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.

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 High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

L_ZI_SARSONNE_2001_LIM_L_019

The study determined the flooding areas of the Sarsonne at Ussel by hydrogeomorphological approach on the upstream and downstream areas of the urbanised part (maximum right-of-way only). The hydrogeomorphological approach is a naturalistic approach based on reading the morphology of the alluvial plain. It is supplemented here by extrapolation of the water line from a flood known to establish the reference flood water line. This reading of the morphology of the alluvial plain has been clarified on the urbanised area, thanks in particular to topographical works, allowing the definition of heights and flow rates in the crossing of the urbanised area. The flooding zone was determined by calculating the water line of a centennial flood (reference) determined by extrapolation of the 1994 flood water line, extrapolation based on topographical knowledge, the difference in flows between the 1994 event and a centennial event. Topographic knowledge has been used to determine water heights (between 0 and 1 m and > 1 m); flow velocities were defined qualitatively from valley morphology primarily. The crossing of these two parameters allowed to map the hazard (strength = H > 2 m or 1 m < H < 2 m and V > 0.5 m/s or 0 m < H < 1 m and V > 1 m/s; mean = 1 m < H < 2 m and V < 0.5 m/s or 0 m < H < 1 m and 0.5 m/s < V < 1 m/s; low hazard 0 m < H < 1 m and V < 0.5 m/s).

Specific sub-watershed located upstream from a remarkable point. A sub-watershed may be divided into several entities for DDT purposes.

Definition of a catchment area: feeding surface of a watercourse or body of water. The catchment area is defined as the water collection area, considered from an outlet: it is bounded by the contour within which all water flows on the surface and underground to this outlet. Its boundaries are the dividing lines of the waters. In addition to the catchment areas defined in the BD Carthage repository on the basis of the reference river areas, procedures (e.g. control basins defined by drought protocols) or the specific needs of the services (e.g. river basins or watersheds upstream of a water intake) may lead to the creation of dedicated sub-watersheds, known as “specific watersheds” or “use watersheds” associated with particular points.

This dataset contains 63 shapefiles that represent the areas of relevance for each research project under the National Environmental Science Program Marine and Coastal Hub, northern and southern node projects for Rounds 1, 2 & 3.

Methods:
Each project map is developed using the following steps:
1. The project map was drawn based on the information provided in the research project proposals.
2. The map was refined based on feedback during the first data discussions with the project leader.
3. Where projects are finished most maps were updated based on the extents of datasets generated by the project and followup checks with the project leader.

The area mapped includes on-ground activities of the project, but also where the outputs of the project are likely to be relevant. The maps were refined by project leads, by showing them the initial map developed from the proposal, then asking them "How would you change this map to better represent the area where your project is relevant?". In general, this would result in changes such as removing areas where they were no longer intending research to be, or trimming of the extents to better represent the habitats that are relevant.

The project extent maps are intentionally low resolution (low number of polygon vertices), limiting the number of vertices 100s of points. This is to allow their easy integration into project metadata records and for presenting via interactive web maps and spatial searching. The goal of the maps was to define the project extent in a manner that was significantly more accurate than a bounding box, reducing the number of false positives generated from a spatial search. The geometry was intended to be simple enough that projects leaders could describe the locations verbally and the rough nature of the mapping made it clear that the regions of relevance are approximate.

In some cases, boundaries were drawn manually using a low number of vertices, in the process adjusting them to be more relevant to the project. In others, high resolution GIS datasets (such as the EEZ, or the Australian coastline) were used, but simplified at a resolution of 5-10km to ensure an appopriate vertices count for the final polygon extent. Reference datasets were frequently used to make adjustments to the maps, for example maps of wetlands and rivers were used to better represent the inner boundary of projects that were relevant for wetlands.

In general, the areas represented in the maps tend to show an area larger then the actual project activities, for example a project focusing on coastal restoration might include marine areas up to 50 km offshore and 50 km inshore. This buffering allows the coastline to be represented with a low number of verticies without leading to false negatives, where a project doesn't come up in a search because the area being searched is just outside the core area of a project.


Limitations of the data:
The areas represented in this data are intentionally low resolution. The polygon features from the various projects overlap significantly and thus many boundaries are hidden with default styling. This dataset is not a complete representation of the work being done by the NESP MaC projects as it was collected only 3 years into a 7 year program.

Format of the data:
The maps were drawn in QGIS using relevant reference layers and saved as shapefiles. These are then converted to GeoJSON or WKT (Well-known Text) and incorporated into the ISO19115-3 project metadata records in GeoNetwork. Updates to the map are made to the original shapefiles, and the metadata record subsequently updated.

All projects are represented as a single multi-polygon. The multiple polygons was developed by merging of separate areas into a single multi-polygon. This was done to improve compatibility with web platforms, allowing easy conversion to GeoJSON and WKT.

This dataset will be updated periodically as new NESP MaC projects are developed and as project progress and the map layers are improved. These updates will typically be annual.


Data dictionary:
NAME - Title of the layer
PROJ - Project code of the project relating to the layer
NODE - Whether the project is part of the Northern or Southern Nodes
TITLE - Title of the project
P_LEADER - Name of the Project leader and institution managing the project
PROJ_LINK - Link to the project metadata
MAP_DESC - Brief text description of the map area
MAP_TYPE - Describes whether the map extent is a 'general' area of relevance for the project work, or 'specific' where there is on ground survey or sampling activities
MOD_DATE - Last modification date to the individual map layer (prior to merging)


Updates & Processing:
These maps were created by eAtlas and IMAS Data Wranglers as part of the NESP MaC Data Management activities. As new project information is made available, the maps may be updated and republished. The update log will appear below with notes to indicate when individual project maps are updated:
20220626 - Dataset published (All shapefiles have MOD_DATE 20230626)


Location of the data:
This dataset is filed in the eAtlas enduring data repository at: data\custodian
esp-mac-3\AU_AIMS-UTAS_NESP-MaC_Project-extents-maps

The National Land Cover Database (NLCD) is a land cover monitoring program providing land cover information for the United States. NLCD2016 extended temporal coverage to 15 years (2001–2016). We collected land cover reference data for the 2011 and 2016 nominal dates to report land cover accuracy for the NLCD2016 database 2011 and 2016 land cover components. We measured land cover accuracy at Level II and Level I, and change accuracy at Level I. For both the 2011 and 2016 land cover components, single-date Level II overall accuracies (OA) were 72% (standard error of ±0.9%) when agreement was defined as match between the map label and primary reference label only and 86% (± 0.7%) when agreement also included the alternate reference label. The corresponding level I OA for both dates were 79% (± 0.9%) and 91% (± 1.0%). For land cover change, the 2011–2016 user’s and producer’s accuracies (UA and PA) were ~ 75% for forest loss. PA for water loss, grassland loss, and grass gain were > 70% when agreement included a match between the map label and either the primary or alternate reference label. Depending on agreement definition and level of the classification hierarchy, OA for the 2011 land cover component of the NLCD2016 database was about 4% to 7% higher than OA for the 2011 land cover component of the NLCD2011 database, suggesting that the changes in mapping methodologies initiated for production of the NLCD2016 database have led to improved product quality.

This Annual NLCD Reference Data Product includes the collection of an independent dataset of 8,360 30-meter by 30-meter samples across the Conterminous United States (CONUS). The Annual NLCD Collection 1 sample design was developed as a two-phase collection by a team of image interpreters as follows: an initial base sample containing 5,000 sample plots chosen purely by simple random selection, following by another collection of 3,360 sample plots (some of which were selected similarly, while others were targeted at particular map-defined strata) upon completion of the map. This approach results in a final stratified reference sample of size 8,360. The Annual NLCD CONUS Reference Data Product collected variables related to primary and alternate land cover and land use, change processes, and other ancillary variables annually across CONUS from 1984–2023. This product contains Annual National Land Cover Database (NLCD) CONUS Reference plot location data, annual land cover, land use, ...

More information about this seam.This datafile shows a highly generalized depth to the top of the Jamestown Coal in Illinois. These 100-foot contours were created by Earthvision software using data from drill holes. Because the depth of the coal was contoured directly from drill hole data (as opposed to creating a map of coal elevation and subtracting it from a map of surface topography) the resulting map is essentially based on the assumption that the land surface is a level plain. Consequently, the accuracy of the map is lowest where the coal is shallowest. This data set is intended for use at a scale of 1:750,000. This data set was created as part of the ISGS GIS database to show general depth of the Jamestown Coal seam. The data are appropriate for regional analysis.These data are appropriate for use in local and regional thematic analysis. The data are not appropriate as a geodetic, legal or engineering base. The data set was not and is not intended as a substitute for surveyed locations, such as can be determined by a registered Public Land Surveyor. Although useful in a GIS as a reference base layer for maps, the data set has no legal basis in the definition of boundaries or property lines.

More information about this seam.This datafile shows a highly generalized depth to the top of the Danville (No. 7) coal in Illinois. These 100-foot contours were created by Earthvision software using more than 9,500 data from drill holes. Because the depth of the coal was contoured directly from drill hole data (as opposed to creating a map of coal elevation and subtracting it from a map of surface topography) the resulting map is essentially based on the assumption that the land surface is a level plain. Consequently, the accuracy of the map is lowest where the coal is shallowest. Data control was very poor in north-central Illinois and the Eagle Valley area in southeastern Illinois. Because a revised crop of the Danville Coal was not available, this file was originally constructed using the crop of the Herrin Coal. This data set is intended for use at a scale of 1:750,000. This data set was created as part of the ISGS GIS database to show general depth of the Danville (No. 7) Coal seam. The data are appropriate for regional analysis.These data are appropriate for use in local and regional thematic analysis. The data are not appropriate as a geodetic, legal or engineering base. The data set was not and is not intended as a substitute for surveyed locations, such as can be determined by a registered Public Land Surveyor. Although useful in a GIS as a reference base layer for maps, the data set has no legal basis in the definition of boundaries or property lines.

This is a collection of 124 global and free datasets allowing for spatial (and temporal) analyses of floods, droughts and their interactions with human societies. We have structured the datasets into seven categories: hydrographic baseline, hydrological dynamics, hydrological extremes, land cover & agriculture, human presence, water management, and vulnerability. Please refer to Lindersson et al. (in preparation) for further information about review methodology.

The collection is a descriptive list, holding the following information for each dataset:

• Category - as structured in Lindersson et al. (in preparation).
• Sub-category- as structured in Lindersson et al. (in preparation).
• Abbreviation - official or as specified in Lindersson et al. (in preparation).
• Title - full title of dataset.
• Product(s) - type of product(s) offered by the dataset.
• Period - time period covered by the dataset, not defined for all datasets.
• Temporal resolution - not defined for static datasets.
• Angular spatial resolution - only defined for gridded datasets.
• Metric spatial resolution - only defined for gridded datasets.
• Map scale
• Extent - geographic coverage of dataset given in latitude limits.
• Description
• Creating institute(s)
• Data type - raster, vector or tabular.
• File format
• Primary EO type - specifies if the product primarily is based on remote sensing, ground-based data, or a hybrid between remote sensing and ground-based data.
• Data sources - lists the data sources behind the dataset, to the extent this is feasible.
• Data sources also in this table - data sources that are also included as datasets in this collection.
• Intentionally compatible with - defines other datasets in this collection that the dataset is intentinoally compatible with.
• Citation - dataset reference or credit.
• Documentation - dataset documentation.
• Web address - dataset access link.

NOTE: Carefully consult the data usage licenses as given by the data providers, to assure that the exact permissions and restrictions are followed.

This dataset represents above- and below-ground terrestrial carbon storage (tonnes (t) of C per hectare (ha)) for circa 2010. This layer supports analysis but, if needed, a direct download of the data can be accessed here.

The dataset was constructed by combining the most reliable publicly available datasets and overlying them with the ESA CCI landcover map for the year 2010 [ESA, 2017], assigning to each grid cell the corresponding above-ground biomass value from the biomass map that was most appropriate for the grid cell’s landcover type.

Input carbon datasets were identified through a literature review of existing datasets on biomass carbon in terrestrial ecosystems published in peer-reviewed literature. To determine which datasets to combine to produce the global carbon density map, identified datasets were evaluated based on resolution, accuracy, biomass definition and reference date (see table 1 for further information on datasets selected).

Dataset

Scope

Year

Resolution

Definition

Santoro et al. 2018

Global

2010

100 m

Above-ground woody biomass for trees that are >10 cm diameter-at-breast-height, masked to Landsat-derived canopy cover for 2010; biomass is expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots.

Xia et al. 2014

Global

1982-2006

8 km

Above-ground grassland biomass.

Bouvet et al. 2018

Africa

2010

25 m

Above-ground woodland and savannah biomass; low woody biomass areas, which therefore exclude dense forests and deserts.

Spawn et al. 2017

Global

2010

300 m

Synthetic, global above- and below-ground biomass maps that combine recently released satellite-based data of standing forest biomass with novel estimates for non-forest biomass stocks.

After aggregating each selected dataset to a nominal scale of 300 m resolution, forest categories in the CCI ESA 2010 landcover dataset were used to extract above-ground biomass from Santoro et al. 2018 for forest areas. Woodland and savanna biomass were then incorporated for Africa from Bouvet et al. 2018., and from Santoro et al. 2018 for areas outside of Africa and outside of forest. Biomass from croplands, sparse vegetation and grassland landcover classes from CCI ESA, in addition to shrubland areas outside Africa missing from Santoro et al. 2018, were extracted from were extracted from Xia et al. 2014. and Spawn et al. 2017 averaged by ecological zone for each landcover type.

Below-ground biomass were added using root-to-shoot ratios from the 2006 IPCC guidelines for National Greenhouse Gas Inventories (IPCC, 2006). No below-ground values were assigned to croplands as ratios were unavailable. Above- and below-ground biomass were then summed together and multiplied by 0.5 to convert to carbon, generating a single above-and-below-ground biomass carbon layer.

Medical Emergency Response StructuresThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Geological Survey, displays hospitals, medical centers, ambulance services, fire stations and EMS stations in the U.S. Per the USGS, "Structures data are designed to be used in general mapping and in the analysis of structure related activities using geographic information system technology. The National Map structures data is commonly combined with other data themes, such as boundaries, elevation, hydrography, and transportation, to produce general reference base maps. The types of structures collected are largely determined by the needs of disaster planning and emergency response, and homeland security organizations."Greendale Fire DepartmentData currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Medical & Emergency Response) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 135 (USGS National Structures Dataset - USGS National Map Downloadable Data Collection)OGC API Features Link: (Medical Emergency Response Structures - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: The National MapFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Theme CommunityThis data set is part of the NGDA Real Property Theme Community. Per the Federal Geospatial Data Committee (FGDC), Real Property is defined as "the spatial representation (location) of real property entities, typically consisting of one or more of the following: unimproved land, a building, a structure, site improvements and the underlying land. Complex real property entities (that is "facilities") are used for a broad spectrum of functions or missions. This theme focuses on spatial representation of real property assets only and does not seek to describe special purpose functions of real property such as those found in the Cultural Resources, Transportation, or Utilities themes."For other NGDA Content: Esri Federal Datasets

Gap Analysis Project (GAP) habitat maps are predictions of the spatial distribution of suitable environmental and land cover conditions within the United States for individual species. Mapped areas represent places where the environment is suitable for the species to occur (i.e. suitable to support one or more life history requirements for breeding, resting, or foraging), while areas not included in the map are those predicted to be unsuitable for the species. While the actual distributions of many species are likely to be habitat limited, suitable habitat will not always be occupied because of population dynamics and species interactions. Furthermore, these maps correspond to midscale characterizations of landscapes, but individual animals may deem areas to be unsuitable because of presence or absence of fine-scale features and characteristics that are not represented in our models (e.g. snags, vernal pools, shrubby undergrowth). These maps are intended to be used at a 1:100,000 or smaller map scale. These habitat maps are created by applying a deductive habitat model to remotely-sensed data layers within a species’ range. The deductive habitat models are built by compiling information on species’ habitat associations and entering it into a relational database. Information is compiled from the best available characterizations of species’ habitat, which included species accounts in books and databases, primary peer-reviewed literature. The literature references for each species are included in the "Species Habitat Model Report" and "Machine Readable Habitat Database Parameters" files attached to each habitat map item in the repository. For all species, the compiled habitat information is used by a biologist to determine which of the ecological systems and land use classes represented in the National Gap Analysis Project’s (GAP) Land Cover Map Ver. 1.0 that species is associated with. The name of the biologist who conducted the literature review and assembled the modeling parameters is shown as the "editor" type contact for each habitat map item in the repository. For many species, information on other mapped factors that define the environment that is suitable is also entered into the database. These factors included elevation (i.e. minimum, maximum), proximity to water features, proximity to wetlands, level of human development, forest ecotone width, and forest edge; and each of these factors corresponded to a data layer that is available during the map production. The individual datasets used in the modeling process with these parameters are also made available in the ScienceBase Repository (see the end of this Summary section for details). The "Machine Readable Habitat Database Parameters" JSON file attached to each species habitat map item has an "input_layers" object that contains the specific parameter names and references (via Digital Object Identifier) to the input data used with that parameter. The specific parameters for each species were output from the database used in the modeling and mapping process to the "Species Habitat Model Report" and "Machine Readable Habitat Database Parameters" files attached to each habitat map item in the repository. The maps are generated using a python script that queries the model parameters in the database; reclassifies the GAP Land Cover Ver 1.0 and ancillary data layers within the species’ range; and combines the reclassified layers to produce the final 30m resolution habitat map. Map output is, therefore, not only a reflection of the ecological systems that are selected in the habitat model, but also any other constraints in the model that are represented by the ancillary data layers. Modeling regions were used to stratify the conterminous U.S. into six regions (Northwest, Southwest, Great Plains, Upper Midwest, Southeast, and Northeast). These regions allowed for efficient processing of the species distribution models on smaller, ecologically homogenous extents. The 2008 start date for the models represents the shift in focus from state and regional project efforts to a national one. At that point all of the datasets needed to be standardized across the national extent and the species list derived based on the current understanding of the taxonomy. The end date for the individual models represents when the species model was considered complete, and therefore reflects the current knowledge related to that species concept and the habitat requirements for the species. Versioning, Naming Conventions and Codes: A composite version code is employed to allow the user to track the spatial extent, the date of the ground conditions, and the iteration of the data set for that extent/date. For example, CONUS_2001v1 represents the spatial extent of the conterminous US (CONUS), the ground condition year of 2001, and the first iteration (v1) for that extent/date. In many cases, a GAP species code is used in conjunction with the version code to identify specific data sets or files (i.e. Cooper’s Hawk Habitat Map named bCOHAx_CONUS_2001v1_HabMap). This collection represents the first complete compilation of terrestrial vertebrate species models for the conterminous U.S. based on 2001 ground conditions. The taxonomic concept for the species model being presented is identified through the Integrated Taxonomic Information System – Taxonomic Serial Number. To provide a link to the NatureServe species information the NatureServe Element Code is provided for each species. The identifiers included for each species habitat map item in the repository include references to a vocabulary system in ScienceBase where definitions can be found for each type of identifier. Source Datasets Uses in Species Habitat Modeling: Gap Analysis Project Species Range Maps - Species ranges were used as model delimiters in predicted distribution models. https://www.sciencebase.gov/catalog/item/5951527de4b062508e3b1e79 Hydrologic Units - Modified 12-digit hydrologic units were used as the spatial framework for species ranges. https://www.sciencebase.gov/catalog/item/56d496eee4b015c306f17a42 Modeling regions - Used to stratify the conterminous U.S. into six ecologically homogeneous regions to facilitate efficient processing. https://www.sciencebase.gov/catalog/item/58b9b8cee4b03b285c07ddef Land Cover - Species were linked to individual map units to document habitat affinity in two ways. Primary map units are those land cover types critical for nesting, rearing young, and/or optimal foraging. Secondary or auxiliary map units are those land cover types generally not critical for breeding, but are typically used in conjunction with primary map units for foraging, roosting, and/or sub-optimal nesting locations. These map units are selected only when located within a specified distance from primary map units. https://www.sciencebase.gov/catalog/item/5540e2d7e4b0a658d79395db Human Impact Avoidance - Buffers around urban areas and roads were used to identify areas that would be suitable for urban exploitative species and unsuitable for urban avoiding species. https://www.sciencebase.gov/catalog/item/5540e099e4b0a658d79395d6 Forest & Edge Habitats - The land cover map was used to derive datasets of forest interior and ecotones between forest and open habitats. Forest edge https://www.sciencebase.gov/catalog/item/5540e3fce4b0a658d79395fe Forest/Open Woodland/Shrubland https://www.sciencebase.gov/catalog/item/5540e48fe4b0a658d7939600 Elevation Derivatives - Slope and aspect were used to constrain some of the southwestern models where those variables are good indicators of microclimates (moist north facing slopes) and local topography (cliffs, flats). For species with a documented relationship to altitude the elevation data was used to constrain the mapped distribution. Aspect https://www.sciencebase.gov/catalog/item/5540ec40e4b0a658d7939628 Slope https://www.sciencebase.gov/catalog/item/5540ebe2e4b0a658d7939626 Elevation https://www.sciencebase.gov/catalog/item/5540e111e4b0a658d79395d9 Hydrology - https://www.sciencebase.gov/catalog/item/5540eb44e4b0a658d7939624: A number of water related data layers were used to refine the species distribution including: water type (i.e. flowing, open/standing), distance to and from water, and stream flow and underlying gradient. The source for this data was the USGS National Hydrography Dataset (NHD)(USGS 2007). Hydrographic features were divided into three types: flowing water, open/standing water, and wet vegetation. Canopy Cover - Some species are limited to open woodlands or dense forest, the National Land Cover’s Canopy Cover dataset was used to constrain the species models based on canopy density. https://www.sciencebase.gov/catalog/item/5540eca9e4b0a658d793962b

The "Reconnaissance-Level Inventory of the Amount of Wilderness in the World" basically shows areas which are hardly touched by mankind. For this data set called World Wilderness Areas, wilderness has been defined as "undeveloped land still primarily shaped by the forces of nature". The World Wilderness Areas data set was created by the Sierra Club and the Center for Earth Resource Analysis of the World Bank. UNEP/GRID built a global data set from the many individual country data files.

The base maps used were the Jet Navigation Charts (scale 1:2,000,000) and Operational Navigation Charts (scale 1:1,000,000) of the U. S. Defense Mapping Agency. These maps show increasing levels of detail in respect to human constructs, to provide orienting landmarks, as areas become sparsely settled and remote. In searching for "empty quarters", all areas showing roads, settlements, airports and other constructs were eliminated. Areas of agricultural development and logging were removed by eliminating proximity zones of 6 km. distance from around roads and settlements. Finally, all remaining wilderness areas with a surface area less than 400,000 hectares were not included in the map.

Under the definitions used, about one-third of the total land surface of the globe is still wilderness. This amounts to almost 50 million square kilometers. The continents with the most wilderness are Antarctica (not shown on map), Eurasia, Africa and North America. Individual countries with the most wilderness are the Commonwealth of Independent States (34% wilderness), Canada (65%), Australia (30%), Denmark's Greenland (99%), China (Tibet) (24%), Brazil (24%), Algeria (59%), Mauritania (69%) and Saudi Arabia (28%).

The World Wilderness Areas data set is in the ARC/INFO vector format. The coverage shows only the polygons which fall under the definition of wilderness; polygons are coded with a '1'. The coverage is in the Geographic or Latitude/Longitude reference system, and extends from 83.62 degrees North to -55.65 degrees South latitude, and -180 West longitude to 180 degrees East longitude. It can easily be overlain with other coverages showing (e.g., "WBDTEMP3") national boundaries. In uncompressed ARC/INFO 'EXPORT' format, the data set comprises 3.5 megabytes. The coverage has 1089 polygons and 1310 arcs. The reference to this data set is: "McCloskey, J.M. and H. Spalding, 1989. A reconnaissance level inventory of the amount of wilderness remaining in the world, Ambio, 18(4), 221-227." The correct sources to be cited when using the World Wilderness Areas data set are the Sierra Club and World Bank, as integrated by UNEP/GRID.

The National Land Cover Database (NLCD), a product suite produced through the Multi-resolution Land Characteristics (MRLC) consortium, is an operational land cover monitoring program. The release of NLCD2019 extends the database to 18 years. We collected land cover reference data for the 2016 and 2019 components of the NLCD2019 database at Level II and Level I of the classification hierarchy. For both dates, Level II land cover overall accuracies (OA) were 77.5% ± 1% (± value is the standard error) when agreement was defined as a match between the map label and primary reference label only and increased to 87.1% ± 0.7% when agreement was defined as a match between the map label and either the primary or alternate reference label. At Level I of the classification hierarchy, land cover OA was 83.1% ± 0.9% for both 2016 and 2019 when agreement was defined as a match between the map label and primary reference label only and increased to 90.3% ± 0.7% when agreement also included the alternate reference label. The Level II and Level I OA for the 2016 land cover in the NLCD2019 database were 5% higher compared to the 2016 land cover component of the NLCD2016 database when agreement was defined as a match between the map label and primary reference label only. No improvement was realized by the NLCD2019 database when agreement also included the alternate reference label. User’s accuracies (UA) for forest loss and grass gain were 70% when agreement included either the primary or alternate label, and UA was generally 50% for all other change themes. Producer’s accuracies (PA) were 70% for grass loss and gain and water gain and generally 50% for the other change themes.

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.

This is a collection of 134 global and free datasets allowing for spatial (and temporal) analyses of floods, droughts and their interactions with human societies. We have structured the datasets into seven categories: hydrographic baseline, hydrological dynamics, hydrological extremes, land cover & agriculture, human presence, water management, and vulnerability. Please refer to Lindersson et al. (2020) for further information about review methodology.

The collection is a descriptive list, holding the following information for each dataset:

Category - as structured in Lindersson et al. (2020).

Sub-category- as structured in Lindersson et al. (2020).

Abbreviation - official or as specified in Lindersson et al. (2020).

Title - full title of dataset.

Product(s) - type of product(s) offered by the dataset.

Period - time period covered by the dataset, not defined for all datasets.

Temporal resolution - not defined for static datasets.

Angular spatial resolution - only defined for gridded datasets.

Metric spatial resolution - only defined for gridded datasets.

Map scale

Extent - geographic coverage of dataset given in latitude limits.

Description

Creating institute(s)

Data type - raster, vector or tabular.

File format

Primary EO type - specifies if the product primarily is based on remote sensing, ground-based data, or a hybrid between remote sensing and ground-based data.

Data sources - lists the data sources behind the dataset, to the extent this is feasible.

Data sources also in this table - data sources that are also included as datasets in this collection.

Intentionally compatible with - defines other datasets in this collection that the dataset is intentinoally compatible with.

Citation - dataset reference or credit.

Documentation - dataset documentation.

Web address - dataset access link.

NOTE: Carefully consult the data usage licenses as given by the data providers, to assure that the exact permissions and restrictions are followed.

Australia's Land Borders is a product within the Foundation Spatial Data Framework (FSDF) suite of datasets. It is endorsed by the ANZLIC - the Spatial Information Council and the Intergovernmental Committee on Surveying and Mapping (ICSM) as a nationally consistent and topologically correct representation of the land borders published by the Australian states and territories.

The purpose of this product is to provide: (i) a building block which enables development of other national datasets; (ii) integration with other geospatial frameworks in support of data analysis; and (iii) visualisation of these borders as cartographic depiction on a map. Although this dataset depicts land borders, it is not nor does it suggests to be a legal definition of these borders. Therefore it cannot and must not be used for those use-cases pertaining to legal context.

This product is constructed by Geoscience Australia (GA), on behalf of the ICSM, from authoritative open data published by the land mapping agencies in their respective Australian state and territory jurisdictions. Construction of a nationally consistent dataset required harmonisation and mediation of data issues at abutting land borders. In order to make informed and consistent determinations, other datasets were used as visual aid in determining which elements of published jurisdictional data to promote into the national product. These datasets include, but are not restricted to: (i) PSMA Australia's commercial products such as the cadastral (property) boundaries (CadLite) and Geocoded National Address File (GNAF); (ii) Esri's World Imagery and Imagery with Labels base maps; and (iii) Geoscience Australia's GEODATA TOPO 250K Series 3. Where practical, Land Borders do not cross cadastral boundaries and are logically consistent with addressing data in GNAF.

It is important to reaffirm that although third-party commercial datasets are used for validation, which is within remit of the licence agreement between PSMA and GA, no commercially licenced data has been promoted into the product. Australian Land Borders are constructed exclusively from published open data originating from state, territory and federal agencies.

This foundation dataset consists of edges (polylines) representing mediated segments of state and/or territory borders, connected at the nodes and terminated at the coastline defined as the Mean High Water Mark (MHWM) tidal boundary. These polylines are attributed to convey information about provenance of the source. It is envisaged that land borders will be topologically interoperable with the future national coastline dataset/s, currently being built through the ICSM coastline capture collaboration program. Topological interoperability will enable closure of land mass polygon, permitting spatial analysis operations such as vector overly, intersect, or raster map algebra. In addition to polylines, the product incorporates a number of well-known survey-monumented corners which have historical and cultural significance associated with the place name.

This foundation dataset is constructed from the best-available data, as published by relevant custodian in state and territory jurisdiction. It should be noted that some custodians - in particular the Northern Territory and New South Wales - have opted out or to rely on data from abutting jurisdiction as an agreed portrayal of their border. Accuracy and precision of land borders as depicted by spatial objects (features) may vary according to custodian specifications, although there is topological coherence across all the objects within this integrated product. The guaranteed minimum nominal scale for all use-cases, applying to complete spatial coverage of this product, is 1:25 000. In some areas the accuracy is much better and maybe approaching cadastre survey specification, however, this is an artefact of data assembly from disparate sources, rather than the product design. As the principle, no data was generalised or spatially degraded in the process of constructing this product.

Some use-cases for this product are: general digital and web map-making applications; a reference dataset to use for cartographic generalisation for a smaller-scale map applications; constraining geometric objects for revision and updates to the Mesh Blocks, the building blocks for the larger regions of the Australian Statistical Geography Standard (ASGS) framework; rapid resolution of cross-border data issues to enable construction and visual display of a common operating picture, etc.

This foundation dataset will be maintained at irregular intervals, for example if a state or territory jurisdiction decides to publish or republish their land borders. If there is a new version of this dataset, past version will be archived and information about the changes will be made available in the change log.

This data represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular survey data. The rectangular survey data are a reference system for land tenure based upon meridian, township/range, section, section subdivision and government lots. The non-rectangular survey data represent surveys that were largely performed to protect and/or convey title on specific parcels of land such as mineral surveys and tracts. The data are largely complete in reference to the rectangular survey data at the level of first division. However, the data varies in terms of granularity of its spatial representation as well as its content below the first division. Therefore, depending upon the data source and steward, accurate subdivision of the rectangular data may not be available below the first division and the non-rectangular minerals surveys may not be present. At times, the complexity of surveys rendered the collection of data cost prohibitive such as in areas characterized by numerous, overlapping mineral surveys. In these situations, the data were often not abstracted or were only partially abstracted and incorporated into the data set. These PLSS data were compiled from a broad spectrum or sources including federal, county, and private survey records such as field notes and plats as well as map sources such as USGS 7 ½ minute quadrangles. The metadata in each data set describes the production methods for the data content. This data is optimized for data publication and sharing rather than for specific "production" or operation and maintenance. A complete PLSS data set includes the following: PLSS Townships, First Divisions and Second Divisions (the hierarchical break down of the PLSS Rectangular surveys) PLSS Special surveys (non-rectangular components of the PLSS) Meandered Water, Corners, Metadata at a Glance (which identified last revised date and data steward) and Conflicted Areas (known areas of gaps or overlaps or inconsistencies). The Entity-Attribute section of this metadata describes these components in greater detail. The second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot division of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class. The second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot division of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.