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  • 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.
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    • This dataset updates: Never
  • OCHA Philippines
    Updated May 15, 2017 | Dataset date: Sep 21, 2016
    Who does What Where of Development and Humanitarian actors/activities in Mindanao for 2nd and 3rd Quarter 2016
    • XLSX
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    • This dataset updates: Every three months
  • OCHA Philippines
    Updated May 15, 2017 | Dataset date: Dec 31, 2016
    Who does What Where of Development and Humanitarian actors/activities in Mindanao for 4th Quarter 2016
    • XLSX
    • This dataset updates: Every three months
  • These datasets are derived from the boundaries of the Barangays as observed at the end of April 2016 as per the Philippine Geographic Standard Code (PSGC) dataset. It has been generated on the basis of the layer created by the Philippine Statistics Authority (PSA) in the context of the 2015 population census. These layers are up-to-date as of the second quarter of 2016. Acknowledge PSA and NAMRIA as the sources. LMB is still the source of official administrative boundaries of the Philippines. In the absence of available official administrative boundary, the IMTWG have agreed to clean and use the PSA administrative boundaries which are used to facilitate data collection of surveys and censuses. The dataset can only be considered as indicative boundaries and not official. * For administrative level 4 (Barangay) please contact the contributor (OCHA Philippines) via this page. This COD replaces https://data.humdata.org/dataset/philippines-administrative-boundaries
  • WFP - World Food Programme
    Updated April 6, 2017 | Dataset date: Mar 13, 2017
    The Global Food Prices Database has data on food prices (e.g., beans, rice, fish, and sugar) for 76 countries and some 1,500 markets. The dataset includes around 500,000 records and is updated monthly. The data goes back as far as 1992 for a few countries, although most of the price trends start in 2000-2002.
    • CSV
    • 600+ Downloads
    • This dataset updates: Every month
  • IFRC
    Updated February 14, 2017 | Dataset date: Sep 13, 2016
    This data is a global overview on Zika Virus.
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    • This dataset updates: Every two weeks
  • 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.
  • OCHA Philippines
    Updated February 2, 2017 | Dataset date: Aug 2, 2016
    Sex and age disaggregated population data by various administrative levels (1 to 4) based on 2015 Census with Philippines Standard Geographic Code (PSGC).
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    • This dataset updates: Never
  • BRC Maps Team
    Updated January 29, 2017 | Dataset date: Oct 25, 2016
    This data is about damaged houses in the Philippines after Typhoon Haima (Lawin)
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    • This dataset updates: Every week
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 1990-Jan 1, 2015
    [Source: United Nations Department of Economic and Social Affairs] The maternal mortality ratio (MMR) is the ratio of the number of maternal deaths during a given time period per 100,000 live births during the same time-period.
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    • 80+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 1966-Jan 1, 2014
    [Source: World Health Organization] Percentage of stunting (height-for-age less than -2 standard deviations of the WHO Child Growth Standards median) among children aged 0-5 years
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    • 50+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 1970-Jan 1, 2014
    [Source: World Health Organization] Percentage of overweight (weight-for-height above +2 standard deviations of the WHO Child Growth Standards median) among children aged 0-5 years
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    • 20+ Downloads
    • This dataset updates: Every year
  • This dataset shows the employed Persons by major Industry group and by sex
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    • This dataset updates: Every year
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 1966-Jan 1, 2014
    [Source: World Health Organization] Percentage of (weight-for-height less than -2 standard deviations of the WHO Child Growth Standards median) among children aged 0-5 years
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    • 30+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 1990-Jan 1, 2015
    [Source: United Nations Department of Economic and Social Affairs] The proportion of seats held by women in national parliaments is the number of seats held by women members in single or lower chambers of national parliaments, expressed as a percentage of all occupied seats.
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    • 20+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 1950-Jan 1, 2005
    [Source: United Nations Department of Economic and Social Affairs] Probability of dying between birth and exact age 1. It is expressed as average annual deaths per 1,000 births
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    • 50+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 1950-Jan 1, 2005
    [Source: United Nations Department of Economic and Social Affairs] Probability of dying between birth and exact age 5. It is expressed as average annual deaths per 1,000 births.
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    • 20+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 1950-Jan 1, 2005
    [Source: United Nations Department of Economic and Social Affairs] Number of deaths over a given period. Refers to five-year periods running from 1 July to 30 June of the initial and final years.
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    • 20+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 1950-Jan 1, 2005
    [Source: United Nations Department of Economic and Social Affairs] The average number of years of life expected by a hypothetical cohort of individuals who would be subject during all their lives to the mortality rates of a given period.
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    • 30+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 2000-Jan 1, 2011
    [Source: United Nations Development Programme] This document is an extract of data compiled by automated extraction of data from a variety of online sources and manually compiled sources.
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    • 20+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 2010-Jan 1, 2014
    [Source: United Nations Development Programme] Percentage of the population ages 15 and older who can, with understanding, both read and write a short simple statement on their everyday life.
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    • 60+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 20, 2017 | Dataset date: Jan 1, 1950-Jan 1, 2010
    [Source: United Nations Department of Economic and Social Affairs] Total Population - Both Sexes. De facto population in a country, area or region as of 1 July of the year indicated. Figures are presented in thousands.
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    • 100+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 19, 2017 | Dataset date: Jan 1, 2003-Jan 1, 2011
    [Source: United Nations Office on Drugs and Crime] Number of sexual violence cases
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    • 60+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 19, 2017 | Dataset date: Jan 1, 1960-Jan 1, 2012
    [Source: World Bank] Adult mortality rate is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old dying before reaching age 60, if subject to current age-specific mortality rates between those ages.
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    • 20+ Downloads
    • This dataset updates: Every year
  • HDX
    Updated January 19, 2017 | Dataset date: Jan 1, 1960-Jan 1, 2012
    [Source: World Bank] Adult mortality rate is the probability of dying between the ages of 15 and 60--that is, the probability of a 15-year-old dying before reaching age 60, if subject to current age-specific mortality rates between those ages.
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    • 70+ Downloads
    • This dataset updates: Every year