Land area (sq. km) in United States was reported at 9147420 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Land area (sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

United States US: Population Density: People per Square Km data was reported at 35.608 Person/sq km in 2017. This records an increase from the previous number of 35.355 Person/sq km for 2016. United States US: Population Density: People per Square Km data is updated yearly, averaging 26.948 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 35.608 Person/sq km in 2017 and a record low of 20.056 Person/sq km in 1961. United States US: Population Density: People per Square Km 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: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. 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 and World Bank population estimates.; Weighted average;

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.

Agricultural land (sq. km) in United States was reported at 4058104 sq. Km in 2021, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Agricultural land (sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on June of 2025.

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 US: Urban Land Area Where Elevation is Below 5 Meters data was reported at 17,520.222 sq km in 2010. This stayed constant from the previous number of 17,520.222 sq km for 2000. United States US: Urban Land Area Where Elevation is Below 5 Meters data is updated yearly, averaging 17,520.222 sq km from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 17,520.222 sq km in 2010 and a record low of 17,520.222 sq km in 2010. United States US: Urban Land Area Where Elevation is Below 5 Meters 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 below 5m is the total urban land area in square kilometers where the elevation is 5 meters or less.; ; 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 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.

In 2022, the population density in the United States remained nearly unchanged at around 36.43 inhabitants per square kilometer. Nevertheless, 2022 still represents a peak in the population density in the United States. Population density refers to the average number of residents per square kilometer of land across a given country or region. It is calculated by dividing the total midyear population by the total land area.Find more key insights for the population density in countries like Mexico.

GLORIA data for the Gulf of Alaska Exclusive Economic Zone (EEZ) were acquired during five cruises over a four year period. The first cruise conducted in 1986 (F-1-86-GA) surveyed an area of the north-central mosaic area and covered an area of approximately 40,000 square kilometers (sq km). The second two cruises (F-8-88-AA, F-9-88-WG) were conducted in 1988. One of the 1988 cruises (F-8-88-AA) focused on a survey of the Aleutian Arc. The eastern most portion of that survey extended outside of the Aleutian Arc survey area and covered an area of approximately 52,000 square kilometers (sq km) of seafloor on the western edge of the Gulf of Alaska. The final two cruises (F-6-89-GA, F-7-89-EG) were completed in 1989. As in earlier EEZ reconnaissance surveys, the USGS utilized the GLORIA (Geological LOng-Range Inclined Asdic) sidescan-sonar system to complete the geologic mapping. The collected GLORIA data were processed and digitally mosaicked to produce continuous imagery of the seafloor. Thirty digital mosaics with a 50-meter pixel resolution were completed for the region.

United States Land Cover: Bare Area: Unconsolidated data was reported at 0.000 sq km th in 2019. This stayed constant from the previous number of 0.000 sq km th for 2018. United States Land Cover: Bare Area: Unconsolidated data is updated yearly, averaging 0.000 sq km th from Dec 1992 (Median) to 2019, with 5 observations. The data reached an all-time high of 0.000 sq km th in 2019 and a record low of 0.000 sq km th in 2019. United States Land Cover: Bare Area: Unconsolidated 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.ESG: Environmental: Land Cover: OECD Member: Annual.

The following is from the Methods section of the related journal article. Please see the article and its accompanying appendix to read more about these data.

We considered the spatial configuration of land protection in the coterminous US. Specifically, we used all 3108 US counties as our units of analysis (median county area in coterminous US = 1670 km2). While other choices of spatial unit would have been possible, counties provide meaningful spatial units for many smaller conservation actors, a convenient reporting unit for relevant socioenvironmental data, and a large enough area to encompass a range of conservation actors.

Protected area data

Protected area data were obtained from the PAD-US 2.0 (US Geological Survey Gap Analysis Project 2018). Data for lands managed by the Bureau of Indian Affairs were collected from PAD-US 1.4 (US Geological Survey Gap Analysis Project 2016) because those data are absent from PAD-US 2.0. Data for rental contract lands managed by the USDA Farm ...

United States Land Use: Land Area: Forest data was reported at 3,097,950.000 sq km in 2022. This stayed constant from the previous number of 3,097,950.000 sq km for 2021. United States Land Use: Land Area: Forest data is updated yearly, averaging 3,066,464.000 sq km from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 3,100,950.000 sq km in 2016 and a record low of 3,024,500.000 sq km in 1990. United States Land Use: Land Area: Forest 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.ESG: Environmental: Land Use: OECD Member: Annual.

United States Land Cover: Bare Area: Permanent Snow and Ice data was reported at 64.949 sq km th in 2019. This stayed constant from the previous number of 64.949 sq km th for 2018. United States Land Cover: Bare Area: Permanent Snow and Ice data is updated yearly, averaging 64.949 sq km th from Dec 1992 (Median) to 2019, with 5 observations. The data reached an all-time high of 64.949 sq km th in 2019 and a record low of 64.949 sq km th in 2019. United States Land Cover: Bare Area: Permanent Snow and Ice 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.ESG: Environmental: Land Cover: OECD Member: Annual.

The statistic shows the largest countries in South America, based on land area. Brazil is the largest country by far, with a total area of over 8.5 million square kilometers, followed by Argentina, with almost 2.8 million square kilometers.

United States Protected Areas: Coastal Within 10km: Habitat or Species Management Area data was reported at 108,853.750 sq km in 2024. This stayed constant from the previous number of 108,853.750 sq km for 2023. United States Protected Areas: Coastal Within 10km: Habitat or Species Management Area data is updated yearly, averaging 108,347.250 sq km from Dec 1950 (Median) to 2024, with 32 observations. The data reached an all-time high of 108,853.750 sq km in 2024 and a record low of 43,113.250 sq km in 1950. United States Protected Areas: Coastal Within 10km: Habitat or Species Management Area 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.ESG: Environmental: Protected Areas: Total for Category by Domain: OECD Member: Annual.

The site suitability criteria included in the techno-economic land use screens are listed below. As this list is an update to previous cycles, tribal lands, prime farmland, and flood zones are not included as they are not technically infeasible for development. The techno-economic site suitability exclusion thresholds are presented in Table 1. Distances indicate the minimum distance from each feature for commercial scale solar development.Attributes:Steeply sloped areas: change in vertical elevation compared to horizontal distancePopulation density: the number of people living in a 1 km2 areaUrban areas: defined by the U.S. Census.8Water bodies: defined by the U.S. National Atlas Water Feature Areas, available from Argonne National Lab Energy Zone Mapping Tool9Railways: a comprehensive database of North America's railway system from the Federal Railroad Administration (FRA), available from Argonne National Lab Energy Zone Mapping ToolMajor highways: available from ESRI Living Atlas10Airports: The Airports dataset including other aviation facilities as of July 13, 2018 is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics' (BTS's) National Transportation Atlas Database (NTAD). The Airports database is a geographic point database of aircraft landing facilities in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the landing facility, current usage including enplanements and aircraft operations, congestion levels and usage categories. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product. Available from Argonne National Lab Energy Zone Mapping ToolActive mines: Active Mines and Mineral Processing Plants in the United States in 200311Military Lands: Land owned by the federal government that is part of a US military base, camp, post, station, yard, center or installation.Table 1 Solar Steeply sloped areas >10o Population density >100/km2 Capacity factor <20% Urban areas <500 m Water bodies <250 m Railways <30 m Major highways <125 m Airports <1000 m Active mines <1000 m Military Lands <1000m For more information about the processes and sources used to develop the screening criteria see sources 1-7 in the footnotes. Data updates occur as needed, corresponding to typical 3-year CPUC IRP planning cycles.Footnotes:[1] Lopez, A. et. al. “U.S. Renewable Energy Technical Potentials: A GIS-Based Analysis,” 2012. https://www.nrel.gov/docs/fy12osti/51946.pdf[2] https://greeningthegrid.org/Renewable-Energy-Zones-Toolkit/topics/social-environmental-and-other-impacts#ReadingListAndCaseStudies[3] Multi-Criteria Analysis for Renewable Energy (MapRE), University of California Santa Barbara. https://mapre.es.ucsb.edu/[4] Larson, E. et. al. “Net-Zero America: Potential Pathways, Infrastructure, and Impacts, Interim Report.” Princeton University, 2020. https://environmenthalfcentury.princeton.edu/sites/g/files/toruqf331/files/2020-12/Princeton_NZA_Interim_Report_15_Dec_2020_FINAL.pdf.[5] Wu, G. et. al. “Low-Impact Land Use Pathways to Deep Decarbonization of Electricity.” Environmental Research Letters 15, no. 7 (July 10, 2020). https://doi.org/10.1088/1748-9326/ab87d1.[6] RETI Coordinating Committee, RETI Stakeholder Steering Committee. “Renewable Energy Transmission Initiative Phase 1B Final Report.” California Energy Commission, January 2009.[7] Pletka, Ryan, and Joshua Finn. “Western Renewable Energy Zones, Phase 1: QRA Identification Technical Report.” Black & Veatch and National Renewable Energy Laboratory, 2009. https://www.nrel.gov/docs/fy10osti/46877.pdf.[8] https://www.census.gov/cgi-bin/geo/shapefiles/index.php?year=2019&layergroup=Urban+Areas[9] https://ezmt.anl.gov/[10] https://www.arcgis.com/home/item.html?id=fc870766a3994111bce4a083413988e4[11] https://mrdata.usgs.gov/mineplant/CreditsTitle: Techno-economic screening criteria for utility-scale solar photovoltaic energy installations for Integrated Resource PlanningPurpose for creation: These exclusion criteria are for use in electric system planning, capacity expansion modeling, and integrated resource planning.Keywords: solar, photovoltaic, resource potential, techno-economic, PV, IRPExtent: western states of the contiguous U.S.Use LimitationsThe geospatial data created by the use of these techno-economic screens inform high-level estimates of technical renewable resource potential for electric system planning and should not be used, on their own, to guide siting of generation projects nor assess project-level impacts. Confidentiality: PublicContactEmily Leslie Emily@MontaraMtEnergy.comSam Schreiber sam.schreiber@ethree.com Jared Ferguson Jared.Ferguson@cpuc.ca.gov Oluwafemi Sawyerr femi@ethree.com

This data set represents "water input," the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter, in the conterminous United States. The data set was used as an input data layer for a national model to predict nitrate concentration in ground water used for drinking. Nolan and Hitt (2006) developed two national models to predict contamination of ground water by nonpoint sources of nitrate. The nonlinear approach to national-scale Ground-WAter Vulnerability Assessment (GWAVA) uses components representing nitrogen (N) sources, transport, and attenuation. One model (GWAVA-S) predicts nitrate contamination of shallow (typically less than 5 meters deep), recently recharged ground water, which may or may not be used for drinking. The other (GWAVA-DW) predicts ambient nitrate concentration in deeper supplies used for drinking. This data set is one of 14 data sets (1 output data set and 13 input data sets) associated with the GWAVA-DW model. Full details of the model development are in Nolan and Hitt (2006). For inputs to the model, spatial attributes representing 13 nitrogen loading and transport and attenuation factors were compiled as raster data sets (1-km by 1-km grid cell size) for the conterminous United States (see table 1).

Table 1.-- Parameters of nonlinear regression model for nitrate in ground water used for drinking (GWAVA-DW) and corresponding input spatial data sets. [kg, kilograms; km2, square kilometers.]

Nitrogen Source Factors Data Set Name 1 farm fertilizer (kg/hectare) gwava-dw_ffer 2 confined manure (kg/hectare) gwava-dw_conf 3 orchards/vineyards (percent) gwava-dw_orvi 4 population density (people/km2) gwava-dw_popd

Transport to Aquifer Factors 5 water input (km2/cm) gwava-dw_wtin 6 glacial till (yes/no) gwava-dw_gtil 7 semiconsolidated sand aquifers gwava-dw_semc (yes/no) 8 sandstone and carbonate rocks gwava-dw_sscb (yes/no) 9 drainage ditch (km2) gwava-dw_ddit 10 Hortonian overland flow gwava-dw_hor (percent of streamflow)

Attenuation Factors 11 fresh surface water withdrawal gwava-dw_swus for irrigation (megaliters/day) 12 irrigation tailwater recovery (km2) gwava-dw_twre 13 Dunne overland flow gwava-dw_dun (percent of streamflow) 14 well depth (meters) - "Farm fertilizer" is the average annual nitrogen input from commercial fertilizer applied to agricultural lands, 1992-2001, in kilograms per hectare. "Confined manure" is the average annual nitrogen input from confined animal manure, 1992 and 1997, in kilograms per hectare. "Orchards/vineyards" is the percent of orchards/vineyards land cover classification. "Population density" is 1990 block group population density, in people per square kilometer. "Water input" is the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter. "Glacial till" is the presence or absence of poorly sorted glacial till east of the Rocky Mountains. "Semiconsolidated sand aquifers" is the presence or absence of semiconsolidated sand aquifers. "Sandstone and carbonate rocks" is the presence or absence of sandstone and carbonate rock aquifers. "Drainage ditch" is the area of National Resources Inventory surface drainage, field ditch conservation practice, in square kilometers. "Hortonian overland flow" is infiltration excess overland flow estimated by TOPMODEL, in percent of streamflow. "Fresh surface water withdrawal for irrigation" is the amount of fresh surface water withdrawal for irrigation, in megaliters per day. "Irrigation tailwater recovery" is the area of National Resources Inventory irrigation system, tailwater recovery conservation practice, in square kilometers. "Dunne overland flow" is saturation overland flow estimated by TOPMODEL, in percent of streamflow. "Well depth" is the depth of the well, in meters. Well depth was not compiled as a spatial data set. Well depth equals 50 meters for the model simulation being presented. Reference cited: Nolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Environmental Science and Technology, vol. 40, no. 24, pages 7834-7840.

Coastal Louisiana wetlands are one of the most critically threatened environments in the United States. These wetlands are in peril because Louisiana currently experiences greater coastal wetland loss than all other States in the contiguous United States combined. The datasets presented here were utilized in a larger effort to quantify landscape changes from 1932 to 2016. Analyses show that coastal Louisiana has experienced a net change in land area of approximately -4,833 square kilometers (modeled estimate: -5,197 +/- 443 square kilometers) from 1932 to 2016. This net change in land area amounts to a decrease of approximately 25 percent of the 1932 land area. Previous studies have presented linear rates of change over multidecadal time periods which unintentionally suggest that wetland change occurs at a constant rate, although in many cases, wetland change rates vary with time. A penalized regression spline technique was used to determine the model that best fit the data, rather than fitting the data with linear trends. Trend analyses from model fits indicate that coastwide rates of wetland change have varied from -83.5 +/- 11.8 square kilometers per year to -28.01 +/- 16.37 square kilometers per year. To put these numbers into perspective, this equates to long-term average loss rates of approximately an American football field’s worth of coastal wetlands within 34 minutes when losses are rapid to within 100 minutes at more recent, slower rates. Of note is the slowing of the rate of wetland change since its peak in the mid- 1970s. Not only have rates of wetland loss been decreasing since that time, a further rate reduction has been observed since 2010. Possible reasons for this reduction include recovery from lows affected by the hurricanes of 2005 and 2008, the lack of major storms in the past 8 years, a possible slowing of subsidence rates, the reduction in and relocation of oil and gas extraction and infrastructure since the peak of such activities in the late 1960s, and restoration activities. In addition, many wetlands in more exposed positions in the landscape have already been lost. Most notable of the factors listed above is the lack of major storms over the past 8 years. The observed coastwide net “stability” in land area observed over the past 6–8 years does not imply that loss has ceased. Future disturbance events such as a major hurricane impact could change the trajectory of the loss rates. Sea-level rise is projected to accelerate over time which might also inflate the rate of wetland loss above current or recent trends and conditions.

The U.S. Geological Survey (USGS), in cooperation with the National Oceanic and Atmospheric Administration's National Marine Sanctuary Program, has conducted seabed mapping and related research in the Stellwagen Bank National Marine Sanctuary region since 1993. The area is approximately 3,700 square kilometers (km2) and is subdivided into 18 quadrangles. Seven maps, at a scale of 1:25,000, of quadrangle 6 (211 km2) depict seabed topography, backscatter, ruggedness, geology, substrate mobility, mud content, and areas dominated by fine-grained or coarse-grained sand. Interpretations of bathymetric and seabed backscatter imagery, photographs, video, and grain-size analyses were used to create the geology-based maps. In all, data from 420 stations were analyzed, including sediment samples from 325 locations. The seabed geology map shows the distribution of 10 substrate types ranging from boulder ridges to immobile, muddy sand to mobile, rippled sand. Substrate types are defined on the basis of sediment grain-size composition, surficial morphology, sediment layering, and the mobility or immobility of substrate surfaces. This map series is intended to portray the major geological elements (substrates, features, processes) of environments within quadrangle 6. Additionally, these maps will be the basis for the study of the ecological requirements of invertebrate and vertebrate species that utilize these substrates and guide seabed management in the region.

Russia is the largest country in Europe, and also the largest in the world, its total size amounting to 17 million square kilometers (km2). It should be noted, however, that over three quarters of Russia is located in Asia, and the Ural mountains are often viewed as the meeting point of the two continents in Russia; nonetheless, European Russia is still significantly larger than any other European country. Ukraine, the second largest country on the continent, is only 603,000 km2, making it about 28 times smaller than its eastern neighbor, or seven times smaller than the European part of Russia. France is the third largest country in Europe, but the largest in the European Union. The Vatican City, often referred to as the Holy Sea, is both the smallest country in Europe and in the world, at just one km2. Population Russia is also the most populous country in Europe. It has around 144 million inhabitants across the country; in this case, around three quarters of the population live in the European part, which still gives it the largest population in Europe. Despite having the largest population, Russia is a very sparsely populated country due to its size and the harsh winters. Germany is the second most populous country in Europe, with 83 million inhabitants, while the Vatican has the smallest population. Worldwide, India and China are the most populous countries, with approximately 1.4 billion inhabitants each. Cities Moscow in Russia is ranked as the most populous city in Europe with around 13 million inhabitants, although figures vary, due to differences in the methodologies used by countries and sources. Some statistics include Istanbul in Turkey* as the largest city in Europe with its 15 million inhabitants, bit it has been excluded here as most of the country and parts of the city is located in Asia. Worldwide, Tokyo is the most populous city, with Jakarta the second largest and Delhi the third.