This style comprises 20 distinct hues, plus a white version, of the firefly symbol family for points, lines, and polygons.Points have two flavors of symbols. One is a standard radial opacity decay with a molten white core. The other is a variant with a shimmer effect, if that's what you need.Line symbols are available in solid or dashed. Lines are a stack of colorized semitransparent strokes beneath a white stroke, to create a glow effect.Polygons are also available in two versions. One version applies the glow to the perimeter of the polygon in both inner and outer directions, with a semi-transparent fill. This is effective for non-adjacent polygons. The alternate version only applies an inner glow, to prevent blending and overlapping of adjacent polygons.This is an early version of these symbols and only the points respond to color selection.Learn how to install this style by visiting this salacious blog post.Learn more about Firefly Cartography here.Happy Firefly Mapping! John

Road edges are defined as the edge of the improved surface including the improved shoulder but do not include the unimproved shoulder, only the travel part of the road. The road network is compiled to include all open intersections. Features do not overlap sidewalks, but have the sidewalk area cut out of the road polygons. Overlapping features are acceptable if one of the features is hidden. Road: A generally named thoroughfare, that is usually paved and can be public or private. Unimproved thoroughfares are excluded. Road polygons are formed by a combination of road edge, curb, sidewalk, street intersection closure line, and map sheet edge. Paved Median Island: Perimeter of non-traffic paved areas that separate traffic lanes in opposing directions. Unpaved Median Island: Perimeter of non-traffic grassy, unpaved areas that separate traffic lanes in opposing directions. Paved Traffic Island: Perimeter of non-traffic concrete areas in the middle of streets designed to segregate traffic flow. This does not include linear barriers, e.g., Jersey barriers, walls or guardrails, or point barriers, such as impact attenuators. Features do not overlap sidewalks. Unpaved Traffic Island: Perimeter of non-traffic unpaved, grassy areas in the middle of streets designed to segregate traffic flow. This does not include linear barriers, e.g., Jersey barriers, walls or guardrails, or point barriers, such as impact attenuators. Features do not overlap sidewalks. Alley: Perimeter of alleys first plotted photogrammetrically from other indicators such as building footprints, fence lines, curb lines, walls, paved or unpaved drives, and map sheet edge. Alley polygons are closed along the lines where they intersect with road polygons. Paved Drive: A paved driveway for a building or entranceway for a parking lot. Driveways are neither streets nor alleys, but provide access to public facilities, such as a drive to a monument, museum, hotel, large estate, sports field or golf course, grounds of the U.S. Capitol, etc. If a driveway is less than 200 feet and leads to a parking lot, the entire paved area is captured as Parking Lot. Driveways are photogrammetrically compiled as polygons and not compiled from individual vectors on different levels. Parking Lot: Generally paved surfaces used for cars to park on. Paved drives usually form entrances to these features, if the drive is more than 200 feet. If the driveway is less than 200 feet leading into the parking lot, the entire paved area is captured as Parking Lot. Parking lots sharing a common boundary with linear features must have the common segment captured once, but coded as both polygon and line. Small parking areas, where individuals park their cars in the middle of a block off a public alley, are not captured as parking lots. These are either public space (e.g., alleys) or private space where owners permit parking to occur. Intersection: A location where more than one road comes together. For standard cross streets, intersection polygons are bounded by curbs and four closure lines at street intersection crosswalks (outer line) or placed arbitrarily where crosswalks could logically be placed. For "T" intersections, the polygons are bounded by curbs and three such closure lines. Complex intersections can have more closure lines. Entire traffic circles are coded as intersections. Hidden Road: A section of a road that passes underneath a bridge or overpass and is not visible in an aerial photograph, but the location can be interpreted based on the road on either side of the bridge. Hidden Median: A road median that exists underneath a bridge or overpass and is not fully visible in an aerial photograph, but the location can be interpreted based on the information visible on either side of the bridge.

Important Note: This item is in mature support as of September 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.The USGS Protected Areas Database of the United States (PAD-US) is the official inventory of public parks and other protected open space. The spatial data in PAD-US represents public lands held in trust by thousands of national, state and regional/local governments, as well as non-profit conservation organizations.GAP 1 and 2 areas are primarily managed for biodiversity, GAP 3 are managed for multiple uses including conservation and extraction, GAP 4 no known mandate for biodiversity protection. Provides a general overview of protection status including management designations. PAD-US is published by the U.S. Geological Survey (USGS) Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP). GAP produces data and tools that help meet critical national challenges such as biodiversity conservation, recreation, public health, climate change adaptation, and infrastructure investment. See the GAP webpage for more information about GAP and other GAP data including species and land cover.The USGS Protected Areas Database of the United States (PAD-US) classifies lands into four GAP Status classes:GAP Status 1 - Areas managed for biodiversity where natural disturbances are allowed to proceedGAP Status 2 - Areas managed for biodiversity where natural disturbance is suppressedGAP Status 3 - Areas protected from land cover conversion but subject to extractive uses such as logging and miningGAP Status 4 - Areas with no known mandate for protectionIn the United States, areas that are protected from development and managed for biodiversity conservation include Wilderness Areas, National Parks, National Wildlife Refuges, and Wild & Scenic Rivers. Understanding the geographic distribution of these protected areas and their level of protection is an important part of landscape-scale planning. Dataset SummaryPhenomenon Mapped: Areas protected from development and managed to maintain biodiversity Coordinate System: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, the Northern Mariana Islands and other Pacific Ocean IslandsVisible Scale: 1:1,000,000 and largerSource: USGS Science Analytics and Synthesis (SAS), Gap Analysis Project (GAP) PAD-US version 3.0Publication Date: July 2022Attributes included in this layer are: CategoryOwner TypeOwner NameLocal OwnerManager TypeManager NameLocal ManagerDesignation TypeLocal DesignationUnit NameLocal NameSourcePublic AccessGAP Status - Status 1, 2, or 3GAP Status DescriptionInternational Union for Conservation of Nature (IUCN) Description - I: Strict Nature Reserve, II: National Park, III: Natural Monument or Feature, IV: Habitat/Species Management Area, V: Protected Landscape/Seascape, VI: Protected area with sustainable use of natural resources, Other conservation area, UnassignedDate of EstablishmentThe source data for this layer are available here. What can you do with this Feature Layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter for Gap Status Code = 3 to create a map of only the GAP Status 3 areas.Add labels and set their propertiesCustomize the pop-upArcGIS ProAdd this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Note that many features in the PAD-US database overlap. For example wilderness area designations overlap US Forest Service and other federal lands. Any analysis should take this into consideration. An imagery layer created from the same data set can be used for geoprocessing analysis with larger extents and eliminates some of the complications arising from overlapping polygons.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

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. This dataset was sourced from the Queensland Department of Natural Resources and Mines in 2012. Information provided by the Department describes the dataset as follows: This data was originally provided on DVD and contains the converted shapefiles, layer files, raster images and project .mxd files used on the Queensland geology …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This dataset was sourced from the Queensland Department of Natural Resources and Mines in 2012. Information provided by the Department describes the dataset as follows: This data was originally provided on DVD and contains the converted shapefiles, layer files, raster images and project .mxd files used on the Queensland geology and structural framework map. The maps were done in ArcGIS 9.3.1 and the data stored in file geodatabases, topology created and validated. This provides greater data quality by performing topological validation on the feature's spatial relationships. For the purposes of the DVD, shapefiles were created from the file geodatabases and for MapInfo users MapInfo .tab and .wor files. The shapefiles on the DVD are a revision of the 1975 Queensland geology data, and are both are available for display, query and download on the department's online GIS application. The Queensland geology map is a digital representation of the distribution or extent of geological units within Queensland. In the GIS, polygons have a range of attributes including unit name, type of unit, age, lithological description, dominant rock type, and an abbreviated symbol for use in labelling the polygons. The lines in this dataset are a digital representation of the position of the boundaries of geological units and other linear features such as faults and folds. The lines are attributed with a description of the type of line represented. Approximately 2000 rock units were grouped into the 250 map units in this data set. The digital data was generalised and simplified from the Department's detailed geological data and was captured at 1:500 000 scale for output at 1:2 000 000 scale. In the ESRI version, a layer file is provided which presents the units in the colours and patterns used on the printed hard copy map. For Map Info users, a simplified colour palette is provided without patterns. However a georeferenced image of the hard copy map is included and can be displayed as a background in both Arc Map and Map Info. The geological framework of Queensland is classified by structural or tectonic unit (provinces and basins) in which the rocks formed. These are referred to as basins (or in some cases troughs and depressions) where the original form and structure are still apparent. Provinces (and subprovinces) are generally older basins that have been strongly tectonised and/or metamorphosed so that the original basin extent and form are no longer preserved. Note that intrusive and some related volcanic rocks that overlap these provinces and basins have not been included in this classification. The map was compiled using boundaries modified and generalised from the 1:2 000 000 Queensland Geology map (2012). Outlines of subsurface basins are also shown and these are based on data and published interpretations from petroleum exploration and geophysical surveys (seismic, gravity and magnetics). For the structural framework dataset, two versions are provided. In QLD_STRUCTURAL_FRAMEWORK, polygons are tagged with the name of the surface structural unit, and names of underlying units are imbedded in a text string in the HIERARCHY field. In QLD_STRUCTURAL_FRAMEWORK_MULTI_POLYS, the data is structured into a series of overlapping, multi-part polygons, one for each structural unit. Two layer files are provided with the ESRI data, one where units are symbolised by name. Because the dataset has been designed for units display in the order of superposition, this layer file assigns colours to the units that occur at the surface with concealed units being left uncoloured. Another layer file symbolises them by the orogen of which they are part. A similar set of palettes has been provided for Map Info. Dataset History Details on the source data can be found in the xml file associated with data layer. Data in this release *ESRI.shp and MapInfo .tab files of rock unit polygons and lines with associated layer attributes of Queensland geology *ESRI.shp and MapInfo .tab files of structural unit polygons and lines with associated layer attributes of structural framework *ArcMap .mxd and .lyr files and MapInfo .wor files containing symbology *Georeferenced Queensland geology map, gravity and magnetic images *Queensland geology map, structural framework and schematic diagram PDF files *Data supplied in geographical coordinates (latitude/longitude) based on Geocentric Datum of Australia - GDA94 Accessing the data Programs exist for the viewing and manipulation of the digital spatial data contained on this DVD. Accessing the digital datasets will require GIS software. The following GIS viewers can be downloaded from the internet. ESRI ArcExplorer can be found by a search of www.esriaustralia.com.au and MapInfo ProViewer by a search on www.pbinsight.com.au collectively ("the websites"). Metadata Metadata is contained in .htm files placed in the root folder of each vector data folder. For ArcMap users metadata for viewing in ArcCatalog is held in an .xml file with each shapefile within the ESRI Shapefile folders. Disclaimer The State of Queensland is not responsible for the privacy practices or the content of the websites and makes no statements, representations, or warranties about the content or accuracy or completeness of, any information or products contained on the websites. Despite our best efforts, the State of Queensland makes no warranties that the information or products available on the websites are free from infection by computer viruses or other contamination. The State of Queensland disclaims all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages and costs you might incur as a result of accessing the websites or using the products available on the websites in any way, and for any reason. The State of Queensland has included the websites in this document as an information source only. The State of Queensland does not promote or endorse the websites or the programs contained on them in any way. WARNING: The Queensland Government and the Department of Natural Resources and Mines accept no liability for and give no undertakings, guarantees or warranties concerning the accuracy, completeness or fitness for the purposes of the information provided. The consumer must take all responsible steps to protect the data from unauthorised use, reproduction, distribution or publication by other parties. Please view the 'readme.html' and 'licence.html' file for further, more complete information Dataset Citation Geological Survey of Queensland (2012) Queensland geology and structural framework - GIS data July 2012. Bioregional Assessment Source Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/69da6301-04c1-4993-93c1-4673f3e22762.

This data set contains sensitive biological resource data for threatened/endangered/rare terrrestrial plants and communities in South Florida (2013), Panhandle Florida (2012), and the Saint Johns River (2003). The data were originally delivered as coverages with a region polygon format which allowed overlaps, representing plants and communities geodata. These overlapping polygons are retained in the final geodatabase feature classes. Benthic habitats information are included in the HABITATS layer for the areas outside of the Panhandle and South Florida areas that were updated in 2010-2013. Please see the BENTHIC feature class within the larger Statewide Composite ESI geodata for benthic habitats in South Florida and the Panhandle. Species specific abundance, seasonality, status, life history, and source ID information have been joined to the attribute table. Source details are stored in a separate related SOURCES data table designed to be used in conjunction with this spatial data layer. This data set comprises a portion of the ESI data for 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. Environmental Sensitivity Index (ESI) is more properly known as "Sensitivity of Coastal Habitats and Wildlife to Spilled Oil" Atlases. The term "ESI" is often used in reference to the whole dataset, but the term "ESI" is really a reference to the classification system of shoreline types known as Environmental Sensitivity Index, that classifies a shoreline on a scale from 1 to 10 based upon overall sensitivity to spilled oil. FWRI contracted out updates to Florida's ESI data for the Panhandle and South Florida in the years 2010 through early 2013. These datasets were delivered as coverages in region-polygon format that allow for overlapping polygons in the same manner as FWRI's older ESI GIS data (in Gulf-Wide Information System (GWIS) format/specification). Hundreds of new species were added and the regional products were completed and delivered as promised. However, FWRI wanted and needed a statewide product for use within the Marine Resources Geographic Information System (MRGIS) and the Florida Marine Spill Analysis System (FMSAS). This data set is a compilation of the most recent ESI mapping for each area of Florida.

Land Use Plan (LUP) boundaries for the Greater Sage-Grouse (GRSG) National Planning Effort in the Rocky Mountain Region. Modifications were incorporated (see below) based on clarification on the correct boundaries to use from BLM planners. Additionally, definitions for EISs each LUP is included in for the Sage-grouse effort were added. Acreage calculations for each LUP as well as each full EIS were provided. EIS definitions were approved by BLM Planners on 71912.Note that individual RMPs that are subsets of other RMPs are designated as part of xxxx RMP. These polygons are overlapping polygons in the dataset.

Rocky Mountain answers to LUP Polygon Selection questions (62012):

From:Schardt, Randall D Sent:Friday, June 15, 2012 6:53 AM To:Prill, Kimberly; Carlson, John C Subject:FW: LUP Polygon selection questions

I have made the changes to the LUP data and responded to what polygons that they should be using for Montana:

Billings and Bighorn Basin(Billings_BighornBasin.pdf ) Billings and Bighorn Basin have overlapping polygons in the small area shown on the map. What should we do with this area? Note that if we use the Billings polygon from the Existing data this overlap will not occur (but we were thinking we should use In-Progress as it should be the most recent)Use the Billings RMP polygon from In-Progress which includes the Billings Bighorn BasinWest HiLine and HiLine(WestHiLine_HiLine.pdf) First, do we want both West Hi-Line and HiLine area calculations? The HiLine polygon will cut into the JVP polygon. Do we need to make a new polygon that is the intersection of HiLine with JVP that represents the part of JVP being revised by HiLine? Use the HiLine RMP polygon from In-Progress.

Lewistown(Lewistown.pdf). We do not have a Lewistown polygon, so can we combine Headwaters and JVP to make a Lewistown poly? Same question about do we still need area calcs for Headwaters and JVP? Use the Lewistown RMP polygon from In-Progress

Hi Frank,For your first question, the Roan Plateau does need to be analyzed. It is a separate planning area from the CRV polygon. So I believe you need to use #8220;Existing#8221; polygons in this case so that the Roan Plateau boundary is included in the analysis.

Christina O#8217;Connell GIS Specialist Colorado River Valley Field Office 2300 River Frontage Road Silt, CO 81652 (970) 876-9011

From:Diekman, Douglas A Sent:Thursday, June 14, 2012 4:10 PM To:Quamen, Frank RCc:Dreyfuss, Erin R; OConnell, Christina M; Cagney, James A; Minnick, Delissa L; Munson, Johanna Subject:FW: Questions from Sage Grouse GIS team at the NOC Importance:High

I called Dave Taylor at our state Office and asked him about accessing national datasets, and Dave directed me to a database that has the state data that is replicated up to the national dataset and advised me that what the NOC is using should be the same as what is in this database. I checked the LUP boundaries and the Land Use Planning Area Boundary In-Progress matches the boundary of the current RMP revision that we are working on. The Land Use Planning Area Boundary Existing matches the boundary of the area of our existing RMP.

From:Dreyfuss, Erin R Sent:Wednesday, June 13, 2012 1:52 PM To:OConnell, Christina M; Diekman, Douglas ACc:Munson, Johanna; Quamen, Frank R Subject:Questions from Sage Grouse GIS team at the NOC Importance:High

Hi Christina and Doug #8211;

See questions below from Frank Quamen at the NOC:

Colorado River Valley There is a significant geometry change between the Existing and In-Progress polygons for the Colorado Plateau. In the Existing polygon, there is a large area of the CRV polygon that is cut out and called Roan Plateau. While we were thinking that we would generally want to use the In-Progress polygons, as they should be newer, if we dont use the Colorado River Valley polygon from the Existing data set, and instead use the polygon from In-Progress, we will have a gap in between the Colorado River Valley area and the White River area. Please advise on what polygons to use. Also, what to do with the Roan Plateau area? Do we just not analyze it?

Grand Junction(GrandJunction.pdf). A polygon for Grand Junction exists in both the Existing and In-Progress GIS layers, and they have different geometries. We think we would want to use the In-Progress polygon, as it should be the most recent, but would like confirmation.

Calculates the number and area of patches and gaps within quadrat polygons. Quadrats can be regular, irregular, or overlapping. Gaps can be limited to certain sizes.

This feature class was derived from two GIS polygon datasets: BLM Grazing Allotments and BLM Grazing Pastures, which were downloaded from the Geospatial Gateway in April 2023. Pastures without a number were exported to a separate feature class and treated as Allotments for the purpose of this analysis (noted as "Pastures as Allotments" in the data). Fields were added to the feature classes and calculated as needed to allow the Rangeland Administration System (RAS) tabular data to be joined to the GIS datasets. RAS tabular data for Billed (2022 Grazing Fee Year (3/1/22 - 2/27/23)) was provided by BLM Rangeland Management Specialist William Lutjens on 3/29/2023 and processed as dbfs, with fields added and calculated as needed to match the BLM GIS Grazing Allotments and Pastures data. RAS tables and BLM GIS data for allotments were joined using the State Allotment Number, a concatenation of allotment number and BLM Administrative State for allotments (ST_ALLOT_NUM). To match numbered pastures in the RAS data and BLM GIS data, the pasture number was added to the State Allotment Number (ST_ALLOT_PAST_NUM). RAS records for Billed Allotments, Pastures, and Pastures as Allotments that did not match during a join operation were tracked in a separate excel sheet from the matching records. Matching records were then joined back to each BLM GIS grazing feature class and Allotment and Pasture name fields were edited as necessary. A Status field was added to indicate if the data are either Billed or Authorized and a Source field was added to indicate if the data came from Pastures, Allotments, Pastures treated as Allotments, or Trailing Allotments. An additional field, TR_ALLOT_NUM, was added to designate any Trailing Allotments in the data. Trailing allotments were identified and processed separately for Nevada, since these allotments overlap other allotments. Any overlaps in the data were removed prior to unioning together the four feature classes for Billed (Pastures, Allotments, Pastures as Allotments, and Trailing Allotments) into the final Billed feature class. Because there are overlaps between different source types in this dataset, and for purposes of sorting and querying the data, a new field was added to this unioned feature class (Source_Concat_Field) that is a concatenation of the Source fields from each of the four unioned datasets. Input BLM GIS Grazing data:BLM Grazing Pastures and BLM Grazing Allotments are areas of land designated and managed for grazing of livestock. It may include private, state, and public lands under the jurisdiction of the Bureau of Land Management and/or other federal agencies. An allotment is derived from its pastures, where the grazing of livestock is occurring. The attributes of the BLM Grazing Allotment and Pasture features may be duplicated in RAS, but are considered to be minimum information for unique identification and cartographic purposes.During the physical implementation of Grazing Allotments and Pastures, if an Allotment does not have any associated Pasture information, one Pasture will be created from/matching the Allotment boundary. A code of “99” will be entered into the PAST_NO (Pasture Number) field to indicate that the Pasture arcs and polygons are derived and need to be updated with real information by the appropriate office.Input RAS Data:The Rangeland Administration System (RAS) provides grazing administrative support and management reports for the BLM and the public. The Rangeland Administration system serves as an electronic calendar for issuance of applications and grazing authorizations, including Permits, Leases, and Exchange-of-Use Agreements. The Billed data covers the 2022 Grazing Fee Year (3/1/22 - 2/28/23) and was provided by William Lutjens of the BLM on 3/29/2023.

Source Water Assessment and Protection Program (SWAPP) is designed to provide for a proactive planning and protection for public drinking water supplies. The SWAPP data set provide a three-tiered polygon delineation of the protection areas for the purposes of inventorying potential contaminant sources in each of Zones I, II, and III. This data set is pubished as one merged statewide data set, to show spatial extent of coverage (i.e. where protection areas overlap, the individual polygons are merged). This data set covers surface water protection areas; wellhead protection areas (WHPA) are a separate data set.

This data set contains sensitive biological resource data for marine and estuarine invertebrate species in South Florida (2013), Panhandle Florida (2012), and the rest of Florida (2003). The data were originally delivered as coverages with a region polygon format which allowed overlaps, representing invertebrate distribution and concentration areas. These overlapping polygons are retained in the final geodatabase feature classes. Species specific abundance, seasonality, status, life history, and source ID information have been joined to the attribute table. Source details are stored in a separate related SOURCES data table designed to be used in conjunction with this spatial data layer. This data set comprises a portion of the ESI data for 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. Environmental Sensitivity Index (ESI) is more properly known as "Sensitivity of Coastal Habitats and Wildlife to Spilled Oil" Atlases. The term "ESI" is often used in reference to the whole dataset, but the term "ESI" is really a reference to the classification system of shoreline types known as Environmental Sensitivity Index, that classifies a shoreline on a scale from 1 to 10 based upon overall sensitivity to spilled oil. FWRI contracted out updates to Florida's ESI data for the Panhandle and South Florida in the years 2010 through early 2013. These datasets were delivered as coverages in region-polygon format that allow for overlapping polygons in the same manner as FWRI's older ESI GIS data (in Gulf-Wide Information System (GWIS) format/specification). Hundreds of new species were added and the regional products were completed and delivered as promised. However, FWRI wanted and needed a statewide product for use within the Marine Resources Geographic Information System (MRGIS) and the Florida Marine Spill Analysis System (FMSAS). This data set is a compilation of the most recent ESI mapping for each area of Florida.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple the Queensland geology and structural framework dataset. The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This dataset contains a polygon shapefile of the Belyando Basin province boundary. The Belyando Basin underlies the eastern margin of the Galilee subregion. Extracted from the QLD Geology and Structural Framework of 2012 - the abstract of which is below.

The data on this DVD contains the converted shapefiles, layer files, raster images and project .mxd files used on the Queensland geology and structural framework map. The maps were done in ArcGIS 9.3.1 and the data stored in file geodatabases, topology created and validated. This provides greater data quality by performing topological validation on the feature's spatial relationships. For the purposes of the DVD, shapefiles were created from the file geodatabases and for MapInfo users MapInfo .tab and .wor files. The shapefiles on the DVD are a revision of the 1975 Queensland geology data, and are both are available for display, query and download on the department's online GIS application.

The Queensland geology map is a digital representation of the distribution or extent of geological units within Queensland. In the GIS, polygons have a range of attributes including unit name, type of unit, age, lithological description, dominant rock type, and an abbreviated symbol for use in labelling the polygons. The lines in this dataset are a digital representation of the position of the boundaries of geological units and other linear features such as faults and folds. The lines are attributed with a description of the type of line represented. Approximately 2000 rock units were grouped into the 250 map units in this data set. The digital data was generalised and simplified from the Department's detailed geological data and was captured at 1:500 000 scale for output at 1:2 000 000 scale.

The geological framework of Queensland is classified by structural or tectonic unit (provinces and basins) in which the rocks formed. These are referred to as basins (or in some cases troughs and depressions) where the original form and structure are still apparent. Provinces (and subprovinces) are generally older basins that have been strongly tectonised and/or metamorphosed so that the original basin extent and form are no longer preserved. Note that intrusive and some related volcanic rocks that overlap these provinces and basins have not been included in this classification. The map was compiled using boundaries modified and generalised from the 1:2 000 000 Queensland Geology map (2012). Outlines of subsurface basins are also shown and these are based on data and published interpretations from petroleum exploration and geophysical surveys (seismic, gravity and magnetics).

For the structural framework dataset, two versions are provided. In QLD_STRUCTURAL_FRAMEWORK, polygons are tagged with the name of the surface structural unit, and names of underlying units are imbedded in a text string in the HIERARCHY field. In QLD_STRUCTURAL_FRAMEWORK_MULTI_POLYS, the data is structured into a series of overlapping, multi-part polygons, one for each structural unit. Two layer files are provided with the ESRI data, one where units are symbolised by name. Because the dataset has been designed for units display in the order of superposition, this layer file assigns colours to the units that occur at the surface with concealed units being left uncoloured. Another layer file symbolises them by the orogen of which they are part. A similar set of palettes has been provided for Map Info.

Purpose

This dataset provides a single, merged representation of the Belyando Basin as interpreted by the QLD Geology and Structural Framework of 2012

Dataset History

This dataset has been extracted directly from the QLD Geology and Structural Framework: QLD_STRUCTURAL_FRAMEWORK.shp.

1. Features with the following 'Heirarchy' attributes were selected and extracted:

a) Galilee Basin>Drummond Basin>Belyando Basin>Thomson Orogen

b) Eromanga Basin>Galilee Basin>Drummond Basin>Belyando Basin>Thomson Orogen

c) Drummond Basin>Belyando Basin>Thomson Orogen

d) Galilee Basin>Drummond Basin>Belyando Basin>Thomson Orogen

1. Features were merged together to produce the Belyando Basin province.

The lineage of the QLD Geology and Structural Framework is below:

Data in this release

*ESRI.shp and MapInfo .tab files of rock unit polygons and lines with associated layer attributes of Queensland geology

*ESRI.shp and MapInfo .tab files of structural unit polygons and lines with associated layer attributes of structural framework

*ArcMap .mxd and .lyr files and MapInfo .wor files containing symbology

*Georeferenced Queensland geology map, gravity and magnetic images

*Queensland geology map, structural framework and schematic diagram PDF files

*Data supplied in geographical coordinates (latitude/longitude) based on Geocentric Datum of Australia - GDA94

Accessing the data

Programs exist for the viewing and manipulation of the digital spatial data contained on this DVD. Accessing the digital datasets will require GIS software. The following GIS viewers can be downloaded from the internet. ESRI ArcExplorer can be found by a search of www.esriaustralia.com.au and MapInfo ProViewer by a search on www.pbinsight.com.au collectively ("the websites").

Metadata

Metadata is contained in .htm files placed in the root folder of each vector data folder. For ArcMap users metadata for viewing in ArcCatalog is held in an .xml file with each shapefile within the ESRI Shapefile folders.

Disclaimer

The State of Queensland is not responsible for the privacy practices or the content of the websites and makes no statements, representations, or warranties about the content or accuracy or completeness of, any information or products contained on the websites.

Despite our best efforts, the State of Queensland makes no warranties that the information or products available on the websites are free from infection by computer viruses or other contamination.

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Dataset Citation

Bioregional Assessment Programme (XXXX) Belyando Basin Boundary - QLD Structural Framework. Bioregional Assessment Derived Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/4add856a-eb40-4bb2-bd41-f89788884782.

Dataset Ancestors

• Derived From Queensland geology and structural framework - GIS data July 2012

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.

This data set contains sensitive biological resource data for manatees, whales, and dolphins in South Florida (2013), Panhandle Florida (2012), and the rest of Florida (2003). The data were originally delivered as coverages with a region polygon format which allowed overlaps, representing describe marine mammal distributions. These overlapping polygons are retained in the final geodatabase feature classes. Species specific abundance, seasonality, status, life history, and source ID information have been joined to the attribute table. Source details are stored in a separate related SOURCES data table designed to be used in conjunction with this spatial data layer. This data set comprises a portion of the ESI data for 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. Environmental Sensitivity Index (ESI) is more properly known as "Sensitivity of Coastal Habitats and Wildlife to Spilled Oil" Atlases. The term "ESI" is often used in reference to the whole dataset, but the term "ESI" is really a reference to the classification system of shoreline types known as Environmental Sensitivity Index, that classifies a shoreline on a scale from 1 to 10 based upon overall sensitivity to spilled oil. FWRI contracted out updates to Florida's ESI data for the Panhandle and South Florida in the years 2010 through early 2013. These datasets were delivered as coverages in region-polygon format that allow for overlapping polygons in the same manner as FWRI's older ESI GIS data (in Gulf-Wide Information System (GWIS) format/specification). Hundreds of new species were added and the regional products were completed and delivered as promised. However, FWRI wanted and needed a statewide product for use within the Marine Resources Geographic Information System (MRGIS) and the Florida Marine Spill Analysis System (FMSAS). This data set is a compilation of the most recent ESI mapping for each area of Florida.

As the parent to WTRBDY_AREA, WTRBDY_DET contains more detail (i.e., _DET) than its child product, WTRBDY_AREA. The layer contains the same information as WTRBDY_AREA such as areal waterbodies and double-banked streams and rivers, plus additional detail. This detail includes additional attributes, storage of tidal range information, and coding and overlapping geometry to store over-water manmade features as well as historic change. This change information includes loss of, or significant change in extent for lakes and ponds, as well as significant changes in waterfront geometry or change in major river channels. In addition, Lake Washington, Lake Sammamish, Puget Sound are stored as simple polygons but also as overlapping polygons showing jurisdictional proximity assignments. Over-water features, such as waterfront docks, marinas and industrial areas, are stored as overlapping polygons. These feature types can be unselected to show a more natural shoreline.

This dataset summarises 40 years of seagrass data collection (1983-2022) within Torres Strait and the Gulf of Carpentaria into two GIS shapefiles: (1) a point shapefile that includes survey data for 48,612 geolocated sites, and (2) a polygon geopackage describing seagrass at 641 individual or composite meadows.

Managing seagrass resources in northern Australia requires adequate baseline information on where seagrass is (presence/absence), the mapped extent of meadows, what species are present, and date of collection. This baseline is particularly important as a reference point against which to compare seagrass loss or change through time. The scale of northern Australia and the remoteness of many seagrass meadows from human populations present a challenge for research and management agencies reporting on the state of seagrass ecological indicators. Broad-scale and repeated surveys/studies of areas are logistically and financially impractical. However seagrass data is being collected through various projects which, although designed for specific reasons, are amenable to collating a picture of the extent and state of the seagrass resource.

In this project we compiled seagrass spatial data collected during surveys in Torres Strait and the Gulf of Carpentaria into a standardised form with point-specific and meadow-specific spatial and temporal information. We revisited, evaluated, simplified, standardised, and corrected individual records, including those collected several decades ago by drawing on the knowledge of one of our authors (RG Coles) who led the early seagrass data collection and mapping programs. We also incorporate new data, such as from photo records of an aerial assessment of mangroves in the Gulf of Carpentaria in 2017. This project was funded by the National Environmental Science Programme (NESP) Marine and Coastal Hub and Torres Strait Regional Authority (TSRA) in partnership with the Centre for Tropical Water and Aquatic Ecosystem Research (TropWATER), James Cook University. The project follows on from TropWATER’s previous work compiling 35 years of seagrass spatial point data and 30 years of seagrass meadow extent data for the Great Barrier Reef World Heritage Area (GBRWHA) and adjacent estuaries, funded through successive NESP Tropical Water Quality Hub Projects 3.1 (2015-2016) and 5.4 (2018-2020). These data sets are now publicly available through the eAtlas data portal: https://doi.org/10.25909/y1yk-9w85 . In making this data publicly available for management, the authors and data custodians request being contacted and involved in decision making processes that incorporate this data, to ensure its limitations are fully understood.

Methods: The data were collected using a variety of survey methods to describe and monitor seagrass sites and meadows. For intertidal sites/meadows, these include walking, observations from helicopters in low hover, and observations from hovercraft when intertidal banks were exposed. For subtidal sites/meadows, methods included free diving, scuba diving, video transects from towed cameras attached to a sled with/without a sled net, video drops with filmed quadrats, trawl and net samples, and van Veen grab samples. These methods were selected and tailored by the data custodians to the location, habitat surveyed, and technology available. Important site and method descriptions and contextual information is contained in the original trip reports and publications for each data set provided in Table 1 of Carter et al. (2022).

Geographic Information System (GIS) Mapping data for historic records (1980s) were transcribed from original logged and mapped data based on coastal topography, dead reckoning fixes and RADAR estimations. More recent data (1990’s onwards) is GPS located. All spatial data were converted to shapefiles with the same coordinate system (GDA 1994 Geoscience Australia Lambert), then compiled into a single point shapefile and a single polygon shapefile (seagrass meadows) using ArcMap (ArcGIS version 10.8 Redlands, CA: Environmental Systems Research Institute, ESRI). Some early spatial data was offset by several hundred metres and where this occurred data was repositioned to match the current coastline projection. The satellite base map used throughout this report is courtesy ESRI 2022.

Seagrass Site Layer: This layer contains information on data collected at assessment sites, and includes: 1. Temporal survey details – Survey month and year; 2. Spatial position - Latitude/longitude; 3. Survey name; 4. Depth for each subtidal site is m below MSL Depth and was extracted from the Australian Bathymetry and Topography Grid, June 2009 (Whiteway 2009). This approach was taken due to inconsistencies in depth recordings among data sets, e.g., converted to depth below mean sea level, direct readings from depth sounder with no conversion, or no depth recorded. Depth for intertidal sites was recorded as 0 m MSL, with an intertidal site defined as one surveyed by helicopter, walking, or hovercraft when banks were exposed during low tide;
5. Seagrass information including presence/absence of seagrass, and whether individual species were present/absent at a site; 6. Dominant sediment - Sediment type in the original data sets were based on grain size analysis or deck descriptions. For consistency, in this compilation we include only the most dominant sediment type (mud, sand, shell, rock, rubble), removed descriptors such as “fine”, “very fine”, “coarse”, etc., and replaced redundant terms, e.g. “mud” and “silt” are termed “mud”; 7. Survey methods – In this compilation we have updated and standardised the terms used to describe survey methods from the original reports; and
8. Data custodians.

Seagrass Meadow Layer: Polygons in the meadow layer are drawn from extent data collected during some surveys. Not all surveys collected meadow extent data (e.g., Torres Strait lobster surveys). The seagrass meadow layer is a composite of all the spatial polygon data we could access where meadow boundaries were mapped as part of the survey. All spatial layers were compiled into a single spatial layer using the ArcToolbox ‘merge’ function in ArcMap. Where the same meadow was surveyed multiple times as part of a long-term monitoring program, the overlapping polygons were compiled into a single polygon using the ‘merge’ function in ArcMap. Because meadows surveyed more than once were merged, there were some cases where adjacent polygons overlap each other.

Meadow Data Includes: 1. Temporal survey details – Survey month and year, or a list of survey dates for meadows repeatedly sampled; 2. Survey methods; 3. Meadow persistence – Classified into three categories: a. Unknown – Unknown persistence as the meadow was surveyed less than five times; b. Enduring – Seagrass is present in the meadow ≥90% of the surveys; c. Transitory – Seagrass is present in the meadow <90% of the surveys; 4. Meadow depth – Classified into three categories: a. Intertidal – Meadow was mapped on an exposed bank during low tide, e.g. Karumba monitoring meadow; b. Subtidal – Meadow remains completely submerged during spring low tides, e.g. Dugong Sanctuary meadow; c. Intertidal-Subtidal – Meadow includes sections that expose during low tide and sections that remain completely submerged, e.g. meadows adjacent to the Thursday Island shipping channel; 5. Dominant species of the meadow based on the most recent survey; 6. Presence or absence of individual seagrass species in a meadow; 7. Meadow density categories – Seagrass meadows were classified as light, moderate, dense, variable or unknown based on the consistency of mean above-ground biomass of the dominant species among all surveys, or percent cover of all species combined (see Table 2 in Carter et al. 2022). For example, a Halophila ovalis dominated meadow would be classed as “light” if the mean meadow biomass was always <1 gram dry weight m-2 (g DW m-2) among years, “variable” if mean meadow biomass ranged from <1 - >5 g DW m-2, and “dense” if mean meadow biomass was always >5 g DW m-2 among years. For meadows with density assessments based on both percent cover (generally from older surveys) and biomass, we assessed density categories based on the biomass data as this made the assessment comparable to a greater number of meadows, and comparable to the most recent data. Meadows with only one year of data were assigned a density category based on that year but no assessment of variability could be made and these are classified as “unknown”; 8. The minimum and maximum annual mean above-ground biomass measured in g DW m-2 (+ standard error if available) for each meadow is included for meadows with >1 year of biomass data. For meadows that were only surveyed once the mean meadow biomass (+ standard error if available) is presented as the minimum and maximum biomass of the meadow. “-9999” represents meadows where no above-ground biomass data was collected.; 9. The minimum and maximum annual mean percent cover is included for each meadow with >1 year of percent cover data. For meadows that were only surveyed once the mean meadow percent cover is presented as the minimum and maximum percent cover of the meadow. Older surveys (e.g., 1986 Gulf of Carpentaria surveys) used percent cover rather than biomass. For some surveys percent cover was estimated as discrete categories or ‘data binning’ (e.g., <10% - >50%). “-9999” represents meadows where no percent cover data was collected; 10. Meadow area survey details – The minimum, maximum and total area (hectares; ha) for each meadow: a. Total area - Total area of each meadow was estimated in the GDA 1994 Geoscience Australia Lambert projection using the ‘calculate geometry’ function in ArcMap. For meadows that were mapped multiple times, meadow area represents the merged maximum extent for

This data layer represents a compilation of local zoning districts and is based on zoning information compiled and digitized from each of Westchester's 43 municipalities between 2011 and 2024. It is important to note that it is not an officially adopted map of local zoning, but rather a depiction - or "snapshot" - of local zoning at the time of compilation. As such, this data layer is intended to be used for general reference purposes only. Since local zoning is constantly subject to change, inquiries regarding current status of local zoning districts, zoning designations of specific parcels, and exact use and bulk requirements should be verified at the local level by contacting the local planning or municipal clerk’s office. Selected updates were completed for this data layer in 2015 and 2016 for the municipalities of Rye City, Town of North Castle, Village of Mamaroneck and Port Chester. Updates for the City of Mount Vernon were completed in April 2021. In 2022/2023 Westchester County conducted an outreach project to obtain updated data. The following municipalities were updated during this time: Ardsley, Dobbs Ferry, Greenburgh, Lewisboro, Mount Pleasant, North Salem, New Castle, Village of Ossining, and Peekskill. Updates for Briarcliff Manor and Buchanan were made in 2024. Buchanan has one overlay district which is an exception because no other polygons should be overlapping.

The fully intersected data is the atomic level of the PLSS that is similar to the coverage or the smallest pieces used to build the PLSS. Polygons may overlap in this feature class. This dataset represents the GIS Version of the Public Land Survey System including both rectangular and non-rectangular surveys. The primary source for the data is cadastral survey records housed by the BLM supplemented with local records and geographic control coordinates from states, counties as well as other federal agencies such as the USGS and USFS. The data has been converted from source documents to digital form and transferred into a GIS format that is compliant with FGDC Cadastral Data Content Standards and Guidelines for publication.

This data set contains sensitive biological resource data for sea turtles, crocodiles, mangrove terrapins, and other rare species in South Florida (2013), Panhandle Florida (2012), and the rest of Florida (2003). The data were originally delivered as coverages with a region polygon format which allowed overlaps, representing reptile distribution and nesting areas. These overlapping polygons are retained in the final geodatabase feature classes. Species specific abundance, seasonality, status, life history, and source ID information have been joined to the attribute table. Source details are stored in a separate related SOURCES data table designed to be used in conjunction with this spatial data layer. This data set comprises a portion of the ESI data for 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 REPTILES_PT for additional information on reptiles. Environmental Sensitivity Index (ESI) is more properly known as "Sensitivity of Coastal Habitats and Wildlife to Spilled Oil" Atlases. The term "ESI" is often used in reference to the whole dataset, but the term "ESI" is really a reference to the classification system of shoreline types known as Environmental Sensitivity Index, that classifies a shoreline on a scale from 1 to 10 based upon overall sensitivity to spilled oil. FWRI contracted out updates to Florida's ESI data for the Panhandle and South Florida in the years 2010 through early 2013. These datasets were delivered as coverages in region-polygon format that allow for overlapping polygons in the same manner as FWRI's older ESI GIS data (in Gulf-Wide Information System (GWIS) format/specification). Hundreds of new species were added and the regional products were completed and delivered as promised. However, FWRI wanted and needed a statewide product for use within the Marine Resources Geographic Information System (MRGIS) and the Florida Marine Spill Analysis System (FMSAS). This data set is a compilation of the most recent ESI mapping for each area of Florida.

This parcel layer contains the parcel's situs (physical) address maintained by the Planning Department.The parcel geometry provides a rough representation of tax parcels in relation to one another and to district boundaries. Parcels with multiple site addresses are represented by overlapping polygons.Contact Nevada County GIS for assistance.

A spatial tiling index designed for storage of file-based image and other raster (i.e., LiDAR elevation, landcover) data sets. An irregular grid of overlapping polygons, each enclosing its respective Public Land Survey System (PLSS) township in an orthogonal polygon minimally encompassing all portions of that township, i.e., minimum bounding rectangle. The amount of overlap between adjacent tiles varies depending on the geometry of the underlying township. Currently extended to include all townships within or partially within King County as well as those townships in the southwestern portion of Snohomish County included within King County's ESA/SAO project area. The name of the spatial index is derived from the acronym (I)n(D)e(X) (P)olygons for (T)ownship-(R)ange, (M)inimum (B)ounding (R)ectangle, or idxptrmbr. Tile label is the t(township number)r(range number)as in t24r02. The meridian zone identifiers, N for townships and E for range is inferred as this index is intended as a local index for ease of use by the majority of users of GIS data. Lowercase identifiers are used for consistency between Unix and Windows OS storage. This index or tile level is the primary user-access level for most LiDAR elevation, orthoimagery and high-resolution raster landcover data. However, not all image and raster data is stored at the tiling level if a given data's resolution does not justify storing the data as multiple tiles.