About this dataset

Context

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

Key observations

In 2023, the population of St. Charles was 1,956, a 0.61% decrease year-by-year from 2022. Previously, in 2022, St. Charles population was 1,968, a decline of 0.91% compared to a population of 1,986 in 2021. Over the last 20 plus years, between 2000 and 2023, population of St. Charles decreased by 170. In this period, the peak population was 2,126 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

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

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the St. Charles is shown in this column.
• Year on Year Change: This column displays the change in St. Charles population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for St. Charles Population by Year. You can refer the same here

The 1986 Census was the first mid-decade census to undertake detailed enumeration. Data on demographic, social and economic characteristics, as well as on dwellings, were collected from Canadians. The information is recorded on two data bases, the 100% data base and the 20% sample data base. The 100% data bases includes general demographic, dwelling and household data (for example: age, sex, marital status, mother tongue and structural type of dwelling) collected from the entire population. The 20% sample data base includes the general demographic data, detailed socio-economic data (for example: ethnic origin, labour force activity, schooling, income and dwellings information) collected from one-fifth of the population. The range of the 1986 Census products and services differs somewhat from the 1981 Census. The major changes are: A 40% reduction in the number of publications The replacement of the 1981 Census Summary Tapes program by the Basic Summary Cross-Tabulations Improvements in the Custom Tabulations Service The implementation of a new Semi-Custom product line Focus series is the aggregate statistics (multi-variate cross-tabulations) at census subdivision, census tract, and enumeration area levels. These 7 tables do not correspond to the print Focus series print publications. At present, EA-level tables are available on CD-ROM only.

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system. The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

IPUMS-International is an effort to inventory, preserve, harmonize, and disseminate census microdata from around the world. The project has collected the world's largest archive of publicly available census samples. The data are coded and documented consistently across countries and over time to facillitate comparative research. IPUMS-International makes these data available to qualified researchers free of charge through a web dissemination system. The IPUMS project is a collaboration of the Minnesota Population Center, National Statistical Offices, and international data archives. Major funding is provided by the U.S. National Science Foundation and the Demographic and Behavioral Sciences Branch of the National Institute of Child Health and Human Development. Additional support is provided by the University of Minnesota Office of the Vice President for Research, the Minnesota Population Center, and Sun Microsystems.

Since 1986, surveys in spring and fall each year count the number of wolves found in Denali National Park and Preserve, north of the Alaska Range.

This dataset contains Qatar Population Mid Year Estimates and Census by Gender. Data from Qatar Planning and Statistics Authority. Follow datasource.kapsarc.org for timely data to advance energy economics research1986 , 1997 , 2004 , 2010 , 2015: Results General Population & Housing Census

About this dataset

Context

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

Key observations

In 2022, the population of Springfield was 1,915, a 0.26% increase year-by-year from 2021. Previously, in 2021, Springfield population was 1,910, a decline of 0.10% compared to a population of 1,912 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Springfield increased by 259. In this period, the peak population was 1,986 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

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

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Springfield is shown in this column.
• Year on Year Change: This column displays the change in Springfield population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Springfield Population by Year. You can refer the same here

This dataset presents the total population in Qatar by gender (male and female) across census years 1986, 1997, 2004, 2010, and 2020, along with their percentage of the total and annual growth rate.

For each county or county equivalent, this file provides the provisional population estimate for July 1, 1986 and the corrected 1980 census population figure. In addition, data are tabulated for births, deaths, and residual migration for the 1980-1986 period. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR08862.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Abstract

The Liberia Demographic and Health Survey (LDHS) was conducted as part of the worldwide Demographic and Health Surveys (DHS) program, in which surveys are being carried out in countries in Africa, Asia, Latin America, and the Middle East. Liberia was the second country to conduct a DHS and the first country in Africa to do so. THe LDHS was a national-level survey conducted from February to July 1986, covering a sample of 5,239 women aged 15 to 49.

The major objective of the LDHS was to provide data on fertility, family planning and maternal and child health to planners and policymakers in Liberia for use in designing and evaluating programs. Although a fair amount of demographic data was available from censuses and surveys, almost no information existed concerning family planning, health, or the determinants of fertility, and the data that did exist were drawn from small-scale, sub-national studies. Thus, there was a need for data to make informed policy choices for family planning and health projects.

A more specific objective was to provide baseline data for the Southeast Region Primary Health Care Project. In order to effectively plan strategies and to eventually evaluate the progress of the project in meeting its goals, there was need for data to indicate the health situation in the two target counties prior to the implementation of the project. Many of the desired topics, such as immunizations, family planning use, and prenatal care, were already incorporated into the model DHS questionnaire; nevertheless, the LDHS was able to better accommodate the needs of this project by adding several questions and by oversampling women living in Sinoe and Grand Gedeh Counties.

Another important goal of the LDHS was to enhance tile skills of those participating in the project for conducting high-quality surveys in the future. Finally, the contribution of Liberian data to an expanding international dataset was also an objective of the LDHS.

Geographic coverage

National

Analysis unit

• Households
• Children age 0-5
• Women age 15 to 49
• Men

Kind of data

Sample survey data

Sampling procedure

The sample for the Liberia Demographic and Health Survey was based on the sampling frame of about 4,500 censal enumeration areas (EAs) that were created for the 1984 Population Census. It was decided to eliminate very remote EAs prior to selecting the sample. The definition of remoteness used was "any EA in which the largest village was estimated to be more than 3-4 hours' walk from a road." According to the 1984 census, the excluded areas represent less than 3 percent of the total number of households in the country. Since the major analytic objective of the LDHS was to adequately estimate basic demographic and health indicators including fertility, mortality, and contraceptive prevalence for the whole country and the two sub-universes (Since and Grand Gedeh Counties), it was decided to oversample these two counties. Consequently, three explicit sub-universes of EAs were created: (1) Since County, (2) Grand Gedeh County, and (3) the rest of the country.

The design provided a self-weighted sample within each sub-universe, but, because of the oversampling in Sinoe and Grand Gedeh Counties, the sample is not self-weighting at the national level. Eligible respondents for the survey were women aged 15-49 years who were present the night before the interview in any of the households included in the sample selected for the LDHS.

The total sample size was expected to be about 6,000 women aged 15-49 with a target by sub-universe of 1,000 each in Sinoe and Grand Gedeh Counties and 4,000 in the rest of the country. It was decided that a sample of approximately 5,500 households selected through a two-stage procedure would be appropriate to reach those objectives. Sampling was carried out independently in each sub-universe. In the rest of the country sub-universe, counties were arranged for selection in serpentine order from the northwest (Cape Mount County) to the southeast (Maryland County). In the first stage EAs were selected systematically with probability proportional to size (size = number of households in 1984). Twenty-four EAs were selected in each of Sinoe and Grand Gedeh Counties and 108 EAs in the rest of the country.

See full sample procedure in the survey final report.

Mode of data collection

Face-to-face

Research instrument

The Liberia Demographic and Health Survey (LDHS) utilized two questionnaires: One to list members of the selected households (Household Questionnaire) and the other to record information from all women aged 15-49 who were present in the selected households the night before the interview (Individual Questionnaire).

Both questionnaires were produced in Liberian English and were pretested in September 1985. The Individual Questionnaire was an early version of the DHS model questionnaire. It covered three main topics: (1) fertility, including a birth history and questions concerning desires for future childbearing, (2) family planning knowledge and use, and (3) family health, including prevalence of childhood diseases, immunizations for children under age five, and breasffeeding and weaning practices.

Cleaning operations

Data from the questionnaires were entered onto microcomputers at the Bureau of Statistics office in Monrovia. The data were then subjected to extensive checks for consistency and accuracy.

Errors detected during this operation were resolved either by referring to the original questionnaire, or, in some cases, by logical inference from other information given in the record. Finally, dates were imputed for the small number of cases where complete dates of important events were not given.

Response rate

Out of the total of 6,1306 households selected, 14.5 percent were found not to be valid households in the field, either because the dwelling had been vacated or destroyed, or the household could not be located or did not exist. Of the 5,609 households that were found to exist, 90 percent were successfully interviewed. In the households that were interviewed, a total of 5,340 women were identified as being eligible for individual interview (that is, they were aged 15-49 and had spent the night before the interview in the selected household). This represents an average of slightly over one eligible woman per household.

The response rate for eligible women was 98 percent. The main reason for nonresponse was the absence of the woman. Similar data are presented by sample subuniverse.

Sampling error estimates

The results from sample surveys are affected by two types of errors: (1) nonsampling error and (2) sampling error. Nonsampling error is due to mistakes made in carrying out field activities, such as failure to locate and interview the correct household, errors in the way questions are asked, misunderstanding of the questions on the part of either the interviewer or the respondent, data entry errors, etc. Although efforts were made during the design and implementation of the Liberia Demographic and Health Survey to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

The sample of women selected in the LDHS is only one of many samples of the same size that could have been selected from the same population, using the same design. Each one would have yielded results that differed somewhat from the actual sample selected. The variability observed between all possible samples constitutes sampling error, which, although it is not known exactly, can be estimated from the survey results. Sampling error is usually measured in terms of the "standard error" of a particular statistic (mean, percentage, etc.), which is the square root of the variance of the statistic across all possible samples of equal size and design.

The standard error can be used to calculate confidence intervals within which one can be reasonably assured the true value of the variable for the whole population falls. For example, for any given statistic calculated from a sample survey, the value of that same statistic as measured in 95 percent of all possible samples of identical size and design will fall within a range of plus or minus two times the standard error of that statistic.

If the sample of women had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the LDHS sample design depended on stratification, stages, and clusters and consequently, it was necessary to utilize more complex formulas. The computer package CLUSTERS was used to assist in computing the sampling errors with the proper statistical methodology.

Data appraisal

Information on the completeness of date reporting is of interest in assessing data quality. With regard to dates of birth of individual women, 42 percent of respondents reported both a month and year of birth, 21 percent gave a year of birth in addition to current age, and 37 percent gave only their ages. With regard to children's dates of birth in the birth history, 85 percent of births had both month and year reported, 12 percent had year and age reported, 1 percent had only age reported, and 2 percent had no date information.

This dataset provides population estimates for states and counties as of July 1, 1987. Revised population estimates for July 1 for the years 1981-1986 and corrected census population figures for 1980 are also included. In addition, figures are given for births, deaths, and net migration for 1980-1987. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR09261.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

This data collection supplies annual data on the size of the prison population and the size of the general population in the United States for the period 1925 to 1986. These yearend counts include tabulations for prisons in each of the 50 states and the District of Columbia, as well as the federal prisons, and are intended to provide a measure of the overall size of the prison population. The figures were provided from a voluntary reporting program in which each state, the District of Columbia, and the Federal Bureau of Prisons reported summary statistics as part of the statistical information on prison populations in the United States.

This data collection provides information on population and per capita income for all states, counties, incorporated places, and functioning minor civil divisions in 20 specified states. Included are corrected total population (census complete-count) as of April 1, 1980, per capita income in 1979 (census sample), population estimates as of July 1, 1986, and per capita income estimates for 1985. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR09167.v1. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

About this dataset

Context

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

Key observations

In 2022, the population of Olive Hill was 1,555, a 0.96% decrease year-by-year from 2021. Previously, in 2021, Olive Hill population was 1,570, a decline of 0.70% compared to a population of 1,581 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Olive Hill decreased by 411. In this period, the peak population was 1,986 in the year 2005. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

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

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Olive Hill is shown in this column.
• Year on Year Change: This column displays the change in Olive Hill population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Olive Hill Population by Year. You can refer the same here

Local Government Area (LGA) based data for Usual Residence 1986, from the Australian Bureau of Statistics (ABS) 1986 Census of Population and Housing. The data is by LGA 1986 boundaries. Periodicity: 5-Yearly. This data is used with permission from the ABS. The tabular data was supplied to AURIN by the Australian Data Archives. The cleaned, high resolution 1986 geographic boundaries (cat. no. 1261.0.30.001) are available from Data.gov.au. For more information please refer to the 1986 Census Dictionary (cat. no. 2102.0). Please note: This table includes all persons counted at their place of usual residence on census night as well as usual residents temporarily absent, i.e. persons not at home on census night were coded back to their LGA of usual residence, and were included in the table to enable counts to be determined for the usual resident population of a particular LGA. Persons who usually reside outside the LGA, but were within it on census night, were excluded from the usual resident population of that LGA. They were, however, transferred back to their own LGA of usual residence.

The Raster Based GIS Coverage of Mexican Population is a gridded coverage (1 x 1 km) of Mexican population. The data were converted from vector into raster. The population figures were derived based on available point data (the population of known localities - 30,000 in all). Cell values were derived using a weighted moving average function (Burrough, 1986), and then calculated based on known population by state. The result from this conversion is a coverage whose population data is based on square grid cells rather than a series of vectors. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI).

Echinus esculentus, normally the subject of a small scale commercial fishery in the Isles of Scilly during the winter months, was not collected during the winter of 1985/86. This report presents the results of the third survey of selected Echinus esculentus populations in the Isles of Scilly, which took place during September 1986. These are compared with data from the same sites obtained during September 1984 and 1985. Seven areas were sampled, four of which are classified as unfished and three as fished sites. Within each of the seven areas, sampling sites were located as closely as possible to those studied in the 1985 survey. The mean density of urchins ranges from 0.05/m2 to 0.49/m2. Within the precision of the methodology, these figures compare exactly to the range measured in 1985 (0.05/m2 to 0.40/m2). There is no significant difference in the density of urchins between fished and unfished sites. The mean diameter of E. esculentus ranged from 95 mm to 122 mm with an overall site mean of 105+2 mm (n = 12). This compares with a slightly lower and smaller range of 92 mm to 112 mm during 1985, but a similar overall site mean of 104+2 mm (n = 10). The mean diameter of urchins at unfished sites was smaller than that of fished sites (98+1, n = 4 and 107.6+2, n = 8 respectively), which was also true in both 1984 and 1985. No major physical changes have affected the sites since 1984, and the dominant fauna and flora appear to be unchanged. There is still no evidence of juvenile or young adult individuals (size <5 cm diameter) at the monitoring sites, although it is believed that juveniles are present at deeper sites which cannot be dived or remotely sampled within the remit of the present contract.

This dataset provides the evolution of the number of households and their members in Qatar, including average household size and annual growth rate.

This dataset contains census tract level data for the following topics: census families, demography, dwellings, ethnocultural, households, income, labour force, language, mobility, mother tongue, and schooling.

This dataset contains enumeration area level data for the following topics: census families, demography, dwellings, ethnocultural, households, income, labour force, language, mobility, mother tongue, and schooling.