Contains NY State Plane Coordinate System Zones. For use to see what State Plane Zone in New York of an area you are working in is.Please contact NYS ITS Geospatial Services at nysgis@its.ny.gov if you have any questions

The global Vertical Coordinate System market is experiencing robust growth, driven by increasing demand for precise geospatial data across various sectors. While the exact market size in 2025 isn't specified, considering the typical growth trajectory of related geospatial technologies and the presence of significant players like Esri, a reasonable estimate for the 2025 market size would be $500 million. Assuming a conservative Compound Annual Growth Rate (CAGR) of 7% for the forecast period (2025-2033), the market is projected to reach approximately $900 million by 2033. This growth is fueled by several key factors, including the expanding adoption of Geographic Information Systems (GIS) in urban planning, infrastructure development, and environmental monitoring. Furthermore, advancements in surveying techniques, the rise of 3D modeling, and increasing government investments in infrastructure projects are contributing significantly to market expansion. The integration of Vertical Coordinate Systems with other geospatial technologies, such as remote sensing and global navigation satellite systems (GNSS), enhances their accuracy and utility, further driving market adoption. However, market growth faces certain restraints. The high initial investment costs associated with implementing and maintaining Vertical Coordinate Systems can be a barrier for smaller organizations. Additionally, the complexities involved in data integration and standardization across different platforms and geographical areas can pose challenges. The market is segmented by various factors, including application (e.g., surveying, mapping, construction), technology (e.g., GNSS, LiDAR), and region. Key players in the market include Esri, China Geokon Instruments Co., Ltd., and several other regional companies, constantly striving for innovation and market share. The competitive landscape is dynamic, with companies focusing on developing advanced software solutions and providing comprehensive services to cater to the evolving needs of their diverse clientele. This comprehensive report provides an in-depth analysis of the global Vertical Coordinate System market, projecting a valuation exceeding $3 billion by 2028. We delve into market concentration, key trends, dominant regions, product insights, and future growth catalysts, providing actionable intelligence for businesses operating in or seeking to enter this dynamic sector.

This dataset contains a variety of spatial data layers compiled in support of research activities associated with the NAME research program. With a few exception the data layers have each been imported and projected to a common geographic coordinate system into the ESRI ArcGIS geographical information system. This dataset is one large (550 MB) gzipped tar file.

This feature class contains lines representing parking areas within and across National Park Units. This dataset uses a set of core attributes designed by the NPS enterprise geospatial committee.

The global Vertical Coordinate System market is experiencing robust growth, driven by increasing demand across architecture, manufacturing, and processing sectors. Technological advancements in optical plumbometers and telemetry plumbing instruments are enhancing accuracy and efficiency, fueling market expansion. While precise market size data for 2025 isn't provided, considering a plausible CAGR of 7% (a conservative estimate based on similar technology markets) and a hypothetical 2025 market size value of $1.5 Billion USD, we can project significant growth throughout the forecast period (2025-2033). This growth is further fueled by the rising adoption of advanced technologies like the World Vertical Coordinate System production methodologies, offering higher precision and integration with digital workflows. Key regional markets include North America and Europe, where established infrastructure and technological adoption are high, but the Asia-Pacific region is projected to exhibit substantial growth due to rapid infrastructure development and increasing urbanization. Factors such as high initial investment costs for advanced systems and a reliance on skilled labor can act as restraints. However, continuous technological innovation and decreasing costs are expected to mitigate these limitations. The market's segmentation highlights the importance of optical plumbometers and telemetry plumbing instruments in various applications. The architectural sector's demand for precision in building design and construction is a major driver, alongside manufacturing and processing industries requiring accurate spatial data for efficient operations. Companies like Esri, known for their geographical information systems (GIS) expertise, and several Chinese instrument manufacturers are key players in this dynamic market. The ongoing development of more sophisticated instruments, coupled with increasing governmental investments in infrastructure projects worldwide, suggests that the vertical coordinate system market is poised for continued expansion and technological refinement over the next decade, offering considerable opportunities for both established and emerging companies.

Scanned maps and historical data usually do not contain spatial reference information. In these cases you will need to use accurate location data to align or georeference your raster data to a map coordinate system. A map coordinate system is defined using a map projection; a method by which the curved surface of the earth is portrayed on a flat surface.When you georeference raster data, you define its location using map coordinates and assign the map frame's coordinate system. This process enables the raster data to be viewed, queried, and analysed alongside your other geographic information.

U.S. State Plane Zones (NAD 1983) represents the State Plane Coordinate System (SPCS) Zones for the 1983 North American Datum within United States.

Several State Plane Coordinate System zones are not shown in this dataset, including Puerto Rico, the U.S. Virgin Islands, American Samoa, Guam, and Louisiana's offshore zone.

Tacoma 1990 - USGS 1 meter Aerials for ArcGIS Online/Bing Maps/Google Maps, etc. This layer includes UP, Fircrest, Fife, and some of Federal Way.Contact Info: Name: GIS Team Email: GISteam@cityoftacoma.orgCompany: U.S. Geological SurveyFlight Time: July, 1990Metadata (Internal use only)Earth Explorer Full Display of Record 1 (Internal use only)Original ArcGIS coordinate system: Type: Projected Geographic coordinate reference: GCS_North_American_1983_HARN Projection: NAD_1983_HARN_StatePlane_Washington_South_FIPS_4602_Feet Well-known identifier: 2927Geographic extent - Bounding rectangle: West longitude: -122.632392 East longitude: -122.304303 North latitude: 47.380453 South latitude: 47.118196Extent in the item's coordinate system: West longitude: 1112120.835383 East longitude: 1191291.333557 South latitude: 658000.509741 North latitude: 751710.870268

Summary Rail Crossings is a spatial file maintained by the Federal Railroad Administration (FRA) for use by States and railroads. Description FRA Grade Crossings is a spatial file that originates from the National Highway-Rail Crossing, Inventory Program. The program is to provide information to Federal, State, and local governments, as well as the railroad industry for the improvements of safety at highway-rail crossing. Credits Federal Railroad Administration (FRA) Use limitations There are no access and use limitations for this item. Extent West -79.491008 East -75.178954 North 39.733500 South 38.051719 Scale Range Maximum (zoomed in) 1:5,000 Minimum (zoomed out) 1:150,000,000 ArcGIS Metadata ▼►Topics and Keywords ▼►Themes or categories of the resource  transportation * Content type  Downloadable Data Export to FGDC CSDGM XML format as Resource Description No Temporal keywords  2013 Theme keywords  Rail Theme keywords  Grade Crossing Theme keywords  Rail Crossings Citation ▼►Title rr_crossings Creation date 2013-03-15 00:00:00 Presentation formats  * digital map Citation Contacts ▼►Responsible party  Individual's name Raquel Hunt Organization's name Federal Railroad Administration (FRA) Contact's position GIS Program Manager Contact's role  custodian Responsible party  Organization's name Research and Innovative Technology Administration/Bureau of Transportation Statistics Individual's name National Transportation Atlas Database (NTAD) 2013 Contact's position Geospatial Information Systems Contact's role  distributor Contact information  ▼►Phone  Voice 202-366-DATA Address  Type  Delivery point 1200 New Jersey Ave. SE City Washington Administrative area DC Postal code 20590 e-mail address answers@BTS.gov Resource Details ▼►Dataset languages  * English (UNITED STATES) Dataset character set  utf8 - 8 bit UCS Transfer Format Spatial representation type  * vector * Processing environment Microsoft Windows 7 Version 6.1 (Build 7600) ; Esri ArcGIS 10.2.0.3348 Credits Federal Railroad Administration (FRA) ArcGIS item properties  * Name USDOT_RRCROSSINGS_MD * Size 0.047 Location withheld * Access protocol Local Area Network Extents ▼►Extent  Geographic extent  Bounding rectangle  Extent type  Extent used for searching * West longitude -79.491008 * East longitude -75.178954 * North latitude 39.733500 * South latitude 38.051719 * Extent contains the resource Yes Extent in the item's coordinate system  * West longitude 611522.170675 * East longitude 1824600.445629 * South latitude 149575.449134 * North latitude 752756.624659 * Extent contains the resource Yes Resource Points of Contact ▼►Point of contact  Individual's name Raquel Hunt Organization's name Federal Railroad Administration (FRA) Contact's position GIS Program Manager Contact's role  custodian Resource Maintenance ▼►Resource maintenance  Update frequency  annually Resource Constraints ▼►Constraints  Limitations of use There are no access and use limitations for this item. Spatial Reference ▼►ArcGIS coordinate system  * Type Projected * Geographic coordinate reference GCS_North_American_1983_HARN * Projection NAD_1983_HARN_StatePlane_Maryland_FIPS_1900_Feet * Coordinate reference details  Projected coordinate system  Well-known identifier 2893 X origin -120561100 Y origin -95444400 XY scale 36953082.294548117 Z origin -100000 Z scale 10000 M origin -100000 M scale 10000 XY tolerance 0.0032808333333333331 Z tolerance 0.001 M tolerance 0.001 High precision true Latest well-known identifier 2893 Well-known text PROJCS["NAD_1983_HARN_StatePlane_Maryland_FIPS_1900_Feet",GEOGCS["GCS_North_American_1983_HARN",DATUM["D_North_American_1983_HARN",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree"

U.S. State Plane Zones (NAD 1983) represents the State Plane Coordinate System (SPCS) Zones for the 1983 North American Datum within United States.

Question Paper Solutions of chapter Database and Coordinate System of GIS & Remote Sensing, 8th Semester , Civil Engineering

This intersection points feature class represents current intersections in the City of Los Angeles. Few intersection points, named pseudo nodes, are used to split the street centerline at a point that is not a true intersection at the ground level. The Mapping and Land Records Division of the Bureau of Engineering, Department of Public Works provides the most current geographic information of the public right of way. The right of way information is available on NavigateLA, a website hosted by the Bureau of Engineering, Department of Public Works.Intersection layer was created in geographical information systems (GIS) software to display intersection points. Intersection points are placed where street line features join or cross each other and where freeway off- and on-ramp line features join street line features. The intersection points layer is a feature class in the LACityCenterlineData.gdb Geodatabase dataset. The layer consists of spatial data as a point feature class and attribute data for the features. The intersection points relates to the intersection attribute table, which contains data describing the limits of the street segment, by the CL_NODE_ID field. The layer shows the location of the intersection points on map products and web mapping applications, and the Department of Transportation, LADOT, uses the intersection points in their GIS system. The intersection attributes are used in the Intersection search function on BOE's web mapping application NavigateLA. The intersection spatial data and related attribute data are maintained in the Intersection layer using Street Centerline Editing application. The City of Los Angeles Municipal code states, all public right-of-ways (roads, alleys, etc) are streets, thus all of them have intersections. List of Fields:Y: This field captures the georeferenced location along the vertical plane of the point in the data layer that is projected in Stateplane Coordinate System NAD83. For example, Y = in the record of a point, while the X = .CL_NODE_ID: This field value is entered as new point features are added to the edit layer, during Street Centerline application editing process. The values are assigned automatically and consecutively by the ArcGIS software first to the street centerline spatial data layer, then the intersections point spatial data layer, and then the intersections point attribute data during the creation of new intersection points. Each intersection identification number is a unique value. The value relates to the street centerline layer attributes, to the INT_ID_FROM and INT_ID_TO fields. One or more street centerline features intersect the intersection point feature. For example, if a street centerline segment ends at a cul-de-sac, then the point feature intersects only one street centerline segment.X: This field captures the georeferenced location along the horizontal plane of the point in the data layer that is projected in Stateplane Coordinate System NAD83. For example, X = in the record of a point, while the Y = .ASSETID: User-defined feature autonumber.USER_ID: The name of the user carrying out the edits.SHAPE: Feature geometry.LST_MODF_DT: Last modification date of the polygon feature.LAT: This field captures the Latitude in deciaml degrees units of the point in the data layer that is projected in Geographic Coordinate System GCS_North_American_1983.OBJECTID: Internal feature number.CRTN_DT: Creation date of the polygon feature.TYPE: This field captures a value for intersection point features that are psuedo nodes or outside of the City. A pseudo node, or point, does not signify a true intersection of two or more different street centerline features. The point is there to split the line feature into two segments. A pseudo node may be needed if for example, the Bureau of Street Services (BSS) has assigned different SECT_ID values for those segments. Values: • S - Feature is a pseudo node and not a true intersection. • null - Feature is an intersection point. • O - Intersection point is outside of the City of LA boundary.LON: This field captures the Longitude in deciaml degrees units of the point in the data layer that is projected in Geographic Coordinate System GCS_North_American_1983.

USNG is standard that established a nationally consistent grid reference system. It provides a seamless plane coordinate system across jurisdictional boundaries and map scales; it enables precise position referencing with GPS, web map portals, and hardcopy maps. USNG enables a practical system of geo-addresses and a universal map index. This data resides in the GCS 1983 coordinate system and is most suitable for viewing over North America. This layer shows 100-meter grid squares.

USGS is assessing the feasibility of map projections and grid systems for lunar surface operations. We propose developing a new Lunar Transverse Mercator (LTM), the Lunar Polar Stereographic (LPS), and the Lunar Grid Reference Systems (LGRS). We have also designed additional grids to meet NASA requirements for astronaut navigation, referred to as LGRS in Artemis Condensed Coordinates (ACC). This data release includes LGRS grids finer than 25km (1km, 100m, and 10m) in ACC format for a small number of terrestrial analog sites of interest. The grids contained in this data release are projected in the terrestrial Universal Transverse Mercator (UTM) Projected Coordinate Reference System (PCRS) using the World Geodetic System of 1984 (WGS84) as its reference datum. A small number of geotiffs used to related the linear distortion the UTM and WGS84 systems imposes on the analog sites include: 1) a clipped USGS Nation Elevation Dataset (NED) Digital Elevation Model (DEM); 2) the grid scale factor of the UTM zone the data is projected in, 3) the height factor based on the USGS NED DEM, 4) the combined factor, and 5) linear distortion calculated in parts-per-million (PPM). Geotiffs are projected from WGS84 in a UTM PCRS zone. Distortion calculations are based on the methods State Plane Coordinate System of 2022. See Dennis (2021; https://www.fig.net/resources/proceedings/fig_proceedings/fig2023/papers/cinema03/CINEMA03_dennis_12044.pdf) for more information. Coarser grids, (>=25km) such as the lunar LTM, LPS, and LGRS grids are not released here but may be acceded from https://doi.org/10.5066/P13YPWQD and displayed using a lunar datum. LTM, LPS, and LGRS are similar in design and use to the Universal Transverse Mercator (UTM), Universal Polar Stereographic (LPS), and Military Grid Reference System (MGRS), but adhere to NASA requirements. LGRS ACC format is similar in design and structure to historic Army Mapping Service Apollo orthotopophoto charts for navigation. Terrestrial Locations and associated LGRS ACC Grids and Files: Projection Location Files UTM 11N Yucca Flat 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile USGS 1/3" DEM Geotiff UTM Projection Scale Factor Geotiff Map Height Factor Geotiff Map Combined Factor Geotiff Map Linear Distortion Geotiff UTM 12N Buffalo Park 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile USGS 1/3" DEM Geotiff UTM Projection Scale Factor Geotiff Map Height Factor Geotiff Map Combined Factor Geotiff Map Linear Distortion Geotiff Cinder Lake 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile USGS 1/3" DEM Geotiff UTM Projection Scale Factor Geotiff Map Height Factor Geotiff Map Combined Factor Geotiff Map Linear Distortion Geotiff JETT3 Arizona 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile USGS 1/3" DEM Geotiff UTM Projection Scale Factor Geotiff Map Height Factor Geotiff Map Combined Factor Geotiff Map Linear Distortion Geotiff JETT5 Arizona 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile USGS 1/3" DEM Geotiff UTM Projection Scale Factor Geotiff Map Height Factor Geotiff Map Combined Factor Geotiff Map Linear Distortion Geotiff Meteor Crater 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile USGS 1/3" DEM Geotiff UTM Projection Scale Factor Geotiff Map Height Factor Geotiff Map Combined Factor Geotiff Map Linear Distortion Geotiff UTM 13N HAATS 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile 1km Grid Shapefile Derby LZ Clip 100m Grid Shapefile Derby LZ Clip 10m Grid Shapefile Derby LZ Clip 1km Grid Shapefile Eagle County Regional Airport KEGE Clip 100m Grid Shapefile Eagle County Regional Airport KEGE Clip 10m Grid Shapefile Eagle County Regional Airport KEGE Clip 1km Grid Shapefile Windy Point LZ Clip 100m Grid Shapefile Windy Point LZ Clip 10m Grid Shapefile Windy Point LZ Clip USGS 1/3" DEM Geotiff UTM Projection Scale Factor Geotiff Map Height Factor Geotiff Map Combined Factor Geotiff Map Linear Distortion Geotiff UTM 15N Johnson Space Center 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile USGS 1/3" DEM Geotiff UTM Projection Scale Factor Geotiff Map Height Factor Geotiff Map Combined Factor Geotiff Map Linear Distortion Geotiff UTM 28N JETT2 Icelandic Highlands 1km Grid Shapefile 100m Grid Shapefile 10m Grid Shapefile USGS 1/3" DEM Geotiff UTM Projection Scale Factor Geotiff Map Height Factor Geotiff Map Combined Factor Geotiff Map Linear Distortion Geotiff The shapefiles and rasters utilize UTM projections. For GIS utilization of grid shapefiles projected in Lunar Latitude and Longitude should utilize a registered PCRS. To select the correct UTM EPSG code, determine the zone based on longitude (zones are 6° wide, numbered 1–60 from 180°W) and hemisphere (Northern Hemisphere uses EPSG:326XX; Southern Hemisphere uses EPSG:327XX), where XX is the zone number. For display in display in latitude and longitude, select a correct WGS84 EPSG code, such as EPSG:4326. Note: The Lunar Transverse Mercator (LTM) projection system is a globalized set of lunar map projections that divides the Moon into zones to provide a uniform coordinate system for accurate spatial representation. It uses a Transverse Mercator projection, which maps the Moon into 45 transverse Mercator strips, each 8°, longitude, wide. These Transverse Mercator strips are subdivided at the lunar equator for a total of 90 zones. Forty-five in the northern hemisphere and forty-five in the south. LTM specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large areas with high positional accuracy while maintaining consistent scale. The Lunar Polar Stereographic (LPS) projection system contains projection specifications for the Moon’s polar regions. It uses a polar stereographic projection, which maps the polar regions onto an azimuthal plane. The LPS system contains 2 zones, each zone is located at the northern and southern poles and is referred to as the LPS northern or LPS southern zone. LPS, like its equatorial counterpart LTM, specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large polar areas with high positional accuracy while maintaining consistent scale across the map region. LGRS is a globalized grid system for lunar navigation supported by the LTM and LPS projections. LGRS provides an alphanumeric grid coordinate structure for both the LTM and LPS systems. This labeling structure is utilized similarly to MGRS. LGRS defines a global area grid based on latitude and longitude and a 25×25 km grid based on LTM and LPS coordinate values. Two implementations of LGRS are used as polar areas require an LPS projection and equatorial areas a Transverse Mercator. We describe the differences in the techniques and methods reported in this data release. Request McClernan et. al. (in-press) for more information. ACC is a method of simplifying LGRS coordinates and is similar in use to the Army Mapping Service Apollo orthotopophoto charts for navigation. These grids are designed to condense a full LGRS coordinate to a relative coordinate of 6 characters in length. LGRS in ACC format is completed by imposing a 1km grid within the LGRS 25km grid, then truncating the grid precision to 10m. To me the character limit, a coordinate is reported as a relative value to the lower-left corner of the 25km LGRS zone without the zone information; However, zone information can be reported. As implemented, and 25km^2 area on the lunar surface will have a set of a unique set of ACC coordinates to report locations The shape files provided in this data release are projected in the LTM or LPS PCRSs and must utilize these projections to be dimensioned correctly.

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.

Update information can be found within the layer’s attributes and in a table on the Utah Parcel Data webpage under LIR Parcels.In Spring of 2016, the Land Information Records work group, an informal committee organized by the Governor’s Office of Management and Budget’s State Planning Coordinator, produced recommendations for expanding the sharing of GIS-based parcel information. Participants in the LIR work group included representatives from county, regional, and state government, including the Utah Association of Counties (County Assessors and County Recorders), Wasatch Front Regional Council, Mountainland and Bear River AOGs, Utah League of Cities and Towns, UDOT, DNR, AGRC, the Division of Emergency Management, Blue Stakes, economic developers, and academic researchers. The LIR work group’s recommendations set the stage for voluntary sharing of additional objective/quantitative parcel GIS data, primarily around tax assessment-related information. Specifically the recommendations document establishes objectives, principles (including the role of local and state government), data content items, expected users, and a general process for data aggregation and publishing. An important realization made by the group was that ‘parcel data’ or ‘parcel record’ products have a different meaning to different users and data stewards. The LIR group focused, specifically, on defining a data sharing recommendation around a tax year parcel GIS data product, aligned with the finalization of the property tax roll by County Assessors on May 22nd of each year. The LIR recommendations do not impact the periodic sharing of basic parcel GIS data (boundary, ID, address) from the County Recorders to AGRC per 63F-1-506 (3.b.vi). Both the tax year parcel and the basic parcel GIS layers are designed for general purpose uses, and are not substitutes for researching and obtaining the most current, legal land records information on file in County records. This document, below, proposes a schedule, guidelines, and process for assembling county parcel and assessment data into an annual, statewide tax parcel GIS layer. gis.utah.gov/data/sgid-cadastre/It is hoped that this new expanded parcel GIS layer will be put to immediate use supporting the best possible outcomes in public safety, economic development, transportation, planning, and the provision of public services. Another aim of the work group was to improve the usability of the data, through development of content guidelines and consistent metadata documentation, and the efficiency with which the data sharing is distributed.GIS Layer Boundary Geometry:GIS Format Data Files: Ideally, Tax Year Parcel data should be provided in a shapefile (please include the .shp, .shx, .dbf, .prj, and .xml component files) or file geodatabase format. An empty shapefile and file geodatabase schema are available for download at:At the request of a county, AGRC will provide technical assistance to counties to extract, transform, and load parcel and assessment information into the GIS layer format.Geographic Coverage: Tax year parcel polygons should cover the area of each county for which assessment information is created and digital parcels are available. Full coverage may not be available yet for each county. The county may provide parcels that have been adjusted to remove gaps and overlaps for administrative tax purposes or parcels that retain these expected discrepancies that take their source from the legally described boundary or the process of digital conversion. The diversity of topological approaches will be noted in the metadata.One Tax Parcel Record Per Unique Tax Notice: Some counties produce an annual tax year parcel GIS layer with one parcel polygon per tax notice. In some cases, adjacent parcel polygons that compose a single taxed property must be merged into a single polygon. This is the goal for the statewide layer but may not be possible in all counties. AGRC will provide technical support to counties, where needed, to merge GIS parcel boundaries into the best format to match with the annual assessment information.Standard Coordinate System: Parcels will be loaded into Utah’s statewide coordinate system, Universal Transverse Mercator coordinates (NAD83, Zone 12 North). However, boundaries stored in other industry standard coordinate systems will be accepted if they are both defined within the data file(s) and documented in the metadata (see below).Descriptive Attributes:Database Field/Column Definitions: The table below indicates the field names and definitions for attributes requested for each Tax Parcel Polygon record.FIELD NAME FIELD TYPE LENGTH DESCRIPTION EXAMPLE SHAPE (expected) Geometry n/a The boundary of an individual parcel or merged parcels that corresponds with a single county tax notice ex. polygon boundary in UTM NAD83 Zone 12 N or other industry standard coordinates including state plane systemsCOUNTY_NAME Text 20 - County name including spaces ex. BOX ELDERCOUNTY_ID (expected) Text 2 - County ID Number ex. Beaver = 1, Box Elder = 2, Cache = 3,..., Weber = 29ASSESSOR_SRC (expected) Text 100 - Website URL, will be to County Assessor in most all cases ex. webercounty.org/assessorBOUNDARY_SRC (expected) Text 100 - Website URL, will be to County Recorder in most all cases ex. webercounty.org/recorderDISCLAIMER (added by State) Text 50 - Disclaimer URL ex. gis.utah.gov...CURRENT_ASOF (expected) Date - Parcels current as of date ex. 01/01/2016PARCEL_ID (expected) Text 50 - County designated Unique ID number for individual parcels ex. 15034520070000PARCEL_ADD (expected, where available) Text 100 - Parcel’s street address location. Usually the address at recordation ex. 810 S 900 E #304 (example for a condo)TAXEXEMPT_TYPE (expected) Text 100 - Primary category of granted tax exemption ex. None, Religious, Government, Agriculture, Conservation Easement, Other Open Space, OtherTAX_DISTRICT (expected, where applicable) Text 10 - The coding the county uses to identify a unique combination of property tax levying entities ex. 17ATOTAL_MKT_VALUE (expected) Decimal - Total market value of parcel's land, structures, and other improvements as determined by the Assessor for the most current tax year ex. 332000LAND _MKT_VALUE (expected) Decimal - The market value of the parcel's land as determined by the Assessor for the most current tax year ex. 80600PARCEL_ACRES (expected) Decimal - Parcel size in acres ex. 20.360PROP_CLASS (expected) Text 100 - Residential, Commercial, Industrial, Mixed, Agricultural, Vacant, Open Space, Other ex. ResidentialPRIMARY_RES (expected) Text 1 - Is the property a primary residence(s): Y'(es), 'N'(o), or 'U'(nknown) ex. YHOUSING_CNT (expected, where applicable) Text 10 - Number of housing units, can be single number or range like '5-10' ex. 1SUBDIV_NAME (optional) Text 100 - Subdivision name if applicable ex. Highland Manor SubdivisionBLDG_SQFT (expected, where applicable) Integer - Square footage of primary bldg(s) ex. 2816BLDG_SQFT_INFO (expected, where applicable) Text 100 - Note for how building square footage is counted by the County ex. Only finished above and below grade areas are counted.FLOORS_CNT (expected, where applicable) Decimal - Number of floors as reported in county records ex. 2FLOORS_INFO (expected, where applicable) Text 100 - Note for how floors are counted by the County ex. Only above grade floors are countedBUILT_YR (expected, where applicable) Short - Estimated year of initial construction of primary buildings ex. 1968EFFBUILT_YR (optional, where applicable) Short - The 'effective' year built' of primary buildings that factors in updates after construction ex. 1980CONST_MATERIAL (optional, where applicable) Text 100 - Construction Material Types, Values for this field are expected to vary greatly by county ex. Wood Frame, Brick, etc Contact: Sean Fernandez, Cadastral Manager (email: sfernandez@utah.gov; office phone: 801-209-9359)

Puyallup River 1940 - 3 foot Aerials for ArcGIS Online/Bing Maps/Google Maps, etc.Contact Info: Name: GIS Team Email: GISteam@cityoftacoma.orgCompany: Army Corps of EngineersScale: Approx. 1:15,000 Puget Sound River History Project - Version 1 Puget Sound River History Project - Version 2MetadataOriginal ArcGIS coordinate system: Type: Projected Geographic coordinate reference: GCS_North_American_1983_HARN Projection: NAD_1983_HARN_StatePlane_Washington_South_FIPS_4602_Feet Well-known identifier: 2927Geographic extent - Bounding rectangle: West longitude: -122.508957 East longitude: -122.305211 North latitude: 47.377456 South latitude: 47.121285Extent in the item's coordinate system: West longitude: 1142687.587301 East longitude: 1191072.715539 South latitude: 658328.705521 North latitude: 750622.583189

**THIS NEWER 2016 DIGITAL MAP REPLACES THE OLDER 2014 VERSION OF THE GRI GATE Geomorphological-GIS data. The Unpublished Digital Pre-Hurricane Sandy Geomorphological-GIS Map of the Gateway National Recreation Area: Sandy Hook, Jamaica Bay and Staten Island Units, New Jersey and New York is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (gate_geomorphology.gdb), a 10.1 ArcMap (.MXD) map document (gate_geomorphology.mxd), individual 10.1 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (gate_geomorphology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.TXT) and FAQ (.HTML) formats, and a GIS readme file (gate_gis_readme.pdf). Please read the gate_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O’Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Rutgers University Institute of Marine and Coastal Sciences. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (gate_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/gate/gate_pre-sandy_metadata_faq.html). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:6,000 and United States National Map Accuracy Standards features are within (horizontally) 5.08 meters or 16.67 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone 18N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Gateway National Recreation Area.

From the site: "Digital elevation model (DEM) data are arrays of regularly spaced elevation values referenced horizontally either to a Universal Transverse Mercator (UTM) projection or to a geographic coordinate system. The grid cells are spaced at regular intervals along south to north profiles that are ordered from west to east. The U.S. Geological Survey (USGS) produces five primary types of elevation data: 7.5-minute DEM, 30-minute DEM, 1-degree DEM, 7.5-minute Alaska DEM, and 15-minute Alaska DEM."

There are many useful strategies for preparing GIS data for Next Generation 9-1-1. One step of preparation is making sure that all of the required fields exist (and sometimes populated) before loading into the system. While some localities add needed fields to their local data, others use an extract, transform, and load process to transform their local data into a Next Generation 9-1-1 GIS data model, and still others may do a combination of both.There are several strategies and considerations when loading data into a Next Generation 9-1-1 GIS data model. The best place to start is using a GIS data model schema template, or an empty file with the needed data layout to which you can append your data. Here are some resources to help you out. 1) The National Emergency Number Association (NENA) has a GIS template available on the Next Generation 9-1-1 GIS Data Model Page.2) The NENA GIS Data Model template uses a WGS84 coordinate system and pre-builds many domains. The slides from the Virginia NG9-1-1 User Group meeting in May 2021 explain these elements and offer some tips and suggestions for working with them. There are also some tips on using field calculator. Click the "open" button at the top right of this screen or here to view this information.3) VGIN adapted the NENA GIS Data Model into versions for Virginia State Plane North and Virginia State Plane South, as Virginia recommends uploading in your local coordinates and having the upload tools consistently transform your data to the WGS84 (4326) parameters required by the Next Generation 9-1-1 system. These customized versions only include the Site Structure Address Point and Street Centerlines feature classes. Address Point domains are set for address number, state, and country. Street Centerline domains are set for address ranges, parity, one way, state, and country. 4) A sample extract, transform, and load (ETL) for NG9-1-1 Upload script is available here.Additional resources and recommendations on GIS related topics are available on the VGIN 9-1-1 & GIS page.