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.

About this dataset

Context

The dataset tabulates the United States population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for United States. The dataset can be utilized to understand the population distribution of United States by age. For example, using this dataset, we can identify the largest age group in United States.

Key observations

The largest age group in United States was for the group of age 30 to 34 years years with a population of 23.06 million (6.94%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in United States was the 80 to 84 years years with a population of 6.34 million (1.91%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the United States is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of United States total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for United States Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the State Line population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for State Line. The dataset can be utilized to understand the population distribution of State Line by age. For example, using this dataset, we can identify the largest age group in State Line.

Key observations

The largest age group in State Line, ID was for the group of age 25 to 29 years years with a population of 3 (100%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in State Line, ID was the Under 5 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the State Line is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of State Line total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for State Line Population by Age. You can refer the same here

The 1998 Dress Rehearsal was conducted as a prelude to the United States Census of Population and Housing, 2000, in the following locations: (1) Columbia, South Carolina, and surrounding areas, including the town of Irmo and the counties of Chester, Chesterfield, Darlington, Fairfield, Kershaw, Lancaster, Lee, Marlboro, Newberry, Richland, and Union, (2) Sacramento, California, and (3) Menominee County, Wisconsin, including the Menominee American Indian Reservation. This collection contains map files showing various levels of geography (in the form of Census Tract Outline Maps, Voting District/State Legislative District Outline Maps, and County Block Maps), TIGER/Line digital files, and Corner Point files for the Census 2000 Dress Rehearsal sites. The Corner Point data files contain the bounding latitude and longitude coordinates for each individual map sheet of the 1998 Dress Rehearsal Public Law (P.L.) 94-171 map products. These files include a sheet identifier, minimum and maximum longitude, minimum and maximum latitude, and the map scale (integer value) for each map sheet. The latitude and longitude coordinates are in decimal degrees and expressed as integer values with six implied decimal places. There is a separate Corner Point File for each of the three map types: County Block Map, Census Tract Outline Map, and Voting District/State Legislative District Outline Map. Each of the three map file types is provided in two formats: Portable Document Format (PDF), for viewing, and Hewlett-Packard Graphics Language (HP-GL) format, for plotting. The County Block Maps show the greatest detail and the most complete set of geographic information of all the maps. These large-scale maps depict the smallest geographic entities for which the Census Bureau presents data -- the census blocks -- by displaying the features that delineate them and the numbers that identify them. These maps show the boundaries, names, and codes for American Indian/Alaska Native areas, county subdivisions, places, census tracts, and, for this series, the geographic entities that the states delineated in Phase 2, Voting District Project, of the Redistricting Data Program. The HP-GL version of the County Block Maps is broken down into index maps and map sheets. The map sheets cover a small area, and the index maps are composed of multiple map sheets, showing the entire area. The intent of the County Block Map series is to provide a map for each county on the smallest possible number of map sheets at the maximum practical scale, dependent on the area size of the county and the density of the block pattern. The latter affects the display of block numbers and feature identifiers. The Census Tract Outline Maps show the boundaries and numbers of census tracts, and name the features underlying the boundaries. These maps also show the boundaries and names of counties, county subdivisions, and places. They identify census tracts in relation to governmental unit boundaries. The mapping unit is the county. These large-format maps are produced to support the P.L. 94-171 program and all other 1998 Dress Rehearsal data tabulations. The Voting District/State Legislative District Outline Maps show the boundaries and codes for voting districts as delineated by the states in Phase 2, Voting District Project, of the Redistricting Data Program. The features underlying the voting district boundaries are shown, as well as the names of these features. Additionally, for states that submit the information, these maps show the boundaries and codes for state legislative districts and their underlying features. These maps also show the boundaries of and names of American Indian/Alaska Native areas, counties, county subdivisions, and places. The scale of the district maps is optimized to keep the number of map sheets for each area to a minimum, but the scale and number of map sheets will vary by the area size of the county and the voting districts and state legislative districts delineated by the states. The Census 2000 Dress Rehearsal TIGER/Line Files consist of line segments representing physical features and governmental and statistical boundaries. The files contain information distributed over a series of record types for the spatial objects of a county. These TIGER/Line Files are an extract of selected geographic and cartographic information from the Census TIGER (Topological

• The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on Earth, which far exceeds the world population of 7.2 billion in 2015. Our estimate based on UN data shows the world's population surpassing 7.7 billion.
• China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.
• The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.
• Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.
• In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added yearly.
• This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content

• In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc. >Dataset Glossary (Column-Wise):
• Rank: Rank by Population.
• CCA3: 3 Digit Country/Territories Code.
• Country/Territories: Name of the Country/Territories.
• Capital: Name of the Capital.
• Continent: Name of the Continent.
• 2022 Population: Population of the Country/Territories in the year 2022.
• 2020 Population: Population of the Country/Territories in the year 2020.
• 2015 Population: Population of the Country/Territories in the year 2015.
• 2010 Population: Population of the Country/Territories in the year 2010.
• 2000 Population: Population of the Country/Territories in the year 2000.
• 1990 Population: Population of the Country/Territories in the year 1990.
• 1980 Population: Population of the Country/Territories in the year 1980.
• 1970 Population: Population of the Country/Territories in the year 1970.
• Area (km²): Area size of the Country/Territories in square kilometers.
• Density (per km²): Population Density per square kilometer.
• Growth Rate: Population Growth Rate by Country/Territories.
• World Population Percentage: The population percentage by each Country/Territories.

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.

The Census data utilized for developing the Community Layer used 2010 TIGER/Line shapefile datasets (TIGER = Topologically Integrated Geographic Encoding and Referencing). TIGER/Line shapefiles are available for free download from the US Census Bureau and include various legal and statistical geographic areas for which the Census tabulates data. The shapefiles are designed to be used in a GIS environment, with the ability to directly link the geographic areas to Census data via a unique GEOID number.The following TIGER/Line datasets should be used: - Counties and Equivalent Entities –primary legal divisions within each state (counties, parishes, etc)- County Subdivisions –includes both legal areas (Minor Civil Divisions or MCDs) and various statistical areas- Places –includes both legal areas (Incorporated Places) and statistical areas (Census Designated Places or CDPs)- Blocks –the smallest geographical area for which Census population counts are recorded; blocks never cross boundaries of any entity for which the Census Bureau tabulates data, including counties, county subdivisions, places, and American Indian, Alaska Native, and Native Hawaiian (AIANNH) areas- American Indian, Alaska Native, and Native Hawaiian (AIANNH) AreasExtracting and Formatting CIS DataA key component of the community layer is the ability to link CIS information spatially. Data from CIS cannot directly be joined with Census data. The two datasets have community name discrepancies which impede an exact match. Therefore, CIS data needs to be formatted to match Census community names. A custom report can be obtained from CIS to include a CID number, Community Name, County, State, Community Status, and Tribal status for all CIS records. Make sure all CID numbers are six digits and you follow the CIS community naming convention outlined in Table 4.2.1.1 in the Community Layer Update Technical Guide 20131206. Converting the CIS name“ADDISON, VILLAGE OF” to “ADDISON TOWN”involves removing unneeded spaces, comma, and preposition to make the join successful to the Census data. Using a comprehensive report at a national level gains efficiencies as bulk edits can be made. Data for each state should be extracted as needed by separating the CIS data into each type of community corresponding to the Census geography layers used, and a new JoinID column (e.g. ADDISON TOWN) can be created for each dataset allowing the CIS data to be joined to the Census data.

U.S. Census BlocksThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau (USCB), displays Census Blocks in the United States. A brief description of Census Blocks, per USCB, is that "Census blocks are statistical areas bounded by visible features such as roads, streams, and railroad tracks, and by nonvisible boundaries such as property lines, city, township, school district, county limits and short line-of-sight extensions of roads." Also, "the smallest level of geography you can get basic demographic data for, such as total population by age, sex, and race."Census Block 1007Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Census Blocks) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 69 (Series Information for 2020 Census Block State-based TIGER/Line Shapefiles, Current)OGC API Features Link: (U.S. Census Blocks - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: What are census blocksFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

Census Blocks in Macon-Bibb County.

A census block is the smallest geographic unit used by the United States Census Bureau for tabulation of 100-percent data (data collected from all houses, rather than a sample of houses). The number of blocks in the United States, including Puerto Rico, for the 2010 Census was 11,155,486.[1]

Census blocks are grouped into block groups, which are grouped into census tracts. There are on average about 39 blocks per block group. Blocks typically have a four-digit number; the first number indicates which block group the block is in. For example, census block 3019 would be in block group 3.

Blocks are typically bounded by streets, roads or creeks. In cities, a census block may correspond to a city block, but in rural areas where there are fewer roads, blocks may be limited by other features. The population of a census block varies greatly. As of the 2010 census, there were 4,871,270 blocks with a reported population of zero,[2] while a block that is entirely occupied by an apartment complex might have several hundred inhabitants.

Census blocks covering the entire country were introduced with the 1990 census. Before that, back to the 1940 census, only selected areas were divided into blocks.

To review a table detailing Census Block information in the United States visit https://www.census.gov/geo/maps-data/data/tallies/tractblock.

Abstract (en): Extracted from the 2002 Census of Governments, this dataset provides the number of general-purpose local governments in each United States Metropolitan Statistical Area (MSA). Data from Consolidated Metropolitan Statistical Areas (CMSAs) and their component Primary Metropolitan Statistical Areas (PMSAs) are included. There are nine variables in this study. They contain information on locations (city and state); Metropolitan Statistical Areas; population at each location in the year 2000; number of General-Purpose Governments at each location as well as per 100,000 people; water, land, and total area in square miles; and General-Purpose Governments per 100,000 square miles of land area. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created variable labels and/or value labels.; Checked for undocumented or out-of-range codes.. Number of General Purpose Local Governments in United States Metropolitan Statistical Areas Smallest Geographic Unit: Metropolitan Statistical Areas

The TIGER/Line Files are shapefiles and related database files (.dbf) that 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 purpose of this file is to provide the geography for the 2010 Census Blocks along with their 2010 housing unit count and population. Census Blocks are statistical areas bounded on all sides by visible features, such as streets, roads, streams, and railroad tracks, and/or by non-visible boundaries such as city, town, township, and county limits, and short line-of-sight extensions of streets and roads. 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.

This dataset contains population densities of 15 study watersheds in Gwinnett County, Georgia from 2000 to 2020. Population densities were determined for 2000, 2010, and 2020 from the decadal U.S. Census and for 2012 and 2017 from the American Community Survey 5-year estimates of 2010-14 and 2015¬-19 block group data, respectively. Population density within each watershed was determined by clipping the census block group data by the watershed boundaries and area-weighting the block group population density data within each watershed. Census block group data is the smallest geographic unit for which the census provides data.

A dataset listing Georgia cities by population for 2024.

Census Block is the smallest geographic unit used by the United States Census Bureau for tabulation of 100-percent data (data collected from all houses, rather than a sample of houses). The number of blocks in the United States, including Puerto Rico, for the 2010 Census was 11,155,486.Census blocks are grouped into block groups, which are grouped into census tracts. There are on average about 39 blocks per block group. Blocks typically have a four-digit number; the first number indicates which block group the block is in. For example, census block 3019 would be in block group 3.Blocks are typically bounded by streets, roads or creeks. In cities, a census block may correspond to a city block, but in rural areas where there are fewer roads, blocks may be limited by other features. The population of a census block varies greatly. As of the 2010 census, there were 4,871,270 blocks with a reported population of zero, while a block that is entirely occupied by an apartment complex might have several hundred inhabitants. Census blocks covering the entire country were introduced with the 1990 census. Before that, back to the 1940 census, only selected areas were divided into blocks. Census blocks are maintained within the Administration Feature and is dissolved out weekly. Administration is a polygon feature consisting of the smallest statistical areas bounded by visible features such as roads, streams, railroad tracks, and mountain ridges, as well as by nonvisible boundaries such as jurisdictional limits, school district, public safety boundaries, voting precincts, and census blocks. This methodology allows for single stream editing to move coincidental boundaries across many aggregate datasets simultaneously. Administration is maintained though an ArcGIS topology class in conjunction with County Parcels and Zoning. The topology prevents self-intersection and gaps, while ensuring complete coverage amongst the participating features.

Abstract (en): InputsThe synthetic population was generated from the 2010-2014 ACS PUMS housing and person files. United States Department of Commerce. Bureau of the Census. (2017-03-06). American Community Survey 2010-2014 ACS 5-Year PUMS File [Data set]. Ann Arbor, MI: Inter-university Consortium of Political and Social Research [distributor]. http://doi.org/10.3886/E100486V1Persistent URL: http://doi.org/10.3886/E100486V1Funding supportThis work is supported under Grant G-2015-13903 from the Alfred P. Sloan Foundation on "The Economics of Socially-Efficient Privacy and Confidentiality Management for Statistical Agencies" (PI: John M. Abowd)OutputsThere are 17 housing files (data/housing)- repHus0.csv, repHus1.csv, ... repHus16.csvand 32 person files (data/person)- rep_recode_ACSpus0.csv, rep_recode_ACSpus1.csv, ... rep_recode_ACSpus31.csv.Files are split to be roughly equal in size. The files contain data for the entire country. Files are not split along any demographic characteristic. The person files and housing files must be concatenated to form a complete person file and a complete housing file, respectively.ProgramsPrograms that generated this release can be found at https://github.com/labordynamicsinstitute/SynUSpopulation/releases/tag/v201703-beta and http://doi.org/10.5281/zenodo.556424Additional informationFor more information, see README.md Smallest Geographic Unit: United States Funding insitution(s): Alfred P. Sloan Foundation.

A dataset listing Illinois cities by population for 2024.

A dataset listing Florida cities by population for 2024.

Datasets: DS0: Study-Level Files DS1: Illinois Great Lakes Anglers 2006-2015 DS2: Illinois Population 2006-2015 DS3: Illinois Total Anglers 2006-2015 DS4: Indiana Lake Michigan Anglers 2005-2015 DS5: Indiana_Population_2005-2015 DS6: Indiana Total Anglers 2005-2015 DS7: Lake Huron Anglers 2000-2014 DS8: Lake Huron Population 2000-2014 DS9: Lake Michigan Anglers 2000-2014 DS10: Lake Michigan Population 2000-2014 DS11: Lake Superior Anglers 2000-2014 DS12: Lake Superior Population 2000-2014 DS13: Michigan Great Lakes Anglers_2000-2014 DS14: Michigan Population 2000-2014 DS15: Michigan Total Anglers 2000-2014 DS16: Minnesota Lake Superior Anglers 2000-2016 DS17: Minnesota Population 2000-2016 DS18: Minnesota Total Anglers 2000-2016 DS19: Minnesota Trout Stamp Anglers 2000-2016 DS20: Wisconsin Great Lakes Anglers 1999-2014 DS21: Wisconsin Population 1999-2014 DS22: Wisconsin Total Anglers 1999-2014 Individuals who purchase any type of fishing license are counted as "anglers" by their state of residence. Estimates of the number of unique individuals who fish the Great Lakes and their tributaries, up to the first barrier, for salmon or trout are noted as "Great Lakes salmon/trout anglers" and are counted by both state of residence and by lake fished. The datasets include only in-state resident anglers; out-of-state residents are excluded. In some instances, the state Department of Natural Resources provided data that was already filtered by residency. When necessary, in-state residency was determined based on license type sold or zip code of residence. For the state of Michigan, a small number of observations (approximately 0.28 percent of records) were missing information that could be used to determine residency status, and these individuals were removed from the data. This collection includes estimates of the number of in-state resident recreational anglers from the states of Illinois, Indiana, Michigan, Minnesota, and Wisconsin. It also includes estimates of the smaller subset of anglers who fished the Upper Great Lakes (Lake Superior, Lake Michigan, or Lake Huron) or their tributaries for salmon/trout by state of residence and separately by lake fished. All estimates were broken down by year, single year of age, and sex. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. State residents eligible to obtain a fishing license (beginning at 16 or 18 depending on state regulations). Smallest Geographic Unit: State To facilitate investigations into angler demographics among the five Upper Great Lakes states. record abstracts

A dataset listing Oklahoma cities by population for 2024.

Globally, about 25 percent of the population is under 15 years of age and 10 percent is over 65 years of age. Africa has the youngest population worldwide. In Sub-Saharan Africa, more than 40 percent of the population is below 15 years, and only three percent are above 65, indicating the low life expectancy in several of the countries. In Europe, on the other hand, a higher share of the population is above 65 years than the population under 15 years. Fertility rates The high share of children and youth in Africa is connected to the high fertility rates on the continent. For instance, South Sudan and Niger have the highest population growth rates globally. However, about 50 percent of the world’s population live in countries with low fertility, where women have less than 2.1 children. Some countries in Europe, like Latvia and Lithuania, have experienced a population decline of one percent, and in the Cook Islands, it is even above two percent. In Europe, the majority of the population was previously working-aged adults with few dependents, but this trend is expected to reverse soon, and it is predicted that by 2050, the older population will outnumber the young in many developed countries. Growing global population As of 2025, there are 8.1 billion people living on the planet, and this is expected to reach more than nine billion before 2040. Moreover, the global population is expected to reach 10 billions around 2060, before slowing and then even falling slightly by 2100. As the population growth rates indicate, a significant share of the population increase will happen in Africa.