This dataset contains a national-scale geodatabase of stream network and river catchment characteristics in the Philippines. It presents detailed information on 128 medium- to large-sized catchments (catchment area > 250 km2). The quantitative descriptions provide context for enabling geomorphologically-informed sustainable river management. The geodatabase provides a baseline understanding of fundamental topographic characteristics in support of varied geomorphological, hydrological and geohazard susceptibility applications. Data sets include: 1) GIS shapefiles with river catchment properties; 2) GIS shapefiles with stream network properties; 3) spreadsheets containing morphometric and topographic characteristics (n = 91); 4) example MATLAB code and topographic data to replicate the analysis for a selected catchment. The work was supported by the Natural Environment Research Council (NERC) and Department of Science and Technology - Philippine Council for Industry, Energy and Emerging Technology Research and Development (DOST-PCIEERD) – Newton Fund grant NE/S003312/1. Full details about this dataset can be found at https://doi.org/10.5285/49ae11ec-e4e5-4e4a-b091-976d18c4ee3e

This theme includes all OpenStreetMap features in this area matching ( Learn what tags means here ) :

tags['waterway'] IS NOT NULL OR tags['water'] IS NOT NULL OR tags['natural'] IN ('water','wetland','bay')

Features may have these attributes:

• name
• name:en
• waterway
• covered
• width
• depth
• layer
• blockage
• tunnel
• natural
• water
• source
• name:fil

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

This dataset consist of comma-separated values files containing the coordinate location of wells in Cebu and Mactan islands, Philippines expressed as points x (longitude) and y (latitude) in decimal degrees. The dataset is partitioned between a training and test subset at a proportion of 70% and 30%, respectively. The sources of the data were two government agencies tasked to manage the water resources in the Philippines. Shapefiles can be generated directly from the dataset using appropriate GIS software.Â

Attached file provides supplementary data for linked article.

Soil erosion is a global geological hazard which can be mitigated through better future land-use planning. In the current research, a Dempster-Shafer-based evidential belief function (EBF) and frequency ratio (FR) were used to map the soil erosion susceptible areas and their outcomes were compared subsequently. These methods were selected due to their efficiency and popularity in natural hazard studies. Moreover, the application of EBF is poorly examined in this area of research. Nine conditioning factors belonging to the current time, and rainfall intensity for the two time periods of current time and 2100 based on the A2 scenario CSIRO global climate model, were utilized in this research. The main aim was to estimate and compare the soil erosion hazards at Southern Luzon in the Philippines under two time periods, current time and 2100. This region has been highly affected by erosion and has not received much attention in the past. The area under the curve outcomes indicated that the FR model produced 70.6% prediction rate, while EBF showed superior prediction accuracy with a rate of 83.1%. The results also project that soil erosion hazards in the Philippines will increase due to changes in rainfall patterns by 2100.

The dataset shows the Marine Protected Areas as uploaded on Philippine GIS Data Clearing House

WGS 1984 - Lat/Long

Maps with wind speed, wind rose and wind power density potential in The Philippines. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). GIS data is available as JSON and CSV. The second link provides poster size (.pdf) and midsize maps (.png).

The U.S. Geological Survey (USGS) has compiled a geodatabase containing mineral-related geospatial data for 19 countries of interest in the Indo-Pacific region (area of study): Bangladesh, Bhutan, Brunei, Burma, Fiji, Malaysia, Mongolia, Nauru, New Caledonia, New Zealand, Papua New Guinea, Philippines, Singapore, Solomon Islands, South Korea (Republic of Korea), Sri Lanka, Taiwan, Timor-Leste, and Vietnam. The data can be used in analyses of the extractive fuel and nonfuel mineral industries integral for the successful operation of the mineral industries within the area of study. This geodatabase reflects the USGS ongoing commitment to its mission of understanding the nature and distribution of global mineral commodity supply chains by updating and publishing the georeferenced locations of mineral commodity production and processing facilities, mineral exploration sites, and mineral sites and processing facilities under development for the countries in the area of study. The geodatabase contains data feature classes from USGS, foreign governmental, and open-source sources as follows: (1) mineral production and processing facilities, (2) mineral exploration sites, (3) mineral production and processing facilities under development, (4) undiscovered mineral resource tracts for copper, (5) coal occurrence areas, (6) undiscovered, technically recoverable conventional and continuous hydrocarbon resources (by USGS geologic province), and (7) cumulative production and recoverable conventional resources (by province groups).

Map with solar irradiation and PV power potential in the Philippines. The GIS data stems from the Global Solar Atlas (http://globalsolaratlas.info). The link also provides a poster size (.tif) and midsize map (.png). The Global Solar Atlas is continuously updated. Provided GIS data layers include long-term yearly average of: (1) PVOUT – Photovoltaic power potential kWh/kWp GHI – Global horizontal irradiation kWh/m2 DIF – Diffuse horizontal irradiation kWh/m2 GTI – Global irradiation for optimally tilted surface kWh/m2 OPTA – Optimum tilt to maximize yearly yield ° DNI – Direct normal irradiation [kWh/m2]

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.4963 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.0815 and 0.0513 (in million kms), corressponding to 16.4302% and 10.3289% respectively of the total road length in the dataset region. 0.3635 million km or 73.241% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0024 million km of information (corressponding to 0.6728% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

This dataset contains GIS maps (.shp files) of built-up/urban land in the Philippines in 2018 (current) and 2050 (future), considering low/medium/high future urban expansion scenarios. The 2050 urban area maps weregenerated using a cellular automata logistic regression model, which was applied to historical urban area maps and other openly available geospatial datasets. / More information on this dataset can be found in the journal paper entitled "High-resolution urban change modeling and flood exposure estimation at a national scale using open geospatial data: A case study of the Philippines", published inComputers, Environment and Urban Systems. Feel free to email the author (johnson@iges.or.jp) in case of any questions or problems with the maps. / Keywords: Adaptation, Resilient Livelihoods【リソース】Fulltext

A database (NDP-068) was generated from estimates of geographically referenced carbon densities of forest vegetation in tropical Southeast Asia for 1980. A geographic information system (GIS) was used to incorporate spatial databases of climatic, edaphic, and geomorphological indices and vegetation to estimate potential (i.e., in the absence of human intervention and natural disturbance) carbon densities of forests. The resulting map was then modified to estimate actual 1980 carbon density as a function of population density and climatic zone. The database covers the following 13 countries: Bangladesh, Brunei, Cambodia (Campuchea), India, Indonesia, Laos, Malaysia, Myanmar (Burma), Nepal, the Philippines, Sri Lanka, Thailand, and Vietnam.

The data sets within this database are provided in three file formats: ARC/INFOTM exported integer grids; ASCII (American Standard Code for Information Interchange) files formatted for raster-based GIS software packages; and generic ASCII files with x, y coordinates for use with non-GIS software packages.

The database includes ten ARC/INFO exported integer grid files (five with the pixel size 3.75 km x 3.75 km and five with the pixel size 0.25 degree longitude x 0.25 degree latitude) and 27 ASCII files. The first ASCII file contains the documentation associated with this database. Twenty-four of the ASCII files were generated by means of the ARC/INFO GRIDASCII command and can be used by most raster-based GIS software packages. The 24 files can be subdivided into two groups of 12 files each.

The files contain real data values representing actual carbon and potential carbon density in Mg C/ha (1 megagram = 10^6 grams) and integer-coded values for country name, Weck's Climatic Index, ecofloristic zone, elevation, forest or non- forest designation, population density, mean annual precipitation, slope, soil texture, and vegetation classification. One set of 12 files contains these data at a spatial resolution of 3.75 km, whereas the other set of 12 files has a spatial resolution of 0.25 degree. The remaining two ASCII data files combine all of the data from the 24 ASCII data files into 2 single generic data files. The first file has a spatial resolution of 3.75 km, and the second has a resolution of 0.25 degree. Both files also provide a grid-cell identification number and the longitude and latitude of the centerpoint of each grid cell.

The 3.75-km data in this numeric data package yield an actual total carbon estimate of 42.1 Pg (1 petagram = 10^15 grams) and a potential carbon estimate of 73.6 Pg; whereas the 0.25-degree data produced an actual total carbon estimate of 41.8 Pg and a total potential carbon estimate of 73.9 Pg.

Fortran and SASTM access codes are provided to read the ASCII data files, and ARC/INFO and ARCVIEW command syntax are provided to import the ARC/INFO exported integer grid files. The data files and this documentation are available without charge on a variety of media and via the Internet from the Carbon Dioxide Information Analysis Center (CDIAC).

OpenStreetMap exports for use in GIS applications.

This theme includes all OpenStreetMap features in this area matching:

amenity IN ('mobile_money_agent','bureau_de_change','bank','microfinance','atm','sacco','money_transfer','post_office')

Features may have these attributes:

• addr:city
• name
• network
• addr:full
• source
• amenity
• operator

This dataset is one of many "https://data.humdata.org/organization/hot">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 "/dataset?tags=openstreetmap">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 "/dataset?tags=openstreetmap">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:

• highway
• width
• name
• lanes
• smoothness
• surface
• layer
• source
• bridge
• oneway

This dataset is one of many "https://data.humdata.org/organization/hot">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

Mineral resource occurrence data covering the world, most thoroughly within the U.S. This database contains the records previously provided in the Mineral Resource Data System (MRDS) of USGS and the Mineral Availability System/Mineral Industry Locator System (MAS/MILS) originated in the U.S. Bureau of Mines, which is now part of USGS. The MRDS is a large and complex relational database developed over several decades by hundreds of researchers and reporters. While database records describe mineral resources worldwide, the compilation of information was intended to cover the United States completely, and its coverage of resources in other countries is incomplete. The content of MRDS records was drawn from reports previously published or made available to USGS researchers. Some of those original source materials are no longer available. The information contained in MRDS was intended to reflect the reports used as sources and is current only as of the date of those source reports. Consequently MRDS does not reflect up-to-date changes to the operating status of mines, ownership, land status, production figures and estimates of reserves and resources, or the nature, size, and extent of workings. Information on the geological characteristics of the mineral resource are likely to remain correct, but aspects involving human activity are likely to be out of date.

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 "/dataset?tags=openstreetmap">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.

Selected attributes included in the ArcGIS web-application.

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 "/dataset?tags=openstreetmap">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 "/dataset?tags=openstreetmap">OpenStreetMap exports on HDX. See the Humanitarian OpenStreetMap Team website for more information.