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  • FTS Requirements and Funding Data for Philippines
    OCHA FTS
    Updated July 24, 2017 | Dataset date: Jul 24, 2017
    FTS publishes data on humanitarian funding flows as reported by donors and recipient organizations. It presents all humanitarian funding to a country and funding that is specifically reported or that can be specifically mapped against funding requirements stated in humanitarian response plans. The data comes from OCHA's Financial Tracking Service, is encoded as utf-8 and the second row of the CSV contains HXL tags.
    • CSV
    • This dataset updates: Every day
  • World Bank Indicators for Philippines
    HDX
    Updated July 20, 2017 | Dataset date: Jan 1, 1960-Dec 31, 2016
    Contains data from World Bank's data portal covering various indicators (one per resource).
    • JSON
    • This dataset updates: Every year
  • Satellite Detected Water Extent in Maguindanao and Cotabato Provinces, Philli...
    UN Operational Satellite Applications Programme (UNOSAT)
    Updated July 20, 2017 | Dataset date: Jun 9, 2017
    This map illustrates the satellite-detected water extent in Maguindanao and Cotabato provinces, Phillipines. The UNITAR-UNOSAT analysis used a Sentinel-1 satellite image acquired on the 07 June 2017 and could observe areas with standing waters mainly affecting agricultural fields. Within the map extent, the UNITAR-UNOSAT analysis identified ~34,000 ha of those areas and ~127 km of potentially affected road mainly local roads. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
  • WorldPop Philippines
    WorldPop
    Updated July 20, 2017 | 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.
  • Philippines - Who is Doing What Where in Marawi Conflict as of 5 July 2017
    OCHA Philippines
    Updated July 13, 2017 | Dataset date: Jul 5, 2017
    Who does What Where of Humanitarian actors/activities in Marawi Conflict as of 5 July 2017
    • XLSX
    • This dataset updates: Every two weeks
  • Mindanao Displacement Dataset [By Request Only]
    OCHA Philippines
    Updated July 5, 2017 | Dataset date: Jan 1, 2012-Jun 8, 2017
    It is an excel file with incidents in Mindanao, categorized by natural disasters or human induced disaster (conflict, non-state armed groups, crimes). Data is disaggregated down to municipality level. Data includes date of the event, and number of people affected or displaced. Dataset will be quarterly updated.
    Request access
  • Marawi Conflict IDPs as of 29 June 2017
    OCHA Philippines
    Updated June 30, 2017 | Dataset date: Jun 29, 2017
    Number of IDPs affected by Marawi Conflict by municipality
    • XLSX
    • This dataset updates: Never
  • Philippines - Who is Doing What Where in Marawi Conflict as of 28 June 2017
    OCHA Philippines
    Updated June 30, 2017 | Dataset date: Jun 28, 2017
    Who does What Where of Humanitarian actors/activities in Marawi Conflict as of 28June 2017
    • XLSX
    • This dataset updates: Every week
  • Philippines: Pre-Disaster Indicators
    OCHA Philippines
    Updated June 28, 2017 | Dataset date: Sep 1, 2010
    This datasets contains a collection of pre-disaster indicators for the Philippines.
    • XLSX
    • This dataset updates: Never
  • Philippines - Who is Doing What Where in Marawi Conflict as of 20 June 2017
    OCHA Philippines
    Updated June 23, 2017 | Dataset date: Jun 20, 2017
    Who does What Where of Humanitarian actors/activities in Marawi Conflict as of 20 June 2017
    • xlsx,
    • This dataset updates: Every week
  • Philippines - Typhoon Haima - Priority Index
    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
    • CSV
    • This dataset updates: Never
  • Philippines - Who is Doing What Where in Mindanao
    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
    • CSV
    • This dataset updates: Every three months
  • Philippines - Who is Doing What Where in Mindanao
    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
  • Philippines - Administrative boundaries levels 1 - 3 (June2016)
    OCHA Philippines
    Updated April 20, 2017 | 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 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
  • Global Food Prices Database (WFP)
    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
    • This dataset updates: Every month
  • Global - Epidemiological update on Zika Virus
    IFRC
    Updated February 14, 2017 | Dataset date: Sep 13, 2016
    This data is a global overview on Zika Virus.
    • XLSX
    • This dataset updates: Every two weeks
  • Typhoon Nina - windspeed, rainfall and priority index
    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.
  • Philippines - Census Population data
    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).
    • XLS
    • This dataset updates: Never
  • Philippines - Haima House Damage PCODED - NDRRMC Sitrep 9
    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)
    • CSV
    • This dataset updates: Every week
  • Maternal mortality ratio per 100,000 live births
    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.
  • Percentage of children less than 5 stunted (Both sexes)
    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
  • Percentage of children less than 5 overweight (Both sexes)
    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
  • Philippines - Employed Persons by major Industry group and by sex
    OCHA Philippines
    Updated January 20, 2017 | Dataset date: Jan 1, 2011
    This dataset shows the employed Persons by major Industry group and by sex
    • XLS
    • This dataset updates: Every year
  • Percentage of children less than 5 wasted (Both sexes)
    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
  • Percentage of seats held by women in national parliament
    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.