The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. GIS Database 2002-2005: Project Size = 1,898 acres Fort Larned National Historic Site (including the Rut Site) = 705 acres 16 Map Classes 11 Vegetated 5 Non-vegetated Minimum Mapping Unit = ½ hectare is the program standard but this was modified at FOLS to ¼ acre. Total Size = 229 Polygons Average Polygon Size = 8.3 acres Overall Thematic Accuracy = 92% To produce the digital map, a combination of 1:8,500-scale (0.75 meter pixels) color infrared digital ortho-imagery acquired on October 26, 2005 by the Kansas Applied Remote Sensing Program and 1:12,000-scale true color ortho-rectified imagery acquired in 2005 by the U.S. Department of Agriculture - Farm Service Agency’s Aerial Photography Field Office, and all of the GPS referenced ground data were used to interpret the complex patterns of vegetation and land-use. In the end, 16 map units (11 vegetated and 5 land-use) were developed and directly cross-walked or matched to corresponding plant associations and land-use classes. All of the interpreted and remotely sensed data were converted to Geographic Information System (GIS) databases using ArcGIS© software. Draft maps were printed, field tested, reviewed and revised. One hundred and six accuracy assessment (AA) data points were collected in 2006 by KNSHI and used to determine the map’s accuracy. After final revisions, the accuracy assessment revealed an overall thematic accuracy of 92%.

This dashboard is best viewed using a mobile device. For an enhanced viewing experience on a desktop or laptop computer please use the NV Wildfire Info desktop version dashboardAll data displayed on this map is near real-time. There are two ways in which this happens: Web service based data and a mobile mapping application called Field Maps. Web services are updated regularly ranging from every minute to once a month. All web services in this map are refreshed automatically to ensure the latest data being provided is displayed. Data collected through the use of Field Maps is done so by firefighters on the ground. The Field Maps application is consuming, creating, and editing data that are stored in ArcGIS Online. These data are then fed directly in to this map. To learn more about these web mapping technologies, visit the links below:Web ServicesArcGIS Field MapsArcGIS OnlineWeb Services used in this map:(visit link to learn more about each service)IRWIN - A central hub that orchestrates data between various fire reporting applications. When a new incident is created and/or updated by a dispatch center or other fire reporting system, it is then displayed on the map using the Integrated Reporting of Wildland-Fire Information (IRWIN) service. All layers below are derived from the same IRWIN service and automatically refresh every five minutes:New Starts (last 24hrs) - Any incident that has occurred within the last rolling 24 hour time period.Current Large Incidents - Incidents that have created an ICS 209 document at the type 3 Incident Commander (IC) level and above and are less than 100% contained.Ongoing - Incidents that do not have a containment, control, or out date.Contained - Incidents with a containment date but no control or out date.Controlled/Out (last 24hrs) - Incidents with a containment, control, and/or out date within the last rolling 24 hour time period.Controlled/Out - Incidents with a containment, control, and/or out date. Layer turned off by default.Season Summary - All incidents year to date. Layer turned off by default.ArcGIS Online/Field Maps - Part of the Esri Geospatial Cloud, ArcGIS Online and Collector enables firefighters to use web maps created in ArcGIS Online on mobile devices using the Collector application to capture and edit data on the fireline. Data may be captured and edited in both connected and disconnected environments. When data is submitted back to the web service in ArcGIS Online, it is then checked for accuracy and approved for public viewing.Fire Perimeter - Must be set to 'Approved' and 'Public' to be displayed on the map. Automatically refreshes every five minutes.NOAA nowCOAST - Provides web services of near real-time observations, analyses, tide predictions, model guidance, watches/warnings, and forecasts for the coastal United States by integrating data and information across NOAA, other federal agencies and regional ocean and weather observing systems (source). All layers below automatically refresh every five minutes.Tornado Warning - National Weather Service warning for short duration hazard.Severe Thunderstorm Warning - National Weather Service warning for short duration hazard.Flash Flood Warning - National Weather Service warning for short duration hazard.Red Flag Warning - National Weather Service warning for long duration hazard.nowCOAST Lightning Strike Density - 15-minute Satellite Emulated Lightning Strike Density imagery for the last several hours.nowCOAST Radar - Weather Radar (NEXRAD) Reflectivity Mosaics from NOAA MRMS for Alaska, CONUS, Puerto Rico, Guam, and Hawaii for last several hours.

This collection consists of ASCII files containing derived magnetic field maps and equivalent source dipole arrays for the crustal magnetic field of Mercury. Zero values (mainly found along the map edges) indicate no useful data.

All data displayed on this map is near real-time. There are two ways in which this happens: Web service based data and a mobile mapping application called Field Maps. Web services are updated regularly ranging from every minute to once a month. All web services in this map are refreshed automatically to ensure the latest data being provided is displayed. Data collected through the use of Field Maps is done so by firefighters on the ground. The Field Maps application is consuming, creating, and editing data that are stored in ArcGIS Online. These data are then fed directly in to this map. To learn more about these web mapping technologies, visit the links below:Web ServicesArcGIS Field MapsArcGIS OnlineWeb Services used in this map:(visit link to learn more about each service)IRWIN - A central hub that orchestrates data between various fire reporting applications. When a new incident is created and/or updated by a dispatch center or other fire reporting system, it is then displayed on the map using the Integrated Reporting of Wildland-Fire Information (IRWIN) service. Automatically refreshes every five minutes:Fires by Cause - Any incident that has occurred year to date displayed by cause.ArcGIS Online/Field Maps - Part of the Esri Geospatial Cloud, ArcGIS Online and Collector enables firefighters to use web maps created in ArcGIS Online on mobile devices using the Collector application to capture and edit data on the fireline. Data may be captured and edited in both connected and disconnected environments. When data is submitted back to the web service in ArcGIS Online, it is then checked for accuracy and approved for public viewing.Fire Perimeter - Must be set to 'Approved' and 'Public' to be displayed on the map. Automatically refreshes every five minutes.

The Regional Crime Analysis GIS (RCAGIS) is an Environmental Systems Research Institute (ESRI) MapObjects-based system that was developed by the United States Department of Justice Criminal Division Geographic Information Systems (GIS) Staff, in conjunction with the Baltimore County Police Department and the Regional Crime Analysis System (RCAS) group, to facilitate the analysis of crime on a regional basis. The RCAGIS system was designed specifically to assist in the analysis of crime incident data across jurisdictional boundaries. Features of the system include: (1) three modes, each designed for a specific level of analysis (simple queries, crime analysis, or reports), (2) wizard-driven (guided) incident database queries, (3) graphical tools for the creation, saving, and printing of map layout files, (4) an interface with CrimeStat spatial statistics software developed by Ned Levine and Associates for advanced analysis tools such as hot spot surfaces and ellipses, (5) tools for graphically viewing and analyzing historical crime trends in specific areas, and (6) linkage tools for drawing connections between vehicle theft and recovery locations, incident locations and suspects' homes, and between attributes in any two loaded shapefiles. RCAGIS also supports digital imagery, such as orthophotos and other raster data sources, and geographic source data in multiple projections. RCAGIS can be configured to support multiple incident database backends and varying database schemas using a field mapping utility.

Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations. Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals. Data from thegSSURGO databasewas used to create this layer. To download ready-to-use project packages of useful soil data derived from the SSURGO dataset, please visit the USA SSURGO Downloader app. Dataset Summary Phenomenon Mapped:Soils of the United States and associated territoriesGeographic Extent:The 50 United States, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaCoordinate System:Web Mercator Auxiliary SphereVisible Scale:1:144,000 to 1:1,000Source:USDA Natural Resources Conservation Service Update Frequency:AnnualPublication Date:December 2024 What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS Online Add this layer to a map in the map viewer. The layer is limited to scales of approximately 1:144,000 or larger but avector tile layercreated from the same data can be used at smaller scales to produce awebmapthat displays across the full scale range. The layer or a map containing it can be used in an application.Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections and apply filters. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Change the layer’s style and filter the data. For example, you could set a filter forFarmland Class= "All areas are prime farmland" to create a map of only prime farmland.Add labels and set their propertiesCustomize the pop-upArcGIS Pro Add this layer to a 2d or 3d map. The same scale limit as Online applies in ProUse as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of theLiving Atlas of the Worldthat provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Data DictionaryAttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them. Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units. Area SymbolSpatial VersionMap Unit Symbol Map Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field. Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability Rating Legend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field. Project Scale Survey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields. Survey Area VersionTabular Version Map Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field. Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Mapunit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Mapunit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - Presence Rating for Manure and Food Processing Waste - Weighted Average Component Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected. Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent Key Component Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r). Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence -

Repository providing free access to marine geophysical data (e.g. bathymetry, seismic data, magnetics, gravity, images) and related land-based data from NSF-funded research conducted throughout the global oceans. Data Portals include GeoPRISMS, MARGINS, Ridge 2000, Antarctic and Southern Ocean Data Synthesis, the Global Multi-Resolution Topography Synthesis, and Seismic Reflection Field Data Portal. Primary data types served are multibeam bathymetric data from the ocean floor, seismic reflection data imaging below the seafloor, and multi-disciplinary ship based data from the Southern Ocean. Other holdings include deep-sea photographic transects, and ultra-high resolution bathymetry, temperature probe data, biological species compilations, MAPR and CTD data. Derived data products and sets include microseismicity catalogs, images, visualization scenes, magnetic and gravity compilations, grids of seismic layer thickness, velocity models, GIS project files, and 3D visualizations. Tools to discover, explore, and visualize data are available. They deliver catalogs, maps, and data through standard programmatic interfaces. GeoMapApp, a standalone data visualization and analysis tool, permits dynamic data exploration from a map interface and the capability to generate and download custom grids and maps and other data. Through GeoMapApp, users can access data hosted at the MGDS, at other data repositories, and import their own data sets. Global Multi-Resolution Topography (GMRT) is a continuously-updated compilation of seafloor bathymetry integrated with global land topography. It can be used to create maps and grids and it can be accessed through several standard programmatic interfaces including GeoMapApp and Google Earth. The GMRT compilation can also be explored in 3D using Virtual Ocean. The MGDS MediaBank contains high quality images, illustrations, animations and video clips that are organized into galleries. Media can be sorted by category, and keyword and map-based search options are provided. Each item in the MediaBank is accompanied by metadata that provides access to a cruise catalog and data repository.

This data layer contains geothermal resource areas and their technical potential used in long-term electric system modeling for Integrated Resource Planning and SB 100. Geothermal resource areas are delineated by Known Geothermal Resource Areas (KGRAs) (Geothermal Map of California, 2002), other geothermal fields (CalGEM Field Admin Boundaries, 2020), and Bureau of Land Management (BLM) Geothermal Leasing Areas (California BLM State Office GIS Department, 2010). The fields that are considered in our assessment have enough information known about the geothermal reservoir that an electric generation potential was estimated by USGS (Williams et al. 2008) or estimated by a BLM Environmental Impact Statement (El Centro Field Office, 2007). For the USGS identified geothermal systems, any point that lies within 2 km of a field is summed to represent the total mean electrical generation potential from the entire field.

Geothermal field boundaries are constructed for identified geothermal systems that lie outside of an established geothermal field. A circular footprint is assumed with a radius determined by the area needed to support the mean resource potential estimate, assuming a 10 MW/km² power density.

Several geothermal fields have power plants that are currently generating electricity from the geothermal source. The total production for each geothermal field is estimated by the CA Energy Commission’s Quarterly Fuel and Energy Report that tracks all power plants greater than 1 MW. The nameplate capacity of all generators in operation as of 2021 were used to inform how much of the geothermal fields are currently in use. This source yields inconsistent results for the power plants in the Geysers. Instead, an estimate from the net energy generation from those power plants is used. Using these estimates, the net undeveloped geothermal resource potential can be calculated.

Finally, we apply the protected area layer for geothermal to screen out those geothermal fields that lie entirely within a protected area. The protected area layer is compiled from public and private lands that have special designations prohibiting or not aligning with energy development.

This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer.

For more information about this layer and its use in electric system planning, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library.

Change Log:

Version 1.1 (January 18, 2024)

• ProtectedArea_Exclusion field was updated to correct for the changes to the Protected Area Layer. A Development Focus Area on Bureau of Land Management (BLM) land that overlays the Coso Hot Springs allows its resource potential to be considered in the statewide estimate.

Data Dictionary:

Total_MWe_Mean: The estimated resource potential from each geothermal field. All geothermal fields, except for Truckhaven, was given an estimate by Williams et al. 2008. If more than one point resource intersects (within 2km of) the field, the sum of the individual geothermal systems was used to estimate the magnitude of the resource coming from the entire geothermal field. Estimates are given in MW.

Total_QFER_NameplateCapacity: The total nameplate capacities of all generators in operation as of 2021 that intersects (within 2 km of) a geothermal field. The resource potential already in use for the Geysers is determined by Lovekin et al. 2004. Estimates are given in MW.

ProtectedArea_Exclusion: Binary value representing whether a field is excluded by the land-use screen or not. Fields that are excluded have a value of 1; those that aren’t have a value of 0.

NetUndevelopedRP: The net undeveloped resource potential for each geothermal field. This field is determined by subtracting the total resource potential in use (Total_QFER_NameplateCapacity) from the total estimated resource potential (Total_MWe_Mean). Estimates are given in MW.

Acres_GeothermalField: This is the geodesic acreage of each geothermal field. Values are reported in International Acres using a NAD 1983 California (Teale) Albers (Meters) projection.

References:

1. Geothermal Map of California, S-11. California Department of Conservation, 2002. https://www.conservation.ca.gov/calgem/geothermal/maps/Pages/index.aspx
2. CalGEM Field Admin Boundaries, 2020. https://gis.conservation.ca.gov/server/rest/services/CalGEM/Admin_Bounds/MapServer
   
3. California BLM State Office GIS Department, California BLM Verified and Potential Geothermal Leases in California, 2010. https://databasin.org/datasets/5ec77a1438ab4402bf09ef9bfd7f04d9/
   
4. Williams, Colin F., Reed, Marshall J., Mariner, Robert H., DeAngelo, Jacob, Galanis, S. Peter, Jr. 2008. "Assessment of moderate- and high-temperature geothermal resources of the United States: U.S. Geological Survey Fact Sheet 2008-3082." 4 p. https://certmapper.cr.usgs.gov/server/rest/services/geothermal/westus_favoribility_systems/MapServer/0
5. El Centro Field Office, Bureau of Land Management (2007). Final Environmental Impact Statement for the Truckhaven Geothermal Leasing Area (Publication Index Number: BLM/CA/ES-2007-017+3200). United States Department of the Interior Bureau of Land Management.
6. Lovekin, James W., Subir K. Sanyal, Christopher W. Klein. 2004. “New Geothermal Site Identification and Qualification.” Richmond, California:

The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses. For more information on the NHDPlus dataset see the NHDPlus v2 User Guide.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territories not including Alaska.Geographic Extent: The United States not including Alaska, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: EPA and USGSUpdate Frequency: There is new new data since this 2019 version, so no updates planned in the futurePublication Date: March 13, 2019Prior to publication, the NHDPlus network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the NHDPlus Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, On or Off Network (flowlines only), Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original NHDPlus dataset. No data values -9999 and -9998 were converted to Null values for many of the flowline fields.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute. Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map. Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

On May 25, 2014, a rain-on-snow induced rock avalanche occurred in the West Salt Creek Valley on the northern flank of Grand Mesa in western Colorado. The avalanche mobilized from a preexisting rock slide and traveled 4.6 km down the confined valley, killing 3 people. The avalanche was rare for the contiguous U.S. because of its large size (54.5 Mm3) and long travel distance. To understand the avalanche failure sequence, mechanisms, and mobility, we mapped landslide structures, geology, and ponds at 1:1000-scale. We used high-resolution, Unmanned Aircraft System (UAS) imagery from July 2014 as a base for our field mapping. Herein, we present the map data and UAS imagery. The data accompany an interpretive paper published in the journal Geosphere. The full citation for this interpretive journal paper is: Coe, J.A., Baum, R.L., Allstadt, K.E., Kochevar, B.F., Schmitt, R.G., Morgan, M.L., White, J.L., Stratton, B. Hayashi, T.A., and Kean, J.W., 2016, Rock avalanche dynamics revealed by large-scale field mapping and seismic signals at a highly mobile avalanche in the West Salt Creek Valley, western Colorado: Geosphere, v. 12, no. 2, p. 607-631, doi:10.1130/GES01265.1

🚑 Clinical Field Mappings for Healthcare Systems

This synthetic dataset provides a wide variety of alternative names for clinical database fields, mapping them to standardized targets for healthcare data normalization.

Using LLMs, we generated and validated thousands of plausible variations, including misspellings, abbreviations, country-specific nuances, and common real-world typos.

This dataset is perfect for training models that need to standardize, clean, or map heterogeneous healthcare data schemas into unified, normalized formats.

✅ Applications include: - Data cleaning and ETL pipelines for clinical databases - Fine-tuning LLMs for schema matching - Clinical data interoperability projects - Zero-shot field matching research

The dataset is machine-generated and validated with LLM feedback loops to ensure high-quality mappings.

Cadastral Mapping Market Outlook

The global cadastral mapping market size was valued at approximately USD 4.2 billion in 2023 and is projected to reach around USD 7.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.2% during the forecast period. This market growth can be attributed to increasing urbanization, rapid advancements in geospatial technologies, and the growing need for efficient land management systems across various regions.

The expansion of urban areas and the corresponding increase in the need for effective land management infrastructure are significant growth factors driving the cadastral mapping market. As urbanization accelerates globally, local governments and planning agencies require sophisticated tools to manage and record land ownership, boundaries, and property information. Enhanced geospatial technologies, including Geographic Information Systems (GIS) and remote sensing, are pivotal in facilitating accurate and efficient cadastral mapping, thus contributing to market growth.

Another key growth factor is the rising demand for infrastructure development. As nations invest in large-scale infrastructure projects such as roads, railways, and smart cities, there is an increased need for precise land data to ensure the proper allocation of resources and to avoid legal disputes. Cadastral mapping provides the critical data needed for these projects, hence its demand is surging. Additionally, governments worldwide are increasingly adopting digital platforms to streamline land administration processes, further propelling the market.

Furthermore, the agricultural sector is also significantly contributing to the growth of the cadastral mapping market. Modern agriculture relies heavily on accurate land parcel information for planning and optimizing crop production. By integrating cadastral maps with other geospatial data, farmers can improve land use efficiency, monitor crop health, and enhance yield predictions. This integration is particularly valuable in precision farming, which is becoming more prevalent as the world's population grows and the demand for food increases.

Regionally, Asia Pacific is expected to witness the highest growth in the cadastral mapping market. Factors such as rapid urbanization, extensive infrastructure development projects, and the need for improved land management are driving the demand in this region. Moreover, governments in countries like India and China are investing heavily in creating digital land records and implementing smart city initiatives, which further boosts the market. The North American and European markets are also substantial, driven by the advanced technological infrastructure and well-established land administration systems.

Component Analysis

The cadastral mapping market can be segmented by component into software, hardware, and services. The software segment holds a significant share in this market, driven by the increasing adoption of advanced GIS and mapping software solutions. These software solutions enable accurate land parcel mapping, data analysis, and integration with other geospatial data systems, making them indispensable tools for cadastral mapping. Companies are continuously innovating to provide more intuitive and comprehensive software solutions, which is expected to fuel growth in this segment.

Hardware components, including GPS devices, drones, and other surveying equipment, are also critical to the cadastral mapping market. The hardware segment is expected to grow steadily as technological advancements improve the accuracy and efficiency of these devices. Innovations such as high-resolution aerial imaging and LIDAR technology are enhancing the capabilities of cadastral mapping hardware, allowing for more detailed and precise data collection. This segment is particularly essential for field surveying and data acquisition, forming the backbone of cadastral mapping projects.

The services segment encompasses a wide range of offerings, including consulting, implementation, and maintenance services. Professional services are vital for the successful deployment and operation of cadastral mapping systems. Governments and private sector organizations often rely on specialized service providers to implement these systems, train personnel, and ensure ongoing support. As the complexity of cadastral mapping projects increases, the demand for expert services is also expected to rise, contributing to the growth of this segment.

Integration services are another critical component within the

Napa County has used a 2004 edition vegetation map produced using the Manual of California Vegetation classification system (Thorne et al. 2004) as one of the input layers for land use decision and policy. The county decided to update the map because of its utility. A University of California, Davis (UCD) group was engaged to produce the map. The earlier map used black and white digital orthophoto quadrangles from 1993, with a pixel resolution of 3 meters. This image was delineated using a heads up digitization technique produced by ASI (Aerial Services Incorporated). The resulting polygons were the provided vegetation and landcover attributes following the classification system used by California State Department of Fish and Wildlife mappers in the Manual of California Vegetation. That effort included a brief field campaign in which surveyors drove accessible roads and verified or corrected the dominant vegetation of polygons adjacent to roadways or visible using binoculars. There were no field relevé or rapid assessment plots conducted. This update version uses a 2016 edition of 1 meter color aerial imagery taken by the National Agriculture Imagery Program (NAIP) as the base imagery. In consultation with the county we decided to use similar methods to the previous mapping effort, in order to preserve the capacity to assess change in the county over time. This meant forgoing recent data and innovations in remote sensing such as the use LiDAR and Ecognition’s segmentation of imagery to delineate stands, which have been recently used in a concurrent project mapping of Sonoma County. The use of such technologies would have made it more difficult to track changes in landcover, because differences between publication dates would not be definitively attributable to either actual land cover change or to change in methodology. The overall cost of updating the map in the way was approximately 20% of the cost of the Sonoma vegetation mapping program.Therefore, we started with the original map, and on-screen inspections of the 2004 polygons to determine if change had occurred. If so, the boundaries and attributes were modified in this new edition of the map. We also used the time series of imagery available on Google Earth, to further inspect many edited polygons. While funding was not available to do field assessments, we incorporated field expertise and other map data from four projects that overlap with parts of Napa Count: the Angwin Experimental Forest; a 2014 vegetation map of the Knoxville area; agricultural rock piles were identified by Amber Manfree; and parts of a Sonoma Vegetation Map that used 2013 imagery.The Angwin Experimental Forest was mapped by Peter Lecourt from Pacific Union College. He identified several polygons of redwoods in what are potentially the eastern-most extent of that species. We reviewed those polygons with him and incorporated some of the data from his area into this map.The 2014 Knoxville Vegetation map was developed by California Department of Fish and Wildlife. It was made public in February of 2019, close to the end of this project. We reviewed the map, which covers part of the northeast portion of Napa County. We incorporated polygons and vegetation types for 18 vegetation types including the rare ones, we reviewed and incorporated some data for another 6 types, and we noted in comments the presence of another 5 types. There is a separate report specifically addressing the incorporation of this map to our map.Dr Amber Manfree has been conducting research on fire return intervals for parts of Napa County. In her research she identified that large piles of rocks are created when vineyards are put in. These are mapable features. She shared the locations of rock piles she identified, which we incorporated into the map. The Sonoma Vegetation Map mapped some distance into the western side of Napa County. We reviewed that map’s polygons for coast redwood. We then examined our imagery and the Google imagery to see if we could discern the whorled pattern of tree branches. Where we could, we amended or expanded redwood polygons in our map.The Vegetation classification systems used here follows California’s Manual of California Vegetation and the National Vegetation Classification System (MCV and NVCS). We started with the vegetation types listed in the 2004 map. We predominantly use the same set of species names, with modifications/additions particularly from the Knoxville map. The NVCS uses Alliance and Association as the two most taxonomically detailed levels. This map uses those levels. It also refers to vegetation types that have not been sampled in the field and that has 3-6 species and a site descriptor as Groups, which is the next more general level in the NVCS classification. We conducted 3 rounds of quality assessment/quality control exercises.

Remote sensing maps of active layer depth derived from Unmanned Areal System (UAS) data. The UAS datasets were stepwise scaled until matching the AVIRIS-NG (Airborne Visible / Infrared Imaging Spectrometer - Next Generation) and Sentinel-2 spatial resolutions. Using the field observed Active Layer Depth (ALD) measurement in combination with spectral and topographic predictors derivatives from DJI UAS imagery, we used a spatially explicit RF regression model to predict and map ALD across our study landscapes. This package includes maps for Next-Generation Ecosystem Experiment Arctic (NGEE Arctic)’s Teller Mile Marker (MM) 27, and Kougarok MM80 (aka Mile 80) watersheds. The field, map data, and metadata are provided as geoTIF and text (*.csv) formats. These datasets are provided in support of Hantson et al., 2024 (accepted) “Scaling Arctic landscape and permafrost features improves active layer depth modeling”

High Accuracy Map Market Outlook

The global high accuracy map market size was valued at approximately USD 2.4 billion in 2023 and is projected to reach around USD 12.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 20.5% from 2024 to 2032. This impressive growth is primarily driven by advancements in autonomous vehicle technology and increasing demand for precise geospatial data across various sectors. The rapid urbanization and increased investment in smart city projects worldwide are also significant factors contributing to market growth.

One of the primary growth factors fueling the high accuracy map market is the burgeoning development of autonomous vehicles. As the automotive industry continues to innovate, the need for high precision maps that provide detailed and real-time data on road conditions, traffic, and obstacles becomes more crucial. High accuracy maps enable autonomous vehicles to navigate safely and efficiently, reducing the likelihood of accidents and improving overall transportation systems. This demand is anticipated to surge further as governments and corporations strive to deploy autonomous vehicle fleets for both personal and commercial use.

Another significant driver of market growth is the increasing implementation of high accuracy maps in infrastructure development and urban planning. As cities expand and develop, the need for accurate and detailed geographic information systems (GIS) becomes essential for efficient planning and management. High accuracy maps provide critical data for designing and maintaining roads, bridges, utilities, and other infrastructure projects. The integration of high precision mapping technology in smart city initiatives further accelerates the adoption of these systems, enabling better resource management and enhanced quality of life for urban populations.

The agricultural sector is also contributing to the expanding high accuracy map market. Precision agriculture relies heavily on accurate geospatial data to optimize farming practices, enhance crop yields, and ensure sustainable resource use. High accuracy maps enable farmers to monitor field conditions, assess soil health, and implement targeted interventions, leading to increased productivity and reduced environmental impact. As the global demand for food continues to rise, the adoption of advanced mapping technologies in agriculture is expected to grow, driving further market expansion.

Regionally, North America holds a significant share of the high accuracy map market, driven by technological advancements and substantial investments in autonomous vehicle research and development. The presence of leading technology companies and a robust infrastructure network further facilitate market growth in this region. However, Asia Pacific is anticipated to witness the highest growth rate during the forecast period, fueled by rapid urbanization, increasing smart city projects, and rising adoption of advanced mapping technologies across various industries. Europe also remains a key player in the market, supported by strong governmental initiatives and a focus on sustainable development.

Component Analysis

The high accuracy map market can be segmented by component into software, hardware, and services. The software segment, encompassing map creation, data processing, and visualization tools, plays a critical role in the market. The demand for sophisticated mapping software is driven by the need for real-time data processing and the integration of multiple data sources to create comprehensive and precise maps. Companies are continually developing advanced software solutions that leverage artificial intelligence and machine learning to enhance the accuracy and functionality of high precision maps.

The hardware segment includes various devices and sensors used in capturing geospatial data, such as GPS units, LiDAR sensors, and high-resolution cameras. As the demand for high accuracy maps grows, the need for advanced hardware capable of capturing detailed and precise data also increases. Innovations in sensor technology and the development of more compact and cost-effective devices are contributing to the growth of this segment. The hardware segment is crucial for the initial data collection phase, which lays the foundation for accurate map creation.

Services encompass a wide range of offerings, including consulting, system integrati

SIS SOIL:The new Irish Soil Information System concludes a 5 year programme, supported by the Irish Environmental Protection Agency (STRIVE Research Programme 2007-2013) and Teagasc, to develop a new 1:250,000 scale national soil map (http://soils.teagasc.ie). The Irish Soil Information System adopted a unique methodology combining digital soil mapping techniques with traditional soil survey application. Developing earlier work conducted by An Foras Talúntais, the project generated soil-landscape models for previously surveyed counties. These soil-landscape (‘soilscape’) models formed the basis for training statistical ‘inference engines’ for predicting soil mapping units, checked during field survey. 213 soil series are identified, each with differing characteristics, having contrasting environmental and agronomic responses. Properties were recorded in a database able to satisfy national and EU policy requirements. The Irish soil map and related soil property data will also serve public interest, providing the means to learn online about Irish soil resources. Use the Symbology layer file 'SOIL_SISNationalSoil.lyr' based on Value Field 'Association_Unit'. SIS SOIL DRAINAGE:In Ireland, soil drainage category is considered to have a predominant influence on soil processes (Schulte et al., 2012). The maritime climate of Ireland drives wet soil conditions, such that excess soil moisture in combination with heavy textured soils is considered a key constraint in relation to achieving productivity and environmental targets. Both soil moisture content and the rate at which water drains from the soil are critical indicators of soil physical quality and the overall functional capacity of soil. Therefore, a natural extension to the Irish Soil Information System included the development of an indicative soil drainage map for Ireland. The soil subgroup map was used to develop the indicative drainage map, based on diagnostic criteria relating to the subgroup categorization. Use the Symbology layer file 'SOIL_SISSoilDrainage.lyr' based on Value Field 'Drainage'. SIS SOIL DEPTH: Soil depth is a measure of the thickness of the soil cover and reflects the relationship between parent material and length of soil forming processes. Soil depth determines the potential rooting depth of plants and any restrictions within the soil that may hinder rooting depth. Plants derive nearly 80 per cent of their water needs from the upper part of the soil solum, i.e. where the root system is denser. The rooting depths depend on plant physiology, type of soil and water availability. Generally, vegetables (beans, tomatoes, potatoes, parsnip, carrots, leek, broccoli, etc.) are shallow rooted, about 50–60 cm; fruit trees and some other plants have medium rooting depths, 70–120 cm and other crops such as barley, wheat, oats, and maize may have deeper roots. Furthermore, rooting depths vary according to the age of the plants. The exact soil depth is difficult to define accurately due to its high variability across the landscape. The effective soil depth can be reduced by the presence of bedrock or impermeable layers. Use the Symbology layer file 'SOIL_SISSoilDepth.lyr' based on Valued Field 'Depth'. SIS SOIL TEXTURE:Soil texture is an important soil characteristic that influences processes such as water infiltration rates, rootability, gas exchanges, leaching, chemical activity, susceptibility to erosion and water holding capacity. The soil textural class is determined by the percentage of sand, silt, and clay. Soil texture also influences how much water is available to the plant; clay soils have a greater water holding capacity than sandy soils. Use the Symbology layer file 'SOIL_SISSoilTexture.lyr' based on Value Field 'Texture'. SIS SOIL SOC:In the previous national soil survey conducted by An Foras Taluntais, 14 counties were described in detail with soil profile descriptions provided for the representative soil series found within a county. Soil samples were taken at each soil horizon to a depth of 1 meter and analyses performed for a range of measurements, including soil organic carbon, texture, cation exchange capacity, pH; however in most cases no bulk density measurements were taken. This meant that while soil organic carbon concentrations were available this could not be related to a stock for a given soil series. In 2012/2013, 246 profile pits were sampled and analysed as part of the Irish Soil Information System project to fill in gaps in the description of representative profile data for Ireland. Use the Symbology layer file 'SOIL_SISSoilSOC.lyr' based on Value Field 'SOC'.

This file presents, as of 10 June 2024, for Metropolitan France, all the overhead lines of the public electricity transmission network managed by RTE. You will find in the Export tab the different formats available, including ShapeFile. This dataset presents the sections as a broken line of identical characteristics.If multiple power lines share the same towers, they are listed in the attributes Line code n, Line name n, Line owner n. A complete line may require consolidation of multiple entries in the Overhead Lines and Underground Lines datasets if applicable as soon as its identifier appears in one of the Line Code fields. In this dataset, work identifiers refer to Transit Links (LIT - business object), while work names are the names of Links (which are a set of LITs, delimited by substations). Since a link is composed of one or more LITs, it is normal to find several objects with the same work name, while having a different identifier. The change from the old cutting (until June 2022) is the export of broken lines of identical characteristics instead of exporting only right-hand segments. There are therefore much fewer entities to handle, we go from around 256000 in the air to less than 14000 also decreasing the volume of files. Geographic accuracy has been improved and the position of the inflections coincides with the dataset of the pylons. This new division will be the only one maintained from December 2022. In addition to this dataset, for access to our mobility infrastructure data, you will find the open data map on our ArcGis Online system accessible on PC here or on the move by opening the map in ArcGIS Field Maps: INSPIRE TEN Network. This dataset is shared within the framework of Directive 2007/2/EC of the European Parliament and of the Council of 14 March 2007 establishing an Infrastructure for Spatial Information in the European Community (INSPIRE). The INSPIRE Directive applies to digital spatial data held by public authorities and requires data to be made available in accordance with harmonised technical specifications. For further information on this dataset, write to: rte-inspire-infos@rte-france.com The publication of this dataset does not exempt the user from his regulatory obligation under the anti-damage decree (DT/DICT) in the event of works or consultation of the Urban Planning Geoportal for urban planning applications (Servitudes). * * *

The outdoor parks, courts, and fields for this dataset were identified using Google Maps. Each data point was geocoded using the latitude and longitude points of each outdoor park, court, and field that Google Maps identified as being a part of Newark into a spreadsheet.