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  • Updated 20 December 2020 | Dataset date: November 13, 2020-November 13, 2020
    This dataset updates: Never
    UNOSAT code: TC20201111PHL This map illustrates satellite-detected surface waters in Donsol and Pilar municipalities, Sorsogon province, Bicol region of Philippines as observed from a Sentinel-1 image acquired on 13 November 2020 at 05:30 local time. Within the analyzed area of about 180 km2, a total of about 3 km2 of lands appear to be flooded. The water extent appears to have receded of about 3 km2 since 12 November 2020. Based on Worldpop population data and the detected surface waters, about 1,200 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
  • Updated 20 December 2020 | Dataset date: November 12, 2020-November 12, 2020
    This dataset updates: Never
    UNOSAT code: TC20201111PHL This map illustrates satellite-detected surface waters in Daraga, Jovellar municipalities of Albay province & Donsol, Pilar municipalities of Sorsogon province of Philippines as observed from a Sentinel-1 image acquired on 12 November 2020 at 05:38 local time. Within the analyzed area of about 270 km2, a total of about 6 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 3,000 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
  • Updated 20 December 2020 | Dataset date: November 16, 2020-November 16, 2020
    This dataset updates: Never
    UNOSAT code: TC20201111PHL This map illustrates satellite-detected mudslide extent in the Historical Cagsawa and surrounding in Daraga Municipality, Albay Province, Bicol Region, Philippines as observed on Pleiades image acquired on 13th of November 2020 at 10:47 Local time. About 80 buildings are identified as damaged and 30 as potentially damaged. Some roads were also identified as potentially damaged. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
  • Updated 20 December 2020 | Dataset date: November 16, 2020-November 16, 2020
    This dataset updates: Never
    UNOSAT code: TC20201111PHL This map illustrates satellite-detected surface waters in Albay and Camarines Sur provinces of Philippines as observed from a Sentinel-1 image acquired on 13 November 2020 at 17:57 local time. Within the analyzed area of about 2,500 km2, a total of about 210 km2 of lands appear to be flooded. The water extent appears to have receded of about 20 km2 since 12 November 2020. Based on Worldpop population data and the detected surface waters, about 140,000 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
  • 20+ Downloads
    Updated 20 December 2020 | Dataset date: November 18, 2020-November 18, 2020
    This dataset updates: Never
    UNOSAT code: TC20201111PHL This map illustrates satellite-detected surface waters in Ilocos, Central Luzon and National Capital regions of Philippines as observed from a Sentinel-1 image acquired on 17 November 2020 at 05:46 local time. Within the analyzed area of about 16,500 km2, a total of about 300 km2 of lands appear to be flooded. The water extent appears to have receded of about 900 km2 since 13 November 2020. Based on Worldpop population data and the detected surface waters, about 200,000 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
  • 10+ Downloads
    Updated 20 December 2020 | Dataset date: November 12, 2020-November 12, 2020
    This dataset updates: Never
    UNOSAT code: TC20201111PHL This map illustrates satellite-detected surface waters in Albay and Camarines Sur provinces of Philippines as observed from a Sentinel-1 image acquired on 12 November 2020 at 05:38 local time. Within the analyzed area of about 5,000 km2, a total of about 250 km2 of lands appear to be flooded. The water extent appears to have increased of about 130 km2 since 6 November 2020. Based on Worldpop population data and the detected surface waters, about 170,000 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
  • Updated 20 December 2020 | Dataset date: November 12, 2020-November 12, 2020
    This dataset updates: Never
    UNOSAT code: TC20201111PHL This map illustrates satellite-detected surface waters in Calauag and Lopez municipalities of Quezon province of Philippines as observed from a Sentinel-1 image acquired on 12 November 2020 at 05:38 local time. Within the analyzed area of about 260 km2, a total of about 20 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 8,300 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
  • 10+ Downloads
    Updated 20 December 2020 | Dataset date: November 12, 2020-November 12, 2020
    This dataset updates: Never
    UNOSAT code: TC20201111PHL This map illustrates satellite-detected surface waters in Camarines Norte province of Philippines as observed from a Sentinel-1 image acquired on 12 November 2020 at 05:38 local time. Within the analyzed area of about 650 km2, a total of about 60 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 32,000 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
  • 10+ Downloads
    Updated 20 December 2020 | Dataset date: November 16, 2020-November 16, 2020
    This dataset updates: Never
    UNOSAT code: TC20201111PHL This map illustrates satellite-detected surface waters in CAR and Cagayan Valley regions, Philippines as observed from a Sentinel-1 image acquired on 13 November 2020 at 17:58 local time. Within the analyzed area of about 18,000 km2, a total of about 970 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 370,000 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
  • 90+ Downloads
    Updated 14 December 2020 | Dataset date: December 08, 2020-December 08, 2020
    This dataset updates: As needed
    Shelter Cluster 4W report (Who does What, Where, and When) for typhoon Goni (Rolly) and Vamco (Ulysses) in the Philippines
  • Updated 4 December 2020 | Dataset date: May 28, 2020-May 28, 2020
    This data is by request only
    Philippines COVID-19 Rapid Information, Communication, and Accountability Assessment (RICAA) for Metro Manila and Other Regions
  • 1700+ Downloads
    Updated 27 November 2020 | Dataset date: July 10, 2019-July 10, 2019
    This dataset updates: As needed
    Based on Republic Act 8425, otherwise known as Social Reform and Poverty Alleviation Act, dated 11 December 1997, the poor refers to individuals and families whose income fall below the poverty threshold as defined by the government and/or those that cannot afford in a sustained manner to provide their basic needs of food, health, education, housing and other amenities of life. It may be estimated in terms of percentages (poverty incidence) and total number of poor families (magnitude of poor families). Also, this dataset has been generated by combining Philippine Standard Geographic Codes (PSGC) and poverty estimates from Philippine Statistics Authority (PSA). For more details, please refer to the following documents: https://psa.gov.ph/poverty-press-releases/references https://psa.gov.ph/poverty-press-releases/technotes https://psa.gov.ph/poverty-press-releases/glossary https://psa.gov.ph/sites/default/files/Technical%20Notes%20on%202015%20SAE.pdf
  • 200+ Downloads
    Updated 24 November 2020 | Dataset date: January 01, 2000-December 31, 2020
    This dataset updates: Every year
    WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset. Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc-seconds (approximately 1km at the equator) -Unconstrained individual countries 2000-2020: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population count datasets by dividing the number of people in each pixel by the pixel surface area. These are produced using the unconstrained top-down modelling method. -Unconstrained individual countries 2000-2020 UN adjusted: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population UN adjusted count datasets by dividing the number of people in each pixel, adjusted to match the country total from the official United Nations population estimates (UN 2019), by the pixel surface area. These are produced using the unconstrained top-down modelling method. Data for earlier dates is available directly from WorldPop. WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00674
  • 400+ Downloads
    Updated 24 November 2020 | Dataset date: January 01, 2000-December 31, 2020
    This dataset updates: Every year
    WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset. Bespoke methods used to produce datasets for specific individual countries are available through the WorldPop Open Population Repository (WOPR) link below. These are 100m resolution gridded population estimates using customized methods ("bottom-up" and/or "top-down") developed for the latest data available from each country. They can also be visualised and explored through the woprVision App. The remaining datasets in the links below are produced using the "top-down" method, with either the unconstrained or constrained top-down disaggregation method used. Please make sure you read the Top-down estimation modelling overview page to decide on which datasets best meet your needs. Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 3 and 30 arc-seconds (approximately 100m and 1km at the equator, respectively): - Unconstrained individual countries 2000-2020 ( 1km resolution ): Consistent 1km resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020. - Unconstrained individual countries 2000-2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020. - Unconstrained individual countries 2000-2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019) -Unconstrained individual countries 2000-2020 UN adjusted ( 1km resolution ): Consistent 1km resolution population count datasets created using unconstrained top-down methods for all countries of the World for each year 2000-2020 and adjusted to match United Nations national population estimates (UN 2019). -Unconstrained global mosaics 2000-2020 ( 1km resolution ): Mosaiced 1km resolution versions of the "Unconstrained individual countries 2000-2020" datasets. -Constrained individual countries 2020 ( 100m resolution ): Consistent 100m resolution population count datasets created using constrained top-down methods for all countries of the World for 2020. -Constrained individual countries 2020 UN adjusted ( 100m resolution ): Consistent 100m resolution population count datasets created using constrained top-down methods for all countries of the World for 2020 and adjusted to match United Nations national population estimates (UN 2019). Older datasets produced for specific individual countries and continents, using a set of tailored geospatial inputs and differing "top-down" methods and time periods are still available for download here: Individual countries and Whole Continent. Data for earlier dates is available directly from WorldPop. WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645
  • Updated 19 November 2020 | Dataset date: November 06, 2020-November 06, 2020
    This dataset updates: Never
    UNOSAT code: TC20201101PHL This map illustrates satellite-detected surface waters in Albay and Camarines Sur provinces of Philippines as observed from a Sentinel-1 image acquired on 6 November 2020 at 05:38 local time. Within the analyzed area of about 5,000 km2, a total of about 120 km2 of lands appear to be flooded. The water extent appears to have receded of about 120 km2 since 1 November 2020. Based on Worldpop population data and the detected surface waters, about 80,000 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
  • 20+ Downloads
    Updated 19 November 2020 | Dataset date: November 04, 2020-November 04, 2020
    This dataset updates: Never
    UNOSAT code: TC20201101PHL This map illustrates floodwater depth in Nabua and Baao Municipalities, Camarines Sur Provinces & Bato and Libon, Albay Province, Bicol Region (Region V) of Philippines based on surface waters observed from a Sentinel-1 image acquired on 1st of November 2020 and digital elevation model data with the floodwater depth estimation tool (FwDET). This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
  • Updated 19 November 2020 | Dataset date: November 04, 2020-November 04, 2020
    This dataset updates: Never
    UNOSAT code: TC20201101PHL This map illustrates floodwater depth in Floodwater depth in Bula and Minalabac municipalities, Camarines Sur Provinces, Bicol Region (Region V) of Philippines based on surface waters observed from a Sentinel-1 image acquired on 1st of November 2020 and a digital elevation model data with the floodwater depth estimation tool (FwDET). This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
  • 10+ Downloads
    Updated 19 November 2020 | Dataset date: November 03, 2020-November 03, 2020
    This dataset updates: Never
    UNOSAT code: TC20201101PHL This map illustrates floodwater depth in Camarines Sur provinces of Philippines based on surface waters observed from a Sentinel-1 image acquired on 1st of November 2020 and a digital elevation model data with the floodwater depth estimation tool(FwDET). This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
  • Updated 19 November 2020 | Dataset date: November 02, 2020-November 02, 2020
    This dataset updates: Never
    UNOSAT code: TC20201101PHL This map illustrates satellite-detected surface waters in Albay and Camarines Sur provinces Philippines as observed from a Sentinel-1 image acquired on 1 November 2020 at about 17:57 local time. Within the analyzed area of about 5,000 km2, a total of about 240 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 160,000 people are potentially exposed or living close to flooded areas. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT. Important Note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to backscattering properties of the radar signal.
  • 80+ Downloads
    Updated 2 November 2020 | Dataset date: November 01, 2020-November 01, 2020
    This dataset updates: Never
    OpenStreetMap buildings of Camarines Sur (as of 1-nov-2020) and AI predictions on Bing Maps images (approximately 2016-2019). Produced in support of Philippines Red Cross for typhoon Goni (1-nov-2020) Coordinate reference system: WGS 84 / EPSG:4326
  • 90+ Downloads
    Updated 12 October 2020 | Dataset date: March 19, 2020-March 19, 2020
    This dataset updates: Every six months
  • 300+ Downloads
    Updated 4 September 2020 | Dataset date: August 31, 2020-August 31, 2020
    This dataset updates: As needed
    The new and emerging access constraints that people are currently experiencing because of the COVID-19 outbreak.
  • 100+ Downloads
    Updated 2 September 2020 | Dataset date: August 31, 2020-August 31, 2020
    This dataset updates: Every two weeks
    This dataset contains scores for humanitarian access constraints into country, constraints within country, impacts the constraints have led to as well as the mitigation strategies in place to limit the impact. The scores have the following interpretations: 0 = NA, 1 = No or open, 2 = partially open/closed, 3 = Yes or closed
  • 300+ Downloads
    Updated 3 August 2020 | Dataset date: December 29, 2021-December 29, 2021
    This dataset updates: Every month
    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 covered source width natural depth layer waterway water blockage tunnel This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.
  • 100+ Downloads
    Updated 3 August 2020 | Dataset date: December 29, 2021-December 29, 2021
    This dataset updates: Every month
    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 addr:housenumber shop source man_made tourism beds addr:full opening_hours amenity addr:street addr:city rooms This dataset is one of many OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.