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United States US: Land Area data was reported at 9,147,420.000 sq km in 2017. This stayed constant from the previous number of 9,147,420.000 sq km for 2016. United States US: Land Area data is updated yearly, averaging 9,158,960.000 sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 9,161,920.000 sq km in 2007 and a record low of 9,147,420.000 sq km in 2017. United States US: Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Land Use, Protected Areas and National Wealth. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.; ; Food and Agriculture Organization, electronic files and web site.; Sum;
This statistic shows the total land and water area of the United States by state and territory. Alabama covers an area of 52,420 square miles.
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United States US: Urban Land Area data was reported at 802,053.592 sq km in 2010. This stayed constant from the previous number of 802,053.592 sq km for 2000. United States US: Urban Land Area data is updated yearly, averaging 802,053.592 sq km from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 802,053.592 sq km in 2010 and a record low of 802,053.592 sq km in 2010. United States US: Urban Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Land Use, Protected Areas and National Wealth. Urban land area in square kilometers, based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Sum;
Roads and highways are a prominent part of modern transportation systems. Roads impact the quality of our environment in both positive and negative ways. Roads represent proximity to human activity on the landscape, the farther away a place is from roads, the lower the likelihood that it is disturbed by human activity. Roads may act as a barrier to wildlife migration and are also vectors for the movement of invasive species. Roads also open up recreational opportunities to people and provide access for management and commerce.Dataset SummaryThis layer provides access to a 1 km resolution raster of road density calculated as kilometer of road per 1 km raster cell. The raster was created from the U.S. Census Bureau's 2014 TIGER database using data for roads, highways, bike trails, and foot paths. This layer covers the continental U.S., Alaska, Hawaii, Puerto Rico, the Northern Marianas Islands, Guam, American Samoa, and the U.S. Virgin Islands.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.
The statistic shows the 30 largest countries in the world by area. Russia is the largest country by far, with a total area of about 17 million square kilometers.
Population of Russia
Despite its large area, Russia - nowadays the largest country in the world - has a relatively small total population. However, its population is still rather large in numbers in comparison to those of other countries. In mid-2014, it was ranked ninth on a list of countries with the largest population, a ranking led by China with a population of over 1.37 billion people. In 2015, the estimated total population of Russia amounted to around 146 million people. The aforementioned low population density in Russia is a result of its vast landmass; in 2014, there were only around 8.78 inhabitants per square kilometer living in the country. Most of the Russian population lives in the nation’s capital and largest city, Moscow: In 2015, over 12 million people lived in the metropolis.
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.
United States National Grid 1 KM cell size for Tallahassee and Leon County Florida. This layer is used in public safety and search and rescue operations applications.
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United States US: Road Passenger Transport: Passenger Cars data was reported at 5,286,161.874 Person-km mn in 2022. This records a decrease from the previous number of 5,586,348.601 Person-km mn for 2021. United States US: Road Passenger Transport: Passenger Cars data is updated yearly, averaging 4,298,629.006 Person-km mn from Dec 1970 (Median) to 2022, with 37 observations. The data reached an all-time high of 6,060,622.152 Person-km mn in 2019 and a record low of 2,817,796.000 Person-km mn in 1970. United States US: Road Passenger Transport: Passenger Cars data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.ITF: Passenger Transport by Mode of Transport: OECD Member: Annual. [STAT_CONC_DEF] Road passenger transport: any movement of passengers using a road vehicle on a given road network. National road passenger transport: road passenger transport between two places (a place of loading/embarkation and a place of unloading/disembarkation) located in the same country irrespective of the country in which the road motor vehicle is registered. It may involve transit through a second country. International road passenger transport: road passenger transport between a place of loading/embarkation or unloading/disembarkation in the declaring country and a place of loading/embarkation or unloading/disembarkation in another country. Such transport may involve transit through one or more additional countries. Road passenger: any person who makes a journey by a road vehicle. Drivers of passenger cars, excluding taxi drivers, are counted as passengers. Road passenger-kilometre: unit of measurement representing the transport of one passenger by road over one kilometre. [STAT_CONC_DEF] Since 2000, the definition of passenger car is determined by the size of the wheel base. In 2009, there was a change in passenger car occupancy factor, that creates a break in the series. Transport by buses and coaches by the American Public Transportation Association (APTA). [COVERAGE] Data should include urban transport.
This statistics shows a list of the top 20 largest-metropolitan areas in the United States in 2010, by land area. Riverside-San Bernardino-Ontario in California was ranked first enclosing an area of 70,612 square kilometers.
This data set provides a 38-year, 1-km resolution inventory of annual on-road CO2 emissions for the conterminous United States based on roadway-level vehicle traffic data and state-specific emissions factors for multiple vehicle types on urban and rural roads as compiled in the Database of Road Transportation Emissions (DARTE). CO2 emissions from the on-road transportation sector are provided annually for 1980-2017 as a continuous surface at a spatial resolution of 1 km.
This data release documents the data used for the associated publication "Evaluating hydrologic region assignment techniques for ungaged watersheds in Alaska, USA" (Barnhart and others, 2022) The data sets within this release are stored in 14 files: (1) Streamflow observations and sites used. (2) Statistically estimated streamflow values computed for each site. (3) Streamflow statistics computed from observed and estimated streamflow values at each site, basin characteristics for each site, and hydrologic regions (clusters) for each site. (4) A dataset describing the optimal number of hydrologic regions into which the considered sites were grouped. (5) P-values from a multiple comparisons analysis testing for statistical differences between clusters for each basin characteristic and streamflow statistic. (6) A matrix of zeros and ones describing the performance of each hydrologic region assignment technique considered in the publication associated with this release. (7) A dataset of variable importance generated by random forest modeling-based hydrologic region assignment techniques evaluated. (8-14) Daily datasets of simulated SnowModel (Liston and Elder, 2006) runoff (snowmelt + rainfall), precipitation, glacial melt, snow water equivalent, snow covered area, liquid precipitation, and air temperature for Alaska, USA at a 1 km grid cell size.
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Spatial information about the seafloor is critical for decision-making by marine resource science, management and tribal organizations. Coordinating data needs can help organizations leverage collective resources to meet shared goals. To help enable this coordination, the National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) developed a spatial framework, process and online application to identify common data collection priorities for seafloor mapping, sampling and visual surveys off the US Caribbean territories of Puerto Rico and the US Virgin Islands. Fifteen participants from local federal, state, and academic institutions entered their priorities in an online application, using virtual coins to denote their priorities in 2.5x2.5 kilometer (nearshore) and 10x10 kilometer (offshore) grid size. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Results were analyzed and mapped using statistical techniques to identify significant relationships between priorities, reasons for those priorities and data needs. Fifteen high priority locations were broadly identified for future mapping, sampling and visual surveys. These locations include: (1) a coastal location in northwest Puerto Rico (Punta Jacinto to Punta Agujereada), (2) a location approximately 11 km off Punta Agujereada, (3) coastal Rincon, (4) San Juan, (5) Punta Arenas (west of Vieques Island), (6) southwest Vieques, (7) Grappler Seamount, (8) southern Virgin Passage, (9) north St. Thomas, (10) east St. Thomas, (11) south St. John, (12) west offshore St. Croix, (13) west nearshore St. Croix, (14) east nearshore St. Croix, and (15) east offshore St. Croix. Participants consistently selected (1) Biota/Important Natural Area, (2) Commercial Fishing and (3) Coastal/Marine Hazards as their top reasons (i.e., justifications) for prioritizing locations, and (1) Benthic Habitat Map and (2) Sub-bottom Profiles as their top data or product needs. This ESRI shapefile summarizes the results from this spatial prioritization effort. This information will enable US Caribbean organization to more efficiently leverage resources and coordinate their mapping of high priority locations in the region.
This effort was funded by NOAA’s NCCOS and supported by CRCP. The overall goal of the project was to systematically gather and quantify suggestions for seafloor mapping, sampling and visual surveys in the US Caribbean territories of Puerto Rico and the US Virgin Islands. The results are will help organizations in the US Caribbean identify locations where their interests overlap with other organizations, to coordinate their data needs and to leverage collective resources to meet shared goals.
There were four main steps in the US Caribbean spatial prioritization process. The first step was to identify the technical advisory team, which included the 4 CRCP members: 2 from each the Puerto Rico and USVI regions. This advisory team recommended 33 organizations to participate in the prioritization. Each organization was then requested to designate a single representative, or respondent, who would have access to the web tool. The respondent would be responsible for communicating with their team about their needs and inputting their collective priorities. Step two was to develop the spatial framework and an online application. To do this, the US Caribbean was divided into 4 sub regions: nearshore and offshore for both Puerto Rico and USVI. The total inshore regions had 2,387 square grid cells approximately 2.5x2.5 km in size. The total offshore regions consisted of 438 square grid cells 10x10 km in size. Existing relevant spatial datasets (e.g., bathymetry, protected area boundaries, etc.) were compiled to help participants understand information and data gaps and to identify areas they wanted to prioritize for future data collections. These spatial datasets were housed in the online application, which was developed using Esri’s Web AppBuilder. In step three, this online application was used by 15 participants to enter their priorities in each subregion of interest. Respondents allocated virtual coins in the grid cells to denote their priorities for each region. Respondents were given access to all four regions, despite which territory they represented, but were not required to provide input into each region. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Coin values were standardized across the nearshore and offshore zones and used to identify spatial patterns across the US Caribbean region as a whole. The number of coins were standardized because each subregion had a different number of grid cells and participants. Standardized coin values were analyzed and mapped using statistical techniques, including hierarchical cluster analysis, to identify significant relationships between priorities, reasons for those priorities and data needs. This ESRI shapefile contains the 2.5x2.5 km and 10x10 km grid cells used in this prioritization effort and associated the standardized coin values overall, as well as by organization, justification and product. For a complete description of the process and analysis please see: Kraus et al. 2020.
This dataset of 40 square kilometer (sq. km) hexagons was created by the U.S. EPA's Environmental Monitoring and Assessment Program (EMAP) and is being released by the U.S. Geological Survey for public use. The 40 sq. km hexagons were derived from a grid consisting of a triangular array of points that cover the United States and neighboring Canada and Mexico. The base grid of points had a companion areal structure called a tessellation. The base tessellation hexagons constituted this tessellation. In other words, surrounding each grid point was a hexagon that defines the area within which all points are closer to this grid point than to any other, and the set of hexagons defined this way completely and -mutually exclusively covers the space of the grid. The grid had a base density of approximately 648 sq. km per point with a spacing of approximately 27 km between points. The original 40 sq. km hexagons (which do not form a tessellation) were centered about the randomized grid points and are exactly 1/16th the size of the tessellation hexagons (and therefore slightly more than 40 sq. km). Hexagon boundaries are distributed in geodetic coordinates based on the Clarke 1866 model of the Earth, meaning that the coordinates are latitude and longitude on the ellipsoid used by most North American geodetic coordinate systems. Distribution can also be made in GRS 80 coordinates if desired. The precision of the coordinates is to millionths of a degree (i.e., to 6 decimal places of a degree). This corresponds to about 0.1 meter on the surface of the Earth. The point grid was constructed in the plane of a special version of the Lambert azimuthal equal area projection; for subsequent use they may be projected using other map projections. When other projections are used, the geometry of the point grid will not be perfectly triangular nor will the hexagons surrounding the points be perfect, since sizes and/or shapes and/or distances will necessarily be distorted in another projection relative to the one used to construct the grid. This 40 sq. km hexagon tessellation was created by two successive enhancements of the 648 sq. km tessellation by factors of four. See White et al. 1992 in references.
The NABat sampling frame is a grid-based finite-area frame spanning Canada, the United States, and Mexico consisting of 10-km by 10-km (100-km2) grid cell sample units for the continental United States, Canada, and Alaska and 5- by 5-km (25km2) for Hawaii and Puerto Rico. This grain size is biologically appropriate given the scale of movement of most bat species, which routinely travel many kilometers each night between roosts and foraging areas and along foraging routes. A Generalized Random-Tessellation Stratified (GRTS) Survey Design draw was added to the sample units from the raw sampling grids (https://doi.org/10.5066/P9M00P17). This sampling design produces an ordered list of units such that any ordered subset of that list is also randomized and spatially balanced.
This statistics shows the size of the airspace in Europe and the U.S. in 2008. In the U.S, the airspace contained around 10 million square kilometers that year.
These data represent Next-Generation Radar (NEXRAD) and Terminal Doppler Weather Radar (TDWR) weather radar stations within the US. The NEXRAD radar stations are maintained and operated by the National Oceanic and Atmospheric Administration. The TDWR radar stations are maintained and operated by the Federal Aviation Administration. Both radar's are pulsed Doppler types that measure reflectivity out to 460 km, and radial velocity and spectrum width out to 300 km for NEXRAD and 90 km for TDWR. Both radars automatically scan the atmosphere from the surface to 70,000 feet using a rotating parabolic antenna.
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Graph and download economic data for Revenue Passenger Miles for U.S. Air Carrier Domestic and International, Scheduled Passenger Flights (RPM) from Jan 2000 to Feb 2025 about flight, miles, passenger, air travel, travel, revenue, domestic, and USA.
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Cox proportional hazard model results relating first passage time of GPS-collared wild pigs on Fort Hood Military Installation, Texas, USA in 2016–2017 to explanatory variables.
The U.S. rail system stretched across almost ******* km in 2021, making it the largest rail network in the world. It is followed by the the Chinese rail network, which encompasses close to ******* rail kilometers. Railroads include railway routes that are open for public passenger and freight services and excludes dedicated private resource railways and parallel tracks. Rail infrastructure decline in the United States While the United States currently maintains the largest rail network globally, the length of the network has been declining for decades. In the early days of railroading, the country experienced a boom in building railway infrastructure. However, in recent decades the railroad industry has focused on consolidating and maintaining only the more profitable main lines. This has led to a closure of many smaller and especially branch lines. This trend is projected to continue, with the total network length predicted to fall to just under ******* kilometers by 2028. High-speed rail dominated by China While the United States has the largest overall rail network, China boasts the largest highspeed rail network. In 2021 the country operated nearly ****** kilometers of highspeed rail lines. This made the Chinese network more than ** times the size of its closest contender, Spain. Meanwhile the United States only operated *** kilometers of high-speed rail lines.
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Total BIP Score was found by adding the variable scores, Stream Slope + Stream Width + Valley Width (max = 12, min = 0), and adjusted to categories 0–3 for ease of display and analysis.
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United States US: Land Area data was reported at 9,147,420.000 sq km in 2017. This stayed constant from the previous number of 9,147,420.000 sq km for 2016. United States US: Land Area data is updated yearly, averaging 9,158,960.000 sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 9,161,920.000 sq km in 2007 and a record low of 9,147,420.000 sq km in 2017. United States US: Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Land Use, Protected Areas and National Wealth. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.; ; Food and Agriculture Organization, electronic files and web site.; Sum;