Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted GIS data cover administrative and postal divisions with up to 6 precision levels: a zip code layer and up to 5 administrative levels. All levels follow a seamless hierarchical structure with no gaps or overlaps.

The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Boundaries Database (GIS data, Geospatial data)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

• Reporting and Business Intelligence (BI)

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Fast-loading polygons for reporting and BI

• Multi-language support

For additional insights, you can combine the GIS data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our geospatial data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All GIS data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted geospatial data cover postal divisions for the whole world. The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Boundaries Database (Geospatial data, Map data, Polygon daa)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

• Reporting and Business Intelligence (BI)

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Fast-loading polygons for reporting and BI

• Multi-language support

For additional insights, you can combine the map data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our location data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

The World Database on Protected Areas (WDPA) is the most comprehensive global database of marine and terrestrial protected areas and is one of the key global biodiversity datasets being widely used by scientists, businesses, governments, International secretariats and others to inform planning, policy decisions and management.The WDPA is a joint project between the United Nations Environment Programme (UNEP) and the International Union for Conservation of Nature (IUCN). The compilation and management of the WDPA is carried out by UNEP World Conservation Monitoring Centre (UNEP-WCMC), in collaboration with governments, non-governmental organisations, academia and industry. There are monthly updates of the data which are made available online through the Protected Planet website where the data is both viewable and downloadable.Data and information on the world's protected areas compiled in the WDPA are used for reporting to the Convention on Biological Diversity on progress towards reaching the Aichi Biodiversity Targets (particularly Target 11), to the UN to track progress towards the 2030 Sustainable Development Goals, to some of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) core indicators, and other international assessments and reports including the Global Biodiversity Outlook, as well as for the publication of the United Nations List of Protected Areas. Every two years, UNEP-WCMC releases the Protected Planet Report on the status of the world's protected areas and recommendations on how to meet international goals and targets.Many platforms are incorporating the WDPA to provide integrated information to diverse users, including businesses and governments, in a range of sectors including mining, oil and gas, and finance. For example, the WDPA is included in the Integrated Biodiversity Assessment Tool, an innovative decision support tool that gives users easy access to up-to-date information that allows them to identify biodiversity risks and opportunities within a project boundary.The reach of the WDPA is further enhanced in services developed by other parties, such as theGlobal Forest Watch and the Digital Observatory for Protected Areas, which provide decision makers with access to monitoring and alert systems that allow whole landscapes to be managed better. Together, these applications of the WDPA demonstrate the growing value and significance of the Protected Planet initiative.For more details on the WDPA please read through the WDPA User Manual.

Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted geospatial data cover administrative and postal divisions with up to 5 precision levels. All levels follow a seamless hierarchical structure with no gaps or overlaps.

The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Administrative Boundaries Database (Geospatial data, Map data)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

• Reporting and Business Intelligence (BI)

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Fast-loading polygons for reporting and BI

• Multi-language support

For additional insights, you can combine the map data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our location data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

HARV_POLY: This dataset represents completed harvest land treatments on BLM managed lands in the states of Oregon and Washington. Harvest treatments are the cutting and removal or trees or biomass.

The Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data with CIESIN Modifications, Version 2 is a database providing georeferenced data for 1,572 National Priorities List (NPL) Superfund sites. These were selected from the larger set of the ATSDR Hazardous Waste Site Polygon Data, Version 2 data set with polygons from May 26, 2010. The modified data set contains only sites that have been proposed, currently on, or deleted from the final NPL as of October 25, 2013. Of the 2,080 ATSDR polygons from 2010, 1,575 were NPL sites but three sites were excluded - 2 in the Virgin Islands and 1 in Guam. This data set is modified by the Columbia University Center for International Earth Science Information Network (CIESIN). The modified polygon database includes all the attributes for these NPL sites provided in the ATSDR GRASP Hazardous Waste Site Polygon database and selected attributes from the EPA List 9 Active CERCLIS sites and SCAP 12 NPL sites databases. These polygons represent sites considered for cleanup under the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA or Superfund). The Geospatial Research, Analysis, and Services Program (GRASP, Division of Health Studies, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention) has created site boundary data using the best available information for those sites where health assessments or consultations have been requested.

Northeastern United States State Boundary data are intended for geographic display of state boundaries at statewide and regional levels. Use it to map and label states on a map. These data are derived from Northeastern United States Political Boundary Master layer. This information should be displayed and analyzed at scales appropriate for 1:24,000-scale data. The State of Connecticut, Department of Environmental Protection (CTDEP) assembled this regional data layer using data from other states in order to create a single, seamless representation of political boundaries within the vicinity of Connecticut that could be easily incorporated into mapping applications as background information. More accurate and up-to-date information may be available from individual State government Geographic Information System (GIS) offices. Not intended for maps printed at map scales greater or more detailed than 1:24,000 scale (1 inch = 2,000 feet.)

Detailed property polygon data for all pharmacies and drug stores in the US and Canada. Includes custom-drawn polygons for each location, enabling precise spatial analysis. Ideal for healthcare accessibility studies, market penetration strategies, and competitive landscape mapping.

Xtract.io offers comprehensive POI and Polygon data, featuring 6 million locations across 11 industries. With global coverage and detailed geospatial data, get custom polygons drawn for the points of interest you choose.

Our polygon dataset enhances geospatial accuracy by linking over 14.4M U.S. POIs to precise building footprints, enabling better decision-making through polygon-based geofencing and spatial analysis.

Built using satellite imagery, AI, and human validation, this dataset defines accurate location boundaries for retail stores, offices, amenities, and more—ideal for applications needing hyper-local precision.

Key data points include: - Polygon geometry linked to POI - Location name, category, coordinates - Building footprint dimensions - Commercial and amenity POIs across the U.S. - Human-validated and machine-refined accuracy

With national U.S. coverage, this dataset powers use cases in retail site selection, real estate intelligence, foot traffic analysis, and investment modeling.

This geodatabase of point, line and polygon features is an effort to consolidate all of the range improvement locations on BLM-managed land in Idaho into one database. Currently, the polygon feature class has some data for all of the BLM field offices except the Coeur d'Alene and Cottonwood field offices. Range improvements are structures intended to enhance rangeland resources, including wildlife, watershed, and livestock management. Examples of range improvements include water troughs, spring headboxes, culverts, fences, water pipelines, gates, wildlife guzzlers, artificial nest structures, reservoirs, developed springs, corrals, exclosures, etc. These structures were first tracked by the Bureau of Land Management (BLM) in the Job Documentation Report (JDR) System in the early 1960s, which was predominately a paper-based tracking system. In 1988 the JDRs were migrated into and replaced by the automated Range Improvement Project System (RIPS), and version 2.0 is currently being used today. It tracks inventory, status, objectives, treatment, maintenance cycle, maintenance inspection, monetary contributions and reporting. Not all range improvements are documented in the RIPS database; there may be some older range improvements that were built before the JDR tracking system was established. There also may be unauthorized projects that are not in RIPS. Official project files of paper maps, reports, NEPA documents, checklists, etc., document the status of each project and are physically kept in the office with management authority for that project area. In addition, project data is entered into the RIPS system to enable managers to access the data to track progress, run reports, analyze the data, etc. Before Geographic Information System technology most offices kept paper atlases or overlay systems that mapped the locations of the range improvements. The objective of this geodatabase is to migrate the location of historic range improvement projects into a GIS for geospatial use with other data and to centralize the range improvement data for the state. This data set is a work in progress and does not have all range improvement projects that are on BLM lands. Some field offices have not migrated their data into this database, and others are partially completed. New projects may have been built but have not been entered into the system. Historic or unauthorized projects may not have case files and are being mapped and documented as they are found. Many field offices are trying to verify the locations and status of range improvements with GPS, and locations may change or projects that have been abandoned or removed on the ground may be deleted. Attributes may be incomplete or inaccurate. This data was created using the standard for range improvements set forth in Idaho IM 2009-044, dated 6/30/2009. However, it does not have all of the fields the standard requires. Fields that are missing from the polygon feature class that are in the standard are: ALLOT_NO, POLY_TYPE, MGMT_AGCY, ADMIN_ST, and ADMIN_OFF. The polygon feature class also does not have a coincident line feature class, so some of the fields from the polygon arc feature class are included in the polygon feature class: COORD_SRC, COORD_SRC2, DEF_FET, DEF_FEAT2, ACCURACY, CREATE_DT, CREATE_BY, MODIFY_DT, MODIFY_BY, GPS_DATE, and DATAFILE. There is no National BLM standard for GIS range improvement data at this time. For more information contact us at blm_id_stateoffice@blm.gov.

Aurora:GeoStudio® is a cutting-edge geospatial analysis platform that excels in supporting Point of Interest (POI) data, providing detailed and comprehensive information about specific locations or landmarks. POI data includes essential details such as the name, address, coordinates, and category of locations, ranging from restaurants and hotels to parks and tourist attractions. This data is vital for enhancing mapping and navigation applications, making it easier for users to find relevant and nearby points of interest.

Core Features:

  1.    Known Polygon Search (KPS) via OpenStreetMap®:
  •    Aurora:GeoStudio® integrates with OpenStreetMap® to offer Known Polygon Search functionality. This feature enables users to accurately identify and retrieve POI data within defined areas of interest. OpenStreetMap® provides a vast and up-to-date database of POIs, ensuring comprehensive coverage and accurate information.
  2.    Automatic Gridding and Area Analytics:
  •    The platform includes automatic gridding within areas of interest, facilitating detailed Area Usage and Area Visit analytics. This functionality divides the area into manageable grids, allowing users to analyze POI data effectively. The automated process supports the visualization of POI density, visitation patterns, and the impact of various factors on POI popularity and usage.
  3.    POI Data Visualization:
  •    Aurora:GeoStudio® offers advanced visualization capabilities, displaying POI data on customizable maps from providers like Google, Esri, Open, and Stamen. This visualization helps users understand the spatial distribution of POIs and their relationships within an area, aiding in effective decision-making and strategic planning.

Applications:

  1.    Urban Planning:
  •    Urban planners can use POI data to understand the distribution of amenities and services within a city. This information helps in planning new developments, optimizing resource allocation, and ensuring that essential services are accessible to residents. By analyzing POI data, planners can create more livable and well-serviced urban areas.
  2.    Infrastructure Management:
  •    POI data is invaluable for managing infrastructure projects, including the placement of utilities, public transportation, and commercial services. Understanding the location and category of POIs allows for better planning and coordination of infrastructure improvements and expansions.
  3.    Spatial Analysis:
  •    Researchers and analysts can leverage POI data to conduct spatial analyses, such as identifying gaps in service provision, studying the impact of new developments, and evaluating the effectiveness of urban policies. Detailed POI data supports robust analysis and actionable insights.
  4.    Area Usage and Visit Analytics:
  •    Aurora:GeoStudio® enables users to track and analyze area usage and visit patterns related to POIs. This is particularly useful for businesses looking to optimize their location strategies, urban developers aiming to enhance public spaces, and government agencies seeking to improve service accessibility and urban experiences.

Aurora:GeoStudio® provides exceptional support for Point of Interest (POI) data, making it a powerful tool for urban planning, infrastructure management, and spatial analysis. By integrating Known Polygon Search via OpenStreetMap® and incorporating automatic gridding and detailed area analytics, the platform offers valuable insights into POI usage and spatial relationships. This capability enhances decision-making processes, supports efficient resource management, and facilitates the development of vibrant and well-planned urban environments. Aurora:GeoStudio®’s advanced features empower users to gain a comprehensive understanding of urban dynamics and optimize their strategic initiatives.

The hydrographic polygon coverages were created using TIGER/LINE 2000 shapefile data gathered from ESRI's Geography Network. The individual county hydrography line shapefiles were processed into Arc/Info coverages and then appended together to create complete state coverages. They were then edited to remove unwanted features, leaving a state-by-state database of both important and navigable water features. Attributes were added to denote navigable features and names. Also, an attribute was added to the polygons to denote which were water and which were land features. The state databases were then appended together to create a single, nationwide hydrography network containing named arcs and polygons. These features also contain a state FIPS. Because some of the hydro features are represented by lines instead of polygons, the complete hydro dataset consists of 2 shapefiles, one for lines and one for polygons. They must be used together to paint a complete picture.

© US Census and ESRI This layer is sourced from maps.bts.dot.gov.

The hydrographic polygon coverages (NTAD 2015) were created using TIGER/LINE 2000 shapefile data gathered from ESRI's Geography Network. The individual county hydrography line shapefiles were processed into Arc/Info coverages and then appended together to create complete state coverages. They were then edited to remove unwanted features, leaving a state-by-state database of both important and navigable water features. Attributes were added to denote navigable features and names. Also, an attribute was added to the polygons to denote which were water and which were land features. The state databases were then appended together to create a single, nationwide hydrography network containing named arcs and polygons. These features also contain a state FIPS. Because some of the hydro features are represented by lines instead of polygons, the complete hydro dataset consists of 2 shapefiles, one for lines and one for polygons. They must be used together to paint a complete picture.

© US Census and ESRI

New York City’s comprehensive effort to reduce or eliminate potential losses from the hazards described in the Hazard Specific section of the website. The map includes existing and completed mitigation actions that will minimize the effects of a hazard event on New York City’s population, economy, property, building stock, and infrastructure. It is the result of a coordinated effort by 46 New York City agencies and partners to develop and implement a broad range of inventive and effective ways to mitigate hazards. Point, line, polygon features and a table for the Mitigation Actions map on the Hazard Mitigation website: www.nychazardmitigation.com/all-hazards/mitigation/actions-map/

This table contains more information on each project: https://data.cityofnewyork.us/City-Government/Hazard-Mitigation-Plan-Mitigation-Actions-Database/veqt-eu3t

This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. The Maryland Department of Natural resources began updating the National Wetlands Inventory (NWI) mapping of wetlands in Maryland in the early 1990s. This database lists the 3.75' x 3.75' USGS quadrangles for which 'DNR Wetlands' have been mapped. It identifies the date of source photography used to map wetlands - and the status of mapping effort. This database also gives the five-letter abbreviation used for naming 'DNR_Wetlands' files. In most cases - the first five characters are the first 'five characters' of the 'USGS 7.5' Quad Name.' When completed - the series will provide coverage for the entire State of Maryland. Last Updated: Feature Service Layer Link: http://geodata.md.gov/imap/rest/services/Hydrology/MD_Wetlands/MapServer/1 ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

The Surface Geology of Australia 1:1M scale dataset (2012 edition) is a seamless national coverage of outcrop and surficial geology, compiled for use at or around 1:1 million scale. The data maps outcropping bedrock geology and unconsolidated or poorly consolidated regolith material covering bedrock. Geological units are represented as polygon and line geometries, and are attributed with information regarding stratigraphic nomenclature and hierarchy, age, lithology, and primary data source. The dataset also contains geological contacts, structural features such as faults and shears, and miscellaneous supporting lines like the boundaries of water and ice bodies.

Purpose

The data is used to indicate the extent of outcropping bedrock geology and unconsolidated or poorly consolidated regolith material covering bedrock.

Dataset History

The 2012 dataset has been updated from the previous 2010 data by updating geological unit data to 2012 information in the Australian Stratigraphic Units Database (http://www.ga.gov.au/products-services/data-applications/reference-databases/stratigraphic-units.html), incorporating new published mapping in the Northern Territory and Queensland, and correcting errors or inconsistent data identified in the previous edition, particularly in the Phanerozoic geology of Western Australia. The attribute structure of the dataset has also been revised to be more compatible with the GeoSciML data standard, published by the IUGS Commission for Geoscience Information. Original Geosciences Australia dataset name; GeologicalUnits_1m_Polys_PED.shp.

Dataset Citation

SA Department of Environment, Water and Natural Resources (2015) Geology Polygon Dataset - PED. Bioregional Assessment Source Dataset. Viewed 12 October 2016, http://data.bioregionalassessments.gov.au/dataset/9f52f19e-25f9-4006-9af9-951d336bb5a0.

See full Data Guide here. Drainage Basin Set:

Connecticut Drainage Basins is 1:24,000-scale, polygon and line feature data that define natural drainage areas in Connecticut. These are small basin areas that average approximately 1 square mile in size and make up, in order of increasing size, the larger local, subregional, regional, and major drainage basin areas. Connecticut Drainage Basins includes drainage areas for all Connecticut rivers, streams, brooks, lakes, reservoirs and ponds published on 1:24,000-scale 7.5 minute topographic quadrangle maps prepared by the USGS between 1969 and 1984. Data is compiled at 1:24,000 scale (1 inch = 2,000 feet). This information is not updated. Polygon and line features represent drainage basin areas and boundaries, respectively. Each basin area (polygon) feature is outlined by one or more major, regional, subregional, local, impoundment, or river reach boundary (line) feature. These data include 7,076 basin area (polygon) features and 20,945 basin boundary (line) features. Basin area (polygon) attributes include major, regional, subregional, local, (full) basin number, and feature size in acres and square miles. The full basin number (BASIN_NO) uniquely identifies individual basins and is up to 13 characters in length. There are 7,031 unique basin numbers. Examples include 6000-00-1+*, 4300-00-1+L1, and 6002-00-2-R1. The first digit (column 1) designates the major basin, the first two digits (columns 1-2) designate the regional basin, the first 4 digits (columns 1-4) designate the subregional basin, and the first seven digits (columns 1-7) designate the local basin. Note, there are slightly more basin polygon features (7,076) than unique basin numbers (7,031) primarily because a few water supply watershed boundaries split a basin into two polygon features at the location of a small dam or point of diversion along a stream. Basin boundary (line) attributes include a drainage divide type attribute (DIVIDE) used to cartographically represent the hierarchical drainage basin system. This divide type attribute is used to assign different line symbology to major, regional, subregional, local, stream reach, and lake impoundment drainage basin divides. For example, major basin drainage divides are more pronounced and shown with a wider line symbol than regional basin drainage divides. Connecticut Drainage Basins is the data source for other digital spatial data including the Connecticut Major Drainage Basins, Connecticut Regional Drainage Basins, Connecticut Subregional Drainage Basins, and Connectcut Local Drainage Basins. Purpose: The polygon features define the contributing drainage area for individual reservoirs, lakes, ponds and river and stream reaches in Connecticut. These are hydrologic land units where precipitation is collected. Rain falling in a basin may take two courses. It may both run over the land and quickly enter surface watercourses, or it may soak into the ground moving through the earth until it surfaces at a wetland or stream. In an undisturbed natural drainage basin, the surface and ground water arrive as precipitation and leave either by evaporation or as surface runoff at the basin's outlet. A basin is a self-contained hydrologic system, with a clearly defined water budget and cycle. The amount of water that flows into the basins equals the amount that leaves. A drainage divide is the topographic barrier along a ridge or line of hilltops separating adjacent drainage basins. For example, rain or snow melt draining down one side of a hill generally will flow into a different basin and stream than water draining down the other side of the hill. These hillsides are separated by a drainage divided that follows nearby hilltops and ridge lines. Use these basin data to identify where rainfall flows over land and downstream to a particular watercourse. Use these data to categorize and tabulate information according to drainage basin by identifying the basin number for individual reservoir, lake, pond, stream reach, or location of interest. Due to the hierarchical nature of the basin numbering system, a database that records the 13-digit basin number for individual geographic locations of interest will support tabulations by major, regional, subregional or local basin as well as document the unique 13-digit basin number. To identify either all upstream basins draining to a particular location or all downstream basins flowing from a particular location, refer to the Gazetteer of Drainage Basin Areas of Connecticut, Nosal, 1977, CT DEP Water Resources Bulletin 45, for the hydrologic sequence, headwater to outfall, of drainage basins available at http://cteco.uconn.edu/docs/wrb/wrb45_gazetteer_of_drainage_areas_of_connecticut.pdf Not intended for maps printed at map scales greater or more detailed than 1:24,000 scale (1 inch = 2,000 feet.). Not intended for analysis with other digital data compiled at scales greater than or more detailed than 1:24,000 scale. Use these data with 1:24,000-scale hydrography data also available from the State of Connecticut, Department of Environmental Protection.

onnecticut Drainage Basins is 1:24,000-scale, polygon and line feature data that define natural drainage areas in Connecticut. These are small basin areas that average approximately 1 square mile in size and make up, in order of increasing size, the larger local, subregional, regional, and major drainage basin areas. Connecticut Drainage Basins includes drainage areas for all Connecticut rivers, streams, brooks, lakes, reservoirs and ponds published on 1:24,000-scale 7.5 minute topographic quadrangle maps prepared by the USGS between 1969 and 1984. Data is compiled at 1:24,000 scale (1 inch = 2,000 feet). This information is not updated. Polygon and line features represent drainage basin areas and boundaries, respectively. Each basin area (polygon) feature is outlined by one or more major, regional, subregional, local, impoundment, or river reach boundary (line) feature. These data include 7,076 basin area (polygon) features and 20,945 basin boundary (line) features. Basin area (polygon) attributes include major, regional, subregional, local, (full) basin number, and feature size in acres and square miles. The full basin number (BASIN_NO) uniquely identifies individual basins and is up to 13 characters in length. There are 7,031 unique basin numbers. Examples include 6000-00-1+*, 4300-00-1+L1, and 6002-00-2-R1. The first digit (column 1) designates the major basin, the first two digits (columns 1-2) designate the regional basin, the first 4 digits (columns 1-4) designate the subregional basin, and the first seven digits (columns 1-7) designate the local basin. Note, there are slightly more basin polygon features (7,076) than unique basin numbers (7,031) primarily because a few water supply watershed boundaries split a basin into two polygon features at the location of a small dam or point of diversion along a stream. Basin boundary (line) attributes include a drainage divide type attribute (DIVIDE) used to cartographically represent the hierarchical drainage basin system. This divide type attribute is used to assign different line symbology to major, regional, subregional, local, stream reach, and lake impoundment drainage basin divides. For example, major basin drainage divides are more pronounced and shown with a wider line symbol than regional basin drainage divides. Connecticut Drainage Basins is the data source for other digital spatial data including the Connecticut Major Drainage Basins, Connecticut Regional Drainage Basins, Connecticut Subregional Drainage Basins, and Connectcut Local Drainage Basins.

For more information on the Pine Barrens visit https://pb.state.ny.us/central-pine-barrens/overview/ and https://pb.state.ny.us/our-work/our-work/land-use-project-review/development-application-process/.

This spatial dataset represents the locations of aquatic resource area (ARA) polygon segments derived from corresponding polygon features in the Ontario Hydro Network. ARA polygon segments may represent a portion of a water body or an entire water body (such as a lake, river or stream). Attributes for each location may include:

• physical characteristics such as water temperature and depth
• fish species observed

The ARA data classes are the authoritative source for generic spatial data related to fish species in Ontario. The data may be used for:

• forest and fisheries management planning
• municipal planning
• natural heritage and other land use planning
• issuing work permits under the Public Lands Act, 1990
• issuing licenses under the Fish and Wildlife Conservation Act, 1997
• fulfilling public information requests

There are additional sensitive data related to provincially tracked species and species at risk that are not available as part of this open data package. Distribution of sensitive species data is approved on a need-to-know basis. Requests should be sent to geospatial@ontario.ca.

The Global Urban Polygons and Points Dataset (GUPPD), Version 1 is a global data set of 123,034 urban settlements with place names and population for the years 1975-2030 in five-year increments. The data set builds on and expands the European Commission, Joint Research Centre's (JRC) 2015 Global Human Settlement (GHS) Urban Centre Database (UCDB). The JRC Settlement Model (GHS-SMOD) data set includes a hierarchy of urban settlements, from urban centre (level 30), to dense urban cluster (level 23), to semi-dense urban cluster (level 22). The UCDB only includes level 30, whereas the GUPPDv1 adds levels 23 and 22, and uses open data sources to both check and validate the names that JRC assigned to its UCDB polygons and to label the newly added settlements. The methodology described in the documentation was able to consistently label a greater percentage of UCDB polygons than were previously labeled by JRC.