Data Grid Completeness
14/27 Core Data 24 Datasets 8 Organisations Show legend
What is Data Grid Completeness?
Data Grid Completeness defines a set of core data that are essential for preparedness and emergency response. For select countries, the HDX Team and trusted partners evaluate datasets available on HDX and add those meeting the definition of a core data category to the Data Grid Completeness board above. Please help us improve this feature by sending your feedback to hdx@un.org.
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Presence, freshness, and quality of dataset
  • Dataset fully matches criteria and is up-to-date
  • Dataset partially matches criteria and/or is not up-to-date
  • No dataset found matching the criteria
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Affected People
3 Datasets
Refugees & Persons of Concern
Humanitarian Profile Locations
Humanitarian Needs
Casualties
Armed Conflict Location & Event Data Project (ACLED)
Food Security & Nutrition
2 Datasets
Global Acute Malnutrition Rate
Severe Acute Malnutrition Rate
Food Prices
WFP - World Food Programme
Geography & Infrastructure
7 Datasets
Administrative Divisions
Populated Places
Roads
Humanitarian OpenStreetMap Team (HOT)
Humanitarian OpenStreetMap Team (HOT)
Population & Socio-economy
2 Datasets
Baseline Population
Baseline Population by Age & Sex
Poverty Rate
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  • 900+ Downloads
    Updated May 15, 2017 | Dataset date: Dec 31, 2016
    This dataset updates: Every three months
    Who does What Where of Development and Humanitarian actors/activities in Mindanao for 4th Quarter 2016
  • 500+ Downloads
    Updated March 22, 2017 | Dataset date: Sep 21, 2016
    This dataset updates: Every three months
    Who does What Where of Development and Humanitarian actors/activities in Mindanao for 2nd and 3rd Quarter 2016
  • 900+ Downloads
    Updated December 14, 2016 | Dataset date: Sep 13, 2016
    This dataset updates: Every two weeks
    This data is a global overview on Zika Virus.
  • 1800+ Downloads
    Updated December 7, 2016 | Dataset date: Nov 22, 2016
    This dataset updates: Never
    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.
  • 1200+ Downloads
    Updated November 15, 2016 | Dataset date: Oct 24, 2016
    This dataset updates: Never
    NDRRMC report of October 28th 2016
  • 300+ Downloads
    Updated November 3, 2016 | Dataset date: Oct 20, 2016
    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.
  • 1500+ Downloads
    Updated November 2, 2016 | Dataset date: Oct 25, 2016
    This dataset updates: Every week
    This data is about damaged houses in the Philippines after Typhoon Haima (Lawin)
  • 200+ Downloads
    Updated October 18, 2016 | Dataset date: Oct 18, 2016
    This dataset updates: Never
    90m resolution elevation data
  • 400+ Downloads
    Updated September 16, 2016 | Dataset date: Sep 1, 2010
    This dataset updates: Never
    Number of households by municipality by source of drinking water from census 2010
  • 500+ Downloads
    Updated June 21, 2016 | Dataset date: Apr 21, 2016
    This dataset updates: Every three months
    This dataset contains a list of the countries affected by the El Niño as at April 21, 2016 as reported jointly by FAO, the Global Food Security Cluster and WFP on 21 April 2016 in the 2015-2016 El Niño: WFP and FAO Overview update. According to the World Bank, El Niño is likely to have a negative impact in more isolated local food markets, and many countries are already facing increased food prices. Food Security Cluster partners have implemented preparedness activities and are responding in countries where the effects of El Niño have materialised, such as Ethiopia, Papua New Guinea, Malawi and throughout Central America. In Southern Africa, many areas have seen the driest October-December period since at least 1981, and some 14 million people in the region are already facing hunger, which adds to fears of a spike in the numbers of the food insecure later this year through 2017.
  • 800+ Downloads
    Updated November 25, 2015 | Dataset date: Nov 30, 2013
    This dataset updates: Every year
    River system River system in 250km scale. Projection is WGS84. Some segements contain names in the attribute.
  • 800+ Downloads
    Updated November 24, 2015 | Dataset date: May 14, 2015
    This dataset updates: Every month
    The Income Activities dataset includes data on income generation at the household level. Sources of income listed include labor, agriculture, asset sales, and remittances, among others. It is available for 32 countries.
  • 1100+ Downloads
    Updated November 24, 2015 | Dataset date: May 14, 2015
    This dataset updates: Never
    The Coping Strategy Index dataset measures the severity and frequency of the strategies that households use to cope with acute food insecurity. The strategies vary from borrowing food or money from neighbors to selling household assets. This data is available for 31 countries at a sub-national level.
  • 1400+ Downloads
    Updated November 24, 2015 | Dataset date: May 13, 2015
    This dataset updates: Every month
    The Food Consumption Score (FCS) dataset is based on the FCS indicator, which assigns a food security score based on food consumption and diets. This data is available sub-nationally for 38 countries, such as Nepal and Sierra Leone.
  • 200+ Downloads
    Updated November 24, 2015 | Dataset date: Jan 15, 2015
    This dataset updates: Never
    Response assistance matrix for Typhoon Hagupit as of 15 Jan 2015
  • 500+ Downloads
    Updated November 24, 2015 | Dataset date: Jan 1, 2006-Jan 1, 2012
    This dataset updates: Never
    City and municipal-level poverty estimates for 2012, 2009, and 2006 2012 City and Municipal-Level Small Area Poverty Estimates Source: Philippine Statistics Authority, through a national government funded project on the generation of the 2012 small area estimates on poverty http://www.nscb.gov.ph/announce/2014/PSA-NSCB_2012MunCity_Pov.asp Note: Region V, Sorsogon, Bacon is in 2006 and 2009 data but not the 2012 data. According to Wikipedia, Sorgoson City was formed by merging the Bacon and Sorsogon towns. City and Municipal-Level Poverty Estimates; 2006 and 2009 Source: NSCB/World Bank/AusAID Project on the Generation of the 2006 and 2009 City and Municipal Level Poverty Estimates http://www.nscb.gov.ph/poverty/dataCharts.asp PDF download Note: The 2009 city and municipal level poverty estimates for ARMM were revised to reflect on the movement/creation of municipalities and barangays which were not considered in the preliminary estimation of the 2009 city and municipal level poverty estimates published in the NSCB website last 03 August 2013. Column Header / Description Prelim_*year / Preliminary (indicated by "TRUE" or "FALSE")* Pov_*year / Poverty Incidence* SE_*year / Standard Error* CoV_*year / Coefficient of Variation* Con_90lower_*year / 90% Confidence Interval Lower Limit* Con_90upper_*year / 90% Confidence Interval Upper Limit*
  • 400+ Downloads
    Updated November 24, 2015 | Dataset date: Nov 1, 2013-Nov 30, 2013
    This dataset updates: Never
    List of datasources used during typhoon yolanda aka haiyan in 2013
  • 300+ Downloads
    Updated November 24, 2015 | Dataset date: Dec 9, 2014
    This dataset updates: Never
    List of Philippines local government websites, twitter, facebook accounts for Typhoon Ruby.
  • 400+ Downloads
    Updated November 24, 2015 | Dataset date: Jan 1, 2009
    This dataset updates: Never
    City and Municipal-level Small Area Poverty Estimates, 2009
  • 300+ Downloads
    Updated November 24, 2015 | Dataset date: Dec 6, 2014
    This dataset updates: Never
  • 20+ Downloads
    Updated August 28, 2015 | Dataset date: Dec 1, 2014-Dec 10, 2014
    This dataset updates: Never
  • 10+ Downloads
    Updated August 28, 2015 | Dataset date: Dec 6, 2014
    This dataset updates: Never
    Preliminary list of evacuation centers - likely to be updated
  • 10+ Downloads
    Updated August 10, 2015 | Dataset date: Dec 18, 2014
    This dataset updates: Never
    This map illustrates satellite-detected damaged structures in Borongan City, Eastern Samar Province, Philippines. Using an image acquired by the Pleiades satellite on 14 December 2014 and compared with an image collected on 26 April 2014, UNOSAT identified 439 affected structures in the area. Specifically, 87 structures were categorized as destroyed, 154 as severely damaged and 198 as moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.
  • This map illustrates satellite-detected damaged structures in San Julian Area, Eastern Samar Province, Philippines. Using an image acquired by the WorldView-2 satellite on 12 December 2014 and compared with WorldView-1 image collected on 12 July 2014, UNOSAT identified a total of 279 affected structures in the area. Specifically, 66 structures were categorized as destroyed, 148 as severely damaged and 65 as moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.
  • This map illustrates satellite-detected damaged structures in Taft City, Eastern Samar Province, Philippines. Using an image acquired by the WorldView-2 satellite on 12 December 2014 and compared with an image collected on 19 June 2014, UNOSAT identified 277 affected structures in the area. Specifically, 75 structures were categorized as destroyed, 63 as severely damaged and 139 as moderately damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR / UNOSAT.