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 July of 2025.

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;

Surface area (sq. km) in United States was reported at 9831510 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Surface area (sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Population density (people per sq. km of land area) in United States was reported at 36.51 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

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;

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.

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 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.

United States US: Surface Area data was reported at 9,831,510.000 sq km in 2017. This stayed constant from the previous number of 9,831,510.000 sq km for 2016. United States US: Surface Area data is updated yearly, averaging 9,629,090.000 sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 9,831,510.000 sq km in 2017 and a record low of 9,629,090.000 sq km in 1999. United States US: Surface 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. Surface area is a country's total area, including areas under inland bodies of water and some coastal waterways.; ; Food and Agriculture Organization, electronic files and web site.; Sum;

Two datasets provide geographic, land use and population data for US Counties within the contiguous US. Land area, water area, cropland area, farmland area, pastureland area and idle cropland area are given along with latitude and longitude of the county centroid and the county population. Variables in this dataset come from the US Dept. of Agriculture (USDA) Natural Resources Conservation Service (NRCS) and the US Census Bureau.

EOS-WEBSTER provides seven datasets which provide county-level data on agricultural management, crop production, livestock, soil properties, geography and population. These datasets were assembled during the mid-1990's to provide driving variables for an assessment of greenhouse gas production from US agriculture using the DNDC agro-ecosystem model [see, for example, Li et al. (1992), J. Geophys. Res., 97:9759-9776; Li et al. (1996) Global Biogeochem. Cycles, 10:297-306]. The data (except nitrogen fertilizer use) were all derived from publicly available, national databases. Each dataset has a separate DIF.

The US County data has been divided into seven datasets.

US County Data Datasets:

1) Agricultural Management 2) Crop Data (NASS Crop data) 3) Crop Summary (NASS Crop data) 4) Geography and Population 5) Land Use 6) Livestock Populations 7) Soil Properties

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;

We developed a model for analyzing multi-year demographic data for long-lived animals and used data from a population of Agassiz’s desert tortoise (Gopherus agassizii) at the Desert Tortoise Research Natural Area in the western Mojave Desert of California, USA, as a case study. The study area was 7.77 square kilometers and included two locations: inside and outside the fenced boundary. The wildlife-permeable, protective fence was designed to prevent entry from vehicle users and sheep grazing. We collected mark-recapture data from 1,123 tortoises during 7 annual surveys consisting of two censuses each over a 34-year period. We used a Bayesian modeling framework to develop a multistate Jolly-Seber model because of its ability to handle unobserved (latent) states and modified this model to incorporate the additional data from non-survey years. For this model we incorporated 3 size-age states (juvenile, immature, adult), sex (female, male), two location states (inside and outside the fenced boundary) and 3 survival states (not-yet-entered, entered/alive, and dead/removed). We calculated population densities and estimated probabilities of growth of the tortoises from one size-age state to a larger size-age state, survival after 1 year and 5 years, and detection. Our results show a declining population with low estimates for survival after 1 year and 5 years. The probability for tortoises to move from outside to inside the boundary fence was greater than for tortoises to move from inside the fence to outside. The probability for detecting tortoises differed by size-age state and was lowest for the smallest tortoises and highest for the adult tortoises. The framework for the model can be used to analyze other animal populations where vital rates are expected to vary depending on multiple individual states. The model was incorporated into the manuscript that included several other databases for publication in Wildlife Monographs in 2020 by Berry et al.

Data from 11 000 public supply wells in 87 study areas were used to assess the quality of nearly all of the groundwater used for public supply in California. Two metrics were developed for quantifying groundwater quality: area with high concentrations (km2 or proportion) and equivalent-population relying upon groundwater with high concentrations (number of people or proportion). Concentrations are considered high if they are above a human-health benchmark. When expressed as proportions, the metrics are area-weighted and population-weighted detection frequencies. On a statewide-scale, about 20% of the groundwater used for public supply has high concentrations for one or more constituents (23% by area and 18% by equivalent-population). On the basis of both area and equivalent-population, trace elements are more prevalent at high concentrations than either nitrate or organic compounds at the statewide-scale, in eight of nine hydrogeologic provinces, and in about three-quarters of the study areas. At a statewide-scale, nitrate is more prevalent than organic compounds based on area, but not on the basis of equivalent-population. The approach developed for this paper, unlike many studies, recognizes the importance of appropriately weighting information when changing scales, and is broadly applicable to other areas.

Wildfires and prescribed fires are frequent but under-mapped across wetlands of the southeastern United States . High annual precipitation supports rapid post-fire recovery of wetland vegetation, while associated cloud cover limits clear-sky observations. In addition, the low burn severity of prescribed fires and spectral confusion between fluctuating water levels and burned areas have resulted in wetland burned area being chronically under-estimated across the region. In this analysis, we first quantify the increase in clear-sky observations by using Sentinel-2 in addition to Landsat 8. We then present an approach using the Sentinel-2 archive (2016-2019) to train a wetland burned area algorithm at 20 m resolution. We coupled a Python-derived random forest model with Google Earth Engine to apply the algorithm across the southeastern United States (>290,000 km2). The burned area extent was validated (burned, unburned) using points derived from 27 WorldView-2 and WorldView-3 images. The burned area extent was compared to 555 wetland fire perimeters compiled from state and federal agencies. On an annual timestep, combining the Sentinel-2 and Landsat 8 data increased the mean observation count from 17 to 46 in 2016 and from 16 to 78 in 2019. When validating single-scene burned area extent, the Sentinel-2 output had 29% and 30% omission and commission error rates, respectively. We compared this to the U.S. Geological Survey’s Landsat 8 Burned Area Product (L8 BA), which had 47% and 8% omission and commission error rates, respectively. Across the four-year period, by count the Sentinel-2 burned area detected 78% of the wetland fire perimeters, compared to the L8 BA which detected 60% of the wetland fire perimeters. By area, Sentinel-2 burned area mapped 48% of the perimeter area as burned, compared to the L8 BA which mapped 32% of the perimeter area as burned. This analysis demonstrated the potential of Sentinel-2 to support efforts to track burned area extent even across challenging ecosystem types, such as wetlands.

Wildfires are increasingly modifying wildlife habitat in the western United States and managers need ways to scope the pace and degree to which post-fire restoration actions can re-create habitat in dynamic landscapes. We simulated post-fire revegetation and greater sage-grouse (Centrocercus urophasianus) habitat restoration using a spatially explicit state-transition simulation model (STSM) developed for sagebrush ecosystems. The STSM represented the vegetation dynamics of the sagebrush ecosystem and included annual fires, annual grass invasion, conifer encroachment, and sagebrush revegetation restoration. We compared simulated vegetation output with sage-grouse perennial grass and sagebrush cover habitat needs and evaluated trajectories of potential habitat for three sage-grouse Priority Area for Conservation (PACs) populations located along the northwestern, central, and eastern edge of the Great Basin. This data release is organized into two general datasets: the ST-Sim library and the associated projections of potential sage-grouse habitat (organized by population). Habitat layers illustrate a time series of potential habitat and 50-year potential change in habitat classification for sage-grouse across space and time. The structure of these data follow: A) STSM Model – contains the ST-Sim library, input, and output files; SagebrushSteppeRestoration.ssim, B) KLAM Habitat Data – contains habitat data for the Klamath Oregon/California PAC (located in the northwestern region of the Great Basin), C) NWINV Habitat Data – contains habitat data for the NW Interior Nevada PAC (located in the central region of the Great Basin), and D) STRAW Habitat Data – contains habitat data for the Strawberry Utah PAC (located along the eastern edge of the Great Basin). The STSM was built using the Syncrosim ST-Sim platform with the software's integrated stock-flow submodel to simulate and track continuous vegetation component cover changes caused by annual growth, natural regeneration, and post-fire sagebrush seeding and planting restoration. Thirteen restoration scenarios representing a combination of three revegetation alternatives (no restoration, seeding, planting) under three effort levels (average, double, maximum), and two durations (single-year, multi-year) were simulated for each PAC landscape. Seeding and planting effort levels were based on historic treatment area polygon data (median size) for sagebrush seeding (6 km2) and planting (4 km2). Area was used as a measure of effort that represented an annual fire response equivalent to average effort, double effort (2x area median), and maximum effort (45 km2). The ‘maximum effort’ scenario represented a hypothetical management response 7-11 times larger than average post-fire revegetation treatment area sizes. Planting scenarios represented the sagebrush cover gains of planting 4 plants/m2 (low-density; LD planting) and 8 plants/m2 (high-density; HD planting). A combination seeding-planting scenario representing single-year gains from seeding and multi-year gains from HD planting (two additional years of sagebrush cover gains) and a passive no restoration scenario equivalent to ‘no effort’ were simulated to compare with single- and multi-year seeding or planting scenarios. Habitat layers were generated at 10-year intervals using the simulated vegetation outputs from the five best restoration scenarios of each type (no restoration, seeding, LD planting, HD planting, multi-year) for each PAC landscape. Sagebrush and perennial grass cover from projected continuous component cover values tracked in the STSM stock-flow (SF) submodel were used to characterize potential habitat based on sage-grouse seasonal life stage cover requirements. Habitat distinctions were based on a given pixel meeting minimum cover amounts and classified pixels as suitable, marginally suitable, and unsuitable relative to seasonal spring (i.e., breeding period), summer (i.e., brood-rearing period), and winter sagebrush and perennial grass cover requirements. The three PAC study sites represented the range of vegetation composition and dynamics present in sagebrush-steppe systems and contained variable amounts of annual grass and pinyon-juniper cover that exemplified degraded sagebrush shrubland (Klamath Oregon/California PAC), at-risk of annual-grass invasion (NW-Interior Nevada PAC), and at-risk of juniper encroachment (Strawberry Utah PAC) landscapes.

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

These data were compiled here to fit various versions of Bayesian population models and compare their performance, primarily the time required to make inferences using different softwares and versions of code. The humpback chub data were collected by US Geological Survey and US Fish and Wildlife service in the Colorado and Little Colorado Rivers from April 2009 to October 2017. Adult fish were captured using hoop nets and electro-fishing, measured for total length and given individual marks using passive integrated transponders that were scanned when fish were recaptured. The other three datasets were collected by US Forest Service. Owl data for the N-occupancy model was collected between 1990 and 2015. Owl data for the two-species example was collected between 1990 and 2011. Both owl data sets were collected in a ~1000 km2 area in the Roseburg District of the Bureau of Land Management in western Oregon, USA. Owl vocalizations (vocal lures) were used to detect barred owl or spotted owl pairs in 158 survey polygons spread throughout the study area. The avian community occupancy data were collected from 1991 to 1995 across 92 sites in the Chiricahua Mountains of southeastern Arizona, USA. 149 species were detected through repeated point counts in each year.

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.

This data set provides the first global inventory of the spatial distribution and density of constructed impervious surface area (ISA) based on the brightness of satellite observed and calibrated nighttime lights [U.S. Air Force Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS)] and population count from ORNL LandScan 2004 [which includes input from three satellite data sources: NASA MODIS land cover, the topographic data from the Shuttle Radar Topography Mission (SRTM), and the high resolution Controlled Image Base (CIB) from the U.S. National Geospatial Intelligence Agency (NGA)]. Examples of ISA include roads, parking lots, buildings, driveways, sidewalks, and other manmade surfaces. While high spatial resolution is required to observe these features, this product was made at one km2 resolution. The reference data used in the calibration were derived from 30-meter resolution ISA estimates of the USA from the U.S. Geological Survey. Nominally the product is for the years 2000-2001 since both the nighttime lights and reference data are from those two years. Investigators used the product to estimate the world’s total ISA, to rank the leading countries in total ISA and to calculate the quantity of ISA per person for individual countries. In addition, they aggregated the ISA density for the major watershed units of the world to identify those watersheds impacted by the proliferation of ISA. Investigators found that 1.05% of the United States land area is impervious surface (83,337 km2) and 0.43% of the world's land surface (579,703 km2) is constructed impervious surface. China has more ISA than any other country (87,182 km2), but has only 67 m2 of ISA per person, compared to 297 m2 per person in the USA. Hydrologic and environmental impacts of ISA begin to be exhibited when the density of ISA reaches 10% of the land surface. An examination of the areas with 10% or more ISA in watersheds finds that with the exception of Europe, the majority of watershed areas have less than 0.4% of their area at or above the 10% ISA threshold. The investigators believe the next step for improving the product is to include reference ISA data from many more areas around the world. For additional information, see Elvidge, C.D., B.T. Tuttle, P.C. Sutton, K.E. Baugh, A.T. Howard, C. Milesi, B.L. Bhaduri, and R. Nemani. 2007. Global Distribution and Density of Constructed Impervious Surfaces. Sensors 7: 1962-1979.

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;