Three-dimensional studies of range of motion currently plot joint poses in an "Euler space" whose axes are angles measured in the joint's three rotational degrees of freedom. Researchers then compute the volume of a pose cloud to measure rotational mobility. However, pairs of poses that are equally different from one another in orientation are not always plotted equally far apart in Euler space. This distortion causes a single joint's mobility to change when measured based on different joint coordinate systems and precludes fair comparisons among joints. Here we present two alternative spaces inspired by a 16th century map projection -- cosine-corrected and sine-corrected Euler spaces -- that allow coordinate-system-independent comparisons of joint rotational mobilities. When tested with data from a bird hip joint, cosine-corrected Euler space demonstrated a ten-fold reduction in variation among mobilities measured from three joint coordinate systems. This new quantitative framework ena...

CONABIO provides online cartography through cartographic metadata distributed following the guidelines in the Standards for Digital Geospatial Metadata of FGDC-NBII (Federal Geographic Data Committee – National Biological Information Infrastructure), 1996. The cartographic information is queried through a database that is organized based on themes (biotic, physical and social aspects, regionalization and others), scales, and geographic area. The metadata content is presented as basic information, reports of the information (methodology) and spatial data information. The cartography is available online at no charge in distinct formats like: export file for Arc/Info (.E00) and shape file (ESRI), and DXF (Drawing eXchange Format). Maps is presented in cartographic projections: Lambert Conic Conformal, UTM and geographic coordinates system. GIF format of map images can be obtained as well.

Internal view of the parcel layer. This view contains all the attributes that can be seen by County employees.There are approximately 51,300 real property parcels in Napa County. Parcels delineate the approximate boundaries of property ownership as described in Napa County deeds, filed maps, and other source documents. GIS parcel boundaries are maintained by the Information Technology Services GIS team. Assessor Parcel Maps are created and maintained by the Assessor Division Mapping Section. Each parcel has an Assessor Parcel Number (APN) that is its unique identifier. The APN is the link to various Napa County databases containing information such as owner name, situs address, property value, land use, zoning, flood data, and other related information. Data for this map service is sourced from the Napa County Parcels dataset which is updated nightly with any recent changes made by the mapping team. There may at times be a delay between when a document is recorded and when the new parcel boundary configuration and corresponding information is available in the online GIS parcel viewer.From 1850 to early 1900s assessor staff wrote the name of the property owner and the property value on map pages. They began using larger maps, called “tank maps” because of the large steel cabinet they were kept in, organized by school district (before unification) on which names and values were written. In the 1920s, the assessor kept large books of maps by road district on which names were written. In the 1950s, most county assessors contracted with the State Board of Equalization for board staff to draw standardized 11x17 inch maps following the provisions of Assessor Handbook 215. Maps were originally drawn on linen. By the 1980’s Assessor maps were being drawn on mylar rather than linen. In the early 1990s Napa County transitioned from drawing on mylar to creating maps in AutoCAD. When GIS arrived in Napa County in the mid-1990s, the AutoCAD images were copied over into the GIS parcel layer. Sidwell, an independent consultant, was then contracted by the Assessor’s Office to convert these APN files into the current seamless ArcGIS parcel fabric for the entire County. Beginning with the 2024-2025 assessment roll, the maps are being drawn directly in the parcel fabric layer.Parcels in the GIS parcel fabric are drawn according to the legal description using coordinate geometry (COGO) drawing tools and various reference data such as Public Lands Survey section boundaries and road centerlines. The legal descriptions are not defined by the GIS parcel fabric. Any changes made in the GIS parcel fabric via official records, filed maps, and other source documents are uploaded overnight. There is always at least a 6-month delay between when a document is recorded and when the new parcel configuration and corresponding information is available in the online parcel viewer for search or download.Parcel boundary accuracy can vary significantly, with errors ranging from a few feet to several hundred feet. These distortions are caused by several factors such as: the map projection - the error derived when a spherical coordinate system model is projected into a planar coordinate system using the local projected coordinate system; and the ground to grid conversion - the distortion between ground survey measurements and the virtual grid measurements. The aim of the parcel fabric is to construct a visual interpretation that is adequate for basic geographic understanding. This digital data is intended for illustration and demonstration purposes only and is not considered a legal resource, nor legally authoritative.SFAP & CFAP DISCLAIMER: Per the California Code, RTC 606. some legal parcels may have been combined for assessment purposes (CFAP) or separated for assessment purposes (SFAP) into multiple parcels for a variety of tax assessment reasons. SFAP and CFAP parcels are assigned their own APN number and primarily result from a parcel being split by a tax rate area boundary, due to a recorded land use lease, or by request of the property owner. Assessor parcel (APN) maps reflect when parcels have been separated or combined for assessment purposes, and are one legal entity. The goal of the GIS parcel fabric data is to distinguish the SFAP and CFAP parcel configurations from the legal configurations, to convey the legal parcel configurations. This workflow is in progress. Please be advised that while we endeavor to restore SFAP and CFAP parcels back to their legal configurations in the primary parcel fabric layer, SFAP and CFAP parcels may be distributed throughout the dataset. Parcels that have been restored to their legal configurations, do not reflect the SFAP or CFAP parcel configurations that correspond to the current property tax delineations. We intend for parcel reports and parcel data to capture when a parcel has been separated or combined for assessment purposes, however in some cases, information may not be available in GIS for the SFAP/CFAP status of a parcel configuration shown. For help or questions regarding a parcel’s SFAP/CFAP status, or property survey data, please visit Napa County’s Surveying Services or Property Mapping Information. For more information you can visit our website: When a Parcel is Not a Parcel | Napa County, CA

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GIS Market Size 2025-2029

The GIS market size is forecast to increase by USD 24.07 billion, at a CAGR of 20.3% between 2024 and 2029.

The Global Geographic Information System (GIS) market is experiencing significant growth, driven by the increasing integration of Building Information Modeling (BIM) and GIS technologies. This convergence enables more effective spatial analysis and decision-making in various industries, particularly in soil and water management. However, the market faces challenges, including the lack of comprehensive planning and preparation leading to implementation failures of GIS solutions. Companies must address these challenges by investing in thorough project planning and collaboration between GIS and BIM teams to ensure successful implementation and maximize the potential benefits of these advanced technologies.
By focusing on strategic planning and effective implementation, organizations can capitalize on the opportunities presented by the growing adoption of GIS and BIM technologies, ultimately driving operational efficiency and innovation.

What will be the Size of the GIS Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The global Geographic Information Systems (GIS) market continues to evolve, driven by the increasing demand for advanced spatial data analysis and management solutions. GIS technology is finding applications across various sectors, including natural resource management, urban planning, and infrastructure management. The integration of Bing Maps, terrain analysis, vector data, Lidar data, and Geographic Information Systems enables precise spatial data analysis and modeling. Hydrological modeling, spatial statistics, spatial indexing, and route optimization are essential components of GIS, providing valuable insights for sectors such as public safety, transportation planning, and precision agriculture. Location-based services and data visualization further enhance the utility of GIS, enabling real-time mapping and spatial analysis.

The ongoing development of OGC standards, spatial data infrastructure, and mapping APIs continues to expand the capabilities of GIS, making it an indispensable tool for managing and analyzing geospatial data. The continuous unfolding of market activities and evolving patterns in the market reflect the dynamic nature of this technology and its applications.

How is this GIS Industry segmented?

The GIS industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Software
  Data
  Services


Type

  Telematics and navigation
  Mapping
  Surveying
  Location-based services


Device

  Desktop
  Mobile


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

The Global Geographic Information System (GIS) market encompasses a range of applications and technologies, including raster data, urban planning, geospatial data, geocoding APIs, GIS services, routing APIs, aerial photography, satellite imagery, GIS software, geospatial analytics, public safety, field data collection, transportation planning, precision agriculture, OGC standards, location intelligence, remote sensing, asset management, network analysis, spatial analysis, infrastructure management, spatial data standards, disaster management, environmental monitoring, spatial modeling, coordinate systems, spatial overlay, real-time mapping, mapping APIs, spatial join, mapping applications, smart cities, spatial data infrastructure, map projections, spatial databases, natural resource management, Bing Maps, terrain analysis, vector data, Lidar data, and geographic information systems.

The software segment includes desktop, mobile, cloud, and server solutions. Open-source GIS software, with its industry-specific offerings, poses a challenge to the market, while the adoption of cloud-based GIS software represents an emerging trend. However, the lack of standardization and interoperability issues hinder the widespread adoption of cloud-based solutions. Applications in sectors like public safety, transportation planning, and precision agriculture are driving market growth. Additionally, advancements in technologies like remote sensing, spatial modeling, and real-time mapping are expanding the market's scope.

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The Software segment was valued at USD 5.06 billion in 2019

A polygon feature class of the Urban Center boundaries within Miami-Dade County. To identify the boundary of urban centers within Miami-Dade County for mapping, application and analysis purposes.Updated: Annually The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

The Unpublished Digital Geologic-GIS Map of Tuzigoot National Monument, Arizona is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (tuzi_geology.gdb), a 10.1 ArcMap (.MXD) map document (tuzi_geology.mxd), individual 10.1 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (moca_tuzi_geology.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 (moca_tuzi_geology_gis_readme.pdf). Please read the moca_tuzi_geology_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: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (tuzi_geology_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/tuzi/tuzi_geology_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 1:48,000 and United States National Map Accuracy Standards features are within (horizontally) 24.4 meters or 80 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 12N, 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 Tuzigoot National Monument.

The Digital Geologic-GIS Map of Mount Rainier National Park, Washington is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (mora_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 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. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (mora_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) this file (mora_geology.gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (mora_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (mora_geology_metadata_faq.pdf). Please read the mora_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: http://www.google.com/earth/index.html. QGIS software is available for free at: https://www.qgis.org/en/site/. 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). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (mora_geology_metadata.txt or mora_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:62,500 and United States National Map Accuracy Standards features are within (horizontally) 31.8 meters or 104.2 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, QGIS 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: https://www.nps.gov/articles/gri-geodatabase-model.htm). The GIS data projection is NAD83, UTM Zone 10N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth.

The Unpublished Digital Bedrock Geologic-GIS Map of Voyageurs National Park and Vicinity, Minnesota is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (voya_geology.gdb), a 10.1 ArcMap (.MXD) map document (voya_geology.mxd), individual 10.1 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (voya_geology.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 (voya_gis_readme.pdf). Please read the voya_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: Minnesota Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (voya_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/voya/voya_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 1:50,000 and United States National Map Accuracy Standards features are within (horizontally) 50.8 meters or 166.7 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 15N, 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 Voyageurs National Park.

For the coastal regions of Maryland, the Maryland Geological Survey (MGS) has periodically compiled maps depicting shoreline position at several points in time. The most recent compilation -- the first electronic one -- involved (1) digitizing historical (1841-1977) shoreline maps, all derived directly or indirectly from National Oceanic and Atmospheric Administration coastal survey maps (topographic or T-sheets), and (2) interpreting recent (1988-1995) shorelines from digital orthophotography. MGS is using the shorelines to update a series of Shoreline Changes maps and to determine coastal land loss, by watershed and by county, during the last half of the 20th century.

This data set contains past shorelines, dating from 1841 to 1995, for 125 7.5-minute quadrangles covering the coastal regions of Maryland (see below). Originally in MicroImage's TNTmips .rvc format, the shoreline vectors have been converted to Arc/Info "Export" (.e00) format. They are registered to the Maryland State Plane Coordinate System (North American Datum of 83, meters).

Spatial Reference Information:

-Map Projection Name: Lambert Conformal Conic
-Grid Coordinate System Name: State Plane Coordinate System 1983
-SPCS Zone Identifier: 1900 (Maryland)
-Planar Distance Units: meters
-Horizontal Datum Name: North American Datum of 1983
-Ellipsoid Name: Geodetic Reference System 80
-Semi-major Axis: 6,378,137 meters
-Denominator of Flattening Ratio: 298.257
-Depth Datum Name: The aerial photography from which the DOQQs were
 developed was not tide-coordinated. Therefore, shorelines in this data
 set does not represent a consistent vertical datum.

Shoreline Attributes:

-beach
-vegetated
-structure
-water edge

All four categories are linear features, except "beach" which, if sufficiently wide, can be both linear and polygonal. Shorelines were merged into 7.5-minute quadrangles, provided that the aerial photography on which the DOQQs were based was flown in the same year

[Summary provided by the Maryland Geology Survey.]

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

This layer is subset of World Ecological Facets Landform Classes Image Layer. Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines.Dataset SummaryPhenomenon Mapped: LandformsUnits: MetersCell Size: 231.91560581932 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS.The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plainsSmooth plains with some local reliefIrregular plains with moderate relief Irregular plains with low hillsScattered moderate hillsScattered high hillsScattered low mountainsScattered high mountainsModerate hillsHigh hills Tablelands with moderate reliefTablelands with considerable reliefTablelands with high relief Tablelands with very high relief Low mountainsHigh mountainsTo produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes: Percent of neighborhood over 8% of slopeSlope Classes0 - 20%40021% -50%30051% - 80%200>81% 100Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain's texture. Relief was assigned one of six classes:Change in elevationRelief Class ID0 – 30 meters1031 meter – 90 meters2091 meter – 150 meters30151 meter – 300 meters40301 meter – 900 meters50>900 meters60The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:Percent of neighborhood over 8% slope in upland or lowland areasProfile ClassLess than 50% gentle slope is in upland or lowland0More than 75% of gentle slope is in lowland150%-75% of gentle slope is in lowland250-75% of gentle slope is in upland3More than 75% of gentle slope is in upland4Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class.The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them:What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

This airborne laser swath mapping (ALSM) data of the San Andreas fault zone in northern California was acquired by TerraPoint, LLC under contract to the National Aeronautics and Space Administration in collaboration with the United States Geological Survey. The data were acquired by means of LIght Detection And Ranging (LIDAR) using a discrete-return, scanning laser altimeter capable of acquiring up to 4 returns per laser pulse. The data were acquired with a nominal density of 1 laser pulses per square meter achieved with 58% overlap of adjacent data swaths (all areas were mapped at least twice and the data combined to produce final products). The data set consists of 3 parts: (1) the LIDAR point cloud providing the location and elevation of each laser return, along with associated acquisition and classification parameters, (2) a highest-surface digital elevation model (DEM) produced at a 6 foot grid spacing, where each grid cell elevation corresponds to the highest laser return within the cell (cells lacking returns are undefined, usually associated with water or low reflectance surfaces such as fresh asphalt), and (3) a "bald Earth" DEM, with vegetation cover and buildings removed, produced at a 6 foot grid spacing by sampling a triangular irregular network (TIN). The TIN was constructed from those returns classified as being from the ground or water based on spatial filtering of the point cloud. Comparison to GPS-established ground control in flat, vegetation-free areas indicates that the DEM vertical accuracy is 17 cm (RMSE for 85 points). Bald Earth elevations under vegetation and for water bodies are less accurate where laser returns from the ground or water are sparse. The highest surface and bald Earth DEMs are distributed as georeferenced geotiff elevation and shaded relief images. The grid cell values in the elevation images are orthometric elevations in international feet referenced to North American Vertical Datum 1988 (NAVD-88) stored as signed floating point values with undefined grid cells set to -99. The shaded relief images are byte values from 0 (shaded) to 255 (illuminated) computed using ENVI 4.0 shaded relief modeling with an illumination azimuth of 225 degrees, illumination elevation of 60 degrees, and a 3x3 kernel size. The images are mosaics based on USGS 7.5 minute quadrangle boundaries. Each mosaic is an east-west strip covering the northern or southern half of adjacent quadrangles. File names include the quadrangle names, a northern (N) or southern (S) half designation, a bald Earth (BE) or highest-surface (FF) designation, and an elevation image (elev) or shaded relief image (SR) designation. FF refers to full-feature indicating vegetation and buildings have not been removed.These data were developed in order to study the geomorphic expression of natural hazards in support of the National Aeronautics and Space Administration (NASA) Solid Earth and Natural Hazards (SENH) Program, the United States Geological Survey (USGS), and the Geology component of the Earthscope Plate Boundary Observatory.

Spatial Data Organization Information -

Direct Spatial Reference: Raster Raster Object Type: Pixel Row Count: 1285 Column Count: 4398 Vertical Count: 1

Spatial Reference Information - Horizontal Coordinate System Definition - Planar - Map Projection Name: Lambert Conformal Conic Standard Parallel: 38.333333 Standard Parallel: 39.833333 Longitude of Central Meridian: -122.000000 Latitude of Projection Origin: 37.666667 False Easting: 6561666.666667 False Northing: 1640416.666667 Planar Coordinate Encoding Method: row and column Coordinate Representation: Abscissa Resolution: 6.000000 Ordinate Resolution: 6.000000 Distance and Bearing Representation: Planar Distance Units: survey feet

Geodetic Model: Horizontal Datum Name: North American Datum of 1983 Ellipsoid Name: Geodetic Reference System 80 Semi-major Axis: 6378137.000000 Denominator of Flattening Ratio: 298.257222

**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 Staten Island Unit, Gateway National Recreation Area, New York is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (stis_geomorphology.gdb), a 10.1 ArcMap (.MXD) map document (stis_geomorphology.mxd), individual 10.1 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (gate_geology.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 (stis_pre-sandy_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/gate/stis_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.

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.

NOAA’s National Geodetic Survey (NGS) is in the process of modernizing the National Spatial Reference System (NSRS). As part of NSRS Modernization, the State Plane Coordinate System (SPCS) will be updated to SPCS2022. This Tiled Image Layer displays the distortion for the Special Use Zones for the SPCS2022 zone layers. This layer is intended for NGS customers, stakeholders, partners, and other constituents to view and provide feedback on the SPCS2022 zones that are being planned for release. The zone definitions are beta and should not be considered final. Distortion rasters have been reprojected to Web Mercator, which changes distortion values due to resampling of the original unprojected rasters. Although the value changes are generally small, only the original rasters are used by NGS for obtaining distortion values and assessing performance. NGS plans to make the original unprojected rasters available after SPCS2022 is finalized. Beta SPCS2022 ExperienceBeta SPCS2022 All Zones Web MapBeta SPCS2022 Special Use Zones Web MapData SourcesInformation about SPCS2022 can be found on the SPCS2022 Beta web pages.https://beta.ngs.noaa.gov/SPCS/Exact zone definitions can be viewed at the SPCS2022 Beta zone definition web page. https://beta.ngs.noaa.gov/SPCS/zone-definitions.shtmlDistortionLinear distortion is the same as map scale error at the ground surface, given in parts per million (ppm) rather than as a ratio. For example, distortion of 100 ppm is the same as 10 cm per km, 0.53 ft per mile, or a ratio of 1 part in 10,000. So for an actual horizontal distance of 1 mile, the projected (map grid) distance would be 0.53 ft shorter for negative 100 ppm distortion, and 0.53 ft longer for positive 100 ppm distortion.Point of ContactPlease email the NGS Information Center for any questions at ngs.infocenter@noaa.gov

This dataset of 40 square kilometer (sq. km) hexagons was created by the U.S. EPA's Environmental Monitoring and Assessment Program (EMAP) and is being released by the U.S. Geological Survey for public use. The 40 sq. km hexagons were derived from a grid consisting of a triangular array of points that cover the United States and neighboring Canada and Mexico. The base grid of points had a companion areal structure called a tessellation. The base tessellation hexagons constituted this tessellation. In other words, surrounding each grid point was a hexagon that defines the area within which all points are closer to this grid point than to any other, and the set of hexagons defined this way completely and -mutually exclusively covers the space of the grid. The grid had a base density of approximately 648 sq. km per point with a spacing of approximately 27 km between points. The original 40 sq. km hexagons (which do not form a tessellation) were centered about the randomized grid points and are exactly 1/16th the size of the tessellation hexagons (and therefore slightly more than 40 sq. km). Hexagon boundaries are distributed in geodetic coordinates based on the Clarke 1866 model of the Earth, meaning that the coordinates are latitude and longitude on the ellipsoid used by most North American geodetic coordinate systems. Distribution can also be made in GRS 80 coordinates if desired. The precision of the coordinates is to millionths of a degree (i.e., to 6 decimal places of a degree). This corresponds to about 0.1 meter on the surface of the Earth. The point grid was constructed in the plane of a special version of the Lambert azimuthal equal area projection; for subsequent use they may be projected using other map projections. When other projections are used, the geometry of the point grid will not be perfectly triangular nor will the hexagons surrounding the points be perfect, since sizes and/or shapes and/or distances will necessarily be distorted in another projection relative to the one used to construct the grid. This 40 sq. km hexagon tessellation was created by two successive enhancements of the 648 sq. km tessellation by factors of four. See White et al. 1992 in references.

The Unpublished Digital Geologic-GIS Map of Mount Desert Island and Vicinity, Acadia National Park, Maine is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (acad_geology.gdb), a 10.1 ArcMap (.mxd) map document (acad_geology.mxd), individual 10.1 layer (.lyr) files for each GIS data layer, an ancillary map information document (acad_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.txt) and FAQ (.pdf) formats, and a GIS readme file (acad_geology_gis_readme.pdf). Please read the acad_geology_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: Maine Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (acad_geology_metadata.txt or acad_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:50,000 and United States National Map Accuracy Standards features are within (horizontally) 25.4 meters or 83.3 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: https://www.nps.gov/articles/gri-geodatabase-model.htm). The GIS data projection is NAD83, UTM Zone 19N, 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 Acadia National Park.

The Unpublished Digital Geologic-GIS Map of Parts of Great Sand Dunes National Park and Preserve (Sangre de Cristo Mountains and part of the Dunes), Colorado is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (gsam_geology.gdb), a 10.1 ArcMap (.mxd) map document (gsam_geology.mxd), individual 10.1 layer (.lyr) files for each GIS data layer, an ancillary map information document (grsa_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.txt) and FAQ (.pdf) formats, and a GIS readme file (grsa_geology_gis_readme.pdf). Please read the grsa_geology_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: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (gsam_geology_metadata.txt or gsam_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 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 13N, 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 Great Sand Dunes National Park and Preserve.

The Unpublished Digital Geologic-GIS Map of Navajo National Monument and Vicinity, Arizona is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (nava_geology.gdb), a 10.1 ArcMap (.mxd) map document (nava_geology.mxd), individual 10.1 layer (.lyr) files for each GIS data layer, an ancillary map information document (nava_geology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.txt) and FAQ (.pdf) formats, and a GIS readme file (nava_geology_gis_readme.pdf). Please read the nava_geology_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: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (nava_geology_metadata.txt or nava_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:125,000 and United States National Map Accuracy Standards features are within (horizontally) 63.5 meters or 208.3 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: https://www.nps.gov/articles/gri-geodatabase-model.htm). The GIS data projection is NAD83, UTM Zone 12N, 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 Navajo National Monument.

NOAA’s National Geodetic Survey (NGS) is in the process of modernizing the National Spatial Reference System (NSRS). As part of NSRS Modernization, the State Plane Coordinate System (SPCS) will be updated to SPCS2022. This Tiled Image Layer displays the distortion for the Special Use Gulf Zones for the SPCS2022 zone layers. This layer is intended for NGS customers, stakeholders, partners, and other constituents to view and provide feedback on the SPCS2022 zones that are being planned for release. The zone definitions are beta and should not be considered final. Distortion rasters have been reprojected to Web Mercator, which changes distortion values due to resampling of the original unprojected rasters. Although the value changes are generally small, only the original rasters are used by NGS for obtaining distortion values and assessing performance. NGS plans to make the original unprojected rasters available after SPCS2022 is finalized. Beta SPCS2022 ExperienceBeta SPCS2022 All Zones Web MapBeta SPCS2022 Special Use Zones Web MapData SourcesInformation about SPCS2022 can be found on the SPCS2022 Beta web pages.https://beta.ngs.noaa.gov/SPCS/Exact zone definitions can be viewed at the SPCS2022 Beta zone definition web page. https://beta.ngs.noaa.gov/SPCS/zone-definitions.shtmlDistortionLinear distortion is the same as map scale error at the ground surface, given in parts per million (ppm) rather than as a ratio. For example, distortion of 100 ppm is the same as 10 cm per km, 0.53 ft per mile, or a ratio of 1 part in 10,000. So for an actual horizontal distance of 1 mile, the projected (map grid) distance would be 0.53 ft shorter for negative 100 ppm distortion, and 0.53 ft longer for positive 100 ppm distortion.Point of ContactPlease email the NGS Information Center for any questions at ngs.infocenter@noaa.gov