Netherlands Red Cross
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  • In this analysis we have combined several data sources around the floods in Bangladesh in August 2017. Visualization See attached map for a map visualization of this analysis. See http://bit.ly/2uFezkY for a more interactive visualization in Carto. Situation Currently, in Bangladesh many water level measuring stations measure water levels that are above danger levels. This sets in triggers in motion for the partnership of the 510 Data Intitiative and the Red Cross Climate Centre to get into action. Indicators and sources In the attached map, we combined several sources: Locations of waterlevel stations and their respective excess water levels (cms above danger level) at 14/08/2017 (Source: http://www.ffwc.gov.bd/index.php/googlemap?id=20) Population density in Bangladesh to quickly see where many people live in comaprison to these higher water-level stations. (Source: http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00018 >> the People per hectare 2015 UN-adjusted totals file is used.) Vulnerability Index: we constructed a Vulnerability Index (0-10) based on two sources. First poverty incidence was collected from Worldpop (Source: http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00020 >> The estimated likelihood of living below $2.50/day). Second, we used a Deprivation Index which is estimated in the report Lagging District Reports 2015 (Source: http://www.plancomm.gov.bd/wp-content/uploads/2015/02/15_Lagging-Regions-Study.pdf > Appendices > Table 20), which combines many socio-economic variables into one Deprivation Index through PCA analysis. Detailed methodology Vulnerability The above-mentioned poverty source file is on a raster level. This raster level poverty was transformed to admin-4 level geographic areas (source: https://data.humdata.org/dataset/bangladesh-admin-level-4-boundaries), by taking a population-weighted average. (Source population also Worldpop). The district-level PCA components from abovementioned reports were matched to the geodata based on district names, and thus joined to the admin-4 level areas, which now contain a poverty value as well as Deprivation Index value. Note that all admin-4 areas within one district (admin-2) obviously all have the same value. The poverty rates do differ between all admin-4 areas. Lastly, both variables were transformed to a 0-10 score (linearly), and a geomean was taken to calculate the final index of the two. A geomean (as opposed to an arithmetic mean) is often used in calculating composite risk indices, for example in the widely used INFORM-framework (www.inform-index.org).
  • Netherlands Red Cross
    Updated August 15, 2017 | Dataset date: Jul 14, 2017
    A crude version of the INFORM risk-framework is applied to Enumeration Areas (which is unofficial, but is deeper than admin-3), in Southern Malawi. This is done specifically for area selection regarding the ECHO2 project in 3 TA's: Mwambo (Zomba district), Makhwira (Chikwawa district) and Ndamera (Nsanje district). Scope Enumeration areas are retrieved from http://www.masdap.mw/layers/geonode%3Aeas_bnd. These are used, because we want to prioritize on a deeper level than Traditional Authority (admin-3) level, and there are no other official boundaries available. The dataset in principle data for the whole of Malawi, but contains 4 filters, which can be applied, which are the following: Filter_south: this filters out only the South of Malawi, for which the drough and flood analysis has been carried out (see details below). Filter_district: contains all EA's from the 3 pre-identified districts Zomba, Chikwawa and Nsanje. Filter_TA: contains all EA's from the 3 pre-identified TAs Mwambo, Makhwira and Ndamera. Filter_GVH: there are also 44 Group Village Heads pre-identified for the project. As these GVH's are points on a map, all EA's are selected here which have a GVH within their boundaries or very close to their boundaries. INFORM risk-framework The INFORM framework (http://www.inform-index.org/) is applied to assess risk per community, which is considered the main criteria for prioritization within the project. Because of low data availability we apply a crude version for now, with only some important indicators of the framework actually used. Since we feel that these indicators (see below) still constitute together a current good assessment of risk, and we want to stimulate the use and acceptance of the INFORM-framework, we choose to use it anyway. The INFORM risk-score consists of 3 main components: hazards, vulnerability and coping capacity. Hazard: For hazard we focus - in line with the ECHO2 project - on floods and droughts only. Analysis has been carried out (see more details below), to determine flood and drought risk on a scale from 0-10 with a resolution of 250meter grid cells. This has subsequently been aggregated to Enumeration Areas, by taking a population-weighted average. Thereby taking into account where people actually live within the Enumeration Areas. (Population data source: Worldpop: http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00155) Vulnerability: Vulnerability is operationalized here through poverty incidence. Poverty rate (living below $1.25/day) is retrieved from Worldpop (http://www.worldpop.org.uk/data/summary/?doi=10.5258/SOTON/WP00157) and again transformed from a 1km resolution grid to Enumeration Areas through a population-weighted average. Lack of Coping capacity: Coping capacity is measured through traveltimes to various facilities, namely traveltime to nearest hospistal, traveltime to nearest trading centre and traveltime to nearest secondary school. Together these are all proxies of being near/far to facilities, and thereby an indicator of having higher/lower coping capacity. See https://510.global/developing-and-field-testing-a-remoteness-indicator-in-malawi/ for more information on how these traveltimes were calculated and validated. Use All features are stored in a CSV, but can easily be joined to the geographic shapefile to make maps on EACODE. Flood and Drought calculations Drought layer The drought risk map was created by analyzing rainfall data in the past 20 years using standard precipitation index (SPI) , which is a widely used index in drought analysis. Based on SPI6 values for the period October-march, which is the main rainy season in Malawi. Each pixel is classified to drought or no drought for each year based on SPI6 values, drought year if SPI value for a pixel is less than -1. Next, relative frequency is calculated, the number of times drought has occurred in the considered 20 year period. This frequency is then converted to probability of drought occurrence in a given year. We validated our analysis by comparing NDVI values for the drought year against long term average values. Flood layer To identify flood moments in Malawi Landsat imagery was studied (1984-2017). Floods were clearly evidenced in 9 dates. For the clearest and most representative layers the mNDWI (modified Normalized Water Index) was calculated. The index mNDWI (McFeeters 1996; Xu 2006) for Landsat bands is calculated as follows: (b2GREEN-b7MIRSWIR/b2GREEN+b7MIRSWIR). In this variation of the index the higher values are the wettest. A threshold was applied to the mNDWI to separate flood from non-flood or water from non-water pixels. The resulting layers were aggregated and the final stretched from 0-10, where 0 are the pixels where no flood is expected while pixels with 10 are where most frequent flood has been evidenced and therefore expected. The largest flood was observed in 2015, as the scenes were cloudy the flood extent was manually interpreted from several scenes. The evidenced flood dates are: 29 Feb. 1988 low flood, 19 march 1989, 17 march 1997, Feb 1998, March 1999 low flood, 2001 since February 16 until end of April, 2007 17 February since early Feb., 2008 Feb. medium flood, 2015 January – March. The water bodies in this layer are not represented and have a value of 0 like the rest of land where flood is absent.
  • Netherlands Red Cross
    Updated June 8, 2017 | Dataset date: Jun 1, 2017
    Product This priority index was derived by combining a detailed flood extent mapping with detailed human settlement geo-data. Both sources were combined to produce the location and magnitude of population living in flooded areas. This was subsequently aggregated to admin-4 areas (GND) as well as admin-3 areas (DS divisional). The flood extent mapping was derived in turn by combining two sources: Flood extent maps could be produced rather faster using satellite imageries captured by either optical sensors or Synthetic Aperture Radar (SAR) sensors. In most places flood is cause by heavy rainfall which means in most cases cloud is present, this is a limitation for optical sensors as they can’t penetrate clouds. Radar sensors are not affected by cloud, which make them more useful in presence of cloud. In This analysis we analyzed sentinel2 optical image from May 28th and Sentinel 1 SAR image from May 30th. Then we combine the two results adding up the flood extents. Main cloud covered areas and permanent water bodies are removed from the flood extent map using the Sentinel 2 cloud mask. The scale/resolution of the flood extent map is 30mts where as the permanent water body map has 250m scale resolution. This will introduce some discrepancy: part of flood extent map could be permanent water body. Scope Analysis focused on 4 districts in South-West Sri Lanka based on news reports (https://www.dropbox.com/s/n0qdqe7qfgq6fyv/special_situation.pdf?dl=0). Based on the admin-3-level analysis, highest percentages of population living in flooded areas were seen in Matara district. Admin-4 level analysis concentrated only on Matara district for that reason. Caveats The dataset is showing percentage flooded. The data has not yet been corrected for small populations. We believe the product is currently pointing to the high priority areas. In the shp or csv files the user of this data could easily correct for small populations, if there is a wish to target on the amount of people affected. Data used from partners The human settlement data was retrieved from http://ciesin.columbia.edu/data/hrsl/. Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. High Resolution Settlement Layer (HRSL). Source imagery for HRSL © 2016 DigitalGlobe. Accessed 01-06-2017. The Radar imagery analysis was done by NASA JPL, whose input in this product has been crucial. Visualization An example map is available here: http://bit.ly/SriLankaFloodMap Linked data Admin boundaries 3 and 4 can be found here (link on OBJECT_ID): https://data.humdata.org/group/lka?q=&ext_page_size=25&sort=score+desc%2C+metadata_modified+desc&tags=administrative+boundaries#dataset-filter-start How to use The ratio column in the SHPs or CSVs can be multiplied by 100 to get the percentage of flooding in the area.
  • Netherlands Red Cross
    Updated May 19, 2017 | Dataset date: Nov 22, 2016
    Blog post about this prediction can be found here: http://bit.ly/2fWF2jq The predicted priority index of Typhoon Haima is produced by a machine learning algorithm that was trained on four past typhoons: Haiyan, Melor, Hagupit and Rammasun. It uses base line data for the whole country, combined with impact data of windspeeds and rains, and trained on counts by the Philippine government on people affected and houses damaged. First run The Priority Index is a 1-5 classification that can be used to identify the worst hit areas: those that need to be visited for further assessments or support first. Second run The model now predicts two things: a weighted index between partially damaged and completely damaged, where partially damaged is counted as 25% of the completely damaged. This has proven to give he highest accuracy. the precentage of total damage (damaged houses versus all houses) The absolute number of houses damaged / people affected is insufficiently validated at the moment, and should just be used for further trainng and ground-truthing. Data sources: Administrative boundaries (P_Codes) - Philippines Government; Published by GADM and UN OCHA (HDX) Census 2015 (population) - Philippine Statistics Authority; received from UN OCHA (HDX) Avg. wind speed (km/h) - University College London Typhoon path - University College London Houses damaged - NDRRMC Rainfall - GPM Poverty - Pantawid pamilyang pilipino program (aggregated) For the second run of the algorithm we also included: Roof and wall materials New geographical features The result of different models can be found in the file 'Typhoon Haima - performance of different models - second run.csv' A note on how to interpret this. date running date alg_date same alg_model name of the algorithm used alg_predict_on name of the learning variable alg_use_log i s the learning variable transformed in log code_version version of the learn.py code All the columns with feat_ indicates the importance of that feature, if not present that feature was not used. learn_matrix name of the learning matrix with the 5 typhoons run_name unique run name (pickle files and csv files have this name for this model) typhoon_to_predict name of a new typhoon to predict val_accuracy accuracy based on 10 categories of damage 0% 10% 20% … val_perc_down perc of underpredicted categories val_perc_up perc of overpredicted categories Val_best_score best r2 score Val_stdev_best_score error on best score based on the CV Val_score_test r2 score on the test set (this should be around +- 5% of the previus number to not overfit Val_mean_error_num_houses average error on the number of houses val_median_error_num_houses median val_std_error_num_houses std deviation of the errors (lower is better) Algorithm developed by 510.global the data innovation initiative of the Netherlands Red Cross.
    • XLSX
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    • 400+ Downloads
    • This dataset updates: Never
  • Netherlands Red Cross
    Updated March 29, 2017 | Dataset date: Oct 13, 2016
    Accumulated precipitation for Haiti - Hurricane Matthew - October 3-6th 2016 Based on GPM IMERG data. See comments for assumptions and workflow.
    • XLSX
    • excel
    • 100+ Downloads
    • 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.
  • Netherlands Red Cross
    Updated February 18, 2017 | Dataset date: Feb 17, 2017
    Dataset contains windspeeds in miles per hour.
  • Netherlands Red Cross
    Updated February 7, 2017 | Dataset date: Dec 26, 2016
    This dataset contains: windspeeds of Typhoon Nina rainfall of Typhoon Nina Priority Index of Typhoon Nina The predicted priority index of Typhoon Nina is produced by a machine learning algorithm that was trained on five past typhoons: Haiyan, Melor, Hagupit and Rammasun and Haima, It uses base line data for the whole country, combined with impact data of windspeeds and rains, and trained on counts by the Philippine government on houses damaged and completely destroyed. The output is a weighted index between partially damaged and completely damaged, where partially damaged is counted as 25% of the completely damaged. This has proven to give he highest accuracy. The absolute number of houses damaged / people affected is insufficiently validated at the moment, and should just be used for further trainng and ground-truthing. Scoring The model has an best r2 score of 0.794933727 and an accuracy of 0.699470899 Data sources: Administrative boundaries (P_Codes) - Philippines Government; Published by GADM and UN OCHA (HDX) Census 2015 (population) - Philippine Statistics Authority; received from UN OCHA (HDX) Avg. wind speed (mph) - University College London Typhoon path - University College London Houses damaged - NDRRMC Rainfall - GPM Poverty - Pantawid pamilyang pilipino program (aggregated) Roof and wall materials New geographical features All the columns with feat_ indicates the importance of that feature, if not present that feature was not used. learn_matrix name of the learning matrix with the 5 typhoons run_name unique run name (pickle files and csv files have this name for this model) typhoon_to_predict name of a new typhoon to predict val_accuracy accuracy based on 10 categories of damage 0% 10% 20% … val_perc_down perc of underpredicted categories val_perc_up perc of overpredicted categories Val_best_score best r2 score Val_stdev_best_score error on best score based on the CV Val_score_test r2 score on the test set (this should be around +- 5% of the previus number to not overfit Val_mean_error_num_houses average error on the number of houses val_median_error_num_houses median val_std_error_num_houses std deviation of the errors (lower is better) Algorithm developed by 510.global the data innovation initiative of the Netherlands Red Cross.
  • Netherlands Red Cross
    Updated November 15, 2016 | Dataset date: Oct 24, 2016
    NDRRMC report of October 28th 2016
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    • XLSX
    • 300+ Downloads
    • This dataset updates: Never
  • Counts of damage and casualties from official data sets
    • XLSX
    • xslx
    • CSV
    • 100+ Downloads
    • This dataset updates: Never
  • This data is created using GPM data. It contains the accumulated precipitation for the periods October 11-18th, October 18-20th and the total over both periods.
  • Netherlands Red Cross
    Updated October 28, 2016 | Dataset date: Jan 10, 2015
    Flood extend data of Malawi floods in January 2015
  • Netherlands Red Cross
    Updated October 26, 2016 | Dataset date: Oct 20, 2016
    Data provided by Mark Saunders at University College London