Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Cardiff population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Cardiff. The dataset can be utilized to understand the population distribution of Cardiff by age. For example, using this dataset, we can identify the largest age group in Cardiff.
Key observations
The largest age group in Cardiff, AL was for the group of age 40 to 44 years years with a population of 14 (33.33%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Cardiff, AL was the Under 5 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Cardiff Population by Age. You can refer the same here
The population of Wales in 2023 was just approximately 3.16 million, and was quite heavily concentrated on the south coast of the country, especially in the large cities of Cardiff and Swansea where approximately 383,500 and 246,700 people live, respectively.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Cardiff population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Cardiff. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.
Key observations
The largest age group was 18 to 64 years with a poulation of 34 (80.95% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age cohorts:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Cardiff Population by Age. You can refer the same here
This dataset provides information on the ethnicity of people by Welsh local authority.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Non-Hispanic population of Cardiff by race. It includes the distribution of the Non-Hispanic population of Cardiff across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Cardiff across relevant racial categories.
Key observations
With a zero Hispanic population, Cardiff is 100% Non-Hispanic. Among the Non-Hispanic population, the largest racial group is White alone with a population of 42 (100% of the total Non-Hispanic population).
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Racial categories include:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Cardiff Population by Race & Ethnicity. You can refer the same here
This dataset provides population estimates for the local health boards in Wales, for the period from 2009 onwards by sex and single year of age, together with some aggregated age groups. It should be noted that for mid-2020, there are some definitional changes (particularly affecting the migration components) compared with mid-2019 populations estimates data and it is advised users read the Quality and Methodology Information section on the Office for National Statistics website. For Wales, the mid-2021 population estimates are the first population estimates to be based on Census 2021. Internal migration estimates for mid-2023 have been produced using a different method to previous years, following a change to the variables available in the Higher Education Statistics Agency (HESA) data. This material is Crown Copyright and may be re-used (not including logos) free of charge in any format or medium, under the terms of the Open Government Licence.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the data for the Cardiff, AL population pyramid, which represents the Cardiff population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.
Key observations
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Cardiff Population by Age. You can refer the same here
The Cardiff Travel Survey is a longitudinal survey that aims to (a) establish current and previous (before the coronavirus outbreak) travel habits; (b) explore how travel-related attitudes, social norms and perceptions change over time; and (c) examine the interplay between individual (perceptual) and environmental (infrastructural) factors in travel mode choice, in particular in relation to the uptake of active travel such as walking and cycling in the City of Cardiff, Wales. The Cardiff Travel Survey 2022 (Wave 2) is an opportunity sample that was collected in 2022 (n=968) by the Centre for Climate Change and Social Transformations (CAST), and is the second of a longitudinal series of surveys to be held annually for the duration of the centre. Data for the Cardiff Travel Survey 2022 were collected between 03 May 2022 and 20 July 2022. Participants of the Cardiff Travel Survey 2021 who consented (n=512) were recontacted via email to invite them to take part in the 2022 survey. Furthermore, participants were recruited through posts on social media, such as Facebook® and Twitter®. Invitations were posted on CAST and investigator accounts. The posts on Twitter and Facebook were promoted to make them more visible in the area. The survey was hosted on the Qualtrics online survey platform and available in both English and Welsh. Inclusion criteria were that participants had to be at least 18 years of age and live in or travel regularly to Cardiff. The English version of the survey was completed by 1,034 respondents and the Welsh version by 28 respondents. Incomplete responses (n=95), defined as those without any answers beyond socio-demographic, were removed from the dataset. A further 4 respondents did not complete the first section on current travel behaviours and were also removed. This left a final sample of n=968 adults. Two hundred and ten (210) of these had also responded to the Cardiff Travel Survey 2021. Participants were asked to create a unique code that can be used match this survey to the previous and next surveys without knowing their identity. Main topic areas of the questionnaire are: Demographics, Travel behaviours, Physical activity, Physical health and mental wellbeing, Perceptions of infrastructure and environmental quality, Travel-related social identity, Attitudes to active travel, Social norms, Support for transport policies, and Unique ID.
This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.
In 2023, almost nine million people lived in Greater London, making it the most populated ceremonial county in England. The West Midlands Metropolitan County, which contains the large city of Birmingham, was the second-largest county at 2.98 million inhabitants, followed by Greater Manchester and then West Yorkshire with populations of 2.95 million and 2.4 million, respectively. Kent, Essex, and Hampshire were the three next-largest counties in terms of population, each with around 1.89 million people. A patchwork of regions England is just one of the four countries that compose the United Kingdom of Great Britain and Northern Ireland, with England, Scotland and Wales making up Great Britain. England is therefore not to be confused with Great Britain or the United Kingdom as a whole. Within England, the next subdivisions are the nine regions of England, containing various smaller units such as unitary authorities, metropolitan counties and non-metropolitan districts. The counties in this statistic, however, are based on the ceremonial counties of England as defined by the Lieutenancies Act of 1997. Regions of Scotland, Wales, and Northern Ireland Like England, the other countries of the United Kingdom have their own regional subdivisions, although with some different terminology. Scotland’s subdivisions are council areas, while Wales has unitary authorities, and Northern Ireland has local government districts. As of 2022, the most-populated Scottish council area was Glasgow City, with over 622,000 inhabitants. In Wales, Cardiff had the largest population among its unitary authorities, and in Northern Ireland, Belfast was the local government area with the most people living there.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of Cardiff by race. It includes the population of Cardiff across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Cardiff across relevant racial categories.
Key observations
The percent distribution of Cardiff population by race (across all racial categories recognized by the U.S. Census Bureau): 100% are white.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Racial categories include:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Cardiff Population by Race & Ethnicity. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of Cardiff by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Cardiff. The dataset can be utilized to understand the population distribution of Cardiff by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Cardiff. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Cardiff.
Key observations
Largest age group (population): Male # 40-44 years (8) | Female # 40-44 years (6). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Age groups:
Scope of gender :
Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Cardiff Population by Gender. You can refer the same here
This dataset provides population projections for local authorities in Wales by sex, single year of age and each year from the base year of 2018, through the projection period to 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.
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License information was derived automatically
Datasets for MSSeasonal and spatial variation in growth and abundance of zebra mussel (Dreissena polymorpha) in a recently invaded artificial lake: implications for management
In 2023, the population of the United Kingdom was around **** million, with approximately **** million women and **** million men. Since 1953, the male population of the UK has grown by around *** million, while the female population has increased by approximately *** million. Throughout this provided time period, the female population of the UK has consistently outnumbered the male population. UK population one of the largest in Europe As of 2022, the population of the United Kingdom was the largest it has ever been, and with growth expected to continue, the forecasted population of the United Kingdom is expected to reach over ** million by the 2030s. Despite the relatively small size of its territory, the UK has one of the largest populations among European countries, slightly larger than France but smaller than Russia and Germany. As of 2022, the population density of the UK was approximately *** people per square kilometer, with London by far the most densely populated area, and Scotland the most sparsely populated. Dominance of London As seen in the data regarding population density, the population of the United Kingdom is not evenly distributed across the country. Within England, London has a population of almost **** million, making it significantly bigger than the next largest cities of Birmingham and Manchester. As of 2022, Scotland's largest city, Glasgow had a population of around *** million, with the largest cities in Northern Ireland, and Wales being Belfast and Cardiff, which had populations of ******* and ******* respectively.
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Eurasian otters are apex predators of freshwater ecosystems and a recovering species across much of their European range; investigating the dietary variation of this predator over time and space therefore provides opportunities to identify changes in freshwater trophic interactions and factors influencing the conservation of otter populations. Here we sampled faeces from 300 dead otters across England and Wales between 2007 and 2016, conducting both morphological analysis of prey remains and dietary DNA metabarcoding. Comparison of these methods showed that greater taxonomic resolution and breadth could be achieved using DNA metabarcoding but combining data from both methodologies gave the most comprehensive dietary description. All otter demographics exploited a broad range of taxa and variation likely reflected changes in prey distributions and availability across the landscape. This study provides novel insights into the trophic generalism and adaptability of otters across Britain, which is likely to have aided their recent population recovery, and may increase their resilience to future environmental changes. Methods Sample and data collection Samples and associated metadata were acquired from 300 otters collected between 2007 and 2016, obtained from the Cardiff University Otter Project, a national monitoring programme for dead otters sampled from across Great Britain (https://www.cardiff.ac.uk/otter-project). Most otters collected were killed by road traffic accidents, with a minority dying through drowning, being shot, starvation, or disease. Information on date (year and month) and location (as grid reference) of carcass collection were recorded at the site of collection. Grid references were used to plot data for spatial analysis. Detailed post-mortems were performed for each carcass during which biotic data were obtained (e.g., sex and size of individual). Faecal samples were collected from the rectum during post-mortem examination, wrapped in foil and stored at -20 °C. Following post-mortems, scaled mass index (SMI) was calculated for each individual otter using the following equation (Peig and Green 2009; Peig and Green 2010): SMI = Mi [ L0 / Li ] bSMA Mi is the body mass and Li is the length measurement of individual i, L0 is the mean length measurement for the entire study population and bSMA is the scaling exponent. Length was measured from nose to tail-tip to the nearest 5 mm. Mean length and the scaling exponent were both calculated from all otter data available as of January 2017 (n = 2477). The scaling exponent is the slope from the standard major axis regression of log-transformed values of mass against length.Otters were also classified into size categories based on their total length (nose to tail tip) using the ‘bins’ function in R (OneR v2.2 package; von Jouanne-Diedrich 2017), which applies a clustering method using Jenks natural breaks optimisation. Male and female otters were clustered separately into small (males <1046 mm, females <936 mm long), medium (males between 1046 mm and 1131 mm, females between 936 mm and 1031 mm), and large (males > 1131 mm, females > 1031 mm). Spatial data Spatial data describing proximity to the coast, urban land use, altitude, slope, and primary water habitat were collated using QGIS version 3.4.4 (QGIS Development Team 2019). Distance from the coast was calculated as the shortest distance (km) along a river from the location at which the otter was found to the low tide point of the mouth of the river (hereafter referred to as ‘river distance’), using the package RivEX (Hornby 2020), because otters tend to travel along water courses rather than across land. As most otters were found as roadkill, and not all were adjacent to rivers, each otter was first assigned to the nearest river. Locations more than 1000 m from a river were checked, and if there was more than one river along which the otter might have travelled, then river distance was calculated for all rivers and a mean distance used. All otters within 1000 m of the coast were given a distance of zero if they were closer to the coastline than a river.Otter locations were mapped as points, and circular areas of 10 km diameter (hereafter referred to as ‘buffers’) mapped around each. Faecal samples typically reflect diet from the preceding 24-72 hours (in mammals; Deagle et al. 2005; Casper et al. 2007; Thalinger et al. 2016), during which time otters can travel up to 10 km (Chanin 2003), it was therefore deemed appropriate to use this distance to reflect the land used by otters within the sample timeframe. Buffers were used to calculate proportions of urban land-use (i.e., urban and suburban land use extracted from the 25 m resolution UK land cover map from 2007; Morton et al. 2011), mean altitude and mean slope (extracted from European Digital Elevation Model (EU-DEM) map; European Environment Agency 2011). We chose to focus on urban land-use as urbanisation may affect otter diet either through changes to prey assemblages or disturbance affecting an otter's ability to forage. Longitude, altitude, and slope were highly correlated, therefore longitude was used in further analyses as a representative for the three variables.Otters in England and Wales typically feed in freshwater river systems but will opportunistically feed in lakes or at the coast if these habitats are within range (Jędrzejewska et al. 2001; Clavero et al. 2004; Parry et al. 2011). Available prey differ between lakes, coasts, and river systems, as well as between different parts of the river network (e.g., tributary, main river channel). To assess whether water habitat type influenced dietary variation, we designated each otter to one of the following: transitional water (coastal and estuarine), lake, main river channel, or tributary (based on Water Framework Directive 2000/60/EC designations mapped using GIS shapefiles provided by Natural Resources Wales and Environment Agency). Otters within 2.5 km (half of a buffer’s radius) from a lake or transitional water were assigned to that habitat, whilst those further away were assumed to be feeding primarily in the river network. The RivEX network map (Hornby 2020) was used to map all rivers, and individuals were further categorised according to whether their assumed habitat was primarily a main river or tributary. To do this, the total length of main river channels and tributaries was calculated within each 10 km buffer. The length of main channels was weighted 10 times greater to account for the greater cross-section of a main channel compared to tributaries (Benda et al. 2004) since waterways with greater areas are assumed to support more prey (Samarasin et al. 2014). The sum of weighted main river lengths and tributary lengths was calculated, and if more than 50 percent of each buffer was attributed to main river channel, the otter was assigned to the main river channel, otherwise it was assigned to tributary. Morphological analysis Each faecal sample was first thawed, homogenised by hand in a sterile container, and divided into subsamples; three samples weighing 200 mg each were collected for DNA analysis (one sample used for DNA extraction and the other two frozen as back-ups) and the remaining material was used for morphological analysis. Subsamples undergoing morphological analysis were then soaked in a solution of water and liquid biological detergent (water:detergent, 10:1) for 24 hours. Samples were passed through sieves with a 0.5 mm mesh and washed with water to ensure only hard parts remained which were air-dried for 24 hours. A record was made of any samples that did not contain any hard parts. Recognisable remains (bones, fish scales, feathers, fur) underwent microscopic identification using a range of keys (Libois and Hallet-Libois 1987; Coburn and Gaglione 1992; Prenda and Granado-Lorencio 1992; Prenda et al. 1997; Watt et al. 1997; Miranda and Escala 2002; Conroy et al. 2005; Tercerie et al. 2019; University of Nottingham 2020). Prey remains were identified to the finest possible taxonomic resolution and recorded as present within or absent from a sample. DNA metabarcoding analysis Faecal samples were processed for HTS, and subsequent bioinformatic analysis was conducted, as described in Drake et al. (2022). In summary, DNA was extracted from a subsample of faecal material and amplified using two metabarcoding primer pairs, designed to amplify regions of the 16S rRNA and cytochrome c oxidase subunit I (COI) genes, each primer having 10 base pair molecular identifier tags (MID tags) to facilitate post-bioinformatic sample identification.Two primer sets from different barcoding regions were selected to overcome biases associated with each region and broaden the range of taxa amplified: the 16S barcoding region selected for vertebrate DNA, and cytochrome c oxidase subunit I (COI) for invertebrate DNA. For 16S, the novel primer pair FN2199 (5’- yayaagacgagaagaccct -3’) and R8B7 (5’- ttatccctrgggtarcthgg -3’; modified for this study from Deagle et al. 2009) were used, which targeted a 225-267 bp amplicon (including primers). For COI, the primer pair Mod_mCOIintF (5’- ggwacwggwtgaacwgtwtaycc -3’; modified for this study from Leray et al. 2013) and HCO-2198 (5’- taaacttcagggtgaccaaaaaatca -3’; Folmer et al. 1994) were used, which targeted a 365 bp amplicon (including primers). Primers underwent in silico testing using ecoPCR (Boyer et al. 2016) and were further tested in vitro. In silico and vitro tests showed primer sets amplified desired taxa. COI primers were also found to amplify a range of vertebrate taxa but did not cover the same range as the 16S primers, therefore justifying the use of both primer sets beyond the benefit of different primer pairs exhibiting different biases.Faecal samples were processed alongside extraction and PCR negative controls, repeat samples, and mock communities, which comprised standardised mixtures of DNA of
Approximately 28 percent of people in Wales advised that they were able to speak Welsh in 20224. The share of people who could speak Welsh ranged from over three-quarters of the population in Gwynedd, located in the North West of Wales, to 14.5 percent in Blaenau Gwent, a small county borough in the South East of the country.
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Demographic and Risk Factors.
Information on health related lifestyle among adults in Wales by health board.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the population of Cardiff by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Cardiff across both sexes and to determine which sex constitutes the majority.
Key observations
There is a majority of male population, with 64.29% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Scope of gender :
Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Cardiff Population by Race & Ethnicity. You can refer the same here
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the Cardiff population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Cardiff. The dataset can be utilized to understand the population distribution of Cardiff by age. For example, using this dataset, we can identify the largest age group in Cardiff.
Key observations
The largest age group in Cardiff, AL was for the group of age 40 to 44 years years with a population of 14 (33.33%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Cardiff, AL was the Under 5 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates
Age groups:
Variables / Data Columns
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.
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/.
This dataset is a part of the main dataset for Cardiff Population by Age. You can refer the same here