is tracking in/out movement of displaced people in 4 Provinces hit by IDAI cyclone.
  • 40+ Downloads
    Updated November 11, 2019 | Dataset date: Oct 7, 2019
    This dataset updates: As needed
    Based on requests from ECHO partners, REACH has worked to compile and map the location of cyclone shelters in Ukhia and Teknaf in the two Upazilas most affected by the Rohingya refugee influx of August 2017, in order to orient and inform humanitarian actors working in these locations. This database includes three lists of shelters: First, a list of shelters as designated on lists provided by the Cox's Bazar District Relief and Rehabilitation Officer (DRRO). Second, a list of cyclone shelters designated by the DRRO with additional structures reported to be shelters according to lists provided by the Cyclone Preparedness Programme (CPP), the District Disaster Managemen Plan (DDMP), Upazila Nirbahi Officers (UNOs), UNDP, IOM, UNHCR, WFP, the Local Government Engineering Department (LGED), and the Comprehensive Disaster Management Programme (CDMP). The third list mirrors the second list but also includes all of alternative names used by different agencies.
  • 500+ Downloads
    Updated October 31, 2019 | Dataset date: Oct 9, 2019-Oct 16, 2019
    This dataset updates: Every three months
    Tropical Cyclone Idai made landfall in central Mozambique the night of 14 March 2019. On 27 March 2019, IOM in coordination with the Government of Mozambique carried out site assessments in 32 evacuation sites in the Beira district in the Sofala province of Mozambique one of the provinces affected by the storm.
  • 10+ Downloads
    Updated October 30, 2019 | Dataset date: May 9, 2019-May 14, 2019
    This dataset updates: Never
    The data contains number of IDPs dis-aggregated by gender, age at village level.
  • Updated September 11, 2019 | Dataset date: Sep 28, 2017
    This data is by request only
    Hurricane Maria has affected the island of Puerto Rico, a US territory. Standby Task Force has been activated by the US Federal Emergency Management Agency (FEMA) to collect information on hospitals and medical facilities. Here are the collected data on the status of each facility.
  • Updated September 10, 2019 | Dataset date: Feb 4, 2018-Mar 2, 2018
    This data is by request only
    This data is the result of interviews conducted with 444 Dominicans impacted by Hurricane Maria. This round of interviews took place between 4 February and 2 March 2018, roughly four and a half months after Hurricane Maria made landfall. This is the third of five rounds of surveys in Dominica.
  • Updated September 10, 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
  • 100+ Downloads
    Updated September 6, 2019 | Dataset date: Apr 7, 2019
    This dataset updates: As needed
    Malawi has experienced floods and sustained heavy rains caused by the tropical cyclone Idai weather system. IOM, in close coordination with the Government of Malawi through the Department of Disaster Management Affairs (DoDMA), conducted multi-sectoral location assessments in Chikwawa, Nsanje, Phalombe, Zomba districts. The dataset contains number of IDPs, households and their needs at sub-national level.
  • 400+ Downloads
    Updated August 29, 2019 | Dataset date: Jan 1, 2015
    This dataset updates: Never
    The tropical cyclonic strong wind model use information from 2594 historical tropical cyclones, topography, terrain roughness, and bathymetry.
  • 40+ Downloads
    Updated June 5, 2019 | Dataset date: May 25, 2019
    This dataset updates: As needed
    Archive of Global Tropical Cyclone Tracks Tracks from 1980 to May 2019. Data provided by NOAA International Best Track Archive for Climate Stewardship (IBTrACS). Original data and metadata are available here (https://www.ncdc.noaa.gov/ibtracs/index.php?name=ib-v4-access). For data updates after May 2019, please visit the ncdc website.
  • 70+ Downloads
    Updated May 6, 2019 | Dataset date: May 3, 2019-May 3, 2019
    This dataset updates: Never
    TSR gust footprint for Cyclonic Storm FANI - 2019-May 3 - Credit: Tropical Storm Risk/UCL Issued about 3 hours after it made landfall ~15km to the west of Puri in Odisha state, India. The gust footprint shows the magnitude and extent of the peak winds from FANI at landfall and immediately thereafter. Our model indicates that the peak gust at landfall was 150-160 mph and that gusts of 100 mph and over extend over a swathe width of ~100 km. The wind damage from FANI will almost certainly be severe. This damage will be exacerbated by heavy rains and an expected high storm surge. Unfortunately the forecast wind swathe for FANI is in line with the heavily populated cities of Bhubaneshwar and Cuttack.
  • 70+ Downloads
    Updated April 26, 2019 | Dataset date: Apr 23, 2019
    This dataset updates: Never
    Just over a month after the tropical cyclone IDAI-19 a new tropical storm is heading toward Comoros, Tanzania and the north of Mozambique called 'twentyfour-19'. The category 2 tropical storm is expected to make landfall between the 27th and the 28th of April 2018 south of Mocimboa da Praia town located in the province of Cabo Delgado in the extreme northern part of Mozambique. Based on data of the expected tropical cyclone path Twentyfour-19, wind speeds zones from Joint Research Centre (Issued on 23 April 2019 06:00 UTC), and population data from WorldPop 2015, UNITAR-UNOSAT conducted a population exposure analysis for Mozambique. About 750,000 people in Mozambique, mainly in Cabo Delgado province are living inside the wind speed zones of 120 km/h, 90 km/h and 60 km/h accordingly. Cyclone track: Joint Research Centre (JRC) as of 23/04/2019 Wind speed zones: Joint Research Centre (JRC) as of 23/04/2019, 06:00 UTC Administrative Levels: OCHA ROSEA Spatial Demographic Data: WorldPop (2015), 100m spatial resolution Analysis: UNITAR-UNOSAT (23/04/2019)
  • 30+ Downloads
    Updated April 12, 2019 | Dataset date: Apr 12, 2019
    This dataset updates: Live
    Data produced under Thematic National Mapping and Cartographic updating Project at 1/250 000 scale implemented by National Cartography and Tele-detection Centre, Mozambique (CENACARTA).
  • 30+ Downloads
    Updated April 12, 2019 | Dataset date: Dec 31, 2014
    This dataset updates: As needed
    Livelihood zone maps define geographic areas of a country where people generally share similar options for obtaining food and income and similar access to markets. An understanding of geographic livelihood systems is a key component of FEWS NET’s food security analysis. The maps are produced through multi-day workshops during which food security stakeholders and country experts identify zones. Factors considered include agro-climatology, elevation, land-cover, market accessibility, sources of food, and major economic activities. A livelihood zone map is typically accompanied by a livelihood description which outlines the key characteristics of each zone. In some cases, maps may also be accompanied by livelihood profiles, which are in-depth descriptions of the characteristics of wealth groups within each zone. The zone maps are available for download as PDFs, PNGs, and GIS shapefiles. The shapefiles and maps are all produced and maintained by FEWS NET. Please consult the logos in maps for the full list of organizations who participated in and/or funded the zoning workshop. For more information on FEWS NET’s livelihood approach and to view our other livelihood products, please click here.
  • 20+ Downloads
    Updated April 12, 2019 | Dataset date: Apr 12, 2019
    This dataset updates: Every day
    This dataset contains key figures and other data pertaining to the evolving humanitarian situation in during the Tropical Cyclone Idai response in Mozambique.
  • 40+ Downloads
    Updated April 12, 2019 | Dataset date: Apr 12, 2019
    This dataset updates: Live
    Data produced under Thematic National Mapping and Cartographic updating Project at 1/250 000 scale implemented by National Cartography and Tele-detection Centre, Mozambique (CENACARTA).
  • 10+ Downloads
    Updated April 10, 2019 | Dataset date: Mar 29, 2019
    This dataset updates: Never
    This map illustrates the satellite detected surface waters in Manicaland Province, Zimbabwe, as observed from the Sentinel-1 data imagery acquired on 12 and 24 March 2019. Within the analysis extent, over Manicaland Province, 164,130 ha of surface waters were observed the 12 March 2019. and about of 406,600 ha of surface waters were observed the 24 March 2019. It represents an increase of 40 %. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT. Satellite data (pre-event) : Sentinel-1 Imagery date: 12 March 2019 Resolution: 10 m Copyright: Copernicus 2019 / ESA Source: ESA Satellite data (post-event) : Sentinel-1 Imagery date: 24 March 2019 Resolution: 10 m Copyright: Copernicus 2019 / ESA Source: ESA Boundary data: OCHA ROSEA Water body & waterway: COD Analysis : UNITAR-UNOSAT Production: UNITAR - UNOSAT
  • This map illustrates the satellite detected surface waters in Masvingo Province, Zimbabwe, as observed from the Sentinel-1 data imageries acquired on 12 and 24 March 2019. Within the analysis extent, over Manicaland Province, 84,500 ha of surface waters were observed the 12 March 2019. and about of 288,500 ha of surface waters were observed the 24 March 2019. It represents an icrease of 29 %. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT. Satellite data (pre-event) : Sentinel-1 Imagery date: 12 March 2019 Resolution: 10 m Copyright: Copernicus 2019 / ESA Source: ESA Satellite data (post-event) : Sentinel-1 Imagery date: 24 March 2019 Resolution: 10 m Copyright: Copernicus 2019 / ESA Source: ESA Boundary data: OCHA ROSEA Water body & waterway: COD Analysis : UNITAR-UNOSAT Production: UNITAR - UNOSAT
  • This map illustrates the satellite detected surface waters in Mashonaland East Province, Zimbabwe, as observed from the Sentinel-1 data imageries acquired on 12 and 24 March 2019. Within the analysis extent, over Manicaland Province, 108,780 ha of surface waters were observed the 12 March 2019. and about of 469,680 ha of surface waters were observed the 24 March 2019. It represents an icrease of 23 %. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR - UNOSAT. Satellite data (pre-event) : Sentinel-1 Imagery date: 12 March 2019 Resolution: 10 m Copyright: Copernicus 2019 / ESA Source: ESA Satellite data (post-event) : Sentinel-1 Imagery date: 24 March 2019 Resolution: 10 m Copyright: Copernicus 2019 / ESA Source: ESA Boundary data: OCHA ROSEA Water body & waterway: COD Analysis : UNITAR-UNOSAT Production: UNITAR - UNOSAT