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.

Purpose

County 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 Layers

This dataset is part of a grouping of many datasets:

1. Cities: Only the city boundaries and attributes, without any unincorporated areas
   • With Coastal Buffers
   • Without Coastal Buffers
2. Counties: Full county boundaries and attributes, including all cities within as a single polygon
   • With Coastal Buffers
   • Without Coastal Buffers
3. Cities 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 Buffers
4. Place Abbreviations
5. Unincorporated Areas (Coming Soon)
6. Census Designated Places (Coming Soon)
   
7. Cartographic Coastline
   • Polygon
   • Line source (Coming Soon)

Point of Contact

California Department of Technology, Office of Digital Services, odsdataservices@state.ca.gov

Field and Abbreviation Definitions

• 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
• Place Name: CDTFA incorporated (city) or county name
• 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.
• Legal Place Name: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information System
• GNIS_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 System
• Place Abbr: CalTrans Division of Local Assistance abbreviations of incorporated area names
• CNTY Abbr: CalTrans Division of Local Assistance abbreviations of county names
• Area_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.

Accuracy

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. COUNTY = county name; CITY = city name or unincorporated

Used in Missouri Outdoor Connection experience to show data analytics and search for proper stewardship groups in an embedded dashboard.

Road edges are defined as the edge of the improved surface including the improved shoulder but do not include the unimproved shoulder, only the travel part of the road. The road network is compiled to include all open intersections. Features do not overlap sidewalks, but have the sidewalk area cut out of the road polygons. Overlapping features are acceptable if one of the features is hidden. Road: A generally named thoroughfare, that is usually paved and can be public or private. Unimproved thoroughfares are excluded. Road polygons are formed by a combination of road edge, curb, sidewalk, street intersection closure line, and map sheet edge. Paved Median Island: Perimeter of non-traffic paved areas that separate traffic lanes in opposing directions. Unpaved Median Island: Perimeter of non-traffic grassy, unpaved areas that separate traffic lanes in opposing directions. Paved Traffic Island: Perimeter of non-traffic concrete areas in the middle of streets designed to segregate traffic flow. This does not include linear barriers, e.g., Jersey barriers, walls or guardrails, or point barriers, such as impact attenuators. Features do not overlap sidewalks. Unpaved Traffic Island: Perimeter of non-traffic unpaved, grassy areas in the middle of streets designed to segregate traffic flow. This does not include linear barriers, e.g., Jersey barriers, walls or guardrails, or point barriers, such as impact attenuators. Features do not overlap sidewalks. Alley: Perimeter of alleys first plotted photogrammetrically from other indicators such as building footprints, fence lines, curb lines, walls, paved or unpaved drives, and map sheet edge. Alley polygons are closed along the lines where they intersect with road polygons. Paved Drive: A paved driveway for a building or entranceway for a parking lot. Driveways are neither streets nor alleys, but provide access to public facilities, such as a drive to a monument, museum, hotel, large estate, sports field or golf course, grounds of the U.S. Capitol, etc. If a driveway is less than 200 feet and leads to a parking lot, the entire paved area is captured as Parking Lot. Driveways are photogrammetrically compiled as polygons and not compiled from individual vectors on different levels. Parking Lot: Generally paved surfaces used for cars to park on. Paved drives usually form entrances to these features, if the drive is more than 200 feet. If the driveway is less than 200 feet leading into the parking lot, the entire paved area is captured as Parking Lot. Parking lots sharing a common boundary with linear features must have the common segment captured once, but coded as both polygon and line. Small parking areas, where individuals park their cars in the middle of a block off a public alley, are not captured as parking lots. These are either public space (e.g., alleys) or private space where owners permit parking to occur. Intersection: A location where more than one road comes together. For standard cross streets, intersection polygons are bounded by curbs and four closure lines at street intersection crosswalks (outer line) or placed arbitrarily where crosswalks could logically be placed. For "T" intersections, the polygons are bounded by curbs and three such closure lines. Complex intersections can have more closure lines. Entire traffic circles are coded as intersections. Hidden Road: A section of a road that passes underneath a bridge or overpass and is not visible in an aerial photograph, but the location can be interpreted based on the road on either side of the bridge. Hidden Median: A road median that exists underneath a bridge or overpass and is not fully visible in an aerial photograph, but the location can be interpreted based on the information visible on either side of the bridge.

Statewide Ecopia 3 foot Land Cover (2021-2022)This raster land cover data is based off of high-resolution statewide imagery from 2021-2022. It was used by Ecopia to extract and digitize the entire state into 7 different land cover classes. Download Notes:This service can be entered into ArcGIS Pro where "Download Rasters" can be used to download approximately 20 square miles at a time. (Rt. click layer in TOC > Data > Download Rasters)Alternatively, the entire statewide 3ft dataset is available as a zipped download from here (includes colormap file): Ecopia_Statewide_3ft_Raster_TilesClasses available at bottom of this pages.Data SpecificationImagery Used for Extraction: Pixel resolution: 15 cm (6")Camera sensor: Hexagon Pushbroom (Content Mapper)Date of capture: 06/25/2021 - 08/14/2022Date of Vector Extraction: June 2023Extraction Methodology:Ecopia uses proprietary extraction and modeling software to process raw images into high-resolution land cover classifications.Quality Measurements:Measure Name - Threshold across Impervious Polygons:False Negatives <= 5% All PolygonsFalse Positives <= 5% All PolygonsValid Interpretation >= 95% All PolygonsMinimum Area 100% All PolygonsValid Geometry 100% All PolygonsMeasure Name - Threshold across Natural Polygons:False Negatives <=5% All PolygonsFalse Positives <=5% All PolygonsValid Interpretation >=90% All PolygonsMinimum Area 100% All PolygonsValid Geometry 100% All PolygonsLand Cover Classes:UnclassifiedImperviousImpervious, covered by treesShrub/low vegetationTree/forest/high vegetationOpen waterRailroadVegetation (Canopy Mapping)Tree canopy will be captured as a unique polygon layer. It can therefore overlap impervious layers.High vegetation is distinguished from low vegetation based on crown, texture, and derived height models. Leveraging stereo imagery produces results using 3D elevation models used to aid the distinction of vegetation categories. Distinguishing low from high vegetation is based on a 5m threshold, but this is not always feasible, especially in areas where heavy canopy prevents a visualization of the ground. In these circumstances, high vegetation will be given the priority over low vegetation. For more information visit: www.ecopiatech.comClasses:0: No data - Null, clear1: Unclassified2: Impervious3: Impervious, Covered by Tree Canopy6: Shrub/Low Vegetation7: Tree/Forest/High Vegetation8: Open Water12: Railroad

Summary: This dataset contains an inventory of City of Los Angeles Sidewalks and related features (Access Ramps, Curbs, Driveways, and Parkways).Background: This inventory was performed throughout 2017 using a combination of G.I.S software, aerial imagery (2014 LARIAC), and a geographic dataset of property/right-of way lines. The dataset has not been updated since its creation.Description: The following provides more detail about the feature classes in this dataset. All features were digitized (“traced”) as observed in the orthophotography (digital aerial photos) and assigned the Parcel Identification Number (PIN) of their corresponding property:Sidewalk (polygon) – represents paved pedestrian walkways. Typical widths are between 3‐6 feet in residential areas and larger and more variable in commercial and high‐density traffic areas.Alley-Sidewalk (polygon) – represents the prevailing walkway or path of travel at the entrance/exit of an alley. Digitized as Sidewalk features but categorized as Alley Sidewalk and assigned a generic PIN value, ALLEY SIDEWALK.Corner Polygon (polygon) - feature created where sidewalks from two streets meet but do not intersect (i.e. at corner lots). There’s no standard shape/type and configurations vary widely. These are part of the Sidewalk feature class.In commercial and high‐density residential areas where there is only continuous sidewalk (no parkway strip), the sidewalk also functions as a Driveway.Driveway (polygon) – represents area that provides vehicular access to a property. Features are not split by extended parcel lot lines except when two adjacent properties are served by the same driveway approach (e.g. a common driveway), in which case they are and assigned a corresponding PIN.Parkway (polygon) – represents the strip of land behind the curb and in front of the sidewalk. Generally, they are landscaped with ground cover but they may also be filled in with decorative stone, pavers, decomposed granite, or concrete. They are created by offsetting lines, the Back of Curb (BOC) line and the Face of Walk (FOW). The distance between the BOC and FOW is measured off the aerial image and rounded to the nearest 0.5 foot, typically 6 – 10 feet.Curb (polygon) – represents the concrete edging built along the street to form part of the gutter. Features are always 6” wide strips and are digitized using the front of curb and back of curb digitized lines. They are the leading improvement polygon and are created for all corner, parkway, driveway and, sidewalk (if no parkway strip is present) features.Curb Ramp, aka Access Ramp (point) – represents the geographic center (centroid) of Corner Polygon features in the Sidewalk feature class. They have either a “Yes” or “No” attribute that indicates the presence or absence of a wheelchair access ramp, respectively.Fields: All features include the following fields...FeatureID – a unique feature identifier that is populated using the feature class’ OBJECTID fieldAssetID – a unique feature identifier populated by Los Angeles City staff for internal usePIND – a unique Parcel Identification Number (PIN) for all parcels within the City of L.A. All Sidewalk related features will be split, non-overlapping, and have one associated Parcel Identification Number (PIN). CreateDate – indicates date feature was createdModifiedDate – indicates date feature was revised/editedCalc_Width (excluding Access Ramps) – a generalized width of the feature calculated using spatial and mathematical algorithms on the feature. In almost all cases where features have variable widths, the minimum width is used. Widths are rounded to the nearest whole number. In cases where there is no value for the width, the applied algorithms were unable to calculate a reliable value.Calc_Length (excluding Access Ramps) – a generalized length of the feature calculated using spatial and mathematical algorithms on the feature. Lengths are rounded to the nearest whole number. In cases where there is no value for the length, the applied algorithms were unable to calculate a reliable value.Methodology: This dataset was digitized using a combination of G.I.S software, aerial imagery (2014 LARIAC), and a geographic dataset of property/right-of way lines.The general work flow is as follows:Create line work based on digital orthophotography, working from the face‐of‐curb (FOC) inward to the property right-of-way (ROW)Build sidewalk, parkway, driveway, and curb polygons from the digitized line workPopulate all polygons with the adjacent property PIN and classify all featuresCreate Curb Ramp pointsWarnings: This dataset has been provided to allow easy access and a visual display of Sidewalk and related features (Parkways, Driveway, Curb Ramps and Curbs). Every reasonable effort has been made to assure the accuracy of the data provided; nevertheless, some information may not be accurate. The City of Los Angeles assumes no responsibility arising from use of this information. THE MAPS AND ASSOCIATED DATA ARE PROVIDED WITHOUT WARRANTY OF ANY KIND, either expressed or implied, including but not limited to, the implied warranties of merchantability and fitness for a particular purpose. Other things to keep in mind about this dataset are listed below:Obscured Features – The existence of dense tree canopy or dark shadows in the aerial imagery tend to obscure or make it difficult to discern the extent of certain features, such as Driveways. In these cases, they may have been inferred from the path in the corresponding parcel. If a feature and approach was completely obscured, it was not digitized. In certain instances the coloring of the sidewalk and adjacent pavement rendered it impossible to identify the curb line or that a sidewalk existed. Therefore a sidewalk may or may not be shown where one actually may or may not exist.Context: The following links provide information on the policy context surrounding the creation of this dataset. It includes links to City of L.A. websites:Willits v. City of Los Angeles Class Action Lawsuit Settlementhttps://www.lamayor.org/willits-v-city-la-sidewalk-settlement-announcedSafe Sidewalks LA – program implemented to repair broken sidewalks in the City of L.A., partly in response to the above class action lawsuit settlementhttps://sidewalks.lacity.org/Data Source: Bureau of EngineeringNotes: Please be aware that this dataset is not actively being maintainedLast Updated: 5/20/20215/20/2021 - Added Calc_Width and Calc_Length fieldsRefresh Rate: One-time deliverable. Dataset not actively being maintained.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

A polygon shapefile of BILO grid cells for each subregion in the Bioregional Assessment. These data should be used for mapping and indicative purposes only as cells are not mutually exclusive between some adjoining bioregions. See history for more details.

Cells are identified by their GRIDCODE, that being a nine digit integer derived from the centroid lat-long co-ordinates.

The formula is (long nnn.nn) x 100,000 + -(lat nn.nn) x 100

eg a cell with a centroid of 148.05 degrees east and -26.55 degrees south would have an ID of 148052555

Subregions are coded as follows:

No subregion

1 Gippsland

2 Wollongong Coast

3 Georges River

4 Hawkesbury-Nepean

5 Hunter

6 Gloucester

7 Clarence-Moreton

8 Central West

9 Namoi

10 Gwydir

11 Maranoa-Balonne-Condamine

12 Arckaringa

13 Pedirka

14 Galilee

14 Cooper

note: Cooper and Galilee both have code 14

Dataset History

The data were extracted from the BILO grid cells for the whole of Australia using an intersection of the BILO cells with the subregion boundaries in ArcGIS. The order of processing for each subregion polygon determined the coded assigned to each cell polygon such that where a cell intersect more than one subregion boundary (i.e. where subregions share a common boundary) then the boundary used in the most recent selection will over-ride that of previous selections. For individual subregions it is recommended that such selection is done individually for the single subregion polygon if these data are required for further analysis. This data set should only be used for over-view mapping and indicative purposes.

Dataset Citation

Bioregional Assessment Programme (XXXX) BA All Regions BILO cells in subregions shapefile. Bioregional Assessment Derived Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/08933408-de61-4eb0-ab41-a6ff59f92a96.

Dataset Ancestors

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

• Derived From Climate model 0.05x0.05 cells and cell centroids

Natural conservation areas were created by clipping artificial pathways (generally, areas that correspond to major rivers) and intermittent and perennial stream features from the National Hydrography Dataset (NHD) flowline feature class to the Hanover County boundary. Intermittent NHD features that did not intersect the FEMA floodplain layer were deleted from the dataset. These final flowlines were then buffered by 100 feet. NHD water body features were also buffered by 100 feet. Features from the buffered water body layer were deleted if they did not intersect the buffered flowlines or the FEMA floodplain layer. Next, the buffered NHD flowlines, the FEMA floodplain layer, and the buffered water body polygons were all merged into one polygon feature class. The geoprocessing tool 'multipart to singlepart' was then run on the polygons to separate multi-part features into distinct regions. Next, the geoprocessing tool 'simplify by straight lines and circular arcs' was run on the polygon layer to reduce the number of feature vertices and improve performance. Finally, any polygons overlaying developed areas were removed from the dataset by erasing the portion of the region within the property boundary of the developed parcel.

Reason for SelectionProtected natural areas in urban environments provide urban residents a nearby place to connect with nature and offer refugia for some species. They help foster a conservation ethic by providing opportunities for people to connect with nature, and also support ecosystem services like offsetting heat island effects (Greene and Millward 2017, Simpson 1998), water filtration, stormwater retention, and more (Hoover and Hopton 2019). In addition, parks, greenspace, and greenways can help improve physical and psychological health in communities (Gies 2006). Urban park size complements the equitable access to potential parks indicator by capturing the value of existing parks.Input DataSoutheast Blueprint 2024 extentFWS National Realty Tracts, accessed 12-13-2023Protected Areas Database of the United States(PAD-US):PAD-US 3.0national geodatabase -Combined Proclamation Marine Fee Designation Easement, accessed 12-6-20232020 Census Urban Areas from the Census Bureau’s urban-rural classification; download the data, read more about how urban areas were redefined following the 2020 censusOpenStreetMap data “multipolygons” layer, accessed 12-5-2023A polygon from this dataset is considered a beach if the value in the “natural” tag attribute is “beach”. Data for coastal states (VA, NC, SC, GA, FL, AL, MS, LA, TX) were downloaded in .pbf format and translated to an ESRI shapefile using R code. OpenStreetMap® is open data, licensed under theOpen Data Commons Open Database License (ODbL) by theOpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more onthe OSM copyright page.2021 National Land Cover Database (NLCD): Percentdevelopedimperviousness2023NOAA coastal relief model: volumes 2 (Southeast Atlantic), 3 (Florida and East Gulf of America), 4 (Central Gulf of America), and 5 (Western Gulf of America), accessed 3-27-2024Mapping StepsCreate a seamless vector layer to constrain the extent of the urban park size indicator to inland and nearshore marine areas <10 m in depth. The deep offshore areas of marine parks do not meet the intent of this indicator to capture nearby opportunities for urban residents to connect with nature. Shallow areas are more accessible for recreational activities like snorkeling, which typically has a maximum recommended depth of 12-15 meters. This step mirrors the approach taken in the Caribbean version of this indicator.Merge all coastal relief model rasters (.nc format) together using QGIS “create virtual raster”.Save merged raster to .tif and import into ArcPro.Reclassify the NOAA coastal relief model data to assign areas with an elevation of land to -10 m a value of 1. Assign all other areas (deep marine) a value of 0.Convert the raster produced above to vector using the “RasterToPolygon” tool.Clip to 2024 subregions using “Pairwise Clip” tool.Break apart multipart polygons using “Multipart to single parts” tool.Hand-edit to remove deep marine polygon.Dissolve the resulting data layer.This produces a seamless polygon defining land and shallow marine areas.Clip the Census urban area layer to the bounding box of NoData surrounding the extent of Southeast Blueprint 2024.Clip PAD-US 3.0 to the bounding box of NoData surrounding the extent of Southeast Blueprint 2024.Remove the following areas from PAD-US 3.0, which are outside the scope of this indicator to represent parks:All School Trust Lands in Oklahoma and Mississippi (Loc Des = “School Lands” or “School Trust Lands”). These extensive lands are leased out and are not open to the public.All tribal and military lands (“Des_Tp” = "TRIBL" or “Des_Tp” = "MIL"). Generally, these lands are not intended for public recreational use.All BOEM marine lease blocks (“Own_Name” = "BOEM"). These Outer Continental Shelf lease blocks do not represent actively protected marine parks, but serve as the “legal definition for BOEM offshore boundary coordinates...for leasing and administrative purposes” (BOEM).All lands designated as “proclamation” (“Des_Tp” = "PROC"). These typically represent the approved boundary of public lands, within which land protection is authorized to occur, but not all lands within the proclamation boundary are necessarily currently in a conserved status.Retain only selected attribute fields from PAD-US to get rid of irrelevant attributes.Merged the filtered PAD-US layer produced above with the OSM beaches and FWS National Realty Tracts to produce a combined protected areas dataset.The resulting merged data layer contains overlapping polygons. To remove overlapping polygons, use the Dissolve function.Clip the resulting data layer to the inland and nearshore extent.Process all multipart polygons (e.g., separate parcels within a National Wildlife Refuge) to single parts (referred to in Arc software as an “explode”).Select all polygons that intersect the Census urban extent within 0.5 miles. We chose 0.5 miles to represent a reasonable walking distance based on input and feedback from park access experts. Assuming a moderate intensity walking pace of 3 miles per hour, as defined by the U.S. Department of Health and Human Service’s physical activity guidelines, the 0.5 mi distance also corresponds to the 10-minute walk threshold used in the equitable access to potential parks indicator.Dissolve all the park polygons that were selected in the previous step.Process all multipart polygons to single parts (“explode”) again.Add a unique ID to the selected parks. This value will be used in a later step to join the parks to their buffers.Create a 0.5 mi (805 m) buffer ring around each park using the multiring plugin in QGIS. Ensure that “dissolve buffers” is disabled so that a single 0.5 mi buffer is created for each park.Assess the amount of overlap between the buffered park and the Census urban area using “overlap analysis”. This step is necessary to identify parks that do not intersect the urban area, but which lie within an urban matrix (e.g., Umstead Park in Raleigh, NC and Davidson-Arabia Mountain Nature Preserve in Atlanta, GA). This step creates a table that is joined back to the park polygons using the UniqueID.Remove parks that had ≤10% overlap with the urban areas when buffered. This excludes mostly non-urban parks that do not meet the intent of this indicator to capture parks that provide nearby access for urban residents. Note: The 10% threshold is a judgement call based on testing which known urban parks and urban National Wildlife Refuges are captured at different overlap cutoffs and is intended to be as inclusive as possible.Calculate the GIS acres of each remaining park unit using the Add Geometry Attributes function.Buffer the selected parks by 15 m. Buffering prevents very small and narrow parks from being left out of the indicator when the polygons are converted to raster.Reclassify the parks based on their area into the 7 classes seen in the final indicator values below. These thresholds were informed by park classification guidelines from the National Recreation and Park Association, which classify neighborhood parks as 5-10 acres, community parks as 30-50 acres, and large urban parks as optimally 75+ acres (Mertes and Hall 1995).Assess the impervious surface composition of each park using the NLCD 2021 impervious layer and the Zonal Statistics “MEAN” function. Retain only the mean percent impervious value for each park.Extract only parks with a mean impervious pixel value <80%. This step excludes parks that do not meet the intent of the indicator to capture opportunities to connect with nature and offer refugia for species (e.g., the Superdome in New Orleans, LA, the Astrodome in Houston, TX, and City Plaza in Raleigh, NC).Extract again to the inland and nearshore extent.Export the final vector file to a shapefile and import to ArcGIS Pro.Convert the resulting polygons to raster using the ArcPy Feature to Raster function and the area class field.Assign a value of 0 to all other pixels in the Southeast Blueprint 2024 extent not already identified as an urban park in the mapping steps above. Zero values are intended to help users better understand the extent of this indicator and make it perform better in online tools.Use the land and shallow marine layer and “extract by mask” tool to save the final version of this indicator.Add color and legend to raster attribute table.As a final step, clip to the spatial extent of Southeast Blueprint 2024.Note: For more details on the mapping steps, code used to create this layer is available in theSoutheast Blueprint Data Downloadunder > 6_Code.Final indicator valuesIndicator values are assigned as follows:6= 75+ acre urban park5= 50 to <75 acre urban park4= 30 to <50 acre urban park3= 10 to <30 acre urban park2=5 to <10acreurbanpark1 = <5 acre urban park0 = Not identified as an urban parkKnown IssuesThis indicator does not include park amenities that influence how well the park serves people and should not be the only tool used for parks and recreation planning. Park standards should be determined at a local level to account for various community issues, values, needs, and available resources.This indicator includes some protected areas that are not open to the public and not typically thought of as “parks”, like mitigation lands, private easements, and private golf courses. While we experimented with excluding them using the public access attribute in PAD, due to numerous inaccuracies, this inadvertently removed protected lands that are known to be publicly accessible. As a result, we erred on the side of including the non-publicly accessible lands.The NLCD percent impervious layer contains classification inaccuracies. As a result, this indicator may exclude parks that are mostly natural because they are misclassified as mostly impervious. Conversely, this indicator may include parks that are mostly impervious because they are misclassified as mostly

Hydric soils indicate a current or former wetland condition, and for this layer, hydric inclusions (>15% hydric soils) were not added to the layer, while hydric complexes (<15% hydric soils) were included. This is a statewide layer that can be used as one resource in identifying current or former wetland areas. United States Department of Agriculture-National Resource Conservation Service (USDA-NRCS) Soil Survey Geographic database (SSURGO) Soils were used as the base dataset and hydric soil types were queried out on a county by county basis in a GIS environment. USDA-NRCS State Soil Scientists provided the hydric soil types in Excel table format by county, and Wetlands, Lakes, and Streams unit (WLSU) produced this layer from that official list.

Field Name

Descriptions

CoverType

This is the approximate historical landcover type. This data was pulled from the land 1800 Michigan Natural Features Inventory dataset. Its typcially used as part of the Landscape Level Wetland Functional Assesement.

MapUnitSymbol

Map unit symbol is an attribute that tracks the type of soil. This code can be used to look up information for a partical soil.

Acres

Size of the hydric soil polygon.

AreaSymbol

County code for location of the polygon.

AreaName

Name of the county in which the soil is found.

MapUnitCode

A symbol used to uniquely identify the soil map unit in the soil survey

MapUnitName

Correlated name of the mapunit (recommended name or field name for surveys in progress).

Component

Name assigned to a component based on its range of properties. Local Phase - Phase criterion to be used at a local level, in conjunction with "component name" to help identify a soil component.

Representation

The percentage of the component of the mapunit.

Landforms

A word or group of words used to name a feature on the earth's surface, expressed in the plural form.

HydricRating

A yes/no field that indicates whether or not a map unit component is classified as a "hydric soil". If rated as hydric, the specific criteria met are listed in the Component Hydric Criteria table. Because this data is a hydric layer all will be yes

HydricCriteria

Criterion code for the soil characteristic(s) and/or feature(s) that cause the map unit component to be classified as a "hydric soil." These codes are the paragraph numbers in the hydric soil criteria publication.

NWIWater

The approximated National Wetland Inventory water regime code assigned to this soil type. This was done as part of the Landscape Level Functional Assessment.

NWICode

Code generated from the landcover type and NWI water field. The approximated National Wetland Inventory Code approximated for this historic landcover.

HGMCode

Code for the Landscape Level Assessment. Combines each of the coded types. For example TEBAVR = Terrene Basin Vertical Flow

Landform

The type of geological feature in which the wetland resides. Slope (SL) Wetlands occurring on a slope of 5% or greater. Island (IS) A wetland completely surrounded by water. Fringe (FR) Wetland occurs in the shallow water zone of a permanent waterbody. *NWI water regime F, G, and H Floodplain (FP) Wetland occurs on an active alluvial plain along a river and some streams. *Modifiers FPba (Basin) and FPfl ( Flat) Basin (BA) Wetland occurs in a distinct depression. *NWI water regime C and E Flat (FL) Wetland occurs on a nearly level landform. *NWI water regime A and B

Landscape_Position

Landscape position values are determined by cross referencing NWI with hydrology and topography. NWI polygons that spatially intersect a stream/river in the National Hydrography Dataset (NHD) are classified as lotic. Lotic type wetlands can be further refined to indicate their adjacency to a stream or a river (lotic stream or lotic river). High resolution NHD data was used to differentiate rivers from streams in this analysis. A NHD classification completed by MDNR, Institute for Fisheries Research separated rivers by temperature gradient (cold, cool, warm) and size, based on average water flows (cubic feet per second or CFS). This dataset was used in the LLWFA analysis to mark this distinction. NWI Polygons that are determined to be within the basin of a lake are classified as lentic. Identifying the extent of a lake basin, and thus which wetlands fall within it, is done with the assistance of digital elevation models (DEM). NWI Polygons that don’t intersect surface water features or aren’t spatially located within a lake basin are classified as terrene

Waterbody_Type

Waterbody type classification is the simplest of the 4 LLWW descriptors. Ponds, lakes, and rivers are classified as such based explicitly on NWI Cowardin code. Lakes and ponds were separated at the 5-acre mark, all open-water polygons less than or equal to 5 acres were classified as ponds, while all open-water polygons larger than 5 acres were classified as lakes. The 5 acre cutoff was chosen to remain consistent with previously existing EGLE regulations. High resolution NHD data was used to differentiate rivers from streams in this analysis. A NHD classification completed by MDNR, Institute for Fisheries Research separated rivers by temperature gradient (cold, cool, warm) and size, based on average water flows (CFS) This dataset was used in the LLWFA analysis to mark this distinction.

Waterflow_Path

Water flow path, otherwise known as hydrodynamics, is classified by automated and manual interpretation of the intersection of NHD surface water features and NWI. Automated methods include intersecting NHD and NWI to capture throughflow wetlands (in-stream wetlands), both natural and artificial. A distinction is drawn in NHD between natural stream/river features and artificial canal/ditch features. Vegetated NWI wetlands that don’t intersect any surface water body are classified as isolated. Detailed coding was developed in an effort to differentiate intermittent, artificial, and perennial connections between wetlands and other surface waterbodies. Any wetland classified as lentic (Landscape Position) is automatically assigned a water flow path of bidirectional, accounting for the tidal effects of lakes on adjacent wetlands

Landform1

A secondary code used to determine type of floodplain and if a vegetated wetland is associated with a pond. Associated w/Pond (pd) Basin (ba) Flat (fl)

Landscape1

Field used to display if a wetland falls within a Headwater area Headwater (hw)

LLWFAComments

Field used to make notes during the LLWFA coding process.

HMValues

All function Values combined to perform the count.

FunCount

Number of Functions each wetland could be performing.

VegOrNotVeg

Is the wetland vegetated or open water (non veg).

FloodWaterStorage

Function field for Flood Water Storage H (2) = High M (1) = Moderate

StreamflowMaintenance

Function field for Streamflow Maintenance H (2) = High M (1) = Moderate

NutrientTransformation

Function field for Nutrient TransformationH (2) = High M (1) = Moderate

SedimentRetention

Function field for Sediment Retention H (2) = High M (1) = Moderate

ShorelineStabailization

Function field for Shoreline Stabilization H (2) = High M (1) = Moderate

FishHabitat

Function field for Fish Habitat. H (2) = High M (1) = Moderate

StreamShading

Function field for Stream Shading H (2) = High M (1) = Moderate

WaterfowlWaterbirdHabitat

Function field for Waterfowl and Water Bird Habitat. H (2) = High M (1) = Moderate

ShorebirdHabitat

Function field for Shorebird Habitat. H (2) = High M (1) = Moderate

InteriorForestBirdHabitat

Function field for Interior Forest Bird Habitat. H (2) = High M (1) = Moderate

AmphibianHabitat

Function field for Amphibian Habitat. H (2) = High M (1) = Moderate

GroundWaterInfluence

Function field for Ground Water InfluenceH (2) = High M (1) = Moderate

CarbonSequestration

Function field for Carbon Sequestration H (2) = High M (1) = Moderate

PathogenRetention

Function field for Pathogen Retention 1 = Wetlands that intersect 303d listed streams, 2 = Wetlands within a 500 ft buffer of 303d streams, 3 Streams that intersect wetlands that filter Pathogens, 4 wetlands within a 500 ft buffer that filter pathogens. For historical wetlands this would be showing best areas to do potential restoration.

The hydric soils polygons are not updated, however attributes will be updated when Landcape Level Wetland Functional data is completed.For questions about this content reach out to Jeremy Jones at jonesj28@michigan.gov.

WBDHU12: This geospatial dataset represents the 6th level (12-digit) hydrologic unit boundaries from the Watershed Boundary Dataset (WBD) layer for Washington. Hydrologic units within the WBDHUC12 represent drainage areas delineated to the 6th level drainage systems. Their boundaries are defined by hydrographic and topographic criteria that delineate an area of land upstream from a specific point on a river, stream, or similar surface waters. Hydrologic units within the WBDHUC12 can accept surface water directly from upstream drainage areas, and indirectly from associated surface areas such as remnant, non-contributing, and diversions to form a drainage area with single or multiple outlet points. Boundaries within the HU_BOUNDARY_WBD_WA_HUC_12 were delineated by Pacific Northwest (PNW) Hydrography Framework Partners and Natural Resources Conservation Service (NRCS) to meet state requirements and to contribute to the national WBD repository. To meet these goals, the WBD must adhere to the "Federal Standards for Delineation of Hydrologic Unit Boundaries", dated October, 2004. These HUC12 boundaries were made from the Washington dataset that has been nationally certified by Natural Resources Conservation Service (NRCS) Prior to submission the dataset was subjected to an iterative review and edit process to ensure that the hydrologic boundaries fully satisfy the federal standards. This work was completed under a Memorandum of Understanding between the Pacific Northwest Hydrography Framework Partnership, NRCS and the US Geological Survey (USGS). The current dataset includes all 6th level boundaries that are in all 4th level (8-digit) subbasins that fall within or intersect the Washington state boundary. USGS Federal Standards and Procedures for the National Watershed Boundary Dataset (WBD) located here: http://pubs.usgs.gov/tm/11/a3/pdf/tm11-a3.pdfWater areas removed. Clipped to Area of Interest.For Forest Cover:Raster to Polygons (Scratch \ForestCover2006 and ForestCover2011)Intersect with WBD_HU12Dissolve by HUC > FC2006_HUC_diss and FC2011_HUC_dissCreate/Pop field FC11_AREA_SQMI Create/Pop field PrctAreaFC06 and PrctAreaFC11Join and copy field PrctAreaFC11 to HUC for mappingJoin and copy field PrctAreaFC06 to HUCCreate/Pop PrctFCchange in by PrctAreaFC11- PrctAreaFC06 in WBD_HU12

Freshwater Management Units depicting lake catchments. Dune riverine, volcanic and some peat lake types were created from the NIWA REC Catchments. Not all lake FMU catchments are mutually exclusive (overlaps occur). For example; lake FMU catchment "A" can have an outflow to another larger lake FMU catchment "B" resulting in catchment "A" being contained by the larger catchment "B". An attribute - ORDER_RANK - is populated with values ranging from 1 to 3. 1’s are catchments that do not contain any other catchments within them. 2’s are catchments that contain one or more 1 catchments. 3’s are catchments that contain one or more 2 catchments. For cartographic purposes, ensure the display order follows ORDER_RANK; 1's drawn at the top of the display order, then 2's and 3's last. For analytical purposes, where two or more lake FMU catchments intersect the same polygon (ie farm property), use ORDER_RANK to determine which lake FMU catchment the polygon belongs to; 1's take primacy over 2's and 3's, while 2's take primacy over 3's. The dataset includes boundary information for the Waikato and Waipa catchments that fall within the Waikato Regional Council legal boundaries or are covered by the RCP maps. This project is co-managed under the JMAs of Ngāti Maniapoto, Raukawa, Ngāti Tūwharetoa, Te Arawa River Iwi and Waikato-Tainui as at 12 February 2012. See metadata for feature HEALTHY_RIV_FMU_LAKE in Healthy Rivers - Plan Change 1 - GIS Layer

This polygon was created using a combination of geospatial intersection tools and hand digitization methods. First, the 2023 Southeast Conservation Blueprint, the Western Highland Rim ecoregion, and 2021 NLCD Tree Canopy Cover layers where compared to one another to trace the eastern edge of the Nashville Highland Rim Forest, being sure to include some transition areas between developed and forested areas that provide ecosystem services and natural amenities to urban communities. Then, the Davidson county boundary/Metropolitan government of Nashville and Davidson County was used to intersect the western and northern side of the forest to complete the polygon. The Nashville Highland Rim Forest is relative to the county boundary because Nashville's Metro government covers the entire county. Interconnected Highland Rim forest blocks function outside of Davidson county, but focusing on the Davidson county portion makes it Nashville's Highland Rim Forest.The map below illustrates the how the Nashville Highland Rim Forest aligns with the 2023 Southeast Conservation Blueprint, the 2015 State Wildlife Action Plan's Western Highland Rim Conservation Opportunity Area, and some of the parks that fall within the forest.This polygon can be used to bring awareness of the significance of the forest to Nashville residents and visitors.

"Due to the size of this dataset, both Shapefile and Spreadsheet download options will not work as expected. The File Geodatabase is an alternative option for this data download"This is SCAG's 2019 Annual Land Use (ALU v. 2019.1) at the parcel-level, updated as of February 2021. This dataset has been modified to include additional attributes in order to feed SCAG's Housing Element Parcel Tool (HELPR), version 2.0. The dataset will be further reviewed and updated as additional information is released. Please refer to the tables below for data dictionary and SCAG’s land use classification.Field NameData TypeField DescriptionPID19Text2019 SCAG’s parcel unique IDAPN19Text2019 Assessor’s parcel numberCOUNTYTextCounty name (based on 2016 county boundary)COUNTY_IDDoubleCounty FIPS code (based on 2016 county boundary)CITYTextCity name (based on 2016 city boundary)CITY_IDDoubleCity FIPS code (based on 2016 city boundary)MULTIPARTShort IntegerMultipart feature (the number of multiple polygons; '1' = singlepart feature)STACKLong IntegerDuplicate geometry (the number of duplicate polygons; '0' = no duplicate polygons)ACRESDoubleParcel area (in acreage)GEOID20Text2020 Census Block Group GEOIDSLOPEShort IntegerSlope information1APN_DUPLong IntegerDuplicate APN (the number of multiple tax roll property records; '0' = no duplicate APN)IL_RATIODoubleRatio of improvement assessed value to land assessed valueLU19Text2019 existing land useLU19_SRCTextSource of 2019 existing land use2SCAGUID16Text2016 SCAG’s parcel unique IDAPNText2016 Assessor’s parcel numberCITY_GP_COText2016 Jurisdiction’s general plan land use designationSCAG_GP_COText2016 SCAG general plan land use codeSP_INDEXShort IntegerSpecific plan index ('0' = outside specific plan area; '1' = inside specific plan area)CITY_SP_COText2016 Jurisdiction’s specific plan land use designationSCAG_SP_COText2016 SCAG specific plan land use codeCITY_ZN_COText2016 Jurisdiction’s zoning codeSCAG_ZN_COText2016 SCAG zoning codeLU16Text2016 existing land useYEARLong IntegerDataset yearPUB_OWNShort IntegerPublic-owned land index ('1' = owned by public agency)PUB_NAMETextName of public agencyPUB_TYPETextType of public agency3BF_SQFTDoubleBuilding footprint area (in square feet)4BSF_NAMETextName of brownfield/superfund site5BSF_TYPETextType of brownfield/superfund site5FIREShort IntegerParcel intersects CalFire Very High Hazard Local Responsibility Areas or State Responsibility Areas (November 2020 version) (CalFIRE)SEARISE36Short IntegerParcel intersects with USGS Coastal Storm Modeling System (CoSMos)1 Meter Sea Level Rise inundation areas for Southern California (v3.0, Phase 2; 2018)SEARISE72Short IntegerParcel intersects with USGS Coastal Storm Modeling System (CoSMos)2 Meter Sea Level Rise inundation areas for Southern California (v3.0, Phase 2; 2018)FLOODShort IntegerParcel intersects with a FEMA 100 Year Flood Plain data from the Digital Flood Insurance Rate Map (DFIRM), obtained from Federal Emergency Management Agency (FEMA) in August 10, 2017EQUAKEShort IntegerParcel intersects with an Alquist-Priolo Earthquake Fault Zone (California Geological Survey; 2018)LIQUAFAShort IntegerParcel intersects with a Liquefaction Susceptibility Zone (California Geological Survey; 2016)LANDSLIDEShort IntegerParcel intersects with a Landslide Hazard Zone (California Geological Survey; 2016)CPADShort IntegerParcel intersects with a protected area from the California Protected Areas Database(CPAD) – www.calands.org (accessed April 2021)RIPARIANShort IntegerParcel centroid falls within Active River Areas(2010)or parcel intersects with a Wetland Area in the National Wetland Inventory(Version 2)WILDLIFEShort IntegerParcel intersects with wildlife habitat (US Fish & Wildlife ServiceCritical Habitat, Southern California Missing Linkages, Natural Lands & Habitat Corridors from Connect SoCal, CEHC Essential Connectivity Areas,Critical Coastal Habitats)CNDDBShort IntegerThe California Natural Diversity Database (CNDDB)includes the status and locations of rare plants and animals in California. Parcels that overlap locations of rare plants and animals in California from the California Natural Diversity Database (CNDDB)have a greater likelihood of encountering special status plants and animals on the property, potentially leading to further legal requirements to allow development (California Department of Fish and Wildlife). Data accessed in October 2020.HCPRAShort IntegerParcel intersects Natural Community & Habitat Conservation Plans Reserve Designs from the Western Riverside MHSCP, Coachella Valley MHSCP, and the Orange County Central Coastal NCCP/HCP, as accessed in October 2020WETLANDShort IntegerParcel intersects a wetland or deepwater habitat as defined by the US Fish & Wildlife Service National Wetlands Inventory, Version 2.UAZShort IntegerParcel centroid lies within a Caltrans Adjusted Urbanized AreasUNBUILT_SFDoubleDifference between parcel area and building footprint area expressed in square feet.6GRCRY_1MIShort IntegerThe number of grocery stores within a 1-mile drive7HEALTH_1MIShort IntegerThe number of healthcare facilities within a 1-mile drive7OPENSP_1MIShort IntegerQuantity of open space (roughly corresponding to city blocks’ worth) within a 1-mile drive7TCAC_2021TextThe opportunity level based on the 2021 CA HCD/TCAC opportunity scores.HQTA45Short IntegerField takes a value of 1 if parcel centroid lies within a 2045 High-Quality Transit Area (HQTA)JOB_CTRShort IntegerField takes a value of 1 if parcel centroid lies within a job centerNMAShort IntegerField takes a value of 1 if parcel centroid lies within a neighborhood mobility area.ABS_CONSTRShort IntegerField takes a value of 1 if parcel centroid lies within an absolute constraint area. See the Sustainable Communities Strategy Technical Reportfor details.VAR_CONSTRShort IntegerField takes a value of 1 if parcel centroid lies within a variable constraint area. See the Sustainable Communities Strategy Technical Reportfor details.EJAShort IntegerField takes a value of 1 if parcel centroid lies within an Environmental Justice Area. See the Environmental Justice Technical Reportfor details.SB535Short IntegerField takes a value of 1 if parcel centroid lies within an SB535 Disadvantaged Community area. See the Environmental Justice Technical Reportfor details.COCShort IntegerField takes a value of 1 if parcel centroid lies within a Community of Concern See the Environmental Justice Technical Reportfor details.STATEShort IntegerThis field is a rudimentary estimate of which parcels have adequate physical space to accommodate a typical detached Accessory Dwelling Unit (ADU)8.SBShort IntegerIndex of ADU eligibility according to the setback reduction policy scenario (from 4 to 2 feet) (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SMShort IntegerIndex of ADU eligibility according to the small ADU policy scenario (from 800 to 600 square feet ADU) (1 = ADU eligible parcel, Null = Not ADU eligible parcel)PKShort IntegerIndex of ADU eligibility according to parking space exemption (200 square feet) policy scenario (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SB_SMShort IntegerIndex of ADU eligibility according to both the setback reduction and small ADU policy scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SB_PKShort IntegerIndex of ADU eligibility according to both the setback reduction and parking space exemption scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SM_PKShort IntegerIndex of ADU eligibility according to both the small ADU policy and parking space exemption scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)SB_SM_PKShort IntegerIndex of ADU eligibility according to the setback reduction, small ADU, and parking space exemption scenarios (1 = ADU eligible parcel, Null = Not ADU eligible parcel)1. Slope: '0' - 0~4 percent; '5' - 5~9 percent; '10' - 10~14 percent; '15' = 15~19 percent; '20' - 20~24 percent; '25' = 25 percent or greater.2. Source of 2019 existing land use: SCAG_REF- SCAG's regional geospatial datasets;ASSESSOR- Assessor's 2019 tax roll records; CPAD- California Protected Areas Database (version 2020a; accessed in September 2020); CSCD- California School Campus Database (version 2018; accessed in September 2020); FMMP- Farmland Mapping and Monitoring Program's Important Farmland GIS data (accessed in September 2020); MIRTA- U.S. Department of Defense's Military Installations, Ranges, and Training Areas GIS data (accessed in September 2020)3. Type of public agency includes federal, state, county, city, special district, school district, college/university, military.4. Based on 2019 building footprint data obtained from BuildingFootprintUSA (except that 2014 building footprint data was used for Imperial County). Please note that 2019 building footprint data does not cover the entire SCAG region (overlapped with 83% of parcels in the SCAG Region).5. Includes brownfield/superfund site whose address information are matched by SCAG rooftop address locator. Brownfield data was obtained from EPA's Assessment, Cleanup and Redevelopment Exchange System (ACRES) database, Cleanups in my community (CIMC), DTSC brownfield Memorandum of Agreement (MOA). Superfund site data was obtained from EPA's Superfund Enterprise Management System (SEMS) database.6. Parcels with a zero value for building footprint area are marked as NULL to indicate this field is not reliable.7. These values are intended as a rudimentary indicator of accessibility developed by SCAG using 2016 InfoUSA business establishment data and 2017 California Protected Areas data. See documentation for details.8. A detailed study conducted by Cal Poly Pomona (CPP) and available hereconducted an extensive review of state and local requirements and development trends for ADUs in the SCAG region and developed a baseline set of assumptions for estimating how many of a jurisdiction’s parcels

THESE BASE DATA LAYERS (DESCRIBED BELOW) WERE CLIPPED TO THE USFS DECISION SPACE - THOSE USFS LANDS WITHIN SAGE GROUSE HABITAT MANAGEMENT AREAS (HMAS). THE BASE DATA LAYERS WERE CLASSIFIED INTO 3-4 LEVELS BASED ON EIS DEFINED ACTIVITY RESTRICTIONS, FOR EXAMPLE THE OIL AND GAS DEVELOPMENT LAYER WAS CLASSIFIED INTO 'CLOSED', 'MAJOR STIPULATIONS', AND 'STANDARD STIPULATIONS'. WE SELECTED THE MOST LENIENT OF THESE CLASSIFICATIONS (‘STANDARD STIPULATIONS’) AND INTERSECTED THESE POLYGONS WITH THE HMA POLYGONS. OUR INTENTION HERE WAS TO FOCUS THE USER ON EIS ACTIVITY GUIDELINES WITHIN THE HMAS, AND THAT THE COMBINATION OF MORE RESTRICTIVE LMP GUIDELINES AND HMA RESTRICTIONS WOULD PRECLUDE ANY ACTIVITY IN THE OTHER AREAS. WE ASSUME THAT DETAILED PROJECT-LEVEL ANALYSIS WOULD SORT OUT THE COMPLEXITIES INVOLVED WITH PROJECTS THAT INTERSECT MULTIPLE HMAS AND LMP DESIGNATIONS.

THEN INTERSECTED WITH THE HMAs This data supports the final 2015-2016 Greater Sage-grouse EIS's and associated Records of Decision. This dataset represents the consolidated submissions of No Action (Baseline) data from each individual USFS EIS for Tier II analysis. These data were submitted to the BLM’s Wildlife Habitat Spatial Analysis Lab who compiled all information between September 2013 and August 2015. The processed 'mer_dis' modeling inputs were used, with Alternative A No Action selected for each EIS as applicable. Data from each EIS were then merged with this selection applied to form this dataset. Finally, the data was isolated to NFS lands within the official USFS GRSG EIS boundaries.All of the data used to create this file was submitted by USFS staff associated with each EIS planning area or occassionally from BLM staff in communication with USFS staff. Quality Assurance/Quality Control (QA/QC) employed by the BLM Wildlife Habitat Spatial Analysis lab was limited to: ensuring that the submitted decision data did not overlap with associated decision data; ensuring that the submitted data did not span the entire EIS boundary; ensuring that the submitted decision data was limited to the decision space; and ensuring that the submitted decision data was not clipped to the Greater Sage-grouse (GRSG) habitat data. The EIS-associated staff who submitted data is solely responsible for the content and quality of data used to create this file. The decision data submitted by EIS-associated staff was identified by the EIS name and alternative through the addition and calculation of the EIS_ALT field.The EIS_ALT field calculation was performed according to the following template:"EISName_Alternative"