Unadjusted decennial census data from 1950-2000 and projected figures from 2010-2040: summary table of New York City population numbers and percentage share by Borough, including school-age (5 to 17), 65 and Over, and total population.

This statistic shows the population of the top twenty largest urban agglomerations in the United States from 2018 to 2035. By 2035, the population of the New York-Newark agglomeration is projected to be roughly **** million people.

According to a population projection based on 2020 Census Data, in 2040, California's population will amount to ***** million inhabitants.

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.

The Groundswell Spatial Population and Migration Projections at One-Eighth Degree According to SSPs and RCPs, 2010-2050, data set provides a baseline population distribution for 2010 and projections from 2020 to 2050, in ten-year increments, of population distribution and internal climate-related and other migration. The projections are produced using the NCAR-CIDR Spatial Population Downscaling Model developed by the CUNY Institute for Demographic Research (CIDR) and the National Center for Atmospheric Research (NCAR). The model incorporates assumptions based on future development scenarios (Shared Socioeconomic Pathways or SSPs) and emissions trajectories (Representative Concentration Pathways or RCPs). The SSPs include SSP2, representing a middle-of-the road future, and SSP4, representing an unequal development future. Climate models using low and high emissions scenarios, RCP2.6 and RCP8.5, then drive climate impact models on crop productivity and water availability from the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). Sea-level rise impacts in the coastal zone are estimated to be 1 meter under RCP2.6 and 2 meters under RCP8.5, to account for potential storm surge or coastal flooding. Three scenarios are generated, a pessimistic reference scenario combining SSP4 and RCP8.5, a more climate-friendly scenario combining SSP4 and RCP2.6, and a more inclusive development scenario combining SSP2 and RCP8.5, and each scenario represents an ensemble of four model runs combining different climate impact models. The modeling work was funded and developed jointly with The World Bank, and covers most World Bank client countries, with reports released in 2018 and 2021 that address different regions and provide full methodological details. To provide a data set that allows users to understand how slow-onset climate change impacts on water availability and crop productivity, coupled with sea-level rise and storm surge, may affect the future population distribution and climate-related internal migration in low to middle income countries.

About this dataset

Context

The dataset tabulates the population of Orangetown town by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Orangetown town. The dataset can be utilized to understand the population distribution of Orangetown town by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Orangetown town. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Orangetown town.

Key observations

Largest age group (population): Male # 20-24 years (2,018) | Female # 55-59 years (2,050). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Orangetown town population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Orangetown town is shown in the following column.
• Population (Female): The female population in the Orangetown town is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Orangetown town for each age group.

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 Orangetown town Population by Gender. You can refer the same here

The Groundswell Africa Spatial Population and Migration Projections at One-Eighth Degree According to SSPs and RCPs, 2010-2050 data set provides a baseline population distribution for 2010 and projections from 2020 to 2050, in five-year increments, of population distribution and internal climate-related and other migration for West Africa and the Lake Victoria Basin. The projections are produced using the NCAR-CIDR Spatial Population Downscaling Model developed by the CUNY Institute for Demographic Research (CIDR) and the National Center for Atmospheric Research (NCAR). The model incorporates assumptions based on future development scenarios (Shared Socioeconomic Pathways or SSPs) and emissions trajectories (Representative Concentration Pathways or RCPs). The SSPs include SSP2, representing a middle-of-the road future, and SSP4, representing an unequal development future. Climate models using low and high emissions scenarios, RCP2.6 and RCP8.5, then drive climate impact models on water availability, crop productivity, and pasturelands (where cropping does not occur), as well as flood impacts, from the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). Sea-level rise impacts in the coastal zone are estimated to be 1 meter under RCP2.6 and 2 meters under RCP8.5, to account for potential storm surge or coastal flooding. Four scenarios are generated, a pessimistic reference scenario combining SSP4 and RCP8.5, a more climate-friendly scenario combining SSP4 and RCP2.6, a more inclusive development scenario combining SSP2 and RCP8.5, and an optimistic scenario combining SSP2 and RCP2.6. Each scenario provides an ensemble average of four model runs combining different climate impact models as well as confidence intervals to better capture uncertainties. The modeling work was funded and developed jointly with The World Bank. To provide a data set that allows users to understand how climate change impacts on water availability, crop productivity, and pasturelands (where no cropping occurs), coupled with sea-level rise, storm surge and flooding, may affect the future population distribution and climate-related internal migration in West Africa and the Lake Victoria Basin.

About this dataset

Context

The dataset tabulates the Bethel town population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Bethel town. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 2,050 (51.29% of the total population). 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 cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Bethel town population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Bethel town is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Bethel town is shown in the following column.

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 Bethel town Population by Age. You can refer the same here

The Africa Climate Mobility Initiative (ACMI) Internal Migration Projections project internal migration flows at 5-year intervals from 2020 to 2050 for a combination of 2 sets of Shared Socioeconomic Pathways (SSPs) scenarios and 3 sets of Representative Concentration Pathways (RCPs) scenarios. The unit of analysis is 7.5 arc-minutes, covering the entire African continent (approximately 15 km at the equator). These grid cells are drawn using a vector representation, and not a tessellation of grid cells (rasters). While the calibration and projection took place at a grid cell resolution of 2.5 arc-minutes, these data have been aggregated to a 7.5 arc-minute resolution. These data underpin the African Shifts Report, produced by the ACMI and enabled by the Global Centre for Climate Mobility (GCCM). The ACMI was launched in September 2021 as a joint initiative of the GCCM, the African Union Commission (AUC), the United Nations System (UN), and the World Bank. To provide data on future internal migration projections for the African continent, including future impacts of climate change on migration.