The United States State Boundaries database is a geographic database of state political boundaries. The database includes boundaries for all 50 states plus Puerto Rico, Washington D.C., American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands. In order for others to use the information in the Census MAF/TIGER database in a geographic information system (GIS) or for other geographic applications, the Census Bureau releases to the public extracts of the database in the form of TIGER/Line Shapefiles. State boundaries with shorelines cut in. The State Boundary with Detailed Shorelines database was created using TIGER/LINE 2011 shapefile data gathered from ESRI's Geography Network. The individual county shapefiles were processed into Arc/Info coverages and then appended together to create complete state coverages. OST-R/BTS Hydrographic data was integrated to create detailed shorelines. The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. States and equivalent entities are the primary governmental divisions of the United States. In addition to the fifty States, the Census Bureau treats the District of Columbia, Puerto Rico, and each of the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands) as the statistical equivalents of States for the purpose of data presentation.

description: State boundaries with shorelines cut in (NTAD). The State Boundary with Detailed Shorelines database was created using TIGER/LINE 2011 shapefile data gathered from ESRI's Geography Network. The individual county shapefiles were processed into Arc/Info coverages and then appended together to create complete state coverages. OST-R/BTS Hydrographic data was integrated to create detailed shorelines. The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. States and equivalent entities are the primary governmental divisions of the United States. In addition to the fifty States, the Census Bureau treats the District of Columbia, Puerto Rico, and each of the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands) as the statistical equivalents of States for the purpose of data presentation.; abstract: State boundaries with shorelines cut in (NTAD). The State Boundary with Detailed Shorelines database was created using TIGER/LINE 2011 shapefile data gathered from ESRI's Geography Network. The individual county shapefiles were processed into Arc/Info coverages and then appended together to create complete state coverages. OST-R/BTS Hydrographic data was integrated to create detailed shorelines. The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. States and equivalent entities are the primary governmental divisions of the United States. In addition to the fifty States, the Census Bureau treats the District of Columbia, Puerto Rico, and each of the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands) as the statistical equivalents of States for the purpose of data presentation.

The National Estuarine Research Reserve System is a network of 29 estuarine areasâ places where freshwater from the land mixes with saltwater from the seaâ established across the nation for long-term research, education, and coastal stewardship.

State boundaries with political limit - boundaries extending into the ocean. The State Boundary with Detailed Shorelines database was created using TIGER/LINE 2011 shapefile data gathered from ESRI's Geography Network. The individual county shapefiles were processed into Arc/Info coverages and then appended together to create complete state coverages. OST-R/BTS Hydrographic data was integrated to create detailed shorelines. The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. States and equivalent entities are the primary governmental divisions of the United States. In addition to the fifty States, the Census Bureau treats the District of Columbia, Puerto Rico, and each of the Island Areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the U.S. Virgin Islands) as the statistical equivalents of States for the purpose of data presentation.

The rainfall-runoff erosivity factor (R-Factor) quantifies the effects of raindrop impacts and reflects the amount and rate of runoff associated with the rain. The R-factor is one of the parameters used by the Revised Unified Soil Loss Equation (RUSLE) to estimate annual rates of erosion. This product is a raster representation of R-Factor derived from isoerodent maps published in the Agriculture Handbook Number 703 (Renard et al.,1997). Lines connecting points of equal rainfall ersoivity are called isoerodents. The iserodents plotted on a map of the coterminous U.S. were digitized, then values between these lines were obtained by linear interpolation. The final R-Factor data are in raster GeoTiff format at 800 meter resolution in Albers Conic Equal Area, GRS80, NAD83.

The American Mosaic Project (AMP) is a research initiative housed at the University of Minnesota aiming to contribute to an understanding of what brings Americans together, what divides Americans, and the implications of American diversity for political and civic life. With support from the National Science Foundation, the AMP designed the Boundaries in the American Mosaic Survey (BAM), focusing on Americans' attitudes towards racial and religious diversity. This survey was fielded to a nationally representative sample in the early spring of 2014.

This map shows the access to mental health providers in every county and state in the United States according to the 2024 County Health Rankings & Roadmaps data for counties, states, and the nation. It translates the numbers to explain how many additional mental health providers are needed in each county and state. According to the data, in the United States overall there are 319 people per mental health provider in the U.S. The maps clearly illustrate that access to mental health providers varies widely across the country.The data comes from this County Health Rankings 2024 layer. An updated layer is usually published each year, which allows comparisons from year to year. This map contains layers for 2024 and also for 2022 as a comparison.County Health Rankings & Roadmaps (CHR&R), a program of the University of Wisconsin Population Health Institute with support provided by the Robert Wood Johnson Foundation, draws attention to why there are differences in health within and across communities by measuring the health of nearly all counties in the nation. This map's layers contain 2024 CHR&R data for nation, state, and county levels. The CHR&R Annual Data Release is compiled using county-level measures from a variety of national and state data sources. CHR&R provides a snapshot of the health of nearly every county in the nation. A wide range of factors influence how long and how well we live, including: opportunities for education, income, safe housing and the right to shape policies and practices that impact our lives and futures. Health Outcomes tell us how long people live on average within a community, and how people experience physical and mental health in a community. Health Factors represent the things we can improve to support longer and healthier lives. They are indicators of the future health of our communities.Some example measures are:Life ExpectancyAccess to Exercise OpportunitiesUninsuredFlu VaccinationsChildren in PovertySchool Funding AdequacySevere Housing Cost BurdenBroadband AccessTo see a full list of variables, definitions and descriptions, explore the Fields information by clicking the Data tab here in the Item Details of this layer. For full documentation, visit the Measures page on the CHR&R website. Notable changes in the 2024 CHR&R Annual Data Release:Measures of birth and death now provide more detailed race categories including a separate category for ‘Native Hawaiian or Other Pacific Islander’ and a ‘Two or more races’ category where possible. Find more information on the CHR&R website.Ranks are no longer calculated nor included in the dataset. CHR&R introduced a new graphic to the County Health Snapshots on their website that shows how a county fares relative to other counties in a state and nation. Data Processing:County Health Rankings data and metadata were prepared and formatted for Living Atlas use by the CHR&R team. 2021 U.S. boundaries are used in this dataset for a total of 3,143 counties. Analytic data files can be downloaded from the CHR&R website.

This package consists of a geo-spatial data file, specifically, an ESRI polygon shape file, containing approximately 1700 polygons depicting the Bailey's Eco Regions classification system. (Consult http://www.nationalatlas.gov/mld/ecoregp.html for a summary of this system). Also included is an R-language script that applies a regularly-spaced point grid on top of the polygon map, and produces a geospatial point file containing samples of the Bailey's region code at each point in the grid.

The Fireshed Registry is a geospatial dashboard and decision tool built to organize information about wildfire transmission to communities and monitor progress towards risk reduction for communities from management investments. The concept behind the Fireshed Registry is to identify and map the source of risk rather than what is at risk across all lands in the continental United States and Hawaii. While the Fireshed Registry was organized around mapping the source of fire risk to communities, the framework does not preclude the assessment of other resource management priorities and trends such as water, fish and aquatic or wildlife habitat, or recreation. The Fireshed Registry is also a multi-scale decision tool for quantifying, prioritizing, and geospatially displaying wildfire transmission to buildings in adjacent or nearby communities.

Fireshed areas in the Fireshed Registry are approximately 250,000-acre accounting units that are delineated based on smoothed building exposure maps of the continental United States and Hawaii. These boundaries were created by dividing up the landscape into regular-sized units that represent similar source levels of community exposure to wildfire risk. Subfiresheds are approximately 25,000-acre accounting units nested within firesheds. This data publication includes three separate geodatabases, one for the conterminous United States (CONUS), one for Alaska, and one for Hawaii. All three geodatabases contain both firesheds and subfiresheds. See metadata for the individual geodatabases and feature classes for more details.The fireshed and subfireshed boundaries are designed to delineate hotspots of fire transmission to adjacent or nearby communities to facilitate cohesive cross-boundary risk mitigation planning.This data publication is the fourth edition of the Fireshed Registry. The first edition included the conterminous United States (https://doi.org/10.2737/RDS-2020-0054). The second edition (https://doi.org/10.2737/RDS-2020-0054-2) was created to include firesheds and project areas for Alaska. The third edition (https://doi.org/10.2737/RDS-2020-0054-3) changed the name of fireshed "project areas" to "subfiresheds".

This fourth edition includes firesheds and subfiresheds for Hawaii; corrects 23 duplicated fireshed names in the CONUS dataset; updates 89 additional names in the CONUS dataset so that the named place used as the fireshed name is actually located in the state having the fireshed's majority area; and removes building exposure and disturbance estimates from the attribute tables of the firesheds and subfiresheds. The smoothed building exposure rasters for the continental United States that were used to calculate the exposure attributes can be found in the data publication https://doi.org/10.2737/RDS-2022-0015-3. The Alaska dataset is unchanged from previous versions.

Shapefile for 492 Coastal Zone Management Program (CZMP) counties and county equivalents, 2009, extracted from the U.S. Census Bureau's MAF/TIGER database of U.S. counties and cross-referenced to a list of CZMP counties published by the NOAA/NOS Office of Ocean and Coastal Resource Management (OCRM). Data extent to the nearest quarter degree is 141.00 E to 64.50 W longitude and 14.75 S to 71.50 N latitude. TL2009 in this document refers to metadata content inherited from the original U.S. Census Bureau (2009) TIGER/Line shapefile. TL2009: The TIGER/Line Shapefiles are an extract of selected geographic and cartographic information from the Census MAF/TIGER database. The Census MAF/TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each TIGER/Line Shapefile is designed to stand alone as an independent dataset or the shapefiles can be combined to cover the whole nation.

SPACE USE INDEX CALCULATIONLek coordinates and associated trend count data were obtained from the 2013 Nevada Sage-grouse Lek Database compiled by the Nevada Department of Wildlife (NDOW, S. Espinosa, 9/10/2013). 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. 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’. 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 (2009 – 2013) for the number of males grouse (or unknown gender if males were not identified) attending each lek was calculated. The final dataset comprised 907 leks. 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 (2009 – 2013) 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 clipped by the SGMA polygon, and values were 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 clipped by the SGMA polygon, and re-scaled between zero and one by dividing by the maximum pixel value.A Spatial Use Index (SUI) was calculated taking the average of the lek utilization distribution and non-linear distance to lek rasters in ArcGIS, and re-scaled between zero and 1 by dividing by the maximum pixel value.The volume of the SUI at cumulative 5% increments (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 relationship between percent land area within each isopleth and isopleth volume (VanderWal and Rodgers 2012) indicated statistically concentrated use at the 70% 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 SGMA clipped by the Nevada state boundary, which only included habitat within the state of Nevada.Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online)REFERENCES Beyer HL. 2012. Geospatial Modelling Environment (Version 0.7.2.0). http://www.spatialecology.com/gmeCoates PS, Casazza ML, Blomberg EJ, Gardner SC, Espinosa SP, Yee JL, Wiechman L, Halstead BJ. 2013. “Evaluating greater sage-grouse seasonal space use relative to leks: Implications for surface use designations in sagebrush ecosystems.” The Journal of Wildlife Management 77: 1598-1609.Doherty KE, Tack JD, Evans JS, Naugle DE. 2010. Mapping breeding densities of greater sage-grouse: A tool for range-wide conservation planning. Bureau of Land Management. Report Number: L10PG00911. Accessed at: http://www.conservationgateway.org/ConservationByGeography/NorthAmerica/Pages/sagegrouse.aspx# Duong T. 2012. ks: Kernel smoothing. R package version 1.8.10. http://CRAN.R-project.org/package=ksHorne JS, Garton EO. 2006. “Likelihood cross-validation versus least squares cross-validation for choosing the smoothing parameter in kerne... Visit https://dataone.org/datasets/e12d29b0-83eb-40fa-bc71-b0c3d1c616df for complete metadata about this dataset.

Zip archive of files (one per state and sex) of county-level life tables from 1928-2019 by 5yr age group in R-binary (.rds) format. We group low-population counties and geographically coterminous neighbors (1084 total) together into county-groups (401 total) with historically consistent boundaries that exceed a minimum population threshold of 10,000 at any time point in our series. County groupings never cross state borders. 2071 counties are left ungrouped. A comma-separated variable lookup table linking the 1084 individual counties (by FIPS code) to the 401 groups are available in the USMDBcountyGroupings.csv. The individual counties in the life table files are identified by their FIPS code.

This CD consists of the TIGER/Line Census Files, 1990. The type of File is geographic. The 1990 Census TIGER/Line file is an extract of selected geographic and cartographic information from the Census Bureau's TIGER data base. The Census Bureau is releasing the 1990 Census TIGER/Line files to provide data users with the final 1990 census boundaries (including voting districts) and to support the 1990 Census Data Products Program. The 1990 Census TIGER/Line file provides digital data for all 1 990 census map features and boundaries, the associated 1990 census final tabulation geographic area codes (such as 1990 census block numbers), and the codes for the January 1, 1990 legal and statistical areas on both sides of each line segment of every mapped feature. This version also contains the final voting district codes and the 1990 census designated place codes. The 1990 Census TIGER/Line file contains basic information for 1990 census geographic area codes, basic map features and their names, and address ranges in the form of 12 'Record Types.' The record types are as follows: 1. Basic Data Records (Individual Feature Segment Records) 2. Shape Coordinate Points (Feature Shape Records) 3. Additional Decennial Census Geographic Area Codes 4. Index to Alternate Feature Names 5. Feature Name List 6. Additional Address Range and ZIP Code(2) Information 7. Landmark Features 8. Area Landmarks A. Additional Polygon Geographic Area Codes I. Area Boundaries P. Polygon Location R. Record Number Range Each segment record contains appropriate decennial census and, when appropriate, FIPS(1) geographic area codes, latitude/longitude coordinates for all line segments and point features, the name of the feature Geographic Coverage: The 1990 Census TIGER/Line files cover the entire United States, Puerto Rico, the Virgin Islands of the United States, American Samoa, Guam, the Northern Mariana Islands, Palau, the other Pacific entities that were part of the Trust Territory of the United States for the 1980 census (the Marshall Islands and the Federated States of Micronesia), and the Midway Islands (to p rovide complete mapping within the boundaries of the State of Hawaii). The data in the 1990 Census TIGER/Line files include information comparable to what was in the 1980 GBF/DIME-Files, which covered roughly 2 percent of the land area of the United States. The remaining 98 percent of the land area has been added using data that originated with the U.S. Geological Survey (USGS) based on the 1:100,000-scale USGS maps, supplemented with Census Bureau-compiled information and from other USGS map sheets. NOSB= Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

This metadata report documents a shapefile of watershed boundaries for 84 selected United States Geological Survey (USGS) water quality stations that are part of the Chesapeake Bay non-tidal network (NTN).

This is a polygon coverage of major land uses in the United States. The source of the coverage is the map of major land uses in the National Atlas, pages 158-159, which was adapted from U.S. Department of Agriculture, "Major Land Uses in the United States," by Francis J. Marschner, revised by James R. Anderson, 1967.

Data presented here include a shapefile that combines fault data for the United States and Canada (Chorlton, 2007; Reed and others, 2005; Styron and Pagani, 2020) and a shapefile of faults for Australia (Chorlton, 2007; Raymond and others, 2012; Styron and Pagani, 2020). These two shapefiles were used as an evidential layer to evaluate the mineral prospectivity for sediment-hosted Pb-Zn deposits (Lawley and others, 2022). References Chorlton, L.B., 2007, Generalized geology of the world: Bedrock domains and major faults in GIS format: a small-scale world geology map with an extended geological attribute database: Geological Survey of Canada Open File 5529, https://doi.org/10.4095/223767. Lawley, C.J.M., McCafferty, A.E., Graham, G.E., Huston, D.L., Kelley, K.D., Czarnota, K., Paradis, S., Peter, J.M., Hayward, N., Barlow, M., Emsbo, P., Coyan, J., San Juan, C.A., and Gadd, M.G., 2022, Data-driven prospectivity modelling of sediment-hosted Zn-Pb mineral systems and their critical raw materials: Ore Geology Reviews, v. 141, no. 104635, https://doi.org/10.1016/j.oregeorev.2021.104635. Raymond, O.L., Liu, S., Gallagher, R., Zhang, W., and Highet, L.M., 2012, Surface Geology of Australia 1:1 million scale dataset 2012 edition: Geoscience Australia, http://pid.geoscience.gov.au/dataset/ga/74619. Reed, J.C., Jr., Wheeler, J.O., Tucholke, B.E., Stettner, W.R., and Soller, D.R., 2005, Decade of North American Geology Geologic Map of North America - Perspectives and explanation: Geological Society of America, v. 1, https://doi.org/10.1130/DNAG-CSMS-v1. Styron, R., and Pagani, M., 2020, The GEM global active faults database: Earthquake Spectra, v. 36, p. 160-180, https://doi.org/10.1177/8755293020944182.

This image dataset details the U.S. Commonwealth of Puerto Rico above-ground forest biomass (AGB) (baseline 2000) developed by the United States (US) Environmental Protection Agency (EPA). The USEPA AGB product (15 m) was created to support the development of landscape watershed predictor metrics for sediment and nutrient loadings associated with stream reaches. Above-ground forest biomass was estimated at a 15 m spatial resolution implementing methodology first posited by the Woods Hole Research Center where they developed the National Biomass and Carbon Dataset (NBCD2000) ─ an above-ground forest biomass map (30 m) for the conterminous United States. For EPA’s effort, spatial predictor layers for AGB estimation included derived products from the United States Geologic Survey (USGS) National Land Cover Dataset 2001 (NLCD) cover type and tree canopy density data, the USGS Gap Analysis Program (GAP) forest type classification data, USGS National Elevation Dataset (NED) topographic data, and the National Aeronautical and Space Administration’s (NASA’s) Shuttle Radar Topography Mission (SRTM) tree height data. These predictor variables and Forest Inventory and Analysis (FIA) response variables (observed canopy height and AGB) were related through multivariate tree-based regression models. Units for this AGB map are in Mg/ha for each 15m pixel. Mean biomass (forest only) for the 15 m pixels was 72.59 Mg/ha (σ = 26.83). This estimate is close in agreement to an assessment of structure and condition of PR forests (2003) (Brandeis, 2006) where mean AGB was estimated at 80 Mg/ha. Brandeis, T.J., M.B. Delaney, R. Parresol, L. Royer, 2006. Development of equations for predicting Puerto Rican subtropical dry forest biomass and volume, Forest Ecology and Management, 233:133-142. This dataset is not publicly accessible because: This data exceeds one GB in size and cannot be stored directly on ScienceHub. It can be accessed through the following means: ftp://newftp.epa.gov/Exposure/A-tqkc/. Format: This dataset is in an ERDAS Imagine *.img format which is easily converted to other formats in software packages such as ESRI ArcMap. This dataset is associated with the following publication: Iiames , J., J. Riegel, and R. Lunetta. The Development and Evaluation of a High-Resolution Above Ground Biomass Product for the Commonwealth of Puerto Rico (2000). Ecosystem Services. Elsevier Online, New York, NY, USA, 83(4): 293-306, (2017).