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  • 900+ Downloads
    Updated 14 March 2019 | Dataset date: December 31, 2018-December 31, 2018
    This dataset updates: Every year
    This shape file consists of consolidated history of tropical storm paths over the past 50 years in the West Pacific, South Pacific, South Indian and North Indian basin. Attributes provides details such as storm Name, Date, Time, wind speed and GPS points for each advisory point. Wind speeds are in knots for more details on speeds conversion and storm categories please visit the original source of data: UNISYS (http://weather.unisys.com/hurricane/index.php), NOAA (http://rammb.cira.colostate.edu/products/tc_realtime/index.asp)
  • 1000+ Downloads
    Updated 20 October 2016 | Dataset date: October 20, 2016-October 20, 2016
    This dataset updates: Never
    Data provided by Mark Saunders at University College London
  • 100+ Downloads
    Updated 8 June 2022 | Dataset date: January 28, 2022-January 31, 2022
    This dataset updates: Never
    Following the passage of tropical storm Ana across southern Malawi, the districts of Chikwawa, Mulanje, Nsanje and Phalombe have been severely hit by torrential and persistent rains, although there are reports of other districts also been affected by the onset, such as Mulanje, Chiradzulu and Neno. Situational overviews conducted by the United Nations Satellite Centre (UNOSAT) reported a flooded area of 20Km2 in the districts of Balaka, Blantyre, Neno and Zomba, where 5,400 people are potentially exposed or living close to flooded areas. Also, the Malawian government agency of Department of Disaster Management Affairs (DoDMA) reported several blocked roads in the Phalombe district, which negatively affects the daily lives of several households whom are relegated to camps and obstructing their access to essential services, such as health facilities.
  • 800+ Downloads
    Updated 10 March 2017 | Dataset date: March 08, 2017-March 08, 2017
    This dataset updates: Never
    Please note that the windspeed and track dataset only covers the part where the this was still a tropical cyclone. For explanation see below caveats. Due to this we will not release a priority index, since windspeed data is missing for most of the country. Dataset of windspeed and track was kindly provided by University College London. The rainfall data is calculated based on GPM. It is the accumulated rainfall from March 6th midnight to March 10th 10:00am Madagascar time.
  • 200+ Downloads
    Updated 21 July 2018 | Dataset date: July 22, 2018-July 22, 2018
    This dataset updates: Every month
    Flood-affected populations by settlement type as of June 2018. The dataset presents the data to LGA level (Admin 2) in all the three crisis-affected states of north eastern Nigeria, thus Borno, Yobe and Adamawa. The affected populations include households and individuals. The settlement types include Households in IDP Camps, Households in Host Communities and provides a computation of affected people by settlement type; Camp flood risk mapping as of June 2018. The dataset presents the data to LGA level (Admin 2) in IDP camps across all the three crisis-affected states of north eastern Nigeria, thus Borno, Yobe and Adamawa; Host Community flood risk analysis and mapping as of June 2018. Also includes a CSV file on Flood-affected populations by settlement type as of June 2018.
  • 1100+ Downloads
    Updated 15 November 2019 | Dataset date: September 13, 2018-September 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.
  • 100+ Downloads
    Updated 26 April 2019 | Dataset date: April 23, 2019-April 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)
  • 3800+ Downloads
    Updated 23 June 2022 | Dataset date: March 26, 2019-October 01, 2021
    This dataset updates: Every six 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.
  • 2200+ Downloads
    Updated 30 March 2022 | Dataset date: November 12, 2021-December 01, 2021
    This dataset updates: Every year
    The dataset contains number of IDPs, Returnees and host communities and their needs at sub national level.