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

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

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

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

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

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

Methodology

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

This dataset will be updated annually, in two releases.

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

The U.S. Census Bureau releases annual estimates of population by counties and municipalities as part of the Population Estimates Program (PEP). This is an estimate of population on July 1 of each year. Adjustments to previous estimate years are made with each release, dating back to the year of the last decennial census. Decennial figures for April 1 of the most recent decennial year will not get updated, but the July 1 estimate for that same year can adjust with each PEP release. The U.S. Census Bureau produces these estimates based on administrative records. At the municipal level, the PEP reports only population totals. At the county level, PEP data gives estimates for age, sex, race, and ethnicity. PEP releases come out in the spring following the latest estimate year. The demographic estimates of the PEP are used as control totals for the American Community Survey results released later that year.

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

Dataset contains census usually resident population counts from the 2013, 2018, and 2023 Censuses, as well as the percentage change in the population count between the 2013 and 2018 Censuses, and between the 2018 and 2023 Censuses. Data is available by statistical area 2.

Map shows the percentage change in the census usually resident population count between the 2018 and 2023 Censuses.

Download lookup file from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

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Footnotes

Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city.

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

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Census usually resident population count concept quality rating

The census usually resident population count is rated as very high quality.

Census usually resident population count – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Symbol

-998 Not applicable

About this dataset

Context

The dataset tabulates the Dillingham Census Area 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 Dillingham Census Area 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 Dillingham Census Area was 4,607, a 2.48% decrease year-by-year from 2022. Previously, in 2022, Dillingham Census Area population was 4,724, a decline of 1.50% compared to a population of 4,796 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Dillingham Census Area decreased by 284. In this period, the peak population was 5,003 in the year 2018. 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 Dillingham Census Area is shown in this column.
• Year on Year Change: This column displays the change in Dillingham Census Area 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 Dillingham Census Area Population by Year. You can refer the same here

This layer contains WorldPop's 100m resolution annual estimates of population density from the year 2000 to 2020. Usage notes: This layer is configured to be viewed only at a scale range for large-scale maps, i.e., zoomed into small areas of the world. Because the underlying data for this layer is relatively large and because raster pyramids cannot accurately represent aggregated population density, there are no pyramids. Thus, this layer may at times require 10 to 15 seconds to draw. We recommend using this layer in conjunction with WorldPop's 1-km resolution Population Density layer to create web maps that allow users to pan and zoom to wider areas; this web map contains an example of this combination. The population estimates in this layer are derived WorldPop's total population data, which use a Top-down unconstrained method which estimates the total population for each cell with a Random Forest-based dasymetric model (Stevens, F. R., Gaughan, A. E., Linard, C., & Tatem, A. J. (2015). Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PloS one, 10(2), e0107042) and converts these values to population density by dividing the number of people in each pixel by the pixel surface area. This diagram visually describes this model that uses known populated locations to analyze imagery to find similarly populated locations. The DOI for the original WorldPop.org total population population data is 10.5258/SOTON/WP00645.Recommended Citation: WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation. Accessed from https://worldpop.arcgis.com/arcgis/rest/services/WorldPop_Total_Population_100m/ImageServer, which was acquired from WorldPop in December 2021.

This dataset consists of housing unit, household, and population estimates for census tracts, census block groups, Transportation Analysis Zones (TAZs), school districts, and ZIP codes in the Twin Cities Region. These data provide a more precise and timely picture of current conditions than the American Community Survey, another source of small area data that is better suited for statistics like percentages and averages than for actual counts. It may be possible to calculate estimates for other small areas upon request; contact Research@metc.state.mn.us for more information.

These data were developed by the Research & Analytics Department at the Atlanta Regional Commission using data from the U.S. Census Bureau across all standard and custom geographies at statewide summary level where applicable.For a deep dive into the data model including every specific metric, see the ACS 2019-2023. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics. Find naming convention prefixes/suffixes, geography definitions and user notes below.Prefixes:NoneCountpPercentrRatemMedianaMean (average)tAggregate (total)chChange in absolute terms (value in t2 - value in t1)pchPercent change ((value in t2 - value in t1) / value in t1)chpChange in percent (percent in t2 - percent in t1)sSignificance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computedSuffixes:_e23Estimate from 2019-23 ACS_m23Margin of Error from 2019-23 ACS_e102006-10 ACS, re-estimated to 2020 geography_m10Margin of Error from 2006-10 ACS, re-estimated to 2020 geography_e10_23Change, 2010-23 (holding constant at 2020 geography)GeographiesAAA = Area Agency on Aging (12 geographic units formed from counties providing statewide coverage)ARC21 = Atlanta Regional Commission modeling area (21 counties merged to a single geographic unit)ARWDB7 = Atlanta Regional Workforce Development Board (7 counties merged to a single geographic unit)BeltLineStatistical (buffer)BeltLineStatisticalSub (subareas)Census Tract (statewide)CFGA23 = Community Foundation for Greater Atlanta (23 counties merged to a single geographic unit)City (statewide)City of Atlanta Council Districts (City of Atlanta)City of Atlanta Neighborhood Planning Unit (City of Atlanta)City of Atlanta Neighborhood Statistical Areas (City of Atlanta)County (statewide)CCDIST = County Commission Districts (statewide where applicable)CCSUPERDIST = County Commission Superdistricts (DeKalb)Georgia House (statewide)Georgia Senate (statewide)HSSA = High School Statistical Area (11 county region)MetroWater15 = Atlanta Metropolitan Water District (15 counties merged to a single geographic unit)Regional Commissions (statewide)State of Georgia (single geographic unit)Superdistrict (ARC region)US Congress (statewide)UWGA13 = United Way of Greater Atlanta (13 counties merged to a single geographic unit)ZIP Code Tabulation Areas (statewide)The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2019-2023). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2019-2023Open Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the data manifest: https://opendata.atlantaregional.com/documents/182e6fcf8201449086b95adf39471831/about

Mid-year (30 June) estimates of the usual resident population for Lower layer Super Output Areas (LSOAs) in England and Wales by broad age groups and sex.

All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name

The population density in Rwanda increased by 12 inhabitants per square kilometer (+2.22 percent) in 2022. Therefore, the population density in Rwanda reached a peak in 2022 with 553.35 inhabitants per square kilometer. Notably, the population density continuously increased over the last years.Population density refers to the average number of residents per square kilometer of land across a given country or region. It is calculated by dividing the total midyear population by the total land area.Find more key insights for the population density in countries like Burundi and Tanzania.

The world's population first reached one billion people in 1803, and reach eight billion in 2023, and will peak at almost 11 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two thirds of the world's population live in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a decade later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

About this dataset

Context

The dataset tabulates the Petersburg Census Area 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 Petersburg Census Area 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 Petersburg Census Area was 3,360, a 0.39% decrease year-by-year from 2021. Previously, in 2021, Petersburg Census Area population was 3,373, a decline of 0.94% compared to a population of 3,405 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Petersburg Census Area decreased by 848. In this period, the peak population was 4,208 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 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 Petersburg Census Area is shown in this column.
• Year on Year Change: This column displays the change in Petersburg Census Area 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 Petersburg Census Area Population by Year. You can refer the same here

description: In this dataset we present two maps that estimate the location and population served by domestic wells in the contiguous United States. The first methodology, called the Block Group Method or BGM, builds upon the original block-group data from the 1990 census (the last time the U.S. Census queried the population regarding their source of water) by incorporating higher resolution census block data. The second methodology, called the Road-Enhanced Method or REM, refines the locations by using a buffer expansion and shrinkage technique along roadways to define areas where domestic wells exist. The fundamental assumption with this method is that houses (and therefore domestic wells) are located near a named road. The results are presented as two nationally consistent domestic-well population datasets. While both methods can be considered valid, the REM map is more precise in locating domestic wells; the REM map had a smaller amount of spatial bias (nearly equal vs biased in type 1 error), total error (10.9% vs 23.7%,), and distance error (2.0 km vs 2.7 km), when comparing the REM and BGM maps to a California calibration map. However, the BGM map is more inclusive of all potential locations for domestic wells. The primary difference in the BGM and the REM is the mapping of low density areas. The REM has a 57% reduction in areas mapped as low density (populations greater than 0 but less than 1 person per km), concentrating populations into denser regions. Therefore, if one is trying to capture all of the potential areas of domestic-well usage, then the BGM map may be more applicable. If location is more imperative, then the REM map is better at identifying areas of the landscape with the highest probability of finding a domestic well. Depending on the purpose of a study, a combination of both maps can be used. For space concerns, the datasets have been divided into two separate geodatabases. The BGM map geodatabase and the REM map database.; abstract: In this dataset we present two maps that estimate the location and population served by domestic wells in the contiguous United States. The first methodology, called the Block Group Method or BGM, builds upon the original block-group data from the 1990 census (the last time the U.S. Census queried the population regarding their source of water) by incorporating higher resolution census block data. The second methodology, called the Road-Enhanced Method or REM, refines the locations by using a buffer expansion and shrinkage technique along roadways to define areas where domestic wells exist. The fundamental assumption with this method is that houses (and therefore domestic wells) are located near a named road. The results are presented as two nationally consistent domestic-well population datasets. While both methods can be considered valid, the REM map is more precise in locating domestic wells; the REM map had a smaller amount of spatial bias (nearly equal vs biased in type 1 error), total error (10.9% vs 23.7%,), and distance error (2.0 km vs 2.7 km), when comparing the REM and BGM maps to a California calibration map. However, the BGM map is more inclusive of all potential locations for domestic wells. The primary difference in the BGM and the REM is the mapping of low density areas. The REM has a 57% reduction in areas mapped as low density (populations greater than 0 but less than 1 person per km), concentrating populations into denser regions. Therefore, if one is trying to capture all of the potential areas of domestic-well usage, then the BGM map may be more applicable. If location is more imperative, then the REM map is better at identifying areas of the landscape with the highest probability of finding a domestic well. Depending on the purpose of a study, a combination of both maps can be used. For space concerns, the datasets have been divided into two separate geodatabases. The BGM map geodatabase and the REM map database.

Abstract

The census date was midnight, the 23rd of July 2018.

The Census is the official count of population, household and dwellings in Wallis & Futuna and it gives a general overview of the country at one specific point in time: 23rd of July 2018. Since 1969 until 2003, Census has been taken once in every 7 or 6 years and every 5 years from 2003.

The census can be the source of information for allocation of public funding, more particularly in areas such as health, education and social policy. The main users of the information provided by the Census are the government, education facilities (such as schools and tertiary organizations), local authorities, businesses, community organizations and the public in general.

The objectives of Census changed over time shifting from earlier years where they were essentially household registrations and counts, to now where a national population census stands supreme as the most valuable single source of statistical data for Wallis & Futuna. This Census allowed to determine the legal population of Wallis and Futuna in all geographical aspects: Wallis island, Futuna island, the 3 "circonsriptions" (Alo, Sigave, Uvea) and 5 districts (Alo, Sigave, Hahake, Hihifo, Mua).

Census data is now widely used to evaluate: - The availability of basic household needs in key sectors, to identify disadvantaged areas and help set priorities for action plans; - Benefits of development programmes in particular areas, such as literacy, employment and family planning;

In addition, census data is useful to asses manpower resources, identify areas of social concern and for the improvement in the social and economic status of women by giving more information about this part of the population and formulating housing policies and programmes and investment of development funds.

Geographic coverage

National coverage.

Analysis unit

Households and Individuals.

Universe

The Census is covering all people alive on the reference date (23rd of July 2018), that are usually living in Wallis and Futuna - whichever nationality they are, for at least 12 months. The Census covered all household and communitiy members. Communities are considered to be: boarding schools, gendarmerie, retirement homes, religious communities, but also people living in mobile dwelling (e.g. boats) and homeless people.

Kind of data

Census/enumeration data [cen]

Sampling procedure

Not applicable as it is a full coverage.

Mode of data collection

Face-to-face [f2f]

Research instrument

There are two types of questionnaire for this Census:

Individual sheet (Feuille de Logement or "FL"): describing the dwelling characteristics and enlisting all the individuals living in it; Individual form (Bulletin Individuel or "BI"): information on each individual that are usually living in the household.

The questionnaires were distributed in French and are available in the "External Resources" section.

Cleaning operations

Data editing was done by SPC in collaboration with Wallis and Futuna NSO.

Sampling error estimates

Not applicable.

Dataset contains counts and measures for individuals from the 2013, 2018, and 2023 Censuses. Data is available by statistical area 2.

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The variables included in this dataset are for the census usually resident population count (unless otherwise stated). All data is for level 1 of the classification (unless otherwise stated).

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The variables for part 1 of the dataset are:

• Census usually resident population count
• Census night population count
• Age (5-year groups)
• Age (life cycle groups)
• Median age
• Birthplace (NZ born/overseas born)
• Birthplace (broad geographic areas)
• Ethnicity (total responses) for level 1 and ‘Other Ethnicity’ grouped by ‘New Zealander’ and ‘Other Ethnicity nec’
• Māori descent indicator
• Languages spoken (total responses)
• Official language indicator
• Gender
• Cisgender and transgender status – census usually resident population count aged 15 years and over
• Sex at birth
• Rainbow/LGBTIQ+ indicator for the census usually resident population count aged 15 years and over
• Sexual identity for the census usually resident population count aged 15 years and over
• Legally registered relationship status for the census usually resident population count aged 15 years and over
• Partnership status in current relationship for the census usually resident population count aged 15 years and over
• Number of children born for the sex at birth female census usually resident population count aged 15 years and over
• Average number of children born for the sex at birth female census usually resident population count aged 15 years and over
• Religious affiliation (total responses)
• Cigarette smoking behaviour for the census usually resident population count aged 15 years and over
• Disability indicator for the census usually resident population count aged 5 years and over
• Difficulty communicating for the census usually resident population count aged 5 years and over
• Difficulty hearing for the census usually resident population count aged 5 years and over
• Difficulty remembering or concentrating for the census usually resident population count aged 5 years and over
• Difficulty seeing for the census usually resident population count aged 5 years and over
• Difficulty walking for the census usually resident population count aged 5 years and over
• Difficulty washing for the census usually resident population count aged 5 years and over.

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Download lookup file for part 1 from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

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Footnotes

Te Whata

Under the Mana Ōrite Relationship Agreement, Te Kāhui Raraunga (TKR) will be publishing Māori descent and iwi affiliation data from the 2023 Census in partnership with Stats NZ. This will be available on Te Whata, a TKR platform.

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Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

​

Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city.

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Population counts

Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts.

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

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Study participation time series

In the 2013 Census study participation was only collected for the census usually resident population count aged 15 years and over.

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Concept descriptions and quality ratings

Data quality ratings for 2023 Census variables has additional details about variables found within totals by topic, for example, definitions and data quality.

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Disability indicator

This data should not be used as an official measure of disability prevalence. Disability prevalence estimates are only available from the 2023 Household Disability Survey. Household Disability Survey 2023: Final content has more information about the survey.

Activity limitations are measured using the Washington Group Short Set (WGSS). The WGSS asks about six basic activities that a person might have difficulty with: seeing, hearing, walking or climbing stairs, remembering or concentrating, washing all over or dressing, and communicating. A person was classified as disabled in the 2023 Census if there was at least one of these activities that they had a lot of difficulty with or could not do at all.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Measures

Measures like averages, medians, and other quantiles are calculated from unrounded counts, with input noise added to or subtracted from each contributing value during measures calculations. Averages and medians based on less than six units (e.g. individuals, dwellings, households, families, or extended families) are suppressed. This suppression threshold changes for other quantiles. Where the cells have been suppressed, a placeholder value has been used.

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Percentages

To calculate percentages, divide the figure for the category of interest by the figure for 'Total stated' where this applies.

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Symbol

-997 Not available

-999 Confidential

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Inconsistencies in definitions

Please note that there may be differences in definitions between census classifications and those used for other data collections.

The population density in Cameroon increased by 1.5 inhabitants per square kilometer (+2.63 percent) in 2022. With 58.46 inhabitants per square kilometer, the population density thereby reached its highest value in the observed period. Notably, the population density continuously increased over the last years.Population density refers to the average number of residents per square kilometer of land across a given country or region. It is calculated by dividing the total midyear population by the total land area.Find more key insights for the population density in countries like Republic of the Congo and Chad.

About this dataset

Context

The dataset tabulates the Bethel Census Area 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 Bethel Census Area 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 Bethel Census Area was 18,224, a 0.19% decrease year-by-year from 2022. Previously, in 2022, Bethel Census Area population was 18,259, a decline of 1.80% compared to a population of 18,594 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Bethel Census Area increased by 2,157. In this period, the peak population was 18,661 in the year 2020. 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 Bethel Census Area is shown in this column.
• Year on Year Change: This column displays the change in Bethel Census Area 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 Bethel Census Area Population by Year. You can refer the same here

Abstract

Dataset quality ***: High quality dataset that was created by and quality-checked by the EIDC team

This dataset identifies the communities served by water systems across the U.S., based on the very first national dataset of drinking water service area boundaries (SAB) provided by the Environmental Policy Innovation Center (EPIC). The dataset leverages the a raster approach developed by the EIDC team and provides crosswalking weights to link water service area boundaries with census geography. This alignment allows for the computation of census demographics within water service areas, providing valuable insights into the populations served by these water systems.

Methodology

The crosswalking data is generated using a fine-tuned raster approach, which is designed to realign mismatched geographical units. This approach utilizes a high resolution raster layer of population density to map water service areas and census geography. Essentially, this realignment is based on population density residing within the overlapping areas between water service areas and census blocks. In other words, the realignment is adjusted to more accurately reflect the distribution of the population within these areas. The final output of this process is a set of crosswalking weights from census blocks to water service areas. These weights represent the proportion of the population within each census block that falls within a given water service area.

The current version of the data is based on SimpleLab, EPIC (2022). U.S. Community Water Systems Service Boundaries, v2.0.0, HydroShare.

Usage

Crosswalk Weights from Census to Service Area Bounds is the crosswalking file from census blocks to water service area boundaries. Each water service area (identified by PWSID) is matched with one or more census blocks. WEIGHT can be applied to calculate the weighted estimates from census data to the water service area.

Demographics for Service Area Bounds includes the information of the water service areas provided by EPIC and some pre-supplied race-ethnicity and income information calculated using the cross-walking weights.

This dataset contains information about the demographics of all US cities and census-designated places with a population greater or equal to 65,000. This data comes from the US Census Bureau's 2015 American Community Survey. This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.