The Digital Geologic-GIS Map of Everglades National Park and Vicinity, Florida 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 (ever_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 (ever_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 (ever_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.) A GIS readme file (ever_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (ever_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 (ever_geology_metadata_faq.pdf). Please read the ever_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: https://www.google.com/earth/versions/. 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: Florida Geological Survey and 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 (ever_geology_metadata.txt or ever_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 1:675,000 and United States National Map Accuracy Standards features are within (horizontally) 342.9 meters or 1125 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).

description: This tool provides a no-cost downloadable software tool that allows users to interact with professional quality GIS maps. Users access pre-compiled projects through a free software product called ArcReader, and are able to open and explore HUD-specific project data as well as design and print custom maps. No special software/map skills beyond basic computer skills are required, meaning users can quickly get started working with maps of their communities.; abstract: This tool provides a no-cost downloadable software tool that allows users to interact with professional quality GIS maps. Users access pre-compiled projects through a free software product called ArcReader, and are able to open and explore HUD-specific project data as well as design and print custom maps. No special software/map skills beyond basic computer skills are required, meaning users can quickly get started working with maps of their communities.

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 California’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 California’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 “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’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 Bay’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 Service–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 Point Conception 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 “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” 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 “potential habitats” 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 Point Conception map area data layers. Data layers are symbolized as shown on the associated map sheets.

This layer contains polygons that split the City of Cambridge into 286 city blocks. Each block boundary is associated with an Assessors map, and a group of parcels. Each individual parcel has a map and lot associated to it. The 'map' refers to one of these blocks, the 'lot' is the tax parcel within the block. Explore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription PCIX_NO type: Stringwidth: 6precision: 0 Block number

BSIZE_MAP_SCALE type: Integerwidth: 4precision: 10

BSIZE_INCH_TO_FOOT type: Integerwidth: 4precision: 10

DSIZE_MAP_SCALE type: Integerwidth: 4precision: 10

DSIZE_INCH_TO_FOOT type: Integerwidth: 4precision: 10

ROTATION type: Doublewidth: 8precision: 38

Yearly effective energy and mass transfer (EEMT) (MJ m−2 yr−1) was calculated for the Valles Calders, upper part of the Jemez River basin by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT flux into the upper soil surface can be estimated by calculating EEMT on a monthly basis as constrained by solar radiation (Rs), temperature (T), precipitation (PPT), and the vapor pressure deficit (VPD): EEMT = f(Rs,T,PPT,VPD). Here we used a multiple linear regression model to calculate the monthly EEMT that accounts for VPD, PPT, and locally modified T across the terrain surface. These EEMT calculations were made using data from the PRISM Climate Group at Oregon State University (www.prismclimate.org). Climate data are provided at an 800-m spatial resolution for input precipitation and minimum and maximum temperature normals and at a 4000-m spatial resolution for dew-point temperature (Daly et al., 2002). The PRISM climate data, however, do not account for localized variation in EEMT that results from smaller spatial scale changes in slope and aspect as occurs within catchments. To address this issue, these data were then combined with 10-m digital elevation maps to compute the effects of local slope and aspect on incoming solar radiation and hence locally modified temperature (Yang et al., 2007). Monthly average dew-point temperatures were computed using 10 yr of monthly data (2000–2009) and converted to vapor pressure. Precipitation, temperature, and dew-point data were resampled on a 10-m grid using spline interpolation. Monthly solar radiation data (direct and diffuse) were computed using ArcGIS Solar Analyst extension (ESRI, Redlands, CA) and 10-m elevation data (USGS National Elevation Dataset [NED] 1/3 Arc-Second downloaded from the National Map Seamless Server at seamless.usgs.gov). Locally modified temperature was used to compute the saturated vapor pressure, and the local VPD was estimated as the difference between the saturated and actual vapor pressures. The regression model was derived using the ISOHYS climate data set comprised of approximately 30-yr average monthly means for more than 300 weather stations spanning all latitudes and longitudes (IAEA).

Geographic Information System Software Market Outlook

The global Geographic Information System (GIS) Software market size was valued at approximately USD 7.8 billion in 2023 and is projected to reach USD 15.6 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.3% during the forecast period. This impressive growth can be attributed to the increasing demand for efficient data management tools across various industries, which rely on spatial data for decision-making and strategic planning. The rapid advancements in technology, such as the integration of AI and IoT with GIS software, have further propelled the market, enabling organizations to harness the full potential of geographic data in innovative ways.

One of the primary growth drivers of the GIS Software market is the burgeoning need for urban planning and smart city initiatives worldwide. As urbanization trends escalate, cities are increasingly relying on GIS technology to manage resources more effectively, optimize transportation networks, and enhance public safety. The ability of GIS software to provide real-time data and spatial analysis is vital for city planners and administrators faced with the challenges of modern urban environments. Furthermore, the trend towards digital transformation in governmental organizations is boosting the adoption of GIS solutions, as they seek to improve operational efficiency and service delivery.

The agricultural sector is also experiencing significant transformations due to the integration of GIS software, which is another pivotal growth factor for the market. Precision agriculture, which involves the use of GIS technologies to monitor and manage farming practices, is enabling farmers to increase crop yields while reducing resource consumption. By leveraging spatial data, farmers can make informed decisions about planting, irrigation, and harvesting, ultimately leading to more sustainable agricultural practices. This trend is particularly prominent in regions where agriculture forms a substantial portion of the economy, encouraging the adoption of advanced GIS tools to maintain competitive advantage.

Another influential factor contributing to the growth of the GIS Software market is the increasing importance of environmental management and disaster response. GIS technology plays a crucial role in assessing environmental changes, managing natural resources, and planning responses to natural disasters. The ability to overlay various data sets onto geographic maps allows for better analysis and understanding of environmental phenomena, making GIS indispensable in tackling issues such as climate change and resource depletion. Moreover, governments and organizations are investing heavily in GIS tools that aid in disaster preparedness and response, ensuring timely and effective action during emergencies.

The evolution of GIS Mapping Software has been instrumental in transforming how spatial data is utilized across various sectors. These software solutions offer robust tools for visualizing, analyzing, and interpreting geographic data, enabling users to make informed decisions based on spatial insights. With the ability to integrate multiple data sources, GIS Mapping Software provides a comprehensive platform for conducting spatial analysis, which is crucial for applications ranging from urban planning to environmental management. As technology continues to advance, the capabilities of GIS Mapping Software are expanding, offering more sophisticated features such as 3D visualization and real-time data processing. These advancements are not only enhancing the utility of GIS tools but also making them more accessible to a wider range of users, thereby driving their adoption across different industries.

Regionally, North America and Europe have traditionally dominated the GIS Software market, thanks to their robust technological infrastructure and higher adoption rates of advanced technologies. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid urbanization, increased government spending on infrastructure development, and the expanding telecommunications sector. The growing awareness and adoption of GIS solutions in countries like China and India are significant contributors to this regional growth. Furthermore, Latin America and the Middle East & Africa regions are slowly catching up, with ongoing investments in smart city projects and infrastructure development driving the demand for GIS software.

Component Analysis</h2&

Abstract

This mapping project aimed to create a database of all financial, health, education, and agricultural service access points in Zambia.

Geographic coverage

National coverage except Ikelenge district

Analysis unit

Health care facilities, financial institutions, educational institutions, agricultural service providers

Universe

The project aimed to capture all open and operational touch-points at the time of fieldwork. Active points were considered to have done a transaction in the last 90 days. Not all points are captured due to several factors including:

i) non-location of the points ii) security areas iii) resistance or lack of cooperation iv) dormancy v) safety of fieldwork staff

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires used in the MSMP are specific to each sector service intermediary. This means that there are 14 different questionnaires, each with its own set of specific questions.

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 California’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 California’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 “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’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 Bay’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 Service–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 Point Sur to Point Arguello 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 “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” 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 “potential habitats” 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 Point Sur to Point Arguello map area data layers. Data layers are symbolized as shown on the associated map sheets.

LANDISVIEW is a tool, developed at the Knowledge Engineering Laboratory at Texas A&M University, to visualize and animate 8-bit/16-bit ERDAS GIS format (e.g., LANDIS and LANDIS-II output maps). It can also convert 8-bit/16-bit ERDAS GIS format into ASCII and batch files. LANDISVIEW provides two major functions: 1) File Viewer: Files can be viewed sequentially and an output can be generated as a movie file or as an image file. 2) File converter: It will convert the loaded files for compatibility with 3rd party software, such as Fragstats, a widely used spatial analysis tool. Some available features of LANDISVIEW include: 1) Display cell coordinates and values. 2) Apply user-defined color palette to visualize files. 3) Save maps as pictures and animations as video files (*.avi). 4) Convert ERDAS files into ASCII grids for compatibility with Fragstats. (Source: http://kelab.tamu.edu/)

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

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

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

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

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

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

References:

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

Coastal Area & Boundary Polygon:

The Coastal Area layer is a 1:24,000-scale, polygon feature-based layer that includes the land and waters that lie within the Coastal Area as defined by Connecticut General Statute (C.G.S.) 22a-94(a). Activities and actions conducted within the coastal area by Federal and State Agencies (i.e., U.S. Army Corps of Engineers (USACOE), DEP regulatory programs, and state plans and actions) must be consistent with all of the applicable standards and criteria contained in the Connecticut Coastal Management Act (C.G.S. 22a-90 to 22a-113). A subset of the Coastal Area, the Coastal Boundary, represents an area within which activities regulated or conducted by coastal municipalities must be consistent with the Coastal Management Act. As defined in this section of the statutes, the Coastal Area includes the land and water within the area delineated by the following: the westerly, southerly and easterly limits of the state's jurisdiction in Long Island Sound; the towns of Greenwich, Stamford, Darien, Norwalk, Westport, Fairfield, Bridgeport, Stratford, Shelton, Milford, Orange, West Haven, New Haven, Hamden, North Haven, East Haven, Branford, Guilford, Madison, Clinton, Westbrook, Deep River, Chester, Essex, Old Saybrook, Lyme, Old Lyme, East Lyme, Waterford, New London, Montville, Norwich, Preston, Ledyard, Groton and Stonington. This layer includes a single polygon feature defined by the boundaries described above. Attribute information is comprised of an Av_Legend to denote the coastal area. Data is compiled at 1:24,000 scale. This data is not updated.

The Coastal Boundary layer is a 1:24,000-scale, polygon feature-based layer of the legal mylar-based maps adopted by the Commissioner of the Department of Environmental Protection (DEP) (i.e., maps were adopted on a town by town basis) showing the extent of lands and coastal waters as defined by Connecticut General Statute (C.G.S.) 22a-93(5)) within Connecticut's coastal area (defined by C.G.S. 22a-94(c)). The coastal boundary is a hybrid of the original 1:24,000 version maps prepared by DEP consistent with C.G.S. 22a-94(d) (Coastal Area) and the revised boundary mapping undertaken by twenty-two coastal towns prepared pursuant to C.G.S. 22a-94(f). This layer therefore does not replace the legal maps and may not be used for legal determinations. The Coastal Boundary layer includes a single polygon feature that represents the coastal boundary. No other features are included in this layer. Data is compiled at 1:24,000 scale. Attribute information is comprised of an Av_Legend attribute and a CoastB_Flg attribute to denote the coastal boundary. Other attributes include automatically calculated Shape_Length and Shape_Area fields. This data is not updated. Any regulated activity conducted within the coastal boundary by a municipal agency (i.e., plans of development, zoning regulations, municipal coastal programs and coastal site plan review (i.e., site plans submitted to zoning commission, subdivision or resubdivision plans submitted to planning commission, application for special permit or exception to the zoning or planning commissions or zoning board of appeals, variance submitted to zoning board of appeals and a referral of a municipal project)) must be conducted in a manner consistent with the requirements of the Connecticut Coastal Management Act (CMA; C.G.S. 22a-90 to 22a-113). As the Coastal Boundary is a hybrid of the Coastal Area, all state and federal agency activities must be consistent with the requirements of the CMA. As defined in C.G.S. 22a-94(b) the coastal boundary is a "continuous line delineated on the landward side by the interior contour elevation of the one hundred year frequency coastal flood zone, as defined and determined by the National Flood Insurance Act, as amended (USC 42 Section 4101, P.L. 93-234), or a one thousand foot linear setback measured from the mean high water mark in coastal waters, or a one thousand foot linear setback measured from the inland boundary of tidal wetlands mapped under section 22a-20, whichever is farthest inland; and shall be delineated on the seaward side by the seaward extent of the jurisdiction of the state." The original boundary maps were created in 1979 on stable mylar overlay using the 1:24,000-scale US Geological Survey topographic quadrangle maps (mylar film format). The source for tidal wetland maps were the legal 1:24,000 maps (mylar format) adopted by the Commissioner of DEP and transformed to 1:24,000 mylar-scale maps by the Office of Policy and Management (OPM) using an accurate pantograph. OPM similarly converted FEMA's flood insurance maps (various scales) to a 1:24,000 mylar overlay. The inland extent of coastal waters was plotted on 1:24,000 USGS topographic maps following the procedures and sources described in The Boundary Between Saltwater and Freshwater in Connecticut, December 1978 prepared by the State of Connecticut, Department of Environmental Protection, Coastal Area Management Program. The following twenty-two towns have adopted municipal coastal boundaries: Chester, Clinton, Darien, Deep River, East Haven, Essex, Fairfield, Greenwich, Groton, Guilford, Hamden, Ledyard, Madison, Milford, New Haven, New London, North Haven, Norwalk, Old Lyme, Old Saybrook, Stamford and Waterford. The coastal boundary maps for these towns may be at different scales than the original DEP draft maps and may contain minor adjustments to the boundary as permitted in C.G.S. 22a-94(f).

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

City of Cambridge, MA, GIS basemap development project encompasses the land area of City of Cambridge with a 200-foot fringe surrounding the area and Charles River shoreline towards Boston. The basemap data was developed at 1" = 40' mapping scale using digital photogrammetric techniques. Planimetric features; both man-made and natural features like vegetation, rivers have been depicted. These features are important to all GIS/mapping applications and publication. A set of data layers such as Buildings, Roads, Rivers, Utility structures, 1 ft interval contours are developed and represented in the geodatabase. The features are labeled and coded in order to represent specific feature class for thematic representation and topology between the features is maintained for an accurate representation at the 1:40 mapping scale for both publication and analysis. The basemap data has been developed using procedures designed to produce data to the National Standard for Spatial Data Accuracy (NSSDA) and is intended for use at 1" = 40 ' mapping scale. Where applicable, the vertical datum is NAVD1988.Explore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription TYPE type: Stringwidth: 50precision: 0 Type of curb

EditDate type: Stringwidth: 4precision: 0

GRADE type: Stringwidth: 50precision: 0

This dataset is a compilation of available oil and gas pipeline data and is maintained by BSEE. Pipelines are used to transport and monitor oil and/or gas from wells within the outer continental shelf (OCS) to resource collection locations. Currently, pipelines managed by BSEE are found in Gulf of Mexico and southern California waters.

© MarineCadastre.gov This layer is a component of BOEMRE Layers.

This Map Service contains many of the primary data types created by both the Bureau of Ocean Energy Management (BOEM) and the Bureau of Safety and Environmental Enforcement (BSEE) within the Department of Interior (DOI) for the purpose of managing offshore federal real estate leases for oil, gas, minerals, renewable energy, sand and gravel. These data layers are being made available as REST mapping services for the purpose of web viewing and map overlay viewing in GIS systems. Due to re-projection issues which occur when converting multiple UTM zone data to a single national or regional projected space, and line type changes that occur when converting from UTM to geographic projections, these data layers should not be used for official or legal purposes. Only the original data found within BOEM/BSEE’s official internal database, federal register notices or official paper or pdf map products may be considered as the official information or mapping products used by BOEM or BSEE. A variety of data layers are represented within this REST service are described further below. These and other cadastre information the BOEM and BSEE produces are generated in accordance with 30 Code of Federal Regulations (CFR) 256.8 to support Federal land ownership and mineral resource management.

For more information – Contact: Branch Chief, Mapping and Boundary Branch, BOEM, 381 Elden Street, Herndon, VA 20170. Telephone (703) 787-1312; Email: mapping.boundary.branch@boem.gov

The REST services for National Level Data can be found here: http://gis.boemre.gov/arcgis/rest/services/BOEM_BSEE/MMC_Layers/MapServer

REST services for regional level data can be found by clicking on the region of interest from the following URL: http://gis.boemre.gov/arcgis/rest/services/BOEM_BSEE

Individual Regional Data or in depth metadata for download can be obtained in ESRI Shape file format by clicking on the region of interest from the following URL: http://www.boem.gov/Oil-and-Gas-Energy-Program/Mapping-and-Data/Index.aspx

Currently the following layers are available from this REST location:

OCS Drilling Platforms -Locations of structures at and beneath the water surface used for the purpose of exploration and resource extraction. Only platforms in federal Outer Continental Shelf (OCS) waters are included. A database of platforms and rigs is maintained by BSEE.

OCS Oil and Natural Gas Wells -Existing wells drilled for exploration or extraction of oil and/or gas products. Additional information includes the lease number, well name, spud date, the well class, surface area/block number, and statistics on well status summary. Only wells found in federal Outer Continental Shelf (OCS) waters are included. Wells information is updated daily. Additional files are available on well completions and well tests. A database of wells is maintained by BSEE.

OCS Oil & Gas Pipelines -This dataset is a compilation of available oil and gas pipeline data and is maintained by BSEE. Pipelines are used to transport and monitor oil and/or gas from wells within the outer continental shelf (OCS) to resource collection locations. Currently, pipelines managed by BSEE are found in Gulf of Mexico and southern California waters.

Unofficial State Lateral Boundaries - The approximate location of the boundary between two states seaward of the coastline and terminating at the Submerged Lands Act Boundary. Because most State boundary locations have not been officially described beyond the coast, are disputed between states or in some cases the coastal land boundary description is not available, these lines serve as an approximation that was used to determine a starting point for creation of BOEM’s OCS Administrative Boundaries. GIS files are not available for this layer due to its unofficial status.

BOEM OCS Administrative Boundaries - Outer Continental Shelf (OCS) Administrative Boundaries Extending from the Submerged Lands Act Boundary seaward to the Limit of the United States OCS (The U.S. 200 nautical mile Limit, or other marine boundary)For additional details please see the January 3, 2006 Federal Register Notice.

BOEM Limit of OCSLA ‘8(g)’ zone - The Outer Continental Shelf Lands Act '8(g) Zone' lies between the Submerged Lands Act (SLA) boundary line and a line projected 3 nautical miles seaward of the SLA boundary line. Within this zone, oil and gas revenues are shared with the coastal state(s). The official version of the ‘8(g)’ Boundaries can only be found on the BOEM Official Protraction Diagrams (OPDs) or Supplemental Official Protraction described below.

Submerged Lands Act Boundary - The SLA boundary defines the seaward limit of a state's submerged lands and the landward boundary of federally managed OCS lands. The official version of the SLA Boundaries can only be found on the BOEM Official Protraction Diagrams (OPDs) or Supplemental Official Protraction Diagrams described below.

Atlantic Wildlife Survey Tracklines(2005-2012) - These data depict tracklines of wildlife surveys conducted in the Mid-Atlantic region since 2005. The tracklines are comprised of aerial and shipboard surveys. These data are intended to be used as a working compendium to inform the diverse number of groups that conduct surveys in the Mid-Atlantic region.The tracklines as depicted in this dataset have been derived from source tracklines and transects. The tracklines have been simplified (modified from their original form) due to the large size of the Mid-Atlantic region and the limited ability to map all areas simultaneously.The tracklines are to be used as a general reference and should not be considered definitive or authoritative. This data can be downloaded from http://www.boem.gov/uploadedFiles/BOEM/Renewable_Energy_Program/Mapping_and_Data/ATL_WILDLIFE_SURVEYS.zip

BOEM OCS Protraction Diagrams & Leasing Maps - This data set contains a national scale spatial footprint of the outer boundaries of the Bureau of Ocean Energy Management’s (BOEM’s) Official Protraction Diagrams (OPDs) and Leasing Maps (LMs). It is updated as needed. OPDs and LMs are mapping products produced and used by the BOEM to delimit areas available for potential offshore mineral leases, determine the State/Federal offshore boundaries, and determine the limits of revenue sharing and other boundaries to be considered for leasing offshore waters. This dataset shows only the outline of the maps that are available from BOEM.Only the most recently published paper or pdf versions of the OPDs or LMs should be used for official or legal purposes. The pdf maps can be found by going to the following link and selecting the appropriate region of interest. http://www.boem.gov/Oil-and-Gas-Energy-Program/Mapping-and-Data/Index.aspx Both OPDs and LMs are further subdivided into individual Outer Continental Shelf(OCS) blocks which are available as a separate layer. Some OCS blocks that also contain other boundary information are known as Supplemental Official Block Diagrams (SOBDs.) Further information on the historic development of OPD's can be found in OCS Report MMS 99-0006: Boundary Development on the Outer Continental Shelf: http://www.boemre.gov/itd/pubs/1999/99-0006.PDF Also see the metadata for each of the individual GIS data layers available for download. The Official Protraction Diagrams (OPDs) and Supplemental Official Block Diagrams (SOBDs), serve as the legal definition for BOEM offshore boundary coordinates and area descriptions.

BOEM OCS Lease Blocks - Outer Continental Shelf (OCS) lease blocks serve as the legal definition for BOEM offshore boundary coordinates used to define small geographic areas within an Official Protraction Diagram (OPD) for leasing and administrative purposes. OCS blocks relate back to individual Official Protraction Diagrams and are not uniquely numbered. Only the most recently published paper or pdf

City of Cambridge, MA, GIS basemap development project encompasses the land area of City of Cambridge with a 200-foot fringe surrounding the area and Charles River shoreline towards Boston. The basemap data was developed at 1" = 40' mapping scale using digital photogrammetric techniques. Planimetric features; both man-made and natural features like vegetation, rivers have been depicted. These features are important to all GIS/mapping applications and publication. A set of data layers such as Buildings, Roads, Rivers, Utility structures, 1 ft interval contours are developed and represented in the geodatabase. The features are labeled and coded in order to represent specific feature class for thematic representation and topology between the features is maintained for an accurate representation at the 1:40 mapping scale for both publication and analysis. The basemap data has been developed using procedures designed to produce data to the National Standard for Spatial Data Accuracy (NSSDA) and is intended for use at 1" = 40 ' mapping scale. Where applicable, the vertical datum is NAVD1988.Explore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription TYPE type: Stringwidth: 50precision: 0 Type of wall (retaining, sitting, urban plaza structure, other)

Width type: Doublewidth: 8precision: 38

Digital line graph (DLG) data are digital representations of cartographic information. DLGs of map features are converted to digital form from maps and related sources. Intermediate-scale DLG data are derived from USGS 1:100,000-scale 30- by 60-minute quadrangle maps. If these maps are not available, Bureau of Land Management planimetric maps at a scale of 1:100,000 are used. Intermediate-scale DLGs are sold in five categories: (1) Public Land Survey System; (2) boundaries; (3) transportation; (4) hydrography; and (5) hypsography. All DLG data distributed by the USGS are DLG-Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, and have passed certain quality-control checks.

Yearly effective energy and mass transfer (EEMT) (MJ m−2 yr−1) was calculated for the Catalina Mountains by summing the 12 monthly values. Effective energy and mass flux varies seasonally, especially in the desert southwestern United States where contemporary climate includes a bimodal precipitation distribution that concentrates in winter (rain or snow depending on elevation) and summer monsoon periods. This seasonality of EEMT flux into the upper soil surface can be estimated by calculating EEMT on a monthly basis as constrained by solar radiation (Rs), temperature (T), precipitation (PPT), and the vapor pressure deficit (VPD): EEMT = f(Rs,T,PPT,VPD). Here we used a multiple linear regression model to calculate the monthly EEMT that accounts for VPD, PPT, and locally modified T across the terrain surface. These EEMT calculations were made using data from the PRISM Climate Group at Oregon State University (www.prismclimate.org). Climate data are provided at an 800-m spatial resolution for input precipitation and minimum and maximum temperature normals and at a 4000-m spatial resolution for dew-point temperature (Daly et al., 2002). The PRISM climate data, however, do not account for localized variation in EEMT that results from smaller spatial scale changes in slope and aspect as occurs within catchments. To address this issue, these data were then combined with 10-m digital elevation maps to compute the effects of local slope and aspect on incoming solar radiation and hence locally modified temperature (Yang et al., 2007). Monthly average dew-point temperatures were computed using 10 yr of monthly data (2000–2009) and converted to vapor pressure. Precipitation, temperature, and dew-point data were resampled on a 10-m grid using spline interpolation. Monthly solar radiation data (direct and diffuse) were computed using ArcGIS Solar Analyst extension (ESRI, Redlands, CA) and 10-m elevation data (USGS National Elevation Dataset [NED] 1/3 Arc-Second downloaded from the National Map Seamless Server at seamless.usgs.gov). Locally modified temperature was used to compute the saturated vapor pressure, and the local VPD was estimated as the difference between the saturated and actual vapor pressures. The regression model was derived using the ISOHYS climate data set comprised of approximately 30-yr average monthly means for more than 300 weather stations spanning all latitudes and longitudes (IAEA).

The Digital Geologic-GIS Map of Everglades National Park and Vicinity, Florida 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 (ever_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 (ever_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 (ever_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.) A GIS readme file (ever_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (ever_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 (ever_geology_metadata_faq.pdf). Please read the ever_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: https://www.google.com/earth/versions/. 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: Florida Geological Survey and 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 (ever_geology_metadata.txt or ever_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 1:675,000 and United States National Map Accuracy Standards features are within (horizontally) 342.9 meters or 1125 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).

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 California’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 California’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 “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’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 Bay’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 Service–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 Point Reyes 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 “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” 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 “potential habitats” 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 Point Reyes map area data layers. Data layers are symbolized as shown on the associated map sheets.