“Aggregates” is the term geologists use to describe rocks used for building and construction purposes. Aggregate Potential Mapping aims to identify areas where aggregate is most likely to be found.It is a vector dataset. Vector data portray the world using points, lines, and polygons (areas). The data is shown as points and polygonsPlease read the metadata lineage for each layer for further information.

1,572,685 polygon records with EASTING,NORTHING and PAREA (postcode area) fields. Individual polygons could be aggregated using PAREA field and entire coverage clipped to a coastline dataset to give a GB Postcode Area dataset. Dataset created by reducing all records in Code-Point Open to a unique point set based on Easting & Northing, allocating the most frequently occurring postcode area at each unique location by sub-stringing unit postcode labels and then creating a thiessen polygon coverage from the pointset in arcinfo workstation. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2010-09-13 and migrated to Edinburgh DataShare on 2017-02-21.

For full data description please visit: Vicmap Hydro (land.vic.gov.au)Vicmap Hydro represents the natural and man-made water resources for Victoria and consists of point, line and polygon vector features in a seamless, networked and topologically structured dataset series. It comprises a basic framework of linear features supplemented by related point and polygon features to value add data for the water networks across the State. Attribute tables classify and describe the real-world features using code lists that can be used for search, discovery and analysis. The following hydrographic features are contained within Vicmap Hydro:• Watercourse (line)- watercourse, connector, channel, drain• Water Area (polygon)- lake, flat, wetland, pondage, watercourse area• Water Area Boundary (line)- shoreline, junction• Water Point (point)- rapids, spring, waterfall, waterbody point• Water Area Fuzzy (polygon) – bay, beach, bend, entrance, inlet, passage, reach, seaIn inland and coastal areas, point and line features are used to describe various waterline related structures:• Water Structure Line (line)- wharf, marina, offshore platform, breakwater, launching ramp, dam wall, spillway, lock• Water Structure Point (point)- lock, well• Water Structure Area (polygon)- dam batter, spillwayPolygon voidsPolygon features may contain an inner set of lines, holes or voids that cannot be assigned to any feature class within that layer. For example, a Lake in the Water Area layer may have in the middle of it an area of dry land. This would appear in the data as a polygon with no paracentroid. Coincident featuresThere will be no coincident polygons, lines (whole or in part) or points of the same feature type in the data (also frequently known as double digitising). Differing features may be coincident, as may be the case where a dam wall also forms part of a dam polygon, (in these cases, the common data repeats for each feature type, and is appropriately tagged and supplied as part of each feature type)ConnectorDrainage patterns are made up of both linear (narrow streams) and polygon features (such as watercourse areas, lakes and swamps) and consequently do not constitute a rigorous linear network. To allow linear analysis of drainage networks an artificial feature called a "Connector" has been added to the data.This Connector feature is used to connect linear watercourse features where they are separated by water areas such as lakes, swamps and watercourses depicted as area features. The points that make up this chain cannot be given any value for planimetric accuracy. The Connector will only be used if there is flow across a waterbody polygon feature. Thus, if there is only inflow to a lake and no outflow the Connector feature will not be used. Tributary watercourses flowing into a polygon area will be linked to the areas with Connectors.Connectors are also used for drainage conveyed by pipelines (Connector_structure). The diagram below demonstrates the relationship between underground pipelines and other drainage features for the situation where pipelines cross drainage features. In this situation the underground pipeline will form the connection with connector features in the watercourse layer and needs to be cloned in the watercourse layer as a connector. A node is to be created in the watercourse layer, on the connector, at the intersection of any drainage lines crossing pipeline connector.Junction The Junction is a linear feature which is an artificial line used to separate adjacent polygon areas across which flow can occur. For example, a Junction feature will separate the confluence of two watercourses where both are depicted as polygons. A Junction also separates watercourse polygons from the Sea. The Junction feature is arbitrarily placed and cannot be given any value for planimetric accuracy.Junction devices carry the attributes of the area entity they enclose.Junction features will not be placed:• separating 2 water bodies with identical attributes.• separating polygons of different feature class except separating watercourse polygons, canal polygons, lakes, reservoirs and the sea from one another.Junction features will be placed:• separating double line watercourses from other water bodies such as lakes and reservoirs.• separating waterbody polygons of the same class but with different attributes.• closing the mouth of rivers (waterbodies).• filling the coastal gaps in the framework layer. Cross border data is not subject to the same data structures or accuracy as the content within Victoria. This is due to the differences in the data models between the States.

“Aggregates” is the term geologists use to describe rocks used for building and construction purposes. Aggregate Potential Mapping (APM) aims to identify areas where aggregate is most likely to be found. In this project, “pits” are excavations into Quaternary sediments, and the gravel pit inventory is used to score Granular Aggregate Potential, “quarries” are excavations into bedrock, and the quarry inventory is used to score Crushed Rock Aggregate Potential.Evidence of extraction over time is a key indicator of aggregate potential. While older pits and quarries are important to include, the generally larger operations of recent times receive the highest scores in the APM process. These latter are also of more relevance in that they reflect the demands of modern aggregates production, both in market type and in material standards.This map shows the areas where pits and quarries occur across Ireland. The areas are displayed according to the period of activity.It is a vector dataset. Vector data portray the world using points, lines, and polygons (areas). The data is shown as polygons. Each polygon holds information on the county it is located, Pit or Quarry, Local Authority Reference, Site name, Site address, Operator, Status, Mineral, Processing, Products, Additional Details, Hours of operation, Source dataset and Activity Period.This map shows the locations where pits and quarries occur across Ireland. The areas are displayed according to type.It is a vector dataset. Vector data portray the world using points, lines, and polygons (areas). The data is shown as points. Each point holds information on the ID, Coordinates, Pit or Quarry Type, Age, Notes and Mineral Location Number.Please read the metadata lineage for further information.Please zoom in beyond 1:400,000 to see the data.

Geographic Information System (GIS) analyses are an essential part of natural resource management and research. Calculating and summarizing data within intersecting GIS layers is common practice for analysts and researchers. However, the various tools and steps required to complete this process are slow and tedious, requiring many tools iterating over hundreds, or even thousands of datasets. USGS scientists will combine a series of ArcGIS geoprocessing capabilities with custom scripts to create tools that will calculate, summarize, and organize large amounts of data that can span many temporal and spatial scales with minimal user input. The tools work with polygons, lines, points, and rasters to calculate relevant summary data and combine them into a single output table that can be easily incorporated into statistical analyses. These tools are useful for anyone interested in using an automated script to quickly compile summary information within all areas of interest in a GIS dataset

The method to create the Wind Resource Area datasets is to:Query Power Plant point locations from the California Energy Commission, California Power Plants data set by operational status and capacity greater than or equal to 2 MW at each facility from the Quarterly Fuel and Energy Report, CEC-1304A. Plants tracked include those of at least 1 MW, which are considered of commercial size. A polygon was generated around the resulting operational, commercial wind facilities using the Aggregate Points geoprocessing tool with an aggregation distance of 15 survey miles. A 5 mile spatial buffer was added to the resulting polygons. The buffer does not represent information regarding environmental analysis. It is used only to depict plant concentration regions.

“Aggregates” is the term geologists use to describe rocks used for building and construction purposes. Aggregate Potential Mapping (APM) aims to identify areas where aggregate is most likely to be found. In this project, “pits” are excavations into Quaternary sediments, and the gravel pit inventory is used to score Granular Aggregate Potential, “quarries” are excavations into bedrock, and the quarry inventory is used to score Crushed Rock Aggregate Potential.Evidence of extraction over time is a key indicator of aggregate potential. While older pits and quarries are important to include, the generally larger operations of recent times receive the highest scores in the APM process. These latter are also of more relevance in that they reflect the demands of modern aggregates production, both in market type and in material standards.This map shows the areas where pits and quarries occur across Ireland. The areas are displayed according to the period of activity.It is a vector dataset. Vector data portray the world using points, lines, and polygons (areas). The data is shown as polygons. Each polygon holds information on the county it is located, Pit or Quarry, Local Authority Reference, Site name, Site address, Operator, Status, Mineral, Processing, Products, Additional Details, Hours of operation, Source dataset and Activity Period.This map shows the locations where pits and quarries occur across Ireland. The areas are displayed according to type.It is a vector dataset. Vector data portray the world using points, lines, and polygons (areas). The data is shown as points. Each point holds information on the ID, Coordinates, Pit or Quarry Type, Age, Notes and Mineral Location Number.Please read the metadata lineage for further information.Please zoom in beyond 1:400,000 to see the data.

This point dataset represents both applications for a Mines Act permit and issued authorizations for mining activities proposed in the application. Applications for regional Mines Act permits are known as Notice of Work (NoW) applications. Regional mine permits are issued for mineral and coal exploration activities, sand and gravel production, quarry production, and placer mining. Major mine permits are issued for producing mineral and coal mines. Permits are issued by the chief permitting officer under section 10 of the Mines Act and administered by the ministry. Regional mines include: * Exploration — mineral, coal, rock quarry, industrial mineral or dimension stone * Sand and gravel — aggregate, rock or natural substances used for construction purposes * Placer Most exploration and development activities require a permit under the Mines Act. A decision marks the end of the permitting process for a NoW application. The decision can either be to reject the application or to authorize the mining activities proposed in the NoW. * For new NoW authorizations, a Mines Act permit is issued * For an existing open Mines Act permit, the newly authorized mining activities are amended to the existing permit and the permit is re-issued NOTE: Administrative amendments to a NoW are not captured in this dataset. We are currently working to include this addition for a more complete view of the data. For proponents, please log into https://minespace.gov.bc.ca/ to confirm any authorizations on your permits. The ministry is transitioning from point data to polygonal data for permits and authorizations. The polygon dataset is added to when data becomes available. Users should use both the point dataset and the polygon dataset in combination. Polygon representation of permits with authorizations are referred to as Notice of Work (NoW) – Permitted Mine Areas – Regional Mine - Public https://catalogue.data.gov.bc.ca/dataset/c728435d-410e-42f9-81d5-95978c90e44a The downloaded product from the BCGW is a complete set of all the NoW public records. In addition to the records that contain lat/long references to be mapped spatially, this includes records that do not have NoW lat/long or mine lat/long coordinates to spatialize. These records are listed in a CSV file (HSP_NOTICE_OF_WORK_PA_POINT_SV_NullGeom). Notice of Work categories include: Notice of Work application type, Notice of Work application status. Notice of Work application type Field: NOW_APPLICATION_TYPE_DESC (NW_APPTYPD) * Coal * Mineral * Placer Operations * Quarry – Construction Aggregate * Quarry – Industrial Mineral * Sand and Gravel Notice of Work application status Field: NOW_APPLICATION_STATUS_DESC (NW_APPSTAD) * Approved – mining activities in the NoW application have been authorized * Client Delayed – Client needs to provide additional information or reclamation security * Government Action Required – Government needs to action; this status is triggered once a client has provided the requested information and client delay is no longer valid * Referred – Includes referral to agencies and stakeholders, and consultation with First Nations * Rejected – An application is rejected. Either the application standards are not met or requested documentation or security was not provided * Received – The NoW application has been sent from virtual FrontCounterBC to the ministry database * No Permit Required – Category when an inspector reviews the application and non-mechanized disturbance does not require a permit, for example an IP survey * Referral Complete – Referral and consultation is complete. The government needs to action the next steps * Withdrawn – Client requested the application to be withdrawn * Pending verification – Category for records needing review and verification. Typically, these are historical NoW applications with approvals. Manual review is required * NULL value – missing status and application type For the public view, please be aware that the ministry: * Only shows mine commodities of gold or jade/nephrite in the MINE_COMMODITY_DESC (MN_COMD) field. All other commodity values remain NULL (empty)

This is a shapefile of points summarizing inundation metrics and geomorphic variables for bends in the downstream-most 800 km of the Lower Missouri River. Points are located at the centroids of 10-km bends of the river. The metrics were developed through analysis of inundation maps calculated from a 1-dimensional hydraulic model for the channel and floodplain. Water-surface elevations were extended across the valley bottom and intersected with lidar-derived floodplain topography to calculate inundation depth and extent on a daily basis. Detailed methods are documented in Bulliner and others (2017). We evaluated longitudinal spatial variation by aggregating floodplain inundation estimates by Thiessen polygons centered at 1-km address points along the Lower Missouri River channel navigation line and extending to the valley walls. The Thiessen polygons sample the area of the floodplain so that the attributes within the polygon are geometrically closest to the address point. These data were then aggregated into 10-km reaches to associate inundation metrics (in average hectare-days per year) with potential explanatory variables that had been developed in previous studies (Jacobson and others, 2017; Jacobson and others, 2018). The explanatory variables in the dataset quantify hypothesized physical controls on inundation at the 10-km reach scale; that is, they do not include fine-scale details of bathymetry or topography. We summarized the inundation metrics for the existing conditions, wherein only floodplain area river-ward of the levees (batture area) is inundated, and for the entire floodplain, for both historical daily discharges (1930 – 2012) and for the future-climate-adjusted daily discharge scenario for the same time series. The data are summarized as point attributes in the released shapefile. Bulliner, E.A., Lindner, G., Bouska, K., Jacobson, R.B., and Paukert, C., 2017, Science to Inform Management of Floodplain Conservation Lands under Non-Stationary Conditions U.S. Geological Survey Data Release, https://doi.org/10.5066/F7HM56KG. Jacobson, R.B., Elliott, C.M., and Bulliner, E.A., 2017, Missouri River bend classification data sets: U.S. Geological Survey Data Release, U.S. Geological Survey data release, https://doi.org/10.5066/F7FF3R9D. Jacobson, R.B., Colvin, M.E., Bulliner, E.A., Pickard, D., and Elliott, C.M., 2018, Bend-scale geomorphic classification and assessment of the Lower Missouri River from Sioux City, Iowa, to the Mississippi River for application to pallid sturgeon management 2018-5069, 46 p., https://doi.org/10.3133/sir20185069.

Abstract Coastline for Antarctica created from various mapping and remote sensing sources, provided as polygons with surface values for 'land', 'ice shelf', 'ice tongue', or 'rumple'. Covering all land and ice shelves south of 60°S. Suitable for topographic mapping and analysis. This dataset has been generalised from the high resolution vector polygons. Medium resolution versions of ADD data are suitable for scales smaller than 1:1,000,000, although certain regions will appear more detailed than others due to variable data availability and coastline characteristics.Changes in v7.11 include updates to the coastline of Adelaide Island and surrounding islands, the grounding line of Alexander Island and the surrounding region, and the ice shelf front of the Brunt Ice Shelf.Data compiled, managed and distributed by the Mapping and Geographic Information Centre and the UK Polar Data Centre, British Antarctic Survey on behalf of the Scientific Committee on Antarctic Research. Further information and useful linksMap projection: WGS84 Antarctic Polar Stereographic, EPSG 3031. Note: by default, opening this layer in the Map Viewer will display the data in Web Mercator. To display this layer in its native projection use an Antarctic basemap. The currency of this dataset is November 2024 and will be reviewed every 6 months. This feature layer will always reflect the most recent version. For more information on, and access to other Antarctic Digital Database (ADD) datasets, refer to the SCAR ADD data catalogue. A related high resolution dataset is also published via Living Atlas, as well medium and high resolution line datasets. For background information on the ADD project, please see the British Antarctic Survey ADD project page. LineageDataset compiled from a variety of Antarctic map and satellite image sources. The dataset was created using ArcGIS and QGIS GIS software programmes and has been checked for basic topography and geometry errors, but does not contain strict topology. Quality varies across the dataset, certain areas where high resolution source data were available are suitable for large scale maps, whereas other areas are only suitable for smaller scales. Each polygon contains a surface attribute with either 'land', 'ice shelf', 'ice tongue' or 'rumple'. Details of when and how each line was created can be found in the attributes of the high or medium resolution polyline coastline dataset. Data sources range in time from the 1990s to 2025. This medium resolution version has been generalised from the high resolution version. All polygons <0.1km² not intersecting anything else were deleted and the simplify tool was used in ArcGIS with the retain critical points algorithm and a smoothing tolerance of 50m. Citation Gerrish, L., Ireland, L., Fretwell, P., Cooper, P., & Skachkova, A. (2025). Medium resolution vector polygons of the Antarctic coastline (Version 7.11) [Data set]. NERC EDS UK Polar Data Centre. https://doi.org/10.5285/981b1444-c57e-40f1-b6e9-884b44cad00eIf using for a graphic or if short on space, please cite as 'Data from the SCAR Antarctic Digital Database, 2025'.

The dataset on aggregate extractions in the European seas was created in 2014 by AZTI for the European Marine Observation and Data Network (EMODnet). It is the result of the aggregation and harmonization of datasets provided by several sources from all across Europe. It is available for viewing and download on EMODnet web portal (Human Activities, https://emodnet.ec.europa.eu/en/human-activities). The dataset contains points representing aggregate extraction sites, by year (although some data are indicated by a period of years), in the following countries: Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Lithuania, Poland, Portugal, Spain, Sweden, The Netherlands and United Kingdom. Where available, each point has the following attributes: Id (Identifier), Position Info (e.g.: Estimated, Original, Polygon centroid of dredging area, Estimated polygon centroid of dredging area), Country, Sea basin, Sea, Name of the extraction area, Area of activity (km2), Year (the year when the extraction took place; when a time period is available, the first year of the period is indicated), Permitted Amount (m3) (permitted amount of material to be extracted, in m3), Permitted Amount (t) (permitted amount of material to be extracted, in tonnes), Requested Amount (m3) (requested amount of material to be extracted, in m3), Requested Amount (t) (requested amount of material to be extracted, in tonnes), Extracted Amount (m3) (extracted amount of material, in m3), Extracted Amount (t) (extracted amount of material, in tonnes), Extraction Type (Marine sediment extraction), Purpose (e.g.: Commercial, Others, N/A), End Use (e.g.: Beach nourishment, Construction, Reclamation fill, N/A), Material type (e.g.: sand, gravel, maerl), Notes, Link to Web Sources. In 2018, a feature on areas for aggregate extractions was included. It contains polygons representing areas of seabed licensed for exploration or extraction of aggregates, in the following countries: Belgium, Denmark, Estonia, Finland, France, Germany, Italy, Lithuania, Poland, Portugal, Russia, Spain, Sweden, The Netherlands and United Kingdom. Where available, each polygon has the following attributes: Id (Identifier), Area code, Area name, Country, Sea basin, Sea, Starting year (the year when the license starts), End year (the year when the license ends), Site Type (exploration area, extraction area, extraction area (in use)), License status (Active, not active, expired, unknown), Material type (e.g.: sand, gravel, maerl), Notes, Distance to coast (in metres), Link to Web Sources. In the 2025 update, extraction data until 2024 and new areas have been included.

“Aggregates” is the term geologists use to describe rocks used for building and construction purposes. Aggregate Potential Mapping aims to identify areas where aggregate is most likely to be found.This map shows the granular aggregate potential across Ireland. To produce this map, scores from 1 to 10 were given to each area based on several factors: Genesis-Petrology (2)Number of pits (1.2)Area (2)*Thickness (2)*Elevation (0.5)Markets (1.2)*In the case of ‘alluvium’ the factor is 1.0.The factors were weighted by a multiplication factor (in brackets after the listed factors above). The final score is obtained by summing the weighted scores to give a final score ranging from 3.5 to 100. This map shows the scores sorted into five different ranges:Very High potential - redHigh potential - orangeModerate Potential - yellowLow Potential - greenVery Low Potential – blueUnlike the crushed rock potential map there are large areas uncoloured because sand or gravel has not been mapped in these areas.This map is to the scale 1:50,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 500m.It is a vector dataset. Vector data portray the world using points, lines, and polygons (areas). The data is shown as polygons. Each polygon holds information on the Granular Aggregate Potential, the county it is located in and the area in m2.Please read the metadata lineage for further information.

These data represent average annual precipitation amounts in Utah for the 1991-2020 period. The dataset was created to provide a generalized polygon alternative to the original raster data that is typically used in the scientific community. The polygon version makes it easier to work with for GIS users in Utah.The purpose of this dataset is to provide generalized average annual precipitation data for the state of Utah, to be used for general analysis and cartographic purposes.Each polygon feature in this dataset represents a 1-inch range of average annual precipitation. The data has attributes for the minimum (min_inches) and maximum (max_inches) average annual precipitation found within a particular polygon, along with a generalized attribute (inches), which is equal to min_inches. For example, a polygon with min_inches = 15 and max_inches = 16, has average annual precipitation values between 15 and 16 inches. For more precise data at a given point location, users are encouraged to obtain the original source raster data from the PRISM Group's 30-Year Normals page.UGRC derived this dataset from a nationwide analysis completed by the PRISM Group at Oregon State University. The original raster dataset (approximately 800m resolution) was downloaded from the PRISM site, clipped to a buffer around Utah, and converted from millimeters to inches. Then it was contoured for every whole inch as an interval of 1 inch, to create a polygon dataset. The polygon data was then projected into UTM 12N (WKID: 26912), and clipped to the state of Utah boundary.

In order to demonstrate the density of accommodation along the Irish Coast all accommodation points provided by Failte Ireland where processed by using kernel density analysis to aggregate points which are spatially clustered, highlighting areas of high density. Data points were plotted from a Fáilte Ireland Accommodation dataset. Using Kernel Density, a raster of high to low density was produced and contours demonstrating the transitions were created. These polylines were then merged with the bounds of a 25km buffer polygon to create a polygon. The separate polygons were then categorised into their different categories similar to the original raster.

This data set contains commercial fisheries in South Florida. Vector polygons in this data set represent statistical reporting grids used to aggregate commercial fishing data. Species-specific landings, catch per unit effort (CPUE), value, fishing seasons, and fishery types are stored in a relational data table designed to be used in conjunction with this spatial data layer. This data set comprises a portion of the ESI data for South Florida. ESI data characterize the marine and coastal environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources. See also the data layers Fish (Fish Polygons), MGT (Management Area Polygons), and SOCECON (Socioeconomic Resource Points and Lines), part of the larger South Florida ESI database, for additional fisheries and human-use information.

1. Plant to plant interactions are probably the most important driver of species coexistence at fine spatial scales, but their detection represents a challenge in Ecology. Spatial point pattern analysis (SPPA) is likely the approach most used to identify them however, it suffers from some limitations related to the over-simplification of individuals to points.
2. Here, we propose a new approach called Overlapping Area Analysis (OAA) to test whether the consideration of the shape and orientation of the individuals reveal signs of interactions between species that would remain undetected with SPPA. We used this approach to analyze a fully-mapped cryophilic grassland in Sierra de Guadarrama National Park (Spain), where the crown of each individual plant (i.e. the canopy) was approximated by a polygon. We then computed and compared the total overlapping area between the canopy of a focal species and that of any other species in the community with the expectations of a null model of random ...

For full data description please visit: Vicmap Hydro (land.vic.gov.au)Vicmap Hydro represents the natural and man-made water resources for Victoria and consists of point, line and polygon vector features in a seamless, networked and topologically structured dataset series. It comprises a basic framework of linear features supplemented by related point and polygon features to value add data for the water networks across the State. Attribute tables classify and describe the real-world features using code lists that can be used for search, discovery and analysis. The following hydrographic features are contained within Vicmap Hydro:• Watercourse (line)- watercourse, connector, channel, drain• Water Area (polygon)- lake, flat, wetland, pondage, watercourse area• Water Area Boundary (line)- shoreline, junction• Water Point (point)- rapids, spring, waterfall, waterbody point• Water Area Fuzzy (polygon) – bay, beach, bend, entrance, inlet, passage, reach, seaIn inland and coastal areas, point and line features are used to describe various waterline related structures:• Water Structure Line (line)- wharf, marina, offshore platform, breakwater, launching ramp, dam wall, spillway, lock• Water Structure Point (point)- lock, well• Water Structure Area (polygon)- dam batter, spillwayPolygon voidsPolygon features may contain an inner set of lines, holes or voids that cannot be assigned to any feature class within that layer. For example, a Lake in the Water Area layer may have in the middle of it an area of dry land. This would appear in the data as a polygon with no paracentroid. Coincident featuresThere will be no coincident polygons, lines (whole or in part) or points of the same feature type in the data (also frequently known as double digitising). Differing features may be coincident, as may be the case where a dam wall also forms part of a dam polygon, (in these cases, the common data repeats for each feature type, and is appropriately tagged and supplied as part of each feature type)ConnectorDrainage patterns are made up of both linear (narrow streams) and polygon features (such as watercourse areas, lakes and swamps) and consequently do not constitute a rigorous linear network. To allow linear analysis of drainage networks an artificial feature called a "Connector" has been added to the data.This Connector feature is used to connect linear watercourse features where they are separated by water areas such as lakes, swamps and watercourses depicted as area features. The points that make up this chain cannot be given any value for planimetric accuracy. The Connector will only be used if there is flow across a waterbody polygon feature. Thus, if there is only inflow to a lake and no outflow the Connector feature will not be used. Tributary watercourses flowing into a polygon area will be linked to the areas with Connectors.Connectors are also used for drainage conveyed by pipelines (Connector_structure). The diagram below demonstrates the relationship between underground pipelines and other drainage features for the situation where pipelines cross drainage features. In this situation the underground pipeline will form the connection with connector features in the watercourse layer and needs to be cloned in the watercourse layer as a connector. A node is to be created in the watercourse layer, on the connector, at the intersection of any drainage lines crossing pipeline connector.Junction The Junction is a linear feature which is an artificial line used to separate adjacent polygon areas across which flow can occur. For example, a Junction feature will separate the confluence of two watercourses where both are depicted as polygons. A Junction also separates watercourse polygons from the Sea. The Junction feature is arbitrarily placed and cannot be given any value for planimetric accuracy.Junction devices carry the attributes of the area entity they enclose.Junction features will not be placed:• separating 2 water bodies with identical attributes.• separating polygons of different feature class except separating watercourse polygons, canal polygons, lakes, reservoirs and the sea from one another.Junction features will be placed:• separating double line watercourses from other water bodies such as lakes and reservoirs.• separating waterbody polygons of the same class but with different attributes.• closing the mouth of rivers (waterbodies).• filling the coastal gaps in the framework layer. Cross border data is not subject to the same data structures or accuracy as the content within Victoria. This is due to the differences in the data models between the States.

This HydroShare resource contains Jupyter Notebooks with instructions and code for accessing and subsetting the NOAA Analysis of Record for Calibration (AORC) dataset. The resource includes four Jupyter Notebooks: 1. AORC_LL_PointRetrieval.ipynb: Retrieves and aggregates data from the latitude-longitude gridded dataset for a specific point specified using geographic coordinates. 2. AORC_LL_ZoneRetrieval.ipynb: Retrieves and aggregates data from the latitude-longitude gridded dataset for an area defined by a polygon shapefile. 3. AORC_NWMProj_PointRetrieval.ipynb: Retrieves and aggregates data from the NWM projected dataset for a specific point specified using geographic coordinates. 4. AORC_NWMProj_ZoneRetrieval.ipynb: Retrieves and aggregates data from the NWM projected dataset for an area defined by a polygon shapefile.

These notebooks programmatically retrieve the data from Amazon Web Services, aggregate it at user-defined time scales (which may differ from NOAA’s original time steps), and, in the case of shapefile-based data retrieval, compute the average over the shapes in the given shapefile.

The provided notebooks are coded to retrieve data from AORC version 1.1 in Zarr format, either from the latitude-longitude gridded dataset (https://registry.opendata.aws/noaa-nws-aorc/) or the NWM projected dataset (https://registry.opendata.aws/nwm-archive/).

The H3 indexing system provides a standardized and high-performance grid for aggregating data at various levels. This layer contains the spatial representation of the IDs that cover the state of Utah. While it can be used for normal GIS point-in-polygon operations, you will get much better performance using the H3 API to assign a hex ID to your points, aggregating/analyzing your points based on ID, and then joining your aggregated data to this layer on the hex IDs.Our H3 blog post provides an overview of the system, an explanation of how we created these geometries, and an example analysis. Once the geometries were created in EPSG 4326 (WGS 84 lon/lat), they were projected to EPSG 26912 (UTM 12N) using the NAD_1983_To_WGS_1984_5 transformation.

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. The CURE vegetation mapping project area was divided into 11,133 polygons and 42 map classes. A total of 10,520 map polygons represent 27 natural and semi-natural vegetation map classes. Fifteen land use map classes describe 613 other polygons within the mapping area. Average polygon size across all map classes is 4.4 ha (10.8 acres). The mapping component of the CURE project used a combination of methods to interpret and delineate vegetation polygons. Initial line work was prepared by USBOR photointerpreters who delineated the most contrasting signatures, e.g., water bodies, exposed shoreline, unvegetated geology, land use types, and vegetation at the physiognomic level. The project photo interpreter used this baseline mapping and refined it by examining digital orthophotos in stereo. The stereo photography was used as needed to distinguish fine scale vegetation patterns. Ancillary datasets including plot and observation point data and classification and local descriptions of plant associations were used by the photointerpreter to assist with map class definitions and guide manual delineations. Polygons were drawn on Mylar overlays of printed orthophotos that were later scanned, or were drawn digitally on a computer screen. Heads-up digitizing consisted of delineating map class polygons on an electronic version of the digital orthophotos at a computer workstation. Digitizing was performed using vector editing in ArcGIS. The line work was refined and finalized by the SEUG GIS Specialist and the map class and other descriptive attributes for each polygon were assigned. The recreation area and the environs were interpreted and mapped to the same level of detail.