The United States has a total coastline measuring approximately 12,383 miles, and a shoreline measuring 88,633 miles. Alaska is, by far, the state with the longest individual coastline or shoreline, and its coastline is even considered longer than all other states combined. The mainland's west coast has a combined length of 1,293 miles (shore: 7,863 miles), while the southern and eastern coast (from Texas to Maine) have a combined length of 3,700 miles (shore: 45,814 miles).

Sediments off the eastern United States vary markedly in texture - the size, shape, and arrangement of their grains. However, for descriptive purposes, it is typically most useful to classify these sediments according to their grain-size distributions. Starting in 1962, the U.S. Geological Survey (USGS) and the Woods Hole Oceanographic Institution (WHOI) began a joint program to study the marine geology of the continental margin off the Atlantic coast of the United States. As part of this program and numerous subsequent projects, thousands of sediment samples were collected and analyzed for particle size. The sediment map of the Continental Margin Mapping Program (CONMAP) series is a compilation of grain-size data produced in the sedimentation laboratory of the Woods Hole Science Center (WHSC) of the Coastal and Marine Geology Program (CMGP) of the U.S. Geological Survey (USGS) and from both published and unpublished studies. Sediment was classified using the Wentworth (1929) grain-size scale and the Shepard (1954) scheme of sediment classification. Certain grain-size categories are combined because of the paucity of some sediment textures; blank parts of the maps indicate areas where data are insufficient to infer sediment type. Bathymetry is used as a guide in placing some of the contacts between different sediment types. However, because the true boundaries between sediment types are probably highly irregular or gradational, because the extreme textural variability that characterizes some areas does not appear at this scale, and because the accuracy of the navigational systems used during the earlier studies is limited, all contacts should be considered to be inferred. The sediment classification for any given polygon (i.e. area) reflects the dominant surficial sediment type for that polygon. It does not mean that other sediment types are not present within the polygon, only that the dominant sediment type is the one that is most common.

The United States shares a border of 5,525 miles with Canada to the north, and 1,933 miles with Mexico to the south. Alaska is the state with the longest international border, while Texas has the largest border of any state on the mainland. Michigan is the state with the third longest border, however the majority of this is on water, as the border is located on the Great Lakes.

Railroad data is represented by the physical class of railroad, i.e., gauge or type of track, which provides a basis for understanding how that track is used; and by the owner. Railroads represent proximity to human activity on the landscape, and conversely, the farther away a place is from roads, the lower the likelihood that it is disturbed by human activity. Railroad right-of-ways frequently provide refuge for wildlife and plants and act both as barriers and sometimes corridors for wildlife.Dataset SummaryThis layer contains a raster density surface of railroads from the U.S. Census Bureau's TIGER database. The cell size is 1 kilometer and the values represent railroad density based on the sum of the length of all railroads in each 1 km cell. This layer covers the continental U.S., Alaska, Hawaii, and Puerto Rico.Link to source metadataWhat can you do with this layer?The layer is restricted to an 24,000 x 24,000 pixel limit for these services, which represents an area roughly the size of North America. The source data for this layer is available here. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.

description: This data set contains sediment grain size and textural information from the Continental Margin Program. The program was a joint collaboration between the U.S. Geological Survey and the Woods Hole Oceanographic Institution during the 1960s to conduct a geological reconnaissance investigation of the continental shelf and slope off the Atlantic coast of the United States. Only those records with complete size analyses are included in this data set. Other stations where only lithologic descriptions are available have been excluded.; abstract: This data set contains sediment grain size and textural information from the Continental Margin Program. The program was a joint collaboration between the U.S. Geological Survey and the Woods Hole Oceanographic Institution during the 1960s to conduct a geological reconnaissance investigation of the continental shelf and slope off the Atlantic coast of the United States. Only those records with complete size analyses are included in this data set. Other stations where only lithologic descriptions are available have been excluded.

About this dataset

Context

The dataset presents median household incomes for various household sizes in Continental, OH, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Continental did not include 4, 5, 6, or 7-person households. Across the different household sizes in Continental the mean income is $73,490, and the standard deviation is $39,293. The coefficient of variation (CV) is 53.47%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $28,270. It then further increased to $99,309 for 3-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/continental-oh-median-household-income-by-household-size.jpeg" alt="Continental, OH median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Continental median household income. You can refer the same here

The purpose of the Continental Margin Mapping Program sediment layer is to show the sediment grain size distributions. The maps depicted in this series are old and do not accurately depict small-scale sediment distributions or sea-floor variability. This data layer is supplied primarily as a gross overview and to show general textural trends. Sediments off the eastern United States vary markedly in texture - the size, shape, and arrangement of their grains. However, for descriptive purposes, it is typically most useful to classify these sediments according to their grain-size distributions. Starting in 1962, the U.S. Geological Survey (USGS) and the Woods Hole Oceanographic Institution (WHOI) began a joint program to study the marine geology of the continental margin off the Atlantic coast of the United States. As part of this program and numerous subsequent projects, thousands of sediment samples were collected and analyzed for particle size. The sediment map of the Continental Margin Mapping Program (CONMAP) series is a compilation of grain-size data produced in the sedimentation laboratory of the Woods Hole Science Center (WHSC) of the Coastal and Marine Geology Program (CMGP) of the U.S. Geological Survey (USGS) and from both published and unpublished studies. Sediment was classified using the Wentworth (1929) grain-size scale and the Shepard (1954) scheme of sediment classification. Certain grain-size categories are combined because of the paucity of some sediment textures; blank parts of the maps indicate areas where data are insufficient to infer sediment type. Bathymetry is used as a guide in placing some of the contacts between different sediment types. However, because the true boundaries between sediment types are probably highly irregular or gradational, because the extreme textural variability that characterizes some areas does not appear at this scale, and because the accuracy of the navigational systems used during the earlier studies is limited, all contacts should be considered to be inferred. The sediment classification for any given polygon (i.e. area) reflects the dominant surficial sediment type for that polygon. It does not mean that other sediment types are not present within the polygon, only that the dominant sediment type is the one that is most common.View Dataset on the Gateway

The international land border between the United States and Canada is the longest in the world at almost 8,900 kilometers. It includes the border between Canada and the continental U.S. as well as the border between Alaska and northern Canada.

description: This data set contains grain size data from samples acquired under the NOAA Outer Continental Shelf Environmental Assessment Program (OCSEAP) from the Outer Continental Shelf around Alaska. Data were contributed by the University of Alaska, the University of South Carolina, the Scripps Institution of Oceanography, and the US Geological Survey with funding from NOAA. Data fields include collecting institution, ship, cruise, sample id, latitude/longitude, date of collection, water depth, sampling device, method of analysis, sample weight, sampled interval, raw weight percentages of sediment, within a given phi range. Some samples also have percentages of total gravel, sand, silt, clay, and statistical measurements such as mean, median, skewness, kurtosis, and standard deviation of grain size. These data are part of the larger NGDC Seafloor Sediment Grain Size Database (doi:10.7289/V5G44N6W).; abstract: This data set contains grain size data from samples acquired under the NOAA Outer Continental Shelf Environmental Assessment Program (OCSEAP) from the Outer Continental Shelf around Alaska. Data were contributed by the University of Alaska, the University of South Carolina, the Scripps Institution of Oceanography, and the US Geological Survey with funding from NOAA. Data fields include collecting institution, ship, cruise, sample id, latitude/longitude, date of collection, water depth, sampling device, method of analysis, sample weight, sampled interval, raw weight percentages of sediment, within a given phi range. Some samples also have percentages of total gravel, sand, silt, clay, and statistical measurements such as mean, median, skewness, kurtosis, and standard deviation of grain size. These data are part of the larger NGDC Seafloor Sediment Grain Size Database (doi:10.7289/V5G44N6W).

Accurate representation of stream networks at various scales in a hydrogeologic system is integral to modeling groundwater-stream interactions at the continental scale. To assess the accurate representation of stream networks, the distance of a point on the land surface to the nearest stream (DS) has been calculated. DS was calculated from the 30-meter Multi Order Hydrologic Position (MOHP) raster datasets for 18 watersheds in the United States that have been prioritized for intensive monitoring and assessment by the U.S. Geological Survey. DS was calculated by multiplying the 30-meter MOHP Lateral Position (LP) datasets by the 30-meter MOHP Distance from Stream Divide (DSD) datasets for stream orders one through five. DS was calculated for the purposes of considering the spatial scale needed for accurate representation of groundwater-stream interaction at the continental scale if a grid with 1-kilometer cell spacing. The data are available as multi-banded GeoTIFF files, where each band is a stream order.

Watershed works with either interactively contributing points or by specifying a layer containing points. Watershed has several optional parameters:Point Identification Field: A string or integer field that supplies a name or ID to each point.Snap Distance: This is the maximum distance that an input point can be moved by the tool. Interactively contributed points or gauge locations may not be exactly coincident with stream locations in the elevation model. The service will move points to be optimally located relative to the flows in the elevation model if they are not already there.Snap Distance Units: The units of measurement for the Snap Distance.Source Database: Available source databases include US30m National Elevation Dataset (NED) for the continental United States (US Geological Survey, 2010-2011), US10m National Elevation Dataset (NED) for Hawaii, American Samoa, Northern Mariana Islands, Guam, Puerto Rico and US Virgin Islands, and the 90m HydroSHEDS for the world between 60 degrees North and 56 degrees South, plus some areas in Canada and Alaska north of 60 degrees. Please refer to the locator map for detailed information about available areas. Additional areas and resolutions will be available in the future.Input Features: The maximum number of input features is 100.Esri processed the source data to derive additional layers required to support high performance and scalable watershed delineation and downstream trace tasks. The source data were the same elevation, Hydrologic Unit boundaries, streams and waterbodies that were used by the Environmental Protection Agency and US Geological Survey to produce NHDPlus V2.1. Sinks were filled unless they fell within a Watershed Boundary Dataset (WBD) closed basin, ensuring that flow will only terminate in endorheic regions. The Esri hydroconditioning process differs from the NHDPlus V2.1 process; therefore the resulting watershed delineations and downstream traces do not always match those from NHDPlus V2.1. To learn more, read the developer documentation for Watershed.

This dataset includes 12,326 DNA barcode records to explore the phylogeography of the North American butterflies.This dataset consists of two files: a fasta file containing DNA sequences, and an Excel file containing associated metadata such as taxonomic details and collection information.

A vector GIS dataset of candidate areas for terrestrial ecological restoration based on landscape context. The dataset was created using NLCD 2011 (www.mrlc.gov) and morphological spatial pattern analysis (MSPA) (http://forest.jrc.ec.europa.eu/download/software/guidos/mspa/). There are 13 attributes for the polygons in the dataset, including presence and length of roads, candidate area size, size of surround contiguous natural areas, soil productivity, presence and length of road, areas suitable for wetland restoration, and others. This dataset is associated with the following publication: Wickham, J., K. Riiters, P. Vogt, J. Costanza, and A. Neale. An inventory of continental U.S. terrestrial candidate ecological restoration areas based on landscape context. RESTORATION ECOLOGY. Blackwell Publishing, Malden, MA, USA, 25(6): 894-902, (2017).

Variability in sediment properties with depth and the thickness of individual sedimentary layers are critical determinants of seabed acoustic response. The New England Mud Patch (NEMP), located south of Cape Cod, is an unusual feature on the U.S. Continental Shelf in that it is composed of fine-grained sediment layers containing a relatively-homogeneous mix of sand, silt, and clay-sized particles bounded by more typical sandy shelf sediments. The unique characteristics and nature of this deposit is due to a derivation of sediments that have been transported to, and deposited in, a basal bowl-shaped depression since the last glacial maximum. Ninety-two piston, vibra-, and gravity cores with a maximum length of 8.2 meters were collected from across the New England Mud Patch during a 2-leg, 10-day cruise aboard the R/V Endeavor in the spring of 2016. Geologic characterization and analysis of a subset of the cores including grain size, CaCO3, mineral composition, and bulk index properties (undrained shear strength, water content, density, and porosity) of discrete samples was carried out at the USGS Woods Hole Coastal and Marine Science Center's (WHCMSC) Sediment Analysis Laboratory. This data release contains the results of these analyses, along with visual core descriptions and summary sheets for each core analyzed for this study.

This web map is used by Esri's Green Infrastructure Initiative to power the "Filter Your Intact Landscape Cores" app and the "Prioritize Your Intact Landscape Cores" app. It displays modeled Intact Habitat Cores, or minimally disturbed natural areas. This map represents modeled Intact Habitat Cores, or minimally disturbed natural areas at least 100 acres in size and greater than 200 meters wide. Esri created these data following a methodology outlined by the Green Infrastructure Center Inc. These data were generated using 2011 National Land Cover Data. Cores were derived from all “natural” land cover classes and excluded all “developed” and “agricultural” classes including crop, hay and pasture lands. The resulting cores were tested for size and width requirements (at least 100 acres in size and greater than 200 meters wide) and then converted into unique polygons. This process resulted in the generation of over 550,000 cores.

Cores were then overlaid with a diverse assortment of physiographic, biologic and hydrographic layers to populate each core with attributes (53 in total) related to the landscape characteristics found within. These data were also compiled to compute a “core quality index”, or score related to the perceived ecological value of each core, to provide users with additional insight related to the importance of each core when compared to all others. See this map image layer for a version that includes popups and ability to query the data.

The source data used to derive this attribution is as follows:

Number of endemic species (Mammals, Fish, Reptiles, Amphibians, Trees) (Jenkins, Clinton N., et. al, (April 21, 2015) US protected lands mismatch biodiversity priorities, PNAS vol.112, no. 16)

Priority Index areas: Endemic species, small home range size and low protection status. (Jenkins, Clinton N., et. al, (April 21, 2015) US protected lands mismatch biodiversity priorities, PNAS vol.112, no. 16)

Unique ecological systems (based upon work by Aycrig, Jocelyn L, et. al. (2013) Representation of Ecological Systems within the Protected Areas Network of the Continental United States. PLos One 8(1):e54689). New data constructed by Esri staff, using TNC Ecological Regions as summary areas.

Ecologically relevant landforms (Theobald DM, Harrison-Atlas D, Monahan WB, Albano CM (2015) Ecologically-Relevant Maps of Landforms and Physiographic Diversity for Climate Adaptation Planning. PLoS ONE 10(12): e0143619. doi:10.1371/journal.pone.0143619

Local Landforms (produced 3/2016) by Deniz Basaran and Charlie Frye, Esri, 30 m* resolution. "Improved Hammond’s Landform Classification and Method for Global 250-m Elevation Data" by Karagulle, Deniz; Frye, Charlie; Sayre, Roger; Breyer, Sean; Aniello, Peter; Vaughan, Randy; Wright, Dawn, has been successfully submitted online and is presently being given consideration for publication in Transactions in GIS. *We scaled the neighborhood windows from the 250-meter method described in the paper, and then applied that to 30-meter data in the U.S.

National Elevation Dataset, USGS, 30 m resolution

NWI – National Wetlands Inventory “ Classification of Wetlands and Deepwater Habitats of the United States”. U.S. Department of the Interior, Fish and Wildlife Service, Washington, DC. FWS/OBS-79/31 , U.S. Fish and Wildlife Service, Division of Habitat and Resource Conservation (prepared 10/2015)

NLCD 2011 – National LandCover Database 2011 (downloaded 1/2016) Homer, C.G., et. al. 2015,Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing, v. 81, no. 5, p. 345-354

NHDPlusV2 Received from Charlie Frye, Esri 3/2016. Produced by the EPA with support from the USGS.

gSSURGO –Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Web Soil Survey. Accessed 3/2016, 30 m resolution

GAP Level 3 Ecological System Boundaries (downloaded 4/ 2016) NOAA CCAP Coastal Change Analysis Program Regional Land Cover and Change–downloaded by state (3/2016) from: C-CAP FTP Tool, see Description of this 30 m resolution, 2010 edition of data

NHD USGS National Hydrography Dataset

TNC Terrestrial Ecoregions (downloaded 3/2016)

2015 LCC Network Areas

Evaluation:

The creation of a national core quality index is a very ambitious objective, given the extreme variability in ecosystem conditions across the United States. The additional attributes were intended to provide flexibility in accommodating regional or local environmental differences across the U.S.

Scripts for constructing local cores and scoring them using the Green Infrastructure Center’s methodology are available on Esri's Green Infrastructure web site.

Two general approaches were used in the developing core quality index values. The first (default) follows the guidance of the Green Infrastructure Center’s scoring approach developed for the southeastern US where size of the core is the primary determinant of quality. The second; Bio-Weights puts more emphasis on bio-diversity and uniqueness ecosystem type and de-emphasizes slightly the importance of core size. This is to compensate for the very large intact core habitat areas in the west and southwest which also have comparatively low biodiversity values.

Scoring values:

Default Weights

0.4, # Acres0.1, # THICKNESS0.05, # TOPOGRAPHIC DIVERSITY (Standard Deviation)0.1, # Biodiversity Priority Index (SPECIES RICHNESS in GIC original version)0.05, # PERCENTAGE WETLAND COVER0.03, # Ecological Land Unit – Shannon-Weaver Index (SOIL VARIETY in GIC original version)0.02, # COMPACTNESS RATIO (AREA RELATIVE TO THE AREA OF A CIRCLE WITH THE SAME PERIMETER LENGTH)0.1, # STREAM DENSITY (LINEAR FEET/ACRE)0.05, # Ecological System Redundancy (RARE/THREATENED/ENDANGERED SPECIES ABUNDANCE (Number of occurrences) in GIC original version) 0.1, # Endemic Species Max (RARE/THREATENED/ENDANGERED SPECIES DIVERSITY (Number of unique species in a core) in GIC original version)

Bio-Weights

0.2, # Acres0.1, # THICKNESS0.05, # TOPOGRAPHIC DIVERSITY (Standard Deviation)0.25, # Biodiversity Priority Index (SPECIES RICHNESS in GIC original version)0.05, # PERCENTAGE WETLAND COVER0.03, # Ecological Land Unit – Shannon-Weaver Index (SOIL VARIETY in GIC original version)0.02, # COMPACTNESS RATIO (AREA RELATIVE TO THE AREA OF A CIRCLE WITH THE SAME PERIMETER LENGTH)0.1, # STREAM DENSITY (LINEAR FEET/ACRE)0.1, # Ecological System Redundancy (RARE/THREATENED/ENDANGERED SPECIES ABUNDANCE (Number of occurrences) in GIC original version) 0.1, # Endemic Species Max (RARE/THREATENED/ENDANGERED SPECIES DIVERSITY (Number of unique species in a core) in GIC original version)

Digital Elevation Models (DEM) are widely used to derive information for the modeling of hydrologic processes. The basic model for hydrologic terrain analysis involving hydrologic conditioning, determination of flow field (flow directions) and derivation of hydrologic derivatives is available in multiple software packages and GIS systems. However as areas of interest for terrain analysis have increased and DEM resolutions become finer there remain challenges related to data size, software and a platform to run it on, as well as opportunities to derive new kinds of information useful for hydrologic modeling. This presentation will illustrate new functionality associated with the TauDEM software (http://hydrology.usu.edu/taudem) and new web based deployments of TauDEM to make this capability more accessible and easier to use. Height Above Nearest Drainage (HAND) is a special case of distance down the flow field to an arbitrary target, with the target being a stream and distance measured vertically. HAND is one example of a general class of hydrologic proximity measures available in TauDEM. As we have implemented it, HAND uses multi-directional flow directions derived from a digital elevation model (DEM) using the Dinifinity method in TauDEM to determine the height of each grid cell above the nearest stream along the flow path from that cell to the stream. With this information, and the depth of flow in the stream, the potential for, and depth of flood inundation can be determined. Furthermore, by dividing streams into reaches or segments, the area draining to each reach can be isolated and a series of threshold depths applied to the grid of HAND values in that isolated reach catchment, to determine inundation volume, surface area and wetted bed area. Dividing these by length yields reach average cross section area, width, and wetted perimeter, information that is useful for hydraulic routing and stage-discharge rating calculations in hydrologic modeling. This presentation will describe the calculation of HAND and its use to determine hydraulic properties across the US for prediction of stage and flood inundation in each NHDPlus reach modeled by the US NOAA’s National Water Model. This presentation will also describe two web based deployments of TauDEM functionality. The first is within a Jupyter Notebook web application attached to HydroShare that provides users the ability to execute TauDEM on this cloud infrastructure without the limitations associated with desktop software installation and data/computational capacity. The second is a web based rapid watershed delineation function deployed as part of Model My Watershed (https://app.wikiwatershed.org/) that enables delineation of watersheds, based on NHDPlus gridded data anywhere in the continental US for watershed based hydrologic modeling and analysis.

Presentation for European Geophysical Union Meeting, April 2018, Vienna. Tarboton, D. G., N. Sazib, A. Castronova, Y. Liu, X. Zheng, D. Maidment, A. Aufdenkampe and S. Wang, (2018), "Hydrologic Terrain Analysis Using Web Based Tools," European Geophysical Union General Assembly, Vienna, April 12, Geophysical Research Abstracts 20, EGU2018-10337, https://meetingorganizer.copernicus.org/EGU2018/EGU2018-10337.pdf.

Body size distributions of modern mammals differ with spatial scale; continental faunas are right-skewed and highly modal, whereas local faunas are essentially uniform. Modal-sized species turn over more rapidly across space, while larger and smaller species occur repeatedly in different local faunas. Such patterns imply that body size mediated competition plays an important role in the structuring of local communities. Because there were considerable changes in the distributions of species, both terrestrial and marine, in response to the last glaciation cycle, the Late Pleistocene of North America is an ideal system with which to examine the role of body size in the structuring of local communities through time. I examined the body size distributions of local assemblages of mammals across the last 40000 yrs. Faunal lists for localities were taken from FAUNMAP along with the age and geographic position of the locality. Body size data were taken from a compilation by Smith et al., 2003. The distributions were evaluated for biases with respect to sampling and degree of time averaging. Localities with less than 20 species tended to cover a smaller range of body sizes and were eliminated from analyses. Data for modern localities were taken from Brown and Nicoletto (1991). Body size distributions of Late Pleistocene localities were compared to continental distributions and uniform distributions using Kolmorgorov-Smirnov two-sample tests. Because of the extinction of megafauna 10000 yra, the continental distribution used for comparison differed depending upon the age of the locality. A majority of the localities were significantly different from their continental distribution, and were not different from a uniform distribution. Given the considerable turnover in local community composition during the last glaciation, the similarity between the body size distributions of Late Pleistocene communities and modern communities implies that body size plays a key role in local community structure.

Maps of the number, size, and species of trees in forests across the western United States are desirable for many applications such as estimating terrestrial carbon resources, predicting tree mortality following wildfires, and for forest inventory. However, detailed mapping of trees for large areas is not feasible with current technologies, but statistical methods for matching the forest plot data with biophysical characteristics of the landscape offer a practical means to populate landscapes with a limited set of forest plot inventory data. We used a modified random forests approach with Landscape Fire and Resource Management Planning Tools (LANDFIRE) vegetation and biophysical predictors as the target data, to which we imputed plot data collected by the USDA Forest Service’s Forest Inventory Analysis (FIA) to the landscape at 30-meter (m) grid resolution (Riley et al. 2016). This method imputes the plot with the best statistical match, according to a “forest” of decision trees, to each pixel of gridded landscape data. In this work, we used the LANDFIRE data set as the gridded target data because it is publicly available, offers seamless coverage of variables needed for fire models, and is consistent with other data sets, including burn probabilities and flame length probabilities generated for the continental United States. The main output of this project (the GeoTIFF available in this data publication) is a map of imputed plot identifiers at 30×30 m spatial resolution for the western United States for landscape conditions circa 2009. The map of plot identifiers can be linked to the FIA databases available through the FIA DataMart or to the ACCDB/CSV files included in this data publication to produce tree-level maps or to map other plot attributes. These ACCDB/CSV files also contain attributes regarding the FIA PLOT CN (a unique identifier for each time a plot is measured), the inventory year, the state code and abbreviation, the unit code, the county code, the plot number, the subplot number, the tree record number, and for each tree: the status (live or dead), species, diameter, height, actual height (where broken), crown ratio, number of trees per acre, and a unique identifier for each tree and tree visit. Application of the dataset to research questions other than those related to aboveground biomass and carbon should be investigated by the researcher before proceeding. The dataset may be suitable for other applications and for use across various scales (stand, landscape, and region), however, the researcher should test the dataset's applicability to a particular research question before proceeding.Geospatial data describing tree species or forest structure are required for many analyses and models of forest landscape dynamics. Forest data must have resolution and continuity sufficient to reflect site gradients in mountainous terrain and stand boundaries imposed by historical events, such as wildland fire and timber harvest. Such detailed forest structure data are not available for large areas of public and private lands in the United States, which rely on forest inventory at fixed plot locations at sparse densities. While direct sampling technologies such as light detection and ranging (LiDAR) may eventually make broad coverage of detailed forest inventory feasible, no such data sets at the scale of the western United States are currently available.When linking the tree list raster (“CN_text” field) to the FIA data via the plot CN field (“CN” in the “PLOT” table and “PLT_CN” in other tables), note that this field is unique to a single visit to a plot. The raster contains a “Value” field, which also appears in the ACCDB/CSV files in the “tl_id” field in order to facilitate this linkage. All plot CNs utilized in this analysis were single condition, 100% forested, physically located in the Rocky Mountain Research Station (RMRS) and Pacific Northwest Research Station (PNW) obtained from FIA in December of 2012.

Original metadata date was 01/03/2018. Minor metadata updates made on 04/30/2019.

The results of the sediment size analysis performed by the U.S.Naval Oceanographic Office Geological Laboratory for six Phleger gravity cores are presented in this data layer. Some of the data in this set were originally released as part of the Deck 41 Database available from the National Geophysical Data Center. The attribute for sediment classification was added by the compilers to make these size data more useful for mapping the regional surficial sediment distribution.

This study was undertaken to provide information on the characteristics and distribution of surficial sediments off the eastern United States. Accordingly, long traverses were run across the continental shelf and in most case carrying over the shelf break. This data set includes data from those 9 traverses which were conducted north of Virginia. These data constitute the first systematic sampling of the U.S. Atlantic margin to show the effects of environmental factors (e.g. increasing distance from shore, water depth) on the sediment distribution. Sampling was performed with a primitive grab sampler; navigational methods were not discussed in this report.