Digital Divide Index normalized to the United States national DDI dataset managed by Purdue University. This layer is displayed for Washington State.

THE U.S. HISTORY GEOINQUIRY COLLECTIONhttp://www.esri.com/geoinquiriesTo support Esri’s involvement in the White House ConnectED Initiative, GeoInquiry instructional materials using ArcGIS Online for Earth Science education are now freely available. The U.S. History GeoInquiry collection contains 15 free, web-mapping activities that correspond and extend map-based concepts in leading high school U.S. History textbooks. The activities use a standard inquiry-based instructional model, require only 15 minutes for a teacher to deliver, and are device agnostic. The activities harmonize with the C3 curriculum standards for social studies education. Activity topics include:· The Great Exchange· The 13 Colonies - 1700s· The War Before Independence (The American Revolution)· The War of 1812· Westward, ho! (Trails west)· The Underground Railroad· From Compromise to Conflict· A nation divided: The Civil War· Native American Lands· Steel and the birth of a city (natural resources)· World War I· Dust Bowl· A day that lived in infamy (Pearl Harbor)· Operation Overlord - D-Day· Hot spots in the Cold WarTeachers, GeoMentors, and administrators can learn more at http://www.esri.com/geoinquiries.

Metadata record for the dataset depicting the Continental Divide of the United States at the scale 1:2,000,000; link to zip file download in record.

This line shapefile represents the Continental Divide of the United States. The map layer was created by extracting Hydrologic Unit Boundary line features from an existing National Atlas layer. The source data are aligned with the individual 1:2,000,000-scale Digital Line Graph (DLG) hypsography files produced by the U.S. Geological Survey. This layer is part of the 1997-2014 edition National Atlas of the United States.

This map layer depicts the climate zone designations used by the U.S. Department of Energy Building America Program by county boundaries (generalized version). It is intended as an aid in helping builders to identify the appropriate climate designation for the counties in which they are building. The guide can be used in conjunction with guidance in the Building America Solution Center and the Best Practices builders’ guides produced by the DOE Building America Program to help builders determine which climate-specific guidance they should use. This data for this layer is taken from Building America Best Practices Series, Volume 7.3 - Guide to Determining Climate Regions by County. The eight U.S. Building America climate regions described here are based on the climate designations used by the International Energy Conservation Code (IECC) and the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE). The IECC climate zone map was developed by DOE researchers at Pacific Northwest National Laboratory with input from Building America team members, in particular Joseph Lstiburek of Building Science Corporation.a,b The IECC map was developed to provide a simplified, consistent approach to defining climate for implementation of various codes; it was based on widely accepted classifications of world climates that have been applied in a variety of different disciplines. The PNNL-developed map was adopted by the IECC and was first included in the IECC in the 2004 Supplement to the IECC. It first appeared in ASHRAE 90.1 in the 2004 edition. The IECC map divided the United States into eight temperatureoriented climate zones. These zones are further divided into three moisture regimes designated A, B, and C. Thus the IECC map allows for up to 24 potential climate designations. In 2003, with direction from the Building America teams, researchers at DOE’s National Renewable Energy Laboratory simplified the IECC map for purposes of the Building America Program, into eight climate zones. For reporting purposes, these are further combined into five climate categories: Hot-humid,hot-dry/mixed drymixed-humidmarinecold/very coldsubarctic.The Building America and IECC climate maps are shown in Figures 1 and 2. The climate regions are described below. Climate zone boundaries follow county boundary lines. A listing of counties comprising each climate zone is provided below, beginning on page 5. The climate region definitions are based on heating degree days, average temperatures, and precipitation as follows:Hot-HumidA hot-humid climate is defined as a region that receives more than 20 inches (50 cm) of annual precipitation and where one or both of the following occur:• A 67°F (19.5°C) or higher wet bulb temperature for 3,000 or more hours during the warmest six consecutive months of the year; or• A 73°F (23°C) or higher wet bulb temperature for 1,500 or more hours during the warmest six consecutive months of the year.The Building America hot-humid climate zone includes the portions of IECC zones 1, 2, and 3 that are in the moist category (A) below the “warm-humid” line shown on the IECC map. Mixed-HumidA mixed-humid climate is defined as a region that receives more than 20 inches (50 cm) of annual precipitation, has approximately 5,400 heating degree days (65°F basis) or fewer, and where the average monthly outdoor temperature drops below 45°F (7°C) during the winter months.The Building America mixed-humid climate zone includes the portions of IECC zones 4 and 3 in category A above the “warmhumid” line. Hot-DryA hot-dry climate is defined as a region that receives less than 20 inches (50 cm) of annual precipitation and where the monthly average outdoor temperature remains above 45°F (7°C) throughout the year.The Building America hot-dry climate zone corresponds to the portions of IECC zones 2 and 3 in the dry category.Mixed-Dry A mixed-dry climate is defined as a region that receives less than 20 inches (50 cm) of annual precipitation, has approximately 5,400 heating degree days (65°F basis) or less, and where the average monthly outdoor temperature drops below 45°F (7°C) during the winter months.The Building America mixed-dry climate zone corresponds to IECC climate zone 4 B (dry).Cold A cold climate is defined as a region with between 5,400 and 9,000 heating degree days (65°F basis).The Building America cold climate corresponds to the IECC climate zones 5 and 6.Very-Cold A very cold climate is defined as a region with between 9,000 and 12,600 heating degree days (65°F basis).The Building America very cold climate corresponds to IECC climate zone 7.SubarcticA subarctic climate is defined as a region with 12,600 heating degree days (65° basis) or more. The only subarctic regions in the United States are in found Alaska, which is not shown in Figure 1.The Building America subarctic climate zone corresponds to IECC climate zone 8.Marine A marine climate is defined as a region that meets all of the following criteria: • A coldest month mean temperature between 27°F (-3°C) and 65°F (18°C)• A warmest month mean of less than 72°F (22°C)• At least 4 months with mean temperatures higher than 50°F (10°C)• A dry season in summer. The month with the heaviest precipitation in the cold season has at least three times as much precipitation as the month with the least precipitation in the rest of the year. The cold season is October through March in the Northern Hemisphere and April through September in the Southern Hemisphere.The Building America marine climate corresponds to those portions of IECC climate zones 3 and 4 located in the “C” moisture category. Building America and IECC Climate ZonesThe table below shows the relationship between the Building America and IECC climate zones.

Building America
IECC


Subarctic
Zone 8


Very Cold
Zone 7


Cold
Zone 5 and 6


Mixed-Humid
4A and 3A counties above warm-humid line


Mixed-Dry
Zone 4B


Hot-Humid
2A and 3A counties below warm-humid line


Hot-Dry
Zone 3B


Marine
All counties with a “C” moisture regime

Index map page boundaries of the Mat-Su Borough Tax Map Page Index. The entire Borough is divided into a series of "base maps" and "index" or "grid maps". Base maps are given names that represent the geographical area represented (similar to USGS quad mapping) and index maps are numbered sequentially within the base map. The result is a base map with a two-character name (for example: ("WA" for Wasilla) and numbered index maps (usually numbered "1" thru "16"). The Mat-Su Borough tax map set is published using these pages.

In this dataset we present two maps that estimate the _location and population served by domestic wells in the contiguous United States. The first methodology, called the “Block Group Method” or BGM, builds upon the original block-group data from the 1990 census (the last time the U.S. Census queried the population regarding their source of water) by incorporating higher resolution census block data. The second methodology, called the “Road-Enhanced Method” or REM, refines the locations by using a buffer expansion and shrinkage technique along roadways to define areas where domestic wells exist. The fundamental assumption with this method is that houses (and therefore domestic wells) are located near a named road. The results are presented as two nationally consistent domestic-well population datasets. While both methods can be considered valid, the REM map is more precise in locating domestic wells; the REM map had a smaller amount of spatial bias (nearly equal vs biased in type 1 error), total error (10.9% vs 23.7%,), and distance error (2.0 km vs 2.7 km), when comparing the REM and BGM maps to a California calibration map. However, the BGM map is more inclusive of all potential locations for domestic wells. The primary difference in the BGM and the REM is the mapping of low density areas. The REM has a 57% reduction in areas mapped as low density (populations greater than 0 but less than 1 person per km), concentrating populations into denser regions. Therefore, if one is trying to capture all of the potential areas of domestic-well usage, then the BGM map may be more applicable. If _location is more imperative, then the REM map is better at identifying areas of the landscape with the highest probability of finding a domestic well. Depending on the purpose of a study, a combination of both maps can be used. For space concerns, the datasets have been divided into two separate geodatabases. The BGM map geodatabase and the REM map database.

The typical statewide or county-wide red/blue map (shown at left) depicts presidential voting results on a winner-take-all basis, so they award an entire geographical area to the Republican or Democratic candidate no matter how close the actual vote tally The large map in the attachment factors in both the percentage of the popular vote won by each candidate as well as the population density of each county. So, the sparsely populated Great Plains and Rocky Mountain West are shown in a much lighter color than the Eastern Seaboard, and the map as a whole is more purple than either red or blue. Perhaps the United States is less divided than some maps would lead us to believe.

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 2022 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. This file depicts the shape of the United States clipped back to a generalized coastline. This nation layer covers the extent of the fifty states, 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) when scale appropriate.

The National Water Quality Network (NWQN) for Rivers and Streams includes 113 surface-water river and stream sites monitored by the U.S. Geological Survey (USGS) National Water Quality Program (NWQP). The NWQN represents the consolidation of four historical national networks: the USGS National Water-Quality Assessment (NAWQA) Project, the USGS National Stream Quality Accounting Network (NASQAN), the National Monitoring Network (NMN), and the Hydrologic Benchmark Network (HBN). The NWQN includes 22 large river coastal sites, 41 large river inland sites, 30 wadeable stream reference sites, 10 wadeable stream urban sites, and 10 wadeable stream agricultural sites. In addition to the 113 NWQN sites, 3 large inland river monitoring sites from the USGS Cooperative Matching Funds (Co-op) program are also included in this annual water-quality reporting Web site to be consistent with previous USGS studies of nutrient transport in the Mississippi-Atchafalaya River Basin. This data release contains geo-referenced digital data and associated attributes of watershed boundaries for 113 NWQN and 3 Co-op sites. Two sites, "Wax Lake Outlet at Calumet, LA"; 07381590, and "Lower Atchafalaya River at Morgan City, LA"; 07381600, are outflow distributaries into the Gulf of Mexico. Watershed boundaries were delineated for the portion of the watersheds between "Red River near Alexandria, LA"; 07355500 and "Atchafalaya River at Melville, LA"; 07381495 to the two distributary sites respectively. Drainage area was undetermined for these two distributary sites because the main stream channel outflows into many smaller channels so that streamflow is no longer relative to the watershed area. NWQN watershed boundaries were derived from the Watershed Boundary Dataset-12-digit hydrologic units (WBD-12). The development of the WBD-12 was a coordinated effort between the United States Department of Agriculture-Natural Resources Conservation Service (USDA-NRCS), the USGS, and the Environmental Protection Agency (EPA) (U.S. Department of Agriculture-Natural Resources Conservation Service, 2012). A hydrologic unit is a drainage area delineated to nest in a multi-level, hierarchical drainage system. Its boundaries are defined by hydrographic and topographic criteria that delineate an area of land upstream from a specific point on a river, stream or similar surface waters. The United States is divided and sub-divided into successively smaller hydrologic units identified by a unique hydrologic unit code (HUC) consisting of two to 12 digits based on the six levels of classification in the hydrologic unit system: regions, sub-regions, accounting units, cataloging units, watersheds, and sub-watersheds. NWQN watershed boundaries were delineated by selecting all sub-watershed polygons that flow into the most downstream WBD-12 polygon in which the NWQN site is located. The WBD-12 attribute table contains 8-digit, 10-digit, and 12-digit HUCs which were used to identify which sub-watersheds flow into the watershed pour point at the NWQN site location. When the NWQN site was located above the pour point of the most downstream sub-watershed, the sub-watershed was edited to make the NWQN site the pour point of that sub-watershed. To aid editing, USGS 1:24,000 digital topographic maps were used to determine the hydrologic divide from the sub-watershed boundary to the NWQN pour point. The number of sub-watersheds which are contained within the NWQN watersheds ranged from less than one to nearly 32,000 internal sub-watersheds. Internal sub-watershed boundaries were dissolved so that a single watershed boundary was generated for each NWQN watershed. Data from this release are presented at the USGS Tracking Water Quality page: http://cida.usgs.gov/quality/rivers/home (Deacon and others, 2015). Watershed boundaries delineated for this release do not take into account non-contributing area, diversions out of the watershed, or return flows into the watershed. Delineations are based solely on contributing WBD-12 polygons with modifications done only to the watershed boundary at the NWQN site location pour point. For this reason calculated drainage areas for these delineated watersheds may not match National Water Information System (MWIS) published drainage areas (http://dx.doi.org/10.5066/F7P55KJN). Deacon, J.R., Lee, C.J., Toccalino, P.L., Warren, M.P., Baker, N.T., Crawford, C.G., Gilliom, R.G., and Woodside, M.D., 2015, Tracking water-quality of the Nation’s rivers and streams, U.S. Geological Survey Web page: http://cida.usgs.gov/quality/rivers, https://dx.doi.org/doi:10.5066/F70G3H51. U.S. Department of Agriculture-Natural Resources Conservation Service, 2012, Watershed Boundary Dataset-12-digit hydrologic units: NRCS National Cartography and Geospatial Center, Fort Worth, Tex., WBDHU12_10May2012_9.3 version, accessed June 2012 at http://datagateway.nrcs.usda.gov.

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.

Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and ‘other’ as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and the National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) was used as the dependent variable. We were able to generate a NRT crop cover map by the first day of September through a process of incrementally removing weekly and monthly data from the CCM and comparing the subsequent map results with the original maps and NASS CDLs. Initially, our NRT results revealed training error of 1.4% and test error of 8.3%, as compared to 1.0% and 7.6%, respectively for the original CCM. Through the implementation of a new ‘two-mapping model’ approach, we were able to substantially improve the results of the NRT crop cover model. We divided the NRT model into one ‘crop type model’ to handle the classification of the nine specific crops and a second, binary model to classify crops as presence or absence of the ‘other’ crop. Under the two-mapping model approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4% for crop type and binary model, respectively.With overall mapping accuracy for the map reaching 69.88 percent, this approach shows strong potential for generating crop type maps of current year in September.

This web map presents five-digit ZIP Code areas used by the U.S. Postal Service to deliver mail more effectively. The first digit of a five-digit ZIP Code divides the United States into 10 large groups of states numbered from 0 in the Northeast to 9 in the far West. Within these areas, each state is divided into an average of 10 smaller geographical areas, identified by the second and third digits. These digits, in conjunction with the first digit, represent a sectional center facility or a mail processing facility area. The fourth and fifth digits identify a post office, station, branch or local delivery area. It provides area, post office name, and population for each ZIP Code area in the United States.The ZIP Code boundaries are from 2024. The population estimates are from Esri Demographics.

This map shows Congressional District boundaries for the United States. The map is set to middle Georgia.

Congressional districts are the 435 areas from which members are elected to the U.S. House of Representatives. After the apportionment of congressional seats among the states, which is based on decennial census population counts, each state with multiple seats is responsible for establishing congressional districts for the purpose of electing representatives. Each congressional district is to be as equal in population to all other congressional districts in a state as practicable. The boundaries and numbers shown for the congressional districts are those specified in the state laws or court orders establishing the districts within each state.

Congressional districts for the 108th through 112th sessions were established by the states based on the result of the 2000 Census. Congressional districts for the 113th through 115th sessions were established by the states based on the result of the 2010 Census. Boundaries are effective until January of odd number years (for example, January 2015, January 2017, etc.), unless a state initiative or court ordered redistricting requires a change. All states established new congressional districts in 2011-2012, with the exception of the seven single member states (Alaska, Delaware, Montana, North Dakota, South Dakota, Vermont, and Wyoming).

For the states that have more than one representative, the Census Bureau requested a copy of the state laws or applicable court order(s) for each state from each secretary of state and each 2010 Redistricting Data Program state liaison requesting a copy of the state laws and/or applicable court order(s) for each state. Additionally, the states were asked to furnish their newly established congressional district boundaries and numbers by means of geographic equivalency files. States submitted equivalency files since most redistricting was based on whole census blocks. Kentucky was the only state where congressional district boundaries split some of the 2010 Census tabulation blocks. For further information on these blocks, please see the user-note at the bottom of the tables for this state.

The Census Bureau entered this information into its geographic database and produced tabulation block equivalency files that depicted the newly defined congressional district boundaries. Each state liaison was furnished with their file and requested to review, submit corrections, and certify the accuracy of the boundaries.

description: In this dataset we present two maps that estimate the location and population served by domestic wells in the contiguous United States. The first methodology, called the Block Group Method or BGM, builds upon the original block-group data from the 1990 census (the last time the U.S. Census queried the population regarding their source of water) by incorporating higher resolution census block data. The second methodology, called the Road-Enhanced Method or REM, refines the locations by using a buffer expansion and shrinkage technique along roadways to define areas where domestic wells exist. The fundamental assumption with this method is that houses (and therefore domestic wells) are located near a named road. The results are presented as two nationally consistent domestic-well population datasets. While both methods can be considered valid, the REM map is more precise in locating domestic wells; the REM map had a smaller amount of spatial bias (nearly equal vs biased in type 1 error), total error (10.9% vs 23.7%,), and distance error (2.0 km vs 2.7 km), when comparing the REM and BGM maps to a California calibration map. However, the BGM map is more inclusive of all potential locations for domestic wells. The primary difference in the BGM and the REM is the mapping of low density areas. The REM has a 57% reduction in areas mapped as low density (populations greater than 0 but less than 1 person per km), concentrating populations into denser regions. Therefore, if one is trying to capture all of the potential areas of domestic-well usage, then the BGM map may be more applicable. If location is more imperative, then the REM map is better at identifying areas of the landscape with the highest probability of finding a domestic well. Depending on the purpose of a study, a combination of both maps can be used. For space concerns, the datasets have been divided into two separate geodatabases. The BGM map geodatabase and the REM map database.; abstract: In this dataset we present two maps that estimate the location and population served by domestic wells in the contiguous United States. The first methodology, called the Block Group Method or BGM, builds upon the original block-group data from the 1990 census (the last time the U.S. Census queried the population regarding their source of water) by incorporating higher resolution census block data. The second methodology, called the Road-Enhanced Method or REM, refines the locations by using a buffer expansion and shrinkage technique along roadways to define areas where domestic wells exist. The fundamental assumption with this method is that houses (and therefore domestic wells) are located near a named road. The results are presented as two nationally consistent domestic-well population datasets. While both methods can be considered valid, the REM map is more precise in locating domestic wells; the REM map had a smaller amount of spatial bias (nearly equal vs biased in type 1 error), total error (10.9% vs 23.7%,), and distance error (2.0 km vs 2.7 km), when comparing the REM and BGM maps to a California calibration map. However, the BGM map is more inclusive of all potential locations for domestic wells. The primary difference in the BGM and the REM is the mapping of low density areas. The REM has a 57% reduction in areas mapped as low density (populations greater than 0 but less than 1 person per km), concentrating populations into denser regions. Therefore, if one is trying to capture all of the potential areas of domestic-well usage, then the BGM map may be more applicable. If location is more imperative, then the REM map is better at identifying areas of the landscape with the highest probability of finding a domestic well. Depending on the purpose of a study, a combination of both maps can be used. For space concerns, the datasets have been divided into two separate geodatabases. The BGM map geodatabase and the REM map database.

IntroductionClimate Central’s Surging Seas: Risk Zone map shows areas vulnerable to near-term flooding from different combinations of sea level rise, storm surge, tides, and tsunamis, or to permanent submersion by long-term sea level rise. Within the U.S., it incorporates the latest, high-resolution, high-accuracy lidar elevation data supplied by NOAA (exceptions: see Sources), displays points of interest, and contains layers displaying social vulnerability, population density, and property value. Outside the U.S., it utilizes satellite-based elevation data from NASA in some locations, and Climate Central’s more accurate CoastalDEM in others (see Methods and Qualifiers). It provides the ability to search by location name or postal code.The accompanying Risk Finder is an interactive data toolkit available for some countries that provides local projections and assessments of exposure to sea level rise and coastal flooding tabulated for many sub-national districts, down to cities and postal codes in the U.S. Exposure assessments always include land and population, and in the U.S. extend to over 100 demographic, economic, infrastructure and environmental variables using data drawn mainly from federal sources, including NOAA, USGS, FEMA, DOT, DOE, DOI, EPA, FCC and the Census.This web tool was highlighted at the launch of The White House's Climate Data Initiative in March 2014. Climate Central's original Surging Seas was featured on NBC, CBS, and PBS U.S. national news, the cover of The New York Times, in hundreds of other stories, and in testimony for the U.S. Senate. The Atlantic Cities named it the most important map of 2012. Both the Risk Zone map and the Risk Finder are grounded in peer-reviewed science.Back to topMethods and QualifiersThis map is based on analysis of digital elevation models mosaicked together for near-total coverage of the global coast. Details and sources for U.S. and international data are below. Elevations are transformed so they are expressed relative to local high tide lines (Mean Higher High Water, or MHHW). A simple elevation threshold-based “bathtub method” is then applied to determine areas below different water levels, relative to MHHW. Within the U.S., areas below the selected water level but apparently not connected to the ocean at that level are shown in a stippled green (as opposed to solid blue) on the map. Outside the U.S., due to data quality issues and data limitations, all areas below the selected level are shown as solid blue, unless separated from the ocean by a ridge at least 20 meters (66 feet) above MHHW, in which case they are shown as not affected (no blue).Areas using lidar-based elevation data: U.S. coastal states except AlaskaElevation data used for parts of this map within the U.S. come almost entirely from ~5-meter horizontal resolution digital elevation models curated and distributed by NOAA in its Coastal Lidar collection, derived from high-accuracy laser-rangefinding measurements. The same data are used in NOAA’s Sea Level Rise Viewer. (High-resolution elevation data for Louisiana, southeast Virginia, and limited other areas comes from the U.S. Geological Survey (USGS)). Areas using CoastalDEM™ elevation data: Antigua and Barbuda, Barbados, Corn Island (Nicaragua), Dominica, Dominican Republic, Grenada, Guyana, Haiti, Jamaica, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, San Blas (Panama), Suriname, The Bahamas, Trinidad and Tobago. CoastalDEM™ is a proprietary high-accuracy bare earth elevation dataset developed especially for low-lying coastal areas by Climate Central. Use our contact form to request more information.Warning for areas using other elevation data (all other areas)Areas of this map not listed above use elevation data on a roughly 90-meter horizontal resolution grid derived from NASA’s Shuttle Radar Topography Mission (SRTM). SRTM provides surface elevations, not bare earth elevations, causing it to commonly overestimate elevations, especially in areas with dense and tall buildings or vegetation. Therefore, the map under-portrays areas that could be submerged at each water level, and exposure is greater than shown (Kulp and Strauss, 2016). However, SRTM includes error in both directions, so some areas showing exposure may not be at risk.SRTM data do not cover latitudes farther north than 60 degrees or farther south than 56 degrees, meaning that sparsely populated parts of Arctic Circle nations are not mapped here, and may show visual artifacts.Areas of this map in Alaska use elevation data on a roughly 60-meter horizontal resolution grid supplied by the U.S. Geological Survey (USGS). This data is referenced to a vertical reference frame from 1929, based on historic sea levels, and with no established conversion to modern reference frames. The data also do not take into account subsequent land uplift and subsidence, widespread in the state. As a consequence, low confidence should be placed in Alaska map portions.Flood control structures (U.S.)Levees, walls, dams or other features may protect some areas, especially at lower elevations. Levees and other flood control structures are included in this map within but not outside of the U.S., due to poor and missing data. Within the U.S., data limitations, such as an incomplete inventory of levees, and a lack of levee height data, still make assessing protection difficult. For this map, levees are assumed high and strong enough for flood protection. However, it is important to note that only 8% of monitored levees in the U.S. are rated in “Acceptable” condition (ASCE). Also note that the map implicitly includes unmapped levees and their heights, if broad enough to be effectively captured directly by the elevation data.For more information on how Surging Seas incorporates levees and elevation data in Louisiana, view our Louisiana levees and DEMs methods PDF. For more information on how Surging Seas incorporates dams in Massachusetts, view the Surging Seas column of the web tools comparison matrix for Massachusetts.ErrorErrors or omissions in elevation or levee data may lead to areas being misclassified. Furthermore, this analysis does not account for future erosion, marsh migration, or construction. As is general best practice, local detail should be verified with a site visit. Sites located in zones below a given water level may or may not be subject to flooding at that level, and sites shown as isolated may or may not be be so. Areas may be connected to water via porous bedrock geology, and also may also be connected via channels, holes, or passages for drainage that the elevation data fails to or cannot pick up. In addition, sea level rise may cause problems even in isolated low zones during rainstorms by inhibiting drainage.ConnectivityAt any water height, there will be isolated, low-lying areas whose elevation falls below the water level, but are protected from coastal flooding by either man-made flood control structures (such as levees), or the natural topography of the surrounding land. In areas using lidar-based elevation data or CoastalDEM (see above), elevation data is accurate enough that non-connected areas can be clearly identified and treated separately in analysis (these areas are colored green on the map). In the U.S., levee data are complete enough to factor levees into determining connectivity as well.However, in other areas, elevation data is much less accurate, and noisy error often produces “speckled” artifacts in the flood maps, commonly in areas that should show complete inundation. Removing non-connected areas in these places could greatly underestimate the potential for flood exposure. For this reason, in these regions, the only areas removed from the map and excluded from analysis are separated from the ocean by a ridge of at least 20 meters (66 feet) above the local high tide line, according to the data, so coastal flooding would almost certainly be impossible (e.g., the Caspian Sea region).Back to topData LayersWater Level | Projections | Legend | Social Vulnerability | Population | Ethnicity | Income | Property | LandmarksWater LevelWater level means feet or meters above the local high tide line (“Mean Higher High Water”) instead of standard elevation. Methods described above explain how each map is generated based on a selected water level. Water can reach different levels in different time frames through combinations of sea level rise, tide and storm surge. Tide gauges shown on the map show related projections (see just below).The highest water levels on this map (10, 20 and 30 meters) provide reference points for possible flood risk from tsunamis, in regions prone to them.

This activity uses Map Viewer.ResourcesMapTeacher guide Student worksheetGet startedOpen the map.Use the teacher guide to explore the map with your class or have students work through it on their own with the worksheet.New to GeoInquiriesTM? See Getting to Know GeoInquiries.Social Studies standardsC3: D2.His.1.9-12 – Evaluate how historical events and developments were shaped by unique circumstances of time and place as well as broader historical contexts.C3: D2.His.2.9-12 – Analyze change and continuity in historical eras.C3: D2.His.3.9-12 – Use questions generated about individuals and groups to assess how the significance of their actions changes over time and is shaped by the historical context.Learning outcomesStudents will compare and contrast the chronology of Civil War battle locations and Union-controlled land between 1861 and 1865.Students will identify Confederate states, Union states, border states, Richmond, and Washington, D.C.More activitiesAll US History GeoInquiriesAll GeoInquiries

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

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.