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

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

Interagency Wildland Fire Perimeter History (IFPH) Overview This national fire history perimeter data layer of conglomerated agency perimeters was developed in support of the WFDSS application and wildfire decision support. The layer encompasses the fire perimeter datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, CalFire, and WFIGS History. Perimeters are included thru the 2024 fire season. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies. WFIGS, NPS and CALFIRE data now include Prescribed Burns. Data InputSeveral data sources were used in the development of this layer, links are provided where possible below. In addition, many agencies are now using WFIGS as their authoritative source, beginning in mid-2020.Alaska fire history (WFIGS pull for updates began 2022)USDA FS Regional Fire History Data (WFIGS pull for updates began 2024)BLM Fire Planning and Fuels (WFIGS pull for updates began 2020)National Park Service - Includes Prescribed Burns (WFIGS pull for updates began 2020)Fish and Wildlife Service (WFIGS pull for updates began 2024)Bureau of Indian Affairs (Incomplete, 2017-2018 from BIA, WFIGS pull for updates began 2020)CalFire FRAS - Includes Prescribed Burns (CALFIRE only source, non-fed fires)WFIGS - updates included since mid-2020, unless otherwise noted Data LimitationsFire perimeter data are often collected at the local level, and fire management agencies have differing guidelines for submitting fire perimeter data. Often data are collected by agencies only once annually. If you do not see your fire perimeters in this layer, they were not present in the sources used to create the layer at the time the data were submitted. A companion service for perimeters entered into the WFDSS application is also available, if a perimeter is found in the WFDSS service that is missing in this Agency Authoritative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.Attributes This dataset implements the NWCG Wildland Fire Perimeters (polygon) data standard.https://www.nwcg.gov/sites/default/files/stds/WildlandFirePerimeters_definition.pdfIRWINID - Primary key for linking to the IRWIN Incident dataset. The origin of this GUID is the wildland fire locations point data layer maintained by IrWIN. (This unique identifier may NOT replace the GeometryID core attribute) FORID - Unique identifier assigned to each incident record in the Fire Occurence Data Records system. (This unique identifier may NOT replace the GeometryID core attribute) INCIDENT - The name assigned to an incident; assigned by responsible land management unit. (IRWIN required). Officially recorded name. FIRE_YEAR (Alias) - Calendar year in which the fire started. Example: 2013. Value is of type integer (FIRE_YEAR_INT). AGENCY - Agency assigned for this fire - should be based on jurisdiction at origin. SOURCE - System/agency source of record from which the perimeter came. DATE_CUR - The last edit, update, or other valid date of this GIS Record. Example: mm/dd/yyyy. MAP_METHOD - Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality.GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; Digitized-Topo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Other GIS_ACRES - GIS calculated acres within the fire perimeter. Not adjusted for unburned areas within the fire perimeter. Total should include 1 decimal place. (ArcGIS: Precision=10; Scale=1). Example: 23.9 UNQE_FIRE_ - Unique fire identifier is the Year-Unit Identifier-Local Incident Identifier (yyyy-SSXXX-xxxxxx). SS = State Code or International Code, XXX or XXXX = A code assigned to an organizational unit, xxxxx = Alphanumeric with hyphens or periods. The unit identifier portion corresponds to the POINT OF ORIGIN RESPONSIBLE AGENCY UNIT IDENTIFIER (POOResonsibleUnit) from the responsible unit’s corresponding fire report. Example: 2013-CORMP-000001 LOCAL_NUM - Local incident identifier (dispatch number). A number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year. Field is string to allow for leading zeros when the local incident identifier is less than 6 characters. (IRWIN required). Example: 123456. UNIT_ID - NWCG Unit Identifier of landowner/jurisdictional agency unit at the point of origin of a fire. (NFIRS ID should be used only when no NWCG Unit Identifier exists). Example: CORMP COMMENTS - Additional information describing the feature. Free Text.FEATURE_CA - Type of wildland fire polygon: Wildfire (represents final fire perimeter or last daily fire perimeter available) or Prescribed Fire or Unknown GEO_ID - Primary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature. Globally Unique Identifier (GUID). Cross-Walk from sources (GeoID) and other processing notesAK: GEOID = OBJECT ID of provided file geodatabase (4,781 Records thru 2021), other federal sources for AK data removed. No RX data included.CA: GEOID = OBJECT ID of downloaded file geodatabase (8,480 Records, federal fires removed, includes RX. Significant cleanup occurred between 2023 and 2024 data pulls resulting in fewer perimeters).FWS: GEOID = OBJECTID of service download combined history 2005-2021 (2,959 Records), includes RX.BIA: GEOID = "FireID" 2017/2018 data (382 records). No RX data included.NPS: GEOID = EVENT ID 15,237 records, includes RX. In 2024/2023 dataset was reduced by combining singlepart to multpart based on valid Irwin, FORID or Unique Fire IDs. RX data included.BLM: GEOID = GUID from BLM FPER (23,730 features). No RX data included.USFS: GEOID=GLOBALID from EDW records (48,569 features), includes RXWFIGS: GEOID=polySourceGlobalID (9724 records added or replaced agency record since mid-2020)Attempts to repair Unique Fire ID not made. Attempts to repair dates not made. Verified all IrWIN IDs and FODRIDs present via joins and cross checks to the respective dataset. Stripped leading and trailing spaces, fixed empty values to

The Federal Emergency Management Agency (FEMA) produces Flood Insurance Rate maps and identifies Special Flood Hazard Areas as part of the National Flood Insurance Program's floodplain management. Special Flood Hazard Areas have regulations that include the mandatory purchase of flood insurance for holders of federally regulated mortgages. In addition, this layer can help planners and firms avoid areas of flood risk and also avoid additional cost to carry insurance for certain planned activities. Dataset SummaryPhenomenon Mapped: Flood Hazard AreasGeographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Mariana Islands and American Samoa.Projection: Web Mercator Auxiliary SphereData Coordinate System: USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WGS 1984 Albers (Alaska), Hawaii Albers Equal Area Conic (Hawaii), Western Pacific Albers Equal Area Conic (Guam, Northern Mariana Islands, and American Samoa)Cell Sizes: 10 meters (default), 30 meters, and 90 metersUnits: NoneSource Type: ThematicPixel Type: Unsigned integerSource: Federal Emergency Management Agency (FEMA)Update Frequency: AnnualPublication Date: May 7, 2025 This layer is derived from the May 7, 2025 version Flood Insurance Rate Map feature class S_FLD_HAZ_AR. The vector data were then flagged with an index of 94 classes, representing a unique combination of values displayed by three renderers. (In three resolutions the three renderers make nine processing templates.) Repair Geometry was run on the set of features, then the features were rasterized using the 94 class index at a resolutions of 10, 30, and 90 meters, using the Polygon to Raster tool and the "MAXIMUM_COMBINED_AREA" option. Not every part of the United States is covered by flood rate maps. This layer compiles all the flood insurance maps available at the time of publication. To make analysis easier, areas that were NOT mapped by FEMA for flood insurance rates no longer are served as NODATA but are filled in with a value of 250, representing any unmapped areas which appear in the US Census boundary of the USA states and territories. The attribute table corresponding to value 250 will indicate that the area was not mapped.What can you do with this layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application. Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "flood hazard areas" 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 "flood hazard areas" in the search box, browse to the layer then click OK. In ArcGIS Pro you can use the built-in raster functions to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro. 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. Processing TemplatesCartographic Renderer - The default. These are meaningful classes grouped by FEMA which group its own Flood Zone Type and Subtype fields. This renderer uses FEMA's own cartographic interpretations of its flood zone and zone subtype fields to help you identify and assess risk. Flood Zone Type Renderer - Specifically renders FEMA FLD_ZONE (flood zone) attribute, which distinguishes the original, broadest categories of flood zones. This renderer displays high level categories of flood zones, and is less nuanced than the Cartographic Renderer. For example, a fld_zone value of X can either have moderate or low risk depending on location. This renderer will simply render fld_zone X as its own color without identifying "500 year" flood zones within that category.Flood Insurance Requirement Renderer - Shows Special Flood Hazard Area (SFHA) true-false status. This may be helpful if you want to show just the places where flood insurance is required. A value of True means flood insurance is mandatory in a majority of the area covered by each 10m pixel. Each of these three renderers have templates at three different raster resolutions depending on your analysis needs. To include the layer in web maps to serve maps and queries, the 10 meter renderers are the preferred option. These are served with overviews and render at all resolutions. However, when doing analysis of larger areas, we now offer two coarser resolutions of 30 and 90 meters in processing templates for added convenience and time savings.Data DictionaryMaking a copy of your area of interest using copyraster in arcgis pro will copy the layer's attribute table to your network alongside the local output raster. The raster attribute table in the copied raster will contain the flood zone, zone subtype, and special flood hazard area true/false flag which corresponds to each value in the layer for your area of interest. For your convienence, we also included a table in CSV format in the box below as a data dictionary you can use as an index to every value in the layer. Value,FLD_ZONE,ZONE_SUBTY,SFHA_TF 2,A,, 3,A,,F 4,A,,T 5,A,,T 6,A,,T 7,A,1 PCT ANNUAL CHANCE FLOOD HAZARD CONTAINED IN CHANNEL,T 8,A,1 PCT ANNUAL CHANCE FLOOD HAZARD CONTAINED IN STRUCTURE,T 9,A,ADMINISTRATIVE FLOODWAY,T 10,A,COASTAL FLOODPLAIN,T 11,A,FLOWAGE EASEMENT AREA,T 12,A99,,T 13,A99,AREA WITH REDUCED FLOOD RISK DUE TO LEVEE,T 14,AE,,F 15,AE,,T 16,AE,,T 17,AE,,T 18,AE,1 PCT ANNUAL CHANCE FLOOD HAZARD CONTAINED IN CHANNEL,T 19,AE,1 PCT ANNUAL CHANCE FLOOD HAZARD CONTAINED IN STRUCTURE,T 20,AE,"1 PCT CONTAINED IN STRUCTURE, COMMUNITY ENCROACHMENT",T 21,AE,"1 PCT CONTAINED IN STRUCTURE, FLOODWAY",T 22,AE,ADMINISTRATIVE FLOODWAY,T 23,AE,AREA OF SPECIAL CONSIDERATION,T 24,AE,COASTAL FLOODPLAIN,T 25,AE,COLORADO RIVER FLOODWAY,T 26,AE,COMBINED RIVERINE AND COASTAL FLOODPLAIN,T 27,AE,COMMUNITY ENCROACHMENT,T 28,AE,COMMUNITY ENCROACHMENT AREA,T 29,AE,DENSITY FRINGE AREA,T 30,AE,FLOODWAY,T 31,AE,FLOODWAY CONTAINED IN CHANNEL,T 32,AE,FLOODWAY CONTAINED IN STRUCTURE,T 33,AE,FLOWAGE EASEMENT AREA,T 34,AE,RIVERINE FLOODWAY IN COMBINED RIVERINE AND COASTAL ZONE,T 35,AE,RIVERINE FLOODWAY SHOWN IN COASTAL ZONE,T 36,AE,STATE ENCROACHMENT AREA,T 37,AH,,T 38,AH,,T 39,AH,FLOODWAY,T 40,AO,,T 41,AO,COASTAL FLOODPLAIN,T 42,AO,FLOODWAY,T 43,AREA NOT INCLUDED,,F 44,AREA NOT INCLUDED,,T 45,AREA NOT INCLUDED,,U 46,D,,F 47,D,,T 48,D,AREA WITH FLOOD RISK DUE TO LEVEE,F 49,OPEN WATER,,F 50,OPEN WATER,,T 51,OPEN WATER,,U 52,V,,T 53,V,COASTAL FLOODPLAIN,T 54,VE,,T 55,VE,,T 56,VE,COASTAL FLOODPLAIN,T 57,VE,RIVERINE FLOODWAY SHOWN IN COASTAL ZONE,T 58,X,,F 59,X,0.2 PCT ANNUAL CHANCE FLOOD HAZARD,F 60,X,0.2 PCT ANNUAL CHANCE FLOOD HAZARD,T 61,X,0.2 PCT ANNUAL CHANCE FLOOD HAZARD,U 62,X,0.2 PCT ANNUAL CHANCE FLOOD HAZARD CONTAINED IN CHANNEL,F 63,X,0.2 PCT ANNUAL CHANCE FLOOD HAZARD CONTAINED IN STRUCTURE,F 64,X,0.2 PCT ANNUAL CHANCE FLOOD HAZARD IN COASTAL ZONE,F 65,X,0.2 PCT ANNUAL CHANCE FLOOD HAZARD IN COMBINED RIVERINE AND COASTAL ZONE,F 66,X,"1 PCT CONTAINED IN STRUCTURE, COMMUNITY ENCROACHMENT",F 67,X,"1 PCT CONTAINED IN STRUCTURE, FLOODWAY",F 68,X,1 PCT DEPTH LESS THAN 1 FOOT,F 69,X,1 PCT DRAINAGE AREA LESS THAN 1 SQUARE MILE,F 70,X,1 PCT FUTURE CONDITIONS,F 71,X,1 PCT FUTURE CONDITIONS CONTAINED IN STRUCTURE,F 72,X,"1 PCT FUTURE CONDITIONS, COMMUNITY ENCROACHMENT",F 73,X,"1 PCT FUTURE CONDITIONS, FLOODWAY",F 74,X,"1 PCT FUTURE IN STRUCTURE, COMMUNITY ENCROACHMENT",F 75,X,"1 PCT FUTURE IN STRUCTURE, FLOODWAY",F 76,X,AREA OF MINIMAL FLOOD HAZARD, 77,X,AREA OF MINIMAL FLOOD HAZARD,F 78,X,AREA OF MINIMAL FLOOD HAZARD,T 79,X,AREA OF MINIMAL FLOOD HAZARD,U 80,X,AREA OF SPECIAL CONSIDERATION,F 81,X,AREA WITH REDUCED FLOOD RISK DUE TO LEVEE,F 82,X,AREA WITH REDUCED FLOOD RISK DUE TO LEVEE,T 83,X,FLOWAGE EASEMENT AREA,F 84,X,1 PCT FUTURE CONDITIONS,T 85,AH,COASTAL FLOODPLAIN,T 86,AE,,U 87,AE,FLOODWAY,F 88,X,AREA WITH REDUCED FLOOD HAZARD DUE TO ACCREDITED LEVEE SYSTEM,F 89,X,530,F 90,VE,100,T 91,AE,100,T 92,A99,AREA WITH REDUCED FLOOD HAZARD DUE TO LEVEE SYSTEM,T 93,A99,AREA WITH REDUCED FLOOD HAZARD DUE TO NON-ACCREDITED LEVEE SYSTEM,T 94,A,COMBINED RIVERINE AND COASTAL FLOODPLAIN,T 250,AREA NOT INCLUDED,Not Mapped by FEMA, Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

Hospitals: shows all hospitals in the state of Minnesota, arranged by county. Downloaded from the Minnesota Department of Health February 24, 2013. Hospital names and health system associations updated on April 10, 2013.

http://www.health.state.mn.us/divs/fpc/directory/fpcdir.html

Addresses cleaned and geocoded by the Hennepin County Human Services and Public Health Dept (HSPHD). Match rate 100% (135). Where possible road centerline geocoded locations were improved to parcel centroid. The field NAME contains the hospital name.

Link to Attribute Table Information: http://gis.hennepin.us/OpenData/Metadata/Minnesota%20Hospitals.pdf

Use Limitations: This data (i) is furnished "AS IS" with no representation as to completeness or accuracy; (ii) is furnished with no warranty of any kind; and (iii) is not suitable for legal, engineering or surveying purposes. Hennepin County shall not be liable for any damage, injury or loss resulting from this data.

© Initial creation completed by the Hennepin County Human Services and Public Health Dept (HSPHD), with additional information provided by the Hennepin County Medical Center. Hospital names and health system association updated by HCMC on April 10, 2013. Maintenance and update stewardship responsibilities will be completed by HSPHD. This layer is a component of Health data.

To quantify shoreline rates of change (erosion or accretion), Maryland Geological Survey (MGS) used historical and recent shorelines spanning 1993-2010 as input into the Digital Shoreline Analysis System (DSAS) Version 4.3. DSAS, a computer program developed by the U.S. Geological Survey (USGS), determines linear rates of shoreline change along closely spaced, shore-normal transects. Based on DSAS output, MGS assigned generalized rate of change categories as attributes to a recent shoreline for Anne Arundel County. This recent shoreline consisted of the National Oceanic and Atmospheric Administration (NOAA) Continually Updated Shoreline Product (CUSP) digital shoreline currently available for Anne Arundel County; and 2) portions of the Maryland Department of Natural Resources (MD DNR) Critical Area Commission (CAC) digital shoreline for Anne Arundel County. Based on the results of an End Point Rate (EPR) analysis on the ca. 1990s shoreline and the ca. 2000/2010 shoreline (recent shoreline), MGS grouped the rate results into the following general categories: (a) No change (-0.01 to 0.01 feet/year), (b) Accretion (greater than 0.01 feet/year), (c) Slight erosion rate (0 to -2 feet/year), (d) Low erosion rate (-2 to -4 feet/year), (e) Moderate erosion rate (-4 to -8 ft/yr), (f) High erosion rate (greater than -8 feet/year), (g) Protected, (h) No data (insufficient shorelines to calculate 10-year EPR rate), (i) No data (no transects cast; unprotected or unknown shoreline condition), and (j) No data (rates not delivered; calculated rates suspect). The NOAA CUSP shoreline represents the shoreline position in Anne Arundel County between the years 2005-2010. The CAC shoreline represents the shoreline position in Anne Arundel County in 2007. In Anne Arundel County, the NOAA CUSP data set covered all shoreline in the county with the exception of the Patuxent River along the western county border. As such, MGS utilized the CAC shoreline data set in the Patuxent River area. To identify which shoreline source is responsible for a shoreline segment, view the "DSAS_SRC" attribute table field -- "CAC" indicates a shoreline sourced from MD DNR CAC data set; "NOAA CUSP" indicates a shoreline sourced from the NOAA CUSP data set. Negative rate of change values indicate erosion, and positive values indicate accretion. In general, MGS tried to attribute lengths of shoreline of at least 80 meters in length sharing similar rates of change.Funding for this data set was provided by two Projects of Special Merit (CZM # 14-14-1868 CZM 143 and CZM # 14-15-2005 CZM 143), funded by the National Oceanic and Atmospheric Administration (NOAA) and made available to MGS through the Department of Natural Resources (MD DNR) Chesapeake and Coastal Service (CCS). MGS wishes to thank the following project partners: 1) MD DNR CCS, Contact: Mr. Chris Cortina, Role: CCS Project Manager; 2) NOAA, Contact: Mr. Doug Graham, NOAA National Geodetic Survey, Role: Project partner & source of historical and recent shorelines; 3) MD DNR Critical Areas Commission (CAC), Contact: Ms. Lisa Hoerger, Role: Project partner & source of recent shorelines; 4) Eastern Shore Regional GIS Cooperative (ESRGC), Salisbury University, Contact: Ryan Mello, Role: Performing the critical area re-mapping for MD DNR CAC and supplying MGS with CAC shorelines; and 5) Ms. Lamere Hennesse, MGS Geologist, retired, Role: Project guidance & technical support. This is a MD iMAP hosted service. Find more information on https://imap.maryland.gov.Map Service Link:https://mdgeodata.md.gov/imap/rest/services/Hydrology/MD_ShorelineChanges/MapServer/4

To quantify shoreline rates of change (erosion or accretion), Maryland Geological Survey (MGS) used historical and recent shorelines spanning 1993-2010 as input into the Digital Shoreline Analysis System (DSAS) Version 4.3. DSAS, a computer program developed by the U.S. Geological Survey (USGS), determines linear rates of shoreline change along closely spaced, shore-normal transects. Based on DSAS output, MGS assigned generalized rate of change categories as attributes to a recent shoreline for Prince George's County. This recent shoreline consisted of the National Oceanic and Atmospheric Administration (NOAA) Continually Updated Shoreline Product (CUSP) digital shoreline currently available for Prince George's County; and 2) portions of the Maryland Department of Natural Resources (MD DNR) Critical Area Commission (CAC) digital shoreline for Prince George's County. Based on the results of an End Point Rate (EPR) analysis on the ca. 1990s shoreline and the ca. 2000/2010 shoreline (recent shoreline), MGS grouped the rate results into the following general categories: (a) No change (-0.01 to 0.01 feet/year), (b) Accretion (greater than 0.01 feet/year), (c) Slight erosion rate (0 to -2 feet/year), (d) Low erosion rate (-2 to -4 feet/year), (e) Moderate erosion rate (-4 to -8 ft/yr), (f) High erosion rate (greater than -8 feet/year), (g) Protected, (h) No data (insufficient shorelines to calculate 10-year EPR rate), (i) No data (no transects cast; unprotected or unknown shoreline condition), and (j) Rates not delivered (calculated rates suspect). The NOAA CUSP shoreline represents the shoreline position in Prince George's County between the years 2005-2010. The CAC shoreline represents the shoreline position in Prince George's County in 2007. In Prince George’s County, the NOAA CUSP data set covered approximately 60% of the shoreline in the county. MGS decided to supplement the NOAA CUSP data with CAC data in the following areas where NOAA CUSP data was missing: Anacostia River, from approximately the Bladensburg Road bridge, north to the Decatur Street bridge; Northwest Branch Anacostia River, from approximately the 38th Street bridge, south to its confluence with the Anacostia River; the lower reaches of Swanson Creek, west of Chalk Point; Spice Creek and an unnamed creek north of Spice Creek; and Patuxent River and its major tributaries, from approximately the Merkle Wildlife Sanctuary, north to the upper reaches of the Patuxent River. To identify which shoreline source is responsible for a shoreline segment, view the "DSAS_SRC" attribute table field -- "CAC" indicates a shoreline sourced from MD DNR CAC data set; "NOAA CUSP" indicates a shoreline sourced from the NOAA CUSP data set. Negative rate of change values indicate erosion, and positive values indicate accretion. In general, MGS tried to attribute lengths of shoreline of at least 80 meters in length sharing similar rates of change.Funding for this data set was provided by Project CZM # 14-15-2040 CZM 136, funded by a National Oceanic and Atmospheric Administration (NOAA) grant (NA13NOS4190136) and made available to MGS through the Department of Natural Resources (MD DNR) Chesapeake and Coastal Service (CCS). MGS wishes to thank the following entities/individuals: 1) MD DNR CCS, Contact: Mr. Chris Cortina, Role: CCS Project Manager; 2) NOAA, Contact: Mr. Doug Graham, NOAA National Geodetic Survey, Role: Guidance on NOAA shoreline data sets; 3) MD DNR Critical Areas Commission (CAC), Contact: Ms. Lisa Hoerger, Role: Permitted use of CAC shorelines; 4) Eastern Shore Regional GIS Cooperative (ESRGC), Salisbury University, Contact: Ryan Mello, Role: Performing the critical area re-mapping for MD DNR CAC and supplying MGS with CAC shorelines; and 5) Ms. Lamere Hennesse, MGS Geologist, retired, Role: Project guidance & technical support.This is a MD iMAP hosted service. Find more information on https://imap.maryland.gov.Map Service Link: https://mdgeodata.md.gov/imap/rest/services/Hydrology/MD_ShorelineChanges/MapServer/12

10 x10 kilometer grids identifying the survey priority ratings and geographical extent of the rusty patched bumble bee to be used in conjunction with our online survey guidance available at:https://www.fws.gov/sites/default/files/documents/Survey_Protocols_RPBB_12April2019.pdf.Attribute table for shapefile can be found at:https://www.arcgis.com/home/item.html?id=4927a7a146e84cefa8daadcec852b538.We encourage people to do bee surveys.We are particularly interested in surveys near recent records of the rusty patched bumble bee (in or near High and Low Potential Zones), but are also interested in surveys across the entire historical range of the species.Bumble bee surveys can provide baseline data, even if rusty patched bumble bee are not present. Bumble bee community data and negative data (surveys where RPBB was not detected) is all important as we plan for recovery.

The geographic extent of a local government (City, Village, Township).Data downloaded from TNRIS 07/2014 (the data sources for each city polygon vary. Sources used were the Texas Department of Transportation data and local data from the council of governments or its component governments).The City of Richardson boundary replaced the given Richardson boundary in the downloaded data and then the boundaries of the cities touching Richardson were topologically cleaned up.Cities were selected within a distance of 75 miles from Richardson were kept and the others were deleted.Local FIPS codes were acquired from the census website and added to the attribute table. http://www.census.gov/census2010/xls/fips_codes_website.xlsMetadata edited 10/2014

This data is a feature class of PLSS sections with various aquifer properties in the attributes. The attributes in this data were created by the Kansas Geological Survey on May 30, 2024. Water levels originate from WIZARD and have been processed to represent winter conditions. All measurements are in feet. Attributes include:depth_to_water: Depth to water in the High Plains aquifer, represented at the PLSS section level.Can be applied to PREDEV_DTW_UPDATED (depth to water during predevelopment, which is ~late 1940s) and DTW_2022-2024 (average over the last three years)DTW_2022-2024: Computed by subtracting a raster dataset of the average 2022 to 2024 water table elevation (interpolated from measured values at wells) from a digital elevation model of the land surface (in this case, the National Elevation Data set).PREDEV_DTW_UPDATED : Computed by subtracting a raster dataset of the predevelopment water table elevation (interpolated from historic measured values in geologic bulletins) from a digital elevation model of the land surface (in this case, the National Elevation Data set).Example map.water_table_elevation_2022_2024: Average 2022 to 2024 water table elevations (WTE_2022_2024) in the High Plains aquifer, represented at the PLSS section level.Example map.saturated_thickness: Thickness of the High Plains aquifer, represented at the PLSS section levelComputed by subtracted the estimated elevation of the underlying bedrock of the High Plains aquifer (taken from KGS Technical Series report no. 20) from the water table elevations. Can be applied to PREDEV_SATTHICK_UPDATE (thickness in predevelopment) and SATTHICK_2022_2024 (average thickness from 2022 to 2024)Example map.chng_predev_2022_2024: Change in feet in the High Plains aquifer thickness from predevelopment to average 2022 to 2024 (CHNG_PREDEV_20222024_ACT).Example mapchng_predev_2022_2024_percent: Percent change in the High Plains aquifer thickness from predevelopment to average 2022 to 2024 (CHNG_PREDEV_20222024_PCT).Example map.chng_10_20_years: 20 year water-level change, from all wells (not just the High Plains aquifer)Change in feet from the average 2002-2004 to average 2022-2024 water tables (chng_3yr_2003_2023)Example map.10 year water-level change, from all wells (not just the High Plains aquifer)Change in feet from the average 2012-2014 to average 2022-2024 water tables (chng_3yr_2013_2023)Example map.chng_5_years: 5 year water-level change, from all wells (not just the High Plains aquifer)Change in feet from the average 2017-2019 to average 2022-2024 water tables (chng_3yr_2018_2023)Example map.

The site suitability criteria included in the techno-economic land use screens are listed below. As this list is an update to previous cycles, tribal lands, prime farmland, and flood zones are not included as they are not technically infeasible for development. The techno-economic site suitability exclusion thresholds are presented in Table 1. Distances indicate the minimum distance from each feature for commercial scale solar development.Attributes:Steeply sloped areas: change in vertical elevation compared to horizontal distancePopulation density: the number of people living in a 1 km2 areaUrban areas: defined by the U.S. Census.8Water bodies: defined by the U.S. National Atlas Water Feature Areas, available from Argonne National Lab Energy Zone Mapping Tool9Railways: a comprehensive database of North America's railway system from the Federal Railroad Administration (FRA), available from Argonne National Lab Energy Zone Mapping ToolMajor highways: available from ESRI Living Atlas10Airports: The Airports dataset including other aviation facilities as of July 13, 2018 is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics' (BTS's) National Transportation Atlas Database (NTAD). The Airports database is a geographic point database of aircraft landing facilities in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the landing facility, current usage including enplanements and aircraft operations, congestion levels and usage categories. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product. Available from Argonne National Lab Energy Zone Mapping ToolActive mines: Active Mines and Mineral Processing Plants in the United States in 200311Military Lands: Land owned by the federal government that is part of a US military base, camp, post, station, yard, center or installation.Table 1

Solar

Steeply sloped areas

10o

Population density

100/km2

Capacity factor

<20%

Urban areas

<500 m

Water bodies

<250 m

Railways

<30 m

Major highways

<125 m

Airports

<1000 m

Active mines

<1000 m

Military Lands

<1000m

For more information about the processes and sources used to develop the screening criteria see sources 1-7 in the footnotes.

Data updates occur as needed, corresponding to typical 3-year CPUC IRP planning cycles.Footnotes:[1] Lopez, A. et. al. “U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis,” 2012. https://www.nrel.gov/docs/fy12osti/51946.pdf[2] https://greeningthegrid.org/Renewable-Energy-Zones-Toolkit/topics/social-environmental-and-other-impacts#ReadingListAndCaseStudies[3] Multi-Criteria Analysis for Renewable Energy (MapRE), University of California Santa Barbara. https://mapre.es.ucsb.edu/[4] Larson, E. et. al. “Net-Zero America: Potential Pathways, Infrastructure, and Impacts, Interim Report.” Princeton University, 2020. https://environmenthalfcentury.princeton.edu/sites/g/files/toruqf331/files/2020-12/Princeton_NZA_Interim_Report_15_Dec_2020_FINAL.pdf.[5] Wu, G. et. al. “Low-Impact Land Use Pathways to Deep Decarbonization of Electricity.” Environmental Research Letters 15, no. 7 (July 10, 2020). https://doi.org/10.1088/1748-9326/ab87d1.[6] RETI Coordinating Committee, RETI Stakeholder Steering Committee. “Renewable Energy Transmission Initiative Phase 1B Final Report.” California Energy Commission, January 2009.[7] Pletka, Ryan, and Joshua Finn. “Western Renewable Energy Zones, Phase 1: QRA Identification Technical Report.” Black & Veatch and National Renewable Energy Laboratory, 2009. https://www.nrel.gov/docs/fy10osti/46877.pdf.[8] https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Urban+Areas[9] https://ezmt.anl.gov/[10] https://www.arcgis.com/home/item.html?id=fc870766a3994111bce4a083413988e4[11] https://mrdata.usgs.gov/mineplant/CreditsTitle: Techno-economic screening criteria for utility-scale solar photovoltaic energy installations for Integrated Resource PlanningPurpose for creation: These exclusion criteria are for use in electric system planning, capacity expansion modeling, and integrated resource planning.Keywords: solar, photovoltaic, resource potential, techno-economic, PV, IRPExtent: western states of the contiguous U.S.Use LimitationsThe geospatial data created by the use of these techno-economic screens inform high-level estimates of technical renewable resource potential for electric system planning and should not be used, on their own, to guide siting of generation projects nor assess project-level impacts. Confidentiality: PublicContactEmily Leslie Emily@MontaraMtEnergy.comSam Schreiber sam.schreiber@ethree.com Jared Ferguson Jared.Ferguson@cpuc.ca.gov

Oluwafemi Sawyerr femi@ethree.com

This dataset represents a compilation of data from various government agencies throughout the City of New York. The underlying geography is derived from the Tax Lot Polygon feature class that is part of the Department of Finance's Digital Tax Map (DTM). The tax lots have been clipped to the shoreline, as defined by NYCMap planimetric features. The attribute information is from the Department of City Planning's PLUTO data. The attribute data pertains to tax lot and building characteristics and geographic, political and administrative information for each tax lot in New York City.The Tax Lot Polygon feature class and PLUTO are derived from different sources. As a result, some PLUTO records do not have a corresponding tax lot in the Tax Lot polygon feature class at the time of release. These records are included in a separate non-geographic PLUTO Only table. There are a number of reasons why there can be a tax lot in PLUTO that does not match the DTM; the most common reason is that the various source files are maintained by different departments and divisions with varying update cycles and criteria for adding and removing records. The attribute definitions for the PLUTO Only table are the same as those for MapPLUTO. DCP Mapping Lots includes some features that are not on the tax maps. They have been added by DCP for cartographic purposes. They include street center 'malls', traffic islands and some built streets through parks. These features have very few associated attributes.To report problems, please open a GitHub issue or email DCPOpendata@planning.nyc.gov.DATES OF INPUT DATASETS:Department of City Planning - E-Designations: 2/5/2021Department of City Planning - Zoning Map Index: 7/31/2019Department of City Planning - NYC City Owned and Leased Properties: 11/15/2020Department of City Planning - NYC GIS Zoning Features: 2/5/2021Department of City Planning - Polictical and Administrative Districts: 11/17/2020Department of City Planning - Geosupport version 20D: 11/17/2020Department of Finance - Digital Tax Map: 1/30/2021Department of Finance - Mass Appraisal System (CAMA): 2/10/2021Department of Finance - Property Tax System (PTS): 2/6/2021Landmarks Preservation Commission - Historic Districts: 2/4/2021Landmarks Preservation Commission - Individual Landmarks: 2/4/2021Department of Information Telecommunications & Technology - Building Footprints: 2/10/2021Department of Parks and Recreation - GreenThumb Garden Info: 1/4/2021

Reason for Selection Protected 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 3.0 national 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

This digital soil survey information is used by soil scientists, hydrologists, ecologists, planners and other land managers to locate, compare, and select suitable areas for major kinds of land uses; to identify areas that need more intensive investigations; and to evaluate various management alternatives and predict the effects of the particular alternative on the land. Other intended uses of the soil survey include, but are not limited to, providing federal, state, and private organizations with resource information as it relates to activities such as power transmission right-of-way, coastal zone management, forest land management plans, mineral and energy exploration and development, and site suitability for buildings and dwellings. Tongass National Forest soil scientists began mapping soils in the early 1960s. By 1992 mapping was largely completed for approximately 10 million acres of the forest. During mapping, polylines were created using tones and textures on aerial photographs and field-verification. Soil map units were digitized from polygons drawn on 1:31,680 scale Mylar maps. Polygons on aerial photos were traced onto Mylar overlay sheets using a rapidiograph pen, which is accurate to .035 inches of the source data. Polygons were digitized to .001 inches of their location of the digitizing source (Mylar overlay). Accounting for the possibility of cumulative errors during transfer and digitizing, positional accuracy may vary by ± 250 feet. More recent inventories like Yakutat and South Kruzof were pre-mapped using on-screen digitizing with orthophotos and contours as base maps. Historically, the forest was divided into three soil survey areas-Stikine, Chatham, and Ketchikan. These areas are indicated in the FOREST field of the attribute table as follows: 2 = Stikine, 3 = Chatham, 5 = Ketchikan. By the end of the 1990s the digital soil inventory for the three survey areas on the forest were aggregated into one feature class. Beginning in the late 2000s an effort was made to move the soil inventory to Web Soil Survey (WSS). Each survey area was correlated separately. Updates to line work have occurred since 2010 to include areas not previously mapped. In 2020 a fourth area, the Yakutat Forelands was incorporated in WSS and the forest-wide feature class updated with that information. Line work for the southern half of Kruzof Island is included in this feature class but is currently in the correlation process and is not yet available on WSS. The update in 2020 also used all available line work from WSS to make the forest-wide dataset consistent with the data on WSS. Stikine Area (FOREST = 2): All lands within the Stikine Administrative Area have been mapped. This includes all federal, state, and private lands, including wilderness. The soil is mapped at different intensities across the area based on their Land Use Designations (LUDs) in the Tongass Land Management Plan, USDA-FS, 1979. Generally, areas designated for intensive land use (LUD III) are mapped at larger scales (Order 3 level, 1:15,840), while other areas designated for low intensity land use (LUD I&II) are mapped at smaller scales (Order 4 level, 1:31,680). Some areas that are currently LUD I&II were mapped to Order 3 prior to designation. All of the Stikine Area is mapped to an Order 3 level with the exception of the following, which were mapped to Order 4: the Stikine-LeConte Wilderness Area (Farm and Dry Islands are mapped to Order 3), Anan Creek area, and mainland areas designated for semi-remote recreation use. For exact locations, see Preliminary Soil Resource Inventory Report, Stikine Area. Order 3 surveys were mapped on 1:15,840 scale aerial photos. This resulted in map delineations no smaller than approximately 3 acres, ranging up to several hundred acres. The map units in the Order 3 survey area are composed of soil associations, some consociations and some complexes. The Order 4 surveys were mapped on 1:31,680 scale high-altitude infrared aerial photographs. This resulted in map units no smaller than approximately 10 acres and ranged as high as 500 acres in size. The map units in the Order 4 survey area are composed of phases of soil families, or subgroups. Design of initial mapping units in the Stikine area was strongly influenced by soil-vegetation relationships. This is referred to as the "Soil Ecosystem" type of mapping units, which are defined based on natural vegetation types, corresponding soil properties and associated landform types. Map units were also broken out by slope class.Chatham Area (FOREST = 3): The Chatham Area soil survey covers approximately 4.5 million acres of the Tongass National Forest. The inventory occurred in two stages and was done at two levels of detail. An Order 3 survey was conducted from 1981 to 1984, and an Order 4 survey was conducted from 1987 to 1989. Wilderness areas, national monuments, ANILCA additions, state, private and native lands were not mapped. The Order 3 survey is composed primarily of areas referred to as "Land Use Designations (LUDs) III and IV in the Tongass Land Management Plan, USDA-FS, 1979. LUD III were managed for a combination of uses, including recreation and some timber harvest. LUD IV were allocated to intensive resource use and development opportunities, primarily timber harvest and mining. Both LUD III and IV areas required the greater detail of an Order 3 survey. The Order 4 survey is composed primarily of LUD II. LUD II areas were allocated to roadless area management. The lower intensity management of LUD II justified a less detailed Order 4 survey. For exact locations, see Chatham Area Ecological Unit Inventory User Guide, figure 1. The inventory area was pre-mapped on either color aerial photographs at a scale of 1:15,840 (Order 3) or high altitude, color infrared aerial photographs at a scale of 1:63,360 (Order 4). South Kruzof soil survey covers about 60,795 acres of the Tongass National Forest. It represents the soils on the young Mount Edgecumbe volcanic field. The area was initially mapped during the 1981 to 1984 Order 3 Chatham soil survey. A second effort to gather more data began in 1994 but was not completed at that time. The effort to map South Kruzof restarted during 2009 and was completed in 2011. It was mapped digitally at a scale of 1:31,680 on 1998 2-meter black and white Digital Ortho Quads. The Yakutat soil survey covers about 487,758 acres of the Tongass, primarily on the Yakutat Forelands. This survey was also started during the 1981 to 1984 Order 3 soil survey. Additional data was collected in 1987, 1989, 1991, 1992, and 1993. The Yakutat survey was picked up again in 2009 and completed in 2013, although the mountainous areas are still unmapped. Yakutat was mapped digitally at a scale of 1:31,680 on 2008 Color 1 meter Digital Ortho Quarter Quads. The NRCS completed correlation on the Yakutat mapping area in 2020 but has not completed correlation of South Kruzof. The Chatham inventory was strongly influenced by soil-landform relationships. Additionally, vegetation, geology, and soils information was used to stratify the landscape into natural integral units that reflect ecological processes. Map units were also broken out by slope classes. The mapping criteria are based on features that may be either directly observed or inferred from natural landscape and vegetative features viewed on an aerial photograph. The intent of the mapping is to delineate integral ecological units that provide information required to achieve National Forest System management objectives. The Yakutat SMUs are nested in the landtype associations (LTAs) that were mapped in Landtype Associations of the Yakutat Foreland by Michael Shephard and Terry Brock (Technical Publication No. R10-TP-109, 2002). These LTAs were generalized for the soil survey.Ketchikan Area (FOREST = 5): The Ketchikan soil survey area covers approximately 3 million acres. It includes all of the area previously known as the Ketchikan Administrative Area except the following: Misty Fjords National Monument Wilderness and non-wilderness areas, the South Prince of Wales area and large tracts of federal (Bureau of Land Management), state, private borough and municipal lands. These unmapped lands are found on Cleveland Peninsula, Revillagigedo Island, Sukkwan Island, Long Island, Dall Island and Prince of Wales Island. Areas within the Ketchikan Area Soil Survey are mapped at different levels of intensity. Those designated as moderate and intensive development under the 1997 Tongass Land Management Plan (1997 TLMP) Revision, are mapped at an Order 3 level. Most wilderness areas were not included in the soil survey, although some areas now designated as wilderness and National Monument or 'Mostly Natural Setting' were mapped prior to those designations. These areas include: outside islands (Noyes, Lulu and Baker), Mt Calder/Mt. Holbrook Area, Salmon Bay, Coronation Island, Maurelle Islands, Warren Island, and the Karta River. Some other lands identified in the 1997 TLMP Revision under Wilderness and National Monument and 'Mostly Natural' settings were mapped at an Order 4 level. These areas include: Duke, Hotspur and Cat Islands, Cleveland Peninsula (North of Yes Bay), Bell Island, area east of Naha Bay, and area north of Cholmondeley Sound. For exact locations, see Ketchikan Area Soil Survey User Guide, Tongass N.F., p. 13. The Order 3 surveys were mapped on 1:15,840 or 1:40,000 aerial photos. This resulted in map delineations no smaller than approximately 3 acres ranging up to several hundred acres. The Order 3 survey areas are composed approximately of one-third each of map units of soil consociations, associations and complexes. The Order 4 surveys were mapped on 1:15,840 colored aerial photographs. This resulted in map delineations no smaller than approximately 10 acres and ranging as high as 500 acres in size. The map units are composed of phases of series, soil families, or subgroups.The criteria