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.

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;

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.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Tree Ring. The data include parameters of tree ring with a geographic location of Massachusetts, United States Of America. The time period coverage is from 463 to 268 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

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;

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Tree Ring. The data include parameters of tree ring with a geographic location of Massachusetts, United States Of America. The time period coverage is from 496 to 267 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Publicly accessible open spaces provide valuable opportunities for people to exercise, play, socialize, and build community. People are more likely to use public open spaces that are close (ideally within walking distance) to their homes. To assess the spatial distribution of access to open space for recreation in the southeastern United States, we constructed an index of open space access based on the size of the largest publicly accessible open space within 10 miles of each point on the landscape, using three distance categories to represent whether people can reach the open spaces by walking (within 0.5 mile), via a short drive (within 3 miles), or via a longer drive (within 10 miles).

U.S. Census Bureau, data file from Geography Division based on the TIGER/Geographic Identification Code Scheme (TIGER/GICS) computer file. Land area updated every 10 years. http://www.census.gov/geo/www/tiger/index.html or http://factfinder2.census.gov.

Land area is the size, in square units (metric and nonmetric) of all areas designated as land in the Census Bureau's national geographic (TIGER®) database.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Tree Ring. The data include parameters of tree ring with a geographic location of Alaska, United States Of America. The time period coverage is from 38 to -51 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Our model is a full-annual-cycle population model {hostetler2015full} that tracks groups of bat surviving through four seasons: breeding season/summer, fall migration, non-breeding/winter, and spring migration. Our state variables are groups of bats that use a specific maternity colony/breeding site and hibernaculum/non-breeding site. Bats are also accounted for by life stages (juveniles/first-year breeders versus adults) and seasonal habitats (breeding versus non-breeding) during each year, This leads to four states variable (here depicted in vector notation): the population of juveniles during the non-breeding season, the population of adults during the non-breeding season, the population of juveniles during the breeding season, and the population of adults during the breeding season, Each vector's elements depict a specific migratory pathway, e.g., is comprised of elements, {non-breeding sites}, {breeding sites}The variables may be summed by either breeding site or non-breeding site to calculate the total population using a specific geographic location. Within our code, we account for this using an index column for breeding sites and an index column for non-breeding sides within the data table. Our choice of state variables caused the time step (i.e. (t)) to be 1 year. However, we recorded the population of each group during the breeding and non-breeding season as an artifact of our state-variable choice. We choose these state variables partially for their biological information and partially to simplify programming. We ran our simulation for 30 years because the USFWS currently issues Indiana Bat take permits for 30 years. Our model covers the range of the Indiana Bat, which is approximately the eastern half of the contiguous United States (Figure \ref{fig:BatInput}). The boundaries of our range was based upon the United States boundary, the NatureServe Range map, and observations of the species. The maximum migration distance was 500-km, which was based upon field observations reported in the literature \citep{gardner2002seasonal, winhold2006aspects}. The landscape was covered with approximately 33,000, 6475-ha grid cells and the grid size was based upon management considerations. The U.S.~Fish and Wildlife Service considers a 2.5 mile radius around a known maternity colony to be its summer habitat range and all of the hibernaculum within a 2.5 miles radius to be a single management unit. Hence the choice of 5-by-5 square grids (25 miles(^2) or 6475 ha). Each group of bats within the model has a summer and winter grid cell as well as a pathway connecting the cells. It is possible for a group to be in the cell for both seasons, but improbable for females (which we modeled). The straight line between summer and winter cells were buffered with different distances (1-km, 2-km, 10-km, 20-km, 100-km, and 200-km) as part of the turbine sensitivity and uncertainty analysis. We dropped the largest two buffer sizes during the model development processes because they were biologically unrealistic and including them caused all populations to go extinct all of the time. Note a 1-km buffer would be a 2-km wide path. An example of two pathways are included in Figure \ref{fig:BatPath}. The buffers accounts for bats not migrating in a straight line. If we had precise locations for all summer maternity colonies, other approaches such as Circuitscape \citep{hanks2013circuit} could have been used to model migration routes and this would have reduced migration uncertainty.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Tree Ring. The data include parameters of tree ring with a geographic location of Massachusetts, United States Of America. The time period coverage is from 423 to 270 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Tree Ring. The data include parameters of tree ring with a geographic location of Massachusetts, United States Of America. The time period coverage is from 496 to 181 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

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.

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.

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 evolving dynamics of the transportation market are significantly influenced by cost concerns, prompting companies to adopt innovative strategies to effectively meet consumer demands. Free shipping remains a popular feature in parcel delivery services, yet it often requires a minimum purchase amount. Advanced logistical solutions are necessary to manage the surge in value-added goods purchased in higher volumes. These shifts in consumer behavior have affected cost structures, sparking the need for companies to optimize operations for economic efficiency without sacrificing service quality, especially in free shipping. Strategies such as load unitization for space-saving and refined route planning software for efficient delivery have become essential to address these evolving needs. Also, robust packet protection during transit ensures items remain undamaged. Labor shortages have accelerated the adoption of automation, albeit at a slower pace, because of the associated costs. Introducing New Distribution Capabilities (NDCs) in the airline sector poses a challenge because of potentially high costs for travel agencies, necessitating careful system integration. Despite these complexities, the transportation sector has maintained profitability, with revenue growing at a CAGR of 0.8% over the past five years, reaching $1.2 trillion. However, a 0.6% revenue drop will happen in 2025. The sector will significantly transform as consumer loyalty becomes increasingly unpredictable, necessitating predictive analytics to track market momentum. Timeliness will be crucial, prompting investments in on-demand packaging services. Legal challenges concerning safety practices, particularly in the trucking industry, could raise costs and complicate growth strategies until resolved. Also, the desire for retailers to have control over their supply chains is driving the growth of the fourth-party logistics (4PL) market. This shift will spur investment in operational renovations to efficiently capture rising market activity. These factors cause revenue to climb at a CAGR of 2.1% over the next five years, reaching $1.3 trillion by 2030.

The Wind Integration National Dataset (WIND) Toolkit, developed by the National Renewable Energy Laboratory (NREL), provides modeled wind speeds at multiple elevations. Instantaneous wind measurements were analyzed from more than 126,000 sites in the continental United States for the years 2007–2013. The model results were mapped on a 2-km grid. A subset of the contiguous United States data for 2012 is shown here. Offshore data is shown to 50 nautical miles.Time Extent: Annual 2012Units: m/sCell Size: 2 kmSource Type: StretchedPixel Type: 32 Bit FloatData Projection: GCS WGS84Mosaic Projection:  WGS 1984 Web MercatorExtent: Contiguous United StatesSource: NREL Wind Integration National Dataset v1.1WIND is an update and expansion of the Eastern Wind Integration Data Set and Western Wind Integration Data Set. It supports the next generation of wind integration studies.Accessing Elevation InformationEach of the 9 elevation slices can be accessed, visualized, and analyzed. In ArcGIS Pro, go to the Multidimensional Ribbon and use the Elevation pull-down menu. In ArcGIS Online, it is best to use Web Map Viewer Classic where the elevation slider will automatically appear on the righthand side. The elevation slider will be available in the new Map Viewer in an upcoming release. What can you do with this layer?This layer may be added to maps to visualize and quickly interrogate each pixel value. The pop-up provides the pixel’s wind speed value.This analytical imagery tile layer can be used in analysis. For example, the layer may be added to ArcGIS Pro and proposed wind turbine locations can be used to Sample the layer at multiple elevation to determine the optimal hub height. Source data can be accessed on Amazon Web ServicesUsers of the WIND Toolkit should use the following citations:Draxl, C., B.M. Hodge, A. Clifton, and J. McCaa. 2015. Overview and Meteorological Validation of the Wind Integration National Dataset Toolkit (Technical Report, NREL/TP-5000-61740). Golden, CO: National Renewable Energy Laboratory.Draxl, C., B.M. Hodge, A. Clifton, and J. McCaa. 2015. "The Wind Integration National Dataset (WIND) Toolkit." Applied Energy 151: 355366.King, J., A. Clifton, and B.M. Hodge. 2014. Validation of Power Output for the WIND Toolkit (Technical Report, NREL/TP-5D00-61714). Golden, CO: National Renewable Energy Laboratory.

Feature layer containing authoritative greenway mile marker points for Sioux Falls, South Dakota.

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 2018, the last mile delivery market in North America was sized at ***** billion U.S. dollars. It is expected to grow to just under ** billion U.S. dollars in 2022. Last mile delivery involved the journey of goods from a transportation hub to a final delivery point, usually a personal residence.