This map shows the population density and total population in the United States in 2010. This is shown by state, county, tract, and block group. The color shows the population per square mile (population density), while the size of each feature shows the total population living there. This is a valuable way to represent population by understanding the quantity and density of the people living there. Areas with high population density are more tightly packed, while low population density means the population is more spread out.The map shows this pattern for states, counties, tracts, and block groups. There is increasing geographic detail as you zoom in, and only one geography is configured to show at any time. The data source is the US Census Bureau, and the vintage is 2010. The original service and data metadata can be found here.

This map shows density surfaces derived from the 2010 US Census block points.This data shows % of people who identified themselves as single race and whiteThe block points were interpolated using the density function to a 2km x 2km grid of the continental US (with water and coastal data masks). There are many stories in these Maps:- What is that clean North/South Line through the center? Why do so many people live East of that line?- Notice the paths of the towns in the west – why are they so linear? And it seems there is a pattern to the spaces between the towns, why?- Looking at the ethnic maps, what explains the patterns? Look at the % Native American map – what are the areas of higher values? (note I did not make a % Asian map as at this scale there was not enough % to show any significant clusters.)

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.

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.

This map is designed to work in the new ArcGIS Online Map Viewer. Open in Map Viewer to view map. What does this map show?This map shows the population in the US by race. The map shows this pattern nationwide for states, counties, and tracts. Open the map in the new ArcGIS Online Map Viewer Beta to see the dot density pattern. What is dot density?The density is visualized by randomly placing one dot per a given value for the desired attribute. Unlike choropleth visualizations, dot density can be mapped using total counts since the size of the polygon plays a significant role in the perceived density of the attribute.Where is the data from?The data in this map comes from the most current American Community Survey (ACS) from the U.S. Census Bureau. Table B03002. The layer being used if updated with the most current data each year when the Census releases new estimates. The layer can be found in ArcGIS Living Atlas of the World: ACS Race and Hispanic Origin Variables - Boundaries.What questions does this map answer?Where do people of different races live?Do people of a similar race live close to people of their own race?Which cities have a diverse range of different races? Less diverse?

This map shows population density of the United States. Areas in darker magenta have much higher population per square mile than areas in orange or yellow. Data is from the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics. The map's layers contain total population counts by sex, age, and race groups for Nation, State, County, Census Tract, and Block Group in the United States and Puerto Rico. From the Census:"Population density allows for broad comparison of settlement intensity across geographic areas. In the U.S., population density is typically expressed as the number of people per square mile of land area. The U.S. value is calculated by dividing the total U.S. population (316 million in 2013) by the total U.S. land area (3.5 million square miles).When comparing population density values for different geographic areas, then, it is helpful to keep in mind that the values are most useful for small areas, such as neighborhoods. For larger areas (especially at the state or country scale), overall population density values are less likely to provide a meaningful measure of the density levels at which people actually live, but can be useful for comparing settlement intensity across geographies of similar scale." SourceAbout the dataYou can use this map as is and you can also modify it to use other attributes included in its layers. This map's layers contain total population counts by sex, age, and race groups data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, State, County, Census Tract, Block Group boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this feature layer.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, State, County, Census Tract, Block GroupNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This map is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

This map shows density surfaces derived from the 2010 US Census block points.The block points were interpolated using the density function to a 2km x 2km grid of the continental US (with water and coastal data masks). There are many stories in these Maps:- What is that clean North/South Line through the center? Why do so many people live East of that line?- Notice the paths of the towns in the west – why are they so linear? And it seems there is a pattern to the spaces between the towns, why?- Looking at the ethnic maps, what explains the patterns? Look at the % Native American map – what are the areas of higher values? (note I did not make a % Asian map as at this scale there was not enough % to show any significant clusters.)

The world's most accurate population datasets. Seven maps/datasets for the distribution of various populations in American Samoa: (1) Overall population density (2) Women (3) Men (4) Children (ages 0-5) (5) Youth (ages 15-24) (6) Elderly (ages 60+) (7) Women of reproductive age (ages 15-49).

Link to landing page referenced by identifier. Service Protocol: Link to landing page referenced by identifier. Link Function: information-- dc:identifier.

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.

This map shows density surfaces derived from the 2010 US Census block points.This data shows % of people who identified themselves as single race and hispanic.The block points were interpolated using the density function to a 2km x 2km grid of the continental US (with water and coastal data masks). There are many stories in these Maps:- What is that clean North/South Line through the center? Why do so many people live East of that line?- Notice the paths of the towns in the west – why are they so linear? And it seems there is a pattern to the spaces between the towns, why?- Looking at the ethnic maps, what explains the patterns? Look at the % Native American map – what are the areas of higher values? (note I did not make a % Asian map as at this scale there was not enough % to show any significant clusters.)

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.

Population per hexagon, using 5-year American Community Survey data from 2011. Since each hexagon is equivalent in area, this also serves as a population density map. The data was received as population per census tract. Then a ratio was created: Tract Population/Tract Area = Hexagon Population/Hexagon Area. This was rearranged so that: Hexagon population = HexArea(TractPop/TractArea).

This map service displays data derived from the 2008-2012 American Community Survey (ACS). Values derived from the ACS and used for this map service include: Total Population, Population Density (per square mile), Percent Minority, Percent Below Poverty Level, Percent Age (less than 5, less than 18, and greater than 64), Percent Housing Units Built Before 1950, Percent (population) 25 years and over (with less than a High School Degree and with a High School Degree), Percent Linguistically Isolated Households, Population of American Indians and Alaskan Natives, Population of American Indians and Alaskan Natives Below Poverty Level, and Percent Low Income Population (Less Than 2X Poverty Level). The map service was created for inclusion in US EPA mapping applications.

The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery.

Population Raster American Samoa 2020 Data Input: Settlement footprint from Facebook's High-Resolution Population Density Maps (https://data.humdata.org/dataset/american-samoa-high-resolution-population-density-maps-demographic-estimates) Population allocated proportionally using 2011 census population counts at enumeration area level. Year Population Growth Rate of 0.23% has been applied to update population up to 2020

This map shows schools, school districts, and population density throughout the US. Click on the map to learn more about the school districts and schools within an area. A few things you can learn within this map:How many public/private schools fall within the district?What type of population density lives within this district? Socioeconomic factors about the Census Tracts which fall within the district:School enrollment of under 19 by grade Children living below the poverty level Children with no internet at home Children without a working parentRace/ethnicity breakdown of the population within the districtFor more information about the data sources:Socioeconomic factors:The American Community Survey (ACS) helps us understand the population in the US. This app uses the 5-year estimates, and the data is updated annually when the U.S. Census Bureau releases the newest estimates. For detailed metadata, visit the links in the bullet points above. Current School Districts layer:The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated school district boundary composite files that include public elementary, secondary, and unified school district boundaries clipped to the U.S. shoreline. School districts are single-purpose administrative units designed by state and local officials to organize and provide public education for local residents. District boundaries are collected for NCES by the U.S. Census Bureau to support educational research and program administration, and the boundaries are essential for constructing district-level estimates of the number of children in poverty.The Census Bureau’s School District Boundary Review program (SDRP) (https://www.census.gov/programs-surveys/sdrp.html) obtains the boundaries, names, and grade ranges from state officials, and integrates these updates into Census TIGER. Census TIGER boundaries include legal maritime buffers for coastal areas by default, but the NCES composite file removes these buffers to facilitate broader use and cleaner cartographic representation. The NCES EDGE program collaborates with the U.S. Census Bureau’s Education Demographic, Geographic, and Economic Statistics (EDGE) Branch to develop the composite school district files. The inputs for this data layer were developed from Census TIGER/Line and represent the most current boundaries available. For more information about NCES school district boundary data, see https://nces.ed.gov/programs/edge/Geographic/DistrictBoundaries.Private Schools layer:This Private Schools feature dataset is composed of private elementary and secondary education facilities in the United States as defined by the Private School Survey (PSS, https://nces.ed.gov/surveys/pss/), National Center for Education Statistics (NCES, https://nces.ed.gov), US Department of Education for the 2017-2018 school year. This includes all prekindergarten through 12th grade schools as tracked by the PSS. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 2675 new records, modifications to the spatial location and/or attribution of 19836 records, the removal of 254 records no longer applicable. Additionally, 10,870 records were removed that previously had a STATUS value of 2 (Unknown; not represented in the most recent PSS data) and duplicate records identified by ORNL.Public Schools layer:This Public Schools feature dataset is composed of all Public elementary and secondary education facilities in the United States as defined by the Common Core of Data (CCD, https://nces.ed.gov/ccd/ ), National Center for Education Statistics (NCES, https://nces.ed.gov ), US Department of Education for the 2017-2018 school year. This includes all Kindergarten through 12th grade schools as tracked by the Common Core of Data. Included in this dataset are military schools in US territories and referenced in the city field with an APO or FPO address. DOD schools represented in the NCES data that are outside of the United States or US territories have been omitted. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 3065 new records, modifications to the spatial location and/or attribution of 99,287 records, and removal of 2996 records not present in the NCES CCD data.WorldPop Populated Foorprint layer:This layer represents an estimate of the footprint of human settlement in 2020. It is intended as a fast-drawing cartographic layer to augment base maps and to focus a map reader's attention on the location of human population. This layer is not intended for analysis.This layer was derived from the 2020 slice of the WorldPop Population Density 2000-2020 100m and 1km layers. WorldPop modeled this population footprint based on imagery datasets and population data from national statistical organizations and the United Nations. Zooming in to very large scales will often show discrepancies between reality and this or any model. Like all data sources imagery and population counts are subject to many types of error, thus this gridded footprint contains errors of omission and commission. The imagery base maps available in ArcGIS Online were not used in WorldPop's model. Imagery only informs the model of characteristics that indicate a potential for settlement, and cannot intrinsically indicate whether any or how many people live in a building.

The typical statewide or county-wide red/blue map (shown at left) depicts presidential voting results on a winner-take-all basis, so they award an entire geographical area to the Republican or Democratic candidate no matter how close the actual vote tally The large map in the attachment factors in both the percentage of the popular vote won by each candidate as well as the population density of each county. So, the sparsely populated Great Plains and Rocky Mountain West are shown in a much lighter color than the Eastern Seaboard, and the map as a whole is more purple than either red or blue. Perhaps the United States is less divided than some maps would lead us to believe.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a plate that shows the distribution of population in what is now Canada circa 1851, 1871, 1901, 1921 and 1941. The five maps display the boundaries of the various colonies, provinces and territories for each date. Also shown on these five maps are the locations of principal cities and settlements. These places are shown on all of the maps for reference purposes even though they may not have been in existence in the earlier years. Each map is accompanied by a pie chart providing the percentage distribution of Canadian population by province and territory corresponding to the date the map is based on. It should be noted that the pie chart entitled Percentage Distribution of Total Population, 1851, refers to the whole of what was then British North America. The name Canada in this chart refers to the province of Canada which entered confederation in 1867 as Ontario and Quebec. The other pie charts, however, show only percentage distribution of population in what was Canada at the date indicated. Three additional graphs are included on this plate and show changes in the distribution of the population of Canada from 1867 to 1951, changes in the percentage distribution of the population of Canada by provinces and territories from 1867 to 1951 and elements in the growth of the population of Canada for each ten-year period from 1891 to 1951.

This map service displays data derived from the 2006-2010 American Community Survey (ACS). Values derived from the ACS and used for this map service include: Total Population, Population Density (per square mile), Percent Minority, Percent Below Poverty Level, Percent Age (less than 5, less than 18, and greater than 64), Percent Housing Units Built Before 1950, Percent (population) 25 years and over (with less than a High School Degree and with a High School Degree), Percent Linguistically Isolated Households, Population of American Indians and Alaskan Natives, Population of American Indians and Alaskan Natives Below Poverty Level. The map service was created for inclusion in US EPA mapping applications.