The statistic shows population projections for the United States for 2015 and 2060, by race and Hispanic origin. In 2060, about 119 million Hispanics will be living in the U.S.

Annual Population Estimates, Estimated Components of Resident Population Change, and Rates of the Components of Resident Population Change for the United States, States, and Puerto Rico // Source: U.S. Census Bureau, Population Division // Note: Total population change includes a residual. This residual represents the change in population that cannot be attributed to any specific demographic component. See Population Estimates Terms and Definitions at http://www.census.gov/popest/about/terms.html. // Net international migration (except for Puerto Rico) includes the international migration of both native and foreign-born populations. Specifically, it includes: (a) the net international migration of the foreign born, (b) the net migration between the United States and Puerto Rico, (c) the net migration of natives to and from the United States, and (d) the net movement of the Armed Forces population between the United States and overseas. Net international migration for Puerto Rico includes the migration of native and foreign-born populations between the United States and Puerto Rico. // The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. See Geographic Terms and Definitions at http://www.census.gov/popest/about/geo/terms.html for a list of the states that are included in each region and division. // For detailed information about the methods used to create the population estimates, see http://www.census.gov/popest/methodology/index.html. // Each year, the Census Bureaus Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2014) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: http://www.census.gov/popest/index.html.

Projected Population by Single Year of Age, Sex, Race, and Hispanic Origin for the United States: 2012 to 2060 File: 2012 National Population Projections Source: U.S. Census Bureau, Population Division Release Date: December 2012 NOTE: Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. The projections generally do not precisely agree with population estimates available elsewhere on the Census Bureau website for methodological reasons. Where both estimates and projections are available for a given time reference, we recommend that you use the population estimates as the measure of the current population. For detailed information about the methods used to create the population projections, see http://www.census.gov/population/projections/methodology/. *** The U.S. Census Bureau periodically produces projections of the United States resident population by age, sex, race, and Hispanic origin. Population projections are estimates of the population for future dates. They are typically based on an estimated population consistent with the most recent decennial census and are produced using the cohort-component method. Projections illustrate possible courses of population change based on assumptions about future births, deaths, net international migration, and domestic migration. In some cases, several series of projections are produced based on alternative assumptions for future fertility, life expectancy, net international migration, and (for state-level projections) state-to-state or domestic migration. Additional information is available on the Population Projections website: http://www.census.gov/population/projections/.

This graph displays the Hispanic population in the U.S. from 2010 to 2015, with projection till 2035. It is estimated that the Hispanic population in the U.S. will amount to 63.37 millions by 2020.

This dataset consist of annual (2020-2100) county-level population projections for the United States (U.S.) for Shared Socioeconomic Pathways (SSPs) 3 and 5. The original decadal state-level data is used as an input to the gridded population data model to downscale the state-level projections for each SSP to a 1 km grid. The 1 km data is then aggregated to the county-level using the 2020 TIGER/Line county shapefiles from the U.S. Census Bureau. The two data files are shared as .csv files with the following structure: Rows = Data for individual counties which are identified using their Federal Information Processing Standard (FIPS) code. The FIPS code is stored in the first column. Columns = Each year from 2020-2100 is an individual column. For easier reference, the state name associated with each row is stored in the last column. Values in each cell are the downscaled estimated population for that county-year combination. Values are fractional due to the weighting scheme used in the downscaling. The paper and model source code cited in the "Related Works" section describes the initial source of the population projections.

This layer shares SEDAC's population projections for U.S. counties for 2020-2100 in increments of 5 years, for each of five population projection scenarios known as Shared Socioeconomic Pathways (SSPs). This layer supports mapping, data visualizations, analysis and data exports.Before using this layer, read:The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview by Keywan Riahi, Detlef P. van Vuuren, Elmar Kriegler, Jae Edmonds, Brian C. O’Neill, Shinichiro Fujimori, Nico Bauer, Katherine Calvin, Rob Dellink, Oliver Fricko, Wolfgang Lutz, Alexander Popp, Jesus Crespo Cuaresma, Samir KC, Marian Leimbach, Leiwen Jiang, Tom Kram, Shilpa Rao, Johannes Emmerling, Kristie Ebi, Tomoko Hasegawa, Petr Havlik, Florian Humpenöder, Lara Aleluia Da Silva, Steve Smith, Elke Stehfest, Valentina Bosetti, Jiyong Eom, David Gernaat, Toshihiko Masui, Joeri Rogelj, Jessica Strefler, Laurent Drouet, Volker Krey, Gunnar Luderer, Mathijs Harmsen, Kiyoshi Takahashi, Lavinia Baumstark, Jonathan C. Doelman, Mikiko Kainuma, Zbigniew Klimont, Giacomo Marangoni, Hermann Lotze-Campen, Michael Obersteiner, Andrzej Tabeau, Massimo Tavoni. Global Environmental Change, Volume 42, 2017, Pages 153-168, ISSN 0959-3780, https://doi.org/10.1016/j.gloenvcha.2016.05.009.From the 2017 paper: "The SSPs are part of a new scenario framework, established by the climate change research community in order to facilitate the integrated analysis of future climate impacts, vulnerabilities, adaptation, and mitigation. The pathways were developed over the last years as a joint community effort and describe plausible major global developments that together would lead in the future to different challenges for mitigation and adaptation to climate change. The SSPs are based on five narratives describing alternative socio-economic developments, including sustainable development, regional rivalry, inequality, fossil-fueled development, and middle-of-the-road development. The long-term demographic and economic projections of the SSPs depict a wide uncertainty range consistent with the scenario literature."According to SEDAC, the purpose of this data is:"To provide subnational (county) population projection scenarios for the United States essential for understanding long-term demographic changes, planning for the future, and decision-making in a variety of applications."According to Francesco Bassetti of Foresight, "The SSP’s baseline worlds are useful because they allow us to see how different socioeconomic factors impact climate change. They include: a world of sustainability-focused growth and equality (SSP1); a “middle of the road” world where trends broadly follow their historical patterns (SSP2); a fragmented world of “resurgent nationalism” (SSP3); a world of ever-increasing inequality (SSP4);a world of rapid and unconstrained growth in economic output and energy use (SSP5).There are seven sublayers, each with county boundaries and an identical set of attribute fields containing projections for these seven groupings across the five SSPs and nine decades.Total PopulationBlack Non-Hispanic PopulationWhite Non-Hispanic PopulationOther Non-Hispanic PopulationHispanic PopulationMale PopulationFemale PopulationMethodology: Documentation for the Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, v1 (2020 – 2100)Data currency: This layer was created from a shapefile downloaded April 18, 2023 from SEDAC's Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, v1 (2020 – 2100)Enhancements found in this layer: Every field was given a field alias and field description created from SEDAC's Data Dictionary downloaded April 18, 2023. Citation: Hauer, M., and Center for International Earth Science Information Network - CIESIN - Columbia University. 2021. Georeferenced U.S. County-Level Population Projections, Total and by Sex, Race and Age, Based on the SSPs, 2020-2100. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/dv72-s254. Accessed 18 April 2023.Hauer, M. E. 2019. Population Projections for U.S. Counties by Age, Sex, and Race Controlled to Shared Socioeconomic Pathway. Scientific Data 6: 190005. https://doi.org/10.1038/sdata.2019.5.Distribution Liability: CIESIN follows procedures designed to ensure that data disseminated by CIESIN are of reasonable quality. If, despite these procedures, users encounter apparent errors or misstatements in the data, they should contact SEDAC User Services at +1 845-465-8920 or via email at ciesin.info@ciesin.columbia.edu. Neither CIESIN nor NASA verifies or guarantees the accuracy, reliability, or completeness of any data provided. CIESIN provides this data without warranty of any kind whatsoever, either expressed or implied. CIESIN shall not be liable for incidental, consequential, or special damages arising out of the use of any data provided by CIESIN.

Annual Housing Unit Estimates for the United States, States, and Counties // Source: U.S. Census Bureau, Population Division // Note: The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 housing units due to the Count Question Resolution program and geographic program revisions. For the housing unit estimates methodology statement, see http://www.census.gov/popest/methodology/index.html.// Each year, the Census Bureau's Population and Housing Unit Estimates Program utilizes current data on new residential construction, placements of manufactured housing, and housing unit loss to calculate change in the housing stock since the most recent decennial census, and produces a time series of housing unit estimates.. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2015) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population and Housing Unit Estimates Program provides additional information including population estimates, historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: http://www.census.gov/popest/index.html.

Annual Housing Unit Estimates for the United States, States, and Counties // Source: U.S. Census Bureau, Population Division // Note: The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 housing units due to the Count Question Resolution program and geographic program revisions. For the housing unit estimates methodology statement, see https://www.census.gov/programs-surveys/popest/technical-documentation/methodology.html.// Each year, the Census Bureau's Population and Housing Unit Estimates Program utilizes current data on new residential construction, placements of manufactured housing, and housing unit loss to calculate change in the housing stock since the most recent decennial census, and produces a time series of housing unit estimates.. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2015) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population and Housing Unit Estimates Program provides additional information including population estimates, historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/programs-surveys/popest.html.

This graph displays the African-American population in the U.S. from 2010 to 2015, with projection till 2035. It is estimated that the African-American population in the U.S. will amount to 41.84 millions by 2020.

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.

United States US: Urban Population Growth data was reported at 0.952 % in 2017. This records a decrease from the previous number of 0.968 % for 2016. United States US: Urban Population Growth data is updated yearly, averaging 1.152 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 2.449 % in 1960 and a record low of 0.927 % in 1974. United States US: Urban Population Growth data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Population and Urbanization Statistics. Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects.; ; World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.; Weighted average;

About this dataset

Context

The dataset tabulates the Prentiss County population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Prentiss County across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Prentiss County was 25,135, a 0.98% increase year-by-year from 2022. Previously, in 2022, Prentiss County population was 24,891, a decline of 0.28% compared to a population of 24,962 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Prentiss County decreased by 515. In this period, the peak population was 25,855 in the year 2009. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Prentiss County is shown in this column.
• Year on Year Change: This column displays the change in Prentiss County population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Prentiss County Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Newman population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Newman across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Newman was 769, a 0% decrease year-by-year from 2022. Previously, in 2022, Newman population was 769, a decline of 0.65% compared to a population of 774 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Newman decreased by 200. In this period, the peak population was 969 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Newman is shown in this column.
• Year on Year Change: This column displays the change in Newman population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Newman Population by Year. You can refer the same here

Annual Resident Population Estimates by Age Group, Sex, Race, and Hispanic Origin: April 1, 2010 to July 1, 2018 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through June. // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. For more information, see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/modified-race-summary-file-method/mrsf2010.pdf. // The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // For detailed information about the methods used to create the population estimates, see https://www.census.gov/programs-surveys/popest/technical-documentation/methodology.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2017) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/programs-surveys/popest.html.

South East US historical (2001-2019) and projected (2020-2100) population per county for 6 states (NC, SC, TN, GA, AL, FL). Historical data come from The National Vital Statistics System (https://seer.cancer.gov/popdata/download.html) and future data are projected by Hauer 2019 (https://doi.org/10.1038/sdata.2019.5) for SSP2 scenario. Data are formatted for r.futures.demand module, which is a GRASS GIS addon for computing future land demand for FUTURES urban growth model.

Each year, the Forecasting and Trends Office (FTO) publishes population estimates and future year projections. The population estimates can be used for a variety of planning studies including statewide and regional transportation plan updates, subarea and corridor studies, and funding allocations for various planning agencies.The 2020 population estimates reported are based on the US Census Bureau 2020 Decennial Census. The 2021 population estimates are based on the population estimates developed by the Bureau of Economic and Business Research (BEBR) at the University of Florida. BEBR uses the decennial census count for April 1, 2020, as the starting point for state-level projections. More information is available from BEBR here.This dataset contains boundaries for all 2010 Census Urban Clusters (UCs) in the State of Florida with 2020 census population and 2021 population estimates. All legal boundaries and names in this dataset are from the US Census Bureau’s TIGER/Line Files (2021).BEBR provides 2021 population estimates for counties in Florida. However, UC boundaries may not coincide with the jurisdictional boundaries of counties and UCs often spread into several counties. To estimate the population for an UC, first the ratio of the subject UC that is contained within a county (or sub-area) to the area of the entire county was determined. That ratio was multiplied by the estimated county population to obtain the population for that sub-area. The population for the entire UC is the sum of all sub-area populations estimated from the counties they are located within.For the 2010 Census, urban areas comprised a “densely settled core of census tracts and/or census blocks that meet minimum population density requirements, along with adjacent territory containing non-residential urban land uses as well as territory with low population density included to link outlying densely settled territory with the densely settled core.” In 2010, the US Census Bureau identified two types of urban areas—Urbanized Areas (UAs) and UCs. UCs have a population of at least 2,500 and less than 50,000 people. Note: Century, FL--AL Urban Cluster is located in two states: Florida (Escambia County) and Alabama (Escambia County). 2021 population of Escambia County, AL used for this estimation is from the US Census annual population estimates (2020-2021). All other Urban Clusters are located entirely within the state of Florida. Please see the Data Dictionary for more information on data fields. Data Sources:US Census Bureau 2020 Decennial CensusUS Census Bureau’s TIGER/Line Files (2021)Bureau of Economic and Business Research (BEBR) – Florida Estimates of Population 2021 Data Coverage: StatewideData Time Period: 2020 – 2021 Date of Publication: July 2022 Point of Contact:Dana Reiding, ManagerForecasting and Trends OfficeFlorida Department of TransportationDana.Reiding@dot.state.fl.us605 Suwannee Street, Tallahassee, Florida 32399850-414-4719

About this dataset

Context

The dataset tabulates the Cowpens population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Cowpens across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Cowpens was 2,190, a 3.06% increase year-by-year from 2022. Previously, in 2022, Cowpens population was 2,125, an increase of 3.06% compared to a population of 2,062 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Cowpens decreased by 143. In this period, the peak population was 2,511 in the year 2009. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Cowpens is shown in this column.
• Year on Year Change: This column displays the change in Cowpens population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Cowpens Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Rochester population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Rochester across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Rochester was 122,413, a 0.42% increase year-by-year from 2022. Previously, in 2022, Rochester population was 121,896, an increase of 0.05% compared to a population of 121,839 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Rochester increased by 31,810. In this period, the peak population was 122,413 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Rochester is shown in this column.
• Year on Year Change: This column displays the change in Rochester population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Rochester Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Nebraska population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Nebraska across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2024, the population of Nebraska was 2.01 million, a 0.89% increase year-by-year from 2023. Previously, in 2023, Nebraska population was 1.99 million, an increase of 0.79% compared to a population of 1.97 million in 2022. Over the last 20 plus years, between 2000 and 2024, population of Nebraska increased by 292,186. In this period, the peak population was 2.01 million in the year 2024. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2024

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2024)
• Population: The population for the specific year for the Nebraska is shown in this column.
• Year on Year Change: This column displays the change in Nebraska population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Nebraska Population by Year. You can refer the same here

These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the United Nations Children's Fund ( UNICEF ) - Population Modelling for use in Routine Health Planning and Monitoring project (contract no. 43335861). Projects partners included the Kenya Unicef Regional and Country Offices, WorldPop research group at the University of Southampton and the Center for International Earth Science Information Network in the Columbia Climate School at Columbia University. Assane Gadiaga (WorldPop) led the input processing and the modelling work following the Random Forest (RF)-based dasymetric mapping approach developed by Stevens et al. (2015). Thomas Abbott supported the covariates processing work. In-country engagements were done by David Kyalo, Olena Borkovska ( GRID3 , Maria Muniz (Unicef). Using the 2009 and 2019 census data from the Kenya’s National Bureau of Statistics (KNBS), the US Census Bureau released the census-based total population projections, population by age and sex and digital sub-counties boundaries. Duygu Cihan helped in the preparation of these input population data. Attila N Lazar, Edith Darin and Heather Chamberlain advised on the modelling procedure. The work was overseen by Attila N Lazar and Andy J Tatem.

Recommended citations

Gadiaga A. N., Abbott T. J., Chamberlain H., Lazar A. N., Darin E., Tatem A. J. 2023. Census disaggregated gridded population estimates for Kenya (2022), version 2.0. University of Southampton. doi:10.5258/SOTON/WP00762

License

These data may be distributed using a Creative Commons Attribution 4.0 International (CC BY 4.0) License, specified in legal code. Contact release[at]worldpop.org for more information.

The authors followed rigorous procedures designed to ensure that the used data, the applied method and thus the results are appropriate and of reasonable quality. If users encounter apparent errors or misstatements, they should contact WorldPop at release[at]worldpop.org.

WorldPop, University of Southampton, and their sponsors offer these data on a "where is, as is" basis; do not offer an express or implied warranty of any kind; do not guarantee the quality, applicability, accuracy, reliability or completeness of any data provided; and shall not be liable for incidental, consequential, or special damages arising out of the use of any data that they offer.