Graph and download economic data for 50-Year High Quality Market (HQM) Corporate Bond Spot Rate (HQMCB50YR) from Jan 1984 to Jul 2025 about bonds, corporate, interest rate, interest, rate, and USA.

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.

Unemployment Rate in the United States increased to 4.20 percent in July from 4.10 percent in June of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The average temperature in the contiguous United States reached 55.5 degrees Fahrenheit (13 degrees Celsius) in 2024, approximately 3.5 degrees Fahrenheit higher than the 20th-century average. These levels represented a record since measurements started in ****. Monthly average temperatures in the U.S. were also indicative of this trend. Temperatures and emissions are on the rise The rise in temperatures since 1975 is similar to the increase in carbon dioxide emissions in the U.S. Although CO₂ emissions in recent years were lower than when they peaked in 2007, they were still generally higher than levels recorded before 1990. Carbon dioxide is a greenhouse gas and is the main driver of climate change. Extreme weather Scientists worldwide have found links between the rise in temperatures and changing weather patterns. Extreme weather in the U.S. has resulted in natural disasters such as hurricanes and extreme heat waves becoming more likely. Economic damage caused by extreme temperatures in the U.S. has amounted to hundreds of billions of U.S. dollars over the past few decades.

The Gross Domestic Product (GDP) in the United States was worth 29184.89 billion US dollars in 2024, according to official data from the World Bank. The GDP value of the United States represents 27.49 percent of the world economy. This dataset provides - United States GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2050 high scenario represented here, the modeled water level for a 50-day frequency is 138 cm (104 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions. Tipping points (large and sudden changes) are triggered, and worst-case possibilities arise. It is recommended using this scenario for planning construction of infrastructure with highly critical use and longer lifespans, such as a new hospital. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

Although the founding fathers declared American independence in 1776, and the subsequent Revolutionary War ended in 1783, individual states did not officially join the union until 1787. The first states to ratify the U.S. Constitution were Delaware, Pennsylvania and New Jersey, in December 1787, and they were joined by the remainder of the thirteen ex-British colonies by 1790. Another three states joined before the turn of the nineteenth century, and there were 45 states by 1900. The final states, Alaska and Hawaii, were admitted to the union in 1959, almost 172 years after the first colonies became federal states. Secession in the American Civil War The issues of slavery and territorial expansion in the mid nineteenth century eventually led to the American Civil War, which lasted from 1861 until 1865. As the U.S. expanded westwards, a moral and economic argument developed about the legality of slavery in these new states; northern states were generally opposed to the expansion of slavery, whereas the southern states (who were economically dependent on slavery) saw this lack of extension as a stepping stone towards nationwide abolition. In 1861, eleven southern states seceded from the Union, and formed the Confederate States of America. When President Lincoln refused to relinquish federal property in the south, the Confederacy attacked, setting in motion the American Civil War. After four years, the Union emerged victorious, and the Confederate States of America was disbanded, and each individual state was readmitted to Congress gradually, between 1866 and 1870. Expansion of other territories Along with the fifty U.S. states, there is one federal district (Washington D.C., the capital city), and fourteen overseas territories, five of which with a resident population (American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, and the U.S. Virgin Islands). In 2019, President Trump inquired about the U.S. purchasing the territory of Greenland from Denmark, and, although Denmark's response indicated that this would be unlikely, this does suggest that the US may be open to further expansion of it's states and territories in the future. There is also a movement to make Washington D.C. the 51st state to be admitted to the union, as citizens of the nation's capital (over 700,000 people) do not have voting representation in the houses of Congress nor control over many local affairs; as of 2020, the U.S. public appears to be divided on the issue, and politicians are split along party lines, as D.C. votes overwhelmingly for the Democratic nominee in presidential elections.

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100.

We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates.

When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency.

In the 2050 intermediate-low scenario represented here, the modeled water level for a 50-day frequency is 118 cm (84 cm for Rose and Swains). In this scenario, world-wide society limits increase of emissions, and sea level rises without reaching any tipping points, i.e. large and sudden changes such as a rapid increase in ice sheets melting. It is recommended to use this scenario only for planning construction of non-critical infrastructure that owners can afford to lose, such as a beach "fale".

Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level.

It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

United States US: Population: Male: Ages 50-54: % of Male Population data was reported at 6.741 % in 2017. This records a decrease from the previous number of 6.885 % for 2016. United States US: Population: Male: Ages 50-54: % of Male Population data is updated yearly, averaging 5.415 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 7.196 % in 2011 and a record low of 4.426 % in 1986. United States US: Population: Male: Ages 50-54: % of Male Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Population and Urbanization Statistics. Male population between the ages 50 to 54 as a percentage of the total male population.; ; World Bank staff estimates based on age/sex distributions of United Nations Population Division's World Population Prospects: 2017 Revision.; ;

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2090 low scenario represented here, the modeled water level for a 50-day frequency is 129 cm (93 cm for Rose and Swains). In this scenario, significant world-wide emissions reductions are implemented now, which is highly unlikely. It is not recommended to use this scenario for planning purposes. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2100 intermediate scenario represented here, the modeled water level for a 50-day frequency is 205 cm (168 cm for Rose and Swains). In this scenario, world-wide society continues current emissions rates, and sea level rises at increased rates compared to the intermediate-low scenario. Tipping points, i.e. large and sudden changes, are still not crossed. It is recommended using this scenario for planning construction of infrastructure with low-to-medium critical use and lifespans extending into the second half of the century, such as a new storefront. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

The main stock market index of United States, the US500, rose to 6467 points on August 22, 2025, gaining 1.52% from the previous session. Over the past month, the index has climbed 1.70% and is up 14.77% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on August of 2025.

The estimated population of the U.S. was approximately 334.9 million in 2023, and the largest age group was adults aged 30 to 34. There were 11.88 million males in this age category and around 11.64 million females. Which U.S. state has the largest population? The population of the United States continues to increase, and the country is the third most populous in the world behind China and India. The gender distribution has remained consistent for many years, with the number of females narrowly outnumbering males. In terms of where the residents are located, California was the state with the highest population in 2023. The U.S. population by race and ethnicity The United States is well known the world over for having a diverse population. In 2023, the number of Black or African American individuals was estimated to be 45.76 million, which represented an increase of over four million since the 2010 census. The number of Asian residents has increased at a similar rate during the same time period and the Hispanic population in the U.S. has also continued to grow.

Monthly totals of precipitation in millimeters (mm), monthly means of daily maximum air temperature in degrees Celsius (C), and monthly means of daily minimum air temperature (C) were developed at the 5 arc minute grid level for the conterminous United States (US). Also, included are computed monthly mean of daily potential evapotranspiration (mm) and mean grid elevation in meters (m). These data were developed from the SRES (Special Report on Emissions Scenarios) climate scenario B2 used in the Third Assessment of the Intergovernmental Panel on Climate Change, as modeled by these climate models: GCGM2 (Climate Centre for Modelling and Analysis), CSIRO MK2 (Australia's Commonwealth Scientific and Industrial Research Organisation), and HadCM3 (Hadley Centre for Climate Prediction and Research UK). The monthly change factors were developed from global model output and downscaled to the 5 arc minute spatial grid using ANUSPLIN following the work of Price et al. (2004). The 30 year mean climatology (1961-1990) was developed from PRISM (Parameter-elevation Regressions on Independent Slopes Model) data at the 2.5 arc minute scale and aggregated to the 5 arc minute grid scale. The change factors were imposed upon the 30-year period (1961-1990) to develop the projections for each climate scenario.The USDA Forest Service (USFS) produces a periodic assessment of the condition and trends of the Nation's renewable resources as required by the Forest and Rangeland Renewable Resources Planning Act (RPA) of 1974. This RPA Assessment provides a snapshot of current US forest and rangeland conditions and trends on all ownerships, identifies drivers of change, and projects 50 years into the future (https://www.fs.fed.us/research/rpa/, accessed 07/06/2015). For 2010 RPA Assessment, an integrated modeling framework is being used in which the potential implications of climate change can be analyzed across some resource areas (Langner et al. 2012). The nature of the climate variables needed to address climate change impacts for these resource analyses in the 2010 RPA Assessment were determined to be monthly precipitation and temperature variables at the 5 arc minute grid level spatial scale, and for some resources at the county spatial scale.Original metadata dated 08/02/2010. Metadata modified on 02/22/2012 to adjust citation to include the addition of a DOI (digital object identifier) and update to the cross-reference section. Minor metadata updates on 02/26/2013. Metadata modified on 07/22/2015 to update cross-reference citations and other minor updates. Additional minor metadata updates on 12/13/2016 and 04/18/2019.

In 2023, around ** percent of U.S. men aged 50 years and older who were college graduates had a prostate cancer test in the past year, compared to ** percent of those with a high school diploma. Prostate cancer is the most common type of cancer among men in the United States. This statistic shows the percentage of men aged 50 years and older who had a prostate cancer test in the U.S. in the past year as of 2023, by education.

United States Current Population Survey: Population: Female: 50 to 54 Yrs data was reported at 10,612.000 Person th in Jun 2018. This records a decrease from the previous number of 10,635.000 Person th for May 2018. United States Current Population Survey: Population: Female: 50 to 54 Yrs data is updated monthly, averaging 7,687.000 Person th from Jun 1976 (Median) to Jun 2018, with 505 observations. The data reached an all-time high of 11,466.000 Person th in Apr 2012 and a record low of 5,602.000 Person th in May 1986. United States Current Population Survey: Population: Female: 50 to 54 Yrs data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.G007: Current Population Survey: Population.

Esri ArcGIS Online (AGOL) Imagery Layer which includes the MDOT SHA 2050 Mean Sea Level 2% Annual Chance (50-Year Storm) - Flood Depth Grid data product.MDOT SHA 2050 Mean Sea Level 2% Annual Chance (50-Year Storm) - Flood Depth Grid consists of a depth grid image service depicting conditions of sea level change based on the 2% annual chance event (50-Year Storm) scenario for coastal areas throughout the State of Maryland in year 2050. This data product supports Maryland Department of Transportation State Highway Administration (MDOT SHA) leadership and planners as they endeavor to mitigate or prevent the impacts of sea level change resulting from land surface subsidence and rising sea levels.MDOT SHA 2050 Mean Sea Level 2% Annual Chance (50-Year Storm) - Flood Depth Grid data was produced as a result of efforts by the Maryland Department of Transportation State Highway Administration (MDOT SHA), Eastern Shore Regional GIS Cooperative (ESRGC), Salisbury University (SU), United States Corps of Engineers (USACE), National Oceanic & Atmospheric Administration (NOAA), and the United States Geological Survey (USGS). The US Army Corps of Engineers provide the sea level change estimate. Sea level change is localized using water elevations collected from a qualifying National Oceanic and Atmospheric Administration (NOAA) tidal reference station - NOAA observations are transformed from tidal datum to North American Vertical Datum of 1988. A final correction for glacial isostatic adjustment and land creates an sea level change value for the official project year, 2050.MDOT SHA 2050 Mean Sea Level 2% Annual Chance (50-Year Storm) - Flood Depth Grid data was task-based, and will only be updated on an As-Needed basis where necessary.For more information, contact MDOT SHA OIT Enterprise Information Services:Email: GIS@mdot.maryland.gov

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2030 intermediate scenario represented here, the modeled water level for a 50-day frequency is 102 cm (75 cm for Rose and Swains). In this scenario, world-wide society continues current emissions rates, and sea level rises at increased rates compared to the intermediate-low scenario. Tipping points, i.e. large and sudden changes, are still not crossed. It is recommended using this scenario for planning construction of infrastructure with low-to-medium critical use and lifespans extending into the second half of the century, such as a new storefront. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

This extreme high-tide flooding layer provides a prediction of future sea level rise (SLR) inundation and was produced using a passive flooding model, often referred to as a "bathtub" model. It provides an assessment of flooded areas according to a specific water level. These water levels are determined using projections from the U.S. Interagency Task Force (ITF) (Sweet et al., 2022) in combination with land subsidence projections modeled by Han et al. (2019). The latter is included only for Tutuila, Aunuu, and Manua Islands (Ofu, Olosega, and Tau). In contrast, SLR projections for Swains Island and Rose Atoll only include the climate-related processes (ITF). The projections are modeled following both scenarios and time. The five scenarios range from low to high depending on the amount of greenhouse gases emissions, while time is divided by decade from 2030 to 2100. We apply this model to the 2022 National Geodetic Survey (NGS) lidar DEM for American Samoa with 1-meter resolution. The DEM was leveled from NAD83 (PA11) to mean sea level at 0 m (MSL=0) in 2005. The adjustment of the DEM may lead to inaccuracies due to the lack of historic information. It is also important to acknowledge that any inaccuracies in the DEM will lead to inaccuracies in the flooding estimates. When assessing the impacts of future sea level rise, it is important to consider how often flood conditions will occur in a given year. A low-lying location will begin to see impacts of being flooded a few times per year. Then, as sea level rise increases, it will flood tens of times per year. Eventually, that location may be flooded under a daily high tide. The present scenario models a frequency of 50 flooding days per year. Please note that this frequency represents an average number of times per year (Thompson et al., 2021). Any particular year may have substantially more or less flooding days depending on local climate variability (such as the El Nino, La Nina cycle) and year-to-year variability in the tides due to changes in the alignment of the Earth, Moon, and Sun. Secondly, flooding frequencies are based on data from the Pago Pago tide gauge on Tutuila, which means that estimates may not perfectly represent local conditions outside the harbor or on other islands. However, this is the best source of information available, and we do not expect this to lead to significant inaccuracies in the estimates of flooding frequency. In the 2030 high scenario represented here, the modeled water level for a 50-day frequency is 105 cm (78 cm for Rose and Swains). In this scenario, world-wide society continues to increase emissions. Tipping points (large and sudden changes) are triggered, and worst-case possibilities arise. It is recommended using this scenario for planning construction of infrastructure with highly critical use and longer lifespans, such as a new hospital. Flood depth is provided in centimeters above the 2005 mean higher high water (MHHW) tide level. It is essential to emphasize that the passive flooding model used to produce this data layer does not include the effects of waves on flooding. As a result, the extent and impacts of future flooding under high-wave conditions are not represented, which should be accounted for in planning efforts. In addition, the DEM is assumed to be unchanged as sea level rises, but in fact there will be erosion and changes in the shape of the land surface, and continued subsidence. This also must be considered, and it is best practice to consider any flooding extent or depth represented in this data layer as a best-case scenario, with the effects of dynamic shoreline processes leading to greater flood extent and depth than presented.

About this dataset

Context

The dataset tabulates the Fifty-Six 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 Fifty-Six. The dataset can be utilized to understand the population distribution of Fifty-Six by age. For example, using this dataset, we can identify the largest age group in Fifty-Six.

Key observations

The largest age group in Fifty-Six, AR was for the group of age 70 to 74 years years with a population of 36 (20.34%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Fifty-Six, AR was the 20 to 24 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 Fifty-Six is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Fifty-Six 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 Fifty-Six Population by Age. You can refer the same here