METWP24PD depicts dissolved political boundaries for all Organized Towns and Unorganized Territories in Maine at 1:24,000 scale. "Dissolved" means that municipalities or townships with multiple disconnected entities (ex. islands) are grouped as multipart polygons in a single geometry with the appropriate municipality or township label and attribute data. This approach reduces the number of labels required and improves layer drawing performance for low-bandwidth environments. Example: a town has 430 distinct island entities that are all labeled as "town" in addition to the municipality itself. When dissolved, it has only one geometry that includes all 430 entities' combined area and attributes with the municipality, and one label of "town". METWP24PD includes common town names and authoritative geocodes in its attribute information. The layer was created using the USGS 7.5-minute map series and the Maine GIS base layer COAST, which contains Maine's coastal Mean High Water (MHW) mark and Maine islands. To correct mapping errors and reflect changes to Minor Civil Division (MCD) boundaries, arcs and polygons were added or updated using the following data sources: photorevised USGS data; Maine GIS base layer coincident features; legal descriptions; GPS data; and Maine Department of Transportation (MEDOT) engineering plans. METWP24P also contains USGS 1:100,000-scale data and U.S. Department of Commerce Census Bureau TIGER Line Files from 1990 and 2000 where these provide a more correct or best available representation of a feature in question.

Maine County Boundary Polygons Dissolved contains county boundary polygons for all sixteen counties in Maine, mapped at the 1:24,000 scale. "Dissolved" means that counties with multiple disconnected entities (ex. islands) are grouped as multipart polygons in a single geometry with the appropriate county label and attribute data. This approach reduces the number of labels required and improves layer drawing performance for low-bandwidth environments. The data layer has polygon topology and was originally created in ArcInfo using METWP24P with a selection on arcs coded "TYPE = state, county, and coastline".

This Administration feature is the single most valuable feature maintained by the GIS Services staff. It combines the maintenance of many individual polygon features in one main overall feature.It is part of a ArcGIS Topology class maintained with our parcel and zoning features in the Editing Feature Data Set.We use the shared editing capabilities of this topology class to leverage our maintenance procedures as simply as possible. Weekly, the individual features maintained with our Administration feature are created with ArcGIS dissolve function. These include Jurisdiction boundaries, Public Safety Response areas, Voting Precincts, Schools Attendance Zones, Inspections, Library Service Zones, and more.Generally, maintenance of this feature is controlled thru shared editing performed with our parcel/zoning edits with the use of the Topology features in ArcGIS. Changes to features maintained in the Administration feature are caused by a number of issues. Parcel edits, new Public Safety Stations, changes in Voting Precincts, Police Reporting districts and other changes occur often. Most changes can be facilitated by selecting one or more “Administrative” polygons and changing the appropriate attribute value. Use of the “Cut Polygon” task may be necessary in those cases where part of a polygon must be changed from a district to another. The appropriate attribute can be changed in the affected area as necessary.

scripts.zip

arcgisTools.atbx: terrainDerivatives: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). rasterizeFeatures: convert vector polygons to raster masks (1 = feature, 0 = background).

makeChips.R: R function to break terrain derivatives and chips into image chips of a defined size. makeTerrainDerivatives.R: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). merge_logs.R: R script to merge training logs into a single file. predictToExtents.ipynb: Python notebook to use trained model to predict to new data. trainExperiments.ipynb: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. assessmentExperiments.ipynb: Python code to generate assessment metrics using PyTorch and the torchmetrics library. graphs_results.R: R code to make graphs with ggplot2 to summarize results. makeChipsList.R: R code to generate lists of chips in a directory. makeMasks.R: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool).

terraceDL.zip

dems: LiDAR DTM data partitioned into training, testing, and validation datasets based on HUC8 watershed boundaries. Original DTM data were provided by the Iowa BMP mapping project: https://www.gis.iastate.edu/BMPs. extents: extents of the training, testing, and validation areas as defined by HUC 8 watershed boundaries. vectors: vector features representing agricultural terraces and partitioned into separate training, testing, and validation datasets. Original digitized features were provided by the Iowa BMP Mapping Project: https://www.gis.iastate.edu/BMPs.

The California Association Local Agency Formation Commissions defines a sphere of influence (SOI) as "a planning boundary outside of an agency’s legal boundary (such as the city limit line) that designates the agency’s probable future boundary and service area." This feature set represents the SOIs of the incorporated jurisdictions for the San Francisco Bay Region. The Metropolitan Transportation Commission (MTC) updated the feature set in late 2019 as part of the jurisdiction review process for the BASIS data gathering project. Changes were made to the growth boundaries of the following jurisdictions based on BASIS feedback and associated work: Antioch, Brentwood, Campbell, Daly City, Dublin, Fremont, Hayward, Los Gatos, Monte Sereno, Newark, Oakland, Oakley, Pacifica, Petaluma, Pittsburg, Pleasanton, San Bruno, San Francisco (added to reflect other jurisdictions whose SOI is the same as their jurisdiction boundary), San Jose, San Leandro, Santa Clara, Saratoga, and Sunnyvale. Notes: With the exception of San Mateo and Solano Counties, counties included jurisdiction (city/town) areas as part of their SOI boundary data. San Mateo County and Solano County only provided polygons representing the SOI areas outside the jurisdiction areas. To create a consistent, regional feature set, the Metropolitan Transportation Commission (MTC) added the jurisdiction areas to the original, SOI-only features and dissolved the features by name.Because of differences in base data used by the counties and the MTC, edits were made to the San Mateo County and Solano County SOI features that should have been adjacent to their jurisdiction boundary so the dissolve function would create a minimum number of features. Original sphere of influence boundary acquisitions:Alameda County - CityLimits_SOI.shp received as e-mail attachment from Alameda County Community Development Agency on 30 August 2019 Contra Costa County - BND_LAFCO_Cities_SOI.zip downloaded from https://gis.cccounty.us/Downloads/Planning/ on 15 August 2019Marin County - 'Sphere of Influence - City' feature service data downloaded from Marin GeoHub on 15 August 2019Napa County - city_soi.zip downloaded from their GIS Data Catalog on 15 August 2019 City and County of San Francisco - does not have a sphere of influence San Mateo County - 'Sphere of Influence' feature service data downloaded from San Mateo County GIS open data on 15 August 2019 Santa Clara County - 'City Spheres of Influence' feature service data downloaded from Santa Clara County Planning Office GIS Data on 15 August 2019 Solano County - SphereOfInfluence feature service data downloaded from Solano GeoHub on 15 August 2019 Sonoma County - 'SoCo PRMD GIS Spheres Influence.zip' downloaded from County of Sonoma on 15 August 2019

This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into country boundaries, administrative 1 boundaries, and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The layer shows the annual average PM 2.5 from 1998 to 2016, highlighting if the overall mean for an area meets the World Health Organization guideline of 10 micrograms per cubic meter annually. Areas that don't meet the guideline and are above the threshold are shown in red, and areas that are lower than the guideline are in grey.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality globally. Some of the things we can learn from this layer:What is the average annual PM 2.5 value over 19 years? (1998-2016)What is the annual average PM 2.5 value for each year from 1998 to 2016?What is the statistical trend for PM 2.5 over the 19 years? (downward or upward)Are there hot spots (or cold spots) of PM 2.5 over the 19 years?How many people are impacted by the air quality in an area?What is the death rate caused by the joint effects of air pollution?Choose a different attribute to symbolize in order to reveal any of the patterns above.A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis, trends, and a 19-year average. The country and administrative 1 layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries and population figures:Antarctica is excluded from all maps because it was not included in the original NASA grids.50km hex bins generated using the Generate Tessellation tool - projected to Behrmann Equal Area projection for analysesPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Administrative boundaries from World Administrative Divisions layer from ArcGIS Living Atlas - projected to Behrmann Equal Area projection for analyses and hosted in Web MercatorSources: Garmin, CIA World FactbookPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Country boundaries from Esri 2019 10.8 Data and Maps - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator. Sources: Garmin, Factbook, CIAPopulation figures attached to the country boundaries come from the World Population Estimate 2016 Sources Living Atlas layer Data processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The layers are hosted in Web Mercator Auxillary Sphere projection, but were processed using an equal area projection: Behrmann. If using this layer for analysis, it is recommended to start by projecting the data back to Behrmann.The country and administrative layer were dissolved and joined with population figures in order to visualize human impact.The dissolve tool ensures that each geographic area is only symbolized once within the map.Country boundaries were generalized post-analysis for visualization purposes. The tolerance used was 700m. If performing analysis with this layer, find detailed country boundaries in ArcGIS Living Atlas. To create the population-weighted attributes on the country and Admin 1 layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and the PM2.5 and population figures were summarized within the country and Admin 1 boundaries.The summation of the PM 2.5 values were then divided by the total population of each geography. This population value was determined by summarizing the population values from the hex bins within each geography.Some artifacts in the hex bin layer as a result of the input NASA rasters. Because the gridded surface is created from multiple satellites, there are strips within some areas that are a result of satellite paths. Some areas also have more of a continuous pattern between hex bins as a result of the input rasters.Within the country layer, an air pollution attributable death rate is included. 2016 figures are offered by the World Health Organization (WHO). Values are offered as a mean, upper value, lower value, and also offered as age standardized. Values are for deaths caused by all possible air pollution related diseases, for both sexes, and all age groups. For more information visit this page, and here for methodology. According to WHO, the world average was 95 deaths per 100,000 people.To learn the techniques used in this analysis, visit the Learn ArcGIS lesson Investigate Pollution Patterns with Space-Time Analysis by Esri's Kevin Bulter and Lynne Buie.

An updated Permit Data Model that includes relationships between the component feature classes. The Dissolved Use Impacts (SDOT.V_SU_PERMIT_USE_IMPACT_DISS) feature class is derived from dissolving the Use Impacts (SDOT.V_SU_PERMIT_USE_IMPACTS) feature class by Permit Number. The Impacts feature class is the Use Impact street line segments that are associated with any give Permit point (V_SU_PERMITS). The relationships connect the Permit points to the Dissolved Use Impacts and then the Dissolved Use Impacts to the component Use Impacts. This data model allows you to see all impacted street line segments associated with any given Permit easily, while also being able to drill down to any specific Use Impact for a given Permit. Service is constructed for use in the Right of Way Map. Data set to Nightly Refresh. Any Questions or Concerns contact the SDOT Street Use Data and GIS Team: Craig Moore/Bryan Bommersbach

This data set represents the 2025 elk hunt area, herd unit, and regions boundaries for Wyoming. The layer was originally digitized at a scale of 1:100,000, using USGS 1:100,000 DRGs as a backdrop for heads up digitizing. Updates requested by Wyoming Game and Fish Biological Services were completed by selecting needed features from other layers, including roads, streams, HUCs, NAIP rasters and others. Hunt area boundary descriptions are part of hunting regulations, which are approved and published annually by the Wyoming Game and Fish Commission. When needed, the 2008 edition (1st Edition) of the Wyoming Road and Recreation Atlas (Benchmark Maps) was consulted for road and other information.NOTE: A layer of herd units is derived from this hunt area layer by dissolving on the "HERDUNIT" and "HERDNAME" attributes (Dissolve_Fields), and unchecking the box "Create multipart features (optional)". All of the same metadata is used from the hunt area layer except that the citation title is modified so that "Herd Unit" replaces "Hunt Area". The "Dissolve" tool: ArcToolbox > Data Management Tools > Generalization.NOTE: A layer of nonresident regions is derived from this hunt area layer by dissolving on the "Region" attribute (Dissolve_Field), and unchecking the box "Create multipart features (optional)". All of the same metadata is used from the hunt area layer except that the citation title is modified so that "Nonresident Region" replaces "Hunt Area". The "Dissolve" tool: ArcToolbox > Data Management Tools > Generalization.

This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into country boundaries, administrative 1 boundaries, and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The layer shows the annual average PM 2.5 from 1998 to 2016, highlighting if the overall mean for an area meets the World Health Organization guideline of 10 micrograms per cubic meter annually. Areas that don't meet the guideline and are above the threshold are shown in red, and areas that are lower than the guideline are in grey.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality globally. Some of the things we can learn from this layer:What is the average annual PM 2.5 value over 19 years? (1998-2016)What is the annual average PM 2.5 value for each year from 1998 to 2016?What is the statistical trend for PM 2.5 over the 19 years? (downward or upward)Are there hot spots (or cold spots) of PM 2.5 over the 19 years?How many people are impacted by the air quality in an area?What is the death rate caused by the joint effects of air pollution?Choose a different attribute to symbolize in order to reveal any of the patterns above.A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis, trends, and a 19-year average. The country and administrative 1 layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries and population figures:Antarctica is excluded from all maps because it was not included in the original NASA grids.50km hex bins generated using the Generate Tessellation tool - projected to Behrmann Equal Area projection for analysesPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Administrative boundaries from World Administrative Divisions layer from ArcGIS Living Atlas - projected to Behrmann Equal Area projection for analyses and hosted in Web MercatorSources: Garmin, CIA World FactbookPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Country boundaries from Esri 2019 10.8 Data and Maps - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator. Sources: Garmin, Factbook, CIAPopulation figures attached to the country boundaries come from the World Population Estimate 2016 Sources Living Atlas layer Data processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The layers are hosted in Web Mercator Auxillary Sphere projection, but were processed using an equal area projection: Behrmann. If using this layer for analysis, it is recommended to start by projecting the data back to Behrmann.The country and administrative layer were dissolved and joined with population figures in order to visualize human impact.The dissolve tool ensures that each geographic area is only symbolized once within the map.Country boundaries were generalized post-analysis for visualization purposes. The tolerance used was 700m. If performing analysis with this layer, find detailed country boundaries in ArcGIS Living Atlas. To create the population-weighted attributes on the country and Admin 1 layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and the PM2.5 and population figures were summarized within the country and Admin 1 boundaries.The summation of the PM 2.5 values were then divided by the total population of each geography. This population value was determined by summarizing the population values from the hex bins within each geography.Some artifacts in the hex bin layer as a result of the input NASA rasters. Because the gridded surface is created from multiple satellites, there are strips within some areas that are a result of satellite paths. Some areas also have more of a continuous pattern between hex bins as a result of the input rasters.Within the country layer, an air pollution attributable death rate is included. 2016 figures are offered by the World Health Organization (WHO). Values are offered as a mean, upper value, lower value, and also offered as age standardized. Values are for deaths caused by all possible air pollution related diseases, for both sexes, and all age groups. For more information visit this page, and here for methodology. According to WHO, the world average was 95 deaths per 100,000 people.To learn the techniques used in this analysis, visit the Learn ArcGIS lesson Investigate Pollution Patterns with Space-Time Analysis by Esri's Kevin Bulter and Lynne Buie.

This data set represents the 2023 moose huntarea and herdunit boundaries for Wyoming. It was digitized at a scale of 1:100,000, using USGS 1:100,000 DRGs as a backdrop for heads up digitizing. Updates requested by Wyoming Game and Fish Biological Services Department, were completed by selecting needed features from other layers, including roads, streams, HUCs, NAIP 2009 rasters. Huntarea boundary descriptions are part of hunting regulations, which are approved and published annually by the Wyoming Game and Fish Commission. When needed, the 2008 edition (1st Edition) of the Wyoming Road and Recreation Atlas (Benchmark Maps) was consulted for road and other information. NOTE: A layer of herd units is derived from this hunt area layer by dissolving on the "HERDUNIT" and "HERDNAME" attributes (Dissolve_Fields), and unchecking the box "Create multipart features (optional)". All of the same metadata is used from the hunt area layer except that the citation title is modified so that "Herd Unit" replaces "Hunt Area". The "Dissolve" tool: ArcToolbox > Data Management Tools > Generalization.

Seafloor geomorphology is the study of physical features on the seafloor. This layer represents the characterizations of geomorphic features and the zones within the ocean where they occur. The data for this layer are from the first map of seafloor geomorphology ever published; this map was published 2014 by GRID Arendal.“The new seafloor features map provides a foundation on which to build an understanding of the living and non-living resources of the ocean and to improve decision making on a range of global issues like food security, resource use and conservation.” - Dr. Peter Harris, the project leader and Managing Director of GRID-Arendal.Dataset SummaryThe geomorphic features in this layer were created by automated and manual processes over the course of many months. The source data for the process is a modified 30-arc second (~1 km) resolution version of SRTM30_PLUS global bathymetry produced in 2009. The following features and zones are included as sub-layers:FeaturesCanyons - Submarine canyons are defined as steep-walled, sinuous valleys with V-shaped cross sections, axes sloping outward as continuously as river-cut land canyons and relief comparable to even the largest of land canyons.Seamounts - Seamounts are a single or group of peaks, greater than 1,000 meters in relief above the sea floor, characteristically of conical form.Guyots - Guyots are isolated or a group of seamount having a comparatively smooth flat top. Also called tablemounts.Troughs - Troughs are long depressions of the sea floor characteristically flat bottomed and steep sided and normally shallower than a trench. In this study we found that troughs are also commonly open at one end (i.e. not defined by closed bathymetric contours) and their broad, flat floors may exhibit a continuous gradient. Troughs may originate from glacial erosion processes or have form through tectonic processes.Glacial Troughs - Glacial troughs are the largest of the shelf valleys at high latitudes incised by glacial erosion during the Pleistocene ice ages to form elongate troughs, typically trending across the continental shelf and extending inland as fjord complexes. Glacial troughs are characterized by depths of over 100 m (often exceeding 1,000 m depth) and are distinguished from shelf valleys by an over-deepened longitudinal profile that reaches a maximum depth inboard of the shelf break, thus creating a perched basin on the shelf with an associated sill.Trenches - Trenches are long narrow very deep asymmetrical depressions of the sea floor, with relatively steep sides. Trenches are generally distinguished from troughs by their “V” shape in cross section (in contrast with flat-bottomed troughs). Bridges - Bridge features are blocks of material that partially infill Trenches forming a “bridge”across the trench.Sills - Sills are a sea floor barrier of relatively shallow depth restricting water movement between basins. Thus every basin has a sill, over which fluid would escape if the basin were filled to overflowing. Shelf Valleys - Shelf valleys are greater than 10 km in length and greater than 10 m in depth overall with an elongate shape more than 4 times greater in length than width.Rift Valleys - Rift valleys are confined to the central axis of mid-ocean spreading ridges; they are elongate, local depressions flanked generally on both sides by ridges.Ridges - Ridges are isolated or a group of elongated narrow elevations of varying complexity with steep sides, often separating basin features. Ridges have greater than 1,000 meters of relief.Spreading Ridges - Spreading ridges are mid-oceanic mountain systems of global extent.Terraces - Terraces an isolated or a group of relatively flat horizontal or gently inclined surface(s), sometimes long and narrow, which is (are) bounded by a steeper ascending slope on one side and by a steeper descending slope on the opposite side. Fans - Fans are relatively smooth, fan-like, depositional featured normally sloping away from the outer termination of a canyon or canyon system. Fans overlay and comprise part of the continental rise and are located offshore from the base of the continental slope. Fans are inter-related with submarine canyons and sediment drift deposits; in cases where canyon axes extend across the rise, the canyon-channels may be flanked by sediment drift deposits, which have been grouped with fans in this study. Fans are defined in the present study by 100 m isobaths that form a concentric series exhibiting an expanding spacing in a seaward direction away from the base of the slope, sometimes clearly associated with a canyon mouth, but also comprising low-relief ridges between canyon-channels on the abyssal plain.Rises - Continental rises are areas with sediment thickness greater than 300 meters and the occurrence of a smooth sloping seabed as indicated by evenly-spaced, slope-parallel contours. In this study, the term “Rise” was restricted to features that abut continental margins and does not include the mid-ocean ridge.Plateaus - Plateaus are flat or nearly flat elevations of considerable areal extent, dropping off abruptly on one or more sides. TerrainMountains - Greater than 1,000 meters of local relief within ~25 kilometers.Hills - Between 300 and 1,000 meters of local relief within ~25 kilometers.Plains - Less than 300 meters of local relief within ~25 kilometers.Basins - Basins are depressions in the sea floor that are more or less equi-dimensional in plan, of variable extent, and are restricted to seafloor depressions defined by closed bathymetric contours.Escarpments - Escarpments are “an elongated, characteristically linear, steep slope separating horizontal or gently sloping sectors of the sea floor in non-shelf areas. Also abbreviated to scarp” (IHO, 2008). Escarpments, like basins, overlay other features (i.e. other individual features may be partly or wholly covered by escarpments). Thus features like the continental slope, seamounts, guyots, ridges and submarine canyons (for example) may be sub-classified in terms of their area of overlain escarpment.ZonesShelf - The zone adjacent to the continents or islands. Slope - The deepening seafloor from the edge of the shelf to the top of the continental rise.Abyss - Areas below the foot of the continental rise and includes all depths up to 6,000 meters.Hadal - Depths greater than 6,000 metersNote that the above definitions are brief summarizations of the definitions contained in Geomorphology of the Oceans.Esri staff edited several of the layers: Zones, Terrain, Basins, and Glacial Troughs to improve drawing performance. All of these edits were split polygon operations; no vertexes were moved, only at cut points were vertexes introduced. If these layers are downloaded, these edits can be removed by using the Dissolve tool, with all fields, including shape, and producing no multi-part polygons in the output.For metadata info, please see Bluehabitats.org.What can you do with this layer?This layer is based on a dynamic map service, which means there are several sub-layers of vector features that can be used for visualization and analysis throughout the ArcGIS Platform. This layer is not editable.This layer is part of a larger collection of Oceans layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about oceans layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.

Statewide polygon feature class of seamlines dissolved by date of flight capture for image used in delievery product from USDA Farm Service Agency. Individual shapefiles provided by USDA were appended into one feature class, then dissolved on the IDATE field and re-projected to NAD83 NJ State Plane Feet in order to overlay easily with other state GIS data. The dissolve tool was run with the multipart polygon option.

ME SALS Patches with Protected lands from the ME Conservation Lands data and NWR interest data.This tool identifies the suite of patches (8680) with potential to support Saltmarsh Sparrows in the near and long term. The patches are ranked according to a formula that assesses the relative importance of a variety of factors known to positively or negatively influence Saltmarsh Sparrow populations (see Influence Factors tab). Patches in darker green colors are assumed to provide higher quality habitat than patches in lighter green colors and indicate important areas to focus conservation attention. This prioritization does not factor current density or abundance of Saltmarsh Sparrows into the results. Rather, it ranks patches according to their suitability to provide high quality Sparrow habitat according to expert opinion. THESE DATA HAVE BEEN REPLCE BY THE https://fws.maps.arcgis.com/home/item.html?id=be7290ec39084f62874dcde6316a0bd3

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

Input Data

NOAA Continuously Updated Shoreline Product (CUSP), accessed 1-11-2023; read a 1-page factsheet about CUSP; view and download CUSP data in the NOAA Shoreline Data Explorer (to download, select “Download CUSP by Region” and select Southeast Caribbean)
Southeast Blueprint 2023 subregions: Caribbean

Mapping Steps

Make a copy of the Southeast Caribbean CUSP feature line dataset and reproject it to ESPG 5070.
For the big island of Puerto Rico, special steps were required to deal with CUSP shorelines that did not connect across large rivers.
  Add and calculate a field to use to dissolve the lines.
    Dissolve the lines using the dissolve function, which reveals where there are gaps in the shoreline.
    Use the integrate tool to snap together nearby nodes, using a tolerance of 8 m. This connects the disconnected lines on the big island of Puerto Rico.
    Convert these modified shorelines to a polygon.
    Add and calculate a dissolve field, then dissolve using the dissolve tool. This is necessary because interior waterbodies on the big island of Puerto Rico also have shorelines in the CUSP data. This step produces a layer where inland waterbodies are included as a part of the island where they occur.
    From the resulting layer, select the big island of Puerto Rico and create a separate polygon feature layer from it. This extracts a modified shoreline boundary for the big island of Puerto Rico only. We don’t want to use the modified shorelines created above for other islands that didn’t have an issue of disconnected shoreline segments near large rivers.

Go back to the original Caribbean CUSP lines and convert them to polygons.
Add a dissolve field and dissolve using the dissolve tool. This produces a layer where all inland waterbodies are included as a part of the island where they occur.
From the island boundaries derived from the original CUSP data, remove the polygons that overlap with the big island of Puerto Rico derived from the modified CUSP data. This produces a layer representing all U.S. Caribbean islands except the big island of Puerto Rico.
Merge the modified big island of Puerto Rico layer with the layer for all other islands.
Create and populate a field that has unique IDs for all islands.
Convert the island polygon to a raster using the ArcPy Feature to Raster function. This makes a raster that correctly represents the interior of the islands. However, because the Feature to Raster function for polygons works differently than the Line to Raster function, the shoreline doesn’t perfectly match the result we get when we convert the CUSP lines to a raster. 
Because the Caribbean coastal shoreline condition indicator is created from the CUSP lines, we need the shorelines to match exactly. To reconcile this, go back to the original Caribbean CUSP line data and use the Feature to Raster function again, this time converting the lines to a raster. 
Use the ArcPy Cell Statistics “MAXIMUM” function to combine the two rasters above (one created from the CUSP lines and one created from the CUSP-derived polygons).
Export the raster that represents the extent of Caribbean islands.
Use the Region Group function to give unique values to each island.
Reclassify to make 3 island size classes. The big island of Puerto Rico is the only island in the highest class. The medium island class contains the following islands: Isla Mona, Isla de Vieques, Isla de Culebra, St. Thomas, St. John, and St. Croix. All other islands were put in the smaller class. All other non-island pixels in the Caribbean were given a value of marine.

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint 2023 Data Download or Caribbean-only Southeast Blueprint 2023 Data Download under > 6_Code. Literature Cited National Oceanic and Atmospheric Administration (NOAA), National Ocean Service, National Geodetic Survey. NOAA Continually Updated Shoreline Product (CUSP): Southeast Caribbean. [https://coast.noaa.gov/digitalcoast/data/cusp.html].

Whole grids from the v3.3 Michigan Great Lakes Grids GIS layer were assigned to statistical districts to match boundaries in existing GIS layers (see maps 1-3 in the 2000 Consent Decree). The Dissolve tool in ArcGIS 10.5 was used to dissolve the Michigan Great Lakes Grid layer (v3.3) to create statistical district boundaries that were then bounded by waterbody descriptions as follows: 1) The MI-8 and MH-1 statistical districts were clipped to end at the boundary of the St. Mary’s River as described in the 2000 Consent Decree. 2) MH-6 was clipped to end at the Blue Water Bridge as described in Fisheries Order 200, and 3) “MICH”in Lake Erie was modified to end at the Lake Erie/Detroit River boundary as described in the 2016-2017 Michigan Fishing Guide. Note that all districts consist of whole or clippped (as described above) 10-minute grids except for Lake Erie which consists of whole aggregated 10-minute grids and two clipped (as described above) 5-minute grids (le_602 and le_603). Also note that statistical district boundaries are almost identical to those of the lake trout management units (where they exist) as described in FO-200.GIS layer last updated 10/02/2019. Metadata last updated 10/2/2019. SourcesConsent Decree, United States of America, and Bay Mills Indian Community, Sault Ste. Marie Tribe of Chippewa Indians, Grand Travers Band of Ottawa and Chippewa Indians, Little River Band of Ottawa Indians, and Little Travers Bay Bands of Odawa Indians v. State of Michigan et al. (Case No. 2:73 CV 26, August, 2000). https://www.michigan.gov/documents/dnr/consent_decree_2000_197687_7.pdfFisheries Order 200. Statewide Trout, Salmon, Whitefish, Lake Herring, and Smelt Regulations. Michigan Natural Resources Commission and the Michigan Department of Natural Resources. https://www.michigan.gov/documents/dnr/FO_200.10_317498_7.pdf?111313Hansen, M. J., 1996. A lake trout restoration plan for Lake Superior. Great Lakes Fishery Commission. Michigan Fishing Guide. Department of Natural Resources, State of Michigan. https://www.michigan.gov/documents/dnr/2019MIFishingGuide-Feb26_647890_7.pdfSmith, S. H., Buettner, H. J., and R. Hile. 1961. Fishery Statistical Districts of the Great Lakes. Great Lakes Fishery Commission Technical Report No. 2, September 1961. https://www.glfc.org/pubs/TechReports/Tr02.pdf

Karst is a type of landscape where the bedrock has dissolved and created features such as caves, enclosed depressions (sinkholes), disappearing streams, springs and turloughs (seasonal lakes). Limestone is the most common type of soluble rock. As rain falls it picks up carbon dioxide (CO2) in the air. When this rain reaches the ground and passes through the soil it picks up more CO2 and forms a weak acid solution. The acidified rain water trickles down through cracks and holes in the limestone and over time dissolves the rock. After traveling underground, sometimes for long distances, this water is then discharged at springs, many of which are cave entrances.There are many kinds of karst landforms, ranging in size from millimetres to kilometres. Dolines or sinkholes are small to medium sized enclosed depressions. Uvalas and poljes are large enclosed depressions. A swallow hole is the point where surface stream sinks underground. Turloughs are seasonal lakes. Springs occur where groundwater comes out at the surface, karst springs are usually much bigger than non-karst springs. Estevelles can act as springs or swallow holes. Dry valleys are similar to normal river valleys except they do not have a stream flowing at the bottom. A cave is a natural underground opening in rock large enough for a person to enter. Superficial Solution Features can be seen on rocks dissolved by rain and include pits, grooves, channels, clints (blocks) and grikes (joints). Please read the lineage for further details.This map shows the currently mapped karst landforms in Ireland.Geologists map and record information in the field. They also examine old maps and aerial photos.We collect new data to update our map and also use data made available from other sources such as academia and consultants. It is NOT a complete database and only shows areas that have been mapped by GSI, or submitted to the GSI. Many karst features are not included in this database. The user should not rely only on this database, and should undertake their own site study for karst features in the area of interest if needed.It is a vector dataset. Vector data portray the world using points, lines, and polygons (areas).The karst data is shown as points. Each point holds information on: Karst Feature Unique ID, Historic GSI Karst Feature ID, Karst Feature Type, Karst Feature Name, if it’s within another Karst Feature, Location Accuracy, Data Source, Comments, Details and County.Water tracing means ‘tagging’ water, usually by adding a colour or dye, to see where it goes. Dye is usually added to a sinking stream and all possible outlet points (such as springs and rivers) are tested for the dye.Water traces are recorded as a straight line between the location of tracer input (e.g. swallow hole) and detection (e.g. spring), but they don’t show the actual path water may take underground, which is likely to be much more winding.It is mainly used in karst areas to find out groundwater flow rates, the direction the water is travelling underground and to help define catchments (Zone of Contributions).The dataset should be used alongside the Karst Landforms 1:40,000 Ireland (ROI/NI) ITM.Geologists map and record information in the field. We collect new data to update our map and also use data made available from other sources such as Academia and Consultants. It is a vector dataset. Vector data portray the world using points, lines, and polygons (areas).The karst data is shown as lines. Each line holds information on: Tracer Line Unique ID, Input Site, Input Historic GSI Karst Feature ID, Output Site. Output Historic GSI Karst Feature ID, Tracer Test Date, Weather Conditions, Tracer Used, Quantity, Operator, Results, Minimum Groundwater Flow Rate, Hydraulic Gradient (slope of water table), Data Source, Catchment, Peak Concentration, Other Information, Flow Path, County, Length (m), Direction and Quality Checked.

Aboout the Estuarine Processes, Hazards, and Ecosystems TeamAt the U.S. Geological Survey's Woods Hole Coastal and Marine Science Center we undertake interdisciplinary projects that aim to quantify and understand estuarine processes through observations and numerical modeling. Both the spatial and temporal scales of these mechanisms are important, and therefore require modern instrumentation and state-of-the-art hydrodynamic models. These are mostly collaborative projects that include participation from other U.S. Geological Survey offices, other federal and state agencies, and academic institutions. Estuaries are dynamic environments where complex interactions between the atmosphere, ocean, watershed, ecosystems, and human infrastructure take place. They serve as valuable ecological habitat and provide numerous ecosystem services and recreational opportunities. However, they are modified by physical processes such as storms and sea-level rise, while anthropogenic impacts such as nutrient loading threaten ecosystem function within estuaries. This project collects basic observational data on these processes, develops numerical models of the processes, and applies the models to understand the past, present, and future states of estuaries.Measuring parameters such as water velocity, salinity, sediment concentration, dissolved oxygen and other constituents in watersheds, tidal wetlands, estuaries, and coasts is critical for evaluating the socioeconomic and ecological function of those regions. Technological advances have made it possible to autonomously measure these parameters over timescales of weeks to months. These measurements are necessary to evaluate three-dimensional numerical models that can represent the spatial and temporal complexity of these parameters. Once the models adequately represent relevant aspects of the physical system, they can be used to evaluate possible future scenarios including sea-level rise, streamflow changes, land-use modifications, and geomorphic evolution.

This feature class contains the external ANILCA boundaries of the 16 National Wildlife Refuges in Alaska. The original refuge boundray feature class contains several refuges which are comprised of multiple units. For example, Alaska Peninsula NWR contains four units (Ugashik, Chignik, Pavlof, and North Creek). Alaska Maritime, Innoko, Togiak, and Yukon Delta also have multiple units. This configuration works well for most purposes, however some use cases require that each refuge be composed of only one overall polygon. To address this, the refuge boundaries were dissolved so that there is only one multipart polygon per refuge. This dissolved feature class is suitable for situations where the map should be zoomed to the extent of the entire refuge rather than to the extent of just one of the refuge units.

Merged, dissolved, and generalized polygon feature class; should be used in conjunction with the merged, dissolved, and generalized line feature class "All_critical_habitat_line_YYYYMMDD_generalized_for_Section7_consultations".Refer to the metadata in each feature class for more information on specific proposed or designated critical habitat.The spatial data represent critical habitat locations; however, the complete description and official boundaries of critical habitat proposed or designated by NMFS are provided in proposed rules, final rules, and the Code of Federal Regulations (50 CFR 226). Official critical habitat boundaries may include regulatory text that modifies or clarifies maps and spatial data. Proposed rules, final rules, and the CFR also describe any areas that are excluded from critical habitat or otherwise not part of critical habitat (e.g., ineligible areas), some of which have not been clipped out of the spatial data. Both proposed and designated critical habitat are included in this feature class. Proposed critical habitat will be replaced by final designations soon after a final rule is published in the Federal Register. This feature class version may not include spatial data for recently proposed, modified, or designated critical habitat.