Philippines GIS data
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uav imagery by Oceanus Foundation, acquired 2025-03-27 to 2025-03-28, 3.6 cm per pixel, published on OpenAerialMap.
geoBoundaries ADM2 boundaries for Philippines: 87 units, representing 2020. Source: National Mapping and Resource Information Authority (NAMRIA), Philippines Statistics Authority (PSA), OCHA Philippines.
geoBoundaries ADM1 boundaries for Philippines: 17 units, representing 2020. Source: National Mapping and Resource Information Authority (NAMRIA), Philippines Statistics Authority (PSA), OCHA Philippines.
geoBoundaries ADM3 boundaries for Philippines: 1647 units, representing 2020. Source: National Mapping and Resource Information Authority (NAMRIA), Philippines Statistics Authority (PSA), OCHA Philippines.
Sea ports, harbours and ferry terminals. Useful for maritime logistics, supply-chain analysis, and coastal access planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: industrial=port, harbour, port, amenity=ferry_terminal, building=ferry_terminal - Part of the country exports on HDX by HOT
Point features describing soil series, agro-ecological zones and land cover classes in Laguna province, Philippines, with each point representing a soil and land mapping unit. Published by Orion Terrametrics.
Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Philippines Country Boundary provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2022 boundaries are provided by Michael Bauer Research GmbH. They are sourced from Philippines Statistics Authority, contributor OCHA Philippines. These were published in December 2022. Other administrative boundaries for this country are also available: Region Province Municipality
Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Philippines Region Boundaries provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2022 boundaries are provided by Michael Bauer Research GmbH. They are sourced from Philippines Statistics Authority, contributor OCHA Philippines. These were published in December 2022. Other administrative boundaries for this country are also available: Country Province Municipality
Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Philippines Province Boundaries provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2022 boundaries are provided by Michael Bauer Research GmbH. They are sourced from Philippines Statistics Authority, contributor OCHA Philippines. These were published in December 2022. Other administrative boundaries for this country are also available: Country Region Municipality
Retirement Notice: This item is in mature support as of September 2025 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Philippines Municipality Boundaries provides a 2022 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online. The 2022 boundaries are provided by Michael Bauer Research GmbH. They are sourced from Philippines Statistics Authority, contributor OCHA Philippines. These were published in December 2022. Other administrative boundaries for this country are also available: Country Region Province
**UNOSAT code: TC20211216PHL** This map illustrates potentially damaged structures and buildings in Jimalalud and La Libertad municipalities, Negros Oriental province, Region VII (Central Visayas), Philippines as observed from a Pleiades image acquired on 22 December 2021. Within the analyzed area, UNOSAT identified 566 damaged structures and 154 potentially damaged structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
Shuttle Radar Topographic Mission (SRTM) global 5 degree tile reference.
Natural Earth grids subdivided by 1,5,10,15,20, and 30 kilometers for the entire globe.
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.
## 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.
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.
A collection of the strongest typhoon (tracks and windspeed) data for the Philippines with category levels 4 and 5. Data were scrapped from http://weather.unisys.com/.
A radar interferogram of the Magnitude 6.5 earthquake that hit Ormoc City Leyte last 6 July 2017. Data were processed using 2 Sentinel C-band Synthetic Aperture Radar data (Repeat Pass)
Archive of Global Tropical Cyclone Tracks Tracks from 1980 to May 2019. Data provided by NOAA International Best Track Archive for Climate Stewardship (IBTrACS). Original data and metadata are available here (https://www.ncdc.noaa.gov/ibtracs/index.php?name=ib-v4-access). For data updates after May 2019, please visit the ncdc website.
Database of Global Landslide Catalog from NASA collected from 1970 - 2019. It was generated using news feeds and is mostly capturing rain-induced landslide events. More information on metadata and original update source here: https://catalog.data.gov/dataset/global-landslide-catalog-export
A polygon of the Philippine Area of Responsibility (PAR) from PAGASA
Point shapefile of capital cities of Philippines administrative level 2 features. Excludes the four districts of the National Capital Region. P-coded in accordance with their administrative 2 feature.
This data includes both the terrestrial, marine and marine+terrestrial key biodiversity areas in of the Philippines as of 2020. Key biodiversity areas are landscapes that showcases high species endemicity and sometimes facing threats due to various human related factors. These places are mostly identified by experts and are not necessarily included in the declared protected areas.
**UNOSAT code TC20221028PHL, GDACS Id: 1000942** This map illustrates satellite-detected surface waters in Capiz Province, Region VI, Philippines as observed from a Sentinel-1 image acquired on 2 November 2022 at 05:39 local time. Within analyzed area of about 1,300 km2, about 28 km2 of lands appear to be flooded. Based on Worldpop population data and the detected surface waters, about 15,000 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 United Nations Satellite Centre (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 TC20221028PHL, GDACS Id: 1000942** This map illustrates the extent of surface waters in Bangsamoro Autonomous Region in Muslim Mindanao and Region XII, Philippines as observed from an RCM-3 image acquired on 3 November 2022 at 17:57 local time. Within the analysis extent of about 2,000 km2, approximately 160 km2 of land appear to be flooded. Based on Worldpop population data and detected surface waters, approximately 41,000 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 United Nations Satellite Centre (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: FL20220412PHL** This map illustrates satellite detected waters, the potentially exposed population, and the related potentially affected croplands as deduced using a satellite TerraSAR-X image acquired on 26 April 2021 at 05:34 local time acquired over Leyte Province, Region VIII, Philippines. Within the analyzed area of about 30,000 hectares, a total of about 2,000 hectares of lands appear to be flooded. The water extent appears to have increased of about 900 hectares since 21 April 2021. Based on Worldpop population data and the detected surface waters, about 8,000 people are potentially exposed or living close to flooded areas. The exposed population appears to have increased with about 3,000 people since 21 April 2021. In this area, about 1,800 km2 of croplands appear to be likely affected by the flood waters. The affected croplands seems to have increased with about 900 hectares since 21 April 2021. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (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.
A cyclone (category 4) during the period Aug 23 2023-Aug 29 2023 in Philippines. It impacted 3624 people.
**UNOSAT code TC20221028PHL, GDACS Id: 1000942** This map illustrates satellite-detected surface waters in Cabuyao City and City of Calamba, Laguna Province, Region IV-A, Philippines as observed from a Kanopus-V image acquired 5 November 2022 at 11:12 local time. Within the analysis extent of about 2,200 hectares, approximately 120 hectares of land appear to be flooded. Based on Worldpop population data and the detected surface waters within the analysis extent, approximately 4,400 people are potentially exposed to or living close to flooded areas along with 256 structures and a port potentially affected by flooding. 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: TC20211216PHL** This map illustrates potentially damaged structures and buildings in City of Naga & Minglanilla municipalities, Cebu province, Region VII (Central Visayas), Philippines as observed from a Pleiades image acquired on 21 December 2021. Within the analyzed area, UNOSAT identified 876 damaged structures and 1,361 potentially damaged structures. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to UNITAR-UNOSAT.
**UNOSAT code TC20221028PHL, GDACS Id: 1000942** This map illustrates the extent of surface waters in Bulacan, Nueva Ecija, Pampanga, and Tarlac Provinces, Region III, Philippines as observed from a TerraSAR-X image acquired on 3 November 2022 at 05:59 local time. Within the analysis extent of about 1,800 km2, approximately 160 km2 of land appears to be inundated and 140 km2 of croplands appear to be likely affected by the flood waters. Based on Worldpop population data and detected surface waters, approximately 84,000 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 United Nations Satellite Centre (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.