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  • WFP - World Food Programme
    Updated October 21, 2018 | Dataset date: Jan 1, 1992-Oct 15, 2018
    This dataset contains Global Food Prices data from the World Food Programme covering foods such as maize, rice, beans, fish, and sugar for 76 countries and some 1,500 markets. It is updated weekly but contains to a large extent monthly data. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.
    • CSV
    • 1900+ Downloads
    • This dataset updates: Every week
  • WFP - World Food Programme
    Updated October 21, 2018 | Dataset date: Jan 15, 2000-Apr 15, 2017
    This dataset contains Food Prices data for Philippines. Food prices data comes from the World Food Programme and covers foods such as maize, rice, beans, fish, and sugar for 76 countries and some 1,500 markets. It is updated weekly but contains to a large extent monthly data. The data goes back as far as 1992 for a few countries, although many countries started reporting from 2003 or thereafter.
    • CSV
    • 20+ Downloads
    • This dataset updates: Every week
  • OCHA FTS
    Updated October 21, 2018 | Dataset date: Oct 21, 2018
    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
    • 2000+ Downloads
    • This dataset updates: Every day
  • Insecurity Insight
    Updated October 18, 2018 | Dataset date: Jan 1, 2017-Dec 31, 2017
    The data has been collected from a wide variety of sources. These include incidents reported in the Safeguarding Healthcare Monthly News Briefs and reported by Aid in Danger partner agencies using the Security in Numbers Database (SiND); incident reports supplied to the SHCC by Médecins Sans Frontières, the Syrian American Medical Society and the World Health Organisation; reports from other UN agencies, including the Office for the Coordination of Humanitarian Affairs and the Office of the High Commissioner for Human Rights; independent NGOs; and media reports.
    • XLSX
    • 200+ Downloads
    • This dataset updates: Every year
  • Insecurity Insight
    Updated October 18, 2018 | Dataset date: Jan 1, 2017-Sep 30, 2018
    This dataset contains agency- and publicly-reported data for events in which an aid worker was killed, kidnapped, or arrested. Categorized by country.
    • XLSX
    • 500+ Downloads
    • This dataset updates: Every month
  • Insecurity Insight
    Updated October 17, 2018 | Dataset date: Jan 1, 2017-Dec 31, 2017
    This dataset includes incidents affecting the delivery of aid in eight countries in 2017. The data contains incidents identified in open sources and reported by Aid in Danger partner agencies using the Security in Numbers Database (SiND).
    • XLSX
    • 70+ Downloads
    • This dataset updates: Every year
  • Insecurity Insight
    Updated October 17, 2018 | Dataset date: Sep 1, 2018-Sep 30, 2018
    This dataset includes incidents affecting the affecting the provision of education. The data contains incidents identified in open sources. Categorized by country.
    • XLSX
    • This dataset updates: Every month
  • OCHA Philippines
    Updated October 10, 2018 | Dataset date: Sep 21, 2018
    Typhoon Mangkhut (Ompong) Who does What Where (3W) matrix
    • XLSX
    • 100+ Downloads
    • This dataset updates: Every month
  • Insecurity Insight
    Updated October 9, 2018 | Dataset date: Jan 1, 2017-Dec 31, 2017
    Attacks on educational staff and facilities in 2017. This dataset contains agency- and publicly-reported data for events in which affected the provision of education. Categorized by country.
    • XLSX
    • 200+ Downloads
    • This dataset updates: Every year
  • This is a standing water (possible inundated/flooded areas) in tarlac, pampanga and parts of pangasinan Philippines due to several typhoons and habagat (July 2018). Data were processed using Sentinel 1A GRD data (July 20, 2018) to map out areas with standing water . Thanks to ESA Copernicus program for the Sentinel 1 data and Google for their cloud computing reource. This was processed in Google Earth Engine. For more detailed information, please contact michael.manalili@wfp.org or michaelandrew.manalili@gmail.com
    • tif
    • 10+ Downloads
    • This dataset updates: Every six months
  • OCHA Philippines
    Updated October 4, 2018 | Dataset date: Oct 1, 2018
    Who What Where of Humanitarian actors/activities in Marawi Conflict as of 01 Oct 2018
    • XLSX
    • 4400+ Downloads
    • This dataset updates: Every three months
  • OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: waterway IS NOT NULL OR water IS NOT NULL OR natural IN ('water','wetland','bay') Features may have these attributes: name waterway covered width depth layer blockage tunnel natural water This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: building IS NOT NULL Features may have these attributes: name building building:levels building:materials addr:full addr:housenumber addr:street addr:city office This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: amenity IS NOT NULL OR man_made IS NOT NULL OR shop IS NOT NULL OR tourism IS NOT NULL Features may have these attributes: name amenity man_made shop tourism opening_hours beds rooms addr:full addr:housenumber addr:street addr:city This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: highway IS NOT NULL Features may have these attributes: name highway surface smoothness width lanes oneway bridge layer This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: waterway IS NOT NULL OR water IS NOT NULL OR natural IN ('water','wetland','bay') Features may have these attributes: name waterway covered width depth layer blockage tunnel natural water This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: building IS NOT NULL Features may have these attributes: name building building:levels building:materials addr:full addr:housenumber addr:street addr:city office This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: amenity IS NOT NULL OR man_made IS NOT NULL OR shop IS NOT NULL OR tourism IS NOT NULL Features may have these attributes: name amenity man_made shop tourism opening_hours beds rooms addr:full addr:housenumber addr:street addr:city This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • OpenStreetMap exports for use in GIS applications. This theme includes all OpenStreetMap features in this area matching: highway IS NOT NULL Features may have these attributes: name highway surface smoothness width lanes oneway bridge layer This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • The urban indicators data available here are analyzed, compiled and published by UN-Habitat’s Global Urban Observatory which supports governments, local authorities and civil society organizations to develop urban indicators, data and statistics. Urban statistics are collected through household surveys and censuses conducted by national statistics authorities. Global Urban Observatory team analyses and compiles urban indicators statistics from surveys and censuses. Additionally, Local urban observatories collect, compile and analyze urban data for national policy development. Population statistics are produced by the United Nations Department of Economic and Social Affairs, World Urbanization Prospects.
    • CSV
    • This dataset updates: Every year
  • WFP - World Food Programme
    Updated September 24, 2018 | Dataset date: Sep 19, 2018
    Generated in Google Earth Engine using Sentinel 1 A data.
    • tif
    • 20+ Downloads
    • This dataset updates: Every year
  • Update 15/09 (POST-EVENT) Now that the typhoon has passed the country, the model is not run with forecasted wind speeds and typhoon track any more, but with actual estimated wind speeds and typhoon track. They come from the same source (Tropical Storm Risk - UCL), and are of the exact same format. All output (map in PDF, data in Excel and in Shapefile) is of the exact same format and interpretation. Full methodology 1. Based on existing Priority Index model: 510 has previously developed the Priority Index model for typhoons in the Philippines One day after a typhoon has passed the Philippines .. .. the model predicts ‘% of completely damaged houses’ per municipality Based on 12 large typhoons in the last 5 years in the Philippines, for which detailed damage reports were available through NDRRMC (https://www.ndrrmc.gov.ph/) For these same events, we also collected possible explanatory indicators, such as wind speed (event-specific) and building materials of houses (PH national census). We built a statistical model, which could explain differences in damage on the basis of differences in wind speed and building materials (etc.) When dividing all municipalities in 5 equal damage classes (class 1 being the 20% municipalities with lowest damage; class 5 the 20% with highest damage) .. .. we found that in 73% of the cases we are at most 1 class off. 2. Mangkhut methodology: In the case of typhoon Mangkhut, we are dealing with an upcoming typhoon, which is still awaiting landfall on Saturday 15/09. This is a new situation, which requires the following noteworthy changes in methodology. Our wind speed source (UCL Tropical Storm Risk) has – in addition to post-event wind speed data as used above – also forecast wind speed data for 5 days ahead. This forecasted wind speed (and typhoon track) are plugged as input into the above-mentioned prediction model, which - still in combination with building materials - lead to the predicted damage class per municipality that can be seen in the map. Note that the results are strongly dependent on the input of windspeed, which is itself still an unknown. (see accuracy below). 3. How to use this product: The map contains damage classes (1-5) per municipality. As such, we advise to put priority on municipalities in damage class 5, and depending on available resources continue with class 4, etc. This damage class is based on ‘% of houses that are completely damaged’. As priority might also be based on exposure and vulnerability, we have added to the Excel a couple of relevant indicators, from the Community Risk Assessment dashboard. PRC can decide if and how to combine these various features. If needed, 510 can be asked for assistance of course. 4. Important notes: ACCURACY: it should be realized that during the course of the coming 3 days, the typhoon might change course, or increase/decrease in terms of strength. This will affect the quality of these predictions. The accuracy figure of 73% that is mentioned in the post-event case should be seen as an upper bound. Given the added inaccuracy of wind speed, the overall accuracy will be lower. This damage prediction is only about completely damaged houses, not about partially damaged houses. We only included municipalities that are within 100km of the forecasted typhoon track, as we have seen from previous typhoons (with comparable wind speeds) that damage figures outside of this area are low. 5. Sources The wind speed is provided by Tropical Storm Risk (University College London). It is the ‘maximum 1-minute sustained wind speed’. An average of this is calculated per municipality. (Latest forecast date: 2018-09-14 00:00 UT >> 7:00AM Manila time) Typhoon track (from which ‘distance to typhoon track’ per municipality is calculated), is provided by UCL as well. (Latest forecast date: 2018-09-14 00:00 UT >> 7:00AM Manila time) Additionally, various wall and roof type categories from the Philippines national census. The model uses 2010 census data, as it was developed using this data (2015 census data on municipality level only became available in 2018). The 2015 census data could not be easily plugged in, because of some differences in roof/wall categories. We believe that this would not change the result much though, as even if there are large differences from 2010 to 2015, these would still be dominated by wind speed effects in the model. All additional indicators, that are added to the Excel table (population, poverty) are derived from the Community Risk Assessment dashboard (Go to this link and click ‘Export to CSV’ on top-right.) The sources for these indicators can be found in the dashboard itself.
  • Armed Conflict Location & Event Data Project (ACLED)
    Updated September 16, 2018 | Dataset date: Jan 1, 2016-Dec 31, 2018
    The ACLED project codes reported information on the type, agents, exact location, date, and other characteristics of political violence events, demonstrations and select politically relevant non-violent events. ACLED focuses on tracking a range of violent and non-violent actions by political agents, including governments, rebels, militias, communal groups, political parties, external actors, rioters, protesters and civilians. Data contain specific information on the date, location, group names, interaction type, event type, reported fatalities and contextual notes.
    • CSV
    • 10+ Downloads
    • This dataset updates: Live
  • OCHA Philippines
    Updated September 14, 2018 | Dataset date: Sep 1, 2010
    This datasets contains a collection of pre-disaster indicators for the Philippines.
    • XLSX
    • 1400+ Downloads
    • This dataset updates: Never
  • OCHA Philippines
    Updated September 14, 2018 | Dataset date: Sep 20, 2017
    Sex and age disaggregated population data by various administrative levels (1 to 4) based on 2015 Census with Philippines Standard Geographic Code (PSGC). These CSV population statistics files are suitable for database or ArcGIS joins to the shapefiles found on HDX here.
    • CSV
    • XLSX
    • 2000+ Downloads
    • This dataset updates: Every year