Annual Resident Population Estimates, Estimated Components of Resident Population Change, and Rates of the Components of Resident Population Change; for the United States, States, Metropolitan Statistical Areas, Micropolitan Statistical Areas, Counties, and Puerto Rico: April 1, 2010 to July 1, 2019 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through March. // Note: Total population change includes a residual. This residual represents the change in population that cannot be attributed to any specific demographic component. // Note: 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. // The Office of Management and Budget's statistical area delineations for metropolitan, micropolitan, and combined statistical areas, as well as metropolitan divisions, are those issued by that agency in September 2018. // Current data on births, deaths, and migration are used to calculate population change since the 2010 Census. An annual time series of estimates is produced, beginning with the census and extending to the vintage year. The vintage year (e.g., Vintage 2019) 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 entire estimates series is revised. Additional information, including historical and intercensal estimates, evaluation estimates, demographic analysis, research papers, and methodology is available on website: https://www.census.gov/programs-surveys/popest.html.

The latest 8 and 10 digit HUC boundaries, along with the calculated US Census population within each subbasin and watershed for 2020, 2010, and 2000.

HUC boundaries are from the USGS National Hydrography Watershed Boundary Dataset. US Census 2020, 2010, and 2000 Block Data was acquired through NC OneMap.

Subbasin and watershed population estimates were derived from the 2020, 2010, and 2000 Block population data from the US Census. The ArcGIS Tool "Summarize Within" was used to calculate the total population within each subbasin and watershed for each census period. As census blocks and HUC boundaries do not always coincide, the calculated population is only an estimate and is not to be used as an exact figure.

Annual Resident Population Estimates by Age Group, Sex, Race, and Hispanic Origin; for the United States, States, Counties; and for Puerto Rico and its Municipios: April 1, 2010 to July 1, 2019 // 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. 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. // Current data on births, deaths, and migration are used to calculate population change since the 2010 Census. An annual time series of estimates is produced, beginning with the census and extending to the vintage year. The vintage year (e.g., Vintage 2019) 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 entire estimates series is revised. Additional information, including historical and intercensal estimates, evaluation estimates, demographic analysis, research papers, and methodology is available on website: https://www.census.gov/programs-surveys/popest.html.

Wildlife populations often exhibit unequal catchability between subgroups such as males and females. This heterogeneity of capture probabilities can bias both population size and sex ratio estimates. Several authors have suggested that this problem can be overcome by treating males and females as separate populations and calculating a population estimate for each of them. However, this suggestion has received little testing, and many researchers do not implement it. Therefore, we used two simulations to test the utility of this method. One simulated a closed population, while the other simulated an open population and used the robust design to calculate population sizes. We tested both simulations with multiple levels of heterogeneity, and we used a third simulation to test several methods for detecting heterogeneity of capture probabilities. We found that treating males and females as separate populations produced more accurate population and sex ratio estimates. The benefits of this method were particularly pronounced for sex ratio estimates. When males and females were included as a single population, the sex ratio estimates became inaccurate when even slight heterogeneity was present, but when males and females were treated separately, the estimates were accurate even when large biases were present. Nevertheless, treating males and females separately reduced precision, and this method may not be appropriate when capture and recapture rates are low. None of the methods for detecting heterogeneity were robust, and we do not recommend that researchers rely on them. Rather, we suggest separating populations by sex, age, or other subgroups whenever sample sizes permit.

Graph and download economic data for Population Estimate, Total (5-year estimate) in Cochise County, AZ (B03002001E004003) from 2009 to 2023 about Cochise County, AZ; AZ; estimate; persons; 5-year; population; and USA.

Office for National Statistics’ national and subnational mid-year population estimates for England and Wales for a selection of administrative and census areas by additional useful age for 2012 to 2020. Age categories include: 0-15, 5-11, 11-15, 16-17, 16-29, 16-64, 18-24, 30-44, 45-64, 65+ & 70+. The data is source is from ONS Population Estimates. Find out more about this dataset here.

This data is issued at (BGC) Generalised (20m) boundary type for:

Country, Region, Upper Tier Local Authority (2021), Lower Tier Local Authority (2021), Middle Super Output Area (2011), and Lower Super Output Area (2011).

If you require the data at full resolution boundaries, or if you are interested in the range of statistical data that Esri UK make available in ArcGIS Online please enquire at dataenquiries@esriuk.com.

The Office for National Statistics (ONS) produces annual estimates of the resident population of England and Wales at 30 June every year. The most authoritative population estimates come from the census, which takes place every 10 years in the UK. Population estimates from a census are updated each year to produce mid-year population estimates (MYEs), which are broken down by local authority, sex and age. More detailed information on the methods used to generate the mid-year population estimates can be found here.

For further information on the usefulness of the data and guidance on small area geographies please see here.The currency of this data is 2021.

Methodology

The total and 5-year breakdown population counts are reproduced directly from the source data. The age range estimates have been calculated from the published estimates by single year of age. The percentages are calculated using the gender specific (total, female or male) total population count as a denominator except in the case of the male and female total population where the total population is used to give female and male proportions.

This dataset will be updated annually, in two releases.

Creator: Office for National Statistics. Aggregated age groupings and percentages calculated by Esri UK._The data services available from this page are derived from the National Data Service. The NDS delivers thousands of open national statistical indicators for the UK as data-as-a-service. Data are sourced from major providers such as the Office for National Statistics, Public Health England and Police UK and made available for your area at standard geographies such as counties, districts and wards and census output areas. This premium service can be consumed as online web services or on-premise for use throughout the ArcGIS system.Read more about the NDS.

It is known that when people generate externalities, a birth also generates an externality and efficiency requires a Pigou tax/subsidy on having children. The size of the externality from a birth is important for studying policy. We calculate the size of this "population externality" in a specific case: we consider a maintained hypothesis that greenhouse gas emissions are a serious problem and assume government reacts by optimally restricting emissions. Calculated population externalities are large under many assumptions (JEL D62, H23, J11, J13, Q54, Q58)

Abstract

The Estimating the Size of Populations through a Household Survey (EPSHS), sought to assess the feasibility of the network scale-up and proxy respondent methods for estimating the sizes of key populations at higher risk of HIV infection and to compare the results to other estimates of the population sizes. The study was undertaken based on the assumption that if these methods proved to be feasible with a reasonable amount of data collection for making adjustments, countries would be able to add this module to their standard household survey to produce size estimates for their key populations at higher risk of HIV infection. This would facilitate better programmatic responses for prevention and caring for people living with HIV and would improve the understanding of how HIV is being transmitted in the country.

The specific objectives of the ESPHS were: 1. To assess the feasibility of the network scale-up method for estimating the sizes of key populations at higher risk of HIV infection in a Sub-Saharan African context; 2. To assess the feasibility of the proxy respondent method for estimating the sizes of key populations at higher risk of HIV infection in a Sub-Saharan African context; 3. To estimate the population size of MSM, FSW, IDU, and clients of sex workers in Rwanda at a national level; 4. To compare the estimates of the sizes of key populations at higher risk for HIV produced by the network scale-up and proxy respondent methods with estimates produced using other methods; and 5. To collect data to be used in scientific publications comparing the use of the network scale-up method in different national and cultural environments.

Geographic coverage

National

Analysis unit

• Household
• Individual

Sampling procedure

The Estimating the Size of Populations through a Household Survey (ESPHS) used a two-stage sample design, implemented in a representative sample of 2,125 households selected nationwide in which all women and men age 15 years and above where eligible for an individual interview. The sampling frame used was the preparatory frame for the Rwanda Population and Housing Census (RPHC), which was conducted in 2012; it was provided by the National Institute of Statistics of Rwanda (NISR).

The sampling frame was a complete list of natural villages covering the whole country (14,837 villages). Two strata were defined: the city of Kigali and the rest of the country. One hundred and thirty Primary Sampling Units (PSU) were selected from the sampling frame (35 in Kigali and 95 in the other stratum). To reduce clustering effect, only 20 households were selected per cluster in Kigali and 15 in the other clusters. As a result, 33 percent of the households in the sample were located in Kigali.

The list of households in each cluster was updated upon arrival of the survey team in the cluster. Once the listing had been updated, a number was assigned to each existing household in the cluster. The supervisor then identified the households to be interviewed in the survey by using a table in which the households were randomly pre-selected. This table also provided the list of households pre-selected for each of the two different definitions of what it means "to know" someone.

For further details on sample design and implementation, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Estimating the Size of Populations through a Household Survey (ESPHS) used two types of questionnaires: a household questionnaire and an individual questionnaire. The same individual questionnaire was used to interview both women and men. In addition, two versions of the individual questionnaire were developed, using two different definitions of what it means “to know” someone. Each version of the individual questionnaire was used in half of the selected households.

Cleaning operations

The processing of the ESPHS data began shortly after the fieldwork commenced. Completed questionnaires were returned periodically from the field to the SPH office in Kigali, where they were entered and checked for consistency by data processing personnel who were specially trained for this task. Data were entered using CSPro, a programme specially developed for use in DHS surveys. All data were entered twice (100 percent verification). The concurrent processing of the data was a distinct advantage for data quality, because the School of Public Health had the opportunity to advise field teams of problems detected during data entry. The data entry and editing phase of the survey was completed in late August 2011.

Response rate

A total of 2,125 households were selected in the sample, of which 2,120 were actually occupied at the time of the interview. The number of occupied households successfully interviewed was 2,102, yielding a household response rate of 99 percent.

From the households interviewed, 2,629 women were found to be eligible and 2,567 were interviewed, giving a response rate of 98 percent. Interviews with men covered 2,102 of the eligible 2,149 men, yielding a response rate of 98 percent. The response rates do not significantly vary by type of questionnaire or residence.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) non-sampling errors, and (2) sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made to minimize this type of error during the implementation of the Rwanda ESPHS 2011, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the ESPHS 2011 is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the ESPHS 2011 sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the ESPHS 2011 is a SAS program. This program uses the Taylor linearization method for variance estimation for survey estimates that are means or proportions.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey report.

Resident population of New York State and counties produced by the U.S. Census Bureau. Estimates are based on decennial census counts (base population), intercensal estimates, postcensal estimates and administrative records. Updates are made annually using current data on births, deaths, and migration to estimate population change. Each year beginning with the most recent decennial census the series is revised, these new series of estimates are called vintages.

Graph and download economic data for Population Estimate, Total (5-year estimate) in Lexington city, VA (B03002001E051678) from 2009 to 2023 about Lexington City, VA; VA; estimate; persons; 5-year; population; and USA.

The latest 8 and 10 digit HUC boundaries, along with the calculated US Census population within each subbasin and watershed for 2020, 2010, and 2000.

HUC boundaries are from the USGS National Hydrography Watershed Boundary Dataset. US Census 2020, 2010, and 2000 Block Data was acquired through NC OneMap.

Subbasin and watershed population estimates were derived from the 2020, 2010, and 2000 Block population data from the US Census. The ArcGIS Tool "Summarize Within" was used to calculate the total population within each subbasin and watershed for each census period. As census blocks and HUC boundaries do not always coincide, the calculated population is only an estimate and is not to be used as an exact figure.

Population Estimate, Total, Hispanic or Latino (5-year estimate) in Daggett County, UT was 22.00000 Persons in January of 2023, according to the United States Federal Reserve. Historically, Population Estimate, Total, Hispanic or Latino (5-year estimate) in Daggett County, UT reached a record high of 57.00000 in January of 2012 and a record low of 13.00000 in January of 2019. Trading Economics provides the current actual value, an historical data chart and related indicators for Population Estimate, Total, Hispanic or Latino (5-year estimate) in Daggett County, UT - last updated from the United States Federal Reserve on June of 2025.

Population Estimate, Total, Hispanic or Latino (5-year estimate) in Kimball County, NE was 332.00000 Persons in January of 2023, according to the United States Federal Reserve. Historically, Population Estimate, Total, Hispanic or Latino (5-year estimate) in Kimball County, NE reached a record high of 394.00000 in January of 2018 and a record low of 170.00000 in January of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for Population Estimate, Total, Hispanic or Latino (5-year estimate) in Kimball County, NE - last updated from the United States Federal Reserve on July of 2025.

The GLA Population Yield Calculator is a tool for estimating population yield from new housing development. Please see the methodology paper for guidance using the calculator. There are two versions of the calculator. Both versions draw on the same underlying census and LDD data. Following user feedback, version 2 of the calculator contains a simlified user interface and broader geographic aggregations. The calculator is in XLSX (Excel 2007 and later) format.

Estimate Population By Category Of Patients & Age Groups By State 2018-2020 Notes: 1. Data for live birth CURRENT year is not yet available from Department of Statistics, Malaysia (DOSM). Therefore, data for PREVIOUS year is provided for reference. 2. P - Preliminary figure 3. The added total differ due to rounding. (1) Current Population Estimates (related year) (2) Primary & Secondary School enrolment (PG 203 & PG204) (3) Data calculated by HIC (3)(b) Input for calculating Estimated number of antenatal mothers is number of live births. Therefore, the estimated number of antenatal mothers is based on previous year live births. (4)(b) Estimated number of antenatal mothers based on new attendances to MCHC. Sources: (a) Department of Statistics, Malaysia (b) Health Informatics Centre, Planning Division, MOH No. of Views : 215

Graph and download economic data for Population Estimate, Total (5-year estimate) in Muskingum County, OH (B03002001E039119) from 2009 to 2023 about Muskingum County, OH; OH; estimate; persons; 5-year; population; and USA.

This data set contains the documentation of the BALTIC data set analysis as a part of the manuscript 'Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale' by

Kissling, W. D., et al. (2018), Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale. Biol Rev, 93: 600-625. doi:10.1111/brv.12359

The data set contains input and output files, geographic locations, and R scripts.

This data set contains the documentation of the BALTIC data set analysis as a part of the manuscript 'Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale' by

Kissling, W. D., et al. (2018), Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale. Biol Rev, 93: 600-625. doi:10.1111/brv.12359

The data set contains input and output files, geographic locations, and R scripts.

Graph and download economic data for Population Estimate, Total (5-year estimate) in Tuscola County, MI (B03002001E026157) from 2009 to 2023 about Tuscola County, MI; MI; estimate; persons; 5-year; population; and USA.

Estimate, Median Age by Sex, Total Population (5-year estimate) in Seneca County, NY was 42.80000 Years of Age in January of 2023, according to the United States Federal Reserve. Historically, Estimate, Median Age by Sex, Total Population (5-year estimate) in Seneca County, NY reached a record high of 43.00000 in January of 2022 and a record low of 40.30000 in January of 2009. Trading Economics provides the current actual value, an historical data chart and related indicators for Estimate, Median Age by Sex, Total Population (5-year estimate) in Seneca County, NY - last updated from the United States Federal Reserve on July of 2025.

The GLA Population Yield Calculator is a tool for estimating population yield from new housing development. The calculator provides users with an indication of the possible number and age of children that could be expected to live in a new housing development of a given bedroom or tenure mix.

Please see the calculator’s methodology document for further details and user guidance.

The calculator is in XLSX (Excel 2007 and later) format.

The tool was updated 23rd October 2019.

Previously published versions are available in the compressed archive file