The statistic shows the share of U.S. population, by race and Hispanic origin, in 2016 and a projection for 2060. As of 2016, about 17.79 percent of the U.S. population was of Hispanic origin. Race and ethnicity in the U.S. For decades, America was a melting pot of the racial and ethnical diversity of its population. The number of people of different ethnic groups in the United States has been growing steadily over the last decade, as has the population in total. For example, 35.81 million Black or African Americans were counted in the U.S. in 2000, while 43.5 million Black or African Americans were counted in 2017.

The median annual family income in the United States in 2017 earned by Black families was about 50,870 U.S. dollars, while the average family income earned by the Asian population was about 92,784 U.S. dollars. This is more than 15,000 U.S. dollars higher than the U.S. average family income, which was 75,938 U.S. dollars.

The unemployment rate varies by ethnicity as well. In 2018, about 6.5 percent of the Black or African American population in the United States were unemployed. In contrast to that, only three percent of the population with Asian origin was unemployed.

The Population Projections for the State of Tennessee, produced for the Tennessee State Data Center, contain projections for each county in Tennessee by race, age, and sex for each year from 2020 to 2070. Age is defined by five-year bands, starting with an “age 0-4” group and ending with an “age 85+” group. Race is delineated as one of four categories that combine race and ethnic definitions:White Non-HispanicBlack Non-HispanicAll HispanicOther non-Hispanic, including two or more races. Our forecast implements a cohort-component methodology. We specify the base year as 2000 and the launch year as 2021. Thus, we inform the forecast with trends from 2000 to 2020. Using vital statistics data from the Tennessee Department of Health, we project the population change resulting from natural components (births minus deaths). Differences between actual population values as reported by the Census and values predicted using births and deaths are used to establish net migration patterns. The forecast used these predicted net migration patterns; life tables from the Social Security Administration; recent average birth rates by county, race, and age of female; and forecast future U.S. populations.The 2020 base year population estimates for Tennessee Counties are from the 2020 Vintage Estimates of Population and Housing Units produced by the US Census Bureau.

Brazil and the United States are the two most populous countries in the Americas today. In 1500, the year that Pedro Álvares Cabral made landfall in present-day Brazil and claimed it for the Portuguese crown, it is estimated that there were roughly one million people living in the region. Some estimates for the present-day United States give a population of two million in the year 1500, although estimates vary greatly. By 1820, the population of the U.S. was still roughly double that of Brazil, but rapid growth in the 19th century would see it grow 4.5 times larger by 1890, before the difference shrunk during the 20th century. In 2024, the U.S. has a population over 340 million people, making it the third most populous country in the world, while Brazil has a population of almost 218 million and is the sixth most populous. Looking to the future, population growth is expected to be lower in Brazil than in the U.S. in the coming decades, as Brazil's fertility rates are already lower, and migration rates into the United States will be much higher. Historical development The indigenous peoples of present-day Brazil and the U.S. were highly susceptible to diseases brought from the Old World; combined with mass displacement and violence, their population growth rates were generally low, therefore migration from Europe and the import of enslaved Africans drove population growth in both regions. In absolute numbers, more Europeans migrated to North America than Brazil, whereas more slaves were transported to Brazil than the U.S., but European migration to Brazil increased significantly in the early 1900s. The U.S. also underwent its demographic transition much earlier than in Brazil, therefore its peak period of population growth was almost a century earlier than Brazil. Impact of ethnicity The demographics of these countries are often compared, not only because of their size, location, and historical development, but also due to the role played by ethnicity. In the mid-1800s, these countries had the largest slave societies in the world, but a major difference between the two was the attitude towards interracial procreation. In Brazil, relationships between people of different ethnic groups were more common and less stigmatized than in the U.S., where anti-miscegenation laws prohibited interracial relationships in many states until the 1960s. Racial classification was also more rigid in the U.S., and those of mixed ethnicity were usually classified by their non-white background. In contrast, as Brazil has a higher degree of mixing between those of ethnic African, American, and European heritage, classification is less obvious, and factors such as physical appearance or societal background were often used to determine racial standing. For most of the 20th century, Brazil's government promoted the idea that race was a non-issue and that Brazil was racially harmonious, but most now acknowledge that this actually ignored inequality and hindered progress. Racial inequality has been a prevalent problem in both countries since their founding, and today, whites generally fare better in terms of education, income, political representation, and even life expectancy. Despite this adversity, significant progress has been made in recent decades, as public awareness of inequality has increased, and authorities in both countries have made steps to tackle disparities in areas such as education, housing, and employment.

The world's population first reached one billion people in 1805, and reached eight billion in 2022, and will peak at almost 10.2 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two-thirds of the world's population lives in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a few years later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

Whereas the population is expected to decrease somewhat until 2100 in Asia, Europe, and South America, it is predicted to grow significantly in Africa. While there were 1.55 billion inhabitants on the continent at the beginning of 2025, the number of inhabitants is expected to reach 3.81 billion by 2100. In total, the global population is expected to reach nearly 10.18 billion by 2100. Worldwide population In the United States, the total population is expected to steadily increase over the next couple of years. In 2024, Asia held over half of the global population and is expected to have the highest number of people living in urban areas in 2050. Asia is home to the two most populous countries, India and China, both with a population of over one billion people. However, the small country of Monaco had the highest population density worldwide in 2024. Effects of overpopulation Alongside the growing worldwide population, there are negative effects of overpopulation. The increasing population puts a higher pressure on existing resources and contributes to pollution. As the population grows, the demand for food grows, which requires more water, which in turn takes away from the freshwater available. Concurrently, food needs to be transported through different mechanisms, which contributes to air pollution. Not every resource is renewable, meaning the world is using up limited resources that will eventually run out. Furthermore, more species will become extinct which harms the ecosystem and food chain. Overpopulation was considered to be one of the most important environmental issues worldwide in 2020.

The data collection contains population projections for UK ethnic groups and all local area by age (single year of age up to 100+) and sex. Included in the data set are also input data to the cohort component model that was used to project populations into the future-fertility rates, mortality rates, international migration flows and internal migration probabilities. Also included in data set are output data: Number of deaths, births and internal migrants. All data included are for the years 2011 to 2061. We have produced two ethnic population projections for UK local authorities, based on information on 2011 Census ethnic populations and 2010-2011-2012 ethnic components. Both projections align fertility and mortality assumptions to ONS assumptions. Where they differ is in the migration assumptions. In LEEDS L1 we employ internal migration rates for 2001 to 2011, including periods of boom and bust. We use a new assumption about international migration anticipating that the UK may leave the EU (BREXIT). In LEEDS L2 we use average internal migration rates for the 5 year period 2006-11 and the official international migration flow assumptions with a long term balance of +185 thousand per annum. This project aims to understand and to forecast the ethnic transition in the United Kingdom's population at national and sub-national levels. The ethnic transition is the change in population composition from one dominated by the White British to much greater diversity. In the decade 2001-2011 the UK population grew strongly as a result of high immigration, increased fertility and reduced mortality. Both the Office for National Statistics (ONS) and Leeds University estimated the growth or decline in the sixteen ethnic groups making up the UK's population in 2001. The 2011 Census results revealed that both teams had over-estimated the growth of the White British population and under-estimated the growth of the ethnic minority populations. The wide variation between our local authority projected populations in 2011 and the Census suggested inaccurate forecasting of internal migration. We propose to develop, working closely with ONS as our first external partner, fresh estimates of mid-year ethnic populations and their components of change using new data on the later years of the decade and new methods to ensure the estimates agree in 2011 with the Census. This will involve using population accounting theory and an adjustment technique known as iterative proportional fitting to generate a fully consistent set of ethnic population estimates between 2001 and 2011. We will study, at national and local scales, the development of demographic rates for ethnic group populations (fertility, mortality, internal migration and international migration). The ten year time series of component summary indicators and age-specific rates will provide a basis for modelling future assumptions for projections. We will, in our main projection, align the assumptions to the ONS 2012-based principal projection. The national assumptions will need conversion to ethnic groups and to local scale. The ten years of revised ethnic-specific component rates will enable us to study the relationships between national and local demographic trends. In addition, we will analyse a consistent time series of local authority internal migration. We cannot be sure, at this stage, how the national-local relationships for each ethnic group will be modelled but we will be able to test our models using the time series. Of course, all future projections of the population are uncertain. We will therefore work to measure the uncertainty of component rates. The error distributions can be used to construct probability distributions of future populations via stochastic projections so that we can define confidence intervals around our projections. Users of projections are always interested in the impact of the component assumptions on future populations. We will run a set of reference projections to estimate the magnitude and direction of impact of international migrations assumptions (net effect of immigration less emigration), of internal migration assumptions (the net effect of in-migration less out-migration), of fertility assumptions compared with replacement level, of mortality assumptions compared with no change and finally the effect of the initial age distribution (i.e. demographic potential). The outputs from the project will be a set of technical reports on each aspect of the research, journal papers submitted for peer review and a database of projection inputs and outputs available to users via the web. The demographic inputs will be subject to quality assurance by Edge Analytics, our second external partner. They will also help in disseminating these inputs to local government users who want to use them in their own ethnic projections. In sum, the project will show how a wide range of secondary data sources can be used in theoretically refined demographic models to provide us with a more reliable picture of how the UK population is going to change in ethnic composition. Base year data (2011) are derived from the 2011 census, vital statistics and ONS migration data. Subsequent population data are computed with a cohort component model.

The Population Projections for the State of Tennessee, produced for the Tennessee State Data Center, contain projections for each county in Tennessee by race, age, and sex for each year from 2018 to 2070. Age is defined by five-year bands, starting with an “age 0-4” group and ending with an “age 85+” group. Race is delineated as one of four categories that combine race and ethnic definitions:White Non-HispanicBlack Non-HispanicAll HispanicOther non-Hispanic, including two or more races. Our forecast implements a cohort-component methodology. We specify the base year as 2000 and the launch year as 2018. Thus, we inform the forecast with trends from 2000 to 2018. Using vital statistics data from the Tennessee Department of Health, we project the population change resulting from natural components (births minus deaths). Differences between actual population values as reported by the Census and values predicted using births and deaths are used to establish net migration patterns.The forecast used these predicted net migration patterns; life tables from the Social Security Administration; recent average birth rates by county, race, and age of female; and forecast future U.S. populations.The 2018 base year population estimates for Tennessee Counties are from the 2018 Vintage Estimates of Population and Housing Units produced by the US Census Bureau.

The statistic shows the total population in the United States from 2015 to 2021, with projections up until 2027. In 2021, the total population of the U.S. amounted to approximately 332.18 million inhabitants.

The United States' economy over the last decade

The United States of America is the world’s largest national economy and the second most prominent trader globally, trailing just behind China. The country is also one of the most populated countries in the world, trailing only China and India. The United States' economy prospers primarily due to having a plentiful amount of natural resources and advanced infrastructure to cope with the production of goods and services, as well as the population and workforce to enable high productivity. Efficient productivity led to a slight growth in GDP almost every year over the past decade, despite undergoing several economic hardships towards the late 2000's.

In addition, the United States holds arguably one of the most important financial markets, with the majority of countries around the world having commercial connections with American companies. Dependency on a single market like the United States has however caused several global dilemmas, most evidently seen during the 2008 financial crisis. What initially started off as a bursting of the U.S. housing bubble lead to a worldwide recession and the necessity to reform national economics. The global financial crisis affected the United States most drastically, especially within the unemployment market as well as national debt, which continued to rise due to the United States having to borrow money in order to stimulate its economy.

A. SUMMARY This dataset includes San Francisco COVID-19 tests by race/ethnicity and by date. This dataset represents the daily count of tests collected, and the breakdown of test results (positive, negative, or indeterminate). Tests in this dataset include all those collected from persons who listed San Francisco as their home address at the time of testing. It also includes tests that were collected by San Francisco providers for persons who were missing a locating address. This dataset does not include tests for residents listing a locating address outside of San Francisco, even if they were tested in San Francisco.

The data were de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected). If a person tested multiple times on the same date, only one test is included from that date. When there are multiple tests on the same date, a positive result, if one exists, will always be selected as the record for the person. If a PCR and antigen test are taken on the same day, the PCR test will supersede. If a person tests multiple times on the same day and the results are all the same (e.g. all negative or all positive) then the first test done is selected as the record for the person.

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco.

When a person gets tested for COVID-19, they may be asked to report information about themselves. One piece of information that might be requested is a person's race and ethnicity. These data are often incomplete in the laboratory and provider reports of the test results sent to the health department. The data can be missing or incomplete for several possible reasons:

• The person was not asked about their race and ethnicity.
• The person was asked, but refused to answer.
• The person answered, but the testing provider did not include the person's answers in the reports.
• The testing provider reported the person's answers in a format that could not be used by the health department.

For any of these reasons, a person's race/ethnicity will be recorded in the dataset as “Unknown.”

B. NOTE ON RACE/ETHNICITY The different values for Race/Ethnicity in this dataset are "Asian;" "Black or African American;" "Hispanic or Latino/a, all races;" "American Indian or Alaska Native;" "Native Hawaiian or Other Pacific Islander;" "White;" "Multi-racial;" "Other;" and “Unknown."

The Race/Ethnicity categorization increases data clarity by emulating the methodology used by the U.S. Census in the American Community Survey. Specifically, persons who identify as "Asian," "Black or African American," "American Indian or Alaska Native," "Native Hawaiian or Other Pacific Islander," "White," "Multi-racial," or "Other" do NOT include any person who identified as Hispanic/Latino at any time in their testing reports that either (1) identified them as SF residents or (2) as someone who tested without a locating address by an SF provider. All persons across all races who identify as Hispanic/Latino are recorded as “"Hispanic or Latino/a, all races." This categorization increases data accuracy by correcting the way “Other” persons were counted. Previously, when a person reported “Other” for Race/Ethnicity, they would be recorded “Unknown.” Under the new categorization, they are counted as “Other” and are distinct from “Unknown.”

If a person records their race/ethnicity as “Asian,” “Black or African American,” “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” “White,” or “Other” for their first COVID-19 test, then this data will not change—even if a different race/ethnicity is reported for this person for any future COVID-19 test. There are two exceptions to this rule. The first exception is if a person’s race/ethnicity value is reported as “Unknown” on their first test and then on a subsequent test they report “Asian;” "Black or African American;" "Hispanic or Latino/a, all races;" "American Indian or Alaska Native;" "Native Hawaiian or Other Pacific Islander;" or "White”, then this subsequent reported race/ethnicity will overwrite the previous recording of “Unknown”. If a person has only ever selected “Unknown” as their race/ethnicity, then it will be recorded as “Unknown.” This change provides more specific and actionable data on who is tested in San Francisco.

The second exception is if a person ever marks “Hispanic or Latino/a, all races” for race/ethnicity then this choice will always overwrite any previous or future response. This is because it is an overarching category that can include any and all other races and is mutually exclusive with the other responses.

A person's race/ethnicity will be recorded as “Multi-racial” if they select two or more values among the following choices: “Asian,” “Black or African American,” “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” “White,” or “Other.” If a person selects a combination of two or more race/ethnicity answers that includes “Hispanic or Latino/a, all races” then they will still be recorded as “Hispanic or Latino/a, all races”—not as “Multi-racial.”

C. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information.

D. UPDATE PROCESS Updates automatically at 5:00AM Pacific Time each day. Redundant runs are scheduled at 7:00AM and 9:00AM in case of pipeline failure.

E. HOW TO USE THIS DATASET San Francisco population estimates for race/ethnicity can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24, 2020 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments.

In order to track trends over time, a user can analyze this data by sorting or filtering by the "specimen_collection_date" field.

Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of percent positive. When there are fewer than 20 positives tests for a given race/ethnicity and time period, the positivity rate is not calculated for the public tracker because rates of small test counts are less reliable.

Calculating Testing Rates: To calculate the testing rate per 10,000 residents, divide the total number of tests collected (positive, negative, and indeterminate results) for the specified race/ethnicity by the total number of residents who identify as that race/ethnicity (according to the 2016-2020 American Community Survey (ACS) population estimate), then multiply by 10,000. When there are fewer than 20 total tests for a given race/ethnicity and time period, the testing rate is not calculated for the public tracker because rates of small test counts are less reliable.

Read more about how this data is updated and validated daily: https://sf.gov/information/covid-19-data-questions

F. CHANGE LOG

• 1/12/2024 - This dataset will stop updating as of 1/12/2024
• 6/21/2023 - A small number of additional COVID-19 testing records were released as part of our ongoing data cleaning efforts. An update to the race or ethnicity designation among a subset of testing records was simultaneously released.
• 1/31/2023 - updated “population_estimate” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
• 2/10/2022 - race/ethnicity categorization was changed. See section NOTE ON RACE/ETHNICITY for additional information.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

In 2023, about 17.7 percent of the American population was 65 years old or over; an increase from the last few years and a figure which is expected to reach 22.8 percent by 2050. This is a significant increase from 1950, when only eight percent of the population was 65 or over. A rapidly aging population In recent years, the aging population of the United States has come into focus as a cause for concern, as the nature of work and retirement is expected to change to keep up. If a population is expected to live longer than the generations before, the economy will have to change as well to fulfill the needs of the citizens. In addition, the birth rate in the U.S. has been falling over the last 20 years, meaning that there are not as many young people to replace the individuals leaving the workforce. The future population It’s not only the American population that is aging -- the global population is, too. By 2025, the median age of the global workforce is expected to be 39.6 years, up from 33.8 years in 1990. Additionally, it is projected that there will be over three million people worldwide aged 100 years and over by 2050.

Kīlauea Point National Wildlife Refuge (KPNWR) is an important seabird breeding site located at the northeastern tip of Kauaʻi in the main Hawaiian Islands (MHI). Despite the regional significance of KPNWR as one of the most important breeding sites for Red-tailed Tropicbirds (Phaethon rubricauda), Red-footed Boobies (Sula sula), and Wedge-tailed Shearwaters (Ardenna pacifica) in MHI, robust and accurate population surveys have not been consistently conducted and recent information is lacking. In this study, we completed comprehensive population surveys for these three species during the 2019 breeding season. Using direct censusing methods (ground-searching, visual and photographic counts), we determined that 387 Red-tailed Tropicbird and 5,049 Red-footed Booby breeding pairs nested at KPNWR in 2019. Additionally, we performed surveys of aerially displaying tropicbirds to estimate a potential population of 30 White-tailed Tropicbird (P. lepturus) breeding pairs at KPWNR. Using a stratified-random plot-sampling method, we estimated that 20,998 Wedge-tailed Shearwater pairs nested at KPNWR in 2019. The breeding population size results in this study are greater than those reported in the past for KPNWR. We suggest that the Red-tailed Tropicbird breeding population has increased since the mid-2000s (when population estimates were last made), whereas Red-footed Booby numbers have likely remained similar and 2019 results show an increase over past estimates because of the more comprehensive methods used in this study. The results of these surveys provide current and accurate population sizes for these species that can serve as 1) benchmarks for future management and monitoring at KPNWR, and 2) important components of population-level assessments of seabird vulnerability to potential offshore wind energy development in the MHI. The data provided in this release support all population estimates in this study. The data are organized as: 1. RFBO_PhotoList: a tabular list of all photograph filenames and ancillary information (camera settings, location) used for conducting Red-footed Booby counts 2. RFBO_Photos: photographic images used for conducting Red-footed Booby counts and output software files of count results; photographs are grouped by sub-colony and platform (ground or aerial) 3. TropicbirdNests: a tabular list of all tropicbird nests, locations, and ancillary information 4. TropicbirdSearch: a polyline shapefile of all ground-based search effort for tropicbird nests created from GPS tracks of field crew 5. WTSH_plots: a tabular list of all survey plots, locations, and ancillary information used for estimating Wedge-tailed Shearwater population 6. WTSH_stratum: a polygon shapefile of sampling strata used for estimating Wedge-tailed Shearwater population Detailed descriptions of data are found in each associated metadata file and additional information useful for interpreting these data can be found in the data series report: Felis, J.J., Kelsey, E.C., Adams, J., Stenske, J., and White, L., 2020, Population estimates for selected breeding seabirds at Kīlauea Point National Wildlife Refuge, Kauaʻi, in 2019: U.S. Geological Survey Data Series 1130, 32 p., https://doi.org/10.3133/ds1130.

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

Kīlauea Point National Wildlife Refuge (KPNWR) is an important seabird breeding site located at the northeastern tip of Kauaʻi in the main Hawaiian Islands (MHI). Despite the regional significance of KPNWR as one of the most important breeding sites for Red-tailed Tropicbirds (Phaethon rubricauda), Red-footed Boobies (Sula sula), and Wedge-tailed Shearwaters (Ardenna pacifica) in MHI, robust and accurate population surveys have not been consistently conducted and recent information is lacking. In this study, we completed comprehensive population surveys for these three species during the 2019 breeding season. Using direct censusing methods (ground-searching, visual and photographic counts), we determined that 387 Red-tailed Tropicbird and 5,049 Red-footed Booby breeding pairs nested at KPNWR in 2019. Additionally, we performed surveys of aerially displaying tropicbirds to estimate a potential population of 30 White-tailed Tropicbird (P. lepturus) breeding pairs at KPWNR. Using a stratified-random plot-sampling method, we estimated that 20,998 Wedge-tailed Shearwater pairs nested at KPNWR in 2019. The breeding population size results in this study are greater than those reported in the past for KPNWR. We suggest that the Red-tailed Tropicbird breeding population has increased since the mid-2000s (when population estimates were last made), whereas Red-footed Booby numbers have likely remained similar and 2019 results show an increase over past estimates because of the more comprehensive methods used in this study. The results of these surveys provide current and accurate population sizes for these species that can serve as 1) benchmarks for future management and monitoring at KPNWR, and 2) important components of population-level assessments of seabird vulnerability to potential offshore wind energy development in the MHI. The data provided in this release support all population estimates in this study. The data are organized as: 1. RFBO_PhotoList: a tabular list of all photograph filenames and ancillary information (camera settings, location) used for conducting Red-footed Booby counts 2. RFBO_Photos: photographic images used for conducting Red-footed Booby counts and output software files of count results; photographs are grouped by sub-colony and platform (ground or aerial) 3. TropicbirdNests: a tabular list of all tropicbird nests, locations, and ancillary information 4. TropicbirdSearch: a polyline shapefile of all ground-based search effort for tropicbird nests created from GPS tracks of field crew 5. WTSH_plots: a tabular list of all survey plots, locations, and ancillary information used for estimating Wedge-tailed Shearwater population 6. WTSH_stratum: a polygon shapefile of sampling strata used for estimating Wedge-tailed Shearwater population Detailed descriptions of data are found in each associated metadata file and additional information useful for interpreting these data can be found in the data series report: Felis, J.J., Kelsey, E.C., Adams, J., Stenske, J., and White, L., 2020, Population estimates for selected breeding seabirds at Kīlauea Point National Wildlife Refuge, Kauaʻi, in 2019: U.S. Geological Survey Data Series 1130, 32 p., https://doi.org/10.3133/ds1130.

In 2008, the Quality Deer Management Association (QDMA) developed a map of white-tailed deer density with information obtained from state wildlife agencies. The map contains information from 2001 to 2005, with noticeable changes since the development of the first deer density map made by QDMA in 2001. The University of Minnesota, Forest Ecosystem Health Lab and the US Department of Agriculture, Forest Service-Northern Research Station have digitized the deer density map to provide information on the status and trends of forest health across the eastern United States. The QDMA spatial map depicting deer density (deer per square mile) was digitized across the eastern United States. Estimates of deer density were: White = rare, absent, or urban area with unknown population, Green = less than 15 deer per square mile, Yellow = 15 to 30 deer per square mile, Orange = 30 to 40 deer per square mile, or Red = greater than 45 deer per square mile. These categories represent coarse deer density levels as identified in the QDMA report in 2009 and should not be used to represent current or future deer densities across the study region. Sponsorship: Quality Deer Management Association; US Department of Agriculture, Forest Service-Northern Research Station; Minnesota Agricultural Experiment Station. Resources in this dataset:Resource Title: Link to DRUM catalog record. File Name: Web Page, url: https://conservancy.umn.edu/handle/11299/178246

The NC Center for Geographic Information and Analysis developed the GIS data set, Type A Future Public Sewer Systems, as mapped by contractors to the NC Rural Center (engineering firms McGill & Associates and Hobbs, Upchurch & Associates) during 2004, 2005, and 2006 to facilitate planning, siting and impact analysis in the 100 individual counties of North Carolina. This file enables the user to make various county-level determinations when used in conjunction with other data layers. The purpose of this data set is to inform users of key information about public sewer systems in North Carolina, with particular focus on system size and growth, sewage discharge annd treatment, and capital improvement needs of the systems.

The White-faced Ibis (Plegadis chihi) in the Great Basin and surrounding area was listed as a Species of Management Concern (Sharp 1985, USFWS 1995) based on its small population size and vulnerability to breeding habitat loss. Traditionally, most Great Basin ibises have bred in Utah and Nevada with peripheral but growing colonies in Idaho, California, and Oregon (Sharp 1985, Ryder and Manry 1994). After apparently declining precipitously in the 1960's and 1970's (Capen 1977), the Great Basin population was estimated at only 7,500 breeding pairs in 1984 (Sharp 1985). In addition to the Great Basin population (as defined here), small numbers of ibises breed locally in Colorado, Wyoming, Montana, North and South Dakota, and southern Alberta, and large numbers breed in Louisiana, Texas, Mexico, and South America (reviewed in Ryder and Manry 1994). Interchange among these sites and Great Basin colonies has not been investigated. In the arid Great Basin region, ibises breed in semi-permanent wetlands which are susceptible to naturally occurring droughts and floods. Local population fluctuations and colony abandonment reflect this vulnerability. The highly nomadic White-faced Ibis apparently compensates for wetland dynamics by moving among breeding colonies and colonizing new wetlands within and between years (e.g., Ryder 1967, Capen 1977, Ivey et al. 1988, Henny and Herron 1989). The nomadic nature of the White-faced Ibis, like that of several other colonial ciconiiforms, suggests that conservation efforts be undertaken at the landscape level and that population dynamics, distribution, and trends be monitored at the regional or population scale (e.g., Frederick et al. 1996). The status of the Great Basin breeding population has not been reviewed since 1984 (Sharp 1985). Increases in breeding numbers in Oregon, Idaho, and California during the 1980's and 1990's suggested either that ibises were increasing regionally or individuals displaced from flooded Great Salt Lake marshes were colonizing elsewhere (e.g., Ivey et al. 1988, Follansbee and Mauser 1994, Trost and GersteIl1994). An increase in wintering numbers also suggested a population increase (Shuford et al. 1989). Recognizing the need for a comprehensive estimate of the breeding population, U.S. Fish and Wildlife Service (USFWS) coordinated a regional survey of all historic, active, and probable colony sites in 1995. To further assess the 1985-1997 population trend, we compiled available annual survey data for all known colonies. Here we report results of the 1995 survey and annual counts for 1985-1997. The objectives of this report are as follows: I. Document changes in the distribution, abundance, and population trend of White-faced Ibis breeding in the Great Basin and surrounding area during 1985-1997. 2. Interpret population-wide changes in ibis distribution and abundance in relation to wetland dynamics throughout the region. 3. Discuss implications for future monitoring, research, and conservation.

The statistic shows the total population in Canada from 2020 to 2024, with projections up until 2030. In 2024, the total population in Canada amounted to about 41.14 million inhabitants. Population of Canada Canada ranks second among the largest countries in the world in terms of area size, right behind Russia, despite having a relatively low total population. The reason for this is that most of Canada remains uninhabited due to inhospitable conditions. Approximately 90 percent of all Canadians live within about 160 km of the U.S. border because of better living conditions and larger cities. On a year to year basis, Canada’s total population has continued to increase, although not dramatically. Population growth as of 2012 has amounted to its highest values in the past decade, reaching a peak in 2009, but was unstable and constantly fluctuating. Simultaneously, Canada’s fertility rate dropped slightly between 2009 and 2011, after experiencing a decade high birth rate in 2008. Standard of living in Canada has remained stable and has kept the country as one of the top 20 countries with the highest Human Development Index rating. The Human Development Index (HDI) measures quality of life based on several indicators, such as life expectancy at birth, literacy rate, education levels and gross national income per capita. Canada has a relatively high life expectancy compared to many other international countries, earning a spot in the top 20 countries and beating out countries such as the United States and the UK. From an economic standpoint, Canada has been slowly recovering from the 2008 financial crisis. Unemployment has gradually decreased, after reaching a decade high in 2009. Additionally, GDP has dramatically increased since 2009 and is expected to continue to increase for the next several years.

In 2023, the median age of the population of the United States was 39.2 years. While this may seem quite young, the median age in 1960 was even younger, at 29.5 years. The aging population in the United States means that society is going to have to find a way to adapt to the larger numbers of older people. Everything from Social Security to employment to the age of retirement will have to change if the population is expected to age more while having fewer children. The world is getting older It’s not only the United States that is facing this particular demographic dilemma. In 1950, the global median age was 23.6 years. This number is projected to increase to 41.9 years by the year 2100. This means that not only the U.S., but the rest of the world will also have to find ways to adapt to the aging population.

In 2024, TV consumption in the United States was highest among African Americans and Black people, who watched an average of **** hours of TV each day. At the same time, the viewing time dropped among Asian Americans to under *** hours TV and streaming engagement among racial groups As of mid 2022, The U.S. population still spent most of their time watching TV and streaming content on traditional media, such as cable and broadcast. Roughly ** percent of their viewing time was allocated to streaming. It was also observed that Hispanics were more likely to use online video platforms than any other ethnic group, with a viewing time share of over ** percent. By contrast, using streaming services accounted for around ****third of the total viewing time among white and Asian consumers. Traditional TV vs. digital video The way consumers watch TV shows and movies has changed significantly. A forecast showed that traditional TV consumption among Americans will continue to steadily decline in the upcoming years. Meanwhile, with an increasing number of consumers adopting streaming and social videos, digital video soars, outpacing TV viewing time by the end of 2023.

Nigeria has the largest population in Africa. As of 2025, the country counted over 237.5 million individuals, whereas Ethiopia, which ranked second, has around 135.5 million inhabitants. Egypt registered the largest population in North Africa, reaching nearly 118.4 million people. In terms of inhabitants per square kilometer, Nigeria only ranked seventh, while Mauritius had the highest population density on the whole African continent in 2023. The fastest-growing world region Africa is the second most populous continent in the world, after Asia. Nevertheless, Africa records the highest growth rate worldwide, with figures rising by over two percent every year. In some countries, such as Chad, South Sudan, Somalia, and the Central African Republic, the population increase peaks at over 3.4 percent. With so many births, Africa is also the youngest continent in the world. However, this coincides with a low life expectancy. African cities on the rise The last decades have seen high urbanization rates in Asia, mainly in China and India. African cities are also growing at large rates. Indeed, the continent has three megacities and is expected to add four more by 2050. Furthermore, Africa's fastest-growing cities are forecast to be Bujumbura, in Burundi, and Zinder, Nigeria, by 2035.