This dataset provides the components of change involved in the calculation of the population projections for local authorities in Wales. Data cover the change between each successive projection year and relate to the change from the middle of each year to the middle of the following year. The first year's data represent the change from the base year of mid-2018 to mid-2019, through the projection period to show the change for mid-2042 to mid-2043. This is the fifth set of population projections published for the 22 local authorities in Wales. Note that the projections become increasingly uncertain the further we try to look into the future.

Data contains 2022 world population data publised by the UN DESA for six most populous countries of the world. File also contains the analysis of decomposition of demographic indicators on population growth.

Datasets archived here consist of all data analyzed in Duan et al. 2015 from Journal of Applied Ecology. Specifically, these data were collected from annual sampling of emerald ash borer (Agrilus planipennis) immature stages and associated parasitoids on infested ash trees (Fraxinus) in Southern Michigan, where three introduced biological control agents had been released between 2007 - 2010. Detailed data collection procedures can be found in Duan et al. 2012, 2013, and 2015. Resources in this dataset:Resource Title: Duan J Data on EAB larval density-bird predation and unknown factor from Journal of Applied Ecology. File Name: Duan J Data on EAB larval density-bird predation and unknown factor from Journal of Applied Ecology.xlsxResource Description: This data set is used to calculate mean EAB density (per m2 of ash phloem area), bird predation rate and mortality rate caused by unknown factors and analyzed with JMP (10.2) scripts for mixed effect linear models in Duan et al. 2015 (Journal of Applied Ecology).Resource Title: DUAN J Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology. File Name: DUAN J Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology.xlsxResource Description: This data set is used to construct life tables and calculation of net population growth rate of emerald ash borer for each site. The net population growth rates were then analyzed with JMP (10.2) scripts for mixed effect linear models in Duan et al. 2015 (Journal of Applied Ecology).Resource Title: DUAN J Data on EAB Life Tables Calculation from Journal of Applied Ecology. File Name: DUAN J Data on EAB Life Tables Calculation from Journal of Applied Ecology.xlsxResource Description: This data set is used to calculate parasitism rate of EAB larvae for each tree and then analyzed with JMP (10.2) scripts for mixed effect linear models on in Duan et al. 2015 (Journal of Applied Ecology).Resource Title: READ ME for Emerald Ash Borer Biocontrol Study from Journal of Applied Ecology. File Name: READ_ME_for_Emerald_Ash_Borer_Biocontrol_Study_from_Journal_of_Applied_Ecology.docxResource Description: Additional information and definitions for the variables/content in the three Emerald Ash Borer Biocontrol Study tables: Data on EAB Life Tables Calculation Data on EAB larval density-bird predation and unknown factor Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology Resource Title: Data Dictionary for Emerald Ash Borer Biocontrol Study from Journal of Applied Ecology. File Name: AshBorerAnd Parasitoids_DataDictionary.csvResource Description: CSV data dictionary for the variables/content in the three Emerald Ash Borer Biocontrol Study tables: Data on EAB Life Tables Calculation Data on EAB larval density-bird predation and unknown factor Data on Parasitism L1-L2 Excluded from Journal of Applied Ecology Fore more information see the related READ ME file.

1Historical estimates for 1–1950 CE are from refs. [72]–[78]. The UN global census data for 1955–2012 is from ref. [24], which provides an open-access web-based summary of these data. The historical world population estimates are also summarized by Cohen [23] in his Appendix 2. Note that the average population values—which are used to calculate (the distance for a given model population trajectory from the average population estimate/census value for the 1750–2012 data)—exclude duplicate estimates, in which a later study relies on an earlier study's result (e.g., Kremer's extensive use of the earlier estimates from McEvedy & Jones [74], [77]).

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.

Monthly Intercensal Estimates of the Civilian Population by Single Year of Age and Sex: April 1, 1990 to April 1, 2000 // Source: U.S. Census Bureau, Population Division // For detailed information about the methods used to create the intercensal population estimates, see https://www.census.gov/popest/methodology/intercensal_nat_meth.pdf. // The Census Bureau's Population Estimates Program produces intercensal estimates each decade by adjusting the existing time series of postcensal estimates for a decade to smooth the transition from one decennial census count to the next. They differ from the postcensal estimates that are released annually because they rely on a formula that redistributes the difference between the April 1 postcensal estimate and April 1 census count for the end of the decade across the estimates for that decade. Meanwhile, the postcensal estimates incorporate current data on births, deaths, and migration to produce each new vintage of estimates, and to revise estimates for years back to the last census. The Population Estimates Program provides additional information including historical and postcensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/popest/index.html.

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and Zip codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential Zip code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality. For the Zip code-level life expectancy calculation, it is assumed that postal Zip codes share the same boundaries as Zip Code Census Tabulation Areas (ZCTAs). More information on the relationship between Zip codes and ZCTAs can be found at https://www.census.gov/geo/reference/zctas.html. Zip code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 Zip code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for Zip codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest Zip code with population. Zip code population for 2000 estimates comes from the Decennial Census. Zip code population for 2013 estimates are from the American Community Survey (5-Year Average). The ACS provides Zip code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to Zip codes based on majority land-area.

Zip codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, Zip codes with populations of less than 5,000 were aggregated with neighboring Zip codes until the merged areas had a population of more than 5,000. In this way, the original 305 Bay Area Zip codes were reduced to 218 Zip code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

Intercensal estimates are produced once a decade by adjusting the existing time series of postcensal estimates for a decade to smooth the transition from one decennial census count to the next They differ from the postcensal estimates that are released annually because they rely on a formula that redistributes the difference between the April 1 postcensal estimate and April 1 census count for the end of the decade across the estimates for that decade Meanwhile, the nbsp postcensal estimates nbsp incorporate current data on births, deaths, and migration to produce each new vintage of estimates, and to revise estimates for years back to the last census Note Intercensal Estimates as of July 1 1 The April 1, 2000 Population Estimates base reflects changes to the Census 2000 population from the Count Question Resolution program, legal boundary updates, and other geographic program revisions 2 The data source for April 1, 2010 is the 2010 Census count 3 The values for 2010 were produced by applying estimates of change in the population between April 1 and July 1 of 2010 to the 2010 Census counts Further details on this methodology are available at http www census gov popest methodology intercensal nat meth pdf

A Population technical workgroup was formed in Sana’a (IOM, UNFPA, OCHA, NAMCHA and CSO ) and another one in Aden (IOM, OCHA, CSO, MOPIC including Executive Unit). IDP data flow figures (from district to district) were collected, cross checked between different sources (IOM DTM, NAMCHA, Executive unit) in cases multiple datasets for the same location were available. This resulted in a country-wide IDP movement database.

The TWGs agreed to use this database to calculate the estimated population using the following formula : Estimated Population = CSO projected population( 2019) – IDPS who left the district + idps who came to the district

All members in both workgroups agreed on the final results on 3 Dec 2018. The attached dataset represents the results of the work of both groups and will be used for 2019 HNO and HRP.

A Population technical workgroup was formed in Sana’a (IOM, UNFPA, OCHA, NAMCHA and CSO ) and another one in Aden (IOM, OCHA, CSO, MOPIC including Executive Unit). IDP data flow figures (from district to district) were collected, cross checked between different sources (IOM DTM, NAMCHA, Executive unit) in cases multiple datasets for the same location were available. This resulted in a country-wide IDP movement database.

The TWGs agreed to use this database to calculate the estimated population using the following formula : Estimated Population = CSO projected population( 2019) – IDPS who left the district + idps who came to the district

All members in both workgroups agreed on the final results on 3 Dec 2018. The attached dataset represents the results of the work of both groups and will be used for 2019 HNO and HRP.

Maintaining up to date population figures going forward Both TWGs are discussing the frequency of population figures updates for 2019. The intent is to agree on quarterly updates, using the same methodological approach

Original dataset title: Yemen Population Estimates 2019

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)

In 1971, John Sved derived an approximate relationship between linkage disequilibrium and effective population size for an ideal finite population. This seminal work was extended by Sved and Feldman (1973) and Weir and Hill (1980) who derived additional equations with the same purpose. These equations yield useful estimates of effective population size, as they require a single sample in time. As these estimates of effective population size are now commonly used on a variety of genomic data, from arrays of single nucleotide polymorphisms to whole genome data, some authors have investigated their bias through simulation studies and proposed corrections for different mating systems. However, the cause of the bias remains elusive. Here we show the problems of using linkage disequilibrium as a statistical measure and, analogously, the problems in estimating effective population size from such measure. For that purpose, we compare three commonly used approaches with a transition probability ...

Candidate gene studies have become very popular but some of their implicit constraints, such as the familial risk and the population attributable fraction (PAF) conferred by the gene under study, are poorly understood. We model here these parameters for susceptibility genes in terms of genotype relative risk (GRR), allele frequency and statistical power in simulated genetic association studies, assuming 500 or 2000 case-control pairs and different modes of inheritance. The results show that the common association studies on genes with minor allele frequency >10% have sufficient power to detect disease-causing variants conferring PAFs >10%, which can be compared to known genes, such as BRCA1 with a PAF of 1.8%. Yet, common low-risk variants confer low familial relative risks (FRRs), typically <1.1. The models show that candidate gene studies may be able to identify genes conferring close to 100% of the PAF, but they may not explain the empirical FRRs. In order to explain FRRs, rare, high-penetrant genes or interacting combinations of common variants need to be uncovered. However, the candidate gene studies for common alleles do not target this class of genes. The results may challenge the common disease-common variant hypothesis, which posits common variants with low GRRs and large PAFs, however failing to accommodate the empirical FRRs.

PerCapita_CO2_Footprint_InDioceses_FULLBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.MethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Since the 5-year data do not benefit from data quality filtering, comparisons are only made for populations of 5,000 or more..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Households not paying cash rent are excluded from the calculation of median gross rent..Complete plumbing in 2016 and later are not directly comparable to complete plumbing in 2015 and prior years. In 2016, the question about whether the housing unit had a toilet was no longer asked. In 2015 and prior years, the requirements for complete plumbing were running water, a flush toilet and bathtub or shower; in 2016 and later, the requirement for complete plumbing is running water and bathtub or shower..Telephone service data are not available for certain geographic areas due to problems with data collection of this question that occurred in 2019. Both ACS 1-year and ACS 5-year files were affected. It may take several years in the ACS 5-year files until the estimates are available for the geographic areas affected..Prior to 2015, if the median, upper, or lower quartile rent was $2,000 or more in a geography, the median, upper, or lower rent displayed as $2,000+. In 2015, the top category for the calculation of median, upper, and lower quartile rent was changed from $2,000 or more to $3,500 or more; consequently, 2015 and later products from the 1 and 5 year ACS files display actual medians, upper, and lower quartiles up to $3,499; $3,500 or more will display as $3,500+..Prior to 2015, if the median, upper, or lower quartile home value was $1,000,000 or more in a geography, the median, upper, or lower home value rent displayed as $1,000,000+. In 2015, the top category for the calculation of median, upper, and lower quartile home value was changed from $1,000,000 or more to $2,000,000 or more; consequently, in 2015 and later products from the 1 and 5 year ACS files display actual medians, upper, and lower quartiles up to $2,000,000; $2,000,000 or more will display as $2,000,000+..Prior to 2015, if the median monthly housing costs for owners without mortgages was $1,000 or more in a geography, the median monthly housing costs for owners without mortgages displayed as $1,000+. In 2015, the top category for the calculation of median monthly housing costs for owners without mortgages was changed from $1,000 or more to $1,500 or more; consequently, in 2015 and later products from the 1 and 5 year ACS files display actual medians up to $1,500; $1,500 or more will display as $1,500+..Prior to 2021, medians presented in the Compariso...

description: A reprint of an article from the Journal of Wildlife Management entitled "Estimating Squirrel Abundance from Live Trapping Data" by Nixon, Edwards and Eberhardt. The material contained in this reprint may be useful in carrying out the proposed wildlife management study on the Delmarva Peninsula Fox Squirrel. Estimates of squirrel (Sciuris carolinersis and S. niger) abundance were derived from several methods of population estimation applied to data obtained by livetrapping squirrels on the Waterloo Wildlife Experiment Station in southeastern Ohio, l 962 and 1963. The 'frequency of capture of marked squirrels suggests that the probability of capture is not the same for all squirrels; as a result, a trapped sample typically contains a disproportionately high number of recaptures. Thus, the multiple census methods of Schnabel and of Schumacher produced estimates lower than the number of animals considered to comprise the population. Frequency of capture approximated the geometric distribution. The simplified equation for maximum likelihood estimation (MLE) for the geometric distribution, presented in 1967 by Edwards and Eberhardt, appeared useful for estimating squirrel abundance from livetrapping data, although estimates tended to be somewhat high. The intercept of a line fitted to a logarithm plot of data on the frequency of capture, using linear regression techniques, gave what appeared to be adequate approximations of the numbers of squirrels in the zero (uncaptured) class. Although estimates derived from M 1 .F. for the geometric distribution and from linear regression are based on assumption: not strictly fulfilled by the data, these methods should prove useful until better techniques are developed. MLE for the Poisson distribution appeared to underestimate the zero class. Similarities in results of evaluations of techniques of population estimation for squirrels and rabbits suggest that further research on population estimation may provide findings applicable to a variety of species.; abstract: A reprint of an article from the Journal of Wildlife Management entitled "Estimating Squirrel Abundance from Live Trapping Data" by Nixon, Edwards and Eberhardt. The material contained in this reprint may be useful in carrying out the proposed wildlife management study on the Delmarva Peninsula Fox Squirrel. Estimates of squirrel (Sciuris carolinersis and S. niger) abundance were derived from several methods of population estimation applied to data obtained by livetrapping squirrels on the Waterloo Wildlife Experiment Station in southeastern Ohio, l 962 and 1963. The 'frequency of capture of marked squirrels suggests that the probability of capture is not the same for all squirrels; as a result, a trapped sample typically contains a disproportionately high number of recaptures. Thus, the multiple census methods of Schnabel and of Schumacher produced estimates lower than the number of animals considered to comprise the population. Frequency of capture approximated the geometric distribution. The simplified equation for maximum likelihood estimation (MLE) for the geometric distribution, presented in 1967 by Edwards and Eberhardt, appeared useful for estimating squirrel abundance from livetrapping data, although estimates tended to be somewhat high. The intercept of a line fitted to a logarithm plot of data on the frequency of capture, using linear regression techniques, gave what appeared to be adequate approximations of the numbers of squirrels in the zero (uncaptured) class. Although estimates derived from M 1 .F. for the geometric distribution and from linear regression are based on assumption: not strictly fulfilled by the data, these methods should prove useful until better techniques are developed. MLE for the Poisson distribution appeared to underestimate the zero class. Similarities in results of evaluations of techniques of population estimation for squirrels and rabbits suggest that further research on population estimation may provide findings applicable to a variety of species.

Abstract

The primary objective of the 2017-18 Jordan Population and Family Health Survey (JPFHS) is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the 2017-18 JPFHS: - Collected data at the national level that allowed calculation of key demographic indicators - Explored the direct and indirect factors that determine levels of and trends in fertility and childhood mortality - Measured levels of contraceptive knowledge and practice - Collected data on key aspects of family health, including immunisation coverage among children, the prevalence and treatment of diarrhoea and other diseases among children under age 5, and maternity care indicators such as antenatal visits and assistance at delivery among ever-married women - Obtained data on child feeding practices, including breastfeeding, and conducted anthropometric measurements to assess the nutritional status of children under age 5 and ever-married women age 15-49 - Conducted haemoglobin testing on children age 6-59 months and ever-married women age 15-49 to provide information on the prevalence of anaemia among these groups - Collected data on knowledge and attitudes of ever-married women and men about sexually transmitted infections (STIs) and HIV/AIDS - Obtained data on ever-married women’s experience of emotional, physical, and sexual violence - Obtained data on household health expenditures

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), children age 0-5 years, women age 15-49 years and men age 15-59 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2017-18 JPFHS is based on Jordan's Population and Housing Census (JPHC) frame for 2015. The current survey is designed to produce results representative of the country as a whole, of urban and rural areas separately, of three regions, of 12 administrative governorates, and of three national groups: Jordanians, Syrians, and a group combined from various other nationalities.

The sample for the 2017-18 JPFHS is a stratified sample selected in two stages from the 2015 census frame. Stratification was achieved by separating each governorate into urban and rural areas. Each of the Syrian camps in the governorates of Zarqa and Mafraq formed its own sampling stratum. In total, 26 sampling strata were constructed. Samples were selected independently in each sampling stratum, through a two-stage selection process, according to the sample allocation. Before the sample selection, the sampling frame was sorted by district and sub-district within each sampling stratum. By using a probability-proportional-to-size selection for the first stage of selection, an implicit stratification and proportional allocation were achieved at each of the lower administrative levels.

In the first stage, 970 clusters were selected with probability proportional to cluster size, with the cluster size being the number of residential households enumerated in the 2015 JPHC. The sample allocation took into account the precision consideration at the governorate level and at the level of each of the three special domains. After selection of PSUs and clusters, a household listing operation was carried out in all selected clusters. The resulting household lists served as the sampling frame for selecting households in the second stage. A fixed number of 20 households per cluster were selected with an equal probability systematic selection from the newly created household listing.

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

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used for the 2017-18 JPFHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. These questionnaires, based on The DHS Program’s standard Demographic and Health Survey questionnaires, were adapted to reflect population and health issues relevant to Jordan. After all questionnaires were finalised in English, they were translated into Arabic.

Cleaning operations

All electronic data files for the 2017-18 JPFHS were transferred via IFSS to the DOS central office in Amman, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. Data editing was accomplished using CSPro software. During the duration of fieldwork, tables were generated to check various data quality parameters, and specific feedback was given to the teams to improve performance. Secondary editing and data processing were initiated in October 2017 and completed in February 2018.

Response rate

A total of 19,384 households were selected for the sample, of which 19,136 were found to be occupied at the time of the fieldwork. Of the occupied households, 18,802 were successfully interviewed, yielding a response rate of 98%.

In the interviewed households, 14,870 women were identified as eligible for an individual interview; interviews were completed with 14,689 women, yielding a response rate of 99%. A total of 6,640 eligible men were identified in the sampled households and 6,429 were successfully interviewed, yielding a response rate of 97%. Response rates for both women and men were similar across urban and rural areas.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling 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 during the implementation of the 2017-18 Jordan Population and Family Health Survey (JPFHS) to minimise this type of error, nonsampling 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 2017-18 JPFHS is only one of many samples that could have been selected from the same population, using the same design and sample 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 among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

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% of all possible samples of identical size and design.

If the sample of respondents had been selected by simple random sampling, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2017-18 JPFHS sample was the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed using SAS programmes developed by ICF International. These programmes use the Taylor linearisation method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

The Taylor linearisation method treats any percentage or average as a ratio estimate, r = y/x, where y represents the total sample value for variable y, and x represents the total number of cases in the group or subgroup under consideration.

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

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months

See details of the data quality tables in Appendix C of the survey final report.

Abstract

The 2023 Jordan Population and Family Health Survey (JPFHS) is the eighth Population and Family Health Survey conducted in Jordan, following those conducted in 1990, 1997, 2002, 2007, 2009, 2012, and 2017–18. It was implemented by the Department of Statistics (DoS) at the request of the Ministry of Health (MoH).

The primary objective of the 2023 JPFHS is to provide up-to-date estimates of key demographic and health indicators. Specifically, the 2023 JPFHS: • Collected data at the national level that allowed calculation of key demographic indicators • Explored the direct and indirect factors that determine levels of and trends in fertility and childhood mortality • Measured contraceptive knowledge and practice • Collected data on key aspects of family health, including immunisation coverage among children, prevalence and treatment of diarrhoea and other diseases among children under age 5, and maternity care indicators such as antenatal visits and assistance at delivery • Obtained data on child feeding practices, including breastfeeding, and conducted anthropometric measurements to assess the nutritional status of children under age 5 and women age 15–49 • Conducted haemoglobin testing with eligible children age 6–59 months and women age 15–49 to gather information on the prevalence of anaemia • Collected data on women’s and men’s knowledge and attitudes regarding sexually transmitted infections and HIV/AIDS • Obtained data on women’s experience of emotional, physical, and sexual violence • Gathered data on disability among household members

The information collected through the 2023 JPFHS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population. The survey also provides indicators relevant to the Sustainable Development Goals (SDGs) for Jordan.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, men aged 15-59, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2023 JPFHS was the 2015 Jordan Population and Housing Census (JPHC) frame. The survey was designed to produce representative results for the country as a whole, for urban and rural areas separately, for each of the country’s 12 governorates, and for four nationality domains: the Jordanian population, the Syrian population living in refugee camps, the Syrian population living outside of camps, and the population of other nationalities. Each of the 12 governorates is subdivided into districts, each district into subdistricts, each subdistrict into localities, and each locality into areas and subareas. In addition to these administrative units, during the 2015 JPHC each subarea was divided into convenient area units called census blocks. An electronic file of a complete list of all of the census blocks is available from DoS. The list contains census information on households, populations, geographical locations, and socioeconomic characteristics of each block. Based on this list, census blocks were regrouped to form a general statistical unit of moderate size, called a cluster, which is widely used in various surveys as the primary sampling unit (PSU). The sample clusters for the 2023 JPFHS were selected from the frame of cluster units provided by the DoS.

The sample for the 2023 JPFHS was a stratified sample selected in two stages from the 2015 census frame. Stratification was achieved by separating each governorate into urban and rural areas. In addition, the Syrian refugee camps in Zarqa and Mafraq each formed a special sampling stratum. In total, 26 sampling strata were constructed. Samples were selected independently in each sampling stratum, through a twostage selection process, according to the sample allocation. Before the sample selection, the sampling frame was sorted by district and subdistrict within each sampling stratum. By using a probability proportional to size selection at the first stage of sampling, an implicit stratification and proportional allocation were achieved at each of the lower administrative levels.

For further details on sample design, see APPENDIX A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Five questionnaires were used for the 2023 JPFHS: (1) the Household Questionnaire, (2) the Woman’s Questionnaire, (3) the Man’s Questionnaire, (4) the Biomarker Questionnaire, and (5) the Fieldworker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Jordan. Input was solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. After all questionnaires were finalised in English, they were translated into Arabic.

Cleaning operations

All electronic data files for the 2023 JPFHS were transferred via SynCloud to the DoS central office in Amman, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. Data editing was accomplished using CSPro software. During the duration of fieldwork, tables were generated to check various data quality parameters, and specific feedback was given to the teams to improve performance. Secondary editing and data processing were initiated in July and completed in September 2023.

Response rate

A total of 20,054 households were selected for the sample, of which 19,809 were occupied. Of the occupied households, 19,475 were successfully interviewed, yielding a response rate of 98%.

In the interviewed households, 13,020 eligible women age 15–49 were identified for individual interviews; interviews were completed with 12,595 women, yielding a response rate of 97%. In the subsample of households selected for the male survey, 6,506 men age 15–59 were identified as eligible for individual interviews and 5,873 were successfully interviewed, yielding a response rate of 90%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and in 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 during the implementation of the 2023 Jordan Population and Family Health Survey (2023 JPFHS) to minimise this type of error, nonsampling 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 2023 JPFHS is only one of many samples that could have been selected from the same population, using the same design and sample 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 among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

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% of all possible samples of identical size and design.

If the sample of respondents had been selected by simple random sampling, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2023 JPFHS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed using SAS programs developed by ICF. These programs use the Taylor linearisation method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

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

Data appraisal

Data Quality Tables

• Household age distribution
• Age distribution of eligible and interviewed women
• Age distribution of eligible and interviewed men
• Age displacement at age 14/15
• Age displacement at age 49/50
• Pregnancy outcomes by years preceding the survey
• Completeness of reporting
• Standardization exercise results from anthropometry training
• Height and weight data completeness and quality for children
• Height measurements from random subsample of measured children
• Interference in height and weight measurements of children
• Interference in height and weight measurements of women
• Heaping in

Population and Housing data for Census Tracts within the State of Montana was compiled from the PL 94-171 Redistricting Summary files released by the U.S. Census Bureau for the 2020 Decennial Census. This data set was created by the Montana Department of Commerce for use by the citizens of Montana and the general public. TIGER shapefiles were joined to the tabular summary file data to create this data set. A subset of variables from the release were selected for this dataset. A description of each variable and calculations are provided here.

VINTAGE - Decennial Census vintage year - Calculation

SUMLEV - Geography summary level - Calculation

GEOID - Geography ID - Calculation

NAME - Geography Name - Calculation

AREALAND - Area of land in square meters - Calculation

AREAWATR - Area of water in square meters - Calculation

INTPTLAT - Geography point latitude - Calculation

INTPTLON - Geography point longitude - Calculation

POPTOT - Population Total - Calculation P0010001

POPPCAP - Population per square mile - Calculation P0010001 / (AREALAND / 2589988.110336)

POPWH - Population White alone - Calculation P0010003

POPBL - Population Black alone - Calculation P0010004

POPAI - Population American Indian or Alaska Native alone - Calculation P0010005

POPAS - Population Asian alone - Calculation P0010006

POPNH - Population Native Hawaiian or Pacific Islander alone - Calculation P0010007

POPOT - Population Some other Race alone - Calculation P0010008

POP2MO - Population 2 or more races - Calculation P0010009

POPWHPCT - Population White alone percent - Calculation P0010003 / P0010001 * 100

POPBLPCT - Population Black alone percent - Calculation P0010004 / P0010001 * 100

POPAIPCT - Population American Indian or Alaska Native alone percent - Calculation P0010005 / P0010001 * 100

POPASPCT - Population Asian alone percent - Calculation P0010006 / P0010001 * 100

POPNHPCT - Population Native Hawaiian or Pacific Islander alone percent - Calculation P0010007 / P0010001 * 100

POPOTPCT - Population Some other Race alone percent - Calculation P0010008 / P0010001 * 100

POP2MOPCT - Population 2 or more races percent - Calculation P0010009 / P0010001 * 100

POPWHC - Population White alone or in combination - Calculation P0010003+ P00100011+ P00100012+ P00100013+ P00100014+ P00100015+ P0010027+ P0010028+ P0010029+ P00100030+ P00100031+ P00100032+ P00100033+ P00100034+ P00100035+ P00100036+ P00100048+ P00100049+ P00100050+ P00100051+ P00100052+ P00100053+ P00100054+ P00100055+ P00100056+ P00100057+ P00100064+ P00100065+ P00100066+ P00100067+ P00100068+ P00100071

POPBLC - Population Black alone or in combination - Calculation P0010004+ P00100011+ P00100016+ P00100017+ P00100018+ P00100019+ P0010027+ P0010028+ P0010029+ P00100030+ P00100037+ P00100038+ P00100039+ P00100040+ P00100041+ P00100042+ P00100048+ P00100049+ P00100050+ P00100051+ P00100052+ P00100053+ P00100058+ P00100059+ P00100060+ P00100061+ P00100064+ P00100065+ P00100066+ P00100067+ P00100069+ P00100071

POPAIC - Population American Indian or Alaska Native alone or in combination - Calculation P0010005+ P00100012+ P00100016+ P0010020+ P0010021+ P0010022+ P0010027+ P00100031+ P00100032+ P00100033+ P00100037+ P00100038+ P00100039+ P00100043+ P00100044+ P00100045+ P00100048+ P00100049+ P00100050+ P00100054+ P00100055+ P00100056+ P00100058+ P00100059+ P00100060+ P00100062+ P00100064+ P00100065+ P00100066+ P00100068+ P00100069+ P00100071

POPASC - Population Asian alone or in combination - Calculation P0010006+ P00100013+ P00100017+ P0010020+ P0010023+ P0010024+ P0010028+ P00100031+ P00100034+ P00100035+ P00100037+ P00100040+ P00100041+ P00100043+ P00100044+ P00100046+ P00100048+ P00100051+ P00100052+ P00100054+ P00100055+ P00100057+ P00100058+ P00100059+ P00100061+ P00100062+ P00100064+ P00100065+ P00100067+ P00100068+ P00100069+ P00100071

POPNHC - Population Native Hawaiian or Pacific Islander alone or in combination - Calculation P0010007+ P00100014+ P00100018+ P0010021+ P0010023+ P0010025+ P0010029+ P00100032+ P00100034+ P00100036+ P00100038+ P00100040+ P00100042+ P00100043+ P00100045+ P00100046+ P00100049+ P00100051+ P00100053+ P00100054+ P00100056+ P00100057+ P00100058+ P00100060+ P00100061+ P00100062+ P00100064+ P00100066+ P00100067+ P00100068+ P00100069+ P00100071

POPOTC - Population Some Other Race alone or in combination - Calculation P0010008+ P00100015+ P00100019+ P0010022+ P0010024+ P0010025+ P00100030+ P00100033+ P00100035+ P00100036+ P00100039+ P00100041+ P00100042+ P00100044+ P00100045+ P00100046+ P00100050+ P00100052+ P00100053+ P00100055+ P00100056+ P00100057+ P00100059+ P00100060+ P00100061+ P00100062+ P00100065+ P00100066+ P00100067+ P00100068+ P00100069+ P00100071

POPWHCPCT - Population White alone or in combination percent - Calculation (P0010003+ P00100011+ P00100012+ P00100013+ P00100014+ P00100015+ P0010027+ P0010028+ P0010029+ P00100030+ P00100031+ P00100032+ P00100033+ P00100034+ P00100035+ P00100036+ P00100048+ P00100049+ P00100050+ P00100051+ P00100052+ P00100053+ P00100054+ P00100055+ P00100056+ P00100057+ P00100064+ P00100065+ P00100066+ P00100067+ P00100068+ P00100071)/ P0010001 * 100

POPBLCPCT - Population Black alone or in combination percent - Calculation (P0010004+ P00100011+ P00100016+ P00100017+ P00100018+ P00100019+ P0010027+ P0010028+ P0010029+ P00100030+ P00100037+ P00100038+ P00100039+ P00100040+ P00100041+ P00100042+ P00100048+ P00100049+ P00100050+ P00100051+ P00100052+ P00100053+ P00100058+ P00100059+ P00100060+ P00100061+ P00100064+ P00100065+ P00100066+ P00100067+ P00100069+ P00100071)/ P0010001 * 100

POPAICPCT - Population American Indian or Alaska Native alone or in combination percent - Calculation (P0010005+ P00100012+ P00100016+ P0010020+ P0010021+ P0010022+ P0010027+ P00100031+ P00100032+ P00100033+ P00100037+ P00100038+ P00100039+ P00100043+ P00100044+ P00100045+ P00100048+ P00100049+ P00100050+ P00100054+ P00100055+ P00100056+ P00100058+ P00100059+ P00100060+ P00100062+ P00100064+ P00100065+ P00100066+ P00100068+ P00100069+ P00100071)/ P0010001 * 100

POPASCPCT - Population Asian alone or in combination percent - Calculation (P0010006+ P00100013+ P00100017+ P0010020+ P0010023+ P0010024+ P0010028+ P00100031+ P00100034+ P00100035+ P00100037+ P00100040+ P00100041+ P00100043+ P00100044+ P00100046+ P00100048+ P00100051+ P00100052+ P00100054+ P00100055+ P00100057+ P00100058+ P00100059+ P00100061+ P00100062+ P00100064+ P00100065+ P00100067+ P00100068+ P00100069+ P00100071)/ P0010001 * 100

POPNHCPCT - Population Native Hawaiian or Pacific Islander alone or in combination percent - Calculation (P0010007+ P00100014+ P00100018+ P0010021+ P0010023+ P0010025+ P0010029+ P00100032+ P00100034+ P00100036+ P00100038+ P00100040+ P00100042+ P00100043+ P00100045+ P00100046+ P00100049+ P00100051+ P00100053+ P00100054+ P00100056+ P00100057+ P00100058+ P00100060+ P00100061+ P00100062+ P00100064+ P00100066+ P00100067+ P00100068+ P00100069+ P00100071)/ P0010001 * 100

POPOTCPCT - Population Some Other Race alone or in combination percent - Calculation (P0010008+ P00100015+ P00100019+ P0010022+ P0010024+ P0010025+ P00100030+ P00100033+ P00100035+ P00100036+ P00100039+ P00100041+ P00100042+ P00100044+ P00100045+ P00100046+ P00100050+ P00100052+ P00100053+ P00100055+ P00100056+ P00100057+ P00100059+ P00100060+ P00100061+ P00100062+ P00100065+ P00100066+ P00100067+ P00100068+ P00100069+ P00100071)/ P0010001 * 100

POPHSP - Population Hispanic - Calculation P0020002

POPNHSP - Population Non-Hispanic - Calculation P0020003

POPHSPPCT - Population Hispanic percent - Calculation P0020002 / P0010001 * 100

POPNHSPPCT - Population Non-Hispanic percent - Calculation P0020003 / P0010001 * 100

POP18OV - Population 18 years and over - Calculation P0030001

POP18OVPCT - Population 18 years and over percent - Calculation P0030001 / P0010001 * 100

HUTOT - Housing Units Total - Calculation H0010001

HUOCC - Housing Units Occupied - Calculation H0010002

HUVAC - Housing Units Vacant - Calculation H0010003

HUOCCPCT - Housing Units Occupied percent - Calculation H0010002 / H0010001 * 100

HUVACPCT - Housing Units Vacant percent - Calculation H0010003 / H0010001 * 100

POPGQ - Population Group Quarters - Calculation P0050001

POPGQIN - Population Group Quarters - Institutionalized - Calculation P0050002

POPGQNI - Population Group Quarters - Non-Institutionalized - Calculation P0050007

POPGQPCT - Population Group Quarters percent - Calculation P0050001 / P0010001 * 100

POPGQINPCT - Population Group Quarters - Institutionalized percent - Calculation P0050002 / P0010001 * 100

POPGQNIPCT - Population Group Quarters - Non-Institutionalized percent - Calculation P0050007 / P0010001 * 100

POPTOT2010 - Population Total 2010 - Calculation

POPCHG - Population Change from 2010 to 2020 - Calculation

POPCHGPCT - Population Percent Change from 2010 to 2020 - Calculation

Monthly Intercensal Estimates of the Resident plus Armed Forces Overseas Population by Single Year of Age and Sex: April 1, 1990 to April 1, 2000 // Source: U.S. Census Bureau, Population Division // For detailed information about the methods used to create the intercensal population estimates, see https://www.census.gov/popest/methodology/intercensal_nat_meth.pdf. // The Census Bureau's Population Estimates Program produces intercensal estimates each decade by adjusting the existing time series of postcensal estimates for a decade to smooth the transition from one decennial census count to the next. They differ from the postcensal estimates that are released annually because they rely on a formula that redistributes the difference between the April 1 postcensal estimate and April 1 census count for the end of the decade across the estimates for that decade. Meanwhile, the postcensal estimates incorporate current data on births, deaths, and migration to produce each new vintage of estimates, and to revise estimates for years back to the last census. The Population Estimates Program provides additional information including historical and postcensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/popest/index.html.