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.

Grid of population density in the conterminous United States at a resolution of one kilometer. The grid was converted from an ASCII file obtained from the Consortium for International Earth Science Information Network (CIESIN).

This graph shows the population density of the United States of America from 1790 to 2019. In 2019, the population density was approximately 92.9 residents per square mile of land area. Population density in the United States Population density has been tracked for over two hundred years in the United States. Over the last two centuries, the number of people living in the United States per square mile has grown from 4.5 in 1790 to 87.4 in 2010. After examining the data in detail, it becomes clear that a major population increase started around 1870. Population density was roughly 11 at the time and has doubled in the last century. Since then, population density grew by about 16 percent each decade. Population density doubled in 1900, and grew in total by around 800 percent until 2010.

The population density of the United States varies from state to state. The most densely populated state is New Jersey, with 1,208 people per square mile living there. Rhode Island is the second most densely populated state, with slightly over 1,000 inhabitants per square mile. A number of New England states follow at the top of the ranking, making the northeastern region of the United States the most densely populated region of the country.

The least populated U.S. state is the vast territory of Alaska. Only 1.3 inhabitants per square mile reside in the largest state of the U.S.

Compared to other countries around the world, the United States does not rank within the top 50, in terms of population density. Most of the leading countries and territories are city states. However, the U.S. is one of the most populous countries in the world, with a total population of over 327 million inhabitants, as of 2018.

The population density in the United States was 36.43 people in 2022. In a steady upward trend, the population density rose by 16.37 people from 1961.

This layer presents population density data by county for states bordering the U.S. Gulf, sourced from the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics. Population density is displayed as the number of people per square kilometer. Broadly speaking, population density indicates how many people would inhabit one square kilometer if the population were evenly distributed across the area. However, population distribution is uneven. People tend to cluster in urban areas, while those in rural regions are spread out over a much more sparsely populated landscape. Population density is a crucial metric for understanding and managing human population dynamics and their effects on society and the environment. It helps assess various environmental challenges, including urban sprawl, pollution, habitat loss, and resource depletion. Coastal areas frequently experience high population density due to urbanization, influencing land use, housing, and infrastructure development. This density can also stimulate tourism and recreation, necessitating careful planning for facilities, transportation, and environmental protection. Additionally, coastal regions are more susceptible to natural disasters such as hurricanes and flooding, making population density data essential for developing effective evacuation plans and emergency services. Data: U.S. Census BureauDocumentation: U.S. Census Bureau This is a component of the Gulf Data Atlas (V2.0) for the Socioeconomic Conditions topic area.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the urban footprint. There are 2,644 Urban Areas (UAs) in this data release with either a minimum population of 5,000 or a housing unit count of 2,000 units. Each urban area is identified by a five-character numeric census code that may contain leading zeros.

This statistics shows a ranking of the metropolitan areas in the United States in 2013 with the highest population density. As of 2013, Los Angeles-Long Beach-Anaheim in California was ranked first with a population density of 1,046 inhabitants per square kilometer.

These high-resolution maps estimate not only the number of people living within 30-meter grid tiles, but also provide insights on demographics at unprecedentedly high resolutions. These maps aren’t built using Facebook data and instead rely on combining the power of machine vision AI with satellite imagery and census information.

Historical dataset showing U.S. population density by year from 1961 to 2022.

This data set represents 2000 population density by block group as a 100-m grid using data from the 2000 Census of Population and Housing. The demographic data is from CensusCD 2000 Short Form Blocks published by GeoLytics, E. Brunswick, NJ, which uses the 2000 Census Summary File 1 (SF 1). Grid cell values represent population density in people per square kilometer multiplied by 10 so that the data could be stored as integer.

Map containing historical census data from 1900 - 2000 throughout the western United States at the county level. Data includes total population, population density, and percent population change by decade for each county. Population data was obtained from the US Census Bureau and joined to 1:2,000,000 scale National Atlas counties shapefile.

The average for 2021 based on 12 countries was 25 people per square km. The highest value was in Ecuador: 72 people per square km and the lowest value was in Guyana: 4 people per square km. The indicator is available from 1961 to 2021. Below is a chart for all countries where data are available.

The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022. The data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.The specific raster datasets included in this publication include:Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.Additional methodology documentation is provided with the data publication download. Metadata and Downloads: (https://www.fs.usda.gov/rds/archive/catalog/RDS-2020-0060-2).Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.

This layer shows Population. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the point by Population Density and size of the point by Total Population. The size of the symbol represents the total count of housing units. Population Density was calculated based on the total population and area of land fields, which both came from the U.S. Census Bureau. Formula used for Calculating the Pop Density (B01001_001E/GEO_LAND_AREA_SQ_KM). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): B01001, B09020Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

Monaco led the ranking for countries with the highest population density in 2024, with nearly 26,000 residents per square kilometer. The Special Administrative Region of Macao came in second, followed by Singapore. The world’s second smallest country Monaco is the world’s second-smallest country, with an area of about two square kilometers and a population of only around 40,000. It is a constitutional monarchy located by the Mediterranean Sea, and while Monaco is not part of the European Union, it does participate in some EU policies. The country is perhaps most famous for the Monte Carlo casino and for hosting the Monaco Grand Prix, the world's most prestigious Formula One race. The global population Globally, the population density per square kilometer is about 60 inhabitants, and Asia is the most densely populated region in the world. The global population is increasing rapidly, so population density is only expected to increase. In 1950, for example, the global population stood at about 2.54 billion people, and it reached over eight billion during 2023.

This statistic shows the change in the regional distribution of the U.S. population each decade from 1790 to 2021. In 2021, 17.2 percent of the population in the United States lived in the Northeast.

This dataset provides detailed information about the population of all the 300 US Cities for the years 2024 and 2020. It includes the annual population change, population density, and the area of all the US cities.

This graph shows population projections for the United States of America from 2015 to 2060, by age group. In 2060, the estimated population of residents of the U.S. over 100 years of age is 604,000.

This tabular data set represents population density, persons per square kilometer from 2000Census block level data, processed by James Falcone (USGS, 2016), compiled for two spatial components of the NHDPlus version 2 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID. The source data for population density was produced by the U.S. Geological Survey (Falcone, 2016). Units are persons per square kilometer. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. (Schwarz and Wieczorek, 2018).

These datasets contain measures of socioeconomic and demographic characteristics by U.S. census tract for the years 1990-2022 and ZIP code tabulation area (ZCTA) for the years 2008-2022. Example measures include population density; population distribution by race, ethnicity, age, and income; income inequality by race and ethnicity; and proportion of population living below the poverty level, receiving public assistance, and female-headed or single parent families with kids. The datasets also contain a set of theoretically derived measures capturing neighborhood socioeconomic disadvantage and affluence, as well as a neighborhood index of Hispanic, foreign born, and limited English.