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) Database (MTDB). The MTDB 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 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.

In 2023, about 25.4 percent of Alabama residents were Black or African American. A further 64.7 percent of the population were white, and 5.6 percent of residents were of two or more races in that year.

38,61 (persons per sq. km) in 2022.

Each year, the Forecasting and Trends Office (FTO) publishes population estimates and future year projections. The population estimates can be used for a variety of planning studies including statewide and regional transportation plan updates, subarea and corridor studies, and funding allocations for various planning agencies.The 2020 population estimates reported are based on the US Census Bureau 2020 Decennial Census. The 2021 population estimates are based on the population estimates developed by the Bureau of Economic and Business Research (BEBR) at the University of Florida. BEBR uses the decennial census count for April 1, 2020, as the starting point for state-level projections. More information is available from BEBR here.This dataset contains boundaries for all 2010 Census Urban Clusters (UCs) in the State of Florida with 2020 census population and 2021 population estimates. All legal boundaries and names in this dataset are from the US Census Bureau’s TIGER/Line Files (2021).BEBR provides 2021 population estimates for counties in Florida. However, UC boundaries may not coincide with the jurisdictional boundaries of counties and UCs often spread into several counties. To estimate the population for an UC, first the ratio of the subject UC that is contained within a county (or sub-area) to the area of the entire county was determined. That ratio was multiplied by the estimated county population to obtain the population for that sub-area. The population for the entire UC is the sum of all sub-area populations estimated from the counties they are located within.For the 2010 Census, urban areas comprised a “densely settled core of census tracts and/or census blocks that meet minimum population density requirements, along with adjacent territory containing non-residential urban land uses as well as territory with low population density included to link outlying densely settled territory with the densely settled core.” In 2010, the US Census Bureau identified two types of urban areas—Urbanized Areas (UAs) and UCs. UCs have a population of at least 2,500 and less than 50,000 people. Note: Century, FL--AL Urban Cluster is located in two states: Florida (Escambia County) and Alabama (Escambia County). 2021 population of Escambia County, AL used for this estimation is from the US Census annual population estimates (2020-2021). All other Urban Clusters are located entirely within the state of Florida. Please see the Data Dictionary for more information on data fields. Data Sources:US Census Bureau 2020 Decennial CensusUS Census Bureau’s TIGER/Line Files (2021)Bureau of Economic and Business Research (BEBR) – Florida Estimates of Population 2021 Data Coverage: StatewideData Time Period: 2020 – 2021 Date of Publication: July 2022 Point of Contact:Dana Reiding, ManagerForecasting and Trends OfficeFlorida Department of TransportationDana.Reiding@dot.state.fl.us605 Suwannee Street, Tallahassee, Florida 32399850-414-4719

Each year, the Forecasting and Trends Office (FTO) publishes population estimates and future year projections. The population estimates can be used for a variety of planning studies including statewide and regional transportation plan updates, subarea and corridor studies, and funding allocations for various planning agencies. The 2021 population estimates are based on the population estimates developed by the Bureau of Economic and Business Research (BEBR) at the University of Florida. BEBR uses the decennial census count for April 1, 2020, as the starting point for state-level projections. More information is available from BEBR here. This dataset contains boundaries for all 2010 Census Urbanized Areas (UAs) in the State of Florida with 2021 population density estimates. All legal boundaries and names in this dataset are from the US Census Bureau’s TIGER/Line Files (2021). BEBR provides 2021 population estimates for counties in Florida. However, UA boundaries may not coincide with the jurisdictional boundaries of counties and UAs often spread into several counties. To estimate the population for an UA, first the ratio of the subject UA that is contained within a county (or sub-area) to the area of the entire county was determined. That ratio was multiplied by the estimated county population to obtain the population for that sub-area. The population for the entire UA is the sum of all sub-area populations estimated from the counties they are located within. For the 2010 Census, urban areas comprised a “densely settled core of census tracts and/or census blocks that meet minimum population density requirements, along with adjacent territory containing non-residential urban land uses as well as territory with low population density included to link outlying densely settled territory with the densely settled core.” In 2010, the US Census Bureau identified two types of urban areas—UAs and Urban Clusters (UCs). UAs have a population of 50,000 or more people. Note: Pensacola, FL--AL Urbanized Area is located in two states: Florida (Escambia County and Santa Rosa County) and Alabama (Baldwin County). 2021 population of Baldwin County, AL used for this estimation is from the US Census annual population estimates (2020-2021). All other Urbanized Areas are located entirely within the state of Florida. Please see the Data Dictionary for more information on data fields. Data Sources:FDOT FTO 2020 and 2021 Population Estimates by Urbanized Area and CountyUS Census Bureau 2020 Decennial CensusUS Census Bureau’s TIGER/Line Files (2021)Bureau of Economic and Business Research (BEBR) – Florida Estimates of Population 2021 Data Coverage: StatewideData Time Period: 2021 Date of Publication: October 2022 Point of Contact:Dana Reiding, ManagerForecasting and Trends OfficeFlorida Department of TransportationDana.Reiding@dot.state.fl.us605 Suwannee Street, Tallahassee, Florida 32399850-414-4719

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.

A dataset listing Alabama counties by population for 2024.

Albania Population Density: People per Square Km data was reported at 101.376 Person/sq km in 2022. This records a decrease from the previous number of 102.616 Person/sq km for 2021. Albania Population Density: People per Square Km data is updated yearly, averaging 105.288 Person/sq km from Dec 1961 (Median) to 2022, with 62 observations. The data reached an all-time high of 119.947 Person/sq km in 1990 and a record low of 60.577 Person/sq km in 1961. Albania 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 Albania – Table AL.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 2015 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. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. 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 two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2010.

GPWv4 is a gridded data product that depicts global population data from the 2010 round of Population and Housing Censuses. The Population Density, 2015 layer represents persons per square kilometer for year 2015. Data Summary:GPWv4 is constructed from national or subnational input areal units of varying resolutions. The native grid cell size is 30 arc-seconds, or ~1 km at the equator. Separate grids are available for population count, population density, estimated land area, and data quality indicators; which include the water mask represented in this service. Population estimates are derived by extrapolating the raw census counts to estimates for the 2010 target year. The development of GPWv4 builds upon previous versions of the data set (Tobler et al., 1997; Deichmann et al., 2001; Balk et al., 2006).The full GPWv4 data collection will consist of population estimates for the years 2000, 2005, 2010, 2015, and 2020, and will include grids for estimates of total population, age, sex, and urban/rural status. However, this release consists only of total population estimates for the year 2015. This data is being released now to allow users access to the population grids.Recommended Citation:Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. Gridded Population of the World, Version 4 (GPWv4): Population Density. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4NP22DQ. Accessed DAY MONTH YEAR

1 computer laser optical disc ; 4 3/4 in.Selected block-level data from Summary tape file 1B, including total population, age, race, and Hispanic origin, number of housing units, tenure, room density, mean contract rent, mean value, and mean number of rooms in housing units.

The 2016 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. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. 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 two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The generalized boundaries for counties and equivalent entities are as of January 1, 2010.

75,64 (persons per sq.km) in 2010.

A mating system is an important life history for animals dealing with changing environments. Population density affects the plasticity of a mating system and subsequently the family structure of animals, but its impacts on mating systems and social structures are rarely investigated by using molecular markers in field conditions. In this study, using microsatellite genetic markers, we examined the changes in the social and genetic mating system and family structure of Brandt’s voles in the grassland of Inner Mongolia, China, under low-, medium-, and high-density enclosures (each enclosure 0.48-ha with 4 replicates.) We found, that with the increase in population density of the founder voles introduced into the enclosure in early spring, both sexes increased their number of genetic mating partners, while males increased their social partners, resulting in a more promiscuous mating system. The number of genetic fathers and mothers per family, the number of social offspring per founder mal..., The study site had pre-constructed twenty-four 0.48-ha enclosures (80 × 60 m) with galvanized iron sheets extending 1 m below the ground’s surface and 1.4 m above the surface to prevent escaping, intrusion and movement of burrowing rodents into, out of, and between enclosures (Li et al., 2016). A raptor†proof nylon netting (10 cm mesh size) covered the top of each enclosure to obstruct avian predators. The integrity of each enclosure’s construction was regularly checked and maintained. Twelve enclosures were randomly assigned to one of three treatments that differed in founder population size: Low Density (6 ♂:6 ♀), Medium Density (12 ♂:12 ♀) and High Density (18 ♂:18 ♀). Each treatment had four replicates. The density level was based on a previous test in which 13-15 pairs of male and female voles were released into each enclosure in April (Li et al., 2016). The highest population density of an enclosure was recorded in one of the high-density enclosures at 138 individuals by the end o..., Data can be opened using Microsoft Excel and other similar software such as LibreOffice Calc. , # Density-dependent changes of mating system and family structure in Brandt's voles (Lasiopodomys brandtii)

https://doi.org/10.5061/dryad.qv9s4mwhx

DATA & FILE OVERVIEW

Dataset.xlxs

This dataset consists of 6 sheets named:

• Population size
• Per capita reproductivity
• Mating system
• Parent structure
• Offspring structure
• Family link

Variables:

LD - low density treatment

MD - medium density treatment

HD - high density treatment

Code/Software

R software (v. 3.6.1)

This dataset is associated with the McDonald et al. paper, entitled "The urban tree cover and temperature disparity in US urbanized areas: Quantifying the effect of income across 5,723 communities". Urban tree cover provides benefits to human health and well-being, but previous studies suggest that tree cover is often inequitably distributed. Here, we use NAIP imagery to survey the tree cover inequality for Census blocks in US large urbanized areas, home to 167 million people across 5,723 municipalities and other places. We compared tree cover to summer surface temperature, as measured using Thematic Mapper imagery. In 92% of the urbanized areas surveyed, low-income blocks have less tree cover than high-income blocks. On average, low-income blocks have 15.2% less tree cover and are 1.5⁰C hotter (surface temperature) than high-income blocks. The greatest difference between low- and high-income blocks was found in urbanized areas in the Northeast of the United States, where low-income blocks often have at least 30% less tree cover and are at least 4.0⁰C hotter. Even after controlling for population density and built-up intensity, the association between income and tree cover is significant, as is the association between race and tree cover. We estimate, after controlling for population density, that low-income blocks have 62 million fewer trees than high-income blocks, a compensatory value of $56 billion dollars ($1,349/person). An investment in tree planting and natural regeneration of $17.6 billion would close the tree cover disparity for 42 million people in low-income blocks.

This dataset is about: Population density of mesozooplankton during POLARSTERN cruise ANT-X/6. Please consult parent dataset @ https://doi.org/10.1594/PANGAEA.728865 for more information.

202,94 (persons per sq.km) in 2015.

The Atlas of the Biosphere is a product of the Center for Sustainability and the Global Environment (SAGE), part of the Gaylord Nelson Institute for Environmental Studies at the University of Wisconsin - Madison. The goal is to provide more information about the environment, and human interactions with the environment, than any other source.

The Atlas provides maps of an ever-growing number of environmental variables, under the following categories:

Human Impacts (Humans and the environment from a socio-economic perspective; i.e., Population, Life Expectancy, Literacy Rates);

Land Use (How humans are using the land; i.e., Croplands, Pastures, Urban Lands);

Ecosystems (The natural ecosystems of the world; i.e., Potential Vegetation, Temperature, Soil Texture); and

Water Resources (Water in the biosphere; i.e., Runoff, Precipitation, Lakes and Wetlands).

Map coverages are global and regional in spatial extent. Users can download map images (jpg) and data (a GIS grid of the data in ESRI ArcView Format), and can view metadata online.

The data for this study and represented in this file were obtained from several sources. Data on reindeer populations throughout Norway were obtained from annual reports submitted by herders for 78 populations covering a majority of the Norwegian reindeer herding area (Tveraa et al., 2007). Reindeer populations were grouped into ten management regions as in Tveraa et al. (2014). Population density was included as an area-adjusted predictor, calculated as a herd’s population size divided by the area of that herd’s summer pasture land in square kilometres. The previous autumn/winter average juvenile slaughter weight was used as a measure of herd body condition previous to birth, as per Tveraa et al. (2014). Plant productivity was measured by the normalized difference vegetation index (NDVI) for locations within the herds’ summer grazing land, data for which were collected by the Advanced Very High Resolution Radiometer (AVHRR) instrument deployed on a satellite system and available for full years since 1982. Average altitudes of the areas from which NDVI values were taken were also recorded. NDVI values were recorded twice a month, and the average NDVI value for all pixels (each pixel is 1km2) within a herd’s summer pasture was calculated for each time point. The average altitude, latitude, and longitude of the summer pastures were calculated for each herd using the GRASS GIS software. Availability of high-quality forage for reindeer was measured by the day of the year (DOY) when the maximum NDVI value first occurred for each herd’s location and each year. Spring onset for each year and each herd’s location was considered as the DOY when NDVI first reached 50% of its yearly maximum. Both the DOY when maximum NDVI occurred and spring onset were calculated from the AVHRR data. Daily snow depth (mm) for each of the herding districts from 1984 to 2013 were obtained from the Norwegian Meteorological Institute. The area under the spline curve (AUC) of ground snow depth was calculated for each year at the summer grazing pastures using daily snow depth values from September to September. The onset of winter for a given year was defined as the first DOY which had at least two consecutive weeks of snow on the ground (snow depth > 0 mm). References Cited: Tveraa, T., Fauchald, P., Gilles Yoccoz, N., Anker Ims, R., Aanes, R., and Arild Høgda, K. (2007). What regulate and limit reindeer populations in Norway? Oikos, 116(4):706–715. Tveraa, T., Stien, A., Brøseth, H., and Yoccoz, N. G. (2014). The role of predation and food limitation on claims for compensation, reindeer demography and population dynamics. Journal of Applied Ecology, 51(5):1264–1272.

We were unable to test two of the hypotheses proposed by Clapham et al. (2012) (Competitor Assessment and Infanticide Avoidance), as well as some of the predictions that involved information on investigatory or age-class information. M = Male, F = Female. BS = Breeding Season, NON-BS = Non-Breeding Season, Y = Yes, N = No, P = Partially. We were unable to determine adult from non-adult in our work, thus where Clapham et al. (2012) distinguish adult and subaduls, we simply pool these groups into their respective sexes but not age classes.