Integrated terrain models covering 16,357 square kilometers of the Massachusetts coastal zone and offshore waters were built to provide a continuous elevation and bathymetry terrain model for ocean planning purposes. The area is divided into the following four geographical areas to reduce file size and facilitate publishing: Massachusetts Bay from the Massachusetts-New Hampshire border south to Provincetown and Scituate and east to Stellwagen Bank; Cape Cod Bay from Provincetown to Scituate and south to Hyannis; Buzzards Bay from the Cape Cod Canal southwest to the State border including the Elizabeth Islands and extending north to Fall River and Mount Hope Bay; and Nantucket and Vineyard Sounds, from Hyannis south to the border of the Massachusetts Coastal zone approximately 8 kilometers south of Nantucket. A Triangulated Irregular Network was created from public-domain bathymetric and LiDAR data using the ArcGIS terrain-model framework and then interpolated into a 32-bit GeoTiff of 10 meter resolution. The grids for each of the four geographical areas are referenced to the Universal Transverse Mercator, Zone 19, North American Datum of 1983 coordinate system, and the North American Vertical Datum of 1988. A polygon shapefile recording the source datasets accompanies each of the four grids.

This feature collection, UCR Timber Harvest & Veg Management Activities_2022, provides the proposed timber harvest and vegetation management data within the ongoing Upper Cheat River project by the U.S. Forest Service for Monongahela National Forest, West Virginia.Purpose:This data was created by the U.S. Forest Service staff for use in analysis of the project’s likely environmental impacts.Source & Date:The source data was created in 2020 and downloaded in zipped ESRI shapefile format (GIS Shapefiles.zip) from the USFS project page (Analysis folder). The data was downloaded on July 1, 2021, and subsequently updated. The data is current as of March 29, 2022. Processing:ABRA published the source shapefiles from ArcMap as a feature layer. That feature layer was published as a feature collection to allow grouping in Map Viewer Classic. The sub-layers were symbolized using the provided map document as an example (Scoping Information and Maps.pdf).UCR Timber Harvest & Veg Management Activities_2022 contains the following data layers:UCR_TSIUCR_WildlifeHabitatEnhancementsUCR_FireBlocksUCR_ExWLOMaintenanceUCR_PotentialCommericalHarvestUnitsUCR_FireLines Symbology:The list below refers to the data layers above, named as shown in the Upper Cheat Project map provided by ABRA.Timber Stand Improvement Units: Light blue polygonWildlife Habitat Enhancements:Cutback Borders: a purple polygonDaylighting: green polygonWildlife Opening Expansion: yellow polygonBurn Blocks: brown polygonExisting Wildlife Opening Maintenance: red polygonPotential Commercial Harvest Units: Cable Timber Units: green polygonConventional Timber Units: green polygon with dark green outlineHelicopter Timber Units: clear polygon with red outlineFire Handlines: purple polylineMore information can be found on ABRA’s project description page, hosted by the National Forest Integrity Project. Additional detailed information is available on the USFS project page.

This dataset is a modified version of the FWS developed data depicting “Highly Important Landscapes”, as outlined in Memorandum FWS/AES/058711 and provided to the Wildlife Habitat Spatial analysis Lab on October 29th 2014. Other names and acronyms used to refer to this dataset have included: Areas of Significance (AoSs - name of GIS data set provided by FWS), Strongholds (FWS), and Sagebrush Focal Areas (SFAs - BLM). The BLM will refer to these data as Sagebrush Focal Areas (SFAs). Data were provided as a series of ArcGIS map packages which, when extracted, contained several datasets each. Based on the recommendation of the FWS Geographer/Ecologist (email communication, see data originator for contact information) the dataset called “Outiline_AreasofSignificance” was utilized as the source for subsequent analysis and refinement. Metadata was not provided by the FWS for this dataset. For detailed information regarding the dataset’s creation refer to Memorandum FWS/AES/058711 or contact the FWS directly. Several operations and modifications were made to this source data, as outlined in the “Description” and “Process Step” sections of this metadata file. Generally: The source data was named by the Wildlife Habitat Spatial Analysis Lab to identify polygons as described (but not identified in the GIS) in the FWS memorandum. The Nevada/California EIS modified portions within their decision space in concert with local FWS personnel and provided the modified data back to the Wildlife Habitat Spatial Analysis Lab. Gaps around Nevada State borders, introduced by the NVCA edits, were then closed as was a large gap between the southern Idaho & southeast Oregon present in the original dataset. Features with an area below 40 acres were then identified and, based on FWS guidance, either removed or retained. Finally, guidance from BLM WO resulted in the removal of additional areas, primarily non-habitat with BLM surface or subsurface management authority. Data were then provided to each EIS for use in FEIS development. Based on guidance from WO, SFAs were to be limited to BLM decision space (surface/sub-surface management areas) within PHMA. Each EIS was asked to provide the limited SFA dataset back to the National Operations Center to ensure consistent representation and analysis. Returned SFA data, modified by each individual EIS, was then consolidated at the BLM’s National Operations Center retaining the three standardized fields contained in this dataset.Several Modifications from the original FWS dataset have been made. Below is a summary of each modification.1. The data as received from FWS: 16,514,163 acres & 1 record.2. Edited to name SFAs by Wildlife Habitat Spatial Analysis Lab:Upon receipt of the “Outiline_AreasofSignificance” dataset from the FWS, a copy was made and the one existing & unnamed record was exploded in an edit session within ArcMap. A text field, “AoS_Name”, was added. Using the maps provided with Memorandum FWS/AES/058711, polygons were manually selected and the “AoS_Name” field was calculated to match the names as illustrated. Once all polygons in the exploded dataset were appropriately named, the dataset was dissolved, resulting in one record representing each of the seven SFAs identified in the memorandum.3. The NVCA EIS made modifications in concert with local FWS staff. Metadata and detailed change descriptions were not returned with the modified data. Contact Leisa Wesch, GIS Specialist, BLM Nevada State Office, 775-861-6421, lwesch@blm.gov, for details.4. Once the data was returned to the Wildlife Habitat Spatial Analysis Lab from the NVCA EIS, gaps surrounding the State of NV were closed. These gaps were introduced by the NVCA edits, exacerbated by them, or existed in the data as provided by the FWS. The gap closing was performed in an edit session by either extending each polygon towards each other or by creating a new polygon, which covered the gap, and merging it with the existing features. In addition to the gaps around state boundaries, a large area between the S. Idaho and S.E. Oregon SFAs was filled in. To accomplish this, ADPP habitat (current as of January 2015) and BLM GSSP SMA data were used to create a new polygon representing PHMA and BLM management that connected the two existing SFAs.5. In an effort to simplify the FWS dataset, features whose areas were less than 40 acres were identified and FWS was consulted for guidance on possible removal. To do so, features from #4 above were exploded once again in an ArcMap edit session. Features whose areas were less than forty acres were selected and exported (770 total features). This dataset was provided to the FWS and then returned with specific guidance on inclusion/exclusion via email by Lara Juliusson (lara_juliusson@fws.gov). The specific guidance was:a. Remove all features whose area is less than 10 acresb. Remove features identified as slivers (the thinness ratio was calculated and slivers identified by Lara Juliusson according to https://tereshenkov.wordpress.com/2014/04/08/fighting-sliver-polygons-in-arcgis-thinness-ratio/) and whose area was less than 20 acres.c. Remove features with areas less than 20 acres NOT identified as slivers and NOT adjacent to other features.d. Keep the remainder of features identified as less than 40 acres.To accomplish “a” and “b”, above, a simple selection was applied to the dataset representing features less than 40 acres. The select by location tool was used, set to select identical, to select these features from the dataset created in step 4 above. The records count was confirmed as matching between the two data sets and then these features were deleted. To accomplish “c” above, a field (“AdjacentSH”, added by FWS but not calculated) was calculated to identify features touching or intersecting other features. A series of selections was used: first to select records 6. Based on direction from the BLM Washington Office, the portion of the Upper Missouri River Breaks National Monument (UMRBNM) that was included in the FWS SFA dataset was removed. The BLM NOC GSSP NLCS dataset was used to erase these areas from #5 above. Resulting sliver polygons were also removed and geometry was repaired.7. In addition to removing UMRBNM, the BLM Washington Office also directed the removal of Non-ADPP habitat within the SFAs, on BLM managed lands, falling outside of Designated Wilderness’ & Wilderness Study Areas. An exception was the retention of the Donkey Hills ACEC and adjacent BLM lands. The BLM NOC GSSP NLCS datasets were used in conjunction with a dataset containing all ADPP habitat, BLM SMA and BLM sub-surface management unioned into one file to identify and delete these areas.8. The resulting dataset, after steps 2 – 8 above were completed, was dissolved to the SFA name field yielding this feature class with one record per SFA area.9. Data were provided to each EIS for use in FEIS allocation decision data development.10. Data were subset to BLM decision space (surface/sub-surface) within PHMA by each EIS and returned to the NOC.11. Due to variations in field names and values, three standardized fields were created and calculated by the NOC:a. SFA Name – The name of the SFA.b. Subsurface – Binary “Yes” or “No” to indicated federal subsurface estate.c. SMA – Represents BLM, USFS, other federal and non-federal surface management 12. The consolidated data (with standardized field names and values) were dissolved on the three fields illustrated above and geometry was repaired, resulting in this dataset.

Integrated terrain models covering 16,357 square kilometers of the Massachusetts coastal zone and offshore waters were built to provide a continuous elevation and bathymetry terrain model for ocean planning purposes. The area is divided into the following four geographical areas to reduce file size and facilitate publishing: Massachusetts Bay from the Massachusetts-New Hampshire border south to Provincetown and Scituate and east to Stellwagen Bank; Cape Cod Bay from Provincetown to Scituate and south to Hyannis; Buzzards Bay from the Cape Cod Canal southwest to the State border including the Elizabeth Islands and extending north to Fall River and Mount Hope Bay; and Nantucket and Vineyard Sounds, from Hyannis south to the border of the Massachusetts Coastal zone approximately 8 kilometers south of Nantucket. A Triangulated Irregular Network was created from public-_domain bathymetric and LiDAR data using the ArcGIS terrain-model framework and then interpolated into a 32-bit GeoTiff of 10 meter resolution. The grids for each of the four geographical areas are referenced to the Universal Transverse Mercator, Zone 19, North American Datum of 1983 coordinate system, and the North American Vertical Datum of 1988. A polygon shapefile recording the source datasets accompanies each of the four grids.

Polygon features that represent the political boundaries of Metropolitan Planning Organizations (MPO) that exist in Maryland and for which the Maryland Department of Transportation (MDOT) is a member. In several instances, these MPO boundaries extend beyond Maryland’s borders into neighboring states as well as the District of Columbia. MPO Boundaries’ data includes information on each boundary's name, geographic location, and the total size / extent of each area. MPO Boundaries data was intended to be used for planning purposes within governments at the National and State level. Maryland's MPO Boundaries data is a sub-set of the U.S. Department of Transportation (USDOT) Office of the Assistant Secretary for Research and Technology's Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). A metropolitan planning organization (MPO) is a federally-mandated and federally-funded transportation policy-making organization that is made up of representatives from local governments and governmental transportation authorities. Federal law requires the formation of an MPO for any urbanized area (UZA) with a population greater than 50,000. Federal funding for transportation projects and programs are channeled through this planning process. Congress created MPOs to ensure that existing and future expenditures of federal funds for transportation projects and programs are based on a continuing, cooperative, and comprehensive (“3‑C”) planning process. MPOs are charged with developing a 20-year long-range transportation plan (LRTP) and a short-term (usually 2-6 years) program called the transportation improvement program (TIP) for each of their respective regions. The seven MPOs of which Maryland jurisdictions and agencies are members are listed below. The Maryland member jurisdictions are listed under each MPO (note that some MPOs cover multi-State regions). The Maryland Department of Transportation is a member of each of the MPOs listed. Each of the listed member jurisdictions has a different level of involvement with its MPO.Maryland's MPOs are as follows: National Capital Region Transportation Planning Board (TPB)https://www.mwcog.org/tpb/- Charles County, Maryland- Frederick County, Maryland- Montgomery County, Maryland- Prince George's County, Maryland- City of Bowie, Maryland- City of College Park, Maryland- City of Frederick, Maryland- City of Gaithersburg, Maryland- City of Greenbelt, Maryland- City of Laurel, Maryland- City of Rockville, Maryland- City of Takoma Park, Maryland- Maryland Department of Transportation (MDOT)Baltimore Regional Transportation Board (BRTB)https://baltometro.org/- Anne Arundel County, Maryland- Baltimore County, Maryland- Carroll County, Maryland- Harford County, Maryland- Howard County, Maryland- Queen Anne's County, Maryland- City of Annapolis, Maryland- City of Baltimore, Maryland- Maryland Department of Transportation (MDOT)Cumberland Area Metropolitan Planning Organization (CAMPO)https://alleganygov.org/473/Metropolitan-Planning-Organization- Allegany County, Maryland- City of Cumberland, Maryland- City of Frostburg, Maryland- Maryland Department of Transportation (MDOT)Hagerstown / Eastern Panhandle Metropolitan Planning Organization (HEPMPO)https://www.hepmpo.net/- Washington County, Maryland- City of Hagerstown, Maryland- Maryland Department of Transportation (MDOT)Wilmington Area Planning Council (WILMAPCO)https://www.wilmapco.org/- Cecil County, Maryland- Maryland Department of Transportation (MDOT)Salisbury / Wicomico Metropolitan Planning Organization (S / WMPO)https://www.swmpo.org/- Wicomico County, Maryland- City of Fruitland, Maryland- City of Salisbury, Maryland- Town of Delmar, Maryland- Maryland Department of Transportation (MDOT)Calvert-St. Mary’s Metropolitan Planning Organization (C - SMMPO)https://www.calvert-stmarysmpo.com/- Calvert County, Maryland- St. Mary's County, Maryland- Maryland Department of Transportation (MDOT)Maryland's MPO Boundaries data is owned and maintained by the Transportation Secretary's Office (TSO) of the Maryland Department of Transportation (MDOT). Being a subset of the USDOT's NTAD, an annual update of Maryland's MPO Boundaries data is performed by TSO in close coordination with each MPO, the Maryland Department of Transportation State Highway Administration (MDOT SHA) and the Federal Highway Administration (FHWA). MPO Boundaries data is a strategic resource for the USDOT, FHWA, MDOT, as well as many other Federal, State, and local government agencies. Maryland's MPO Boundaries data is updated on an annual basis. For additional MPO information, contact MDOT's Office of Planning and Capital Programming:MDOTGIS@mdot.state.md.usFor additional data information, contact the MDOT SHA Geospatial Technologies Team:GIS@sha.state.md.usFor additional information related to the Maryland Department of Transportation (MDOT):https://www.mdot.maryland.gov/For additional information related to the Maryland Department of Transportation State Highway Administration (MDOT SHA):https://www.roads.maryland.gov/This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Feature Service Link:https://geodata.md.gov/imap/rest/services/BusinessEconomy/MD_IncentiveZones/FeatureServer/13

Integrated terrain models covering 16,357 square kilometers of the Massachusetts coastal zone and offshore waters were built to provide a continuous elevation and bathymetry terrain model for ocean planning purposes. The area is divided into the following four geographical areas to reduce file size and facilitate publishing: Massachusetts Bay from the Massachusetts-New Hampshire border south to Provincetown and Scituate and east to Stellwagen Bank; Cape Cod Bay from Provincetown to Scituate and south to Hyannis; Buzzards Bay from the Cape Cod Canal southwest to the State border including the Elizabeth Islands and extending north to Fall River and Mount Hope Bay; and Nantucket and Vineyard Sounds, from Hyannis south to the border of the Massachusetts Coastal zone approximately 8 kilometers south of Nantucket. A Triangulated Irregular Network was created from public-domain bathymetric and LiDAR data using the ArcGIS terrain-model framework and then interpolated into a 32-bit GeoTiff of 10 meter resolution. The grids for each of the four geographical areas are referenced to the Universal Transverse Mercator, Zone 19, North American Datum of 1983 coordinate system, and the North American Vertical Datum of 1988. A polygon shapefile recording the source datasets accompanies each of the four grids.

This dataset contains shapefile boundaries for CA State, counties and places from the US Census Bureau's 2023 MAF/TIGER database. Current geography in the 2023 TIGER/Line Shapefiles generally reflects the boundaries of governmental units in effect as of January 1, 2023.

Joshua tree is a visually distinctive plant found in California''s Mojave Desert and adjacent areas. The unique silhouette and tall stature of Joshua tree relative to typical surrounding vegetation make it one of the most recognizable native plants of California deserts. There are two species of Joshua tree in California, western Joshua Tree (Yucca brevifolia) and eastern Joshua tree (Yucca jaegeriana). Eastern Joshua tree (Yucca brevifolia ssp. jaegeriana) distribution is represented in the data incidentally, but the primary purpose of this dataset is to illustrate the distribution of western Joshua tree. Western Joshua tree is distributed in discontinuous populations in the Mojave Desert and in a portion of the Great Basin Desert. Western Joshua tree is often noted as being abundant near the borders of the Mojave Desert in transition zones. No attempt was made to map Joshua tree distribution outside of California, and therefore the data are limited to geographic areas within California. CDFW possesses vegetation maps that cover a large portion of the California deserts where Joshua tree generally occurs. CDFWs Vegetation Classification and Mapping Program (VegCAMP) uses a combination of aerial imagery and fieldwork to delineate polygons with similar vegetation and to categorize the polygons into vegetation types. In 2013, an effort was made to create a vegetation map that covers a large portion of the California deserts. The vegetation data from this project includes percent absolute cover of Joshua tree and in some instances only Joshua tree presence and absence data. Western Joshua tree and eastern Joshua tree were lumped together as one species in these vegetation maps. A rigorous accuracy assessment of Joshua tree woodland vegetation alliance was performed using field collected data and it was determined to be mapped with approximately 95 percent accuracy. This means that approximately 95 percent of field-verified, polygons mapped as Joshua tree woodland alliance were mapped correctly. While Joshua tree woodland alliance requires even cover of Joshua tree at greater than or equal to 1 percent, the vegetation dataset has polygons recorded with less than 1 percent cover of Joshua tree as well as simple presence and absence data. The CDFW used Joshua tree polygons from vegetation mapping combined with additional point data from other sources including herbarium records, Calflora, and iNaturalist to create the western Joshua tree range boundary used in the March 2022 Status Review of Western Joshua Tree. CDFW reviewed publicly available point observations that appeared to be geographic outliers to ensure that incorrectly mapped and erroneous observations did not substantially expand the presumed range of the species. In a limited region, hand digitized points were used where obvious Joshua tree occurrences that had not been mapped elsewhere were present on aerial photographs. Creating a range map with incomplete presence data can sometimes be misleading because the absence of data does not necessarily mean the absence of the species. Some of the observations used to produce the range map may also be old, particularly if they are based on herbarium records, and trees may no longer be present in some locations. Additionally, different buffer distances around data points can yield wildly different results for occupied areas. To create the the western Joshua tree range boundary used in the March 2022 Status Review of Western Joshua Tree, CDFW buffered presence locations, but did not use a specific buffer value, and instead used the data described above in a geographic information system exercise to extend the range polygons to closely follow known occurrence boundaries while eliminating small gaps between them.

This is a graphical polygon dataset depicting the polygon boundaries of Cities within Bexar County Texas and Surrounding Counties. (excluding San Antonio)Updated per Ordinance No. 564, No. 565, and No. 567 on April 9, 2015 extending the Helotes City limits with the annexation of four parcels of vacant property known as Bricewood Subdivision. Updated previously per Resolution No 2012-007-R From the City of Somerset .Updated per ordinance 2014-09-04-0657 (Savano Park ETJ ONLY release)Updated per ordinance 2014-09-04-0658 (Live Oak City Limit release)Updated per ordinance 2014-08-21-0614 (Fair Oaks Ranch ETJ ONLY release.

Taxi trip data provided as a zip file containing pipe (|) delimited text files or csv for trips by month. DFHV provided OCTO with a taxicab trip text file representing trips. OCTO processed the data to assign a block locations to pick up and drop off locations. The blocks were assigned using the original pick up, drop off lat/long coordinates and searching for the block locations in the DC Master Address Repository (radius tolerance of 250 meters and less). The pick and drop off times were also rounded to the nearest hour. See ReadMe.txt in zip file for summary.In addition, the pick up and drop off locations were assigned to an airport using locator polygons for Reagan, BWI, and Dulles. These polygons generally followed the visual borders of these airports.The Department of For Hire Vehicles continues its growing investment in good governance and public transparency with data sets, research reports, and taxicab trip ratings available for review below. Access to information enables the public to engage in more robust debates about DFHV regulations and programs; better inform the public about the industry and agency policies; encourage innovators to design new programs; and help improve safety. The data provided herein is derived from electronic sources the accuracy of which cannot be guaranteed. While DFHV strives to provide data that is accurate and current, all data provided is for informational purposes only. The District of Columbia disclaims all liability for errors, omissions, completeness, accuracy and currentness of the data provided herein. Use of data provided herein constitutes acceptance of these terms. Revisions to the dashboard have included the addition of Transport DC data and an update to address inaccurate data that was inadvertently posted due to a technical glitch.

After the union of the Lake Huron and 10 min by 10 min grid coverages, some of the resulting polygons were unioned and nearly all were assigned unique ID numbers as to correpond to the generally accepted 10 min grid definition used by the fishery community. This attribute is found under the field titled "Grid10min." For example maps containing said generally accepted grid definitions, see for example: <"Status of the Fishery Resource - 1989"; A Report by the Technical Fisheries Review Committee on the Assessment of Major Fish Stocks in those Waters of the Upper Great Lakes Ceded in the Treaty of 1836; U.S. Fish and Wildlife Service, Michigan Department of Natural Resources, Chippewa/Ottawa Treaty Fishery Management Authority; Appendix Figure B1.2>. Additional attributes include which Whitefish Management Unit "Wfm_units" and Statistical District "Stat_dist" each 10 minute by 10 minute grid belongs to (as defined by the State of Michigan Department of Natural Resources Fisheries Division) and "Area" in square meters (since meters are the map units of Michigan GeoRef), "Perimeter" in meters, and "Hectares" which were all calculated with the ArcView Xtools Meters/Hectares extension (XtoolsMH.avx).For quality information regarding the shoreline, please refer to the associated metadata cited previously. After merging the shoreline pieces there were two known gaps on the Michigan shore (most likely near the intersection of counties). These were sealed by graphically adding arcs to fill the gap (so that polygons that would not "leak out" could be created). There is no guarantee regarding the accuracy of this editing. The two edits were done at approximately: -83.32 lon, 44.50 lat; -84.22 lon, 46.00 lat.The Mackinac Bridge between the Michigan lower peninsula and Michigan upper peninsula was not included with the NOAA shoreline data that was merged. It was also added graphically, extending from approximately -84.73 lon, 45.79 lat to -84.72, 45.85. There is no gurantee regarding the accuracy of this editing.The has been a reduction of detail from the original shoreline data and grid during editing within ArcInfo/ArcView due to tolerances/snapping/etc. which would be evident upon comparison of this coverage with the two original coverages. The original grid coverage was created in ArcInfo using the Generate command - Fishnet option and was "straight" prior to union. After union of the two coverages, close inspection of some of the grid polygon border-arcs will reveal slight shifts in their "straightness". There has been no attempt to remedy said flaw. It is believed that the overall accuracy is within reason for many fishery applications. Suggestions for fundamental edits to this coverage should be directed to the originator.

The project lead for the collection of this data was Julie Garcia. Mule deer (25 females) were captured in 2016 and equipped with Lotek satellite GPS collars, transmitting data from 2016-2019. The Doyle mule deer herd migrates from a winter range in Honey Lake Valley and Upper Long Valley near Doyle, California along US Highway 395 in Lassen County, California and eastward into Plumas County and Plumas National Forest in the Sierra Nevada Mountains for the summer. Winter range also exists on the Nevada side of the border in Washoe County. GPS locations were fixed between 3-13 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual deer is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification and prioritization of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 14 migrating deer, including 44 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. The average migration time and average migration distance for deer was 6.47 days and 27.37 km, respectively. Corridors and stopovers were prioritized based on the number of animals moving through a particular area. BBMMs were produced at a spatial resolution of 50 m using a sequential fix interval of less than 27 hours and a fixed motion variance of 1000. Winter range analyses were based on data from 14 individual deer and 25 wintering sequences using a fixed motion variance of 1000. Winter range designations for this herd may expand with a larger sample, filling in some of the gaps between winter range polygons in the map. Large water bodies were clipped from the final outputs.Corridors are visualized based on deer use per cell, with greater than or equal to 1 deer and greater than or equal to 3 deer (20% of the sample) representing migration corridors and high use corridors, respectively. Stopovers were calculated as the top 10 percent of the population level utilization distribution during migrations and can be interpreted as high use areas. Stopover polygon areas less than 20,000 m2were removed, but remaining small stopovers may be interpreted as short-term resting sites, likely based on a small concentration of points from an individual animal. Winter range is visualized as the 50thpercentile contour of the winter range utilization distribution.

Hydrographic areas (HA) or hydrographic basins in Nevada were delineated by the U.S. Geological Survey (USGS) and Nevada Division of Water Resources in the late 1960s (Cardinalli and others, 1968; Rush, 1968) for scientific and administrative purposes. In 1988, the USGS published Hydrologic Atlas 694-C where the hydrographic areas of Nevada delineated in the 1960's (and hydrographic areas from surrounding states) were used to define the groundwater flow systems of the Great Basin (Harrill and others, 1988). These data were digitized and released in GIS format by the USGS in 2009 (Buto and Reichelt, 2009). As part of the Nevada Water Initiative project, NDWR has updated the 2009 USGS dataset to include flow systems occurring within the State of Nevada that were previously excluded because they were outside of the Great Basin region. Additionally, NDWR has updated existing HAs overlapping the state boundary with adjacent states HUC boundaries to form more hydrologically based units. Within the State of Nevada, the 1:1,000,000-scale HAs digitized by the USGS were spatially joined to the 1:750,000-scale hydrographic areas used by NDWR for administering water rights in the State and the groundwater flow system attributes were transferred from the USGS dataset to the NDWR HAs. Outside of the State, the 1,000,000-scale HAs were spatially joined to the corresponding USGS 10-Digit Hydrologic Units (HUC10) which overlapped with each HA. Along the State boundary, the 1:750,000-scale NDWR HAs were merged with the rest of the hydrologically continuous basin (as a HUC10 boundary) that exist adjacent to the State. In the NDWR HAs where there was no corresponding groundwater flow system delineated by the USGS, each HA was assigned a flow system based on the following: Within the Truckee, Carson, Walker, and Colorado River systems, several NDWR HAs adjacent to the Nevada State boundary were excluded from the original dataset. Based on the known watersheds of these surface water systems, previously delineated NDWR HAs were assigned to the groundwater flow system based on the watershed boundary.Along the northern border of Nevada with Idaho and Oregon, NDWR HAs belonging to tributary watersheds of the Snake River basin were assigned groundwater flow systems based on watershed area. These systems include those belonging to the Owyhee River, Bruneau River, Salmon Falls Creek, and Goose Creek. Along the northwestern border of Nevada with Oregon and California, small portions of several endorheic basins within the adjacent states extend into Nevada. Due to the potentially complex nature of these groundwater flow systems, the NDWR HAs associated with these endorheic basins were assigned an 'Undetermined' groundwater flow system and may be revisited as part of the Nevada Water Initiative in the future.To document the delineation source and status of each groundwater flow system boundary, a line dataset was produced categorizing each boundary into one of five types: 1. Delineated by NDWR, 2. Delineated by NDWR, Extends Beyond Area Shown, 3. Delineated by USGS, 4. Delineated by USGS, Modified by NDWR, and 5. Undetermined. The Nevada State boundary was also included with these data to better show where the HA data source was merged from the NDWR HAs to the USGS HUC10 boundaries. A polygon dataset of all groundwater flow systems was also created by merging all the HAs and HUC10 boundaries associated with each flow system into a single polygon. Each dataset includes the following attributes:Hydrographic Basin-scale polygons: NDWR Basin Number (ID), NDWR Basin Name, NDWR Basin Sub-Area Name, Groundwater Flow System ID, Groundwater Flow System Name, Groundwater Flow System Source, and Polygon Data Source (NDWR HA or USGS HUC10) Boundary lines: boundary typeFlow system scale polygons: Groundwater Flow System Number (ID), Groundwater Flow System Name, and Groundwater Flow System Source Known issues associated with these data include the following:The boundaries of the NDWR HA and the USGS HUC10 datasets do not necessarily align where they were merged along the Nevada State border. This results in apparent jogs in the boundaries along the State border where the data source switches.Along the border with Oregon, the three NDWR HAs of Macy Flat, Guano Valley, and Sage Hen Valley (HAs 010, 006, and 005 respectively) align with two HUC10 boundaries in Oregon and could not be merged with the associated HUC10s without removing one of the NDWR HAs.The extents of the bordering endorheic basin flow systems have not been determined.The southern extent of the Owens River system is known to extend beyond the delineated boundary.References Cited:Buto, S.G. and Reichelt, J.C., 2009, 1:1,000,000-scale Hydrographic Areas of the Great Basin: U.S. Geological Survey data release, https://doi.org/10.5066/P9VCBUAUCardinalli, J.L., Roach, L.M., Rush, F.E., and Vasey, B.J., 1968, State of Nevada hydrographic areas, scale 1:500,000, in Rush, F.E., ed., Index of hydrographic areas: Nevada Division of Water Resources Information Report 6, 38 p, http://images.water.nv.gov/images/publications/Information%20series/6.pdfHarrill, J.R., Gates, J.S., and Thomas, J.M., 1988, Major ground-water flow systems in the Great Basin region of Nevada, Utah, and adjacent states: U.S. Geological Survey Hydrologic Investigations Atlas HA-694-C, scale 1:1,000,000, 2 sheets, https://doi.org/10.3133/ha694C

In June 2000 the Geological Survey of Ireland (GSI) under the auspices of the Department of Public Enterprise (later moved to the Department of Communications, Marine and Natural Resources in 2002) awarded a contract to Global Ocean Technologies Limited (GOTECH) to undertake the Irish National Seabed Survey (INSS), Zone 3 Hydrographic Survey. This area, of some 413,760 square Kilometres, stretches from the 200 metre water depth line on the Western Seaboard of Ireland, westward into the full oceanic depths of the Atlantic Ocean. The INSS mapped to approximately the 200m contour. The project was completed in 2006.The INFOMAR programme is a joint venture between Geological Survey Ireland (GSI) and Marine Institute (MI) and is the successor to the Irish National Seabed Survey. INFOMAR aims to survey the remaining shelf and coastal waters between 2006 to 2026.It is a vector dataset. Vector data portrays the world using points, lines and polygons (areas). The zone data is shown as polygons. Each polygon holds information on the zone number, zone part, area (km2) and perimeter (m).The United Nations Convention on the Law of the Sea (UNCLOS), also called the Law of the Sea Convention or the Law of the Sea Treaty, is an international agreement that establishes a legal framework for all marine and maritime activities. Articles 3 and 4 of UNCLOS sets out what a territorial sea is and what is permitted. Territorial sea, as defined by the 1982 United Nations Convention on the Law of the Sea (UNCLOS), is a belt of coastal waters extending at most 12 nautical miles (22 km; 14 mi) from the baseline (usually the mean low-water mark) of a coastal state. The territorial sea is regarded as the sovereign territory of the state, although foreign ships (military and civilian) are allowed innocent passage through it, or transit passage for straits; this sovereignty also extends to the airspace over and seabed below. Adjustment of these boundaries is called, in international law, maritime delimitation.A state's territorial sea extends up to 12 nm (22 km; 14 mi) from its baseline. A nautical mile is 1,852 metres. If this would overlap with another state's territorial sea, the border is taken as the median point between the states' baselines, unless the states in question agree otherwise. A state can also choose to claim a smaller territorial sea.It is a vector dataset. Vector data portrays the world using points, lines and polygons (areas). The data is shown as a line.The exclusive economic zone is an area beyond and adjacent to the territorial sea, subject to the specific legal regime established in this Part, under which the rights and jurisdiction of the coastal State and the rights and freedoms of other States are governed by the relevant provisions of this Convention. An exclusive economic zone, as prescribed by the 1982 United Nations Convention on the Law of the Sea, is an area of the sea in which a sovereign state has exclusive rights regarding the exploration and use of marine resources, including energy production from water and wind.It stretches from the outer limit of the territorial sea (22.224 Km or 12 NM from the baseline) out to a maximum of 370.4 Km (or 200 nautical miles) from the coast of the state in question. It is also referred to as a maritime continental margin and, in colloquial usage, may include the continental shelf. The term does not include either the territorial sea or the continental shelf beyond the 200 nautical mile limit. The difference between the territorial sea and the exclusive economic zone is that the first confers full sovereignty over the waters, whereas the second is merely a "sovereign right" which refers to the coastal state's rights below the surface of the sea. The surface waters are international waters.It is a vector dataset. Vector data portrays the world using points, lines and polygons (areas). The data is shown as a line.The United Nations Convention on the Law of the Sea (UNCLOS), also called the Law of the Sea Convention or the Law of the Sea Treaty, is an international agreement that establishes a legal framework for all marine and maritime activities. Article 76 of UNCLOS sets out the definition of what the continental shelf is and what is permitted. The Geoscience Regulatory Office (GSRO) (formerly Petroleum Affairs Division (PAD)) a division of the Department of the Environment, Climate and Communications (DECC) has statutory responsibility for Ireland’s Continental Shelf.A state wishing to extend its shelf beyond 200 nautical miles must make a submission to the Commission on the Limits of the Continental Shelf. Ireland’s continental shelf physically extends beyond 200 nautical miles to the west and south of the country and, working together, the Departments of Foreign Affairs and of the Environment, Climate and Communications have in all made three submissions to the Commission – in 2005 in relation to the Porcupine Abyssal Plain, then jointly with France, Spain and the UK for the seabed of the Celtic Sea and Bay of Biscay, and finally for the Hatton Rockall area of the North East Atlantic in 2009.The submission concerning the Porcupine Abyssal Plain successfully resulted in the addition of 39,000 km² of seabed to the State’s continental shelf. The Commission has also made recommendations that would enclose an area of approx. 80,000 km² of seabed in the Celtic Sea and Bay of Biscay and the division of this area is currently under negotiation between the four countries concerned. In addition, regular discussions have taken place for a number of years between Ireland and the UK (who agreed continental shelf boundaries in 1988), Iceland and the Faroe Islands in relation to overlapping claims in the North East Atlantic.It is a vector dataset. Vector data portrays the world using points, lines and polygons (areas). The data is shown as a line.