Philippines
Overview
Population, total 98,393,574
World Bank - Data - Jan 01, 2013
Population density 329.99
World Bank - Data - Jan 01, 2013
Human Development Index rank 117
UNDP HDR Statistics - Jan 01, 2014
GDP per capita, PPP 6,535.88
World Bank - Data - Jan 01, 2013
Land area 298,170
World Bank - Data - Jan 01, 2014
Children under five mortality rate per 1,000 live births
29.9 / 2013
UN DESA Millennium Development Goals - Data - Nov 24, 2015
Prevalence of undernourishment
16.2 / 2011
FAO - Data - Nov 24, 2015
Proportion of the population using improved drinking water sources
92.0 / 2015
UN DESA Millennium Development Goals - Data - Apr 28, 2016
Per capita food supply
2608.0 / 2011
FAOSTAT - Data - Nov 24, 2015
MPI: Population living below $1.25 PPP per day
18.42 / 2014
UNDP HDR Statistics - Data - Nov 24, 2015
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  • Netherlands Red Cross
    Updated December 26, 2016 | 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 December 16, 2016 | Dataset date: Sep 17, 2013
    Dataset shows the Locations of IDP Evacuation Centres in Zamboanga City as of 18 Sept 2013
  • WFP - World Food Programme
    Updated December 15, 2016 | Dataset date: Dec 5, 2016
    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
    • 400+ Downloads
    • This dataset updates: Every month
  • IFRC
    Updated December 14, 2016 | Dataset date: Sep 13, 2016
    This data is a global overview on Zika Virus.
    • XLSX
    • This dataset updates: Every two weeks
  • Netherlands Red Cross
    Updated December 7, 2016 | 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
    • CSV
    • This dataset updates: Never
  • OCHA Philippines
    Updated December 1, 2016 | 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).
    • XLS
    • This dataset updates: Never
  • OCHA Philippines
    Updated November 30, 2016 | Dataset date: Jun 15, 2016
    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 source. * 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
  • OCHA ROAP
    Updated November 17, 2016 | Dataset date: Nov 14, 2016
    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: http://weather.unisys.com/hurricane/index.php.
  • WorldPop
    Updated November 16, 2016 | Dataset date: Jan 1, 2015
    These datasets provide estimates of population counts for each 100 x 100m grid cell in the country for various years. Please refer to the metadata file and WorldPop website (www.worldpop.org) for full information.
  • Netherlands Red Cross
    Updated November 15, 2016 | Dataset date: Oct 24, 2016
    NDRRMC report of October 28th 2016
    • CSV
    • XLSX
    • This dataset updates: Never
  • Counts of damage and casualties from official data sets
  • Who is doing what and where in Philippines for Typhoon Haima (Lawin)
    • XLSX
    • 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.
  • BRC Maps Team
    Updated November 2, 2016 | Dataset date: Oct 25, 2016
    This data is about damaged houses in the Philippines after Typhoon Haima (Lawin)
    • CSV
    • This dataset updates: Every week
  • OCHA Philippines
    Updated October 31, 2016 | Dataset date: Oct 31, 2013
    Pcode table that consists of names and pcodes for administrative levels 1 (Region); 2 (Province); 3 (Municipality); 4(Barangay). Pcodes are stored as "text" in this excel spreadsheet. Historic COD, replaced by https://data.humdata.org/dataset/regional-admin1-boundaries-of-the-philippines-june2016
    • XLSX
    • This dataset updates: Every year
  • DROMIC data by DSWD on municipalities within the 50km radius of the typhoon Haiyan/Yolanda track as of 27 January 2014. Historic COD used during response to Typhoon Haiyan, November 2013.
    • XLSX
    • 10+ Downloads
    • This dataset updates: Every year
  • Municipal population of Regions 4B, 5, 6, 7, 8, 13(Caraga). Regions that were most affected by Typhoon Haiyan (Yolanda) 07 Nov 2013. Historic COD used during response to Typhoon Haiyan, November 2013.
    • XLS
    • This dataset updates: Every year
  • OCHA Philippines
    Updated October 18, 2016 | Dataset date: Apr 30, 2013
    Estimated population statistics for 2013. Former COD, replaced with https://data.humdata.org/dataset/philippines-2015-barangay-admin4-census-population
    • ZIP
    • 10+ Downloads
    • This dataset updates: Every year
  • OCHA Philippines
    Updated October 18, 2016 | Dataset date: May 1, 2010
    The dataset shows the PHL population Statistics for admin level 1 and 2 Historic COD, it has been replaced with https://data.humdata.org/dataset/philippines-2015-barangay-admin4-census-population
    • XLSX
    • This dataset updates: Every year
  • Philippines Provincial Population with pcodes for 2000 and 2007 census. This is Original data from Philippines National Census and Statistics Office. Historic COD, it has been replaced with https://data.humdata.org/dataset/philippines-2015-barangay-admin4-census-population
    • XLS
    • This dataset updates: Never
  • BRC Maps Team
    Updated October 18, 2016 | Dataset date: Oct 18, 2016
    90m resolution elevation data
    • ZIP
    • This dataset updates: Never
  • OCHA Philippines
    Updated September 21, 2016 | Dataset date: Sep 21, 2016
    Who does What Where of Development and Humanitarian actors/activities in Mindanao for 2nd and 3rd Quarter 2016
    • XLSX
    • This dataset updates: Every three months
  • OCHA Philippines
    Updated September 19, 2016 | Dataset date: Sep 1, 2010
    This datasets contains a collection of pre-disaster indicators for the Philippines.
    • XLSX
    • This dataset updates: Never
  • OCHA Philippines
    Updated September 16, 2016 | Dataset date: Sep 1, 2010
    Number of households by municipality by type of housing wall used from census 2010
    • XLSX
    • This dataset updates: Never
  • OCHA Philippines
    Updated September 16, 2016 | Dataset date: Sep 1, 2010
    Number of households by municipality by source of drinking water from census 2010
    • XLSX
    • This dataset updates: Never