Integrated terrain models covering 16,357 square kilometers of the Massachusetts coastal zone and offshore waters were built to provide a continuous elevation and bathymetry terrain model for ocean planning purposes. The area is divided into the following four geographical areas to reduce file size and facilitate publishing: Massachusetts Bay from the Massachusetts-New Hampshire border south to Provincetown and Scituate and east to Stellwagen Bank; Cape Cod Bay from Provincetown to Scituate and south to Hyannis; Buzzards Bay from the Cape Cod Canal southwest to the State border including the Elizabeth Islands and extending north to Fall River and Mount Hope Bay; and Nantucket and Vineyard Sounds, from Hyannis south to the border of the Massachusetts Coastal zone approximately 8 kilometers south of Nantucket. A Triangulated Irregular Network was created from public-domain bathymetric and LiDAR data using the ArcGIS terrain-model framework and then interpolated into a 32-bit GeoTiff of 10 meter resolution. The grids for each of the four geographical areas are referenced to the Universal Transverse Mercator, Zone 19, North American Datum of 1983 coordinate system, and the North American Vertical Datum of 1988. A polygon shapefile recording the source datasets accompanies each of the four grids.

This polygon layer represents the ten USA and Mexico states addressed by the U.S.-Mexico Border Program. States are sourced from Esri's ArcGIS Online: USA States (Generalized) Living Atlas Layer: https://www.arcgis.com/home/item.html?id=99fd67933e754a1181cc755146be21ca These data support the U.S.-Mexico Border Program Map, which highlights the projects funded through the Border 2020 Program (2013-2020) in both Region 9 and Region 6 of the U.S. EPA, including U.S. Federally recognized Tribal communities and states of Texas, New Mexico, Chihuahua, Nuevo Leon, Tamaulipas, Coahuila, California, Baja California, Sonora, and Arizona within 62 miles (100 kilometers) of the U.S.-Mexico Border. The projects stem from the Border 2020 framework that has five goals to reduce air pollution, improve access to clean water, promote materials and waste management, improve emergency preparedness, and enhance environmental stewardship, and fundamental strategies that includes children's health and environmental education and outreach. This layer is tied to the U.S.-Mexico Border Program with EPA and is relevant to past and future programs.

This feature represents the entire area considered as the U.S.-Mexico border region for the U.S.-Mexico Border Program. The extent of the area was agreed upon between the United States and Mexico under the 1983 La Paz Agreement. The framework of this agreement stated that the border area is defined as "the area situated 62 miles (100 kilometers) on either side of the inland and maritime boundaries". This layer was generated by buffering the Homeland Infrastructure Foundation-Level Data (HIFLD) "Mexico and US Border" layer (https://hifld-geoplatform.opendata.arcgis.com/datasets/geoplatform::mexico-and-us-border/about) by [60] Miles. These data support the U.S.-Mexico Border Program Map, which highlights the projects funded through the U.S.-Mexico Border Program (2013-2020) in both Region 9 and Region 6 of the U.S. EPA, including U.S. Federally recognized Tribal communities and states of Texas, New Mexico, Chihuahua, Nuevo Leon, Tamaulipas, Coahuila, California, Baja California, Sonora, and Arizona within 62 miles (100 kilometers) of the U.S.-Mexico Border. The projects stem from the Border 2020 framework that has five goals to reduce air pollution, improve access to clean water, promote materials and waste management, improve emergency preparedness, and enhance environmental stewardship, and fundamental strategies that includes children's health and environmental education and outreach. For more information about Border 2020 and/or current U.S.-Mexico Border program visit this website: https://www.epa.gov/usmexicoborder

This provides a polygon coastline and islands layer which is based on the Topo50 products. It is a combination of the following layers:

• NZ Coastlines (Topo 1:50k)
• NZ Island Polygons (Topo 1:50k)
• NZ Auckland Is Island Polygons (Topo, 1:50k)
• NZ Campbell Is / Motu Ihupuku Island Polygons (Topo, 1:50k)
• NZ Antipodes Is Island Polygons (Topo, 1:25k)
• NZ Kermadec Is Island Polygons (Topo, 1:25k)
• NZ Bounty Is Island Polygons (Topo, 1:25k)
• NZ Snares Is / Tini Heke Island Polygons (Topo, 1:25k)
• NZ Chatham Is island polygons (Topo, 1:50k)

This topographic coastline is the line forming the boundary between the land and sea, defined by mean high water.

Islands from the NZ Island Polygons layer that lie within the NZ Coastline and Chatham Islands areas (i.e. islands in lakes, rivers and estuaries) have been removed.

The GIS workflow to create the layer is:

1. NZ Coastlines were converted from a polyline to a polygon using a polyline to polygon tool.
2. The resulting coastal polygon was then used as an input into an erase tool and run against the NZ Island Polygon layer to remove all islands lying within the NZ Mainland and Stewart Island.
3. This was then merged with the NZ Chatham Is island polygons (Topo, 1:50k) that have had the islands within the main island polygon removed, NZ Auckland Is Island Polygons (Topo, 1:50k), NZ Campbell Is / Motu Ihupuku Island, NZ Antipodes Is Island Polygons (Topo, 1:25k), NZ Kermadec Is Island Polygons (Topo, 1:25k), NZ Bounty Is Island Polygons (Topo, 1:25k) and NZ Snares Is / Tini Heke Island Polygons (Topo, 1:25k) layers using a merge tool.

For more detailed description of each layer refer to the layer urls above.

APIs and web services This dataset is available via ArcGIS Online and ArcGIS REST services, as well as our standard APIs. LDS APIs and OGC web services ArcGIS Online map services ArcGIS REST API

Integrated terrain models covering 16,357 square kilometers of the Massachusetts coastal zone and offshore waters were built to provide a continuous elevation and bathymetry terrain model for ocean planning purposes. The area is divided into the following four geographical areas to reduce file size and facilitate publishing: Massachusetts Bay from the Massachusetts-New Hampshire border south to Provincetown and Scituate and east to Stellwagen Bank; Cape Cod Bay from Provincetown to Scituate and south to Hyannis; Buzzards Bay from the Cape Cod Canal southwest to the State border including the Elizabeth Islands and extending north to Fall River and Mount Hope Bay; and Nantucket and Vineyard Sounds, from Hyannis south to the border of the Massachusetts Coastal zone approximately 8 kilometers south of Nantucket. A Triangulated Irregular Network was created from public-_domain bathymetric and LiDAR data using the ArcGIS terrain-model framework and then interpolated into a 32-bit GeoTiff of 10 meter resolution. The grids for each of the four geographical areas are referenced to the Universal Transverse Mercator, Zone 19, North American Datum of 1983 coordinate system, and the North American Vertical Datum of 1988. A polygon shapefile recording the source datasets accompanies each of the four grids.

Integrated terrain models covering 16,357 square kilometers of the Massachusetts coastal zone and offshore waters were built to provide a continuous elevation and bathymetry terrain model for ocean planning purposes. The area is divided into the following four geographical areas to reduce file size and facilitate publishing: Massachusetts Bay from the Massachusetts-New Hampshire border south to Provincetown and Scituate and east to Stellwagen Bank; Cape Cod Bay from Provincetown to Scituate and south to Hyannis; Buzzards Bay from the Cape Cod Canal southwest to the State border including the Elizabeth Islands and extending north to Fall River and Mount Hope Bay; and Nantucket and Vineyard Sounds, from Hyannis south to the border of the Massachusetts Coastal zone approximately 8 kilometers south of Nantucket. A Triangulated Irregular Network was created from public-domain bathymetric and LiDAR data using the ArcGIS terrain-model framework and then interpolated into a 32-bit GeoTiff of 10 meter resolution. The grids for each of the four geographical areas are referenced to the Universal Transverse Mercator, Zone 19, North American Datum of 1983 coordinate system, and the North American Vertical Datum of 1988. A polygon shapefile recording the source datasets accompanies each of the four grids.

The Inflation Reduction Act of 2022 (IRA) became law on August 8, 2022. Under the law, new qualifying renewable and/or carbon-free electricity generation projects constructed in certain areas of the US, called energy communities, are eligible for bonus worth an additional 10% to the value of the production tax credit or a 10 percentage point increase in the value of the investment tax credit. The IRA does not explicitly map or list these specific communities. Instead, eligible communities are defined by a series of qualifications:

1. a brownfield site,
2. a metropolitan statistical area (MSA) or non-metropolitan statistical area with either (a) 0.17% or greater employment or (b) 25% or greater local tax revenues related to the extraction, processing, transport, or storage of coal, oil, or natural gas; and an unemployment rate at or above the national average for the previous year, or
3. a census tract containing or adjacent to (a) a coal mine closed after December 31, 1999 or (b) a coal-fired electric generating unit retired after December 31, 2009.

These maps and data layers contain GIS data for coal mines, coal-fired power plants, fossil energy related employment, and brownfield sites. Each record represents a point, tract or metropolitan statistical area and non-metropolitan statistical area with attributes including plant type, operating information, GEOID, etc. The input data used includes:

1. Brownfields – Source: EPA. No analysis was performed on this data layer. However, tract polygon layers have a column denoting brownfield presence (0 for no brownfield site, 1 if the tract contains a brownfield somewhere within the polygon).
2. Eligible Employment MSAs (“Final_Employment_Qualifying_MSAs”) – Source: US Census County Business Patterns. MSAs and non-MSA regions with employment over 0.17% in the fossil fuel industry (defined here as NAICS codes 211, 2121, 213, 23712, 324, 4247, and 486) and unemployment greater than or equal to 3.9% (the average national unemployment rate in 2021, according to the Bureau of Labor Statistics).

--Possibly Eligible MSAs (“FossilFuel_Employment_Qualifying_MSAs”) are MSA and non-MSA regions that meet or exceed the 0.17% employment in the fossil fuel industry threshold but do not exceed the unemployment threshold.

--Relevant columns include:

a) SUM_nhgis0: Total employment in 2020.

b) SUM_nhgis1: Total unemployment in 2020.

c) P_Unemp: Percent unemployment in 2020.

d) Q_Unemp: Boolean column indicating if the MSA or non-MSA’s unemployment rate is at or above the national average of 3.9%.

e) FF_Qual: Boolean column indicating if the MSA or non-MSA had employment in the fossil fuel industry at or above 0.17% in the past 11 years.

f) final_Qual: Boolean column indicating if an MSA or non-MSA qualifies for both unemployment rate and fossil fuel employment under the IRA.

1. Retired Power Plants – Source: EIA via HFLID. Qualifying power plants were selected by use of coal in at least one generator, and if they were retired (RET_DATE) on or after January 1, 2010. This data goes through December 2021.

--Adjacent tract data was derived by Cecelia Isaac using ESRI ArcGIS Pro.

1. Abandoned Coal Mines – Source: MSHA. Mines labeled “Abandoned”, “Abandoned and Sealed” or “NonProducing” between January 1, 2000 and September 2022.

--Adjacent tract data was derived by Cecelia Isaac using ESRI ArcGIS Pro.

5) US State Borders– Source: IPUMS NHGIS.

Also included here are polygon shapefiles for Onshore Wind and Solar Candidate Project Areas from Princeton REPEAT. These files have been updated to include columns related to the energy communities.

New columns include:

1. CoalPlantTract: Boolean column indicating if the CPA is within a tract that qualifies because of a retired coal plant.
2. CoalMineTract: Boolean column indicating if the CPA is within a tract that qualifies because of a closed coal mine.
3. FossilFuelEmp: Boolean column indicating if the CPA is within an MSA or non-MSA with greater than or equal to 0.17% employment in the fossil fuel industry.
4. UnempQualification: Boolean column indicating if the CPA is within an MSA or non-MSA with greater than or equal to 0.17% employment in the fossil fuel industry.
5. MSA_non_to: The code of the MSA or non-MSA area that contains the CPA.
6. P_Unemp: The percent unemployment of the MSA or non-MSA that contains the CPA in 2021.

This shapefile represents proposed management categories (Core, Priority, General, and Non-Habitat) derived from the intersection of habitat suitability categories and lek space use. Habitat suitability categories were derived from a composite, continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada and northeastern California formed from the multiplicative product of the spring, summer, and winter HSI surfaces. Summary of steps to create Management Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution and created using ArcGIS 10.2.2) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014) as well as additional telemetry location data from field sites in 2014. The dataset was then split according to calendar date into three seasons. Spring included telemetry locations (n = 14,058) from mid-March to June; summer included locations (n = 11,743) from July to mid-October; winter included locations (n = 4862) from November to March. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated for each subregion using R software (v 3.13) and season using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. For each season, subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell. The three seasonal HSI rasters were then multiplied to create a composite annual HSI. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. HABITAT CATEGORIZATION: Using the same ecoregion boundaries described above, the habitat classification dataset (an independent data set comprising 10% of the total telemetry location sample) was split into locations falling within respective north and south regions. HSI values from the composite and relativized statewide HSI surface were then extracted to each classification dataset location within the north and south region. The distribution of these values were used to identify class break values corresponding to 0.5 (high), 1.0 (moderate), and 1.5 (low) standard deviations (SD) from the mean HSI. These class breaks were used to classify the HSI surface into four discrete categories of habitat suitability: High, Moderate, Low, and Non-Habitat. In terms of percentiles, High habitat comprised greater than 30.9 % of the HSI values, Moderate comprised 15 – 30.9%, Low comprised 6.7 – 15%, and Non-Habitat comprised less than 6.7%.The classified north and south regions were then clipped by the boundary layer and mosaicked to create a statewide categorical surface for habitat selection. Each habitat suitability category was converted to a vector output where gaps within polygons less than 1.2 million square meters were eliminated, polygons within 500 meters of each other were connected to create corridors and polygons less than 1.2 million square meters in one category were incorporated to the adjacent category. The final step was to mask major roads that were buffered by 50m (Census, 2014), lakes (Peterson, 2008) and urban areas, and place those masked areas into the non-habitat category. The existing urban layer (Census 2010) was not sufficient for our needs because it excluded towns with a population lower than 1,500. Hence, we masked smaller towns (populations of 100 to 1500) and development with Census Block polygons (Census 2015) that had at least 50% urban development within their boundaries when viewed with reference imagery (ArcGIS World Imagery Service Layer). SPACE USE INDEX CALCULATION: Updated lek coordinates and associated trend count data were obtained from the 2015 Nevada Sage-grouse Lek Database compiled by the Nevada Department of Wildlife (NDOW, S. Espinosa, 9/20/2015). Leks count data from the California side of the Buffalo-Skedaddle and Modoc PMU's that contributed to the overall space-use model were obtained from the Western Association of Fish and Wildlife Agencies (WAFWA), and included count data up to 2014. We used NDOW data for border leks (n = 12), and WAFWA data for those fully in California and not consistently surveyed by NDOW. We queried the database for leks with a ‘LEKSTATUS’ field classified as ‘Active’ or ‘Pending’. Active leks comprised leks with breeding males observed within the last 5 years (through the 2014 breeding season). Pending leks comprised leks without consistent breeding activity during the prior 3 - 5 surveys or had not been surveyed during the past 5 years; these leks typically trended towards ‘inactive’, or newly discovered leks with at least 2 males. A sage-grouse management area (SGMA) was calculated by buffering Population Management Units developed by NDOW by 10km. This included leks from the Buffalo-Skedaddle PMU that straddles the northeastern California – Nevada border, but excluded leks for the Bi-State Distinct Population Segment. The 5-year average (2011 - 2015) for the number of male grouse (or NDOW classified 'pseudo-males' if males were not clearly identified but likely) attending each lek was calculated. Compared to the 2014 input lek dataset, 36 leks switched from pending to inactive, and 74 new leks were added for 2015 (which included pending ‘new’ leks with one year of counts. A total of 917 leks were used for space use index calculation in 2015 compared to 878 leks in 2014. Utilization distributions describing the probability of lek occurrence were calculated using fixed kernel density estimators (Silverman 1986) with bandwidths estimated from likelihood based cross-validation (CVh) (Horne and Garton 2006). UDs were weighted by the 5-year average (2011 - 2015) for the number of males grouse (or unknown gender if males were not identified) attending leks. UDs and bandwidths were calculated using Geospatial Modelling Environment (Beyer 2012) and the ‘ks’ package (Duong 2012) in Program R. Grid cell size was 30m. The resulting raster was re-scaled between zero and one by dividing by the maximum pixel value. The non-linear effect of distance to lek on the probability of grouse spatial use was estimated using the inverse of the utilization distribution curves described by Coates et al. (2013), where essentially the highest probability of grouse spatial use occurs near leks and then declines precipitously as a non-linear function. Euclidean distance was first calculated in ArcGIS, reclassified into 30-m distance bins (ranging from 0 - 30,000m), and bins reclassified according to the non-linear curve in Coates et al. (2013). The resulting raster was re-scaled between zero and one by dividing by the maximum cell value. A Spatial Use Index (SUI) was calculated by taking the average of the lek utilization distribution and non-linear distance-to-lek rasters in ArcGIS, and re-scaled between zero and one by dividing by the maximum cell value. The volume of the SUI at cumulative at specific isopleths was extracted in Geospatial Modelling Environment (Beyer 2012) with the command ‘isopleth’. Interior polygons (i.e., donuts’ > 1.2 km2) representing no probability of use within a larger polygon of use were erased from each isopleth. The 85% isopleth, which provided greater spatial connectivity and consistency with previously used agency standards (e.g., Doherty et al. 2010), was ultimately recommended by the Sagebrush Ecosystem Technical Team. The 85% SUI isopleth was clipped by the Nevada state boundary. MANAGEMENT CATEGORIES: The process for category determination was directed by the Nevada Sagebrush Ecosystem Technical team. Sage-grouse habitat was categorized into 4 classes: High, Moderate, Low, and Non-Habitat as described above, and intersected with the space use index to form the following management categories . 1) Core habitat: Defined as the intersection between all suitable habitat (High, Moderate, and Low) and the 85% Space Use Index (SUI). 2) Priority habitat: Defined as all high quality habitat

Feature layer containing the authoritative city limit polygon for Sioux Falls, South Dakota and non-authoritative city limit polygons in the surrounding area.

Ecological Response Units (ERUs) facilitate landscape analyses and planning. The framework represents all major ecosystem types of the southwest region, and represents a stratification of biophysical themes. ERUs are used to define historic/reference conditions within a mapping unit by integrating site potential (soil physical and chemical properties, geology, geomorphology, aspect, slope, climate variables, and geographic location), fire regime (historic and contemporary), neighboring vegetation communities, and seral state sequence.The process by which this dataset was created is as follows:The input data were forest Terrestrial Ecological Unit Inventory (TEUI) survey data that were crosswalked to the ERU list, ERU version 4, corrections to ERU version 4 utilizing a climate gradient to identify anomalous attribution, a collaborative review product with the University of Arizona’s Ecologist Jim Malusa, Integrated Landscape Assessment Project (ILAP) data, Regional Riparian Mapping Project (RMAP) data, and subclass information derived from an ILAP grid analysis. The data layers listed above were assembled in a hierarchical order starting with the ERU version 4 data through a series of geoprocessing updates. Highest confidence was placed on TEUI data and RMAP data so those datasets were burned into the product last.During key review periods by resource specialists, there were metrics built to analyze percent of ERUs that lay on the border of a climate gradient or a Southwest Biotic Community (Brown and Lowe). These metrics were used to inform attribution for ERUs that needed evaluation by the resource specialist. Additional processing was performed using the Eliminate tool to reduce the amount of “noise” in the dataset brought on by fragmented polygons smaller than a parametric minimum map unit. The minimum map unit was defined as 1 hectare for grassland, shrubland, forest, woodland, and great plains system types and 1 acre for riparian types. By virtue of the Eliminate tool, polygons that did not meet the minimum map unit were merged to a neighboring polygon that shared the largest boundary. RMAP data was used as an exclusion dataset and no changes were made to that via the Eliminate processing. Multiple QA/QC reviews were performed at many stages of the building of the layer and as products were being finalized. During these reviews, updates were made to the layer as needed.