The ArcGIS Pro Permitting and Environmental Information Tool (APPEIT) Project Package includes all of the layers that are in the NTIA Permitting and Environmental Information Application as well as the APPEIT Tool which will allow users to input a project area and determine what layers from the application overlap with it. An overview of the project package and the APPEIT tool is provided below. User instructions on how to use the tool are available here. Instructions now include how to customize the tool by adding your own data. A video explaining how to use the Project Package is also available here. Project Package OverviewThis map package includes all of the layers from the NTIA Permitting and Environmental Information Application. The layers included are all feature services from various Federal and State agencies. The map package was created with ArcGIS Pro 3.4.0. The map package was created to allow users easy access to all feature services including symbology. The map package will allow users to avoid downloading datasets individually and easily incorporate into their own GIS system. The map package includes three maps.1. Permitting and Environmental Information Application Layers for GIS Analysis - This map includes all of the map tabs shown in the application, except State Data which is provided in another tab. This map includes feature services that can be used for analysis with other project layers such as a route or project area. 2. Permitting and Environmental Information Application Layers – For Reference Only - This map includes layers that cannot be used for analysis since they are either imagery or tile layers.3. State Data - Reference Only - This map includes all relevant state data that is shown in the application.The NTIA Permitting and Environmental Information Application was created to help with your permitting planning and environmental review preparation efforts by providing access to multiple maps from publicly available sources, including federal review, permitting, and resource agencies. The application should be used for informational purposes only and is intended solely to assist users with preliminary identification of areas that may require permits or planning to avoid potentially significant impacts to environmental resources subject to the National Environmental Policy Act (NEPA) and other statutory requirements. Multiple maps are provided in the application which are created from public sources. This application does not have an exhaustive list of everything you need for permitting or environmental review for a project but is an initial starting point to see what might be required.APPEIT Tool OverviewThe Department of Commerce’s National Telecommunications and Information Administration (NTIA) is providing the ArcGIS Pro Permitting and Environmental Information Tool (APPEIT) to help federal broadband grant recipients and subgrantees identify permits and environmental factors as they plan routes for their broadband deployments. Identifying permit requirements early, initiating pre-application coordination with permitting agencies, and avoiding environmental impacts help drive successful infrastructure projects. NTIA’s public release of the APPEIT tool supports government-wide efforts to improve permitting and explore how online and digital technologies can promote efficient environmental reviews. This Esri ArcGIS Pro tool is included in the map package and was created to support permitting, planning, and environmental review preparation efforts by providing access to data layers from publicly available sources, including federal review, permitting, and resource agencies. An SOP on how to use the tool is available here. For the full list of APPEIT layers, see Appendix Table 1 in the SOP. The tool is comprised of an ArcGIS Pro Project containing a custom ArcGIS Toolbox tool, linked web map shared by the NTIA’s National Broadband Map (NBAM), a report template, and a Tasks item to guide users through using the tool. This ArcGIS Pro project and its contents (maps and data) are consolidated into this (.ppkx) project file. To use APPEIT, users will input a project area boundary or project route line in a shapefile or feature class format. The tool will return as a CSV and PDF report that lists any federal layers from the ArcGIS Pro Permitting and Environmental Information Web Map that intersect the project. Users may only input a single project area or line at a time; multiple projects or project segments will need to be screened separately. For project route lines, users are required to specify a buffer distance. The buffer distance that is used for broadband projects should be determined by the area of anticipated impact and should generally not exceed 500 feet. For example, the State of Maryland recommends a 100-foot buffer for broadband permitting. The tool restricts buffers to two miles to ensure relevant results. DisclaimerThis document is intended solely to assist federal broadband grant recipients and subgrantees in better understanding Infrastructure Investment and Jobs Act (IIJA) broadband grant programs and the requirements set forth in the Notice of Funding Opportunity (NOFO) for this program. This document does not and is not intended to supersede, modify, or otherwise alter applicable statutory or regulatory requirements, the terms and conditions of the award, or the specific application requirements set forth in the NOFO. In all cases, statutory and regulatory mandates, the terms and conditions of the award, the requirements set forth in the NOFO, and follow-on policies and guidance, shall prevail over any inconsistencies contained in this document. NTIA’s ArcGIS Pro Permitting and Environmental Information Tool (APPEIT) should be used for informational purposes only and is intended solely to assist users with preliminary identification of broadband deployments that may require permits or planning to avoid potentially significant impacts to environmental resources subject to the National Environmental Policy Act (NEPA) and other statutory requirements. The tool is not an exhaustive or complete resource and does not and is not intended to substitute for, supersede, modify, or otherwise alter any applicable statutory or regulatory requirements, or the specific application requirements set forth in any NTIA NOFO, Terms and Conditions, or Special Award Condition. In all cases, statutory and regulatory mandates, and the requirements set forth in NTIA grant documents, shall prevail over any inconsistencies contained in these templates. The tool relies on publicly available data available on the websites of other federal, state, local, and Tribal agencies, and in some instances, private organizations and research institutions. Layers identified with a double asterisk include information relevant to determining if an “extraordinary circumstance” may warrant more detailed environmental review when a categorical exclusion may otherwise apply. While NTIA continues to make amendments to its websites to comply with Section 508, NTIA cannot ensure Section 508 compliance of federal and non-federal websites or resources users may access from links on NTIA websites. All data is presented “as is,” “as available” for informational purposes. NTIA does not warrant the accuracy, adequacy, or completeness of this information and expressly disclaims liability for any errors or omissions. Please e-mail NTIAanalytics@ntia.gov with any questions.

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

Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000-2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000-2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990-1999 and 2000-2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974-2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer. Maps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990-1999 and 2000-2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990-1999 and 2000-2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000-2009 than during 1990-1999; and that inland water levels were generally lower during 2000-2009 than during 1990-1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000–2009 than during 1990-1999. Mean October water levels during 2000-2009 were generally higher than during 1990-1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.

The Digital Geologic-GIS Map of Eisenhower National Historic Site, Pennsylvania is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (eise_geology.gdb), and a 2.) Open Geospatial Consortium (OGC) geopackage. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (eise_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (eise_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (gett_eise_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (gett_eise_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (eise_geology_metadata_faq.pdf). Please read the gett_eise_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. QGIS software is available for free at: https://www.qgis.org/en/site/. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (eise_geology_metadata.txt or eise_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:62,500 and United States National Map Accuracy Standards features are within (horizontally) 31.8 meters or 104.2 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The National Hydrography Dataset Plus High Resolution (NHDplus High Resolution) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US Geological Survey, NHDPlus High Resolution provides mean annual flow and velocity estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.For more information on the NHDPlus High Resolution dataset see the User’s Guide for the National Hydrography Dataset Plus (NHDPlus) High Resolution.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territoriesGeographic Extent: The Contiguous United States, Hawaii, portions of Alaska, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: USGSUpdate Frequency: AnnualPublication Date: July 2022This layer was symbolized in the ArcGIS Map Viewer and while the features will draw in the Classic Map Viewer the advanced symbology will not. Prior to publication, the network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original dataset. No data values -9999 and -9998 were converted to Null values.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute.Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map.Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

GapMaps Live is an easy-to-use location intelligence platform available across 25 countries globally that allows you to visualise your own store data, combined with the latest demographic, economic and population movement intel right down to the micro level so you can make faster, smarter and surer decisions when planning your network growth strategy.

With one single login, you can access the latest estimates on resident and worker populations, census metrics (eg. age, income, ethnicity), consuming class, retail spend insights and point-of-interest data across a range of categories including fast food, cafe, fitness, supermarket/grocery and more.

Some of the world's biggest brands including McDonalds, Subway, Burger King, Anytime Fitness and Dominos use GapMaps Live as a vital strategic tool where business success relies on up-to-date, easy to understand, location intel that can power business case validation and drive rapid decision making.

Primary Use Cases for GapMaps Live includes:

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 and where to find more of them.
3. Analyse your catchment areas at a granular grid levels using all the key metrics
4. Target Marketing: Develop effective marketing strategies to acquire more customers.
5. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)
6. Customer Profiling
7. Target Marketing
8. Market Share Analysis

Some of features our clients love about GapMaps Live include: - View business locations, competitor locations, demographic, economic and social data around your business or selected location - Understand consumer visitation patterns (“where from” and “where to”), frequency of visits, dwell time of visits, profiles of consumers and much more. - Save searched locations and drop pins - Turn on/off all location listings by category - View and filter data by metadata tags, for example hours of operation, contact details, services provided - Combine public data in GapMaps with views of private data Layers - View data in layers to understand impact of different data Sources - Share maps with teams - Generate demographic reports and comparative analyses on different locations based on drive time, walk time or radius. - Access multiple countries and brands with a single logon - Access multiple brands under a parent login - Capture field data such as photos, notes and documents using GapMaps Connect and integrate with GapMaps Live to get detailed insights on existing and proposed store locations.

Retirement Notice: This item is in mature support as of February 2023 and will be retired in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.This layer displays change in pixels of the Sentinel-2 10m Land Use/Land Cover product developed by Esri, Impact Observatory, and Microsoft. Available years to compare with 2021 are 2018, 2019 and 2020. By default, the layer shows all comparisons together, in effect showing what changed 2018-2021. But the layer may be changed to show one of three specific pairs of years, 2018-2021, 2019-2021, or 2020-2021.Showing just one pair of years in ArcGIS Online Map Viewer To show just one pair of years in ArcGIS Online Map viewer, create a filter. 1. Click the filter button. 2. Next, click add expression. 3. In the expression dialogue, specify a pair of years with the ProductName attribute. Use the following example in your expression dialogue to show only places that changed between 2020 and 2021:ProductNameis2020-2021 By default, places that do not change appear as a transparent symbol in ArcGIS Pro. But in ArcGIS Online Map Viewer, a transparent symbol may need to be set for these places after a filter is chosen. To do this: 4. Click the styles button.5. Under unique values click style options. 6. Click the symbol next to No Change at the bottom of the legend. 7. Click the slider next to "enable fill" to turn the symbol off. Showing just one pair of years in ArcGIS Pro To show just one pair of years in ArcGIS Pro, choose one of the layer's processing templates to single out a particular pair of years. The processing template applies a definition query that works in ArcGIS Pro. 1. To choose a processing template, right click the layer in the table of contents for ArcGIS Pro and choose properties. 2. In the dialogue that comes up, choose the tab that says processing templates. 3. On the right where it says processing template, choose the pair of years you would like to display. The processing template will stay applied for any analysis you may want to perform as well. How the change layer was created, combining LULC classes from two yearsImpact Observatory, Esri, and Microsoft used artificial intelligence to classify the world in 10 Land Use/Land Cover (LULC) classes for the years 2017-2021. Mosaics serve the following sets of change rasters in a single global layer: Change between 2018 and 2021Change between 2019 and 2021Change between 2020 and 2021To make this change layer, Esri used an arithmetic operation combining the cells from a source year and 2021 to make a change index value. ((from year * 16) + to year) In the example of the change between 2020 and 2021, the from year (2020) was multiplied by 16, then added to the to year (2021). Then the combined number is served as an index in an 8 bit unsigned mosaic with an attribute table which describes what changed or did not change in that timeframe. Variable mapped: Change in land cover between 2018, 2019, or 2020 and 2021 Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022 What can you do with this layer?Global LULC maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land cover anywhere on Earth. This layer can also be used in analyses that require land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes. Land Cover processingThis map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world. The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map. Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch. Class definitions1. WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2. TreesAny significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4. Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8. Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. Rangeland Open areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.For questions please email environment@esri.com

Retirement Notice: This item is in mature support as of June 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.Areas protected from conversion include areas that are permanently protected and managed for biodiversity such as Wilderness Areas and National Parks. In addition to protected lands, portions of areas protected from conversion includes multiple-use lands that are subject to extractive uses such as mining, logging, and off-highway vehicle use. These areas are managed to maintain a mostly undeveloped landscape including many areas managed by the Bureau of Land Management and US Forest Service. The Protected Areas Database of the United States classifies lands into four GAP Status classes. This layer displays lands managed for biodiversity conservation (GAP Status 1 and 2) and multiple-use lands (GAP Status 3). Dataset SummaryPhenomenon Mapped: Protected and multiple-use lands (GAP Status 1, 2, and 3) Units: MetersCell Size: 30.92208102 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, the US Virgin Islands, Guam, Northern Mariana Islands and American Samoa.Source: USGS National Gap Analysis Program PAD-US version 3.0Publication Date: July 2022 ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/ This layer displays protected areas from the Protected Areas Database of the United States version 3.0 created by the USGS National Gap Analysis Program. This layer displays areas managed for biodiversity where natural disturbances are allowed to proceed or are mimicked by management (GAP Status 1), areas managed for biodiversity where natural disturbance is suppressed (GAP Status 2), and multiple-use lands where extract activities are allowed (GAP Status 3). The source data for this layer are available here. A feature layer published from this dataset is also available. The polygon vector layer was converted to raster layers using the Polygon to Raster Tool using the National Elevation Dataset 1 arc second product as a snap raster. The service behind this layer was published with 8 functions allowing the user to select different views of the service. Other layers created from this service using functions include:USA Protected AreasUSA Unprotected AreasUSA Protected Areas - Gap Status 1-4USA Protected Areas - Gap Status 1USA Protected Areas - Gap Status 2USA Protected Areas - Gap Status 3USA Protected Areas - Gap Status 4 What can you do with this layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application. Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "Protected from Land Cover Conversion" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "Protected from Land Cover Conversion" in the search box, browse to the layer then click OK. In ArcGIS Pro you can use the built-in raster functions to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of 2024. Expected changes:Metadata is missing or incomplete for some layers at this time and will be continuously improved.We expect to update this layer roughly in line with CDTFA at some point, but will increase the update cadence over time as we are able to automate the final pieces of the process.This dataset is continuously updated as the source data from CDTFA is updated, as often as many times a month. If you require unchanging point-in-time data, export a copy for your own use rather than using the service directly in your applications.PurposeCounty and incorporated place (city) boundaries along with third party identifiers used to join in external data. Boundaries are from the authoritative source the California Department of Tax and Fee Administration (CDTFA), altered to show the counties as one polygon. This layer displays the city polygons on top of the County polygons so the area isn"t interrupted. The GEOID attribute information is added from the US Census. GEOID is based on merged State and County FIPS codes for the Counties. Abbreviations for Counties and Cities were added from Caltrans Division of Local Assistance (DLA) data. Place Type was populated with information extracted from the Census. Names and IDs from the US Board on Geographic Names (BGN), the authoritative source of place names as published in the Geographic Name Information System (GNIS), are attached as well. Finally, the coastline is used to separate coastal buffers from the land-based portions of jurisdictions. This feature layer is for public use.Related LayersThis dataset is part of a grouping of many datasets:Cities: Only the city boundaries and attributes, without any unincorporated areasWith Coastal BuffersWithout Coastal BuffersCounties: Full county boundaries and attributes, including all cities within as a single polygonWith Coastal BuffersWithout Coastal BuffersCities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.With Coastal Buffers (this dataset)Without Coastal BuffersPlace AbbreviationsUnincorporated Areas (Coming Soon)Census Designated Places (Coming Soon)Cartographic CoastlinePolygonLine source (Coming Soon)Working with Coastal BuffersThe dataset you are currently viewing includes the coastal buffers for cities and counties that have them in the authoritative source data from CDTFA. In the versions where they are included, they remain as a second polygon on cities or counties that have them, with all the same identifiers, and a value in the COASTAL field indicating if it"s an ocean or a bay buffer. If you wish to have a single polygon per jurisdiction that includes the coastal buffers, you can run a Dissolve on the version that has the coastal buffers on all the fields except COASTAL, Area_SqMi, Shape_Area, and Shape_Length to get a version with the correct identifiers.Point of ContactCalifornia Department of Technology, Office of Digital Services, odsdataservices@state.ca.govField and Abbreviation DefinitionsCOPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering systemPlace Name: CDTFA incorporated (city) or county nameCounty: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.Legal Place Name: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information SystemGNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.GEOID: numeric geographic identifiers from the US Census Bureau Place Type: Board on Geographic Names authorized nomenclature for boundary type published in the Geographic Name Information SystemPlace Abbr: CalTrans Division of Local Assistance abbreviations of incorporated area namesCNTY Abbr: CalTrans Division of Local Assistance abbreviations of county namesArea_SqMi: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.COASTAL: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.AccuracyCDTFA"s source data notes the following about accuracy:City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. COUNTY = county name; CITY = city name or unincorporated territory; COPRI = county number followed by the 3-digit city primary number used in the California State Board of Equalization"s 6-digit tax rate area numbering system (for the purpose of this map, unincorporated areas are assigned 000 to indicate that the area is not within a city).Boundary ProcessingThese data make a structural change from the source data. While the full boundaries provided by CDTFA include coastal buffers of varying sizes, many users need boundaries to end at the shoreline of the ocean or a bay. As a result, after examining existing city and county boundary layers, these datasets provide a coastline cut generally along the ocean facing coastline. For county boundaries in northern California, the cut runs near the Golden Gate Bridge, while for cities, we cut along the bay shoreline and into the edge of the Delta at the boundaries of Solano, Contra Costa, and Sacramento counties.In the services linked above, the versions that include the coastal buffers contain them as a second (or third) polygon for the city or county, with the value in the COASTAL field set to whether it"s a bay or ocean polygon. These can be processed back into a single polygon by dissolving on all the fields you wish to keep, since the attributes, other than the COASTAL field and geometry attributes (like areas) remain the same between the polygons for this purpose.SliversIn cases where a city or county"s boundary ends near a coastline, our coastline data may cross back and forth many times while roughly paralleling the jurisdiction"s boundary, resulting in many polygon slivers. We post-process the data to remove these slivers using a city/county boundary priority algorithm. That is, when the data run parallel to each other, we discard the coastline cut and keep the CDTFA-provided boundary, even if it extends into the ocean a small amount. This processing supports consistent boundaries for Fort Bragg, Point Arena, San Francisco, Pacifica, Half Moon Bay, and Capitola, in addition to others. More information on this algorithm will be provided soon.Coastline CaveatsSome cities have buffers extending into water bodies that we do not cut at the shoreline. These include South Lake Tahoe and Folsom, which extend into neighboring lakes, and San Diego and surrounding cities that extend into San Diego Bay, which our shoreline encloses. If you have feedback on the exclusion of these items, or others, from the shoreline cuts, please reach out using the contact information above.Offline UseThis service is fully enabled for sync and export using Esri Field Maps or other similar tools. Importantly, the GlobalID field exists only to support that use case and should not be used for any other purpose (see note in field descriptions).Updates and Date of ProcessingConcurrent with CDTFA updates, approximately every two weeks, Last Processed: 12/17/2024 by Nick Santos using code path at https://github.com/CDT-ODS-DevSecOps/cdt-ods-gis-city-county/ at commit 0bf269d24464c14c9cf4f7dea876aa562984db63. It incorporates updates from CDTFA as of 12/12/2024. Future updates will include improvements to metadata and update frequency.

It is an ArcGIS multirole locator with two roles:

1. Point Address - Generally more accurate results from rooftop location points. Includes a Subaddress if a unit number is located.
   
2. Street Address - Less accurate results from an estimated distance along a street centerline address range if a Point Address was not found.

Instructions for using the Geocoder via ArcGIS Pro, ArcGIS Online, and REST Services are below:

ArcGIS Pro
Web Services
ArcGIS Online

Note: The schema changed in February 2025 - please see below. We will post a roadmap of upcoming changes, but service URLs and schema are now stable. For deployment status of new services beginning in February 2025, see https://gis.data.ca.gov/pages/city-and-county-boundary-data-status. Additional roadmap and status links at the bottom of this metadata.This dataset is regularly updated as the source data from CDTFA is updated, as often as many times a month. If you require unchanging point-in-time data, export a copy for your own use rather than using the service directly in your applications. PurposeCounty boundaries along with third party identifiers used to join in external data. Boundaries are from the California Department of Tax and Fee Administration (CDTFA). These boundaries are the best available statewide data source in that CDTFA receives changes in incorporation and boundary lines from the Board of Equalization, who receives them from local jurisdictions for tax purposes. Boundary accuracy is not guaranteed, and though CDTFA works to align boundaries based on historical records and local changes, errors will exist. If you require a legal assessment of boundary location, contact a licensed surveyor.This dataset joins in multiple attributes and identifiers from the US Census Bureau and Board on Geographic Names to facilitate adding additional third party data sources. In addition, we attach attributes of our own to ease and reduce common processing needs and questions. Finally, coastal buffers are separated into separate polygons, leaving the land-based portions of jurisdictions and coastal buffers in adjacent polygons. This feature layer is for public use. Related LayersThis dataset is part of a grouping of many datasets:Cities: Only the city boundaries and attributes, without any unincorporated areasWith Coastal BuffersWithout Coastal BuffersCounties: Full county boundaries and attributes, including all cities within as a single polygonWith Coastal Buffers (this dataset)Without Coastal BuffersCities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.With Coastal BuffersWithout Coastal BuffersCity and County AbbreviationsUnincorporated Areas (Coming Soon)Census Designated PlacesCartographic CoastlinePolygonLine source (Coming Soon)State BoundaryWith Bay CutsWithout Bay Cuts Working with Coastal Buffers The dataset you are currently viewing includes the coastal buffers for cities and counties that have them in the source data from CDTFA. In the versions where they are included, they remain as a second polygon on cities or counties that have them, with all the same identifiers, and a value in the COASTAL field indicating if it"s an ocean or a bay buffer. If you wish to have a single polygon per jurisdiction that includes the coastal buffers, you can run a Dissolve on the version that has the coastal buffers on all the fields except OFFSHORE and AREA_SQMI to get a version with the correct identifiers. Point of ContactCalifornia Department of Technology, Office of Digital Services, gis@state.ca.gov Field and Abbreviation DefinitionsCDTFA_COUNTY: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.CDTFA_COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system. The boundary data originate with CDTFA's teams managing tax rate information, so this field is preserved and flows into this dataset.CENSUS_GEOID: numeric geographic identifiers from the US Census BureauCENSUS_PLACE_TYPE: City, County, or Town, stripped off the census name for identification purpose.GNIS_PLACE_NAME: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information SystemGNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.CDT_COUNTY_ABBR: Abbreviations of county names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 3 characters.CDT_NAME_SHORT: The name of the jurisdiction (city or county) with the word "City" or "County" stripped off the end. Some changes may come to how we process this value to make it more consistent.AREA_SQMI: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.OFFSHORE: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".PRIMARY_DOMAIN: Currently empty/null for all records. Placeholder field for official URL of the city or countyCENSUS_POPULATION: Currently null for all records. In the future, it will include the most recent US Census population estimate for the jurisdiction.GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead. Boundary AccuracyCounty boundaries were originally derived from a 1:24,000 accuracy dataset, with improvements made in some places to boundary alignments based on research into historical records and boundary changes as CDTFA learns of them. City boundary data are derived from pre-GIS tax maps, digitized at BOE and CDTFA, with adjustments made directly in GIS for new annexations, detachments, and corrections.Boundary accuracy within the dataset varies. While CDTFA strives to correctly include or exclude parcels from jurisdictions for accurate tax assessment, this dataset does not guarantee that a parcel is placed in the correct jurisdiction. When a parcel is in the correct jurisdiction, this dataset cannot guarantee accurate placement of boundary lines within or between parcels or rights of way. This dataset also provides no information on parcel boundaries. For exact jurisdictional or parcel boundary locations, please consult the county assessor's office and a licensed surveyor. CDTFA's data is used as the best available source because BOE and CDTFA receive information about changes in jurisdictions which otherwise need to be collected independently by an agency or company to compile into usable map boundaries. CDTFA maintains the best available statewide boundary information. CDTFA's source data notes the following about accuracy: City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. Boundary ProcessingThese data make a structural change from the source data. While the full boundaries provided by CDTFA include coastal buffers of varying sizes, many users need boundaries to end at the shoreline of the ocean or a bay. As a result, after examining existing city and county boundary layers, these datasets provide a coastline cut generally along the ocean facing coastline. For county boundaries in northern California, the cut runs near the Golden Gate Bridge, while for cities, we cut along the bay shoreline and into the edge of the Delta at the boundaries of Solano, Contra Costa, and Sacramento counties. In the services linked above, the versions that include the coastal buffers contain them as a second (or third) polygon for the city or county, with the value in the COASTAL field set to whether it"s a bay or ocean polygon. These can be processed back into a single polygon by dissolving on all the fields you wish to keep, since the attributes, other than the COASTAL field and geometry attributes (like areas) remain the same between the polygons for this purpose. SliversIn cases where a city or county"s boundary ends near a coastline, our coastline data may cross back and forth many times while roughly paralleling the jurisdiction"s boundary, resulting in many polygon slivers. We post-process the data to remove these slivers using a city/county boundary priority algorithm. That is, when the data run parallel to each other, we discard the coastline cut and keep the CDTFA-provided boundary, even if it extends into the ocean a small amount. This processing supports consistent boundaries for Fort Bragg, Point Arena, San Francisco, Pacifica, Half Moon Bay, and Capitola, in addition to others. More information on this algorithm will be provided soon. Coastline CaveatsSome cities have buffers extending into water bodies that we do not cut at the shoreline. These include South Lake Tahoe and Folsom, which extend into neighboring lakes, and San Diego and surrounding cities that extend into San Diego Bay, which our shoreline encloses. If you have feedback on the exclusion of these

Last Revised: February 2016 Map InformationThis nowCOAST™ time-enabled map service provides maps depicting the latest surface weather and marine weather observations at observing sites using the international station model. The station model is a method for representing information collected at an observing station using symbols and numbers. The station model depicts current weather conditions, cloud cover, wind speed, wind direction, visibility, air temperature, dew point temperature, sea surface water temperature, significant wave height, air pressure adjusted to mean sea level, and the change in air pressure over the last 3 hours. The circle in the model is centered over the latitude and longitude coordinates of the station. The total cloud cover is expressed as a fraction of cloud covering the sky and is indicated by the amount of circle filled in; however, all cloud cover values are presently displayed using the "Missing" symbol due to a problem with the source data. Present weather information is also not available for display at this time. Wind speed and direction are represented by a wind barb whose line extends from the cover cloud circle towards the direction from which the wind is blowing. The short lines or flags coming off the end of the long line are called barbs, which indicate wind speed in knots. Each normal barb represents 10 knots, while short barbs indicate 5 knots. A flag represents 50 knots. If there is no wind barb depicted, an outer circle around the cloud cover symbol indicates calm winds.Due to software limitations, the observations included in this map service are organized into three separate group layers: 1) Wind velocity (wind barb) observations, 2) Cloud Cover observations, and 3) All other observations, which are displayed as numerical values (e.g. Air Temperature, Wind Gust, Visibility, Sea Surface Temperature, etc.).Additionally, due to the density of weather/ocean observations in this map service, each of these group data layers has been split into ten individual "Scale Band" layers, with each one visible for a certain range of map scales. Thus, to ensure observations are displayed at any scale, users should make sure to always specify all ten corresponding scale band layers in every map request. This will result in the scale band most appropriate for your present zoom level being shown, resulting in a clean, uncluttered display. As you zoom in, additional observations will appear.The observations in this nowCOAST™ map service are updated approximately every 10 minutes. However, since the reporting frequency varies by network or station, the observations for a particular station may update only once per hour. For more detailed information about layer update frequency and timing, please reference the nowCOAST™ Dataset Update Schedule.Background InformationThe maps of near-real-time surface weather and ocean observations are based on non-restricted data obtained from the NWS Family of Services courtesy of NESDIS/OPSD and also the NWS Meteorological Assimilation Data Ingest System (MADIS). The data includes observations from terrestrial and maritime observing stations from the U.S.A. and other countries. For terrestrial networks, the platforms include but are not limited to ASOS, AWOS, RAWS, non-automated stations, U.S. Climate Reference Networks, many U.S. Geological Survey Stations via NWS HADS, several state DOT Road Weather Information Systems, and U.S. Historical Climatology Network-Modernization. For maritime areas, the platforms include NOS/CO-OPS National Water Level Observation Network (NWLON), NOS/CO-OPS Physical Oceanographic Real-Time System (PORTS), NWS/NDBC Fixed Buoys, NDBC Coastal-Marine Automated Network (C-MAN), drifting buoys, ferries, Regional Ocean Observing System (ROOS) coastal stations and buoys, and ships participating in the Voluntary Ship Observing (VOS) Program. Observations from MADIS are updated approximately every 10 minutes in the map service and those from NESDIS are updated every hour. However, not all stations report that frequently. Many stations only report once per hour sometime between 15 minutes before the hour and 30 minutes past the hour. For these stations, new observations will not appear until approximately 23 minutes past top of the hour for land-based stations and 33 minutes past the top of the hour for maritime stations.Time InformationThis 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 DocumentationReferencesNWS, 2013: Sample Station Plot, NWS/NCEP/WPC, College Park, MD (Available at http://www.wpc.ncep.noaa.gov/html/stationplot.shtml).NWS, 2013: Terminology and Weather Symbols, NWS/NCEP/OPC, College Park, MD (Available at http://www.opc.ncep.noaa.gov/product_description/keyterm.shtml).NWS, 2013: How to read Surface weather maps, JetStream an Online School for Weather (Available at http://www.srh.noaa.gov/jetstream/synoptic/wxmaps.htm).

This dataset represents a unique compiled environmental data set for the circumpolar Arctic ocean region 45N to 90N region. It consists of 170 layers (mostly marine, some terrestrial) in ArcGIS 10 format to be used with a Geographic Information System (GIS) and which are listed below in detail. Most layers are long-term average raster GRIDs for the summer season, often by ocean depth, and represent value-added products easy to use. The sources of the data are manifold such as the World Ocean Atlas 2009 (WOA09), International Bathimetric Chart of the Arctic Ocean (IBCAO), Canadian Earth System Model 2 (CanESM2) data (the newest generation of models available) and data sources such as plankton databases and OBIS. Ocean layers were modeled and predicted into the future and zooplankton species were modeled based on future data: Calanus hyperboreus (AphiaID104467), Metridia longa (AphiaID 104632), M. pacifica (AphiaID 196784) and Thysanoessa raschii (AphiaID 110711). Some layers are derived within ArcGIS. Layers have pixel sizes between 1215.819573 meters and 25257.72929 meters for the best pooled model, and between 224881.2644 and 672240.4095 meters for future climate data. Data was then reprojected into North Pole Stereographic projection in meters (WGS84 as the geographic datum). Also, future layers are included as a selected subset of proposed future climate layers from the Canadian CanESM2 for the next 100 years (scenario runs rcp26 and rcp85). The following layer groups are available: bathymetry (depth, derived slope and aspect); proximity layers (to,glaciers,sea ice, protected areas, wetlands, shelf edge); dissolved oxygen, apparent oxygen, percent oxygen, nitrogen, phosphate, salinity, silicate (all for August and for 9 depth classes); runoff (proximity, annual and August); sea surface temperature; waterbody temperature (12 depth classes); modeled ocean boundary layers (H1, H2, H3 and Wx).This dataset is used for a M.Sc. thesis by the author, and freely available upon request. For questions and details we suggest contacting the authors. Process_Description: Please contact Moritz Schmid for the thesis and detailed explanations. Short version: We model predicted here for the first time ocean layers in the Arctic Ocean based on a unique dataset of physical oceanography. Moreover, we developed presence/random absence models that indicate where the studied zooplankton species are most likely to be present in the Arctic Ocean. Apart from that, we develop the first spatially explicit models known to science that describe the depth in which the studied zooplankton species are most likely to be at, as well as their distribution of life stages. We do not only do this for one present day scenario. We modeled five different scenarios and for future climate data. First, we model predicted ocean layers using the most up to date data from various open access sources, referred here as best-pooled model data. We decided to model this set of stratification layers after discussions and input of expert knowledge by Professor Igor Polyakov from the International Arctic Research Center at the University of Alaska Fairbanks. We predicted those stratification layers because those are the boundaries and layers that the plankton has to cross for diel vertical migration and a change in those would most likely affect the migration. I assigned 4 variables to the stratification layers. H1, H2, H3 and Wx. H1 is the lower boundary of the mixed layer depth. Above this layer a lot of atmospheric disturbance is causing mixing of the water, giving the mixed layer its name. H2, the middle of the halocline is important because in this part of the ocean a strong gradient in salinity and temperature separates water layers. H3, the isotherm is important, because beneath it flows denser and colder Atlantic water. Wx summarizes the overall width of the described water column. Ocean layers were predicted using machine learning algorithms (TreeNet, Salford Systems). Second, ocean layers were included as predictors and used to predict the presence/random absence, most likely depth and life stage layers for the zooplankton species: Calanus hyperboreus, Metridia longa, Metridia pacifica and Thysanoessa raschii, This process was repeated for future predictions based on the CanESM2 data (see in the data section). For zooplankton species the following layers were developed and for the future. C. hyperboreus: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100.For parameters: Presence/random absence, most likely depth and life stage layers M. longa: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100. For parameters: Presence/rand... Visit https://dataone.org/datasets/f63d0f6c-7d53-46ce-b755-42a368007601 for complete metadata about this dataset.

This dataset includes all 7 metro counties that have made their parcel data freely available without a license or fees.

This dataset is a compilation of tax parcel polygon and point layers assembled into a common coordinate system from Twin Cities, Minnesota metropolitan area counties. No attempt has been made to edgematch or rubbersheet between counties. A standard set of attribute fields is included for each county. The attributes are the same for the polygon and points layers. Not all attributes are populated for all counties.

NOTICE: The standard set of attributes changed to the MN Parcel Data Transfer Standard on 1/1/2019.
https://www.mngeo.state.mn.us/committee/standards/parcel_attrib/parcel_attrib.html

See section 5 of the metadata for an attribute summary.

Detailed information about the attributes can be found in the Metro Regional Parcel Attributes document.

The polygon layer contains one record for each real estate/tax parcel polygon within each county's parcel dataset. Some counties have polygons for each individual condominium, and others do not. (See Completeness in Section 2 of the metadata for more information.) The points layer includes the same attribute fields as the polygon dataset. The points are intended to provide information in situations where multiple tax parcels are represented by a single polygon. One primary example of this is the condominium, though some counties stacked polygons for condos. Condominiums, by definition, are legally owned as individual, taxed real estate units. Records for condominiums may not show up in the polygon dataset. The points for the point dataset often will be randomly placed or stacked within the parcel polygon with which they are associated.

The polygon layer is broken into individual county shape files. The points layer is provided as both individual county files and as one file for the entire metro area.

In many places a one-to-one relationship does not exist between these parcel polygons or points and the actual buildings or occupancy units that lie within them. There may be many buildings on one parcel and there may be many occupancy units (e.g. apartments, stores or offices) within each building. Additionally, no information exists within this dataset about residents of parcels. Parcel owner and taxpayer information exists for many, but not all counties.

This is a MetroGIS Regionally Endorsed dataset.

Additional information may be available from each county at the links listed below. Also, any questions or comments about suspected errors or omissions in this dataset can be addressed to the contact person at each individual county.

Anoka = http://www.anokacounty.us/315/GIS
Caver = http://www.co.carver.mn.us/GIS
Dakota = http://www.co.dakota.mn.us/homeproperty/propertymaps/pages/default.aspx
Hennepin = https://gis-hennepin.hub.arcgis.com/pages/open-data
Ramsey = https://www.ramseycounty.us/your-government/open-government/research-data
Scott = http://opendata.gis.co.scott.mn.us/
Washington: http://www.co.washington.mn.us/index.aspx?NID=1606

Historical FiresLast updated on 06/17/2022OverviewThe national fire history perimeter data layer of conglomerated Agency Authoratative perimeters was developed in support of the WFDSS application and wildfire decision support for the 2021 fire season. The layer encompasses the final fire perimeter datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, CalFire, and WFIGS History. Perimeters are included thru the 2021 fire season. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies. WFIGS, NPS and CALFIRE data now include Prescribed Burns. Data InputSeveral data sources were used in the development of this layer:Alaska fire history USDA FS Regional Fire History Data BLM Fire Planning and Fuels National Park Service - Includes Prescribed Burns Fish and Wildlife ServiceBureau of Indian AffairsCalFire FRAS - Includes Prescribed BurnsWFIGS - BLM & BIA and other S&LData LimitationsFire perimeter data are often collected at the local level, and fire management agencies have differing guidelines for submitting fire perimeter data. Often data are collected by agencies only once annually. If you do not see your fire perimeters in this layer, they were not present in the sources used to create the layer at the time the data were submitted. A companion service for perimeters entered into the WFDSS application is also available, if a perimeter is found in the WFDSS service that is missing in this Agency Authoratative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.AttributesThis dataset implements the NWCG Wildland Fire Perimeters (polygon) data standard.https://www.nwcg.gov/sites/default/files/stds/WildlandFirePerimeters_definition.pdfIRWINID - Primary key for linking to the IRWIN Incident dataset. The origin of this GUID is the wildland fire locations point data layer. (This unique identifier may NOT replace the GeometryID core attribute)INCIDENT - The name assigned to an incident; assigned by responsible land management unit. (IRWIN required). Officially recorded name.FIRE_YEAR (Alias) - Calendar year in which the fire started. Example: 2013. Value is of type integer (FIRE_YEAR_INT).AGENCY - Agency assigned for this fire - should be based on jurisdiction at origin.SOURCE - System/agency source of record from which the perimeter came.DATE_CUR - The last edit, update, or other valid date of this GIS Record. Example: mm/dd/yyyy.MAP_METHOD - Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality.GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; Digitized-Topo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; OtherGIS_ACRES - GIS calculated acres within the fire perimeter. Not adjusted for unburned areas within the fire perimeter. Total should include 1 decimal place. (ArcGIS: Precision=10; Scale=1). Example: 23.9UNQE_FIRE_ - Unique fire identifier is the Year-Unit Identifier-Local Incident Identifier (yyyy-SSXXX-xxxxxx). SS = State Code or International Code, XXX or XXXX = A code assigned to an organizational unit, xxxxx = Alphanumeric with hyphens or periods. The unit identifier portion corresponds to the POINT OF ORIGIN RESPONSIBLE AGENCY UNIT IDENTIFIER (POOResonsibleUnit) from the responsible unit’s corresponding fire report. Example: 2013-CORMP-000001LOCAL_NUM - Local incident identifier (dispatch number). A number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year. Field is string to allow for leading zeros when the local incident identifier is less than 6 characters. (IRWIN required). Example: 123456.UNIT_ID - NWCG Unit Identifier of landowner/jurisdictional agency unit at the point of origin of a fire. (NFIRS ID should be used only when no NWCG Unit Identifier exists). Example: CORMPCOMMENTS - Additional information describing the feature. Free Text.FEATURE_CA - Type of wildland fire polygon: Wildfire (represents final fire perimeter or last daily fire perimeter available) or Prescribed Fire or UnknownGEO_ID - Primary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature. Globally Unique Identifier (GUID).Cross-Walk from sources (GeoID) and other processing notesAK: GEOID = OBJECT ID of provided file geodatabase (4580 Records thru 2021), other federal sources for AK data removed. CA: GEOID = OBJECT ID of downloaded file geodatabase (12776 Records, federal fires removed, includes RX)FWS: GEOID = OBJECTID of service download combined history 2005-2021 (2052 Records). Handful of WFIGS (11) fires added that were not in FWS record.BIA: GEOID = "FireID" 2017/2018 data (416 records) provided or WFDSS PID (415 records). An additional 917 fires from WFIGS were added, GEOID=GLOBALID in source.NPS: GEOID = EVENT ID (IRWINID or FRM_ID from FOD), 29,943 records includes RX.BLM: GEOID = GUID from BLM FPER and GLOBALID from WFIGS. Date Current = best available modify_date, create_date, fire_cntrl_dt or fire_dscvr_dt to reduce the number of 9999 entries in FireYear. Source FPER (25,389 features), WFIGS (5357 features)USFS: GEOID=GLOBALID in source, 46,574 features. Also fixed Date Current to best available date from perimeterdatetime, revdate, discoverydatetime, dbsourcedate to reduce number of 1899 entries in FireYear.Relevant Websites and ReferencesAlaska Fire Service: https://afs.ak.blm.gov/CALFIRE: https://frap.fire.ca.gov/mapping/gis-dataBIA - data prior to 2017 from WFDSS, 2017-2018 Agency Provided, 2019 and after WFIGSBLM: https://gis.blm.gov/arcgis/rest/services/fire/BLM_Natl_FirePerimeter/MapServerNPS: New data set provided from NPS Fire & Aviation GIS. cross checked against WFIGS for any missing perimeters in 2021.https://nifc.maps.arcgis.com/home/item.html?id=098ebc8e561143389ca3d42be3707caaFWS -https://services.arcgis.com/QVENGdaPbd4LUkLV/arcgis/rest/services/USFWS_Wildfire_History_gdb/FeatureServerUSFS - https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_FireOccurrenceAndPerimeter_01/MapServerAgency Fire GIS ContactsRD&A Data ManagerVACANTSusan McClendonWFM RD&A GIS Specialist208-258-4244send emailJill KuenziUSFS-NIFC208.387.5283send email Joseph KafkaBIA-NIFC208.387.5572send emailCameron TongierUSFWS-NIFC208.387.5712send emailSkip EdelNPS-NIFC303.969.2947send emailJulie OsterkampBLM-NIFC208.258.0083send email Jennifer L. Jenkins Alaska Fire Service 907.356.5587 send email

Update information can be found within the layer’s attributes and in a table on the Utah Parcel Data webpage under LIR Parcels.In Spring of 2016, the Land Information Records work group, an informal committee organized by the Governor’s Office of Management and Budget’s State Planning Coordinator, produced recommendations for expanding the sharing of GIS-based parcel information. Participants in the LIR work group included representatives from county, regional, and state government, including the Utah Association of Counties (County Assessors and County Recorders), Wasatch Front Regional Council, Mountainland and Bear River AOGs, Utah League of Cities and Towns, UDOT, DNR, AGRC, the Division of Emergency Management, Blue Stakes, economic developers, and academic researchers. The LIR work group’s recommendations set the stage for voluntary sharing of additional objective/quantitative parcel GIS data, primarily around tax assessment-related information. Specifically the recommendations document establishes objectives, principles (including the role of local and state government), data content items, expected users, and a general process for data aggregation and publishing. An important realization made by the group was that ‘parcel data’ or ‘parcel record’ products have a different meaning to different users and data stewards. The LIR group focused, specifically, on defining a data sharing recommendation around a tax year parcel GIS data product, aligned with the finalization of the property tax roll by County Assessors on May 22nd of each year. The LIR recommendations do not impact the periodic sharing of basic parcel GIS data (boundary, ID, address) from the County Recorders to AGRC per 63F-1-506 (3.b.vi). Both the tax year parcel and the basic parcel GIS layers are designed for general purpose uses, and are not substitutes for researching and obtaining the most current, legal land records information on file in County records. This document, below, proposes a schedule, guidelines, and process for assembling county parcel and assessment data into an annual, statewide tax parcel GIS layer. gis.utah.gov/data/sgid-cadastre/ It is hoped that this new expanded parcel GIS layer will be put to immediate use supporting the best possible outcomes in public safety, economic development, transportation, planning, and the provision of public services. Another aim of the work group was to improve the usability of the data, through development of content guidelines and consistent metadata documentation, and the efficiency with which the data sharing is distributed.GIS Layer Boundary Geometry:GIS Format Data Files: Ideally, Tax Year Parcel data should be provided in a shapefile (please include the .shp, .shx, .dbf, .prj, and .xml component files) or file geodatabase format. An empty shapefile and file geodatabase schema are available for download at:At the request of a county, AGRC will provide technical assistance to counties to extract, transform, and load parcel and assessment information into the GIS layer format.Geographic Coverage: Tax year parcel polygons should cover the area of each county for which assessment information is created and digital parcels are available. Full coverage may not be available yet for each county. The county may provide parcels that have been adjusted to remove gaps and overlaps for administrative tax purposes or parcels that retain these expected discrepancies that take their source from the legally described boundary or the process of digital conversion. The diversity of topological approaches will be noted in the metadata.One Tax Parcel Record Per Unique Tax Notice: Some counties produce an annual tax year parcel GIS layer with one parcel polygon per tax notice. In some cases, adjacent parcel polygons that compose a single taxed property must be merged into a single polygon. This is the goal for the statewide layer but may not be possible in all counties. AGRC will provide technical support to counties, where needed, to merge GIS parcel boundaries into the best format to match with the annual assessment information.Standard Coordinate System: Parcels will be loaded into Utah’s statewide coordinate system, Universal Transverse Mercator coordinates (NAD83, Zone 12 North). However, boundaries stored in other industry standard coordinate systems will be accepted if they are both defined within the data file(s) and documented in the metadata (see below).Descriptive Attributes:Database Field/Column Definitions: The table below indicates the field names and definitions for attributes requested for each Tax Parcel Polygon record.FIELD NAME FIELD TYPE LENGTH DESCRIPTION EXAMPLE SHAPE (expected) Geometry n/a The boundary of an individual parcel or merged parcels that corresponds with a single county tax notice ex. polygon boundary in UTM NAD83 Zone 12 N or other industry standard coordinates including state plane systemsCOUNTY_NAME Text 20 - County name including spaces ex. BOX ELDERCOUNTY_ID (expected) Text 2 - County ID Number ex. Beaver = 1, Box Elder = 2, Cache = 3,..., Weber = 29ASSESSOR_SRC (expected) Text 100 - Website URL, will be to County Assessor in most all cases ex. webercounty.org/assessorBOUNDARY_SRC (expected) Text 100 - Website URL, will be to County Recorder in most all cases ex. webercounty.org/recorderDISCLAIMER (added by State) Text 50 - Disclaimer URL ex. gis.utah.gov...CURRENT_ASOF (expected) Date - Parcels current as of date ex. 01/01/2016PARCEL_ID (expected) Text 50 - County designated Unique ID number for individual parcels ex. 15034520070000PARCEL_ADD (expected, where available) Text 100 - Parcel’s street address location. Usually the address at recordation ex. 810 S 900 E #304 (example for a condo)TAXEXEMPT_TYPE (expected) Text 100 - Primary category of granted tax exemption ex. None, Religious, Government, Agriculture, Conservation Easement, Other Open Space, OtherTAX_DISTRICT (expected, where applicable) Text 10 - The coding the county uses to identify a unique combination of property tax levying entities ex. 17ATOTAL_MKT_VALUE (expected) Decimal - Total market value of parcel's land, structures, and other improvements as determined by the Assessor for the most current tax year ex. 332000LAND _MKT_VALUE (expected) Decimal - The market value of the parcel's land as determined by the Assessor for the most current tax year ex. 80600PARCEL_ACRES (expected) Decimal - Parcel size in acres ex. 20.360PROP_CLASS (expected) Text 100 - Residential, Commercial, Industrial, Mixed, Agricultural, Vacant, Open Space, Other ex. ResidentialPRIMARY_RES (expected) Text 1 - Is the property a primary residence(s): Y'(es), 'N'(o), or 'U'(nknown) ex. YHOUSING_CNT (expected, where applicable) Text 10 - Number of housing units, can be single number or range like '5-10' ex. 1SUBDIV_NAME (optional) Text 100 - Subdivision name if applicable ex. Highland Manor SubdivisionBLDG_SQFT (expected, where applicable) Integer - Square footage of primary bldg(s) ex. 2816BLDG_SQFT_INFO (expected, where applicable) Text 100 - Note for how building square footage is counted by the County ex. Only finished above and below grade areas are counted.FLOORS_CNT (expected, where applicable) Decimal - Number of floors as reported in county records ex. 2FLOORS_INFO (expected, where applicable) Text 100 - Note for how floors are counted by the County ex. Only above grade floors are countedBUILT_YR (expected, where applicable) Short - Estimated year of initial construction of primary buildings ex. 1968EFFBUILT_YR (optional, where applicable) Short - The 'effective' year built' of primary buildings that factors in updates after construction ex. 1980CONST_MATERIAL (optional, where applicable) Text 100 - Construction Material Types, Values for this field are expected to vary greatly by county ex. Wood Frame, Brick, etc Contact: Sean Fernandez, Cadastral Manager (email: sfernandez@utah.gov; office phone: 801-209-9359)

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

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

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

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

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

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

References:

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

Last Revised: February 2016

Map Information

This nowCOAST™ time-enabled map service provides maps depicting the NWS Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimate mosaics for 1-, 3-, 6-, 12-, 24-, 48-, and 72-hr time periods at a 1 km (0.6 miles) horizontal resolution for CONUS and southern part of Canada. The precipitation estimates are based only on radar data. The total precipitation amount is indicated by different colors at 0.01, 0.10, 0.25 inches and then at 1/4 inch intervals up to 4.0 inches (e.g. 0.50, 0.75, 1.00, 1.25, etc.), at 1-inch intervals from 4 to 10 inches and then at 2-inch intervals up to 14+ inches. The increments from 0.01 to 1.00 or 2.00 inches are similar to what are used on NCEP's Weather Prediction Center QPF products and the NWS River Forecast Center (RFC) daily precipitation analysis. The 1-hr mosaic is updated every 4 minutes with a latency on nowCOAST™ of about 6-7 minutes from valid time. The 3-, 6-, 12-, and 24-hr QPEs are updated on nowCOAST™ every hour for the period ending at the top of the hour. The 48- and 72-hr QPEs are generated daily for the period ending at 12 UTC (i.e. 7AM EST) and available on nowCOAST™ shortly afterwards. For more detailed information about layer update frequency and timing, please reference the
nowCOAST™ Dataset Update Schedule.

Background Information

The NWS Multi-Radar Multi-Sensor System (MRMS)/Q3 QPEs are radar-only based quantitative precipitation analyses. The 1-hr precipitation accumulation is obtained by aggregating 12 instantaneous rate fields. Missing rate fields are filled with the neighboring rate fields if the data gap is not significantly large (e.g.<=15 minutes). The instantaneous rate is computed from the hybrid scan reflectivity and the precipitation flag fields (both are 2-D derivative products from the National 3-D Reflectivity Mosaic grid which has a 1-km horizontal resolution, 31 vertical levels and a 5-minute update cycle). The instantaneous rate currently uses four Z-R relationships (i.e. tropical, convective, stratiform, or snow). The particular ZR relationship used in any grid cell depends on precipitation type which is indicated by the precipitation flag. The other accumulation products are derived by aggregating the hourly accumulations. The 1-hr QPE are generated every 4 minutes, while the 3-, 6-, 12-, and 24-hr accumulations are generated every hour at the top of the hour. The 48- and 72-hr QPEs are updated daily at approximately 12 UTC. MRMS was developed by NOAA/OAR/National Severe Storms Laboratory and migrated into NWS operations at NOAA Integrated Dissemination Program.

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

For more information about the MRMS/Q3 system, please see http://nmq.ou.edu and http://www.nssl.noaa.gov/projects/mrms.