GISCO (Geographic Information System of the COmmission) is responsible for meeting the European Commission's geographical information needs at three levels: the European Union, its member countries, and its regions.

In addition to creating statistical and other thematic maps, GISCO manages a database of geographical information, and provides related services to the Commission. Its database contains core geographical data covering the whole of Europe, such as administrative boundaries, and thematic geospatial information, such as population grid data. Some data are available for download by the general public and may be used for non-commercial purposes. For further details and information about any forthcoming new or updated datasets, see http://ec.europa.eu/eurostat/web/gisco/geodata.

This metadata refers to the whole content of GISCO reference database extracted in June 2020, which contains both public datasets (also available for the general public through http://ec.europa.eu/eurostat/web/gisco/geodata) and datasets to be used only internally by the EEA (typically, but not only, GISCO datasets at 1:100k). The document GISCO-ConditionsOfUse.pdf provided with the dataset gives information on the copyrighted data sources, the mandatory acknowledgement clauses and re-dissemination rights. The license conditions for EuroGeographic datasets in GISCO are provided in a standalone document "LicenseConditions_EuroGeographics.pdf".

The database is provided in GPKG files, with datasets at scales from 1:60M to 1:100K, with reference years spanning until 2021 (e.g. NUTS 2021). Attribute files are provided in CSV. The database manual, a file with the content of the database, a glossary, and a document with the naming conventions are also provided with the database.

The main updates with respect to the previous version of the full database in the SDI (from Jul. 2018) are the addition of the following datasets: - Administrative boundaries at country level, 2020 (CNTR_2020) - Administrative boundaries at commune level, 2016 (COMM_2016) - Coastline boundaries, 2016 (COAS_2016) - Exclusive Economic Zones, 2016 (EEZ_2016)

- Farm Accountancy Data Network based on NUTS 2016, 2018 (FADN_2018)

             Local Administrative Units, 2018 (LAU_2018)

• Nomenclature of Territorial Units for Statistics, 2021 (NUTS_2021)
• Political regions (NB.: defined by the Committee of the Regions), 2018 (POLREG_2018)
• Pan-European Settlements, 2016 (STLL_2016) and 2018 (STLL_2018)
• Transport Networks (NB.: railway lines, railway stations, roads, road junctions, levelcrossings, ferry routs and custom points), 2019 (TRAN_2019)
• Urban Audit Areas, 2018 (URAU_2018) and 2020 (URAU_2020)

NOTE: This metadata file is only for internal EEA purposes and in no case replaces the official metadata provided by Eurostat. For specific GISCO datasets included in this version there are individual EEA metadata files in the SDI: NUTS_2021 and CNTR_2020. For other GISCO datasets in the SDI, it is recommended to use the version included in this dataset. The original metadata files from Eurostat for the different GISCO datasets are available via ECAS login through the Eurostat metadata portal on https://webgate.ec.europa.eu/inspire-sdi/srv/eng/catalog.search#/home. For the public products metadata can also be downloaded from https://ec.europa.eu/eurostat/web/gisco/geodata. For more information about the full database or any of its datasets, please contact the SDI Team (sdi@eea.europa.eu).

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. GIS Database 2002-2005: Project Size = 1,898 acres Fort Larned National Historic Site (including the Rut Site) = 705 acres 16 Map Classes 11 Vegetated 5 Non-vegetated Minimum Mapping Unit = ½ hectare is the program standard but this was modified at FOLS to ¼ acre. Total Size = 229 Polygons Average Polygon Size = 8.3 acres Overall Thematic Accuracy = 92% To produce the digital map, a combination of 1:8,500-scale (0.75 meter pixels) color infrared digital ortho-imagery acquired on October 26, 2005 by the Kansas Applied Remote Sensing Program and 1:12,000-scale true color ortho-rectified imagery acquired in 2005 by the U.S. Department of Agriculture - Farm Service Agency’s Aerial Photography Field Office, and all of the GPS referenced ground data were used to interpret the complex patterns of vegetation and land-use. In the end, 16 map units (11 vegetated and 5 land-use) were developed and directly cross-walked or matched to corresponding plant associations and land-use classes. All of the interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases using ArcGIS© software. Draft maps were printed, field tested, reviewed and revised. One hundred and six accuracy assessment (AA) data points were collected in 2006 by KNSHI and used to determine the map’s accuracy. After final revisions, the accuracy assessment revealed an overall thematic accuracy of 92%.

The main two products of this data set are (1) an Asia 30-second land cover data set and (2) an Asia 30-second ground truth data set of land cover classes. The purpose to distribute Asia land cover data set is to provide land cover information for global change studies and other global/continental applications. The purpose to distribute ground truth data of land cover classes is to build a better Asian ground truth database by improving coverage and adding new reliable ground truth data of Asia for future application.

The Land Cover Working Group (LCWG) of the Asian Association on Remote Sensing (AARS) prepared the land cover data set. The AARS land cover classification system was developed through discussion with members of the LCWG/AARS and is described in the Methods section of the CD-ROM. A table showing corresponding classes between the LCWG/AARS classification system and IGBP-DIS classification system is provided.

Ground truth data were collected mainly from existing thematic maps by the cooperation of the working group members. The maps used are listed in the documentation. Some of ground truth data were collected by field survey in Central Asia such as Kazakhstan, Uzbekistan and Turkmenistan. Three field trips were performed with the cooperation of WG member of Kazakhstan. Ground truth data of 31 land cover classes were collected from 19 types of information sources (thematic maps or field surveys).

Global land 1-km AVHRR data set was used as the source of satellite data. 10-days composite data of AVHRR NDVI, channel 4 and channel 5 were used for this project. NDVI data from April 1, 1992 to March 31, 1993 and NDVI and land surface temperature (Ts) data from April 1, 1992 to October 31, 1992 were used in the land cover analysis. (See Methods section for discussion of the theoretical support for using the ratio of land surface temperature (Ts) and NDVI in land cover analysis.) The Global Land One-kilometer Base Elevation (GLOBE), Version 1.0, (30 arc-second grid digital elevation data) and the Digital Chart of the World (DCW) data (1:1,000,000 scale vector base map of the world with 17 attibute layers including seashore lines and national boundaries) were used. The following data were prepared for the classification: Ts/NDVI (seven monthly data from April to October 1992); maximum NDVI (the maximum monthly data from April 1992 to March 1993); minimum NDVI (the minimum monthly data from April 1992 to March 1993); and digital elevation data. All these data are registered together in 30-second grid in latitude/longitude.

The land cover classification was done by the following steps: (1) clustering of monthly Ts/NDVI from April to October; (2) finding classification rules for decision tree method; (3) classification by decision tree method; and (4) post-classification modification.

Contribution to add and improve ground truth data is appreciated. It can be sent to Dr. Ryutaro Tateishi (see Data Center Contact information). Contributions should be the following way: (1) digital data or paper map; (2) ground truth data of land cover described either by class code defined in the data set land.htm or by contributor's own land cover class name with its definition; (3) ground truth region covering at least as large as 2.5 minute by 2.5 minute (approximately equivalent to 5 km by 5 km at the equator) in latitude/longitude with a homogeneous land cover type; and (4) four values of latitude/longitude of the north, south, east, and west end of ground truth regions. If you send information of ground truth, it will be included in the Asia ground truth data base as the next product.

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

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 polygons in this dataset are a digital representation of the distribution or extent of geological units within the area. Polygons have a range of attributes including unit name, age, lithological description and an abbreviated symbol for use in labelling the polygons. These have been extracted from the Rock Units Table held in the Department of Natural Resources, Mines and Energy Merlin Database.

Purpose

To display the geology polygons which define the extent of rock units.

Dataset History

Supplemental_Information:

Data captured at 1:40 000 scale. The data set is sourced from the Department's Geoscience and Resources Database (GRDB), a component of the Mineral and Energy Resources Location and Information Network (MERLIN) corporate database.(GRDB), a component of the Mineral and Energy Resources Location and Information Network (MERLIN) corporate database.



NOTE: GEOLDATA was in most cases compiled based on Datum AGD66. The map tile coverages so compiled have now been projected to geographics based on Datum GDA94. Consequently the boundaries for these map tiles will not conform to the Latitude and Longitude graticule based on Datum GDA94.

Entity_and_Attribute_Information:

Detailed_Description:



  Entity_Type:



    Entity_Type_Label: 9341_r

    Entity_Type_Definition:

      Polygons have a range of attributes including unit name, age, lithological description and an abbreviated symbol for use in labelling the polygons. 

    Entity_Type_Definition_Source:

      The Rock Units Table held in the Department of Natural Resources, Mines and Energy Merlin Database. 



  Attribute:



    Attribute_Label: FID

    Attribute_Definition: Internal feature number.

    Attribute_Definition_Source: ESRI

    Attribute_Domain_Values:



      Unrepresentable_Domain:

        Sequential unique whole numbers that are automatically generated. 



    Beginning_Date_of_Attribute_Values: March 2004



  Attribute:



    Attribute_Label: Shape

    Attribute_Definition: Feature geometry.

    Attribute_Definition_Source: ESRI

    Attribute_Domain_Values:



      Unrepresentable_Domain: Coordinates defining the features.



  Attribute:



    Attribute_Label: KEY

    Attribute_Definition: Unique polygon identifier and relate item for poygon attributes



  Attribute:



    Attribute_Label: ROCK_U_NAM

    Attribute_Definition:

      The Map Unit Name of the polygon. In the case of named units it comprises of the standard binomial name. Unnamed subdivisions of named units include the binomial name with a letter symbol as a suffix. Unnamed units are represented by a letter symbol, usually in combination with a map sheet number. 



  Attribute:



    Attribute_Label: AGE

    Attribute_Definition: Geological age of unit



  Attribute:



    Attribute_Label: LITH_SUMMA

    Attribute_Definition:

      Provides a brief description of the map units as they have been described in the course of the project work, or as has appeared on relevant hard copy map legends. 



  Attribute:



    Attribute_Label: ROCK_U_TYP

    Attribute_Definition:

      Provides a means of separating map units, eg for constructing a map reference. This item will contain one of the following: STRAT- Stratigraphic unit, including sedimentary, volcanic and metamorphic rock units. INTRU- Intrusive rock units; COMPST- Compound unit where the polygon includes two or more rock units, either stratigraphic, intrusive or both; COMPST- Compound unit, as above where the dominant or topmost unit is of the STRAT type; COMPIN- Compound unit, as above, where the dominant unit is of the INTRU type; WATER- Water bodies- Large dams, lakes, waterholes. 



  Attribute:



    Attribute_Label: SEQUENCE_N

    Attribute_Definition:

      A numeric field to allow sorting of the rock units in approximate stratigraphic order as they would appear on a map legend. 



  Attribute:



    Attribute_Label: DOMINANT_R

    Attribute_Definition:

      A simplified lithological description to allow generation of thematic maps based on broad rock types. 



  Attribute:



    Attribute_Label: MAP_SYMBOL

    Attribute_Definition:

      Provides an abbreviated label for polygons. Mostly based on the letter symbols as they appear on published maps or the original hard copy compilation sheets. These are not unique across the State, but should be unique within a single map tile, and usually adjacent tiles. 



  Attribute:



    Attribute_Label: NAME_100K

    Attribute_Definition: Name of 1:100 000 map sheet coincident with the data extent.



Overview_Description:



  Entity_and_Attribute_Overview:

    Polygon Attribute information includes Polygon Key, Rock Unit Name, Age, Lithology, Rock Unit Type, Map Symbol and 1:100 000 sheet name.

Dataset Citation

"Queensland Department of Natural Resources, Mines and Energy" (2014) Qld 100k mapsheets - Warwick. Bioregional Assessment Source Dataset. Viewed 28 September 2017, http://data.bioregionalassessments.gov.au/dataset/3e2fa307-1f06-4873-96d3-5c3e5638894a.

The pathway representation consists of segments and intersection elements. A segment is a linear graphic element that represents a continuous physical travel path terminated by path end (dead end) or physical intersection with other travel paths. Segments have one street name, one address range and one set of segment characteristics. A segment may have none or multiple alias street names. Segment types included are Freeways, Highways, Streets, Alleys (named only), Railroads, Walkways, and Bike lanes. SNDSEG_PV is a linear feature class representing the SND Segment Feature, with attributes for Street name, Address Range, Alias Street name and segment Characteristics objects. Part of the Address Range and all of Street name objects are logically shared with the Discrete Address Point-Master Address File layer. Appropriate uses include: Cartography - Used to depict the City's transportation network location and connections, typically on smaller scaled maps or images where a single line representation is appropriate. Used to depict specific classifications of roadway use, also typically at smaller scales. Used to label transportation network feature names typically on larger scaled maps. Used to label address ranges with associated transportation network features typically on larger scaled maps. Geocode reference - Used as a source for derived reference data for address validation and theoretical address location Address Range data repository - This data store is the City's address range repository defining address ranges in association with transportation network features. Polygon boundary reference - Used to define various area boundaries is other feature classes where coincident with the transportation network. Does not contain polygon features. Address based extracts - Used to create flat-file extracts typically indexed by address with reference to business data typically associated with transportation network features. Thematic linear location reference - By providing unique, stable identifiers for each linear feature, thematic data is associated to specific transportation network features via these identifiers. Thematic intersection location reference - By providing unique, stable identifiers for each intersection feature, thematic data is associated to specific transportation network features via these identifiers. Network route tracing - Used as source for derived reference data used to determine point to point travel paths or determine optimal stop allocation along a travel path. Topological connections with segments - Used to provide a specific definition of location for each transportation network feature. Also provides a specific definition of connection between each transportation network feature. (defines where the streets are and the relationship between them ie. 4th Ave is west of 5th Ave and 4th Ave does intersect with Cherry St) Event location reference - Used as source for derived reference data used to locate event and linear referencing.Data source is TRANSPO.SNDSEG_PV. Updated weekly.

The Tree Cover Dynamics (TCD) Conterminous United States (CONUS) dataset is a suite of 30 m wall-to-wall products, derived from USGS Landsat-4, Landsat-5 and Landsat-7 Collection 1 Analysis Ready Data (ARD), defining for each year: (i) the estimated percent tree cover (PTC), (ii) if tree cover loss is detected, the estimated percent tree cover decrease from the previous year (ΔPTC), (iii) if tree cover loss is detected, the Landsat acquisition dates bounding the tree cover loss event (i.e., the last valid observation before the loss, and the first valid observation after the loss) and (iv) a forest status thematic map (three thematic classes: stable forest, stable non-forest, forest cover loss). The products are available for every year from 1985 to 2019. The dataset is provided as georeferenced GeoTIFF images, defined in the CONUS Albers Equal-Area Conic map projection at 30m resolution.

A multidisciplinary study based on several digital (geology, lithology, shoreline evolution, photo-interpretation of aerial and ortho-photographs) and field (topographic and vegetational surveys, grain-size analysis) datasets prompted new insights to a better definition of the processes in action at the Grande beach at São Francisco do Sul Island (Santa Catarina, Brazil). The resulting data enabled us to produce a multi-thematic map at 1:50,000 scale that might be useful in assisting decision-makers to manage the coastal system, taking into account involved factors at once and not separately. In addition, the map may be implemented and integrated with new information, since the database is provided in geographical information system. The results confirmed the importance of addressing coastal systems with a multi-faceted approach that can be applied everywhere, not only in settings similar to São Francisco do Sul Island.

The 2023 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Combined Statistical Areas (CSAs) are defined by the Office of Management and Budget (OMB) and consist of two or more adjacent Core Based Statistical Areas (CBSAs) that have significant employment interchanges. The CBSAs that combine to create a CSA retain separate identities within the larger CSA. Because CSAs represent groupings of CBSAs, they should not be ranked or compared with individual CBSAs. The generalized boundaries in this file are based on those defined by OMB based on the 2020 Census and published in 2023.

Introduction and Rationale: Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product. Contents: Spatial data

Attribute table for merged rasters

Technical validation data

Number and proportion of mismatched pixels Number and proportion of unresolved pixels Producer's and User's accuracy values and coverage of reference data Resources in this dataset:Resource Title: Attribute table for merged rasters. File Name: CombinedRasterAttributeTable_CDLNVC.csvResource Description: Raster attribute table for merged raster product. Class names and recommended color map were taken from USDA-NASS Cropland Data Layer and LANDFIRE National Vegetation Classification. Class values are also identical to source data, except classes from the CDL are now negative values to avoid overlapping NVC values. Resource Title: Number and proportion of mismatched pixels. File Name: pixel_mismatch_byyear_bycounty.csvResource Description: Number and proportion of pixels that were mismatched between the Cropland Data Layer and National Vegetation Classification, per year from 2012-2021, per county in the conterminous United States.Resource Title: Number and proportion of unresolved pixels. File Name: unresolved_conflict_byyear_bycounty.csvResource Description: Number and proportion of unresolved pixels in the final merged rasters, per year from 2012-2021, per county in the conterminous United States. Unresolved pixels are a result of mismatched pixels that we could not resolve based on surrounding agricultural land (no agriculture with 90m radius).Resource Title: Producer's and User's accuracy values and coverage of reference data. File Name: accuracy_datacoverage_byyear_bycounty.csvResource Description: Producer's and User's accuracy values and coverage of reference data, per year from 2012-2021, per county in the conterminous United States. We defined coverage of reference data as the proportional area of land cover classes that were included in the reference data published by USDA-NASS and LANDFIRE for the Cropland Data Layer and National Vegetation Classification, respectively. CDL and NVC classes with reference data also had published accuracy statistics. Resource Title: Data Dictionary. File Name: Data_Dictionary_RasterMerge.csv

The World Terrestrial Ecosystems map classifies the world into areas of similar climate, landform, and land cover, which form the basic components of any terrestrial ecosystem structure. This map is important because it uses objectively derived and globally consistent data to characterize the ecosystems at a much finer spatial resolution (250-m) than existing ecoregionalizations, and a much finer thematic resolution (431 classes) than existing global land cover products. This item was updated on Apr 14, 2023 to distinguish between Boreal and Polar climate regions in the terrestrial ecosystems. Cell Size: 250-meter Source Type: ThematicPixel Type: 16 Bit UnsignedData Projection: GCS WGS84Extent: GlobalSource: USGS, The Nature Conservancy, EsriUpdate Cycle: NoneAnalysis: Optimized for analysis What can you do with this layer?This map allows you to query the land surface pixels and returns the values of all the input parameters (landform type, landcover/vegetation type, climate region) and the name of the terrestrial ecosystem at that location. This layer can be used in analysis at global and local regions. However, for large scale spatial analysis, we have also provided an ArcGIS Pro Package that contains the original raster data with multiple table attributes. For simple mapping applications, there is also a raster tile layer. This layer can be combined with the World Protected Areas Database to assess the types of ecosystems that are protected, and progress towards meeting conservation goals. The WDPA layer updates monthly from the United Nations Environment Programme. Optimized for analysis means this layer does not have size constraints for analysis and it is recommended for multisource analysis with other layers optimized for analysis. See the Living Atlas Imagery Layers Optimized for Analysis Group for a complete list of imagery layers optimized for analysis. Developing the World Terrestrial EcosystemsWorld Terrestrial Ecosystems map was produced by adopting and modifying the Intergovernmental Panel on Climate Change (IPCC) approach on the definition of Terrestrial Ecosystems and development of standardized global climate regions using the values of environmental moisture regime and temperature regime. We then combined the values of Global Climate Regions, Landforms and matrix-forming vegetation assemblage or land use, using the ArcGIS Combine tool (Spatial Analyst) to produce World Ecosystems Dataset. This combination resulted of 431 World Ecosystems classes. Each combination was assigned a color using an algorithm that blended traditional color schemes for each of the three components. Every pixel in this map is symbolized by a combination of values for each of these fields. The work from this collaboration is documented in the publication:Sayre et al. 2020. An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems - Global Ecology and Conservation More information about World Terrestrial Ecosystems can be found in this Story Map.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

The soil texture dataset of the Heihe River Basin (2011) is compiled by LIU Chao et al. (2011) by using the SOLIM model. Based on the famous Jenny equation of soil science, and according to the environmental factors such as climate, biology, topography and parent material, knowledge mining and fuzzy logic are combined on the basis of existing soil texture maps and soil profiles in Heihe River Basin. It is produced and integrated with thematic maps of glaciers and lakes. According to the different characteristics of the six ecological zones in Heihe River Basin, different mapping methods are used in the upper, middle and lower reaches. According to the different characteristics of six ecological zones in Heihe River Basin, different mapping methods are used in the upper, middle and lower reaches. The data is in grid format with 1KM spatial resolution and WGS-84 projection. Soil texture attributes and categories represent 0-30 cm topsoil texture attributes, derived from depth-weighted averages. The texname in the attribute table indicates the soil texture type name. Sandrange, siltrange, and clayrange respectively represent the sand, powder, and clay content ranges in the USDA soil triangle. Sandaverage, siltaverage and clayaverage are taken from the measured soil profiles, the average content of sand, silt and clay particles as the sand, silt and clay content of the soil type. (Note: The soil particle content of clay loam is derived from the soil quality map of Beijing Normal University). The soil texture classification standard is USDA, the sand grain size is defined as (2~0.05mm), the silt particle size is (0.05~0.002mm) and the clay size is defined as (<0.002mm).

The 2020 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. County subdivisions are the primary divisions of counties and their equivalent entities for the reporting of Census Bureau data. They include legally-recognized minor civil divisions (MCDs) and statistical census county divisions (CCDs), and unorganized territories. In MCD states where no MCD exists or no MCD is defined, the Census Bureau creates statistical unorganized territories to complete coverage. The entire area of the United States, Puerto Rico, and the Island Areas are covered by county subdivisions. The generalized boundaries of legal MCDs are based on those as of January 1, 2020 as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The generalized boundaries of all CCDs, delineated in 21 states, are those as reported as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

Australian Mineral Blocks (2020) - Aligned with the current Australian Maritime Boundary Dataset (AMB2020).

ESRI Geodatabase.

Available for download as GDA94 or GDA2020.

The dataset was created by Geoscience Australia using the framework described in Section 17 of the Offshore Minerals Act 1994.

The international, scheduled areas and coastal waters used in this dataset are those found in the current Australian Maritime Boundary Dataset 2020 (AMB2020). The 2020 release has been updated to reflect the 2018 Timor Sea Treaty.

The dataset is comprised of both polygons and points created to very high precision, accurate to within millimetres.

The blocks have been cut by Australia's international boundaries, the scheduled areas and the coastal waters. Each block is assigned a polygon, including partial blocks. All blocks are titled with their block ID, and a list of vertices that make up the blocks. Each vertex of the dataset is also replicated as a discrete point in the points dataset.

The design of the dataset allows for the exact location of every vertex to be known to millimetre precision. The corner coordinates of blocks are now defined to a high precision, and can be found by querying the appropriate point.

The blocks are attributed with fields containing information on: Block ID Parent 1 Million Mapsheet Offshore Area Epoch of the boundaries used to cut the data AMB2014 Datum Origin of the mapsheet in AGD66 The position of all vertices in the block The number of vertices in the block The area of the block in acres The area of the block in hectares The calculation used to find the area of the blocks is estimated to be precise to better than 1%. This is considered to be sufficient as under the permit and licensing arrangements in the Offshore Minerals Act, the area of a block has no relevance. Therefore the area figure is provided solely for reference.

Typology of waters is defined as a group of water bodies having common natural ecological conditions in terms of geo-morphological, hydrological, physico-chemical, and biological characteristics. The type descriptors are permanent characteristics that do not respond to human activities and represent the fixed abiotic conditions that explain natural variability. For the need of large-scale analysis of ecological status, multiple pressures on rivers and lakes, linkages of water body types to habitat types and for comparison of type-specific limit values for nutrients and other quality elements across countries in Europe, a broad river and lake typology was developed. Descriptors categories are dominant geology, region, river catchment, river altitude, river flow, lake size and mean lake depth. The ranges of descriptors largely follow the system A of Water Framework Directive (WFD) (EC, 2000) and are described in Lyche Solheim et al. (2019). Various European data sources were used for spatial allocation of rivers and lakes broad types. The starting point was the European Catchments and Rivers Network System (Ecrins) (EEA, 2012), which is organised into sets of spatial thematic layers: lake polygons, river segments (drains), nodes representing intersection of river and catchments and almost 180,000 “Functional Elementary Catchments (FECs)”. Catchments include “main drains” (connecting together the FECs) and “secondary drains” (internal within a FEC). We assigned one broad type to all segments belonging to “main drain” of each FEC and named them “river segment". The catchment size of river segments in each FEC is defined as the sum of the upstream drainage area and FEC surface area. The upstream drainage area has been derived using data in “Code Arbo” in Ecrins database (Globevnik et al., 2017). The altitude of the lower end points of river segments in each FEC is available in Ecrins river database. Lake surface area is obtained from Ecrins lake area attribute “Area”. Data on mean lake depth were obtained from Waterbase – Water Quality database (EEA, 2016) or estimated from terrain data. The basic map of five geological (geochemical) categories was produced from two thematic maps: bedrock map “International Hydrogeological Map of Europe (IHME 1500_v11)” (Dutcher et al, 2015) and the soil map of the European Union “SGDBE4” (JRC, 2016). The dominant geology for lakes was derived from this map with the overlay procedure. For each FEC we then defined dominant catchment geology (geochemistry) and assigned this geology type to all river segments forming the FEC's main drain. Spatial extent of the Mediterranean region is obtained from spatial layer 'Biogeographical regions of Europe» (EEA, 2019). More details on methodology are in Lyche Solheim et al. (2019).

The 2020 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Alaska Native Regional Corporations (ANRCs) were created pursuant to the Alaska Native Claims Settlement Act (ANCSA), which is federal legislation (Pub. L. 92-203, 85 Stat. 688 (1971); 43 U.S.C. 1602 et seq. (2000)) enacted in 1971, as a "Regional Corporation" and organized under the laws of the State of Alaska to conduct both the for-profit and non-profit affairs of Alaska Natives within a defined region of Alaska. For the Census Bureau, ANRCs are considered legal geographic entities. Twelve ANRCs cover the entire State of Alaska except for the area within the Annette Island Reserve (a federally recognized American Indian reservation under the governmental authority of the Metlakatla Indian Community). A thirteenth ANRC represents Alaska Natives who do not live in Alaska and do not identify with any of the twelve corporations. The Census Bureau does not provide data for this thirteenth ANRC because it has no defined geographic extent and thus it does not appear in the cartographic boundary files. The Census Bureau offers representatives of the twelve non-profit ANRCs in Alaska the opportunity to review and update the ANRC boundaries before each decennial census. The generalzied ANRC boundaries are based on those reported as of January 1, 2020.

The 2023 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. State Legislative Districts (SLDs) are the areas from which members are elected to state legislatures. The SLDs embody the upper (senate) and lower (house) chambers of the state legislature. Nebraska has a unicameral legislature and the District of Columbia has a single council, both of which the Census Bureau treats as upper-chamber legislative areas for the purpose of data presentation; there are no data by SLDL for either Nebraska or the District of Columbia. A unique three-character census code, identified by state participants, is assigned to each SLD within a state. In Connecticut, Illinois, Louisiana, New Hampshire, Wisconsin, and Puerto Rico, the Redistricting Data Program (RDP) participant did not define the SLDs to cover all of the state or state equivalent area. In these areas with no SLDs defined, the code "ZZZ" has been assigned, which is treated as a single SLD for purposes of data presentation. The generarlized boundaries in this file are based on the most recent state legislative district boundaries collected by the Census Bureau for the 2022 election year and provided by state-level participants through the RDP.

CanVec contains more than 60 topographic features classes organized into 8 themes: Transport Features, Administrative Features, Hydro Features, Land Features, Manmade Features, Elevation Features, Resource Management Features and Toponymic Features. This multiscale product originates from the best available geospatial data sources covering Canadian territory. It offers quality topographic information in vector format complying with international geomatics standards. CanVec can be used in Web Map Services (WMS) and geographic information systems (GIS) applications and used to produce thematic maps. Because of its many attributes, CanVec allows for extensive spatial analysis. Related Products: Constructions and Land Use in Canada - CanVec Series - Manmade Features Lakes, Rivers and Glaciers in Canada - CanVec Series - Hydrographic Features Administrative Boundaries in Canada - CanVec Series - Administrative Features Mines, Energy and Communication Networks in Canada - CanVec Series - Resources Management Features Wooded Areas, Saturated Soils and Landscape in Canada - CanVec Series - Land Features Transport Networks in Canada - CanVec Series - Transport Features Elevation in Canada - CanVec Series - Elevation Features Map Labels - CanVec Series - Toponymic Features

From gridded National Soil Survey Geographic Database (gNATSGO). Used Soil Data Development Toolbox > gSSURGO Mapping Toolset > Create Soil Map Tool, Exported Data Layer to TIFF, and Used Spatial Analyst > Reclass > Lookup Tool to create this data layer and display the HYDROLGRP_. Follow instructions in "How to Create an On-Demand Soil Property or Interpretation Grid from gNATSGO". Shows sSSURGO data for California. A - sand, loamy sand, sandy loam B - loam, silt, loam or silt C - sandy clay loam D - clay loam, silty clay loam, sandy clay, silty clay, or clay The gridded National Soil Survey Geographic Database (gNATSGO) is a USDA-NRCS Soil & Plant Science Division (SPSD) composite database that provides complete coverage of the best available soils information for all areas of the United States and Island Territories. It was created by combining data from the Soil Survey Geographic Database (SSURGO), State Soil Geographic Database (STATSGO2), and Raster Soil Survey Databases (RSS) into a single seamless ESRI file geodatabase. The state-wide gNATSGO databases contain a 10-meter raster of the soil map units and 70 related tables of soil properties and interpretations. It is designed to work with the SPSD gSSURGO ArcTools. Users can create full coverage thematic maps and grids of soil properties and interpretations for large geographic areas, such as the extent of a State or the conterminous United States. Please note that for the CONUS database, only a 30 meter raster is included. SSURGO is the SPSD flagship soils database that has over 100 years of field-validated detailed soil mapping data. SSURGO contains soils information for more than 90 percent of the United States and island territories, but unmapped land remains. Click here for the current completion status of SSURGO mapping. STATSGO2 is a general soil map that has soils data for all of the United States and island territories, but the data is not as detailed as the SSURGO data. The Raster Soil Surveys (RSSs) are the next generation soil survey databases developed using advanced digital soil mapping methods. https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625) Use the Create A Soil Map ArcTool from the gSSURGO Mapping Toolset in the Soil Data Development Toolbox to make a TIFF data layer (Instructions: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcseprd1464625#grid). Make a Hydrological Soils Group Map, and display it using the Hydrolgrp_ attribute. NotesThe SPSD refreshes all published soil databases annually. gNATSGO will be included in the refresh cycle, which will provide a new up-to-date version of the database each year. gNATSGO is an ESRI file geodatabase. The soil map units are delivered only as a 10-meter raster version and are uniquely identified by the mukey, which is included in the attribute table. No vectorized version of the soil map units is included in gNATSGO. The database has 70 tables that contain soil attributes, and relationship classes are built into the database to define relationships among tables. The raster can be joined to the Mapunit and Muaggatt tables in the MUKEY field. The database contains a feature class called SAPOLYGON. The “source” field in this feature class indicates whether the data was derived from SSURGO, STATSGO2, or an RSS. A gNATSGO database was created for the conterminous United States and for each state or island territory that does not have complete coverage in SSURGO or has a published RSS. If you encounter an ArcMap error when working with a gNATSGO dataset that reads “The number of unique values exceeds the limit” try increasing the maximum number of unique values to render in your Raster ArcMap Options. Specific instructions can be obtained here: https://support.esri.com/en/technical-article/000010117