The "Map Imager Layer - Administrative Boundaries" is a Map Image Layer of Administrative Boundaries. It has been designed specifically for use in ArcGIS Online (and will not directly work in ArcMap or ArcPro). This data has been modified from the original source data to serve a specific business purpose. This data is for cartographic purposes only.The Administrative Boundaries Data Group contains the following layers: Populated Places (USGS)US Census Urbanized Areas and Urban Clusters (USCB)US Census Minor Civil Divisions (USCB)PLSS Townships (MnDNR, MnGeo)Counties (USCB)American Indian, Alaska Native, Native Hawaiian (AIANNH) Areas (USCB)States (USCB)Countries (MPCA)These datasets have not been optimized for fast display (but rather they maintain their original shape/precision), therefore it is recommend that filtering is used to show only the features of interest. For more information about using filters please see "Work with map layers: Apply Filters": https://doc.arcgis.com/en/arcgis-online/create-maps/apply-filters.htmFor additional information about the Administrative Boundary Dataset please see:United States Census Bureau TIGER/Line Shapefiles and TIGER/Line Files Technical Documentation: https://www.census.gov/programs-surveys/geography/technical-documentation/complete-technical-documentation/tiger-geo-line.htmlUnited States Census Bureau Census Mapping Files: https://www.census.gov/geographies/mapping-files.htmlUnited States Census Bureau TIGER/Line Shapefiles: https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html and https://www.census.gov/cgi-bin/geo/shapefiles/index.php

The National Parks dataset is frequently updated by the National Park Service (NPS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This dataset depicts National Park Service boundary data that was created by the Land Resources Division. NPS Director's Order #25 states: "Land status maps will be prepared to identify the ownership of the lands within the authorized boundaries of the park unit. These maps, showing ownership and acreage, are the 'official record' of the acreage of Federal and non-federal lands within the park boundaries. While these maps are the official record of the lands and acreage within the unit's authorized boundaries, they are not of survey quality and not intended to be used for survey purposes." As such this data is intended for use as a tool for GIS analysis. It is in no way intended for engineering or legal purposes. For the full data description, please go to https://irma.nps.gov/DataStore/Reference/Profile/2224545?lnv=True. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529046

This resource contains the test data for the GeoServer OGC Web Services tutorials for various GIS applications including ArcGIS Pro, ArcMap, ArcGIS Story Maps, and QGIS. The contents of the data include a polygon shapefile, a polyline shapefile, a point shapefile, and a raster dataset; all of which pertain to the state of Utah, USA. The polygon shapefile is of every county in the state of Utah. The polyline is of every trail in the state of Utah. The point shapefile is the current list of GNIS place names in the state of Utah. The raster dataset covers a region in the center of the state of Utah. All datasets are projected to NAD 1983 Zone 12N.

Click here to download these data in a file geodatabase. Log into ArcGIS Online to download data directly from this feature service. Create an ArcGIS Online account.On America Recycles Day, 2020, the U.S. Environmental Protection Agency (EPA) announced a National Recycling Goal to increase the U.S. recycling rate of materials generated in municipal solid waste (MSW) to 50 percent by 2030. To further this goal and support the building of new recycling infrastructure through the Bipartisan Infrastructure Law (BIL), EPA developed a map displaying estimated generation of recyclable materials, estimated recycled quantities, existing recycling infrastructure, potential recycling end markets, and other MSW infrastructure such as landfills and transfer stations. The map can be used to identify infrastructure gaps, facilitate a needs analysis, and better understand where funding could be allocated to enhance markets.

This dataset provides estimated tons generated and recycled by U.S. ZIP code and material. It relies on materials management reports and surveys from various states and regions, State Measurement Program (SMP) data, the Ball Corporation's Fifty States of Recycling, EPA's EPA’s Excess Food Opportunities Map, and the U.S. Census Bureau’s American Community Survey dataset. EPA estimated the quantities generated and recycled by ZIP code by dividing state reported generation and recycled quantities by the population for each state and for each material to arrive at state-specific per capita rates. Those per capita rates were then applied to the population of each ZIP code in each corresponding state. By clicking on the map, the user can see the estimated tons generated per capita and recycled per capita. Estimated recycling potential for each material is the difference between estimated tons generated and estimated tons recycled. The ZIP codes with the greatest difference in generated and recycled tons have higher estimated recycling potential.

The data were then integrated with a U.S. Census Bureau Tiger Database ZIP code shapefile to create the resulting data layer. The ZIP code shapefile was simplified to remove vertices. This dataset includes 16 recyclable material types: aluminum, cardboard, electronics, food waste, glass, HDPE bottles #2, PET bottles #1, PET other #1, PP (polypropylene) containers #5, rigid plastics #3 to #7, steel cans, tires, paper, textiles, yard trimmings, and wood. Note that there are certain materials for which data are not available for every state. In these cases, the layer will only display ZIP codes where data are available.

The map provides estimated U.S. recycling quantities, infrastructure, materials markets, and supporting market factors using the best available data at the time the map was developed (2021-2022). While data sources range from 2011 to 2021, most data are from 2018-2021. The map was created and by Industrial Economics, Inc. (IEc), a consultancy supporting EPA to develop the Recycling Infrastructure and Market Opportunities Map. The map is managed by EPA’s Office of Land and Emergency Management. This project was supported in part by an appointment to the Research Participation Program at the Office of Land and Emergency Management, U.S. Environmental Protection Agency, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and EPA.

The TIGER/Line Roads County-based dataset was released February 28, 2023, by the United States Census Bureau (USCB) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

The North American Rail Network (NARN) Rail Lines dataset was created in 2016 and was updated on July 18, 2025 from the Federal Railroad Administration (FRA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The NARN Rail Lines dataset is a database that provides ownership, trackage rights, type, passenger, STRACNET, and geographic reference for North America's railway system at 1:24,000 or better within the United States. The data set covers all 50 States, the District of Columbia, Mexico, and Canada. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1528950

The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

For the latest Land Use Legend, 2022-DWR-Standard-Land-Use-Legend-Remote-Sensing-Version.pdf, please see the Data and Resources section below.

Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

The Time Zones dataset was compiled on October 04, 2019 and was updated January 05, 2023 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This layer is a digital representation of the geographic boundaries of the nine time zones that cover the United States and its territories (the Atlantic, Eastern, Central, Mountain, Pacific, Alaska, Hawaii–Aleutian, Samoa, and Chamorro time zones). The U.S. Department of Transportation (DOT) oversees the Nation's time zones and the uniform observance of Daylight-Saving Time. The oversight of time zones was assigned to DOT due to the importance of time coordination for transportation related activities. The time zones were established by the Standard Time Act of 1918 and amended by the Uniform Time Act of 1966. Time zones in the U.S. are defined in the U.S. Code, Title 15, Chapter 6, Subchapter IX - Standard Time. The time zone boundaries are defined in the Code of Federal Regulations (CFR), Title 49, Subtitle A, Part 71 - Standard Time Zone Boundaries. Segments used to compile the geospatial layer were derived from the CFR’s time zone descriptions (https://www.ecfr.gov/current/title-49/subtitle-A/part-71). Descriptions consist of segments referencing administrative boundaries, infrastructure, natural features, and geodesic lines. These segments are contained in various data layers in the National Geospatial Data Asset (NGDA) portfolio, the federal government’s authoritative geospatial data repository. Referenced segments were extracted from their NGDA and then merged to form continuous boundaries. In instances where there were multiple scales for a given dataset, the largest scale or most detailed layer was used. The standard time of the Atlantic zone is the Coordinated Universal Time (UTC) minus 4 hours; Eastern zone is UTC minus 5 hours; Central zone is UTC minus 6 hours; Mountain zone is UTC minus 7 hours; Pacific zone is UTC minus 8 hours; Alaska zone is UTC minus 9 hours; Hawaii–Aleutian zone is UTC minus 10 hours; Samoa zone is UTC minus 11 hours; and Chamorro zone is UTC plus 10 hours. For more information, please visit: https://doi.org/10.21949/1519143.

This web map created by the Colorado Governor's Office of Information Technology GIS team, serves as a basemap specific to the state of Colorado. The basemap includes general layers such as counties, municipalities, roads, waterbodies, state parks, national forests, national wilderness areas, and trails.Layers:Layer descriptions and sources can be found below. Layers have been modified to only represent features within Colorado and are not up to date. Layers last updated February 23, 2023. Colorado State Extent: Description: “This layer provides generalized boundaries for the 50 States and the District of Columbia.” Notes: This layer was filtered to only include the State of ColoradoSource: Esri Living Atlas USA States Generalized Boundaries Feature LayerState Wildlife Areas:Description: “This data was created by the CPW GIS Unit. Property boundaries are created by dissolving CDOWParcels by the property name, and property type and appending State Park boundaries designated as having public access. All parcel data correspond to legal transactions made by the CPW Real Estate Unit. The boundaries of the CDOW Parcels were digitized using metes and bounds, BLM's GCDB dataset, the PLSS dataset (where the GCDB dataset was unavailable) and using existing digital data on the boundaries.” Notes: The state wildlife areas layer in this basemap is filtered from the CPW Managed Properties (public access only) layer from this feature layer hosted in ArcGIS Online Source: Colorado Parks and Wildlife CPW Admin Data Feature LayerMunicipal Boundaries:Description: "Boundaries data from the State Demography Office of Colorado Municipalities provided by the Department of Local Affairs (DOLA)"Source: Colorado Information Marketplace Municipal Boundaries in ColoradoCounties:Description: “This layer presents the USA 2020 Census County (or County Equivalent) boundaries of the United States in the 50 states and the District of Columbia. It is updated annually as County (or County Equivalent) boundaries change. The geography is sources from US Census Bureau 2020 TIGER FGDB (National Sub-State) and edited using TIGER Hydrology to add a detailed coastline for cartographic purposes. Geography last updated May 2022.” Notes: This layer was filtered to only include counties in the State of ColoradoSource: Esri USA Census Counties Feature LayerInterstates:Description: Authoritative data from the Colorado Department of Transportation representing Highways Notes: Interstates are filtered by route sign from this CDOT Highways layer Source: Colorado Department of Transportation Highways REST EndpointU.S. Highways:Description: Authoritative data from the Colorado Department of Transportation representing Highways Notes: U.S. Highways are filtered by route sign from this CDOT Highways layer Source: Colorado Department of Transportation Highways REST EndpointState Highways:Description: Authoritative data from the Colorado Department of Transportation representing Highways Notes: State Highways are filtered by route sign from this CDOT Highways layer Source: Colorado Department of Transportation Highways REST EndpointMajor Roads:Description: Authoritative data from the Colorado Department of Transportation representing major roads Source: Colorado Department of Transportation Major Roads REST EndpointLocal Roads:Description: Authoritative data from the Colorado Department of Transportation representing local roads Source: Colorado Department of Transportation Local Roads REST EndpointRail Lines:Description: Authoritative data from the Colorado Department of Transportation representing rail lines Source: Colorado Department of Transportation Rail Lines REST EndpointCOTREX Trails:Description: “The Colorado Trail System, now titled the Colorado Trail Explorer (COTREX), endeavors to map every trail in the state of Colorado. Currently their are nearly 40,000 miles of trails mapped. Trails come from a variety of sources (USFS, BLM, local parks & recreation departments, local governments). Responsibility for accuracy of the data rests with the source.These data were last updated on 2/5/2019” Source: Colorado Parks and Wildlife CPW Admin Data Feature LayerNHD Waterbodies:Description: “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.”Notes: This layer was filtered to only include waterbodies in the State of ColoradoSource: National Hydrography Dataset Plus Version 2.1 Feature LayerNHD Flowlines:Description: “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.”Notes: This layer was filtered to only include flowline features in the State of ColoradoSource: National Hydrography Dataset Plus Version 2.1 Feature LayerState Parks:Description: “This data was created by the CPW GIS Unit. Property boundaries are created by dissolving CDOWParcels by the property name, and property type and appending State Park boundaries designated as having public access. All parcel data correspond to legal transactions made by the CPW Real Estate Unit. The boundaries of the CDOW Parcels were digitized using metes and bounds, BLM's GCDB dataset, the PLSS dataset (where the GCDB dataset was unavailable) and using existing digital data on the boundaries.” Notes: The state parks layer in this basemap is filtered from the CPW Managed Properties (public access only) layer from this feature layer Source: Colorado Parks and Wildlife CPW Admin Data Feature LayerDenver Parks:Description: "This dataset should be used as a reference to locate parks, golf courses, and recreation centers managed by the Department of Parks and Recreation in the City and County of Denver. Data is based on parcel ownership and does not include other areas maintained by the department such as medians and parkways. The data should be used for planning and design purposes and cartographic purposes only."Source: City and County of Denver Parks REST EndpointNational Wilderness Areas:Description: “A parcel of Forest Service land congressionally designated as wilderness such as National Wilderness Area.”Notes: This layer was filtered to only include National Wilderness Areas in the State of ColoradoSource: United States Department of Agriculture National Wilderness Areas REST EndpointNational Forests: Description: “A depiction of the boundaries encompassing the National Forest System (NFS) lands within the original proclaimed National Forests, along with subsequent Executive Orders, Proclamations, Public Laws, Public Land Orders, Secretary of Agriculture Orders, and Secretary of Interior Orders creating modifications thereto, along with lands added to the NFS which have taken on the status of 'reserved from the public domain' under the General Exchange Act. The following area types are included: National Forest, Experimental Area, Experimental Forest, Experimental Range, Land Utilization Project, National Grassland, Purchase Unit, and Special Management Area.”Notes: This layer was filtered to only include National Forests in the State of ColoradoSource: United States Department of Agriculture Original Proclaimed National Forests REST Endpoint

This layer shows housing costs as a percentage of household income. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Income is based on earnings in past 12 months of survey. This layer is symbolized to show the percent of renter households that spend 30.0% or more of their household income on gross rent (contract rent plus tenant-paid utilities). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25070, B25091 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This dataset contains model-based census tract level estimates in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at four geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates are Behavioral Risk Factor Surveillance System (BRFSS) 2022 or 2021 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 estimates. The 2024 release uses 2022 BRFSS data for 36 measures and 2021 BRFSS data for 4 measures (high blood pressure, high cholesterol, cholesterol screening, and taking medicine for high blood pressure control among those with high blood pressure) that the survey collects data on every other year. These data can be joined with the Census tract 2022 boundary file in a GIS system to produce maps for 40 measures at the census tract level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

The Counties dataset was updated on October 31, 2023 from the United States Census Bureau (USCB) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are mostly as of January 1, 2023, as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

These are the main layers that were used in the mapping and analysis for the Santa Monica Mountains Local Coastal Plan, which was adopted by the Board of Supervisors on August 26, 2014, and certified by the California Coastal Commission on October 10, 2014. Below are some links to important documents and web mapping applications, as well as a link to the actual GIS data:

Plan Website – This has links to the actual plan, maps, and a link to our online web mapping application known as SMMLCP-NET. Click here for website. Online Web Mapping Application – This is the online web mapping application that shows all the layers associated with the plan. These are the same layers that are available for download below. Click here for the web mapping application. GIS Layers – This is a link to the GIS layers in the form of an ArcGIS Map Package, click here (LINK TO FOLLOW SOON) for ArcGIS Map Package (version 10.3). Also, included are layers in shapefile format. Those are included below.

Below is a list of the GIS Layers provided (shapefile format):

Recreation (Zipped - 5 MB - click here)

Coastal Zone Campground Trails (2012 National Park Service) Backbone Trail Class III Bike Route – Existing Class III Bike Route – Proposed

Scenic Resources (Zipped - 3 MB - click here)

Significant Ridgeline State-Designated Scenic Highway State-Designated Scenic Highway 200-foot buffer Scenic Route Scenic Route 200-foot buffer Scenic Element

Biological Resources (Zipped - 45 MB - click here)

National Hydrography Dataset – Streams H2 Habitat (High Scrutiny) H1 Habitat H1 Habitat 100-foot buffer H1 Habitat Quiet Zone H2 Habitat H3 Habitat

Hazards (Zipped - 8 MB - click here)

FEMA Flood Zone (100-year flood plain) Liquefaction Zone (Earthquake-Induced Liquefaction Potential) Landslide Area (Earthquake-Induced Landslide Potential) Fire Hazard and Responsibility Area

Zoning and Land Use (Zipped - 13 MB - click here)

Malibu LCP – LUP (1986) Malibu LCP – Zoning (1986) Land Use Policy Zoning

Other Layers (Zipped - 38 MB - click here)

Coastal Commission Appeal Jurisdiction Community Names Santa Monica Mountains (SMM) Coastal Zone Boundary Pepperdine University Long Range Development Plan (LRDP) Rural Village

Contact the L.A. County Dept. of Regional Planning's GIS Section if you have questions. Send to our email.

This dataset collection contains A0 maps of the Keppel Island region based on satellite imagery and fine-scale habitat mapping of the islands and marine environment. This collection provides the source satellite imagery used to produce these maps and the habitat mapping data.

The imagery used to produce these maps was developed by blending high-resolution imagery (1 m) from ArcGIS Online with a clear-sky composite derived from Sentinel 2 imagery (10 m). The Sentinel 2 imagery was used to achieve full coverage of the entire region, while the high-resolution was used to provide detail around island areas.

The blended imagery is a derivative product of the Sentinel 2 imagery and ArcGIS Online imagery, using Photoshop to to manually blend the best portions of each imagery into the final product. The imagery is provided for the sole purpose of reproducing the A0 maps.

Methods:

The high resolution satellite composite composite was developed by manual masking and blending of a Sentinel 2 composite image and high resolution imagery from ArcGIS Online World Imagery (2019).

The Sentinel 2 composite was produced by statistically combining the clearest 10 images from 2016 - 2019. These images were manually chosen based on their very low cloud cover, lack of sun glint and clear water conditions. These images were then combined together to remove clouds and reduce the noise in the image.

The processing of the images was performed using a script in Google Earth Engine. The script combines the manually chosen imagery to estimate the clearest imagery. The dates of the images were chosen using the EOBrowser (https://www.sentinel-hub.com/explore/eobrowser) to preview all the Sentinel 2 imagery from 2015-2019. The images that were mostly free of clouds, with little or no sun glint, were recorded. Each of these dates was then viewed in Google Earth Engine with high contrast settings to identify images that had high water surface noise due to algal blooms, waves, or re-suspension. These were excluded from the list. All the images were then combined by applying a histogram analysis of each pixel, with the final image using the 40th percentile of the time series of the brightness of each pixel. This approach helps exclude effects from clouds.

The contrast of the image was stretched to highlight the marine features, whilst retaining detail in the land features. This was done by choosing a black point for each channel that would provide a dark setting for deep clear water. Gamma correction was then used to lighten up the dark water features, whilst not ove- exposing the brighter shallow areas.

Both the high resolution satellite imagery and Sentinel 2 imagery was combined at 1 m pixel resolution. The resolution of the Sentinel 2 tiles was up sampled to match the resolution of the high-resolution imagery. These two sets of imagery were then layered in Photoshop. The brightness of the high-resolution satellite imagery was then adjusting to match the Sentinel 2 imagery. A mask was then used to retain and blend the imagery that showed the best detail of each area. The blended tiles were then merged with the overall area imagery by performing a GDAL merge, resulting in an upscaling of the Sentinel 2 imagery to 1 m resolution.


Habitat Mapping:

A 5 m resolution habitat mapping was developed based on the satellite imagery, aerial imagery available, and monitoring site information. This habitat mapping was developed to help with monitoring site selection and for the mapping workshop with the Woppaburra TOs on North Keppel Island in Dec 2019.

The habitat maps should be considered as draft as they don't consider all available in water observations. They are primarily based on aerial and satellite images.

The habitat mapping includes: Asphalt, Buildings, Mangrove, Cabbage-tree palm, Sheoak, Other vegetation, Grass, Salt Flat, Rock, Beach Rock, Gravel, Coral, Sparse coral, Unknown not rock (macroalgae on rubble), Marine feature (rock).

This assumed layers allowed the digitisation of these features to be sped up, so for example, if there was coral growing over a marine feature then the boundary of the marine feature would need to be digitised, then the coral feature, but not the boundary between the marine feature and the coral. We knew that the coral was going to cut out from the marine feature because the coral is on top of the marine feature, saving us time in digitising this boundary. Digitisation was performed on an iPad using Procreate software and an Apple pencil to draw the features as layers in a drawing. Due to memory limitations of the iPad the region was digitised using 6000x6000 pixel tiles. The raster images were converted back to polygons and the tiles merged together.

A python script was then used to clip the layer sandwich so that there is no overlap between feature types.

Habitat Validation:

Only limited validation was performed on the habitat map. To assist in the development of the habitat mapping, nearly every YouTube video available, at the time of development (2019), on the Keppel Islands was reviewed and, where possible, georeferenced to provide a better understanding of the local habitats at the scale of the mapping, prior to the mapping being conducted. Several validation points were observed during the workshop. The map should be considered as largely unvalidated.

data/coastline/Keppels_AIMS_Coastline_2017.shp:
The coastline dataset was produced by starting with the Queensland coastline dataset by DNRME (Downloaded from http://qldspatial.information.qld.gov.au/catalogue/custom/detail.page?fid={369DF13C-1BF3-45EA-9B2B-0FA785397B34} on 31 Aug 2019). This was then edited to work at a scale of 1:5000, using the aerial imagery from Queensland Globe as a reference and a high-tide satellite image from 22 Feb 2015 from Google Earth Pro. The perimeter of each island was redrawn. This line feature was then converted to a polygon using the "Lines to Polygon" QGIS tool. The Keppel island features were then saved to a shapefile by exporting with a limited extent.

data/labels/Keppel-Is-Map-Labels.shp:
This contains 70 named places in the Keppel island region. These names were sourced from literature and existing maps. Unfortunately, no provenance of the names was recorded. These names are not official. This includes the following attributes:
- Name: Name of the location. Examples Bald, Bluff
- NameSuffix: End of the name which is often a description of the feature type: Examples: Rock, Point
- TradName: Traditional name of the location
- Scale: Map scale where the label should be displayed.

data/lat/Keppel-Is-Sentinel2-2016-19_B4-LAT_Poly3m_V3.shp:
This corresponds to a rough estimate of the LAT contours around the Keppel Islands. LAT was estimated from tidal differences in Sentinel-2 imagery and light penetration in the red channel. Note this is not very calibrated and should be used as a rough guide. Only one rough in-situ validation was performed at low tide on Ko-no-mie at the edge of the reef near the education centre. This indicated that the LAT estimate was within a depth error range of about +-0.5 m.

data/habitat/Keppels_AIMS_Habitat-mapping_2019.shp:
This shapefile contains the mapped land and marine habitats. The classification type is recorded in the Type attribute.

Format:

GeoTiff (Internal JPEG format - 538 MB)
PDF (A0 regional maps - ~30MB each)
Shapefile (Habitat map, Coastline, Labels, LAT estimate)

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\Keppels_AIMS_Regional-maps

This service contains various Aquaculture data. This includes Shellfish Production, Optimum Sites of Aquaculture potential (AQ1), Bivalve Classification area and Areas of Future Potential for Aquaculture. ------------------------------------------------------------------------------------------------------------The Shellfish Production dataset shows shellfish farm species production data grouped by water body. Water bodies were taken from the water framework directive (WFD) coastal and transitional water bodies database, and joined with the data from CEFAS. Data contains information on species present and production values. This dataset was created by ABPmer under contract to DEFRA (Contract reference MB106). An Excel spreadsheet was supplied to ABPmer by CEFAS which contained a list of waterbodies with the species cultivated per waterbody, production per waterbody and the number of businesses operating for 2007. The production data was joined to a shapefile containing waterbodies based on name of waterbody, and all sites where no shellfish cultivation occurred were removed. The same procedure was repeated with the data of species present. A shapefile containing both number of species grown and tonnes produced per waterbody was created by merging the two datasets based on waterbody name. ------------------------------------------------------------------------------------------------------------The Optimum Sites of Aquaculture Potential (AQ1) dataset shows areas identified through GIS modelling of suitable environmental conditions in East Coast Inshore and Offshore Marine Plan Areas favourable for macroalgae culture, Bivalve Bottom Culture, Finfish Cage, Lobster Restocking, Rope Cultured Bivalve Shellfish or Trestle/Bag Culture of Bivalves. This dataset has been derived from of a wider study assessing aquaculture potential in the South and East Marine Plan Areas for the Marine Management Organisation, project MMO1040. It was created using the Natural Resource model which forms part of the MMO project 1040 Spatial Trends in Aquaculture Potential in the South and East Coast Inshore and Offshore Marine Plan Areas. The Natural Resource model is made up of three existing environmental datasets: bathymetry derived from the Department of Food and Rural Affairs (Defra) Digital Elevation Model (DEM), predicted seabed sediments and combined seabed energy, both from UKSeaMap 2010 (McBreen, et al., 2010). Suitable environmental conditions applied include - low-moderate seabed energy, any sediment type and 10-25 m water depth for current potential. The depth limitations in this instance are based on the industry current reliance on scuba-divers for maintenance and husbandry. It is anticipated that as the industry develops it will become less reliant on divers and be able to move into deeper waters. Note that although the Natural Resource model used the best environmental data available for use in the study but there are significant limitations and gaps. These are outlined below and are discussed in more detail in the final project report: The model does not contain any measure of water quality (e.g. dissolved oxygen, sediment loading or contaminants) and therefore is likely to overestimate the area deemed suitable for aquaculture developments, particularly fin fish cage culture, rope grown bivalve culture and macroalgae culture. The UKSeaMap 2010 predicted seabed sediment map (McBreen, et al., 2010) is modelled at a coarse scale which has led to inaccuracies in the identification of areas which have potential for aquaculture development. UKSeaMap 2010 is known to under-estimate rock habitats because of the type of sampling data (sediment grabs) used to underpin the model. The MMO is working with JNCC to develop these data to lead to improvements in future models. The UKSeaMap 2010 combined seabed energy map included in the model (McBreen, et al., 2010) provides an approximation of the environmental conditions that are likely to limit aquaculture development (e.g. strong currents and large waves) but more accurate results could be obtained by using more precise component datasets such as the maximum wave height and tidal current range, where these datasets are available and the precise limitations of the aquaculture activities of interest are known. The dataset shows potential based on current technologies as defined in Table 10 of the MMO1040 Aquaculture Potential Final Report which is published on the MMO website's evidence pages. ------------------------------------------------------------------------------------------------------------The Bivalve Classification dataset classifies where the production of shellfish can be commercially harvested. All areas listed are designated for species that may be harvested as well as the classification of the shellfish waters. Classification of harvesting areas is required and implemented directly in England and Wales under European Regulation 854/2004. The co-ordination of the shellfish harvesting area classification monitoring programme in England and Wales is carried out by the Centre for Environment, Fisheries and Aquaculture Science, Weymouth (Cefas) on behalf of the Food Standards Agency (FSA). Cefas will make recommendations on classification according to an agreed protocol with the FSA making all final classification decisions and setting out the overall policy. Shellfish production areas are classified according to the extent to which shellfish sampled from the area are contaminated with E. coli. The Classification Zones/Production areas delineate areas where shellfish may be commercially harvested. Coordinates for the zone boundaries are calculated during a sanitary (ground) survey of the production area and where appropriate they are based on the OS Mastermap Mean High Water Line (coordinate accuracy <10m). The maps/zones are correct at time of publication but are updated when necessary depending on hygiene testing results. The current maps (jpgs) are available from the Cefas website ( https://www.cefas.co.uk/publications-data/food-safety/classification-and-microbiological-monitoring/england-and-wales-classification-and-monitoring/classification-zone-maps ) or a listing is available from the FSA website ( http://www.food.gov.uk/enforcement/monitoring/shellfish/shellharvestareas ) ------------------------------------------------------------------------------------------------------------The Current Aquaculture Potential layer highlights areas identified through GIS modelling of suitable environmental conditions in the South and East Marine Plan Areas favourable for macroalgae culture, Bivalve Bottom Culture, Finfish Cage, Lobster Restocking, Rope Cultured Bivalve Shellfish or Trestle/Bag Culture of Bivalves in the South and East Coast Marine Plan Areas. This dataset forms part of a wider study assessing different aquaculture potential in the South and East Marine Plan Areas for the Marine Management Organisation, project MMO1040. This dataset was created using the Natural Resource model which forms part of the MMO project 1040 Spatial Trends in Aquaculture Potential in the South and East Coast Inshore and Offshore Marine Plan Areas. The Natural Resource model is made up of three existing environmental datasets: bathymetry derived from the Department of Food and Rural Affairs (Defra) Digital Elevation Model (DEM), predicted seabed sediments and combined seabed energy, both from UKSeaMap 2010 (McBreen, et al., 2010). Suitable environmental conditions applied include - low-moderate seabed energy, any sediment type, 10-25 m water depth for current potential and 25-50 m water depth for near future potential). The depth limitations in this instance are based on the industry current reliance on scuba-divers for maintenance and husbandry. It is anticipated that as the industry develops it will become less reliant on divers and be able to move into deeper waters. Note that although the Natural Resource model used the best environmental data available for use in the study, there are significant limitations and gaps. These are outlined below and are discussed in more detail in the final project report: The Natural Resource model does not contain any measure of water quality (e.g. dissolved oxygen, sediment loading or contaminants) and therefore is likely to overestimate the area deemed suitable for aquaculture developments, particularly fin fish cage culture, rope grown bivalve culture and macroalgae culture. The UKSeaMap 2010 predicted seabed sediment map (McBreen, et al., 2010) is modelled at a coarse scale which has led to inaccuracies in the identification of areas which have potential for aquaculture development. UKSeaMap 2010 is known to under-estimate rock habitats because of the type of sampling data (sediment grabs) used to underpin the model. It is recommended that this component of the model is supplemented or replaced by higher resolution sediment maps where they are available for the region of interest. The UKSeaMap 2010 combined seabed energy map included in the model (McBreen, et al., 2010) provides an approximation of the environmental conditions that are likely to limit aquaculture development (e.g. strong currents and large waves) but more accurate results could be obtained by using more precise component datasets such as the maximum wave height and tidal current range, where these datasets are available and the precise limitations of the aquaculture activities of interest are known. The potential for development for the feature is "Current" (0-5 years), "Near Future" (5-10 years) or "Future" (10-20 years), the definitions of which are presented in Table 13 within the main report.

The Core Based Statistical Areas dataset was updated on September 22, 2025 from the U.S. Department of Commerce, U.S. Census Bureau, Geography Division and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Metropolitan and Micropolitan Statistical Areas are together termed Core Based Statistical Areas (CBSAs) and are defined by the Office of Management and Budget (OMB) and consist of the county or counties or equivalent entities associated with at least one urban core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core. Categories of CBSAs are: Metropolitan Statistical Areas, based on urban areas of 50,000 or more population; and Micropolitan Statistical Areas, based on urban areas of at least 10,000 population but less than 50,000 population. The CBSA boundaries are those defined by OMB based on the 2020 Census and published in 2023. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529014

In the United States, the federal government manages lands in significant parts of the country. These lands include 193 million acres managed by the US Forest Service in the nation's 154 National Forests and 20 National Grasslands, Bureau of Land Management lands that cover 247 million acres in Alaska and the Western United States, 150 million acres managed for wildlife conservation by the US Fish and Wildlife Service, 84 million acres of National Parks and other lands managed by the National Park Service and over 30 million acres managed by the Department of Defense. The Bureau of Reclamation manages a much smaller land base than the other agencies included in this layer but plays a critical role in managing the country's water resources.The agencies included in this layer are:Bureau of Land ManagementBureau of ReclamationDepartment of DefenseNational Park ServiceUS Fish and Wildlife ServiceUS Forest ServiceDataset SummaryPhenomenon Mapped: United States lands managed by six federal agencies Coordinate System: Web Mercator Auxiliary SphereExtent: 50 United States plus Puerto Rico, US Virgin Islands, Guam, American Samoa, and Northern Mariana Islands. The layer also includes National Monuments and Wildlife Refuges in the Pacific Ocean, Atlantic Ocean, and the Caribbean Sea.Visible Scale: The data is visible at all scales but draws best at scales greater than 1:2,000,000Source: BLM, DoD, USFS, USFWS, NPS, PADUS 2.1Publication Date: Various - Esri compiled and published this layer in May 2022. See individual agency views for data vintage.There are six layer views available that were created from this service. Each layer uses a filter to extract an individual agency from the service. For more information about the layer views or how to use them in your own project, follow these links:USA Bureau of Land Management LandsUSA Bureau of Reclamation LandsUSA Department of Defense LandsUSA National Park Service LandsUSA Fish and Wildlife Service LandsUSA Forest Service LandsWhat 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 "federal lands" 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 "federal lands" in the search box, browse to the layer then click OK.In both ArcGIS Online and Pro you can change the layer's symbology and view its attribute table. You can filter the layer to show subsets of the data using the filter button in Online or a definition query in Pro.The data can be exported to a file geodatabase, a shapefile or other format and downloaded using the Export Data button on the top right of this webpage.This layer can be used as an analytic input in both Online and Pro through the Perform Analysis window Online or as an input to a geoprocessing tool, model, or Python script in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

World Countries provides a detailed basemap layer for the countries of the world. This layer has been designed to be used as a basemap and includes fields for official names and country codes, along with fields for continent and display. Particularly useful are the fields LAND_TYPE and LAND_RANK that separate polygons based on their size. These fields are helpful for rendering at different scales by providing the ability to turn off small islands that may clutter small-scale (zoomed out) views. The sources of this dataset are Esri, Garmin, U.S. Central Intelligence Agency (The World Factbook), and International Organization for Standardization (ISO). This layer was published in October 2024. It is updated every 12-18 months or as significant changes occur.

This data set is part of an ongoing project to consolidate interagency fire perimeter data. Currently only certified perimeters and new perimeters captured starting in 2021 are included. A process for loading additional perimeters is being evaluated.The Wildland Fire Interagency Geospatial Services (WFIGS) Group provides authoritative geospatial data products under the interagency Wildland Fire Data Program. Hosted in the National Interagency Fire Center ArcGIS Online Organization (The NIFC Org), WFIGS provides both internal and public facing data, accessible in a variety of formats.This service includes perimeters for wildland fire incidents that meet the following criteria:Categorized in the IRWIN (Integrated Reporting of Wildland Fire Information) integration service as a Wildfire (WF) or Prescribed Fire (RX)Is Valid and not "quarantined" in IRWIN due to potential conflicts with other recordsAttribution of the source polygon is set to a Feature Access of Public, a Feature Status of Approved, and an Is Visible setting of YesPerimeters are not available for every incident. This data set is an ongoing project with the end goal of providing a national interagency fire history feature service of best-available perimeters.No "fall-off" rules are applied to this service. The date range for this service will extend from present day back indefinitely. Data prior to 2021 will be incomplete and incorporated as an ongoing project.Criteria were determined by an NWCG Geospatial Subcommittee task group. Data are refreshed every 5 minutes. Changes in the perimeter source may take up to 15 minutes to display.Perimeters are pulled from multiple sources with rules in place to ensure the most current or most authoritative shape is used.Warning: Please refrain from repeatedly querying the service using a relative date range. This includes using the “(not) in the last” operators in a Web Map filter and any reference to CURRENT_TIMESTAMP. This type of query puts undue load on the service and may render it temporarily unavailable.Attributes and their definitions can be found below. More detail about the NWCG Wildland Fire Event Polygon standard can be found here.Attributes:poly_SourceOIDThe OBJECTID value of the source record in the source dataset providing the polygon.poly_IncidentNameThe incident name as stored in the polygon source record.poly_MapMethodThe mapping method with which the polygon was derived.poly_GISAcresThe acreage of the polygon as stored in the polygon source record.poly_CreateDateSystem generated date for the date time the source polygon record was created (stored in UTC).poly_DateCurrentSystem generated date for the date time the source polygon record was last edited (stored in UTC).poly_PolygonDateTimeRepresents the date time that the polygon data was captured.poly_IRWINIDIRWIN ID stored in the polygon record.poly_FORIDFORID stored in the polygon record.poly_Acres_AutoCalcSystem calculated acreage of the polygon (geodesic WGS84 acres).poly_SourceGlobalIDThe GlobalID value of the source record in the source dataset providing the polygon.poly_SourceThe source dataset providing the polygon.attr_SourceOIDThe OBJECTID value of the source record in the source dataset providing the attribution.attr_ABCDMiscA FireCode used by USDA FS to track and compile cost information for emergency initial attack fire suppression expenditures. for A, B, C & D size class fires on FS lands.attr_ADSPermissionStateIndicates the permission hierarchy that is currently being applied when a system utilizes the UpdateIncident operation.attr_ContainmentDateTimeThe date and time a wildfire was declared contained. attr_ControlDateTimeThe date and time a wildfire was declared under control.attr_CreatedBySystemArcGIS Server Username of system that created the IRWIN Incident record.attr_IncidentSizeReported for a fire. The minimum size is 0.1.attr_DiscoveryAcresAn estimate of acres burning upon the discovery of the fire. More specifically when the fire is first reported by the first person that calls in the fire. The estimate should include number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.attr_DispatchCenterIDA unique identifier for a dispatch center responsible for supporting the incident.attr_EstimatedCostToDateThe total estimated cost of the incident to date.attr_FinalAcresReported final acreage of incident.attr_FFReportApprovedByTitleThe title of the person that approved the final fire report for the incident.attr_FFReportApprovedByUnitNWCG Unit ID associated with the individual who approved the final report for the incident.attr_FFReportApprovedDateThe date that the final fire report was approved for the incident.attr_FireBehaviorGeneralA general category describing the manner in which the fire is currently reacting to the influences of fuel, weather, and topography. attr_FireBehaviorGeneral1A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireBehaviorGeneral2A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireBehaviorGeneral3A more specific category further describing the general fire behavior (manner in which the fire is currently reacting to the influences of fuel, weather, and topography). attr_FireCauseBroad classification of the reason the fire occurred identified as human, natural or unknown. attr_FireCauseGeneralAgency or circumstance which started a fire or set the stage for its occurrence; source of a fire's ignition. For statistical purposes, fire causes are further broken into specific causes. attr_FireCauseSpecificA further categorization of each General Fire Cause to indicate more specifically the agency or circumstance which started a fire or set the stage for its occurrence; source of a fire's ignition. attr_FireCodeA code used within the interagency wildland fire community to track and compile cost information for emergency fire suppression expenditures for the incident. attr_FireDepartmentIDThe U.S. Fire Administration (USFA) has created a national database of Fire Departments. Most Fire Departments do not have an NWCG Unit ID and so it is the intent of the IRWIN team to create a new field that includes this data element to assist the National Association of State Foresters (NASF) with data collection.attr_FireDiscoveryDateTimeThe date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.attr_FireMgmtComplexityThe highest management level utilized to manage a wildland fire event. attr_FireOutDateTimeThe date and time when a fire is declared out. attr_FireStrategyConfinePercentIndicates the percentage of the incident area where the fire suppression strategy of "Confine" is being implemented.attr_FireStrategyFullSuppPrcntIndicates the percentage of the incident area where the fire suppression strategy of "Full Suppression" is being implemented.attr_FireStrategyMonitorPercentIndicates the percentage of the incident area where the fire suppression strategy of "Monitor" is being implemented.attr_FireStrategyPointZonePrcntIndicates the percentage of the incident area where the fire suppression strategy of "Point Zone Protection" is being implemented.attr_FSJobCodeA code use to indicate the Forest Service job accounting code for the incident. This is specific to the Forest Service. Usually displayed as 2 char prefix on FireCode.attr_FSOverrideCodeA code used to indicate the Forest Service override code for the incident. This is specific to the Forest Service. Usually displayed as a 4 char suffix on FireCode. For example, if the FS is assisting DOI, an override of 1502 will be used.attr_GACCA code that identifies one of the wildland fire geographic area coordination center at the point of origin for the incident.A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.attr_ICS209ReportDateTimeThe date and time of the latest approved ICS-209 report.attr_ICS209RptForTimePeriodFromThe date and time of the beginning of the time period for the current ICS-209 submission.attr_ICS209RptForTimePeriodToThe date and time of the end of the time period for the current ICS-209 submission. attr_ICS209ReportStatusThe version of the ICS-209 report (initial, update, or final). There should never be more than one initial report, but there can be numerous updates, and even multiple finals (as determined by business rules).attr_IncidentManagementOrgThe incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.attr_IncidentNameThe name assigned to an incident.attr_IncidentShortDescriptionGeneral descriptive location of the incident such as the number of miles from an identifiable town. attr_IncidentTypeCategoryThe Event Category is a sub-group of the Event Kind code and description. The Event Category further breaks down the Event Kind into more specific event categories.attr_IncidentTypeKindA general, high-level code and description of the types of incidents and planned events to which the interagency wildland fire community responds.attr_InitialLatitudeThe latitude location of the initial reported point of origin specified in decimal degrees.attr_InitialLongitudeThe longitude location of the initial reported point of origin specified in decimal degrees.attr_InitialResponseAcresAn estimate of acres burning at the time of initial response. More specifically when the IC arrives and performs initial size up. The

This layer, Conservation Easements w/in 5 mi, displays the boundaries for lands placed under a state-recognized conservation easement program that lie within five miles of the Rocky Forge Wind project. This data was downloaded from the Department of Conservation and Recreation. website.Source and date:The original dataset was downloaded as a shapefile from the Department of Conservation and Recreation's Virginia Conservation Lands Database. Accessed November 1, 2021.Purpose:The source map serves as a resource that identifies conservation lands, conservation planning, and various other spatial data, to serve as a land conservation planning tool. The Conservation Lands Database is constantly being edited and updated. Historic easements are held by the Virginia Board of Historic Resources and administered by the Virginia department of Historic Resources. Data is released to the public quarterly. The map can be viewed without downloading by going to the Department of Conservation and Recreation's Virginia Conservation Lands Database and clicking on the map.Processing:ABRA extracted Conservation Easements from the source map and selected data within 5 miles of the Rocky Forge Wind project in order to create this layer. The resulting shapefile was uploaded to ArcGIS Online and published as a feature layer.Symbolization:The following symbolization is how it appears in the Rocky Forge Wind online map provided by ABRA.Conservation Easements: blue polygonsFrom Original Metadata:This dataset contains the boundaries for lands of conservation and recreational interest in Virginia. The Conservation Lands Database is constantly being edited and updated. Historic easements are held by the Virginia Board of Historic Resources and administered by the Virginia department of Historic Resources. Data is released to the public quarterly and posted to the download section of the website: https://www.dcr.virginia.gov/natural_heritage/cldownload.shtml