S-map is the new national soils database for New Zealand. When completed, it will provide a seamless digital soil map coverage for New Zealand. S-map is designed to be applied at any scale from farm to region to nation.

Existing soil databases are patchy in scale, age and quality. Many maps do not adequately describe the underlying properties of the soil types they represent. S-map integrates existing reports and digital information and updates soil maps where existing data are of low quality. Our goal is to provide comprehensive, quantitative soil information to support sustainable development and scientific modelling.

S-map terms of use / More about S-map / Paper on S-map

Miami Dade WASD Sewer Map Book (Atlas page) Index - Atlas layer

The authoritative City of Sioux Falls street map(s).

Index of sewer maps for the City.

The Digital Geomorphic-GIS Map of the Avon Area (1:24,000 scale 2007 mapping), North Carolina 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 (avon_geomorphology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (avon_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (avon_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). 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.) A GIS readme file (caha_fora_wrbr_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (caha_fora_wrbr_geomorphology.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 (avon_geomorphology_metadata_faq.pdf). Please read the caha_fora_wrbr_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. 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: North Carolina 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 (avon_geomorphology_metadata.txt or avon_geomorphology_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 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 NOAA ENC Online map service provides a continuous depiction of all NOAA ENC® coverage over U.S. coastal waters and the Great Lakes as would be shown on Electronic Chart Display and Information Systems (ECDIS). U.S. Chart No. 1 provides information about the symbology used in ECDIS. This service provides features that can be leveraged in various GIS and OGC WMS compliant applications. Generic featuresDisplays the S-57 datasets using S-52 presentation library specification edition 3.4.Provides indexing for the S-57 attribute Object Name (OBJNAM)Provides access to S-57 attribute informationLinks external files to S-57 attributesAllows for the best scale data to be displayed similar to how an ECDIS displays best scale data based on the map scale as a user zooms in and out of the display.For more information about Esri technology, email maritime@esri.com.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within Californiaâ s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map Californiaâ s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of â landsâ from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of Californiaâ s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bayâ s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Serviceâ Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Scott Creek map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these â ground-truthâ surveying data are available from the CSMP Video and Photograph Portal at http://dx.doi.org/10.5066/F7J1015K. The â seafloor characterâ data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The â potential habitatsâ polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore of Scott Creek map area data layers. Data layers are symbolized as shown on the associated map sheets.

Named Landforms of the World (NLW) contains four sub-layers representing geomorphological landforms, provinces, divisions, and their respective cartographic boundaries. The latter is to support map making, while the first three represent basic units such landforms comprise provinces, and provinces comprise divisions. NLW is a substantial update to World Named Landforms in both compilation method and the attributes that describe each landform.For more details, please refer to our paper, Named Landforms of the World: A Geomorphological and Physiographic Compilation, in Annals of the American Assocation of Geographers.Landforms are commonly defined as natural features on the surface of the Earth. The National Geographic Society specifies terrain as the basis for landforms and lists four major types: mountains, hills, plateaus, and plains. Here, however, we define landforms in a richer way that includes properties relating to underlying geologic structure, erosional and depositional character, and tectonic setting and processes. These characteristics were asserted by Dr. Richard E. Murphy in 1968 in his map, titled Landforms of the World. We blended Murphy's definition for landforms with the work E.M. Bridges, who in his 1990 book, World Geomorphology, provided a globally consistent description of geomorphological divisions, provinces, and sections to give names to the landform regions of the world. AttributeDescription Bridges Full NameFull name from E.M. Bridges' 1990 "World Geomorphology" Division and if present province and section - intended for labeling print maps of small extents. Bridges DivisionGeomorphological Division as described in E.M. Bridges' 1990 "World Geomorphology" - All Landforms have a division assigned, i.e., no nulls. Bridges ProvinceGeomorphological Province as described in E.M. Bridges' 1990 "World Geomorphology" - Not all divisions are subdivided into provinces. Bridges SectionGeomorphological Section as described in E.M. Bridges' 1990 "World Geomorphology" - Not all provinces are subdivided into sections. StructureLandform Structure as described in Richard E. Murphy's 1968 "Landforms of the World" map. Coded Value Domain. Values include: - Alpine Systems: Area of mountains formed by orogenic (collisions of tectonic plates) processes in the past 350 to 500 million years. - Caledonian/Hercynian Shield Remnants: Area of mountains formed by orogenic (collisions of tectonic plates) processes 350 to 500 million years ago. - Gondwana or Laurasian Shields: Area underlaid by mostly crystalline rock formations fromed one billion or more years ago and unbroken by tectonic processes. - Rifted Shield Areas: fractures or spreading along or adjacent to tectonic plate edges. - Isolated Volcanic Areas: volcanic activity occurring outside of Alpine Systems and Rifted Shields. - Sedimentary: Areas of deposition occurring within the past 2.5 million years Moist or DryLandform Erosional/Depositional variable as described in Richard E. Murphy's 1968 "Landforms of the World" map. Coded Value Domain. Values include: - Moist: where annual aridity index is 1.0 or higher, which implies precipitation is absorbed or lost via runoff. - Dry: where annual aridity index is less than 1.0, which implies more precipitation evaporates before it can be absorbed or lost via runoff. TopographicLandform Topographic type variable as described in Richard E. Murphy's 1968 "Landforms of the World" map. Karagulle et. al. 2017 - based on rich morphometric characteristics. Coded Value Domain. Values include: - Plains: Areas with less than 90-meters of relief and slopes under 20%. - Hills: Areas with 90- to 300-meters of local relief. - Mountains: Areas with over 300-meters of relief - High Tablelands: Areas with over 300-meters of relief and 50% of highest elevation areas are of gentle slope. - Depressions or Basins: Areas of land surrounded land of higher elevation. Glaciation TypeLandform Erosional/Depositional variable as described in Richard E. Murphy's 1968 "Landforms of the World" map. Values include: - Wisconsin/Wurm Glacial Extent: Areas of most recent glaciation which formed 115,000 years ago and ended 11,000 years ago. - Pre-Wisconsin/Wurm Glacial Extent: Areas subjected only to glaciation prior to 140,000 years ago. ContinentAssigned by Author during data compilation. Bridges Short NameThe name of the smallest of Division, Province, or Section containing this landform feature. Murphy Landform CodeCombination of Richard E. Murphy's 1968 "Landforms of the World" variables expressed as a 3- or 4- letter notation. Used to label medium scale maps. Area_GeoGeodesic area in km2. Primary PlateName of tectonic plate that either completely underlays this landform feature or underlays the largest portion of the landform's area. Secondary PlateWhen a landform is underlaid by two or more tectonic plates, this is the plate that underlays the second largest area. 3rd PlateWhen a landform is underlaid by three or more tectonic plates, this is the plate that underlays the third largest area. 4th PlateWhen a landform is underlaid by four or more tectonic plates, this is the plate that underlays the fourth largest area. 5th PlateWhen a landform is underlaid by five tectonic plates, this is the plate that underlays the fifth largest area. NotesContains standard text to convey additional tectonic process characteristics. Tectonic ProcessAssigns values of orogenic, rift zone, or above subducting plate.

These data are also available as an ArcGIS Pro Map Package: Named_Landforms_of_the_World_v2.0.mpkx.These data supersede the earlier v1.0: World Named Landforms.Change Log:

DateDescription of Change July 20, 2022Corrected spelling of Guiana from incorrect representation, "Guyana", used by Bridges. July 27, 2022Corrected Structure coded value domain value, changing "Caledonian/Hercynian Shield" to "Caledonian , Hercynian, or Appalachian Remnants".

Cite as:Frye, C., Sayre R., Pippi, M., Karagulle, Murphy, A., D. Soller, D.R., Gilbert, M., and Richards, J., 2022. Named Landforms of the World. DOI: 10.13140/RG.2.2.33178.93129. Accessed on:

The electronic map market is experiencing robust growth, driven by increasing adoption of location-based services (LBS), the proliferation of smartphones and connected devices, and the expanding use of GPS technology across various sectors. The market's value, estimated at $15 billion in 2025, is projected to experience a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $45 billion by 2033. Key drivers include the rising demand for precise navigation systems in the automotive industry, the surge in e-commerce and delivery services relying on efficient route optimization, and the growing importance of location intelligence for urban planning and resource management. Furthermore, advancements in mapping technologies, such as 3D mapping and augmented reality (AR) integration, are further fueling market expansion. While data security and privacy concerns represent a potential restraint, the overall outlook remains positive, fueled by continuous technological advancements and increasing reliance on location data across numerous applications. The market is segmented by various factors, including map type (2D, 3D, etc.), application (navigation, GIS, etc.), and end-user (automotive, government, etc.). Leading companies like ESRI, Google, TomTom, and HERE Technologies are actively shaping the market landscape through innovation and strategic partnerships. Regional variations in market penetration exist, with North America and Europe currently holding a significant share. However, Asia-Pacific is expected to witness the fastest growth due to rapid urbanization and increasing smartphone penetration. The competitive landscape is characterized by both established players and emerging technology companies vying for market share through technological advancements, improved data accuracy, and enhanced user experience. The forecast period of 2025-2033 promises significant opportunities for growth, driven by the continuous integration of electronic maps into various aspects of daily life and the emerging importance of location data in diverse industries.

The Nova Map (World Edition) web map provides a detailed world basemap featuring a dark background with glowing blue symbology and colors that are reminiscent of science-fiction shows, where one is looking at a map of the world on a 'head's up' device or a map that would be projected from a transparent glass wall. The map is designed with a grid pattern across the ocean and stripes or square stippled patterns for land use features visible at larger scales. Additional graphics in the oceans presents a futuristic user interface. The futuristic and less terrestrial feel theme continues with the geometric patterns, starburst city dot symbols, and cool color scheme. The fonts displayed are clean and squarish (san serif) with a futuristic, science-fiction, or high technology appearance.This basemap, included in the ArcGIS Living Atlas of the World, uses the Nova vector tile layer.The vector tile layer in this web map is built using the same data sources used for other Esri Vector Basemaps. For details on data sources contributed by the GIS community, view the map of Community Maps Basemap Contributors. Esri Vector Basemaps are updated monthly.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layer referenced in this map.

State Geologic Map CompilationThis web map portrays the U.S. Geological Survey's (USGS) State Geologic Map Compilation (SGMC) geodatabase of the conterminous United States. The SGMC represents a seamless, spatial database of 48 State geologic maps. Per USGS, "A national digital geologic map database is essential in interpreting other datasets that support numerous types of national-scale studies and assessments, such as those that provide geochemistry, remote sensing, or geophysical data. The SGMC is a compilation of the individual USGS releases of the Preliminary Integrated Geologic Map Databases for the U.S."A full discussion of the procedures and methodology used to create this dataset is available in the accompanying report: Horton, J.D., San Juan, C.A., and Stoeser, D.B, 2017, The State Geologic Map Compilation (SGMC) geodatabase of the conterminous United States (ver. 1.1, August 2017): U.S. Geological Survey Data Series 1052, 46p.State Geologic Map CollectionData currency and source: See individual layers listed below.For more information: The State Geologic Map Compilation (SGMC) Geodatabase of the Conterminous United States For feedback please contact: ArcGIScomNationalMaps@esri.comLayers:State Geologic Map Compilation – PointsState Geologic Map Compilation – StructureState Geologic Map Compilation – GeologyState Geologic Map Compilation - Vector TilesU.S. Geological SurveyPer USGS, "The USGS provides science about the natural hazards that threaten lives and livelihoods; the water, energy, minerals, and other natural resources we rely on; the health of our ecosystems and environment; and the impacts of climate and land-use change."

3D Map System Market Outlook

The global 3D map system market size was valued at approximately $4.2 billion in 2023 and is projected to reach around $11.3 billion by 2032, growing at a robust CAGR of 11.5% during the forecast period. The increasing demand for advanced mapping solutions across various sectors such as automotive, urban planning, and infrastructure development is a significant growth factor propelling this market. The adoption of 3D maps, driven by technological advancements and the need for precise spatial data, is transforming how industries manage and utilize geospatial information.

One of the primary growth factors of the 3D map system market is the burgeoning demand within the automotive industry. The rise of autonomous and connected vehicles relies heavily on high-precision 3D mapping systems to ensure safety and efficiency. As vehicles become increasingly sophisticated, the need for accurate terrain and environmental data becomes paramount, driving the integration of these systems into modern automobiles. Additionally, the evolution of smart cities and infrastructure projects around the globe has necessitated the use of 3D maps for planning and management, further fueling market growth.

The aerospace and defense sectors are also major proponents of 3D map systems, utilizing them for navigation, simulation, and mission planning. The accuracy and detailed visualization provided by these maps are indispensable in military applications, where precise terrain understanding can critically impact operations and strategy development. Furthermore, the expansion of drone technology has increased the demand for 3D mapping solutions, as these aerial vehicles increasingly rely on detailed geospatial data to perform a variety of tasks ranging from surveillance to environmental monitoring.

In urban planning, the use of 3D mapping systems has gained significant traction due to their ability to provide a comprehensive view of urban landscapes, aiding in efficient planning and decision-making. These systems enable planners to visualize and simulate different developmental scenarios, assessing their impact on the environment and city infrastructure. Such capabilities are invaluable in developing sustainable urban areas that can accommodate growing populations while minimizing ecological footprints. Moreover, as environmental concerns and regulatory pressures increase, the use of 3D maps is becoming more prevalent in infrastructure planning and development.

Regionally, North America dominates the 3D map system market, driven by technological innovation and high adoption rates across various industries. The presence of key market players and substantial investment in research and development further bolster the region's dominance. Meanwhile, the Asia Pacific is experiencing the fastest growth, attributed to rapid urbanization and infrastructure development, particularly in countries like China and India. The implementation of smart city initiatives and the expansion of automotive and defense sectors are significant factors contributing to the region's market expansion.

Component Analysis

The component segment of the 3D map system market is subdivided into software, hardware, and services, each playing a pivotal role in the overall functionality and utilization of 3D mapping technologies. Software components are at the core of the 3D map system market, offering essential functionalities for creating, editing, and managing 3D spatial data. The demand for sophisticated software solutions is rising as users seek advanced features such as real-time data processing, analytics, and augmented reality integration. These software solutions enable various applications, from navigation and simulation to geospatial data analysis, making them indispensable across multiple industries.

Hardware components include the physical devices and infrastructure required to capture, store, and process 3D mapping data. This includes GPS devices, LiDAR systems, and high-resolution cameras, which are critical for accurate data acquisition. The hardware segment is experiencing growth due to technological advances that enhance data capture accuracy and efficiency. The integration of artificial intelligence and machine learning with hardware components further improves the capability of 3D mapping systems, enabling automated data processing and real-time applications.

The services component encompasses the various support and maintenance services essential for the optimal functioning of 3D map systems. These services include system integration,

Catholic Carbon Footprint Story Map Map:DataBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.Map Development: Molly BurhansMethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

Map DetailsVector tile layer map customization built using ESRI's Vector Tile Style editor. Intended to be used as a standalone basemap that, while intricate in its details, provides a muted, desaturated light appearance. It is most similar to ESRI's Modern Antique basemap, but provides a further distillation of colors and desaturated color palette that is intended to be used as a standalone, unobtrusive basemap. It retains the multi-scale mapping of the Modern Antique map and includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries albeit with varying transparencies for different scales. Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layer item referenced in this map.Customize this MapBecause this map includes a vector tile layer, you can customize the map to change its content and symbology. You are able to turn on and off layers, change symbols for layers, switch to alternate local language (in some areas), and refine the treatment of disputed boundaries. For details on how to customize this map, please refer to the Esri Vector Basemap Reference Document (v2) and vector basemap articles on the ArcGIS Online Blog. Fonts available for use in the style resource directory are under the OFL, Open Font License.This map was designed and created by Andrew Bernish but with substantial (majority) credit for the original design by Cindy Prostak in her Modern Antique basemap available on Living Atlas.

The Digital Geologic-GIS Map of the Cave Creek School Quadrangle, Texas is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (ccsc_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (ccsc_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI 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.) a readme file (lyjo_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (lyjo_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 (ccsc_geology_metadata_faq.pdf). Please read the lyjo_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. 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: 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: Texas Bureau of Economic Geology, University of Texas at Austin. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (ccsc_geology_metadata.txt or ccsc_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 ArcGIS Pro, 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).

Electronic Map Market Outlook

As of 2023, the global electronic map market size is valued at approximately USD 15 billion and is projected to reach USD 40 billion by 2032, growing at a compound annual growth rate (CAGR) of around 12%. This substantial growth is driven by advancements in geographic information systems (GIS), the increasing adoption of real-time mapping solutions, and the proliferation of smart devices requiring precise location data.

One of the significant growth factors for the electronic map market is the rapid adoption of smart devices and the integration of location-based services. Smartphones, tablets, and wearable devices increasingly rely on real-time mapping for navigation, social networking, and various applications. The growing consumer preference for location-based services enhances the demand for electronic maps globally. Furthermore, the development of autonomous and connected vehicles has necessitated highly accurate mapping systems, thereby fueling market growth.

Another key driver is the increasing investment in smart city initiatives. Governments and urban planners worldwide are leveraging electronic maps for urban planning, traffic management, and public safety purposes. These maps provide critical data for optimizing infrastructure development and managing urban growth efficiently. The integration of electronic maps in smart city projects is expected to significantly contribute to the market's expansion over the forecast period.

The rising importance of geospatial data in various industries also propels the market forward. Sectors such as defense, aerospace, and logistics rely heavily on precise mapping for operations, strategic planning, and tracking. The continuous advancements in GIS technologies and their applications across different sectors ensure a steady demand for electronic maps. Additionally, the advent of AI and machine learning in geospatial analytics further enhances the functionality and accuracy of electronic maps, driving their adoption in diverse industries.

Regionally, North America is expected to dominate the electronic map market due to the early adoption of advanced technologies and the presence of key market players. The region's strong technological infrastructure and high investment in R&D activities support market growth. Europe follows closely, driven by the increasing use of electronic maps in automotive and public sector applications. The Asia Pacific region is projected to witness the highest growth rate, attributed to the rapid urbanization, expanding automotive industry, and significant investments in smart city projects.

Component Analysis

Hardware

The hardware segment of the electronic map market encompasses various physical components required for mapping and navigation systems. This includes GPS devices, sensors, and other geospatial data collection tools. The demand for advanced hardware components is driven by the need for high-precision data in automotive navigation systems, smart city infrastructure, and defense applications. The continuous advancements in sensor technology and the integration of IoT devices have further bolstered the growth of the hardware segment.

In the automotive industry, the development of autonomous and connected vehicles has significantly increased the demand for sophisticated hardware components. These vehicles rely on precise mapping data for navigation and safety, requiring advanced GPS systems and sensors. The push towards electric and autonomous vehicles will continue to drive demand for high-quality hardware components in the mapping sector.

Moreover, the rise of smart cities has led to increased use of mapping hardware for urban planning, traffic management, and public safety. Governments and municipalities are investing in advanced hardware solutions to collect and analyze geospatial data, optimizing infrastructure development and urban growth. This trend is expected to continue, driving further growth in the hardware segment.

The defense and aerospace industries are also significant contributors to the hardware segment's growth. These sectors require precise and reliable mapping hardware for strategic planning, surveillance, and navigation. The continuous advancements in GIS technologies and their integration with AI and machine learning enhance the functionality and accuracy of mapping hardware, further driving their demand in defense and aerospace applications.

Overall,

The Digital Geologic-GIS Map of the Maumee Quadrangle, Arkansas 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 (maum_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (maum_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (maum_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). 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.) A GIS readme file (buff_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (buff_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 (maum_geology_metadata_faq.pdf). Please read the buff_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. 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 (maum_geology_metadata.txt or maum_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 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 global Digital HD Map market is experiencing robust growth, projected to reach $1558.9 million in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 24.4% from 2025 to 2033. This expansion is driven by the increasing demand for precise location data across various sectors. The automotive industry, particularly autonomous vehicles, is a major catalyst, relying heavily on highly detailed and accurate maps for navigation and safety features. Furthermore, the burgeoning use of augmented reality (AR) and virtual reality (VR) applications, coupled with the expanding smart city initiatives globally, fuels the market's growth trajectory. The rise of advanced driver-assistance systems (ADAS) and the integration of digital maps into connected car platforms also contribute significantly to this market's expansion. Competition within the market is fierce, with established players like Google, TomTom, and HERE Technologies competing alongside emerging innovative companies. The market segmentation by map type (2D HD Map, 3D HD Map) and application (Commercial Use, Military Use, Others) reflects the diverse range of applications and associated technological advancements shaping this dynamic landscape. Different regions contribute varying levels of market share, with North America and Asia-Pacific anticipated to lead due to significant technological advancements and higher adoption rates. The market's growth is not without its challenges. Data acquisition and maintenance costs remain a significant hurdle, especially for maintaining the accuracy and timeliness of high-resolution map data. Ensuring data security and privacy, particularly with the increased use of location data in various applications, presents another substantial challenge. Regulatory frameworks governing the use and collection of such data vary across different geographies, creating complexities for businesses operating internationally. Despite these challenges, the long-term prospects for the Digital HD Map market remain positive, driven by continuous technological innovations, increasing investment in autonomous driving technologies, and the expanding need for precise location intelligence across diverse industry verticals. The market is expected to see further consolidation through mergers and acquisitions as companies strive to enhance their capabilities and market share.

The Digital Geologic-GIS Map of parts of the Bohemotash Mountain Quadrangle, California is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (bhmt_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro 3.X map file (.mapx) file (bhmt_geology.mapx) and individual Pro 3.X layer (.lyrx) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI 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.) a readme file (whis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (whis_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 (bhmt_geology_metadata_faq.pdf). Please read the whis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. 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: 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 (bhmt_geology_metadata.txt or bhmt_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 ArcGIS Pro, 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 Digital Geologic-GIS Map of the Big Pine 15' Quadrangle, California 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 (bigp_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (bigp_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (bigp_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). 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.) A GIS readme file (seki_manz_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (seki_manz_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 (bigp_geology_metadata_faq.pdf). Please read the seki_manz_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. 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 (bigp_geology_metadata.txt or bigp_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 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).