ArcGIS and QGIS map packages, with ESRI shapefiles for the DSM2 Model Grid. These are not finalized products. Locations in these shapefiles are approximate.

Monitoring Stations - shapefile with approximate locations of monitoring stations.

DSM2 Grid 2025-05-28 Historical

FC_2023.01

DSM2 v8.2.0, calibrated version:

• dsm2_8_2_grid_map_calibrated.mpkx - ArcGIS Pro map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_grid_map_calibrated.mpk - ArcGIS Desktop map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_0_calibrated_grid_map_qgis.zip - QGIS map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_0_calibrated_gridmap_shapefiles.zip - A zip file containing all the shapefiles used in the above map packages:
• dsm2_8_2_0_calibrated_channels_centerlines - channel centerlines, follwing the path of CSDP centerlines
• dsm2_8_2_0_calibrated_network_channels - channels represented by straight line segments which are connected the upstream and downstream nodes
• dsm2_8_2_0_calibrated_nodes - DSM2 nodes
• dsm2_8_2_0_calibrated_dcd_only_nodes - Nodes that are only used by DCD
• dsm2_8_2_0_calibrated_and_dcd_nodes - Nodes that are shared by DSM2 and DCD
• dsm2_8_2_0_calibrated_and_smcd_nodes - Nodes that are shared by DSM2 and SMCD
• dsm2_8_2_0_calibrated_gates_actual_loc - The approximate actual locations of each gate in DSM2
• dsm2_8_2_0_calibrated_gates_grid_loc - The locations of each gate in the DSM2 model grid
• dsm2_8_2_0_calibrated_reservoirs - The approximate locations of the reservoirs in DSM2
• dsm2_8_2_0_calibrated_reservoir_connections - Lines showing connections from reservoirs to nodes in DSM2

DSM2 v8.2.1, historical version:

• DSM2 v8.2.1, historical version grid map release notes (PDF), updated 7/12/2022
• DSM2 v8.2.1, historical version grid map, single zoom level (PDF)
• DSM2 v8.2.1, historical version grid map, multiple zoom levels (PDF) - PDF grid map designed to be printed on 3 foot wide plotter paper.
• DSM2 v8.2.1, historical version map package for ArcGIS Desktop: A map package for ArcGIS Desktop containing the grid map layers with symbology.
• DSM2 v8.2.1, historical version grid map shapefiles (zip): A zip file containing the shapefiles used in the grid map.

Change Log

7/12/2022: The document "DSM2 v8.2.1, historical version grid map release notes (PDF)" was corrected by removing section 4.4, which incorrectly stated that the grid included channels 710-714, representing the Toe Drain, and that the Yolo Flyway restoration area was included.

U.S. National Grid This feature layer, utilizing data from the Federal Geographic Data Committee (FGDC), displays the U.S. National Grid (USNG). The FGDC provides standards for a National Grid. Per the FGDC, "The objective of this standard is to create a more favorable environment for developing location-based services within the United States and to increase the interoperability of location services appliances with printed map products by establishing a nationally consistent grid reference system as the preferred grid for National Spatial Data Infrastructure (NSDI) applications. This standard defines the US National Grid. The U.S. National Grid is based on universally defined coordinate and grid systems and can, therefore, be easily extended for use world-wide as a universal grid reference system."Notes:Popups can be viewed for the USNG 1000m and USNG 100m layers.The USNG 100m layer is only displayed for certain cities. To view those places, please select a row in the attribute table and then center (zoom) on selection. U.S. National Grid - Grid Zone Designations Top: 100,000-meter and 10,000-meter Square IdentificationsBottom: 1,000-meter and 100-meter Square IdentificationsData downloaded: 2025Data source: USNG GDBData modifications: The Percent Complete field was removed from all layers. The following fields were added to the original data for layers:USNG 1000m - UTM ZoneUSNG 100m - Place; Region For more information:Standard for a U.S. National GridUnited States National GridHow to read a United States National Grid (USNG) spatial address For feedback, please contact: ArcGIScomNationalMaps@esri.com Federal Geographic Data Committee (FGDC) Per the FGDC, "The Federal Geographic Data Committee (FGDC) is an organized structure of Federal geospatial professionals and constituents that provide executive, managerial, and advisory direction and oversight for geospatial decisions and initiatives across the Federal government. In accordance with Office of Management and Budget (OMB) Circular A-16, the FGDC is chaired by the Secretary of the Interior with the Deputy Director for Management, OMB as Vice-Chair."

This is a simple map service showing latitude/longitude lines that can be used as an overlay along with other layers for reference.Spatial reference: WGS_1984_Web_Mercator_Auxiliary_Sphere.This map layer is used in NOAA's Data in the Classroom module(s).Data in the Classroom is an online curriculum to foster data literacy. With NOAA’s Data in the Classroom, students use historical and real-time NOAA data to explore today’s most pressing environmental issues. Each of the modules addresses research questions, includes stepped levels of engagement and builds students’ abilities to understand, interpret, and think critically about data. The modules available include:Investigating El NiñoInvestigating Sea LevelInvestigating Coral BleachingMonitoring Estuarine Water QualityUnderstanding Ocean & Coastal AcidificationVisit Data in the Classroom for more information.All Data in the Classroom modules follow guiding principles found in the Next Generation Science Standards (NGSS)* and Common Core State Standards.*NGSS Lead States. 2013. Next Generation Science Standards: For States, By States. Washington, DC: The National Academies Press. Next Generation Science Standards is a registered trademark of Achieve. Neither Achieve nor the lead states and partners that developed the Next Generation Science Standards was involved in the production of, and does not endorse, this product.

Map Index Sheets from Block and Lot Grid of Property Assessment and based on aerial photography, showing 1983 datum with solid line and NAD 27 with 5 second grid tics and italicized grid coordinate markers and outlines of map sheet boundaries.

Each grid square is 3500 x 4500 feet. Each Index Sheet contains 16 lot/block sheets, labeled from left to right, top to bottom (4 across, 4 down): A, B, C, D, E, F, G, H, J, K, L, M, N, P, R, S. The first (4) numeric characters in a parcelID indicate the Index sheet in which the parcel can be found, the alpha character identifies the block in which most (or all) of the property lies.

This Kentucky-specific quadrangle index grid was developed for the KyTopo Map Series. The 60,000' x 40,000' grid tiles are landscape oriented, fit on a standard Arch-D sized sheet, and have newly generated contours based on a KyFromAbove LiDAR-derived DEM. The 60k x 40k grid is a superset of the Kentucky Single Zone based 5k grid that is utilized for organizing and distributing most all of the Commonwealth's raster data holdings. Quadrangle names were developed utilizing a USGS methodology that focuses on the most prominent map features. Clicking on a grid tile shows the names, contour interval, contour index interval, and provides links to download currently available versions of that map.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

USGS is assessing the feasibility of map projections and grid systems for lunar surface operations. We propose developing a new Lunar Transverse Mercator (LTM), the Lunar Polar Stereographic (LPS), and the Lunar Grid Reference Systems (LGRS). We have also designed additional grids designed to NASA requirements for astronaut navigation, referred to as LGRS in Artemis Condensed Coordinates (ACC), but this is not released here. LTM, LPS, and LGRS are similar in design and use to the Universal Transverse Mercator (UTM), Universal Polar Stereographic (LPS), and Military Grid Reference System (MGRS), but adhere to NASA requirements. LGRS ACC format is similar in design and structure to historic Army Mapping Service Apollo orthotopophoto charts for navigation. The Lunar Transverse Mercator (LTM) projection system is a globalized set of lunar map projections that divides the Moon into zones to provide a uniform coordinate system for accurate spatial representation. It uses a transverse Mercator projection, which maps the Moon into 45 transverse Mercator strips, each 8°, longitude, wide. These transverse Mercator strips are subdivided at the lunar equator for a total of 90 zones. Forty-five in the northern hemisphere and forty-five in the south. LTM specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large areas with high positional accuracy while maintaining consistent scale. The Lunar Polar Stereographic (LPS) projection system contains projection specifications for the Moon’s polar regions. It uses a polar stereographic projection, which maps the polar regions onto an azimuthal plane. The LPS system contains 2 zones, each zone is located at the northern and southern poles and is referred to as the LPS northern or LPS southern zone. LPS, like is equatorial counterpart LTM, specifies a topocentric, rectangular, coordinate system (easting and northing coordinates) for spatial referencing. This projection is commonly used in GIS and surveying for its ability to represent large polar areas with high positional accuracy, while maintaining consistent scale across the map region. LGRS is a globalized grid system for lunar navigation supported by the LTM and LPS projections. LGRS provides an alphanumeric grid coordinate structure for both the LTM and LPS systems. This labeling structure is utilized in a similar manner to MGRS. LGRS defines a global area grid based on latitude and longitude and a 25×25 km grid based on LTM and LPS coordinate values. Two implementations of LGRS are used as polar areas require a LPS projection and equatorial areas a transverse Mercator. We describe the difference in the techniques and methods report associated with this data release. Request McClernan et. al. (in-press) for more information. ACC is a method of simplifying LGRS coordinates and is similar in use to the Army Mapping Service Apollo orthotopophoto charts for navigation. These data will be released at a later date. Two versions of the shape files are provided in this data release, PCRS and Display only. See LTM_LPS_LGRS_Shapefiles.zip file. PCRS are limited to a single zone and are projected in either LTM or LPS with topocentric coordinates formatted in Eastings and Northings. Display only shapefiles are formatted in lunar planetocentric latitude and longitude, a Mercator or Equirectangular projection is best for these grids. A description of each grid is provided below: Equatorial (Display Only) Grids: Lunar Transverse Mercator (LTM) Grids: LTM zone borders for each LTM zone Merged LTM zone borders Lunar Polar Stereographic (LPS) Grids: North LPS zone border South LPS zone border Lunar Grid Reference System (LGRS) Grids: Global Areas for North and South LPS zones Merged Global Areas (8°×8° and 8°×10° extended area) for all LTM zones Merged 25km grid for all LTM zones PCRS Shapefiles:` Lunar Transverse Mercator (LTM) Grids: LTM zone borders for each LTM zone Lunar Polar Stereographic (LPS) Grids: North LPS zone border South LPS zone border Lunar Grid Reference System (LGRS) Grids: Global Areas for North and South LPS zones 25km Gird for North and South LPS zones Global Areas (8°×8° and 8°×10° extended area) for each LTM zone 25km grid for each LTM zone The rasters in this data release detail the linear distortions associated with the LTM and LPS system projections. For these products, we utilize the same definitions of distortion as the U.S. State Plane Coordinate System. Scale Factor, k - The scale factor is a ratio that communicates the difference in distances when measured on a map and the distance reported on the reference surface. Symbolically this is the ratio between the maps grid distance and distance on the lunar reference sphere. This value can be precisely calculated and is provided in their defining publication. See Snyder (1987) for derivation of the LPS scale factor. This scale factor is unitless and typically increases from the central scale factor k_0, a projection-defining parameter. For each LPS projection. Request McClernan et. al., (in-press) for more information. Scale Error, (k-1) - Scale-Error, is simply the scale factor differenced from 1. Is a unitless positive or negative value from 0 that is used to express the scale factor’s impact on position values on a map. Distance on the reference surface are expended when (k-1) is positive and contracted when (k-1) is negative. Height Factor, h_F - The Height Factor is used to correct for the difference in distance caused between the lunar surface curvature expressed at different elevations. It is expressed as a ratio between the radius of the lunar reference sphere and elevations measured from the center of the reference sphere. For this work, we utilized a radial distance of 1,737,400 m as recommended by the IAU working group of Rotational Elements (Archinal et. al., 2008). For this calculation, height factor values were derived from a LOLA DEM 118 m v1, Digital Elevation Model (LOLA Science Team, 2021). Combined Factor, C_F – The combined factor is utilized to “Scale-To-Ground” and is used to adjust the distance expressed on the map surface and convert to the position on the actual ground surface. This value is the product of the map scale factor and the height factor, ensuring the positioning measurements can be correctly placed on a map and on the ground. The combined factor is similar to linear distortion in that it is evaluated at the ground, but, as discussed in the next section, differs numerically. Often C_F is scrutinized for map projection optimization. Linear distortion, δ - In keeping with the design definitions of SPCS2022 (Dennis 2023), we refer to scale error when discussing the lunar reference sphere and linear distortion, δ, when discussing the topographic surface. Linear distortion is calculated using C_F simply by subtracting 1. Distances are expended on the topographic surface when δ is positive and compressed when δ is negative. The relevant files associated with the expressed LTM distortion are as follows. The scale factor for the 90 LTM projections: LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_K_grid_scale_factor.tif Height Factor for the LTM portion of the Moon: LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_EF_elevation_factor.tif Combined Factor in LTM portion of the Moon LUNAR_LTM_GLOBAL_PLOT_HEMISPHERES_distortion_CF_combined_factor.tif The relevant files associated with the expressed LPS distortion are as follows. Lunar North Pole The scale factor for the northern LPS zone: LUNAR_LGRS_NP_PLOT_LPS_K_grid_scale_factor.tif Height Factor for the north pole of the Moon: LUNAR_LGRS_NP_PLOT_LPS_EF_elevation_factor.tif Combined Factor for northern LPS zone: LUNAR_LGRS_NP_PLOT_LPS_CF_combined_factor.tif Lunar South Pole Scale factor for the northern LPS zone: LUNAR_LGRS_SP_PLOT_LPS_K_grid_scale_factor.tif Height Factor for the south pole of the Moon: LUNAR_LGRS_SP_PLOT_LPS_EF_elevation_factor.tif Combined Factor for northern LPS zone: LUNAR_LGRS_SP_PLOT_LPS_CF_combined_factor.tif For GIS utilization of grid shapefiles projected in Lunar Latitude and Longitude, referred to as “Display Only”, please utilize a registered lunar geographic coordinate system (GCS) such as IAU_2015:30100 or ESRI:104903. LTM, LPS, and LGRS PCRS shapefiles utilize either a custom transverse Mercator or polar Stereographic projection. For PCRS grids the LTM and LPS projections are recommended for all LTM, LPS, and LGRS grid sizes. See McClernan et. al. (in-press) for such projections. Raster data was calculated using planetocentric latitude and longitude. A LTM and LPS projection or a registered lunar GCS may be utilized to display this data. Note: All data, shapefiles and rasters, require a specific projection and datum. The projection is recommended as LTM and LPS or, when needed, IAU_2015:30100 or ESRI:104903. The datum utilized must be the Jet Propulsion Laboratory (JPL) Development Ephemeris (DE) 421 in the Mean Earth (ME) Principal Axis Orientation as recommended by the International Astronomy Union (IAU) (Archinal et. al., 2008).

The map is designed to be used as a basemap by marine GIS professionals and as a reference map by anyone interested in ocean data. The basemap focuses on bathymetry. It also includes inland waters and roads, overlaid on land cover and shaded relief imagery.The Ocean Base map currently provides coverage for the world down to a scale of ~1:577k; coverage down to ~1:72k in United States coastal areas and various other areas; and coverage down to ~1:9k in limited regional areas.The World Ocean Reference is designed to be drawn on top of this map and provides selected city labels throughout the world. This web map lets you view the World Ocean Base with the Reference service drawn on top. Article in the Fall 2011 ArcUser about this basemap: "A Foundation for Ocean GIS".The map was compiled from a variety of best available sources from several data providers, including General Bathymetric Chart of the Oceans GEBCO_08 Grid version 20100927 and IHO-IOC GEBCO Gazetteer of Undersea Feature Names August 2010 version (https://www.gebco.net), National Oceanic and Atmospheric Administration (NOAA) and National Geographic for the oceans; and Garmin, and Esri for topographic content. You can contribute your bathymetric data to this service and have it served by Esri for the benefit of the Ocean GIS community. For details on the users who contributed bathymetric data for this map via the Community Maps Program, view the list of Contributors for the Ocean Basemap. The basemap was designed and developed by Esri. The GEBCO_08 Grid is largely based on a database of ship-track soundings with interpolation between soundings guided by satellite-derived gravity data. In some areas, data from existing grids are included. The GEBCO_08 Grid does not contain detailed information in shallower water areas, information concerning the generation of the grid can be found on GEBCO's website: https://www.gebco.net/data_and_products/gridded_bathymetry_data/. The GEBCO_08 Grid is accompanied by a Source Identifier (SID) Grid which indicates which cells in the GEBCO_08 Grid are based on soundings or existing grids and which have been interpolated. The latest version of both grids and accompanying documentation is available to download, on behalf of GEBCO, from the British Oceanographic Data Centre (BODC) https://www.bodc.ac.uk/data/online_delivery/gebco/.The names of the IHO (International Hydrographic Organization), IOC (intergovernmental Oceanographic Commission), GEBCO (General Bathymetric Chart of the Oceans), NERC (Natural Environment Research Council) or BODC (British Oceanographic Data Centre) may not be used in any way to imply, directly or otherwise, endorsement or support of either the Licensee or their mapping system.Tip: Here are some famous oceanic locations as they appear this map. Each URL launches this map at a particular location via parameters specified in the URL: Challenger Deep, Galapagos Islands, Hawaiian Islands, Maldive Islands, Mariana Trench, Tahiti, Queen Charlotte Sound, Notre Dame Bay, Labrador Trough, New York Bight, Massachusetts Bay, Mississippi Sound

Through its Natural Heritage Database, the Office of Natural Lands Management (ONLM) documents rare plant species and rare ecological community habitat to inform decision-makers who need to address the conservation of natural resources. The Natural Heritage Grid Map is a geographic information system (GIS) file that provides a general portrayal of the geographic locations of rare plant species and rare ecological communities for the entire state without providing sensitive detailed information. It does not contain data for animal species. The Natural Heritage Grid Map was produced using computer-generated vector-based polygons that divide the boundary lines of each USGS 1:24,000 scale topographic map into 100 grid cells, each cell being between 358 and 372 acres in size. If a rare plant species or ecological community has been documented from anywhere within a cell, the entire cell will be coded as containing an occurrence of a rare plant species/ecological community. An associated data table can be linked or related to the NHPGRID table in order to display information about the individual rare plant species/ecological community occurrences within any cell.

Index map page boundaries of the Mat-Su Borough Tax Map Page Index. The entire Borough is divided into a series of "base maps" and "index" or "grid maps". Base maps are given names that represent the geographical area represented (similar to USGS quad mapping) and index maps are numbered sequentially within the base map. The result is a base map with a two-character name (for example: ("WA" for Wasilla) and numbered index maps (usually numbered "1" thru "16"). The Mat-Su Borough tax map set is published using these pages.

This layer contains the boundaries and IDs of the Maryland tax maps produced by Maryland Department of Planning. Tax maps, also known as assessment maps, property maps or parcel maps, are a graphic representation of real property showing and defining individual property boundaries in relationship to contiguous real property.This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Feature Service Layer Link:https://mdgeodata.md.gov/imap/rest/services/PlanningCadastre/MD_PropertyData/MapServer/2

Sourcing accurate and up-to-date GIS data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent GIS data across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

• Better understand your customers
• Identify optimal locations to expand your retail footprint
• Define sales territories for franchisees
• Run targeted marketing campaigns.

GapMaps GIS data for Asia and MENA can be utilized in any GIS platform and includes the latest Demographic estimates (updated annually) including:

1. Population (how many people live in your local catchment)
2. Census Demographics (who lives within your local catchment)
3. Worker population (how many people work within your local catchment)
4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

GapMaps GIS Data also includes Point-Of-Interest (POI) Data updated monthly across a range of categories including Fast Food, Cafe, Health & Fitness and Supermarket/ Grocery

Primary Use Cases for GapMaps GIS Data:

1. Retail Site Selection - identify optimal locations for future expansion and benchmark performance across existing locations.
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics
4. Target Marketing: Develop effective marketing strategies to acquire more customers.
5. Integrate GapMaps GIS data with your existing GIS or BI platform to generate powerful visualizations.

This website provides a limited number of Substructure Maps in “pdf” format via GIS polygons representing grids containing URL links. Across various areas of Los Angeles County, paper maps were created by Public Works (PW) and its predecessor Departments to show underground utilities such as cable TV, gas, oil, and telephone lines.

Though most of these maps are no longer updated, they can be useful as a research resource. Every reasonable effort has been made to assure the accuracy of this data and the maps referenced. Some cities may provide substructure information for the areas not covered by these grids. Additional and more accurate substructure data and information may also be obtained through the utility companies. Before digging, it is strongly advised to contact the Underground Service Alert (DigAlert Express) at www.digalert.org/digexpress.html or by calling 811.

Please note that California State Law Says, You Must Contact DigAlert!

The County of Los Angeles makes no warranty, representation, or guarantee as to the content, sequence, accuracy, timeliness, or completeness of any of the data provided herein or of any maps referenced. Los Angeles County Public Works recommends that all utility research be conducted under the supervision of a licensed civil engineer.

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

This GIS data package contains airborne electromagnetic (AEM) datasets and interpreted data products for the Boundary Bend-Nyah survey area, as part of the River Murray Corridor (RMC) Salinity Mapping and Interpretation Project. The RMC project was undertaken between 2006 and 2010 to provide information on a range of salinity and land management issues along a 450 kilometre reach of the Murray River from the South Australian border to Gunbower, northwest of Echuca in Victoria. The Boundary Bend-Nyah survey area extends downstream from Wakool junction to Nyah.

This metadata briefly describes the contents of the data package. The user guide included in the package contains more detailed information about the individual datasets and available technical reports.

The main components in the package are: AEM data and images derived from a holistic inversion of the RMC RESOLVE AEM survey; a composite digital elevation model (DEM); a range of interpreted data products designed to map key elements of the hydrogeological system and salinity hazards using the AEM dataset; and a series of ESRI ArcGIS map documents.

The AEM data component consists of grids and images of modelled conductivity data derived from a holistic inversion of the RMC RESOLVE AEM survey. They include: layer conductivity grids below ground surface; depth slice grids representing the average conductivity of various regular depth intervals below ground surface; floodplain slice grids representing the average conductivity of various depth intervals relative to the elevation above or below a surface that approximates the River Murray floodplain; watertable slice grids representing the average conductivity of various intervals relative to the elevation above or below the regional watertable; and AEM cross sections of conductivity versus depth along each of the flight lines. The holistic inversion AEM data are derived from the 'River Murray Corridor RESOLVE AEM Survey, VIC & NSW, 2007 Final Data (P1141)', available as Geoscience Australia product number 67212 (GeoCat #67212).

The DEM data component consists of a 10 metre horizontal resolution composite DEM for the River Murray Corridor AEM survey area derived from airborne light detection and ranging (LiDAR) surveys, AEM surveys and the shuttle radar topography mission (SRTM) survey.

The interpreted data component is organised into product themes to address salinity and land management questions and to map key elements of the hydrogeological system and salinity hazards. An ArcGIS map document is included for each product theme. The products include: Blanchetown Clay; conductive soils; flush zones; groundwater conductivity; stratigraphic extents and reliability; near surface conductive zones; near surface resistive zones; Parilla Sands; Quaternary alluvium; recharge; salt store; surface salt; vegetation health; and Woorinen Formation.

The RMC project was funded through the National Action Plan for Salinity and Water Quality with additional funding from the Lower Murray Catchment Management Authority (CMA), Mallee CMA, Goulburn-Murray Water and the Murray-Darling Basin Authority. The project was administered by the Australian Government Department of Agriculture, Fisheries and Forestry through the Bureau of Rural Sciences, now known as the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES). Geoscience Australia (GA) were contracted to provide geophysical services to manage the AEM system selection and data acquisition, and to process and calibrate the AEM data. The AEM survey was flown by Fugro Airborne Geophysical Services in 2007 using the helicopter-borne RESOLVE frequency domain system. The Cooperative Research Centre for Landscape Environments and Mineral Exploration was sub-contracted through GA to manage the interpretation and reporting component of the RMC project.

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

This is a polygon feature data layer of United States National Grid (1000m x 1000m polygons ) constructed by the Center for Interdisciplinary Geospatial Information Technologies at Delta State University with support from the US Geological Survey under the Cooperative Agreement 07ERAG0083. For correct display, please set the base coordinate system and projection such that it matches the UTM zone for which these data were constructed using the NAD 83 datum. Further information about the US National Grid is available from https://www.fgdc.gov/usng and a viewing of these layers as applied to local geography may be seen at the National Map, https://www.nationalmap.gov. The name of each dataset has the following format - StateAbbv_USNG_UTMXX. For example, for the UTM zone 15 of Mississippi, the dataset is named MS_USNG_UTM15.Data Download: https://ky.box.com/v/kymartian-us-national-grid-1km

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. Large scale final map products were created within ArcMap and designed to show both the orthophoto coverage and the vegetation maps. For the vegetation maps, colors were assigned and the polygons labeled with the dominant vegetation and modifier and, where present, the second vegetation and modifier. For the orthophoto maps, the photos were simply plotted at the same scale and area coverage as the vegetation maps. Additional planimetric map data included roads, trails, hydrology, boundaries and a UTM coordinate grid. Legends are designed to provide full definitions of the vegetation and buffer classes and modifiers, as well as information about the park, map projection, data sources and authorship (Figure 19). All maps are projected to the Universal Transverse Mercator Coordinate System, North American Datum of 1984, in the local zone for the specific park. Photo Date: 10/24/2000 Area (ac): 3945 Area (ha): 1597 Completion Date: Oct, 2008 Veg Class: 20 Polygons: 382 Avg Polygon Size: 4.18 Map Scale: 1:9,000

Last Revised: February 2016

Map Information

This nowCOAST™ time-enabled map service provides maps of lightning strike density data from the NOAA/National Weather Service/NCEP's Ocean Prediction Center (OPC) which emulate (simulate) data from the future NOAA GOES-R Global Lightning Mapper (GLM). The purpose of this product is to provide mariners and others with enhanced "awareness of developing and transitory thunderstorm activity, to give users the ability to determine whether a cloud system is producing lightning and if that activity is increasing or decreasing..." Lightning Strike Density, as opposed to display of individual strikes, highlights the location of lightning cores and trends of increasing and decreasing activity. The maps depict the density of lightning strikes during a 15 minute time period at an 8 km x 8 km spatial resolution. The lightning strike density maps cover the geographic area from 25 degrees South to 80 degrees North latitude and from 110 degrees East to 0 degrees West longitude. The map units are number of strikes per square km per minute multiplied by a scaling factor of 10^3. The strike density is color coded using a color scheme which allows the data to be easily seen when overlaid on GOES imagery and to distinguish areas of low and high density values. The maps are updated on nowCOAST™ approximately every 15 minutes. The latest data depicted on the maps are approximately 12 minutes old (or older). Given the spatial resolution and latency of the data, the data should NOT be used to activite your lightning safety plans. Always follow the safety rule: when you first hear thunder or see lightning in your area, activate your emergency plan. If outdoors, immediately seek shelter in a substantial building or a fully enclosed metal vehicle such as a car, truck or van. Do not resume activities until 30 minutes after the last observed lightning or thunder. For more detailed information about layer update frequency and timing, please reference the
nowCOAST™ Dataset Update Schedule.

Background Information

The source for the data is OPC's gridded lightning strike density data on an 8x8 km grid. The gridded data emulate the spatial resolution of the future Global Lightning Mapper (GLM) instrument to be flown on the NOAA GOES-R series of geostationary satellites, with the first satellite scheduled for launch in late 2016.

The gridded data is based on data from Vaisala's ground based U.S. National Lightning Detection Network (NLDN) and its global lightning detection network referred to as the Global Lightning Dataset (GLD360). These networks are capable of detecting cloud-to-ground strikes, cloud-to-ground flash information and survey level cloud lightning information. According to the National Lightning Safety Institute, NLDN uses radio frequency detectors in the spectrum 1.0 kHz through 400 kHz to measure energy discharges from lightning as well as approximate distance and direction. According to Vaisala, the GLD360 network is capable of a detection efficiency greater than 70% over most of the Northern Hemisphere with a median location accuracy of 5 km or better. OPC's gridded data are coarser than the original source data from Vaisala's networks. The 15-minute gridded source data are updated at OPC every 15 minutes at 10 minutes past the valid time.

The lightning strike density product from NWS/NCEP/OPC is considered a derived product or Level 5 product ("NOAA-generated products using lightning data as input but not displaying the contractor transmitted/provided lightning data") and is appropriate for public distribution.

Time Information

This map service is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.

In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.

This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.

This service is configured with time coverage support, meaning that the service will always return the most relevant available data, if any, to the specified time value. For example, if the service contains data valid today at 12:00 and 12:10 UTC, but a map request specifies a time value of today at 12:07 UTC, the data valid at 12:10 UTC will be returned to the user. This behavior allows more flexibility for users, especially when displaying multiple time-enabled layers together despite slight differences in temporal resolution or update frequency.

When interacting with this time-enabled service, only a single instantaneous time value should be specified in each request. If instead a time range is specified in a request (i.e. separate start time and end time values are given), the data returned may be different than what was intended.

Care must be taken to ensure the time value specified in each request falls within the current time coverage of the service. Because this service is frequently updated as new data becomes available, the user must periodically determine the service's time extent. However, due to software limitations, the time extent of the service and map layers as advertised by ArcGIS Server does not always provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time extent of the service:

  Issue a returnUpdates=true request (ArcGIS REST protocol only)
  for an individual layer or for the service itself, which will return
  the current start and end times of available data, in epoch time format
  (milliseconds since 00:00 January 1, 1970). To see an example, click on
  the "Return Updates" link at the bottom of the REST Service page under
  "Supported Operations". Refer to the
  ArcGIS REST API Map Service Documentation
  for more information.


  Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against
  the proper layer corresponding with the target dataset. For raster
  data, this would be the "Image Footprints with Time Attributes" layer
  in the same group as the target "Image" layer being displayed. For
  vector (point, line, or polygon) data, the target layer can be queried
  directly. In either case, the attributes returned for the matching
  raster(s) or vector feature(s) will include the following:


      validtime: Valid timestamp.


      starttime: Display start time.


      endtime: Display end time.


      reftime: Reference time (sometimes referred to as
      issuance time, cycle time, or initialization time).


      projmins: Number of minutes from reference time to valid
      time.


      desigreftime: Designated reference time; used as a
      common reference time for all items when individual reference
      times do not match.


      desigprojmins: Number of minutes from designated
      reference time to valid time.




  Query the nowCOAST™ LayerInfo web service, which has been created to
  provide additional information about each data layer in a service,
  including a list of all available "time stops" (i.e. "valid times"),
  individual timestamps, or the valid time of a layer's latest available
  data (i.e. "Product Time"). For more information about the LayerInfo
  web service, including examples of various types of requests, refer to
  the 
  nowCOAST™ LayerInfo Help Documentation

References

Kithil, 2015: Overview of Lightning Detection Equipment, National
Lightning Safety Institute, Louisville, CO. (Available from
http://www.lightningsafety.com/nsli_ihm/detectors.html).


NASA and NOAA, 2014: Geostationary Lightning Mapper (GLM). (Available at
http://www.goes-r.gov/spacesegment/glm.html).


NWS, 2013: Lightning Strike Density Product Description Document.
NOAA/NWS/NCEP/Ocean Prediction Center, College Park, MD (Available at
http://www.opc.ncep.noaa.gov/lightning/lightning_pdd.php
and http://products.weather.gov/PDD/Experimental%20Lightning%20Strike%20Density%20Product%2020130913.pdf).


NOAA Knows Lightning. NWS, Silver Spring, MD (Available at
http://www.lightningsafety.noaa.gov/resources/lightning3_050714.pdf).


Siebers, A., 2013: Soliciting Comments until June 3, 2014 on an
Experimental Lightning Strike Density product (Offshore Waters). Public
Information Notice, NOAA/NWS Headquarters, Washington, DC (Available at
http://www.nws.noaa.gov/om/notification/pns13lightning_strike_density.htm).

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. were derived from the NVC. NatureServe developed a preliminary list of potential vegetation types. These data were combined with existing plot data (Cully 2002) to derive an initial list of potential types. Additional data and information were gleaned from a field visit and incorporated into the final list of map units. Because of the park’s small size and the large amount of field data, the map units are equivalent to existing vegetation associations or local associations/descriptions (e.g., Prairie Dog Colony). In addition to vegetation type, vegetation structures were described using three attributes: height, coverage density, and coverage pattern. In addition to vegetation structure and context, a number of attributes for each polygon were stored in the associated table within the GIS database. Many of these attributes were derived from the photointerpretation; others were calculated or crosswalked from other classifications. Table 2.7.2 shows all of the attributes and their sources. Anderson Level 1 and 2 codes are also included (Anderson et al. 1976). These codes should allow for a more regional perspective on the vegetation types. Look-up tables for the names associated with the codes is included within the geodatabase and in Appendix D. The look-up tables contain all the NVC formation information as well as alliance names, unique IDs, and the ecological system codes (El_Code) for the associations. These El_Codes often represent a one-to-many relationship; that is, one association may be related to more than one ecological system. The NatureServe conservation status is included as a separate item. Finally, slope (degrees), aspect, and elevation were calculated for each polygon label point using a digital elevation model and an ArcView script. The slope figure will vary if one uses a TIN (triangulated irregular network) versus a GRID (grid-referenced information display) for the calculation (Jenness 2005). A grid was used for the slope figure in this dataset. Acres and hectares were calculated using XTools Pro for ArcGIS Desktop.