United States US: Population Projection: Mid Year: Growth data was reported at 0.450 % in 2050. This stayed constant from the previous number of 0.450 % for 2049. United States US: Population Projection: Mid Year: Growth data is updated yearly, averaging 0.700 % from Jun 2001 (Median) to 2050, with 50 observations. The data reached an all-time high of 0.980 % in 2006 and a record low of 0.450 % in 2050. United States US: Population Projection: Mid Year: Growth data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.US Census Bureau: Demographic Projection.

This graph shows population projections for the United States of America. The estimated population of the USA in 2050 is 398 million residents. Population The U.S. Census Bureau presents annual projections for the growth of the U.S. population up to the year 2060. By 2050, it is estimated that the American population will surpass 398 million citizens. The U.S. census also projects a regressing annual growth rate, starting at 0.8 percent in 2015 and decreasing to 0.46 percent by 2060.

The UN population division publishes population projections for the entire world up to the year 2100. The United Nations also projects a regressing annual growth rate of the world population. Between 2015 and 2020, the population is expected to increase by 1.04 percent annually. Around 2060, the annual growth rate will have decreased to 0.34 percent.

The statistic shows the degree of urbanization in the United States from 2000 to 2050. In 2015, about **** percent of the total population in the United States lived in urban areas. Projections estimate that the corresponding figure in 2050 will be **** percent.

Content

The current population of the United States of America is 334,464,117 as of Saturday, April 16, 2022, based on Worldometer elaboration of the latest United Nations data. This three datasets contain population data of USA (2020 and histIndiaorical), population forecast and population in major cities.

Attribute Information

• Year - Years from 2020-1955
• Population - Population in the respective year
• Yearly % Change - Percentage Yearly Change in Population
• Yearly Change - Yearly Change in Population
• Migrants (net) - Total number of migrants
• Median Age - Median age of the population
• Fertility Rate - Fertility rate
• Density (P/Km²)- Population density (population per square km)
• Urban Pop %- Percentage of urban population
• Urban Population- Urban population
• Country's Share of World Pop - Population share
• World Population - World Population in the respective year
• India Global Rank - Global Rank in Population

Source

Link : https://www.worldometers.info/world-population/us-population/

Updated Covid 19 and Other Datasets

Link : https://www.kaggle.com/anandhuh/datasets

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The sea level rise (SLR) coastal inundation layers were created using existing federal products: the (1) NOAA Coastal Digital Elevation Models (DEMs) and (2) 2022 Interagency Sea Level Rise Technical Report Data Files. The DEMs for the Continental United States (CONUS) are provided in North American Vertical Datum 1988 (NAVD 88) and were converted to Mean Higher High Water (MHHW) using the NOAA VDatum conversion surfaces; the elevation values are in meters (m). The NOAA Scenarios of Future Mean Sea Level are provided in centimeters (cm). The MHHW DEMs for CONUS were merged and converted to cm and Scenarios of Future Mean Sea Level were subtracted from the merged DEM. Values below 0 represent areas that are below sea level and are “remapped” to 1, all values above 0 are remapped to “No Data”, creating a map that shows only areas impacted by SLR. Areas protected by levees in Louisiana and Texas were then masked or removed from the results. This was done for each of the emissions scenarios (Lower Emissions = 2022 Intermediate SLR Scenario Higher Emissions = 2022 Intermediate High SLR Scenario) at each of the mapped time intervals (Early Century - Year 2030, Middle Century - Year 2050, and Late Century - Year 2090). The resulting maps are displayed in the CMRA Assessment Tool. County, tract, and tribal geographies summaries of percentage SLR inundation were also calculated using Zonal Statistics tools. The Sea Level Rise Scenario year 2020 is considered “baseline” and the impacts are calculated by subtracting the baseline value from each of the near-term, mid-term and long-term timeframes. General Disclaimer The data and maps in this tool illustrate the scale of potential flooding, not the exact location, and do not account for erosion, subsidence, or future construction. Water levels are relative to Mean Higher High Water (MHHW) (excludes wind driven tides). The data, maps, and information provided should be used only as a screening-level tool for management decisions. As with all remotely sensed data, all features should be verified with a site visit. Hydroconnectivity was not considered in the mapping process. The data and maps in this tool are provided “as is,” without warranty to their performance, merchantable state, or fitness for any particular purpose. The entire risk associated with the results and performance of these data is assumed by the user. This tool should be used strictly as a planning reference tool and not for navigation, permitting, or other legal purposes. SLR visualizations and statistics are not available in CMRA for Hawaii, Alaska, or U.S. territories at this time. Levees Disclaimer Enclosed levee areas are displayed as gray areas on the maps. Major federal leveed areas were assumed high enough and strong enough to protect against inundation depicted in this viewer, and therefore no inundation was mapped in these regions. Major federal leveed areas were taken from the National Levee Database. Minor (nonfederal) leveed areas were mapped using the best available elevation data that capture leveed features. In some cases, however, breaks in elevation occur along leveed areas because of flood control features being removed from elevation data, limitations of the horizontal and vertical resolution of the elevation data, the occurrence of levee drainage features, and so forth. Flooding behind levees is only depicted if breaks in elevation data occur or if the levee elevations are overtopped by the water surface. At some flood levels, alternate pathways around—not through—levees, walls, dams, and flood gates may exist that allow water to flow into areas protected at lower levels. In general, imperfect levee and elevation data make assessing protection difficult, and small data errors can have large consequences. Citations 2022 Sea Level Rise Technical Report - Sweet, W.V., B.D. Hamlington, R.E. Kopp, C.P. Weaver, P.L. Barnard, D. Bekaert, W. Brooks, M. Craghan, G. Dusek, T. Frederikse, G. Garner, A.S. Genz, J.P. Krasting, E. Larour, D. Marcy, J.J. Marra, J. Obeysekera, M. Osler, M. Pendleton, D. Roman, L. Schmied, W. Veatch, K.D. White, and C. Zuzak, 2022: Global and Regional Sea Level Rise Scenarios for the United States: Updated Mean Projections and Extreme Water Level Probabilities Along U.S. Coastlines. NOAA Technical Report NOS 01. National Oceanic and Atmospheric Administration, National Ocean Service, Silver Spring, MD, 111 pp. https://oceanservice.noaa.gov/hazards/sealevelrise/noaa-nostechrpt01-global-regional-SLR-scenarios-US.pdf

These raster datasets are derived from output of the Massachusetts Coast Flood Risk Model (MC-FRM) for Present Day - defined by the tidal epoch centered on 2008 (1999 to 2017), 2030, 2050, and 2070 sea level rise and coastal storm simulations as described in the report "Assessing the vulnerability of MassDOT’s coastal transportation systems to future sea level rise and coastal storms, and developing conceptual adaptation strategies" (2020). For each of these four sea level conditions the model output is provided as four separate raster datasets: (1) coastal flood exceedance probabilities (CFEP), (2) flood depths for 1% CFEP, (3) flood depths for 0.5% CFEP, and (4) flood depths for 0.1% CFEP. Details of the project and model are described in the final project report cited herein (Bosma, et. al., 2020).

The agriculture sector could become the largest source of U.S. GHG emissions by mid-century if the country meets all its international commitments. In this secenario, the agriculture sector would account for ** percent of total annual U.S. emissions. Currently, the agriculture sector accounts for roughly ** percent of U.S. GHG emissions.

United States US: Fertility Rate: per Woman data was reported at 1.860 NA in 2050. This stayed constant from the previous number of 1.860 NA for 2049. United States US: Fertility Rate: per Woman data is updated yearly, averaging 1.870 NA from Jun 2010 (Median) to 2050, with 41 observations. The data reached an all-time high of 2.060 NA in 2012 and a record low of 1.860 NA in 2050. United States US: Fertility Rate: per Woman data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.US Census Bureau: Demographic Projection.

The average level of the ocean has been rising since we started measuring and recording this data. According to the National Aeronautics and Space Agency (NASA), since 1900 the global mean sea level has risen more than 200 millimeters (nearly 8 inches) and nearly half of that increase has occurred since 1993 in a concerning change in rate of rise.Sea level rise is one of the many effects of global warming. Scientists attribute sea level rise to two things, melting ice and increased ocean water temperatures. Increasing air temperatures, particularly in the polar regions, has encouraged the melting of land-based ice reserves such as glaciers, ice sheets, and permafrost. Historically, warm season ice melt was balanced by replenishment during the cold season but warming temperatures have created conditions where melting exceeds the buildup of ice. This water flows through rivers and streams to the ocean in quantities sufficient to contribute to sea level rise.Oceans are also massive heat sinks. They pull large quantities of atmospheric heat and greenhouse gases such as carbon dioxide and store it in the ocean. The sea changes temperature much more slowly than the air and over time ocean temperatures have continued to build. As the ocean water warms it expands causing the sea levels to rise.Sea levels are not rising equally across Earth. Some areas are already experiencing significant impacts due to the rising water levels while others have seen minimal changes. This is due to a variety of reasons. First, despite how it is typically illustrated Earth is not perfectly round so the height of the ocean at any given point varies. This can be due to the Earth’s rotation, ocean currents, or prevailing wind speed and direction.Experts consider sea level rise and urgent climatic threat. Many low-lying places such as islands and coastal areas are already experiencing high waters. Higher waters also make storms such as hurricanes more dangerous due to higher storm surges and flooding. As coastlines could lose key infrastructure, land will become uninhabitable, and many people could lose their livelihoods. It is estimated 10 percent of the world’s population could be impacted as the waters rise. Many of the approximately 770 million people could be forced to migrate to higher ground, or in the case of island countries, such as Kiribati, to new countries once theirs sinks below the sea.This map was created with data from the National Oceanic and Atmospheric Administration (NOAA), NASA, and the United States Geological Survey. Experts used an elevation data and the NOAA model Scenarios of Future Mean Seal Level to illustrate the scale of potential coastal flooding. The mapmaker chose to remove levees from the data, so the areas flooded include places, particularly in the states of Texas and Louisiana, that are presently protected by this infrastructure. It is important to note that these are possible outcomes. This model does not include possible erosion, subsidence, or construction that may occur between 2022 when this data was created and 2030, 2050, or 2090 respectively. While models are powerful tools it is difficult to calculate every aspect that shapes our environment.Learn more about how coastal communities are impacted by sea level rise with this StoryMap by NOAA’s Office for Coastal Management, The King Tides Project: Snap the shore, See the Future.

The 2018 edition of Woods and Poole Complete U.S. Database provides annual historical data from 1970 (some variables begin in 1990) and annual projections to 2050 of population by race, sex, and age, employment by industry, earnings of employees by industry, personal income by source, households by income bracket and retail sales by kind of business. The Complete U.S. Database contains annual data for all economic and demographic variables for all geographic areas in the Woods & Poole database (the U.S. total, and all regions, states, counties, and CBSAs). The Complete U.S. Database has following components: Demographic & Economic Desktop Data Files: There are 122 files covering demographic and economic data. The first 31 files (WP001.csv – WP031.csv) cover demographic data. The remaining files (WP032.csv – WP122.csv) cover economic data. Demographic DDFs: Provide population data for the U.S., regions, states, Combined Statistical Areas (CSAs), Metropolitan Statistical Areas (MSAs), Micropolitan Statistical Areas (MICROs), Metropolitan Divisions (MDIVs), and counties. Each variable is in a separate .csv file. Variables: Total Population Population Age (breakdown: 0-4, 5-9, 10-15 etc. all the way to 85 & over) Median Age of Population White Population Population Native American Population Asian & Pacific Islander Population Hispanic Population, any Race Total Population Age (breakdown: 0-17, 15-17, 18-24, 65 & over) Male Population Female Population Economic DDFs: The other files (WP032.csv – WP122.csv) provide employment and income data on: Total Employment (by industry) Total Earnings of Employees (by industry) Total Personal Income (by source) Household income (by brackets) Total Retail & Food Services Sales ( by industry) Net Earnings Gross Regional Product Retail Sales per Household Economic & Demographic Flat File: A single file for total number of people by single year of age (from 0 to 85 and over), race, and gender. It covers all U.S., regions, states, CSAs, MSAs and counties. Years of coverage: 1990 - 2050 Single Year of Age by Race and Gender: Separate files for number of people by single year of age (from 0 years to 85 years and over), race (White, Black, Native American, Asian American & Pacific Islander and Hispanic) and gender. Years of coverage: 1990 through 2050. DATA AVAILABLE FOR 1970-2019; FORECASTS THROUGH 2050

In 2020, about 82.66 percent of the total population in the United States lived in cities and urban areas. As the United States was one of the earliest nations to industrialize, it has had a comparatively high rate of urbanization over the past two centuries. The urban population became larger than the rural population during the 1910s, and by the middle of the century it is expected that almost 90 percent of the population will live in an urban setting. Regional development of urbanization in the U.S. The United States began to urbanize on a larger scale in the 1830s, as technological advancements reduced the labor demand in agriculture, and as European migration began to rise. One major difference between early urbanization in the U.S. and other industrializing economies, such as the UK or Germany, was population distribution. Throughout the 1800s, the Northeastern U.S. became the most industrious and urban region of the country, as this was the main point of arrival for migrants. Disparities in industrialization and urbanization was a key contributor to the Union's victory in the Civil War, not only due to population sizes, but also through production capabilities and transport infrastructure. The Northeast's population reached an urban majority in the 1870s, whereas this did not occur in the South until the 1950s. As more people moved westward in the late 1800s, not only did their population growth increase, but the share of the urban population also rose, with an urban majority established in both the West and Midwest regions in the 1910s. The West would eventually become the most urbanized region in the 1960s, and over 90 percent of the West's population is urbanized today. Urbanization today New York City is the most populous city in the United States, with a population of 8.3 million, while California has the largest urban population of any state. California also has the highest urbanization rate, although the District of Columbia is considered 100 percent urban. Only four U.S. states still have a rural majority, these are Maine, Mississippi, Montana, and West Virginia.

United States EIA Projection: Population: 16 Years & Over data was reported at 326,696.625 Person th in 2050. This records an increase from the previous number of 325,115.143 Person th for 2049. United States EIA Projection: Population: 16 Years & Over data is updated yearly, averaging 295,935.073 Person th from Dec 2015 (Median) to 2050, with 36 observations. The data reached an all-time high of 326,696.625 Person th in 2050 and a record low of 256,707.825 Person th in 2015. United States EIA Projection: Population: 16 Years & Over data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s USA – Table US.G005: Population: Projection: Energy Information Administration.

projected population total, broken down by age and sex, for 2030, 2040, and 2050. They are benchmarked on the 2020 Decennial Census Count data from the U.S. Census Bureau to reflect the effect of the latest demographic trends on the future population. These projections were produced and released by the Cooper Center on July 1, 2024.

The Cooper Center projections research is widely used and well received; this data has been cited by a diverse range of organizations including many federal agencies, state legislatures, businesses, non-profits, think-tanks, academic institutions, and the media. The last vintage of projections for 2020 were found to be highly accurate when evaluated and compared to the actual Census Count data.

United States US: Mortality Rate: Under 5 per 1000 Births data was reported at 4.900 NA in 2050. This stayed constant from the previous number of 4.900 NA for 2049. United States US: Mortality Rate: Under 5 per 1000 Births data is updated yearly, averaging 5.800 NA from Jun 2014 (Median) to 2050, with 37 observations. The data reached an all-time high of 6.900 NA in 2014 and a record low of 4.900 NA in 2050. United States US: Mortality Rate: Under 5 per 1000 Births data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.US Census Bureau: Demographic Projection.

United States US: Number of Births data was reported at 4,413,478.000 Person in 2050. This records an increase from the previous number of 4,397,629.000 Person for 2049. United States US: Number of Births data is updated yearly, averaging 4,195,844.000 Person from Jun 2001 (Median) to 2050, with 50 observations. The data reached an all-time high of 4,413,478.000 Person in 2050 and a record low of 3,921,308.000 Person in 2013. United States US: Number of Births data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.US Census Bureau: Demographic Projection.

Esri ArcGIS Online (AGOL) Imagery Layer which includes the MDOT SHA 2050 Mean Sea Level 1% Annual Chance (100-Year Storm) - Flood Depth Grid data product.MDOT SHA 2050 Mean Sea Level 1% Annual Chance (100-Year Storm) - Flood Depth Grid consists of a depth grid image service depicting conditions of sea level change based on the 1% annual chance event (100-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 1% Annual Chance (100-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 1% Annual Chance (100-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

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

A dataset of modeled nearshore water levels (WLs) was developed for three states (Virginia, Georgia, and Florida) along the U.S. Atlantic coast. Water levels, defined for this dataset as the linear sum of tides and non-tidal residuals (NTR), were produced by Muis and others (2016) using a global tide and surge model (GTSM) forced by global atmospheric fields. Water level outputs were extracted from the global grid at approximately 20 km resolution along the Atlantic coastline. These data were then statistically downscaled using a signal-specific set of corrections to improve skill in comparison to tide gauge observations (Parker and others, 2023). Projected water levels were forced by CMIP6 future period data. Four CMIP6 climate models were selected from the High-Resolution Model Intercomparison project (highresMIP; Haarsma and others, 2016) to sample variability in climate predictions. Similar modeled data for North Carolina and South Carolina are available from Barnard and others, 2023, at https://doi.org/10.5066/P9W91314)

Hydrogen demand in the United States is projected to reach up to ** million metric tons by 2050, considering an ambitious deployment scenario. In the ambitious scenario outlook, hydrogen demand will be largely driven by its use as transportation fuel. Transportation fuel consumption could be as high as ** million metric tons per year, should hydrogen be promoted as an economic growth driver on a federal level.

The U.S. Geological Survey has been forecasting sea-level rise impacts on the landscape to evaluate where coastal land will be available for future use. The purpose of this project is to develop a spatially explicit, probabilistic model of coastal response for the Northeastern U.S. to a variety of sea-level scenarios that take into account the variable nature of the coast and provides outputs at spatial and temporal scales suitable for decision support. Model results provide predictions of adjusted land elevation ranges (AE) with respect to forecast sea-levels, a likelihood estimate of this outcome (PAE), and a probability of coastal response (CR) characterized as either static or dynamic. The predictions span the coastal zone vertically from -12 meters (m) to 10 m above mean high water (MHW). Results are produced at a horizontal resolution of 30 meters for four decades (the 2020s, 2030s, 2050s and 2080s). Adjusted elevations and their respective probabilities are generated using regional geospatial datasets of current sea-level forecasts, vertical land movement rates, and current elevation data. Coastal response type predictions incorporate adjusted elevation predictions with land cover data and expert knowledge to determine the likelihood that an area will be able to accommodate or adapt to water level increases and maintain its initial land class state or transition to a new non-submerged state (dynamic) or become submerged (static). Intended users of these data include scientific researchers, coastal planners, and natural resource management communities.

These GIS layers provide the probability of observing the forecast of adjusted land elevation (PAE) with respect to predicted sea-level rise or the Northeastern U.S. for the 2020s, 2030s, 2050s and 2080s. These data are based on the following inputs: sea-level rise, vertical land movement rates due to glacial isostatic adjustment and elevation data. The output displays the highest probability among the five adjusted elevation ranges (-12 to -1, -1 to 0, 0 to 1, 1 to 5, and 5 to 10 m) to be observed for the forecast year as defined by a probabilistic framework (a Bayesian network), and should be used concurrently with the adjusted land elevation prediction layer (PAE), also available from http://woodshole.er.usgs.gov/project-pages/coastal_response/, which provides users with the likelihood of elevation range occurring when compared with the four other elevation ranges. These data layers primarily show the distribution of adjusted elevation range probabilities over a large spatial scale and should therefore be used qualitatively.