In 2023, Google Maps was the most downloaded map and navigation app in the United States, despite being a standard pre-installed app on Android smartphones. Waze followed, with 9.89 million downloads in the examined period. The app, which comes with maps and the possibility to access information on traffic via users reports, was developed in 2006 by the homonymous Waze company, acquired by Google in 2013.

Usage of navigation apps in the U.S. As of 2021, less than two in 10 U.S. adults were using a voice assistant in their cars, in order to place voice calls or follow voice directions to a destination. Navigation apps generally offer the possibility for users to download maps to access when offline. Native iOS app Apple Maps, which does not offer this possibility, was by far the navigation app with the highest data consumption, while Google-owned Waze used only 0.23 MB per 20 minutes.

Usage of navigation apps worldwide In July 2022, Google Maps was the second most popular Google-owned mobile app, with 13.35 million downloads from global users during the examined month. In China, the Gaode Map app, which is operated along with other navigation services by the Alibaba owned AutoNavi, had approximately 730 million monthly active users as of September 2022.

This webmap features the USGS GAP application of the vegetation cartography design based on NVCS mapping being done at the Alliance level by the California Native Plant Society (CNPS), the California Dept of Fish and Game (CDFG), and the US National Park Service, combined with Ecological Systems Level mapping being done by USGS GAP, Landfire and Natureserve. Although the latter are using 3 different approaches to mapping, this project adopted a common cartography and a common master crossover in order to allow them to be used intercheangably as complements to the detailed NVCS Alliance & Macrogroup Mapping being done in Calif by the California Native Plant Society (CNPS) and Calif Dept of Fish & Wildlife (CDFW). A primary goal of this project was to develop ecological layers to use as overlays on top of high-resolution imagery, in order to help interpret and better understand the natural landscape. You can see the source national GAP rasters by clicking on either of the "USGS GAP Landcover Source RASTER" layers at the bottom of the contents list.Using polygons has several advantages: Polygons are how most conservation plans and land decisions/managment are done so polygon-based outputs are more directly useable in management and planning. Unlike rasters, Polygons permit webmaps with clickable links to provide additional information about that ecological community. At the analysis level, polygons allow vegetation/ecological systems depicted to be enriched with additional ecological attributes for each polygon from multiple overlay sources be they raster or vector. In this map, the "Gap Mac base-mid scale" layers are enriched with links to USGS/USNVC macrogroup summary reports, and the "Gap Eco base scale" layers are enriched with links to the Naturserve Ecological Systems summary reports.Comparsion with finer scale ground ecological mapping is provided by the "Ecol Overlay" layers of Alliance and Macrogroup Mapping from CNPS/CDFW. The CNPS Vegetation Program has worked for over 15 years to provide standards and tools for identifying and representing vegetation, as an important feature of California's natural heritage and biodiversity. Many knowledgeable ecologists and botanists support the program as volunteers and paid staff. Through grants, contracts, and grass-roots efforts, CNPS collects field data and compiles information into reports, manuals, and maps on California's vegetation, ecology and rare plants in order to better protect and manage them. We provide these services to governmental, non-governmental and other organizations, and we collaborate on vegetation resource assessment projects around the state. CNPS is also the publisher of the authoritative Manual of California Vegetation, you can purchase a copy HERE. To support the work of the CNPS, please JOIN NOW and become a member!The CDFG Vegetation Classification and Mapping Program develops and maintains California's expression of the National Vegetation Classification System. We implement its use through assessment and mapping projects in high-priority conservation and management areas, through training programs, and through working continuously on best management practices for field assessment, classification of vegetation data, and fine-scale vegetation mapping.HOW THE OVERLAY LAYERS WERE CREATED:Nserve and GapLC Sources: Early shortcomings in the NVC standard led to Natureserve's development of a mid-scale mapping-friendly "Ecological Systems" standard roughly corresponding to the "Group" level of the NVC, which facilitated NVC-based mapping of entire continents. Current scientific work is leading to the incorporation of Ecological Systems into the NVC as group and macrogroup concepts are revised. Natureserve and Gap Ecological Systems layers differ slightly even though both were created from 30m landsat data and both follow the NVC-related Ecological Systems Classification curated by Natureserve. In either case, the vector overlay was created by first enforcing a .3ha minimum mapping unit, that required deleting any classes consisting of fewer than 4 contiguous landsat cells either side-side or cornerwise. This got around the statistical problem of numerous single-cell classes with types that seemed improbable given their matrix, and would have been inaccurate to use as an n=1 sample compared to the weak but useable n=4 sample. A primary goal in this elimination was to best preserve riparian and road features that might only be one pixel wide, hence the use of cornerwise contiguous groupings. Eliminated cell groups were absorbed into whatever neighboring class they shared the longest boundary with. The remaining raster groups were vectorized with light simplification to smooth out the stairstep patterns of raster data and hopefully improve the fidelity of the boundaries with the landscape. The resultant vectors show a range of fidelity with the landscape, where there is less apparent fidelity it must be remembered that ecosystems are normally classified with a mixture of visible and non-visible characteristics including soil, elevation and slope. Boundaries can be assigned based on the difference between 10% shrub cover and 20% shrub cover. Often large landscape areas would create "godzilla" polygons of more than 50,000 vertices, which can affect performance. These were eliminated using SIMPLIFY POLYGONS to reduce vertex spacing from 30m down to 50-60m where possible. Where not possible DICE was used, which bisects all large polygons with arbitrary internal divisions until no polygon has more than 50,000 vertices. To create midscale layers, ecological systems were dissolved into the macrogroups that they belonged to and resymbolized on macrogroup. This was another frequent source for godzillas as larger landscape units were delineate, so simplify and dice were then run again. Where the base ecol system tiles could only be served up by individual partition tile, macrogroups typically exhibited a 10-1 or 20-1 reduction in feature count allowing them to be assembled into single integrated map services by region, ie NW, SW. CNPS / CDFW / National Park Service Sources: (see also base service definition page) Unlike the Landsat-based raster modelling of the Natureserve and Gap national ecological systems, the CNPS/CDFW/NPS data date back to the origin of the National Vegetation Classification effort to map the US national parks in the mid 1990's.
These mapping efforts are a hybrid of photo-interpretation, satellite and corollary data to create draft ecological land units, which are then sampled by field crews and traditional vegetation plot surveys to quantify and analyze vegetation composition and distribution into the final vector boundaries of the formal NVC classes identified and classified. As such these are much more accurate maps, but the tradeoff is they are only done on one field project area at a time so there is not yet a national or even statewide coverage of these detailed maps.
However, with almost 2/3d's of California already mapped, that time is approaching. The challenge in creating standard map layers for this wide diversity of projects over the 2 decades since NVC began is the extensive evolution in the NVC standard itself as well as evolution in the field techniques and tools. To create a consistent set of map layers, a master crosswalk table was built using every different classification known at the time each map was created and then crosswalking each as best as could be done into a master list of the currently-accepted classifications. This field is called the "NVC_NAME" in each of these layers, and it contains a mixture of scientific names and common names at many levels of the classification from association to division, whatever the ecologists were able to determine at the time. For further precision, this field is split out into scientific name equivalents and common name equivalents.MAP LAYER NAMING: The data sublayers in this webmap are all based on the US National Vegetation Classification, a partnership of the USGS GAP program, US Forest Service, Ecological Society of America and Natureserve, with adoption and support from many federal & state agencies and nonprofit conservation groups. The USNVC grew out of the US National Park Service Vegetation Mapping Program, a mid-1990's effort led by The Nature Conservancy, Esri and the University of California. The classification standard is now an international standard, with associated ecological mapping occurring around the world. NVC is a hierarchical taxonomy of 8 levels, from top down: Class, Subclass, Formation, Division, Macrogroup, Group, Alliance, Association. The layers in this webmap represent 4 distinct programs: 1. The California Native Plant Society/Calif Dept of Fish & Wildlife Vegetation Classification and Mapping Program (Full Description of these layers is at the CNPS MS10 Service Registration Page and Cnps MS10B Service Registration Page . 2. USGS Gap Protected Areas Database, full description at the PADUS registration page . 3. USGS Gap Landcover, full description below 4. Natureserve Ecological Systems, full description belowLAYER NAMING: All Layer names follow this pattern: Source - Program - Level - Scale - RegionSource - Program = who created the data: Nserve = Natureserve, GapLC = USGS Gap Program Landcover Data PADUS = USGS Gap Protected Areas of the USA program Cnps/Cdfw = California Native Plant Society/Calif Dept of Fish & Wildlife, often followed by the project name such as: SFhill = Sierra Foothills, Marin Open Space, MMWD = Marin Municipal Water District etc. National Parks are included and may be named by their standard 4-letter code ie YOSE = Yosemite, PORE = Point Reyes.Level: The level in the NVC Hierarchy which this layer is based on: Base = Alliances and Associations Mac =

Comparison of process documentation tools for SAP including Signavio, ARIS, Blueworks Live, and Microsoft Visio. Covers integration, modeling standards, collaboration, and best use cases.

This webmap is a collaboration between the California Native Plant Society (CNPS) and the California Dept of Fish and Game (CDFG).The CNPS Vegetation Program has worked for over 15 years to provide standards and tools for identifying and representing vegetation, as an important feature of California's natural heritage and biodiversity. Many knowledgeable ecologists and botanists support the program as volunteers and paid staff. Through grants, contracts, and grass-roots efforts, CNPS collects field data and compiles information into reports, manuals, and maps on California's vegetation, ecology and rare plants in order to better protect and manage them. We provide these services to governmental, non-governmental and other organizations, and we collaborate on vegetation resource assessment projects around the state. CNPS is also the publisher of the authoritative Manual of California Vegetation, you can purchase a copy HERE. To support the work of the CNPS, please JOIN NOW and become a member!The CDFG Vegetation Classification and Mapping Program develops and maintains California's expression of the National Vegetation Classification System. We implement its use through assessment and mapping projects in high-priority conservation and management areas, through training programs, and through working continuously on best management practices for field assessment, classification of vegetation data, and fine-scale vegetation mapping.HOW THE OVERLAY LAYERS WERE CREATED:Nserve and GapLC Sources: Early shortcomings in the NVC standard led to Natureserve's development of a mid-scale mapping-friendly "Ecological Systems" standard roughly corresponding to the "Group" level of the NVC, which facilitated NVC-based mapping of entire continents. Current scientific work is leading to the incorporation of Ecological Systems into the NVC as group and macrogroup concepts are revised. Natureserve and Gap Ecological Systems layers differ slightly even though both were created from 30m landsat data and both follow the NVC-related Ecological Systems Classification curated by Natureserve. In either case, the vector overlay was created by first enforcing a .3ha minimum mapping unit, that required deleting any classes consisting of fewer than 4 contiguous landsat cells either side-side or cornerwise. This got around the statistical problem of numerous single-cell classes with types that seemed improbable given their matrix, and would have been inaccurate to use as an n=1 sample compared to the weak but useable n=4 sample. A primary goal in this elimination was to best preserve riparian and road features that might only be one pixel wide, hence the use of cornerwise contiguous groupings. Eliminated cell groups were absorbed into whatever neighboring class they shared the longest boundary with. The remaining raster groups were vectorized with light simplification to smooth out the stairstep patterns of raster data and hopefully improve the fidelity of the boundaries with the landscape. The resultant vectors show a range of fidelity with the landscape, where there is less apparent fidelity it must be remembered that ecosystems are normally classified with a mixture of visible and non-visible characteristics including soil, elevation and slope. Boundaries can be assigned based on the difference between 10% shrub cover and 20% shrub cover. Often large landscape areas would create "godzilla" polygons of more than 50,000 vertices, which can affect performance. These were eliminated using SIMPLIFY POLYGONS to reduce vertex spacing from 30m down to 50-60m where possible. Where not possible DICE was used, which bisects all large polygons with arbitrary internal divisions until no polygon has more than 50,000 vertices. To create midscale layers, ecological systems were dissolved into the macrogroups that they belonged to and resymbolized on macrogroup. This was another frequent source for godzillas as larger landscape units were delineate, so simplify and dice were then run again. Where the base ecol system tiles could only be served up by individual partition tile, macrogroups typically exhibited a 10-1 or 20-1 reduction in feature count allowing them to be assembled into single integrated map services by region, ie NW, SW. CNPS / CDFW / National Park Service Sources: (see also base service definition page) Unlike the Landsat-based raster modelling of the Natureserve and Gap national ecological systems, the CNPS/CDFW/NPS data date back to the origin of the National Vegetation Classification effort to map the US national parks in the mid 1990's.
These mapping efforts are a hybrid of photo-interpretation, satellite and corollary data to create draft ecological land units, which are then sampled by field crews and traditional vegetation plot surveys to quantify and analyze vegetation composition and distribution into the final vector boundaries of the formal NVC classes identified and classified. As such these are much more accurate maps, but the tradeoff is they are only done on one field project area at a time so there is not yet a national or even statewide coverage of these detailed maps.
However, with almost 2/3d's of California already mapped, that time is approaching. The challenge in creating standard map layers for this wide diversity of projects over the 2 decades since NVC began is the extensive evolution in the NVC standard itself as well as evolution in the field techniques and tools. To create a consistent set of map layers, a master crosswalk table was built using every different classification known at the time each map was created and then crosswalking each as best as could be done into a master list of the currently-accepted classifications. This field is called the "NVC_NAME" in each of these layers, and it contains a mixture of scientific names and common names at many levels of the classification from association to division, whatever the ecologists were able to determine at the time. For further precision, this field is split out into scientific name equivalents and common name equivalents.MAP LAYER NAMING: The data sublayers in this webmap are all based on the US National Vegetation Classification, a partnership of the USGS GAP program, US Forest Service, Ecological Society of America and Natureserve, with adoption and support from many federal & state agencies and nonprofit conservation groups. The USNVC grew out of the US National Park Service Vegetation Mapping Program, a mid-1990's effort led by The Nature Conservancy, Esri and the University of California. The classification standard is now an international standard, with associated ecological mapping occurring around the world. NVC is a hierarchical taxonomy of 8 levels, from top down: Class, Subclass, Formation, Division, Macrogroup, Group, Alliance, Association. The layers in this webmap represent 4 distinct programs: 1. The California Native Plant Society/Calif Dept of Fish & Wildlife Vegetation Classification and Mapping Program (Full Description of these layers is at the CNPS MS10 Service Registration Page and Cnps MS10B Service Registration Page . 2. USGS Gap Protected Areas Database, full description at the PADUS registration page . 3. USGS Gap Landcover, full description below 4. Natureserve Ecological Systems, full description belowLAYER NAMING: All Layer names follow this pattern: Source - Program - Level - Scale - RegionSource - Program = who created the data: Nserve = Natureserve, GapLC = USGS Gap Program Landcover Data PADUS = USGS Gap Protected Areas of the USA program Cnps/Cdfw = California Native Plant Society/Calif Dept of Fish & Wildlife, often followed by the project name such as: SFhill = Sierra Foothills, Marin Open Space, MMWD = Marin Municipal Water District etc. National Parks are included and may be named by their standard 4-letter code ie YOSE = Yosemite, PORE = Point Reyes.Level: The level in the NVC Hierarchy which this layer is based on: Base = Alliances and Associations Mac = Macrogroups Sub = SubclassesScale: One of 3 basic scales at which this layer will appear: Base = base scale, approx 1:1k up to 1:36k Mid = 72k to about 500k Out = 1m to 10mRegion: The region that this layer covers, ie USA=USA, WEST= western USA,
Marin = Marin County. May not appear if redundant to the Source-Program text.LABEL & COLOR: These overlays utilize a separate labelling layer to make it easy to include or not include labels, as needed. These are named the same as the layer they label, with "LABEL" added, and often the color used for that label layer in order to help tell them apart on the map. Note there can be multiple different label layers for the same set of polygons, depending upon the attribute or naming style desired, ie scientific names or common names. Finally the order of these services in the sublayers of a map service is normally designed so that ALL of the label services appear above ANY/ALL of the vector services they refer to, to prevent a vector service writing on top of a label and obscuring it.MAP LAYER CATALOGThis map includes a test segment of Natureserve Ecological Systems in the US Southwest, with the following layers and sublayers:GapNsUSA BoundaryMasksALB2: A grid showing the boundaries that define each partition tile of the national vegetation map services, with regional and state boundaries in the USGS Gap US Albers projectionPadus Gap13 WM Base Scale plus Label: (Full PADUS FGDC Metadata here) Overlay vectors at 1k to 288k scale with separate 1k-288k Labelling services for one of 3 different attributes: --Landowner Name: Land owner and primary entity responsible for managing parcel when ‘Manager Name’ is not attributed (e.g. USFS, State Fish and

Global Network Mapping Software Market Report 2022 comes with the extensive industry analysis of development components, patterns, flows and sizes. The report also calculates present and past market values to forecast potential market management through the forecast period between 2022-2028. The report may be the best of what is a geographic area which expands the competitive landscape and industry perspective of the market.

The global Curriculum Mapping Software market is projected to reach USD XX million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). Growing demand for educational software to enhance teaching and learning, increasing need for curriculum alignment, and government initiatives to improve educational standards are the key factors driving market growth. Additionally, the cloud-based deployment model is gaining popularity due to its scalability, cost-effectiveness, and ease of access. The competitive landscape of the Curriculum Mapping Software market is characterized by a mix of established and emerging players. Top players in the market include Top Hat, Kiddom, PlanbookEdu, LearnZillion, Eduphoria!, OnCourse Systems for Education, Skyward, LessonWriter, Workday, School Software Group, Leepfrog Technologies, and currIQūnet. Companies are focusing on strategic partnerships, new product launches, and technological advancements to gain a competitive edge. The market is segmented based on application (higher education institutions, K-12 schools, and others), deployment type (cloud-based and on-premise), and region (North America, Europe, Asia Pacific, Middle East & Africa, and South America). North America holds the largest market share, followed by Europe.

Important Note: this item is marked as Deprecated and will no longer be maintained. Please use one of the following items instead:World Topographic Map Swiss Style (VT)World Topographic Map Swiss Style (VT) - deWorld Topographic Map (Vector Tile)This map is designed to be used as a basemap by GIS professionals and as a reference map by anyone. The map includes cities, water features, physiographic features, parks, landmarks, highways, roads, railways, airports, and administrative boundaries, overlaid on land cover and shaded relief imagery for added context. Alignment of boundaries is a presentation of the feature provided by our data vendors and does not imply endorsement by Esri or any governing authority.The map provides coverage for the world down to a scale of ~1:72k. Coverage is provided down to ~1:4k for the following areas: Africa, Australia and New Zealand; Europe and Russia; India; the continental United States and Hawaii; Canada; Mexico; most of the Middle East; Pacific Island nations; South America and Central America. Coverage down to ~1:1k and ~1:2k is available in select urban areas. This basemap was compiled from a variety of best available sources from several data providers, including the U.S. Geological Survey (USGS), U.S. Environmental Protection Agency (EPA) , U.S. National Park Service (NPS), Food and Agriculture Organization of the United Nations (FAO), Department of Natural Resources Canada (NRCAN), GeoBase, Agriculture and Agri-Food Canada, DeLorme, HERE, and Esri. Data for Africa and Pacific Island nations from ~1:288k to ~1:4k (~1:1k in select areas) was sourced from OpenStreetMap contributors. The data for the World Topographic Map is provided by the GIS community. You can contribute your data to this service and have it served by Esri. For details on the coverage in this map and the users who contributed data for this map via the Community Maps Program, view the list of Contributors for the World Topographic Map.Feedback: Have you ever seen a problem in the Esri World Topographic Map community basemap that you wanted to see fixed? You can use the Topographic Map Feedback web map to provide feedback on issues or errors that you see in the Esri World Topographic Map. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates.

The global network mapping software market size was valued at USD 2,325.4 million in 2025 and is projected to grow at a CAGR of 12.3% during the forecast period (2025-2033). The rapid growth of cloud-based, on-premises, and hybrid IT environments, coupled with the increasing adoption of network management best practices, are some of the key factors driving market growth. Furthermore, the need to enhance network visibility and control, improve performance, and simplify network troubleshooting is also contributing to the growing demand for network mapping software. Cloud-based and on-premises solutions held a significant market share in 2025. However, the cloud-based segment is expected to witness faster growth during the forecast period. The growing adoption of cloud-based services, the need for remote network management, and the cost-effectiveness of cloud-based solutions are driving the growth of this segment. In terms of application, the small and medium enterprises (SMEs) segment dominated the market in 2025, and it is expected to maintain its dominance throughout the forecast period. The increasing number of SMEs, the need for cost-effective network management solutions, and the growing awareness of network security are driving the growth of this segment. Network mapping software is a tool that helps businesses visualize and manage their networks. It can be used to create diagrams of the network, identify potential problems, and track down performance issues. The software can also be used to automate tasks such as device discovery and configuration.

World Imagery provides one meter or better satellite and aerial imagery for most of the world’s landmass and lower resolution satellite imagery worldwide. The map is currently comprised of the following sources:Worldwide 15-m resolution TerraColor imagery at small and medium map scales.Maxar imagery basemap products around the world: Vivid Premium at 15-cm HD resolution for select metropolitan areas, Vivid Advanced 30-cm HD for more than 1,000 metropolitan areas, and Vivid Standard from 1.2-m to 0.6-cm resolution for the most of the world, with 30-cm HD across the United States and parts of Western Europe. More information on the Maxar products is included below. High-resolution aerial photography contributed by the GIS User Community. This imagery ranges from 30-cm to 3-cm resolution. You can contribute your imagery to this map and have it served by Esri via the Community Maps Program.Maxar Basemap ProductsVivid PremiumProvides committed image currency in a high-resolution, high-quality image layer over defined metropolitan and high-interest areas across the globe. The product provides 15-cm HD resolution imagery.Vivid AdvancedProvides committed image currency in a high-resolution, high-quality image layer over defined metropolitan and high-interest areas across the globe. The product includes a mix of native 30-cm and 30-cm HD resolution imagery.Vivid StandardProvides a visually consistent and continuous image layer over large areas through advanced image mosaicking techniques, including tonal balancing and seamline blending across thousands of image strips. Available from 1.2-m down to 30-cm HD. More on Maxar HD.Updates and CoverageYou can use the World Imagery Updates app to learn more about recent updates and map coverage.CitationsThis layer includes imagery provider, collection date, resolution, accuracy, and source of the imagery. With the Identify tool in ArcGIS Desktop or the ArcGIS Online Map Viewer you can see imagery citations. Citations returned apply only to the available imagery at that location and scale. You may need to zoom in to view the best available imagery. Citations can also be accessed in the World Imagery with Metadata web map.UseYou can add this layer to the ArcGIS Online Map Viewer, ArcGIS Desktop, or ArcGIS Pro. To view this layer with a useful reference overlay, open the Imagery Hybrid web map.FeedbackHave you ever seen a problem in the Esri World Imagery Map that you wanted to report? You can use the Imagery Map Feedback web map to provide comments on issues. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates.

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.

Best Management Practices (BMPs) are structural controls used to manage stormwater runoff. Examples include green roofs, rain gardens, and cisterns. BMPs reduce the effects of stormwater pollution and help restore the District’s waterbodies. The District’s stormwater regulations require that large construction or renovation projects install BMPs to manage stormwater runoff once construction is complete. The District also provides financial incentives for properties that install BMPs voluntarily. This dataset includes BMPs that were installed to comply with the District’s stormwater regulations, to participate in the Stormwater Retention Credit (SRC) trading program, to participate in the RiverSmart Homes program, to participate in the Green Roof Rebate program, or to participate in the RiverSmart Rewards stormwater fee discount program. These BMPs have been reviewed by the Department of Energy and Environment (DOEE) as part of these programs. This dataset is updated weekly with data from the District’s Stormwater Database.

5-Digit and 3-Digit ZIP Code data for Maptitude mapping software are from Caliper Corporation and contain boundaries and demographic data.

In 2020, under a State Government funded program to test the application of machine learning (ML) to government data, the Dept for Energy and Mining (DEM) and the Australian Institute of Machine Learning (AIML) ran a trial to test the capability... In 2020, under a State Government funded program to test the application of machine learning (ML) to government data, the Dept for Energy and Mining (DEM) and the Australian Institute of Machine Learning (AIML) ran a trial to test the capability of ML to predict rock outcrop across the state using a variety of different remote sensing datasets and existing mapping data from DEM’s geological mapping programs. The AIML developed a tool that works by taking locations of known, mapped outcrops (known as the ground truth) and remote sensing imagery and uses machine learning, specifically deep learning, to train a model that can predict the location of the ground truth from the remote sensing imagery. The tool can then take the same remote sensing imagery for the state of South Australia and predict the probable locations of outcrops across the entire state and also output a value that indicates the confidence in its predictions. The Automated Outcrop Prediction Package is the outcomes from this project. This file contains the results of running the outcrop prediction tool using The web map tile service of a mosaic of the best available orthoimagery for South Australia (WMTS) only.

As part of the Maine Beach Mapping Program (MBMAP), MGS surveys annual alongshore shoreline positions (see Beach_Mapping_Shorelines). Using these shoreline positions and guidance from the USGS Digital Shoreline Analysis System (DSAS). DSAS is referenced as Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Ergul, Ayhan, 2009, Digital Shoreline Analysis System (DSAS) version 4.0— An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2008-1278. For more information on DSAS and the methodology DSAS employs, please see: https://woodshole.er.usgs.gov/project-pages/DSAS/. The supporting DSAS User Guide which describes how DSAS works and how statistics are calculated is available here: http://www.maine.gov/dacf/mgs/hazards/beach_mapping/DSAS_manual.pdf. MGS wrote a database procedure following protocols outlined in DSAS that allows for the calculation of different shoreline change rates and supporting statistics. This was done so that MGS no longer needed to depend on USGS updates to the DSAS software to keep current with ArcGIS software updates. The script casts shoreline-perpendicular transects at a set spacing (in this case, 10-m intervals along the shoreline), from a preset baseline (located landward of the monitored shorelines), and calculates a range of shoreline change statistics, including: Process Time: The time when the statistics were calculated. TransectID: The ID of the transect (including the group or line section ID; for example, 1-1, is line 1, transect 1) SCE: Shoreline Change Envelope. The distance, in meters, between the shoreline farthest from and closests to the baseline at each transect. NSM: Net Shoreline Movement. The distance, in meters, between the oldest and youngest shorelines for each tranect. EPR: End Point Rate. A shoreline change rate, in meters/year, calculated by dividing the NSM by the time elapsed between the oldest and youngest shorelines at each transect. LRR: Linear Regression Rate. A shoreline change rate, in meters/year, calculated by fitting a least-squares regression line to all of the shoreline points for a particular transect. The distance from the baseline, in meters, is plotted against the shoreline date, and slope of the line that provides the best fit is the LRR. LR2: The R-squared statistic, or coefficient of determination. The percentage of variance in the data that is explained by a regression, or in this case, the LRR value. It is a dimensionless index that ranges from 1.0 (a perfect fit, with the best fit line explaining all variation) to 0.0 (a bad fit, with the best fit line explaining little to no variation) and measures how successfully the best fit line (LRR) accounts for variation in the data. LCI95: Standard error of the slope at the 95% confidence interval. Calculated by muliplying the standard error, or standard deviation, of the slope by the two-tailed test statistic at the user-specified confidence percentage. For example if a reported LRR is 1.34 m/yr and a calculated LCI95 is 0.50, the band of confidence around the LRR is +/- 0.50. In other words, you can be 95% confidence that the true rate of change is between 0.84 and 1.84 m/yr. LRR_ft: The Linear Regression Rate, converted to feet/year. LCI95_ft: The LCI95, converted to feet. EPR_ft: The End Point Rate converted to feet.

The U. S. Army Corps of Engineers (USACE) National Coastal Mapping Program (NCMP) is designed to provide high-resolution elevation and imagery data along U.S. shorelines on a recurring basis. USACE Headquarters funds the NCMP to support regional sediment management, construction, operations, and regulatory functions in the coastal zone.

On January 25, 2018 FEMA replaced this map with a new NFHL map with additional functionality which allows users to print official flood maps. On April 1, 2018 this map and NFHL link will no longer function. Please update your bookmark to https://hazards-fema.maps.arcgis.com/apps/webappviewer/index.html?id=8b0adb51996444d4879338b5529aa9cd. For more information on NFHL data availability, please visit the NFHL GIS Services page at https://hazards.fema.gov/femaportal/wps/portal/NFHLWMSAs of August 1, 2017 all FEMA systems will require the use of the “https” protocol, and “http” links will no longer function. This may impact NFHL web services. The FEMA GeoPlatform (including this map) will not be affected by this change. For more information on how NFHL GIS services will be impacted, please visit the NFHL GIS Services page at https://hazards.fema.gov/femaportal/wps/portal/NFHLWMS.An NFHL FIRMette print service is now available HERE. (For a video tutorial, click here.)OverviewThe National Flood Hazard Layer (NFHL) dataset represents the current effective flood data for the country, where maps have been modernized. It is a compilation of effective Flood Insurance Rate Map (FIRM) databases and Letters of Map Change (LOMCs). The NFHL is updated as studies go effective. For more information, visit FEMA's Map Service Center (MSC). Base Map ConsiderationsThe default base map is from a USGS service and conforms to FEMA's specification for horizontal accuracy. This base map from The National Map (TNM) consists of National Agriculture Imagery Program (NAIP) and high resolution orthoimagery (HRO) that combine the visual attributes of an aerial photograph with the spatial accuracy and reliability of a map. This map should be considered the best online resource to use for official National Flood Insurance Program (NFIP) purposes when determining locations in relation to regulatory flood hazard information. If a different base map is used with the NFHL, the accuracy specification may not be met and the resulting map should be used for general reference only, and not official NFIP purposes. Users can download a simplified base map from the USGS service via: https://viewer.nationalmap.gov/services/ For the specifics of FEMA’s policy on the use of digital flood hazard data for NFIP purposes see: http://www.fema.gov/library/viewRecord.do?id=3235Letter of Map Amendment (LOMA) pointsLOMA point locations are approximate. The location of the LOMA is referenced in the legal description of the letter itself. Click the LOMA point for a link to the letter (use the arrows at the top of the popup window to bring up the LOMA info, if needed).This LOMA database may include LOMAs that are no longer effective. To be certain a particular LOMA is currently valid, please check relevant documentation at https://msc.fema.gov/ . Relevant documents can be found for a particular community by choosing to "Search All Products", and finding the community by State and County. Documents include LOMAs found in the "Effective Products" and "LOMC" folders, as well as Revalidations (those LOMAs which are still considered to be effective after a map is revised).Updates3/27/2017 - Updated all references to https to prevent issues with mixed content.5/11/2016 - Added link to NFHL FIRMette Print Service. Updated LOMA and CBRS popup notes.2/20/2014 - Created a General Reference map for use when the USGS base map service is down. Renamed this map to "Official".Further InformationSpecific questions about FEMA flood maps can be directed to FEMAMapSpecialist@riskmapcds.comFor more flood map data, tool, and viewing options, visit the FEMA NFHL page. Information about connecting to web map services (REST, WMS, WFS) can be found here.Several fact sheets are available to help you learn more about FEMA’s NFHL utility: National Flood Hazard Layer (NFHL) GIS Services Users GuideNational Flood Hazard Layer (NFHL): New Products and Services for FEMA's Flood Hazard Map DataMoving to Digital Flood Hazard Information Standards for Flood Risk Analysis and MappingNFHL GIS Data: Perform Spatial Analyses and Make Custom Maps and Reports

The National Mine Map Repository (NMMR) maintains point locations for mines appearing on maps within its archive. This dataset is intended to help connect the Office of Surface Mining Reclamation and Enforcement, other federal, state, and local government agencies, private industry, and the general public with archived mine maps in the NMMR's collection. The coordinates for mine point locations represent the best information the NMMR has for the location of the mine. As much as possible, the NMMR strives to find precise locations for all historic mines appearing on mine maps. When this is not possible, another feature as close to the mine as is known is used. This information is reflected in the mine point symbols. However, the NMMR cannot guarantee the accuracy of mine point locations or any other information on or derived from mine maps. The NMMR is part of the United States Department of the Interior, Office of Surface Mining Reclamation and Enforcement (OSMRE). The mission of the NMMR is to preserve abandoned mine maps, to correlate those maps to the surface topography, and to provide the public with quality map products and services. It serves as a point of reference for maps and other information on surface and underground coal, metal, and non-metal mines from throughout the United States. It also serves as a location to retrieve mine maps in an emergency. Some of the information that can be found in the repository includes: Mine and company names, Mine plans including mains, rooms, and pillars, Man-ways, shafts, and mine surface openings. Geological information such as coal bed names, bed thicknesses, bed depths and elevations, bed outcrops, drill-hole data, cross-sections, stratigraphic columns, and mineral assays. Geographical information including historic railroad lines, roads, coal towns, surface facilities and structures, ponds, streams, and property survey lines, gas well and drill-hole locations. Please note: Map images are not available for download from this dataset. They can be requested by contacting NMMR staff and providing them with the desired Document Numbers. NMMR staff also have additional search capabilities and can fulfill more complex requests if necessary. See the NMMR website homepage for contact information: https://www.osmre.gov/programs/national-mine-map-repository. There is no charge for noncommercial use of the maps. Commercial uses will incur a $46/hour research fee for fulfilling requests.

This map is designed to be used as a basemap by GIS professionals and as a reference map by anyone. The map includes cities, water features, physiographic features, parks, landmarks, highways, roads, railways, airports, and administrative boundaries, overlaid on land cover and shaded relief imagery for added context. Alignment of boundaries is a presentation of the feature provided by our data vendors and does not imply endorsement by Esri or any governing authority.The map provides coverage for the world down to a scale of ~1:72k. Coverage is provided down to ~1:4k for the following areas: Africa, Australia and New Zealand; Europe and Russia; India; the continental United States and Hawaii; Canada; Mexico; most of the Middle East; Pacific Island nations; South America and Central America. Coverage down to ~1:1k and ~1:2k is available in select urban areas. This basemap was compiled from a variety of best available sources from several data providers, including the U.S. Geological Survey (USGS), U.S. Environmental Protection Agency (EPA) , U.S. National Park Service (NPS), Food and Agriculture Organization of the United Nations (FAO), Department of Natural Resources Canada (NRCAN), GeoBase, Agriculture and Agri-Food Canada, DeLorme, HERE, and Esri. Data for Africa and Pacific Island nations from ~1:288k to ~1:4k (~1:1k in select areas) was sourced from OpenStreetMap contributors. Specific country list and documentation of Esri's process for including OSM data is available to view.The data for the World Topographic Map is provided by the GIS community. You can contribute your data to this service and have it served by Esri. For details on the coverage in this map and the users who contributed data for this map via the Community Maps Program, view the list of Contributors for the World Topographic Map.Feedback: Have you ever seen a problem in the Esri World Topographic Map community basemap that you wanted to see fixed? You can use the Topographic Map Feedback web map to provide feedback on issues or errors that you see in the Esri World Topographic Map. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates.

The California Department of Transportation (Caltrans) and the California Energy Commission (CEC) are partnering to implement the federal National Electric Vehicle Infrastructure (NEVI) Program, which allocates $5 billion to the states to create a nationwide, interconnected network of DC fast chargers along the National Highway Systems. California's share will be $384 million over 5 years. This map was developed to help prospective applicants and interested parties identify eligible areas for infrastructure deployment.

Instructions

Viewers can display Alternative Fuel Corridors, NEVI 3 Project Regions, NEVI 2 (GFO-24-606) corridor groups and corridor segments, NEVI 1 (GFO-23-601) corridor groups, electric vehicle (EV) charging stations, Tribal lands, California-designated low-income or disadvantaged communities, metropolitan planning organizations, regional transportation planning agencies, California state legislative districts, counties, Caltrans districts, utility districts, and congressional districts in this interactive map. The map initially displays the state's Alternative Fuel Corridors and the NEVI 3 Project Regions. Viewers can toggle individual layers on and off using the map layers menu located to the right of the map. Some layers are organized into groups; viewers can toggle all layers within a group or select specific ones. The legend to the left of the map will show the layers that have been turned on. There is a search tool to the right of the map that enables viewers to type in an address and locate the address on the map. A basemap selector allows viewers to view road detail. Additional information on the map can be found under the information icon. Viewers can download the map files by clicking on the Data and Supplemental Links icon.

Map layers include:

• An Alternative Fuel Corridors layer that shows designated corridors for California's NEVI funding program. Users can click on a corridor segment to view the start and end of each corridor. When selected, a pop-up window will appear that shows the corridor name and description.
• A NEVI 3 (GFO-24-606) Project Regions layer that shows the regions the state has been divided into for allocating funding under Round 3 of California's NEVI funding program.
• A NEVI 2 (GFO-24-606) corridor groups layer shows corridor groups eligible for Round 2 of California's NEVI funding program. Note that this layer is only visible when the Alternative Fuels Corridors layer is turned off.
• NEVI 2 (GFO-24-606) corridor group labels for enhanced accessibility. Note that labels are only visible at certain ranges (zoom in and out to view labels) and when the Alternative Fuels Corridors layer is turned off.
• NEVI 2 (GFO-24-606) corridor segment labels for enhanced accessibility. Note that labels are only visible at certain ranges (zoom in and out to view labels) and when the Alternative Fuels Corridors layer is turned off.
• A NEVI 1 (GFO-23-601) corridor groups layer that shows corridor groups eligible for Round 1 of California's NEVI funding program. Note that this layer is only visible when the Alternative Fuels Corridors layer is turned off.
• A layer showing the locations of EV charging stations awarded through Round 1 of California's NEVI funding program that are planned for deployment.
• A layer showing California-designated disadvantaged or low-income communities.
• A layer showing California Federally Recognized Tribal Lands.
• A layer showing Metropolitan Planning Organizations.
• A layer showing Regional Transportation Planning Agencies.
• A layer showing California State Senate Districts.
• A layer showing California State Assembly Districts.
• A layer showing California Counties.
• EV charging stations layers (existing DC fast charging stations that are located within one mile of a NEVI-eligible corridor offramp). One layer shows locations of EV charging stations with DC fast charging capabilities that meet the NEVI power level and four-port minimum requirement and could likely become part of the NEVI network if these stations became compliant with other NEVI program requirements such as data reporting. The other layer shows DC fast charging stations that do not meet NEVI power-level or port count requirements but could be upgraded to be NEVI-compliant. Users can click on EV charging stations and a pop-up window will appear with more information on the station (i.e., station address, total port count, minimum NEVI standard, etc.). These data were last updated in March 2024. Please refer to the Department of Energy's Alternative Fuels Data Center and PlugShare for up-to-date existing and planned DC fast charger site information.
• A layer showing Caltrans Districts.
• A layer showing Electric Utilities (IOUs and POUs).
• A layer showing California Congressional Districts.

Background

The $5 billion NEVI Program is part of the $1.2 trillion Infrastructure Investment and Jobs Act (IIJA) signed into law by President Biden in November 2021. IIJA commits significant federal funding to clean transportation and energy programs throughout the U.S. to reduce climate changing greenhouse gas emissions. Caltrans is the designated lead agency for NEVI. The CEC is their designated state energy partner. Caltrans and the CEC have partnered to create California's Deployment Plan for the National Electric Vehicle Infrastructure Program that describes how the state plans to allocate its $384 million share of federal NEVI funds to build out a network of modern, high-powered DC fast chargers along federally designated Alternative Fuel Corridors throughout California. California's latest NEVI Deployment Plan was submitted to the Joint Office of Energy and Transportation on August 1, 2023 and approved on September 29, 2023. The Plans must be updated each year over 5 years.