Philippines GIS data

The ICA is a process of consultations supported by mapped-out data that produces a strategic plan describing where different combinations of programme themes are appropriate to achieve goals of reducing food insecurity and climate related shock risk. The ICA combines multi-year food security trends with natural shock risk data to highlight sub-national areas where different programme strategies make sense. Food security trend maps shows areas where safety nets can address regular food insecurity, and others where shocks make recovery more important. Climate-related natural shock risk maps show where DRR, preparedness and early warning efforts can complement food-security objectives. Atop this core foundation, mapped data on subjects including nutrition, gender, livelihoods and resilience can enrich theme-level strategic planning in which all pieces work together. The full group of ICA partners discuss these analytical results to arrive at strategic programmatic directions.

**UNOSAT code: TC20211216PHL** A category 5 Tropical Cyclone RAI-21 struck the Philippines on the 16th of December 2021, causing damage, devastation, and destruction. The Tropical Cyclone RAI-21 made landfall north of General Luna city on Siargao Island, the easternmost island of the Philippines, and continued westward through the central part of the country. The Philippines National Disaster Risk Reduction and Management Council (NDRRMC) has stated that 2,182,791 families have been affected in 11 out of 17 regions of the Philippines. About 3M people, ~ 30% of the affected area population – need emergency humanitarian assistance. According to OCHA, the death toll has reached 177 persons, with more than 275 persons injured and 630,000 displaced. More than 159,000 houses have been damaged, 61,900 have been destroyed. These figures are likely to rise as the after-effects of the Tropical Cyclone are fully realized. Preliminary assessment has shown devastating damage to houses, roads, and bridges disrupting aid movement into the affected areas. This report summarizes satellite-derived building damage analysis covering the most affected areas within Region IV-B (Mimaropa), Region VII (Central Visayas), Region VIII (Eastern Visayas), and Region XIII (Caraga) of the Philippines.

This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes: - macroregion (admin-1 including region) - region (admin-2 including state, province, department, governorate) - macrocounty (admin-3 including arrondissement) - county (admin-4 including prefecture, sub-prefecture, regency, canton, commune) - localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb) The dataset also contains human settlement points and polygons for: - localities (city, town, and village) - neighbourhoods (borough, macrohood, neighbourhood, microhood) The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names. Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.

**UNOSAT code: EQ20220727PHL** This map illustrates potentially damaged buildings and damaged buildings in Tayum Town, Abra Province, Cordillera Administrative Region, Philippines as detected by Pléiades satellite image acquired on 29 July 2022. Within the analyzed area, UNOSAT has identified 37 potentially damaged buildings and 1 damaged buildings. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**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.

**UNOSAT code: TC20211216PHL** This map illustrates potentially damaged structures/buildings in Siargao Island, General Luna, Surigao del Norte of Philippines as detected by Pleiades image acquired on 26 December 2021 and WorldView-3 acquired on 23 December 2021. Within the analyzed area, UNOSAT has identified 1,769 damaged structures, 380 potentially damaged structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to the United Nations Satellite Centre (UNOSAT).

**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.

**UNOSAT code: TC20211216PHL** This map illustrates potentially damaged structures and buildings in Dinagat Island, Region XIII, the Philippines as observed from a Pleiades image acquired on 23 December 2021. Within the analyzed area, UNOSAT and Copernicus EMS identified 3,278 damaged structures, 523 potentially damaged structures, and 3 potentially damaged ports. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

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.

**UNOSAT code: TC20191202PHL** This map illustrates satellite-based structural damage assessment in San Pascual municipality in the northeastern part of Masbate region, Bicol Region province, in the Philippines. The area was affected by Typhoon Kammuri that made landfall on 3 December 2019. UNITAR-UNOSAT identified 16 potentially damaged structures around San Pascual town, in the San Pascual municipality, within the extent of this map. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.

**UNOSAT code: TC20211216PHL** This map illustrates potentially damaged structures/buildings in Roxas town, Palawan province, Region IV-B (Mimaropa) of Philippines as observed from a Pleiades image acquired on 21 December 2021 after landfall of the Tropical Cyclone RAI-21. Within the analysis extent, UNITAR-UNOSAT identified 280 damaged structures and 265 potentially damaged structures including Roxas Medicare Hospital and 6 potentially damaged education facilities. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

**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.

**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.

**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.

**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.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of August 05, 2022, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of August 24, 2022, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of October 30, 2022, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of October 28, 2022, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of October 19, 2022, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of August 25, 2022, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of August 27, 2022, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of October 14, 2022, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of September 28, 2022, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of January 17, 2023, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of January 13, 2023, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of January 08, 2023, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of January 03, 2023, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of December 27, 2022, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of November 04, 2022, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of November 02, 2022, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of November 03, 2022, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of October 31, 2022, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of November 01, 2022, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of August 31, 2023, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.

This dataset shows the actual extent of flooding in the Philippines caused by typhoons or tropical cyclones as of August 29, 2023, as observed through available satellite imagery. The dataset can be used to track the progression of floods over time, identify areas at risk, and assess the impact of these natural disasters on infrastructure, agriculture, and local populations.