November 2021

Note: Sample data provided. ・ Eversource's Hosting Capacity Map shows the maximum amount of energy a distributed resource, like solar panels, can be accommodated on the distribution system at a given location. This datacard is for Eastern Massachusetts.

Time series map of Eastern MA demographics.

This data release presents geologic map data for the surficial geology of the Aztec 1-degree by 2-degree quadrangle. The map area lies within two physiographic provinces of Fenneman (1928): the Southern Rocky Mountains province, and the Colorado Plateau province, Navajo section. Geologic mapping is mostly compiled from published geologic map data sources ranging from 1:24,000 to 1:250,000 scale, with limited new interpretive contributions. Gaps in map compilation are related to a lack of published geologic mapping at the time of compilation, and not necessarily a lack of surficial deposits. Much of the geology incorporated from published geologic maps is adjusted based on digital elevation model and natural-color image data sources to improve spatial resolution of the data. Spatial adjustments and new interpretations also eliminate mismatches at source map boundaries. This data set represents only the surficial geology, defined as generally unconsolidated to moderately consolidated sedimentary deposits that are Quaternary or partly Quaternary in age, and faults that have documented Quaternary offset. Bedrock and sedimentary material directly deposited as a result of volcanic activity are not included in this database, nor are faults that are not known to have moved during the Quaternary. Map units in the Aztec quadrangle include alluvium, glacial, eolian, mass-wasting, colluvium, and alluvium/colluvium deposit types. Alluvium map units, present throughout the map area, range in age from Quaternary-Tertiary to Holocene and form stream-channel, floodplain, terrace, alluvial-fan, and pediment deposits. Along glaciated drainages terraces are commonly made up of glacial outwash. Glacial map units are concentrated in the northeast corner of the map area and are mostly undifferentiated till deposited in mountain valleys during Pleistocene glaciations. Eolian map units are mostly middle Pleistocene to Holocene eolian sand deposits forming sand sheets and dunes. Mass-wasting map units are concentrated in the eastern part of the map area, and include deposits formed primarily by slide, slump, earthflow, and rock-fall processes. Colluvium and alluvium/colluvium map units form hillslope and undifferentiated valley floor/hillslope deposits, respectively. The detail of geologic mapping varies from about 1:50,000- to 1:250,000-scale depending on the scale of published geologic maps available at the time of compilation, and for new mapping, the resolution of geologic features on available basemap data. Map units are organized within geologic provinces as described by the Seamless Integrated Geologic Mapping (SIGMa) (Turner and others, 2022) extension to the Geologic Map Schema (GeMS) (USGS, 2020). For this data release, first order geologic provinces are the physiographic provinces of Fenneman (1928), which reflect the major geomorphological setting affecting depositional processes. Second order provinces are physiographic sections of Fenneman (1928) if present. Third and fourth order provinces are defined by deposit type. Attributes derived from published source maps are recorded in the map unit polygons to preserve detail and allow database users the flexibility to create derivative map units. Map units constructed by the authors are based on geologic province, general deposit type and generalized groupings of minimum and maximum age to create a number of units typical for geologic maps of this scale. Polygons representing map units were assigned a host of attributes to make that geology easily searchable. Each polygon contains a general depositional process (‘DepositGeneral’) as well as three fields that describe more detailed depositional processes responsible for some deposition in that polygon (‘LocalGeneticType1’ – ‘LocalGeneticType3’). Three fields describe the materials that make up the deposit (‘LocalMaterial1’ – ‘LocalMaterial3’) and the minimum and maximum chronostratigraphic age of a deposit is stored in the ‘LocalAgeMin’ and ‘LocalAgeMax’ fields, respectively. Where a polygon is associated with a prominent landform or a formal stratigraphic name the ‘LocalLandform’ and ‘LocalStratName’ fields are populated. The field ‘LocalThickness’ provides a textual summary of how thick a source publication described a deposit to be. Where three fields are used to describe the contents of a deposit, we attempt to place descriptors in a relative ordering such that the first field is most prominent, however for remotely interpreted deposits and some sources that provide generalized descriptions this was not possible. Values within these searchable fields are generally taken directly from source maps, however we do perform some conservative adjustments of values based on observations from the landscape and/or adjacent source maps. Where new features were interpreted from remote observations, we derive polygon attributes based on a conservative correlation to neighboring maps. Detail provided at the polygon level is simplified into a map unit by matching its values to the DescriptionOfMapUnits_Surficial table. Specifically, we construct map units within each province based on values of ‘DepositGeneral’ and a set of chronostratigraphic age bins that attempt to capture important aspects of Quaternary landscape evolution. Polygons are assigned to the mapunit with a corresponding ‘DepositGeneral’ and the narrowest chronostratigraphic age bin that entirely contains the ‘LocalAgeMin’ and ‘LocalAgeMax’ values of that polygon. Therefore, users may notice some mismatch between the age range of a polygon and the age range of the assigned map unit, where ‘LocalAgeMin’ and ‘LocalAgeMax’ (e.g., Holocene – Holocene) may define a shorter temporal range than suggested by the map unit (e.g., Holocene – late Pleistocene). This apparent discrepancy allows for detailed information to be preserved in the polygons, while also allowing for an integrated suite of map units that facilitate visualization over a large region.

This map service from MassGIS contains points for the limited access highway exit and interchange locations in Massachusetts. The exit numbers include the updates from the 2020-21 mileage-based exit renumbering project.The two statewide point feature classes in the service are:Exits - One point at each off-ramp, located approximately at the "gore point" (the triangular area of space between the through travel lanes and the off-ramp) or where the exit lane begins. Includes the letter ('A' and 'B', e.g.) designations for north/south, east/west.Interchanges - One point for each junction where there is a unique exit number. The points are located at the approximate midpoint of the interchange and represent all the ramps with that exit number. In most cases these include only the numeric portion of the exit number.See layer metadata.Map service also available.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

The four adjacent Outer Cape communities of Eastham, Truro, Provincetown, and Wellfleet have built an intermunicipal partnership to pursue a regional approach to shoreline management. This partnership promotes short- and long-term science-based decisions that will maximize the effectiveness and efficiency of community responses to the increased threat of coastal hazards. This map set is a product of that partnership, the Intermunicipal Shoreline Management Project, a project first initiated in 2019 with funding from CZM's Coastal Resilience Grant Program.Maps showing the general location of littoral cells, the sediment transport system and ISM management cells along the eastern shoreline of Cape Cod Bay.Management Cells: The spatial base map upon which to implement a regional shoreline management framework for the ISM planning area. Recognizing that nearshore and shoreline characteristics drive coastal change, management cells are organized around the concept of littoral cells or natural coastal compartments that contain a complete cycle of sedimentation including sources, transport paths, and sinks. Management cells can be used to determine a shoreline project’s location within the littoral cell and to aid in the identification of key management considerations for a given project. Ignoring municipal boundaries should enhance each town’s ability to work with the natural processes of coastal change and help facilitate a uniform, science-based regional shoreline management approach. Littoral Cells / Sediment Transport System: Although represented as discrete points and lines, features are not intended to imply point specific locations. Rather the information provided is intended to visualize generally the areas of sediment sources and sinks, the locations of null points, and the directions of net sediment transport along the eastern shore of Cape Cod Bay.DefinitionsLittoral Cell: A coastal compartment that contains a complete cycle of sedimentation including sources, transport paths, and sinks. Net Longshore Sediment Transport (Q): Annual net flow of sediment along the coast expressed as the volume rate of wave-produced sediment transport. Null Point: A point along the shore that defines the updrift or down drift boundary of a littoral cell, (Q=0). Sediment Sink: An area where sediment is removed from a littoral cell (an area of deposition). Sediment Source: An area where sediment in added to a littoral cell (an area of erosion). For more information seeBerman, G.A., 2011, Longshore Sediment Transport, Cape Cod, Massachusetts. Marine Extension Bulletin, Woods Hole Sea Grant & Cape Cod Cooperative Extension. 48 p.Giese, G.S., Borrelli, M., Mague, S.T., Barger, P., McFarland, S., 2018. Assessment of the Century-Scale Sediment Budget for the Eastham and Wellfleet Coasts of Cape Cod Bay. A Report Submitted to the Towns of Eastham and Wellfleet, Center for Coastal Studies, Provincetown, MA. 32p. Giese, G.S., M. Borrelli, S.T. Mague, T. Smith and P. Barger, 2014, Assessment of Multi- Decadal Coastal Change: Provincetown Harbor to Jeremy Point, Wellfleet. A Report Submitted to the Massachusetts Bays Program, .Center for Coastal Studies, Provincetown, MA. 23 p. Giese, G.S., Borrelli, M., Mague, S.T., Smith, T.L., Barger, P., Hughes, P., 2013. Evaluating century-scale coastal change: Provincetown/Truro line to Provincetown Harbor. No. 14- 1, Center for Coastal Studies. 11p.

no abstract provided

The U.S. Geological Survey (USGS), in cooperation with the National Oceanic and Atmospheric Administration's National Marine Sanctuary Program, has conducted seabed mapping and related research in the Stellwagen Bank National Marine Sanctuary region since 1993. The area is approximately 3,700 square kilometers (km2) and is subdivided into 18 quadrangles. Seven maps, at a scale of 1:25,000, of quadrangle 6 (211 km2) depict seabed topography, backscatter, ruggedness, geology, substrate mobility, mud content, and areas dominated by fine-grained or coarse-grained sand. Interpretations of bathymetric and seabed backscatter imagery, photographs, video, and grain-size analyses were used to create the geology-based maps. In all, data from 420 stations were analyzed, including sediment samples from 325 locations. The seabed geology map shows the distribution of 10 substrate types ranging from boulder ridges to immobile, muddy sand to mobile, rippled sand. Substrate types are defined on the basis of sediment grain-size composition, surficial morphology, sediment layering, and the mobility or immobility of substrate surfaces. This map series is intended to portray the major geological elements (substrates, features, processes) of environments within quadrangle 6. Additionally, these maps will be the basis for the study of the ecological requirements of invertebrate and vertebrate species that utilize these substrates and guide seabed management in the region.

Humanity’s role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the ‘anthropocene’, as humans are ‘overwhelming the great forces of nature’. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed ‘manufactured capital’, ‘technomass’, ‘human-made mass’, ‘in-use stocks’ or ‘socioeconomic material stocks’, they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with ‘real’ (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called ‘built structures’) represent the overwhelming majority of all socioeconomic material stocks.

This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors.

Spatial extent This subdataset covers the North East CONUS, i.e.

CT

DC

DE

MA

MD

ME

NH

NJ

NY

PA

RI

VA

For the remaining CONUS, see the related identifiers.

Temporal extent The map is representative for ca. 2018.

Data format The data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided.

Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types).

Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e.

t at 10m x 10m

kt at 100m x 100m

Mt at 1km x 1km

Gt at 10km x 10km

For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming.

Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv.

Material layers Note that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see related identifiers):

A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), doi:10.5281/zenodo.5045337.

Further information For further information, please see the publication. A web-visualization of this dataset is available here. Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.

Publication D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gomez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, H. Haberl. Weighing the US Economy: Map of Built Structures Unveils Patterns in Human-Dominated Landscapes. In prep

Funding This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404.

Acknowledgments We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC.

Study ObjectivesThe primary objective of this study was to generate projections of changes in stream temperature and thermal habitat (i.e., cold water fish habitat) due to climate change across the state of Massachusetts. To achieve this, statistical and machine learning models were developed for predicting stream temperatures based on air temperature and various landscape metrics (e.g., land use, elevation, drainage area). The model was then used in conjunction with climate change projections of air temperature increases to estimate the potential changes in stream temperatures and thermal habitat across the state. The results of this study are made available through this web-based tool to inform conservation and management decisions related to the protection of coldwater fish habitat in MassachusettsModeling MethodologyA regional model was developed for predicting stream temperatures in all streams and rivers across the state, excluding the largest rivers such as the Connecticut and Merrimack. The model was comprised of two components: 1) a non-linear regression model representing the functional relationship between air and water temperatures at a single location, and 2) a machine learning model (boosted decision trees) for estimating the parameters of the air-water temperature model spatially based on landscape characteristics. Together, these models demonstrated strong performance in predicting weekly water temperatures with an RMSE of 1.3 degC and Nash Sutcliffe Efficiency (NSE) of 0.97 based on an independent subset of the observed data that was excluded from model development and training.ResultsUnder historical baseline conditions (average air temperatures over 1971-2000), the model results showed more abundant cold water habitat in the western part of the state compared to the eastern and coastal areas. Forest and tree canopy cover were among the most important predictors of the relationship between air and water temperatures. The amount of impounded water due to dams upstream of each reach was also important. The majority of cold water habitat (82% of all river miles) were found in first order streams (i.e., headwaters), which are also the most abundant accounting for 60% of all river miles overall. The Deerfield and Hudson-Hoosic drainage basins had the most cold water habitat, which accounted for 80% or more of the total river miles within each basin. Coastal basins such as Narragansett, Piscataqua-Salmon Falls, Charles River, and Cape Code each had less than 5% cold water habitat.Using a series of projected air temperature increases for the RCP 8.5 emissions scenario, the model predicted a reduction in cold water habitat (mean July temp < 18.45 °C) from 30% to 8.5% (a 72% reduction) statewide by the 2090 averaging period (2080-2100). Furthermore, projections for larger streams (orders 3–5) were projected to shift from predominately cool-water (18.45–22.30 °C) to the majority (> 50%) of river miles being classified as warm-water habitat (> 22.30 °C).ConclusionsThe projected stream temperatures and thermal classifications generated by this project will be a valuable dataset for researchers and resource managers to assess potential climate change impacts on thermal habitats across the state. With this spatially continuous dataset, researchers and managers can identify specific reaches or basins projected to be the most resilient to climate change, and prioritize them for protection or restoration. As more datasets become available, this model can be readily extended and adapted to increase its spatial extent and resolution, and to incorporate flow data for assessing the impacts of not only rising air temperatures but also changing precipitation patterns.AcknowledgementsI would like to thank Jenn Fair (USGS) for her technical review of the model report and assistance in data gathering at the beginning of the project. I would also like to thank Ben Letcher (USGS) for his feedback and long-term collaboration on EcoSHEDS, which led to this project; Matt Fuller (USDA FS), Jenny Rogers (UMass Amherst), Valerie Ouellet (NOAA NMFS), and Aimee Fullerton (NOAA NMFS) for taking the time to discuss their experience, insights, and ideas regarding regional stream temperature modeling; Lisa Kumpf (CRWA) and Ryan O’Donnell (IRWA) for sharing their data directly; and Sean McCanty (NRWA), Julia Blatt (Mass Rivers Alliance), and Sarah Bower (Mass Rivers Alliance) for their assistance in sending out a request to the Mass Rivers Alliance for stream temperature data. Lastly, I am grateful for the countless individuals who collected the temperature data and without whom this project would not have been possible.FundingThis study was performed by Jeffrey D Walker, PhD of Walker Environmental Research LLC in collaboration with MA Division of Fisheries and Wildlife (MassWildlife). Funding was provided by the 2018 State Hazard Mitigation and Climate Adaptation Plan (SHMCAP) for Massachusetts.

Layered GeoPDF 7.5 Minute Quadrangle Map. Layers of geospatial data include orthoimagery, roads, grids, geographic names, elevation contours, hydrography, and other selected map features.

Noise pollution in cities has major negative effects on the health of both humans and wildlife. Using iPhones, we collected sound-level data at hundreds of locations in four areas of Boston, Massachusetts (USA) before, during, and after the fall 2020 pandemic lockdown, during which most people were required to remain at home. These spatially dispersed measurements allowed us to make detailed maps of noise pollution that are not possible when using standard fixed sound equipment. The four sites were: the Boston University campus (which sits between two highways), the Fenway/Longwood area (which includes an urban park and several hospitals), Harvard Square (home of Harvard University), and East Boston (a residential area near Logan Airport). Across all four sites, sound levels averaged 6.4 dB lower during the pandemic lockdown than after. Fewer high noise measurements occurred during lockdown as well. The resulting sound maps highlight noisy locations such as traffic intersections and qui..., We collected sound measurements within four different urban sites in Boston, Massachusetts. Working in small teams of 2-4 people, we used the mobile app SPLnFFT to collect sound level data in A-weighted decibel readings using smartphones. We exclusively used iPhones for data collection for consistency in hardware and software. Before each collection, we calibrated each iPhone to the same standard, which was used for every collection outing. We recorded the L50 value (the median sound level) for each recording because the L50 value is less affected by short bursts of loud sound than the mean reading. Recordings ran for approximately 20 seconds each. We recorded all sound measurements between 9 am and 5 pm on workdays to avoid the influence of rush-hour traffic, and only collected data on days without rain, snow, or strong wind to prevent inaccuracies due to weather. Within these conditions, we collected sound measurements over multiple days and at different times to ensure representative..., , # Data from: Maps made with smartphones highlight lower noise pollution during COVID-19 pandemic lockdown at four locations in Boston

https://doi.org/10.5061/dryad.ncjsxkt35

Dataset contents include csv files of all data (each file describes collection year and site of data), R script used to create noise maps, and kml files needed to run the map creation code.

Description of the data and file structure

Each csv file contains the L50 values (median sound level) taken from hundreds of 20 second recordings over multiple collection days. The SPLnFFT application exports the latitude and longitude of where the recording was taken, which is also included in the csv files and is used to create the noise maps. The csv files are used as data frames for the R script to create noise maps for each collection site. The R script contains comments and instructions to clearly indicate each step of the map creation. The kml files are used to create bound...

New multidisciplinary data collected as part of the Exploring for the Future (EFTF) Program has changed our understanding of the basement geology of the East Tennant region in the Northern Territory, and its potential to host mineralisation. To ensure this understanding is accurately reflected in geological maps, we undertake a multidisciplinary interpretation of the basement geology in East Tennant. For the purposes of this product, basement comprises polydeformed and variably metamorphosed rocks of the pre-1800 Ma Warramunga Province, which are exposed in outcrop around Tennant Creek, to the west. In the East Tennant region, these rocks are entirely covered by younger flat-lying strata of the Georgina Basin, and locally covered by the Kalkarindji Suite, and South Nicholson Basin (Ahmad 2000).

The data from this solid geology map are designed to be included in mineral potential models and future updates to Geoscience Australia’s chronostratigraphic solid geology maps.

This interpretation comprises a Geographic Information System (GIS) dataset containing basement geology polygons, faults and contacts. Geological units are consistent with the Australian Stratigraphic Units Database and faults utilise existing conventions followed by Geoscience Australia’s chronostratigraphic solid geology products (Stewart et al. 2020). To aid in understanding the data, we have added a three-stage fault hierarchy. Basement geology was interpreted at 1:100000 scale (but is intended for display at 1:250000 scale) using geophysical imagery, namely total magnetic intensity and vertical derivatives of these data, and gravity. The interpretation makes use of numerous new datasets collected as part of the EFTF program. These include a new 2-km spaced gravity grid over most of East Tennant, drill-core lithology from new boreholes drilled as part of the MinEx CRC National Drilling Initiative, airborne electromagnetic data collected under the AusAEM program, new active seismic data, and geochronology from legacy boreholes. These data are available to view and download from the Geoscience Australia portal (https://portal.ga.gov.au).

We interpret that basement in the East Tennant region does represent the eastern continuation of the Warramunga Province. There is no obvious geophysical or geological boundary between Tennant Creek and East Tennant. However, the East Tennant region mostly lacks stratigraphy equivalent to the Ooradidgee Group, which overlies and postdates mineralisation in turbiditic rocks of the Warramunga Formation at Tennant Creek. Instead, East Tennant is underlain by a widespread succession of clastic metapelitic rocks that bear many lithological and geochronological similarities to the Warramunga Formation (Cross et al. 2020). Other important outcomes of this work include the documentation of significant regional faults and shear zones and abundant intrusive rocks at East Tennant. Geophysical and geochronological data suggest that this deformation and magmatism is the eastern continuation of ~1850 Ma tectonism preserved at Tennant Creek (e.g. Cross et al. 2020).

NOTE: Specialised (GIS) software is required to view this data.

References: Ahmad M, 2000. Geological map of the Northern Territory. 1:2 500 000 scale. Northern Territory Geological Survey, Darwin.

Cross AJ, Clark AD, Schofield A and Kositcin N, 2020. New SHRIMP U-Pb zircon and monazite geochronology of the East Tennant region: a possible undercover extension of the Warramunga Province, Tennant Creek. In: Czarnota K, Roach I, Abbott S, Haynes M, Kositcin N, Ray A and Slatter E (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4.

Stewart AJ, Liu SF, Bonnardot M-A, Highet LM, Woods M, Brown C, Czarnota K and Connors K, 2020. Seamless chronostratigraphic solid geology of the North Australian Craton. In: Czarnota K, Roach I, Abbott S, Haynes M, Kositcin N, Ray A and Slatter E (eds.) Exploring for the Future: Extended Abstracts, Geoscience Australia, Canberra, 1–4.

In the spring of 2017, the Commonwealth of Virginia, through the Virginia Geographic Information Network Division (herein referred to as VGIN) of the Virginia Information Technologies Agency (VITA) contracted with Fugro Geospatial, Inc. to provide aerial data acquisition, ground control, aerial triangulation and development of statewide ortho quality DEM and digital orthophotography data. The Virginia Base Mapping Program (VBMP) update project is divided into three collection phases: In 2017, Fugro flew the eastern third of Virginia at one foot resolution, with options for localities and other interested parties to upgrade resolution or purchase other optional products through the state contract. The middle third of Virginia will be flown in 2018 and the western third in 2019. Ortho products are 1-foot resolution statewide with upgrades to 6-inch resolution tiles and 3-inch resolution tiles in various regions within the project area. The Virginia Base Mapping project encompasses the entire land area of the Commonwealth of Virginia over 4 years. The State boundary is buffered by 1000'. Coastal areas of the State bordering the Atlantic Ocean or the Chesapeake Bay are buffered by 1000' or the extent of man-made features extending from shore. This metadata record describes the generation of new Digital Terrain Model (DTM) and contours generated at 2-foot intervals. All products are being delivered in the North American Datum of 1983 (1986), State Plane Virginia North. The vertical datum was the North American Vertical Datum of 1988 (NAVD88) using GEOID12B.

This record is maintained in the National Geologic Map Database (NGMDB). The NGMDB is a Congressionally mandated national archive of geoscience maps, reports, and stratigraphic information, developed according to standards defined by the cooperators, i.e., the USGS and the Association of American State Geologists (AASG). Included in this system is a comprehensive set of publication citations, stratigraphic nomenclature, downloadable content, unpublished source information, and guidance on standards development. The NGMDB contains information on more than 90,000 maps and related geoscience reports published from the early 1800s to the present day, by more than 630 agencies, universities, associations, and private companies. For more information, please see http://ngmdb.usgs.gov/.

This tile layer from MassGIS displays elevation and shaded relief imagery derived from 2013-2021 lidar data for the Commonwealth of Massachusetts. The elevation data is symbolized with a custom color ramp. The shaded relief data is symbolized with the sunlight shining from the northwest (315 degrees) at a sun angle of 45 degrees. The two image datasets are displayed using a blending mode as mapped in ArcGIS Pro software.Data for the eastern and central areas of the mainland was captured in 2021, Nantucket from 2018, and the western part of the state from 2013 and 2014. The tile service will display at scale levels 7 (1:4.6M) to 19 (1:1128).For more information and links to data downloads, see MassGIS' Lidar Terrain Data page.

The U.S. Geological Survey (USGS), in cooperation with the National Oceanic and Atmospheric Administration's National Marine Sanctuary Program, has conducted seabed mapping and related research in the Stellwagen Bank National Marine Sanctuary region since 1993. The area is approximately 3,700 square kilometers (km2) and is subdivided into 18 quadrangles. Seven maps, at a scale of 1:25,000, of quadrangle 6 (211 km2) depict seabed topography, backscatter, ruggedness, geology, substrate mobility, mud content, and areas dominated by fine-grained or coarse-grained sand. Interpretations of bathymetric and seabed backscatter imagery, photographs, video, and grain-size analyses were used to create the geology-based maps. In all, data from 420 stations were analyzed, including sediment samples from 325 locations. The seabed geology map shows the distribution of 10 substrate types ranging from boulder ridges to immobile, muddy sand to mobile, rippled sand. Substrate types are defined on the basis of sediment grain-size composition, surficial morphology, sediment layering, and the mobility or immobility of substrate surfaces. This map series is intended to portray the major geological elements (substrates, features, processes) of environments within quadrangle 6. Additionally, these maps will be the basis for the study of the ecological requirements of invertebrate and vertebrate species that utilize these substrates and guide seabed management in the region.

description: The U.S. Geological Survey (USGS), in cooperation with the National Oceanic and Atmospheric Administration (NOAA) and the Massachusetts Office of Coastal Zone Management (MA CZM), is producing detailed geologic maps of the coastal sea floor. Imagery, originally collected by NOAA for charting purposes, provide a fundamental framework for research and management activities along this part of the Massachusetts coastline, show the composition and terrain of the seabed, and provide information on sediment transport and benthic habitat. Interpretive data layers were derived from multibeam echo-sounder and sidescan sonar data collected in Great Round Shoal Channel, a passage through the shoals at the eastern entrance to Nantucket Sound, off Cape Cod, Massachusetts. In June 2006, bottom photographs and surficial sediment data were acquired as part of a ground-truth reconaissance survey.; abstract: The U.S. Geological Survey (USGS), in cooperation with the National Oceanic and Atmospheric Administration (NOAA) and the Massachusetts Office of Coastal Zone Management (MA CZM), is producing detailed geologic maps of the coastal sea floor. Imagery, originally collected by NOAA for charting purposes, provide a fundamental framework for research and management activities along this part of the Massachusetts coastline, show the composition and terrain of the seabed, and provide information on sediment transport and benthic habitat. Interpretive data layers were derived from multibeam echo-sounder and sidescan sonar data collected in Great Round Shoal Channel, a passage through the shoals at the eastern entrance to Nantucket Sound, off Cape Cod, Massachusetts. In June 2006, bottom photographs and surficial sediment data were acquired as part of a ground-truth reconaissance survey.

Humanity's role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the 'anthropocene', as humans are 'overwhelming the great forces of nature'. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed 'manufactured capital', 'technomass', 'human-made mass', 'in-use stocks' or 'socioeconomic material stocks', they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with 'real' (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called 'built structures') represent the overwhelming majority of all socioeconomic material stocks. This dataset features a detailed map of material stocks in the CONUS on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors. Spatial extentThis dataset covers the whole CONUS. Due to upload constraints, detailed data were split into 7 regions and were uploaded into sub-repositories - see related identifiers. (This repository holds aggregated values for the whole CONUS)

Great Plains Mid West North East Rocky Mountains South South West West Coast Temporal extentThe map is representative for ca. 2018. Data formatThe data are organized by states. Within each state, data are split into 100km x 100km tiles (EQUI7 grid), and mosaics are provided. Within each tile, images for area, volume, and mass at 10m spatial resolution are provided. Units are m², m³, and t, respectively. Each metric is split into buildings, other, rail and street (note: In the paper, other, rail, and street stocks are subsumed to mobility infrastructure). Each category is further split into subcategories (e.g. building types). Additionally, a grand total of all stocks is provided at multiple spatial resolutions and units, i.e.

t at 10m x 10m kt at 100m x 100m Mt at 1km x 1km Gt at 10km x 10km For each state, mosaics of all above-described data are provided in GDAL VRT format, which can readily be opened in most Geographic Information Systems. File paths are relative, i.e. DO NOT change the file structure or file naming. Additionally, the grand total mass per state is tabulated for each county in mass_grand_total_t_10m2.tif.csv. County FIPS code and the ID in this table can be related via FIPS-dictionary_ENLOCALE.csv. Material layersNote that material-specific layers are not included in this repository because of upload limits. Only the totals are provided (i.e. the sum over all materials). However, these can easily be derived by re-applying the material intensity factors from (see related identifiers): A. Baumgart, D. Virág, D. Frantz, F. Schug, D. Wiedenhofer, Material intensity factors for buildings, roads and rail-based infrastructure in the United States. Zenodo (2022), doi:10.5281/zenodo.5045337. Further informationFor further information, please see the publication.A web-visualization of this dataset is available here.Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society. PublicationD. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gómez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, and H. Haberl (2023): Unveiling patterns in human dominated landscapes through mapping the mass of US built structures. Nature Communications 14, 8014. https://doi.org/10.1038/s41467-023-43755-5 FundingThis research was primarly funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). Workflow development was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 414984028-SFB 1404. AcknowledgmentsWe thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on EODC - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC, and Wolfgang Wagner for granting access to preprocessed Sentinel-1 data.