This graph shows the population density in the federal state of Texas from 1960 to 2018. In 2018, the population density of Texas stood at 109.9 residents per square mile of land area.

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.

Explore our new interactive population density maps for MSA, County, Tract, Block Group, Place, School District, and ZCTA geographies in Texas. These pop density maps are based on the latest ACS 5-Year estimates and TIGER/Line data. Inspired by a map of the same produced by the Texas Demography Center.

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. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined because of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division or incorporated place boundaries in some states and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard Census Bureau geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous.

This data set contains population densities of U.S. counties based on land area size and 2010 census data. I have not found a central repository for all U.S. counties, so I started with only Texas and New York counties. Data sources are shown in the file descriptions and in the provenance metadata.

There are many use cases for these data, but I specifically compiled them to be used with the UNCOVER COVID-19 Challenge data set. Population density will likely influence a given county's baseline contagion rate (i.e. the contagion rate before social distancing policies, PPE use, and other preventative measures are put in place).

These are the data for the Demographic Profiles displayed on austintexas.gov/demographics. These profiles were published in 2025, but display data from 2023 and 2024.

Most data are from the 2023 American Community Survey (the most recent available at the time of publication), but some data have other sources. All data come from the American Community Survey estimates except for:

Total Population - City of Austin Planning (2023) (City and Council Districts only) Population Low-Moderate Income - Dept. of Housing and Urban Development LMISD Summary Data (5 year 2016-2020) Occupied Housing Units - City of Austin Planning (2023) (City and Council Districts only) Median Home Closing Price - Austin Board of Realtors (2024) Average Monthly Rent - ApartmentTrends.com by Austin Investor Interests (Q4 2024) Income Restricted Units - City of Austin Affordable Housing Inventory (March 2025) Housing Units - City of Austin Planning (2023)(City only) Population Density - Esri Updated Demographics (2024) (County, MSA, Council Districts) Daytime Population Density - Esri Updated Demographics (2024) (County, MSA, Council Districts) Population Density - Calculation derived from 2023 Population Estimates, City of Austin Demographics & Data Division (City only) Daytime Population Density - 2023 Population Estimates, City of Austin Demographics & Data Division (City only) Selected Land Use Percentages - City of Austin Land Use Inventory (2024) Transit Stops - Capital Metro (January 2025)

City, County, and MSA data are 1-Year ACS estimates. Council Districts are 5-year ACS estimates.

Some datapoints may not be available for all geographies.

More information and links to these alternate sources, when available, can be found at austintexas.gov/demographics.

These profiles are updated annually.

City of Austin Open Data Terms of Use – https://data.austintexas.gov/stories/s/ranj-cccq

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. Census blocks are statistical areas bounded on all sides by visible features, such as streets, roads, streams, and railroad tracks, and/or by nonvisible boundaries such as city, town, township, and county limits, and short line-of-sight extensions of streets and roads. Census blocks are relatively small in area; for example, a block in a city bounded by streets. However, census blocks in remote areas are often large and irregular and may even be many square miles in area. A common misunderstanding is that census blocks are used geographically to build all other census geographic areas, rather all other census geographic areas are updated and then used as the primary constraints, along with roads and water features, to delineate the tabulation blocks. As a result, all 2020 Census blocks nest within every other 2020 Census geographic area, so that Census Bureau statistical data can be tabulated at the block level and aggregated up to the appropriate geographic areas. Census blocks cover all territory in the United States, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands). Blocks are the smallest geographic areas for which the Census Bureau publishes data from the decennial census. A block may consist of one or more faces.

The 2022 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some states and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census and beyond, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

Data analyzing the zebra mussel population dynamics in three Texas reservoirs over a period of 3-5 years. Data includes shell length of adult and planktonic larvae samples collected monthly, growth rates, reproductive periods, and settlement patterns. Additionally, water quality parameters are included for correlational analysis.

Urban areas comprise a densely-settled core of census blocks that meet minimum housing unit density and/or population density requirements. This includes adjacent territory containing non-residential urban land uses. To qualify as an urban area, the territory identified according to criteria must encompass at least 2,000 housing units or a population of at least 5,000. These areas were delineated by the U.S. Census Bureau following the 2020 Decennial Census. For additional information, see: https://www.census.gov/programs-surveys/geography/guidance/geo-areas/urban-rural.html. For FAQs see: https://www2.census.gov/geo/pdfs/reference/ua/Census_UA_2020FAQs.pdf.

Model state variables and parameters.

Intraspecific competition influences population and community dynamics and occurs via two mechanisms. Exploitative competition is an indirect effect that occurs through use of a shared resource and depends on resource availability. Interference competition occurs by obstructing access to a resource and may not depend on resource availability. Our study tested whether the strength of interference competition changes with protozoa population density. We grew experimental microcosms of protozoa and bacteria under different combinations of protozoan density and basal resource availability. We then solved a dynamic predator–prey model for parameters of the functional response using population growth rates measured in our experiment. As population density increased, competition shifted from exploitation to interference, and competition was less dependent on resource levels. Surprisingly, the effect of resources was weakest when competition was the most intense. We found that at low population densities, competition was largely exploitative and resource availability had a large effect on population growth rates, but the effect of resources was much weaker at high densities. This shift in competitive mechanism could have implications for interspecific competition, trophic interactions, community diversity, and natural selection. We also tested whether this shift in the mechanism of competition with protozoa density affected the structure of the bacterial prey community. We found that both resources and protozoa density affected the structure of the bacterial prey community, suggesting that competitive mechanism may also affect trophic interactions.

We conducted population and habitat assessments for Mountain Plovers in Texas during winters of 2019 and 2020. We used roadside surveys and distance-sampling to estimate bird density and calculate population totals for the study area, which included parts of five ecoregions (Chihuahuan Deserts, High Plains, Central Great Plains, Southern Texas Plains, Texas Blackland Prairies, and Western Gulf Coastal Plain). In 2019, we surveyed 103 transects along 3,032 km (1,884 mi) and, in 2020, we surveyed 152 transects along 4,985 km (3,098 mi). When driving along transects, we stopped every 3.2 km (2 mi) to assess habitat conditions (vegetation height, vegetation density, etc.) and land cover (National Land Cover Database categories).

In 2023, the per capita personal income in Texas was 66,252 U.S. dollars. Per capita personal income is calculated as the personal income of the residents of a given area divided by the resident population of the area.

These are the data for displayed in the Demographic Profiles displayed on austintexas.gov/demographics. These profiles were published in 2024, but display data from 2022 and 2023.

Most data are from the 2022 American Community Survey (the most recent available at the time of publication), but some data have other sources. All data come from the American Community Survey estimates except for:

Total Population - City of Austin Planning (2023) Population Low-Moderate Income - Dept. of Housing and Urban Development LMISD Summary Data (2022) Occupied Housing Units - City of Austin Planning (2023) Median Home Closing Price - Austin Board of Realtors (2023) Average Monthly Rent - Austin Investor Interests (Q4 2023) Income Restricted Units - City of Austin Affordable Housing Inventory Housing Units-City of Austin Planning (2023) Population Density - Esri Updated Demographics Daytime Population Density - Esri Updated Demographics Selected Land Use Percentages - City of Austin Land Use Inventory Transit Stops - Capital Metro (2023)

City, County, and MSA data are 1-Year ACS estimates. Council Districts are 5-year ACS estimates.

More information and links to these alternate sources, when available, can be found at austintexas.gov/demographics.

These profiles are updated annually.

City of Austin Open Data Terms of Use – https://data.austintexas.gov/stories/s/ranj-cccq

Vector polygon map data of city limits from Houston, Texas containing 731 features.

City limits GIS (Geographic Information System) data provides valuable information about the boundaries of a city, which is crucial for various planning and decision-making processes. Urban planners and government officials use this data to understand the extent of their jurisdiction and to make informed decisions regarding zoning, land use, and infrastructure development within the city limits.

By overlaying city limits GIS data with other layers such as population density, land parcels, and environmental features, planners can analyze spatial patterns and identify areas for growth, conservation, or redevelopment. This data also aids in emergency management by defining the areas of responsibility for different emergency services, helping to streamline response efforts during crises..

This city limits data is available for viewing and sharing as a map in a Koordinates map viewer. This data is also available for export to DWG for CAD, PDF, KML, CSV, and GIS data formats, including Shapefile, MapInfo, and Geodatabase.