The Protected Areas Database of the United States (PAD-US) is a geodatabase, managed by USGS GAP, that illustrates and describes public land ownership, management and other conservation lands, including voluntarily provided privately protected areas. The State, Regional and LCC geodatabases contain two feature classes. The PADUS1_3_FeeEasement feature class and the national MPA feature class. Legitimate and other protected area overlaps exist in the full inventory, with Easements loaded on top of Fee. Parcel data within a protected area are dissolved in this file that powers the PAD-US Viewer. As overlaps exist, GAP creates separate analytical layers to summarize area statistics for "GAP Status Code" and "Owner Name". Contact the PAD-US Coordinator for more information. The lands included in PAD-US are assigned conservation measures that qualify their intent to manage lands for the preservation of biological diversity and to other natural, recreational and cultural uses; managed for these purposes through legal or other effective means. The geodatabase includes: 1) Geographic boundaries of public land ownership and voluntarily provided private conservation lands (e.g., Nature Conservancy Preserves); 2) The combination land owner, land manager, management designation or type, parcel name, GIS Acres and source of geographic information of each mapped land unit 3) GAP Status Code conservation measure of each parcel based on USGS National Gap Analysis Program (GAP) protection level categories which provide a measurement of management intent for long-term biodiversity conservation 4) IUCN category for a protected area's inclusion into UNEP-World Conservation Monitoring Centre's World Database for Protected Areas. IUCN protected areas are defined as, "A clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values" and are categorized following a classification scheme available through USGS GAP; 5) World Database of Protected Areas (WDPA) Site Codes linking the multiple parcels of a single protected area in PAD-US and connecting them to the Global Community. As legitimate and other overlaps exist in the combined inventory GAP creates separate analytical layers to obtain area statistics for "GAP Status Code" and "Owner Name". PAD-US version 1.3 Combined updates include: 1) State, local government and private protected area updates delivered September 2011 from PAD-US State Data Stewards: CO (Colorado State University), FL (Florida Natural Areas Inventory), ID (Idaho Fish and Game), MA (The Commonwealth's Office of Geographic Information Systems, MassGIS), MO (University of Missouri, MoRAP), MT (Montana Natural Heritage Program), NM (Natural Heritage New Mexico), OR (Oregon Natural Heritage Program), VA (Department of Conservation and Recreation, Virginia Natural Heritage Program). 2) Select local government (i.e. county, city) protected areas (3,632) across the country (to complement the current PAD-US inventory) aggregated by the Trust for Public Land (TPL) for their Conservation Almanac that tracks the conservation finance movement across the country. 3) A new Date of Establishment field that identifies the year an area was designated or otherwise protected, attributed for 86% of GAP Status Code 1 and 2 protected areas. Additional dates will be provided in future updates. 4) A national wilderness area update from wilderness.net 5) The Access field that describes public access to protected areas as defined by data stewards or categorical assignment by Primary Designation Type. . The new Access Source field documents local vs. categorical assignments. See the PAD-US Standard Manual for more information: gapanalysis.usgs.gov/padus 6) The transfer of conservation measures (i.e. GAP Status Codes, IUCN Categories) and documentation (i.e. GAP Code Source, GAP Code Date) from PAD-US version 1.2 or categorical assignments (see PAD-US Standard) when not provided by data stewards 7) Integration of non-sensitive National Conservation Easement Database (NCED) easements from August 2011, July 2012 with PAD-US version 1.2 easements. Duplicates were removed, unless 'Stacked' = Y and multiple easements exist. 8) Unique ID's transferred from NCED or requested for new easements. NCED and PAD-US are linked via Source UID in the PAD-US version 1.3 Easement feature class. 9) Official (member and eligible) MPAs from the NOAA MPA Inventory (March 2011, www.mpa.gov) translated into the PAD-US schema with conservation measures transferred from PAD-US version 1.2 or categorically assigned to new protected areas. Contact the PAD-US Coordinator for documentation of categorical GAP Status Code assignments for MPAs. 10) Identified MPA records that overlap existing protected areas in the PAD-US Fee feature class (i.e. PADUS Overlap field in MPA feature class). For example, many National Wildlife Refuges and National Parks are also MPAs and are represented in the PAD-US MPA and Fee feature classes.

City of Amesbury, MA GIS Viewer

Tagged image tiles as well as the Faster-RCNN framework for automatic extraction of road intersection points from USGS historical maps of the United States of America. The data and code have been prepared for the paper entitled "Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks" submitted to "International Journal of Geographic Information Science". The image tiles have been tagged manually. The Faster RCNN framework (see https://arxiv.org/abs/1611.10012) was captured from:https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md

This Innovative Technology Experiences for Students and Teachers (ITEST) project has developed, implemented, and evaluated a series of innovative Socio-Environmental Science Investigations (SESI) using a geospatial curriculum approach. It is targeted for economically disadvantaged 9th grade high school students in Allentown, PA, and involves hands-on geospatial technology to help develop STEM-related skills. SESI focuses on societal issues related to environmental science. These issues are multi-disciplinary, involve decision-making that is based on the analysis of merged scientific and sociological data, and have direct implications for the social agency and equity milieu faced by these and other school students. This project employed a design partnership between Lehigh University natural science, social science, and education professors, high school science and social studies teachers, and STEM professionals in the local community to develop geospatial investigations with Web-based Geographic Information Systems (GIS). These were designed to provide students with geospatial skills, career awareness, and motivation to pursue appropriate education pathways for STEM-related occupations, in addition to building a more geographically and scientifically literate citizenry. The learning activities provide opportunities for students to collaborate, seek evidence, problem-solve, master technology, develop geospatial thinking and reasoning skills, and practice communication skills that are essential for the STEM workplace and beyond. Despite the accelerating growth in geospatial industries and congruence across STEM, few school-based programs integrate geospatial technology within their curricula, and even fewer are designed to promote interest and aspiration in the STEM-related occupations that will maintain American prominence in science and technology. The SESI project is based on a transformative curriculum approach for geospatial learning using Web GIS to develop STEM-related skills and promote STEM-related career interest in students who are traditionally underrepresented in STEM-related fields. This project attends to a significant challenge in STEM education: the recognized deficiency in quality locally-based and relevant high school curriculum for under-represented students that focuses on local social issues related to the environment. Environmental issues have great societal relevance, and because many environmental problems have a disproportionate impact on underrepresented and disadvantaged groups, they provide a compelling subject of study for students from these groups in developing STEM-related skills. Once piloted in the relatively challenging environment of an urban school with many unengaged learners, the results will be readily transferable to any school district to enhance geospatial reasoning skills nationally.

In Module 2 Lesson 1, we will take a deeper dive into Geographic Information Systems (GIS) technology. We'll explore different types of GIS data, the importance of data attributes and queries, data symbolization, and ways to access GIS technology.

The Department of Fish and Game - Division of Wildlife Conservation's game management units and subunits are the most requested of the Division's GIS data. Hunting and trapping regulations and other wildlife management issues often refer geographically to the effected Game Management Unit (GMU). This file gives the user access to the currently available digital representation of the GMU/UCUs. The purpose of the GMU and associated Subunits and Uniform coding units is to give a uniform, geographic based coding system for all State of Alaska wildlife population and habitat management and regulations. This data can be used for mapping or analysis purposes assuming it is used with comparable data.Uniform Coding UnitsPrior to 1982, Alaska Department of Fish and Game - Division of Wildlife Conservation (ADFG-DWC) had a variety of coding schemes (18) relating harvest and management information to geographical areas. This made it difficult when comparing statewide wildlife information gathered across the state. In 1982, a new standardized statewide, geographically-based, hierarchy system of coding was created called the Uniform Coding Unit or UCU system. Game management units (GMUs), Subunits, and uniform coding units (UCUs) are the underlying geographic foundation of the wildlife and habitat management and regulations for ADFG-DWC. The GMU/UCU system consists of five Regions which are divided into twenty-six (26) Game Management Units (GMUs). Many of the GMUs are divided into Subunits (e.g. GMU 15 has three (3) Subunits, 15A, 15B, and 15C). GMUs that are not divided into subunits have a "Z" designation for the subunit. GMUs and Subunits are further divided into Major Drainages, Minor Drainages and Specific Areas. The smallest of these areas (down to the "specific area") is referred to as a Uniform Coding Unit (UCU) and has a unique 10 character code associated with it. (NOTE: UCU layer is for internal and official use only, not for public use or distribution). The UCU code is used for geographically classifying harvest and management information. Data that cannot be tied to a specific code can be generalized to the next higher level of the hierarchy. For example:a location description that is within multiple "specific areas" within a "minor drainage" can be coded to the minor code with a "00" for the specific area. Unknown "minor drainages" can be coded to the "major drainage" level, etc. If the subunit is unknown or the area covers multiple subunits within a unit, the subunit can be specified as a "Z" code (e.g. an area within subunits 15A and 15B could be recorded as 15Z). If a geographic location covers multiple units or the unit is unknown, the most general code (statewide code) is recorded as 27Z-Z00. The original hardcopy master maps were drawn to portray the UCUs fairly accurately geographically, but were not necessarily precisely drawn (i.e. left vs. right bank of a river, or exact ridge line). Each UCU was represented by drawing boundaries on USGS 1:250,000 scale quadrangle maps with a thick magic marker. A list (database) of place-names and their corresponding UCU codes was created and is still used today to assign permit, harvest, and sealing information to one of these geographic areas. In 1988, the UCU boundaries were digitized (traced) from the original maps into a computerized Geographic Information System (ArcInfo). Minor changes were made in 1989. Effective July 1, 2006 - GMU 24 is now divided up into four subunit 24A, 24B, 24C, 24D. - GMU 21A and 21B - - boundary has been modified. Phase I2006-2008 - initial clean-up of boundaries for GMU 6, 9, 10, 12, 16, 19, 20, 25. These modifications have NOT been verified against the UCU master list or by area biologists. -ras Jan 2009 - Priority has shifted to getting the bulk of the updates into the master. Verification and modifications based on the UCU list and the AB corrections will come at a later date. This shift is to attempt to get the master into a permanent SDE GDB, set it up with the GDB topology, make additional clean-up/edits using the GDB tools, set up versioning, make it easier to replicate to area offices, and to take advantage of the tools/features available thru ArcGIS Server with versioned GDBs. June 2009 - initial clean-up of boundaries for Southeast (GMU 1-5), GMU 17, and GMU 18. These have NOT been verified against the UCU master list or by area biologists. -ras July 1 2009 - initial clean-up of boundaries for GMU 7 and 8. Also some adjustments for 25D based on the NHD 2008 version and ArcHydro Tools "raindrop" feature. These have NOT been verified against the UCU master list or by area biologists. -ras Sept 17, 2009 - initial clean-up of boundaries for GMU 13. These modifications have NOT been verified against the UCU master list or by area biologists. -ras Oct 21, 2009 - initial clean-up of boundaries for GMU 14 These modification have NOT been verified against the UCU master list or by area biologists. -rasNov 19, 2009 - initial clean-up of boundaries for GMU 15. These modifications have NOT been verified against the UCU master list or by area biologists. -ras Dec 7, 2009 - initial clean-up of boundaries for GMU 22. These modification have NOT been verified against the UCU master list or by area biologists. -ras March 3, 2010 - initial clean-up of boundaries for GMU 23. These modification have NOT been verified against the UCU master list or by area biologists. -rasApril 10, 2010 - initial clean-up of boundaries for GMU 26. These modification have NOT been verified against the UCU master list or by area biologists. -ras May 2010 - This completes Phase I of refining the UCUs - bulk heads-up re-digitizing of all arcs. Phase II - Converting and establishing procedures for maintaining the master in an Enterprise GDB is underway. Effective July 1, 2010, Region II was split into Region 2 (GMU's 6, 7, 8, 14C, 15) and Region 4 (GMU's 9, 10, 11, 13, 14AB, 16, 17. This version was updated to reflect the change. An archive of the previous version (with Regions I, II, III, and V) is available on request as GMUMaster_063010. -ras2012-present - minor updates continue as needed and time allows, and as newer base maps are used.2014 minor updates continue as needed, including updates to domain listings (not affecting GIS geometry)Effective July 1, 2014- revision to GMU 18/19/21 boundary to clarify/correct previous insufficient boundary description. Passed during Spring 2014 Board of Game.2015 minor changes as needed

"Hi, I'm Adam Burke. I am the Lead Specialist Advisor for Geospatial at Natural Resources Wales. Read on to find out more about the work I do and how I got here."I graduated from Aberystwyth University with a BSc in Physical Geography and a MSc in Geographic Information Systems.

These data were automated to provide an accurate high-resolution historical shoreline of Nauset Harbor to Pleasant Bay, MA suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey....

These data provide an accurate high-resolution shoreline compiled from imagery of Ship Island, MS . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Sour...

Crowther_Nature_Files.zip This description pertains to the original download. Details on revised (newer) versions of the datasets are listed below. When more than one version of a file exists in Figshare, the original DOI will take users to the latest version, though each version technically has its own DOI. -- Two global maps (raster files) of tree density. These maps highlight how the number of trees varies across the world. One map was generated using biome-level models of tree density, and applied at the biome scale. The other map was generated using ecoregion-level models of tree density, and applied at the ecoregion scale. For this reason, transitions between biomes or between ecoregions may be unrealistically harsh, but large-scale estimates are robust (see Crowther et al 2015 and Glick et al 2016). At the outset, this study was intended to generate reliable estimates at broad spatial scales, which inherently comes at the cost of fine-scale precision. For this reason, country-scale (or larger) estimates are generally more robust than individual pixel-level estimates. Additionally, due to data limitations, estimates for Mangroves and Tropical coniferous forest (as identified by WWF and TNC) were generated using models constructed from Topical moist broadleaf forest data and Temperate coniferous forest data, respectively. Because we used ecological analogy, the estimates for these two biomes should be considered less reliable than those of other biomes . These two maps initially appeared in Crowther et al (2015), with the biome map being featured more prominently. Explicit publication of the data is associated with Glick et al (2016). As they are produced, updated versions of these datasets, as well as alternative formats, will be made available under Additional Versions (see below).

Methods: We collected over 420,000 ground-sources estimates of tree density from around the world. We then constructed linear regression models using vegetative, climatic, topographic, and anthropogenic variables to produce forest tree density estimates for all locations globally. All modeling was done in R. Mapping was done using R and ArcGIS 10.1.

Viewing Instructions: Load the files into an appropriate geographic information system (GIS). For the original download (ArcGIS geodatabase files), load the files into ArcGIS to view or export the data to other formats. Because these datasets are large and have a unique coordinate system that is not read by many GIS, we suggest loading them into an ArcGIS dataframe whose coordinate system matches that of the data (see File Format). For GeoTiff files (see Additional Versions), load them into any compatible GIS or image management program.

Comments: The original download provides a zipped folder that contains (1) an ArcGIS File Geodatabase (.gdb) containing one raster file for each of the two global models of tree density – one based on biomes and one based on ecoregions; (2) a layer file (.lyr) for each of the global models with the symbology used for each respective model in Crowther et al (2015); and an ArcGIS Map Document (.mxd) that contains the layers and symbology for each map in the paper. The data is delivered in the Goode homolosine interrupted projected coordinate system that was used to compute biome, ecoregion, and global estimates of the number and density of trees presented in Crowther et al (2015). To obtain maps like those presented in the official publication, raster files will need to be reprojected to the Eckert III projected coordinate system. Details on subsequent revisions and alternative file formats are list below under Additional Versions.----------

Additional Versions: Crowther_Nature_Files_Revision_01.zip contains tree density predictions for small islands that are not included in the data available in the original dataset. These predictions were not taken into consideration in production of maps and figures presented in Crowther et al (2015), with the exception of the values presented in Supplemental Table 2. The file structure follows that of the original data and includes both biome- and ecoregion-level models.

Crowther_Nature_Files_Revision_01_WGS84_GeoTiff.zip contains Revision_01 of the biome-level model, but stored in WGS84 and GeoTiff format. This file was produced by reprojecting the original Goode homolosine files to WGS84 using nearest neighbor resampling in ArcMap. All areal computations presented in the manuscript were computed using the Goode homolosine projection. This means that comparable computations made with projected versions of this WGS84 data are likely to differ (substantially at greater latitudes) as a product of the resampling. Included in this .zip file are the primary .tif and its visualization support files.

References:

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., Smith, J. R., Hintler, G., Duguid, M. C., Amatulli, G., Tuanmu, M. N., Jetz, W., Salas, C., Stam, C., Piotto, D., Tavani, R., Green, S., Bruce, G., Williams, S. J., Wiser, S. K., Huber, M. O., Hengeveld, G. M., Nabuurs, G. J., Tikhonova, E., Borchardt, P., Li, C. F., Powrie, L. W., Fischer, M., Hemp, A., Homeier, J., Cho, P., Vibrans, A. C., Umunay, P. M., Piao, S. L., Rowe, C. W., Ashton, M. S., Crane, P. R., and Bradford, M. A. 2015. Mapping tree density at a global scale. Nature, 525(7568): 201-205. DOI: http://doi.org/10.1038/nature14967Glick, H. B., Bettigole, C. B., Maynard, D. S., Covey, K. R., Smith, J. R., and Crowther, T. W. 2016. Spatially explicit models of global tree density. Scientific Data, 3(160069), doi:10.1038/sdata.2016.69.

Northeastern United States State Boundary data are intended for geographic display of state boundaries at statewide and regional levels. Use it to map and label states on a map. These data are derived from Northeastern United States Political Boundary Master layer. This information should be displayed and analyzed at scales appropriate for 1:24,000-scale data. The State of Connecticut, Department of Environmental Protection (CTDEP) assembled this regional data layer using data from other states in order to create a single, seamless representation of political boundaries within the vicinity of Connecticut that could be easily incorporated into mapping applications as background information. More accurate and up-to-date information may be available from individual State government Geographic Information System (GIS) offices. Not intended for maps printed at map scales greater or more detailed than 1:24,000 scale (1 inch = 2,000 feet.)

Northeastern United States Town Boundary data are intended for geographic display of state, county and town (municipal) boundaries at statewide and regional levels. Use it to map and label towns on a map. These data are derived from Northeastern United States Political Boundary Master layer. This information should be displayed and analyzed at scales appropriate for 1:24,000-scale data. The State of Connecticut, Department of Environmental Protection (CTDEP) assembled this regional data layer using data from other states in order to create a single, seamless representation of political boundaries within the vicinity of Connecticut that could be easily incorporated into mapping applications as background information. More accurate and up-to-date information may be available from individual State government Geographic Information System (GIS) offices. Not intended for maps printed at map scales greater or more detailed than 1:24,000 scale (1 inch = 2,000 feet.)

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Geography Wemphasis In Geographic Information System Gis. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Geography Wemphasis In Geographic Information System Gis. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Geoinformatics (Geographic Information Systems). It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Geoinformatics (Geographic Information Systems). This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

MassGIS is working very closely with the State 911 Department in the state’s Executive Office of Public Safety and Security on the Next Generation 911 Emergency Call System. MassGIS developed and is maintaining the map and address information that are at the heart of this new system. Statewide deployment of this new 9-1-1 call routing system was completed in 2018.Address sources include the Voter Registration List from the Secretary of the Commonwealth, site addresses from municipal departments (primarily assessors), and customer address lists from utilities. Addresses from utilities were “anonymized” to protect customer privacy. The MAD was also validated for completeness using the Emergency Service List (a list of telephone land line addresses) from Verizon.The MAD contains both tabular and spatial data, with addresses being mapped as point features. At present, the MAD contains 3.2 million address records and 2.2 million address points. As the database is very dynamic with changes being made daily, the data available for download will be refreshed weekly.A Statewide Addressing Standard for Municipalities is another useful asset that has been created as part of this ongoing project. It is a best practices guide for the creation and storage of addresses for Massachusetts Municipalities.Points features with each point having an address to the building/floor/unit level, when that information is available. Where more than one address is located at a single location multiple points are included (i.e. "stacked points"). The points for the most part represent building centroids. Other points are located as assessor parcel centroids.Points will display at scales 1:75,000 and closer.MassGIS' service does not contain points for Boston; they may be accessed at https://data.boston.gov/dataset/live-street-address-management-sam-addresses/resource/873a7659-68b6-4ac0-98b7-6d8af762b6f1.More details about the MAD and Master Address Points.Map service also available.

Town of Hardwick, MA GIS Viewer

Uniform Coding UnitsPrior to 1982, Alaska Department of Fish and Game - Division of Wildlife Conservation (ADFG-DWC) had a variety of coding schemes (18) relating harvest and management information to geographical areas. This made it difficult when comparing statewide wildlife information gathered across the state. In 1982, a new standardized statewide, geographically-based, hierarchy system of coding was created called the Uniform Coding Unit or UCU system. Game management units (GMUs), Subunits, and uniform coding units (UCUs) are the underlying geographic foundation of the wildlife and habitat management and regulations for ADFG-DWC. The GMU/UCU system consists of five Regions which are divided into twenty-six (26) Game Management Units (GMUs). Many of the GMUs are divided into Subunits (e.g. GMU 15 has three (3) Subunits, 15A, 15B, and 15C). GMUs that are not divided into subunits have a "Z" designation for the subunit. GMUs and Subunits are further divided into Major Drainages, Minor Drainages and Specific Areas. The smallest of these areas (down to the "specific area") is referred to as a Uniform Coding Unit (UCU) and has a unique 10 character code associated with it. (NOTE: UCU layer is for internal and official use only, not for public use or distribution). The UCU code is used for geographically classifying harvest and management information. Data that cannot be tied to a specific code can be generalized to the next higher level of the hierarchy. For example:a location description that is within multiple "specific areas" within a "minor drainage" can be coded to the minor code with a "00" for the specific area. Unknown "minor drainages" can be coded to the "major drainage" level, etc. If the subunit is unknown or the area covers multiple subunits within a unit, the subunit can be specified as a "Z" code (e.g. an area within subunits 15A and 15B could be recorded as 15Z). If a geographic location covers multiple units or the unit is unknown, the most general code (statewide code) is recorded as 27Z-Z00. The original hardcopy master maps were drawn to portray the UCUs fairly accurately geographically, but were not necessarily precisely drawn (i.e. left vs. right bank of a river, or exact ridge line). Each UCU was represented by drawing boundaries on USGS 1:250,000 scale quadrangle maps with a thick magic marker. A list (database) of place-names and their corresponding UCU codes was created and is still used today to assign permit, harvest, and sealing information to one of these geographic areas. In 1988, the UCU boundaries were digitized (traced) from the original maps into a computerized Geographic Information System (ArcInfo). Minor changes were made in 1989. Effective July 1, 2006 - GMU 24 is now divided up into four subunit 24A, 24B, 24C, 24D. - GMU 21A and 21B - - boundary has been modified. Phase I2006-2008 - initial clean-up of boundaries for GMU 6, 9, 10, 12, 16, 19, 20, 25. These modifications have NOT been verified against the UCU master list or by area biologists. -ras Jan 2009 - Priority has shifted to getting the bulk of the updates into the master. Verification and modifications based on the UCU list and the AB corrections will come at a later date. This shift is to attempt to get the master into a permanent SDE GDB, set it up with the GDB topology, make additional clean-up/edits using the GDB tools, set up versioning, make it easier to replicate to area offices, and to take advantage of the tools/features available thru ArcGIS Server with versioned GDBs. June 2009 - initial clean-up of boundaries for Southeast (GMU 1-5), GMU 17, and GMU 18. These have NOT been verified against the UCU master list or by area biologists. -ras July 1 2009 - initial clean-up of boundaries for GMU 7 and 8. Also some adjustments for 25D based on the NHD 2008 version and ArcHydro Tools "raindrop" feature. These have NOT been verified against the UCU master list or by area biologists. -ras Sept 17, 2009 - initial clean-up of boundaries for GMU 13. These modifications have NOT been verified against the UCU master list or by area biologists. -ras Oct 21, 2009 - initial clean-up of boundaries for GMU 14 These modification have NOT been verified against the UCU master list or by area biologists. -rasNov 19, 2009 - initial clean-up of boundaries for GMU 15. These modifications have NOT been verified against the UCU master list or by area biologists. -ras Dec 7, 2009 - initial clean-up of boundaries for GMU 22. These modification have NOT been verified against the UCU master list or by area biologists. -ras March 3, 2010 - initial clean-up of boundaries for GMU 23. These modification have NOT been verified against the UCU master list or by area biologists. -rasApril 10, 2010 - initial clean-up of boundaries for GMU 26. These modification have NOT been verified against the UCU master list or by area biologists. -ras May 2010 - This completes Phase I of refining the UCUs - bulk heads-up re-digitizing of all arcs. Phase II - Converting and establishing procedures for maintaining the master in an Enterprise GDB is underway. Effective July 1, 2010, Region II was split into Region 2 (GMU's 6, 7, 8, 14C, 15) and Region 4 (GMU's 9, 10, 11, 13, 14AB, 16, 17. This version was updated to reflect the change. An archive of the previous version (with Regions I, II, III, and V) is available on request as GMUMaster_063010. -ras2012-present - minor updates continue as needed and time allows, and as newer base maps are used.2014 minor updates continue as needed, including updates to domain listings (not affecting GIS geometry)Effective July 1, 2014- revision to GMU 18/19/21 boundary to clarify/correct previous insufficient boundary description. Passed during Spring 2014 Board of Game.

Raw data used in MSc Thesis. Available for reproducing methodology

These data provide an accurate high-resolution shoreline compiled from lidar and imagery of Nantucket Island, MA . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

These data provide an accurate high-resolution shoreline compiled from imagery of ST LOUIS BAY, MS . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Sou...