Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents median household incomes for various household sizes in England, AR, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.
Key observations
https://i.neilsberg.com/ch/england-ar-median-household-income-by-household-size.jpeg" alt="England, AR median household income, by household size (in 2022 inflation-adjusted dollars)">
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Household Sizes:
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 England median household income. You can refer the same here
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically
National and subnational mid-year population estimates for the UK and its constituent countries by administrative area, age and sex (including components of population change, median age and population density).
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents median household incomes for various household sizes in Kentucky, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.
Key observations
https://i.neilsberg.com/ch/kentucky-median-household-income-by-household-size.jpeg" alt="Kentucky median household income, by household size (in 2022 inflation-adjusted dollars)">
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Household Sizes:
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 Kentucky median household income. You can refer the same here
This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.
List of the data tables as part of the Immigration System Statistics Home Office release. Summary and detailed data tables covering the immigration system, including out-of-country and in-country visas, asylum, detention, and returns.
If you have any feedback, please email MigrationStatsEnquiries@homeoffice.gov.uk.
The Microsoft Excel .xlsx files may not be suitable for users of assistive technology.
If you use assistive technology (such as a screen reader) and need a version of these documents in a more accessible format, please email MigrationStatsEnquiries@homeoffice.gov.uk
Please tell us what format you need. It will help us if you say what assistive technology you use.
Immigration system statistics, year ending March 2025
Immigration system statistics quarterly release
Immigration system statistics user guide
Publishing detailed data tables in migration statistics
Policy and legislative changes affecting migration to the UK: timeline
Immigration statistics data archives
https://assets.publishing.service.gov.uk/media/68258d71aa3556876875ec80/passenger-arrivals-summary-mar-2025-tables.xlsx">Passenger arrivals summary tables, year ending March 2025 (MS Excel Spreadsheet, 66.5 KB)
‘Passengers refused entry at the border summary tables’ and ‘Passengers refused entry at the border detailed datasets’ have been discontinued. The latest published versions of these tables are from February 2025 and are available in the ‘Passenger refusals – release discontinued’ section. A similar data series, ‘Refused entry at port and subsequently departed’, is available within the Returns detailed and summary tables.
https://assets.publishing.service.gov.uk/media/681e406753add7d476d8187f/electronic-travel-authorisation-datasets-mar-2025.xlsx">Electronic travel authorisation detailed datasets, year ending March 2025 (MS Excel Spreadsheet, 56.7 KB)
ETA_D01: Applications for electronic travel authorisations, by nationality
ETA_D02: Outcomes of applications for electronic travel authorisations, by nationality
https://assets.publishing.service.gov.uk/media/68247953b296b83ad5262ed7/visas-summary-mar-2025-tables.xlsx">Entry clearance visas summary tables, year ending March 2025 (MS Excel Spreadsheet, 113 KB)
https://assets.publishing.service.gov.uk/media/682c4241010c5c28d1c7e820/entry-clearance-visa-outcomes-datasets-mar-2025.xlsx">Entry clearance visa applications and outcomes detailed datasets, year ending March 2025 (MS Excel Spreadsheet, 29.1 MB)
Vis_D01: Entry clearance visa applications, by nationality and visa type
Vis_D02: Outcomes of entry clearance visa applications, by nationality, visa type, and outcome
Additional dat
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically
Numbers of enterprises and local units produced from a snapshot of the Inter-Departmental Business Register (IDBR) taken on 8 March 2024.
CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
This dataset contains key characteristics about the data described in the Data Descriptor A synthetic population dataset for estimating small area health and socio-economic outcomes in Great Britain. Contents:
1. human readable metadata summary table in CSV format
2. machine readable metadata file in JSON format
SUMMARYTo be viewed on combination with the dataset ‘Area of accessible green and blue space per 1000 population (England)’ and its associated metadata.This dataset identifies administrative areas for which Public Right of Way (PRoW) data was not available. While some gaps in the PRoW data will have been partially filled in by the OS MasterMap Highways Network Paths dataset, due to overlap between the two, some gaps will still remain. The area of accessible green/blue space in the areas highlighted by this dataset could be slightly under represented in the ‘Area of accessible green and blue space per 1000 population (England)’ dataset.COPYRIGHT NOTICEProduced by Ribble Rivers Trust. Contains Ordnance Survey data © Crown copyright and database right 2020. Contains public sector information licensed under the Open Government Licence v3.0.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
United Kingdom UK: Urban Land Area data was reported at 58,698.750 sq km in 2010. This stayed constant from the previous number of 58,698.750 sq km for 2000. United Kingdom UK: Urban Land Area data is updated yearly, averaging 58,698.750 sq km from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 58,698.750 sq km in 2010 and a record low of 58,698.750 sq km in 2010. United Kingdom UK: Urban Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Land Use, Protected Areas and National Wealth. Urban land area in square kilometers, based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Sum;
This dataset contains gridded human population with a spatial resolution of 1 km x 1 km for the UK based on Census 2021 (Census 2022 for Scotland) and Land Cover Map 2021 input data. Data on population distribution for the United Kingdom is available from statistical offices in England, Wales, Northern Ireland and Scotland and provided to the public e.g. via the Office for National Statistics (ONS). Population data is typically provided in tabular form or, based on a range of different geographical units, in file types for geographical information systems (GIS), for instance as ESRI Shapefiles. The geographical units reflect administrative boundaries at different levels of detail, from Devolved Administration to Output Areas (OA), wards or intermediate geographies. While the presentation of data on the level of these geographical units is useful for statistical purposes, accounting for spatial variability for instance of environmental determinants of public health requires a more spatially homogeneous population distribution. For this purpose, the dataset presented here combines 2021/2022 UK Census population data on Output Area level with Land Cover Map 2021 land-use classes 'urban' and 'suburban' to create a consistent and comprehensive gridded population data product at 1 km x 1 km spatial resolution. The mapping product is based on British National Grid (OSGB36 datum).
Success.ai’s Company Data Solutions provide businesses with powerful, enterprise-ready B2B company datasets, enabling you to unlock insights on over 28 million verified company profiles. Our solution is ideal for organizations seeking accurate and detailed B2B contact data, whether you’re targeting large enterprises, mid-sized businesses, or small business contact data.
Success.ai offers B2B marketing data across industries and geographies, tailored to fit your specific business needs. With our white-glove service, you’ll receive curated, ready-to-use company datasets without the hassle of managing data platforms yourself. Whether you’re looking for UK B2B data or global datasets, Success.ai ensures a seamless experience with the most accurate and up-to-date information in the market.
API Features:
Why Choose Success.ai’s Company Data Solution? At Success.ai, we prioritize quality and relevancy. Every company profile is AI-validated for a 99% accuracy rate and manually reviewed to ensure you're accessing actionable and GDPR-compliant data. Our price match guarantee ensures you receive the best deal on the market, while our white-glove service provides personalized assistance in sourcing and delivering the data you need.
Why Choose Success.ai?
Our database spans 195 countries and covers 28 million public and private company profiles, with detailed insights into each company’s structure, size, funding history, and key technologies. We provide B2B company data for businesses of all sizes, from small business contact data to large corporations, with extensive coverage in regions such as North America, Europe, Asia-Pacific, and Latin America.
Comprehensive Data Points: Success.ai delivers in-depth information on each company, with over 15 data points, including:
Company Name: Get the full legal name of the company. LinkedIn URL: Direct link to the company's LinkedIn profile. Company Domain: Website URL for more detailed research. Company Description: Overview of the company’s services and products. Company Location: Geographic location down to the city, state, and country. Company Industry: The sector or industry the company operates in. Employee Count: Number of employees to help identify company size. Technologies Used: Insights into key technologies employed by the company, valuable for tech-based outreach. Funding Information: Track total funding and the most recent funding dates for investment opportunities. Maximize Your Sales Potential: With Success.ai’s B2B contact data and company datasets, sales teams can build tailored lists of target accounts, identify decision-makers, and access real-time company intelligence. Our curated datasets ensure you’re always focused on high-value leads—those who are most likely to convert into clients. Whether you’re conducting account-based marketing (ABM), expanding your sales pipeline, or looking to improve your lead generation strategies, Success.ai offers the resources you need to scale your business efficiently.
Tailored for Your Industry: Success.ai serves multiple industries, including technology, healthcare, finance, manufacturing, and more. Our B2B marketing data solutions are particularly valuable for businesses looking to reach professionals in key sectors. You’ll also have access to small business contact data, perfect for reaching new markets or uncovering high-growth startups.
From UK B2B data to contacts across Europe and Asia, our datasets provide global coverage to expand your business reach and identify new...
Update 29-04-2020: The data is now split into two files based on the variable collection frequency (monthly and yearly). Additional variables added: area size in hectares, number of jobs in the area, number of people living in the area.
I have been inspired by Xavier and his work on Barcelona to explore the city of London! 🇬🇧 💂
The datasets is primarily centered around the housing market of London. However, it contains a lot of additional relevant data: - Monthly average house prices - Yearly number of houses - Yearly number of houses sold - Yearly percentage of households that recycle - Yearly life satisfaction - Yearly median salary of the residents of the area - Yearly mean salary of the residents of the area - Monthly number of crimes committed - Yearly number of jobs - Yearly number of people living in the area - Area size in hectares
The data is split by areas of London called boroughs (a flag exists to identify these), but some of the variables have other geographical UK regions for reference (like England, North East, etc.). There have been no changes made to the data except for melting it into a long format from the original tables.
The data has been extracted from London Datastore. It is released under UK Open Government License v2 and v3. The underlining datasets can be found here: https://data.london.gov.uk/dataset/uk-house-price-index https://data.london.gov.uk/dataset/number-and-density-of-dwellings-by-borough https://data.london.gov.uk/dataset/subjective-personal-well-being-borough https://data.london.gov.uk/dataset/household-waste-recycling-rates-borough https://data.london.gov.uk/dataset/earnings-place-residence-borough https://data.london.gov.uk/dataset/recorded_crime_summary https://data.london.gov.uk/dataset/jobs-and-job-density-borough https://data.london.gov.uk/dataset/ons-mid-year-population-estimates-custom-age-tables
Cover photo by Frans Ruiter from Unsplash
The dataset lends itself for extensive exploratory data analysis. It could also be a great supervised learning regression problem to predict house price changes of different boroughs over time.
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically
The mid-year estimates refer to the population on 30 June of the reference year and are produced in line with the standard United Nations (UN) definition for population estimates. They are the official set of population estimates for the UK and its constituent countries, the regions and counties of England, and local authorities and their equivalents.
https://eidc.ceh.ac.uk/licences/OGL/plainhttps://eidc.ceh.ac.uk/licences/OGL/plain
http://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/noLimitationshttp://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/noLimitations
The data comprise a long-term study of alpine plant community dynamics in the Gunnison National Forest of Colorado. The data comprise annual census data for all plants (including seedlings) in each of 50 2x2m plots, including information on size, reproduction, life stage, and mortality, with all plants identified and geo-located. These data are also made available transformed to provide individual-level estimates of growth, survival, fecundity, and recruitment. The dataset covers several thousand individuals of approximately twenty species, and highlights an apparent pattern of demographic decline. The data also include information on microenvironment / microedaphic variation at 2 m resolution covering the entire research site, including information on temperatures, topography, soil chemistry, soil texture, and other variables. The data also include information on the functional traits of many of the species present at the site, including information on biomass allocation, leaf traits, root traits, seed traits, and floral traits. Full details about this dataset can be found at https://doi.org/10.5285/d850fcd2-b70a-415e-acf4-fc27b38d59c1
This statistical data set includes information on education and training participation and achievements broken down into a number of reports including sector subject areas, participation by gender, age, ethnicity, disability participation.
It also includes data on offender learning.
If you need help finding data please refer to the table finder tool to search for specific breakdowns available for FE statistics.
<p class="gem-c-attachment_metadata"><span class="gem-c-attachment_attribute">MS Excel Spreadsheet</span>, <span class="gem-c-attachment_attribute">33 MB</span></p>
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This dataset includes information on native eyebright plants (Euphrasia, Orobanchaceae) studied and measured at a range of sites across Britain and Ireland, with a special sampling focus on Fair Isle (Shetland, Scotland). Attributes measured are location information (Euphrasia species, coordinates and population description), individual plant trait data (including measures of floral and vegetative traits) and genome sizes. Full details about this dataset can be found at https://doi.org/10.5285/0a77d3b5-03ce-4a14-ab2d-acc4aa7bd0ef
The vertical land change activity focuses on the detection, analysis, and explanation of topographic change. These detection techniques include both quantitative methods, for example, using difference metrics derived from multi-temporal topographic digital elevation models (DEMs), such as, light detection and ranging (lidar), National Elevation Dataset (NED), Shuttle Radar Topography Mission (SRTM), and Interferometric Synthetic Aperture Radar (IFSAR), and qualitative methods, for example, using multi-temporal aerial photography to visualize topographic change. The geographic study area of this activity is Perry County, Kentucky. Available multi-temporal lidar, NED, SRTM, IFSAR, and other topographic elevation datasets, as well as aerial photography and multi-spectral image data were identified and downloaded for this study area county. Available mine maps and mine portal locations were obtained from the Kentucky Mine Mapping Information System, Division of Mine Safety, 300 Sower Boulevard, Frankfort, KY 40601 at http://minemaps.ky.gov/Default.aspx?Src=Downloads. These features were used to spatially locate the study areas within Perry County. Previously developed differencing methods (Gesch, 2006) were used to develop difference raster datasets of NED/SRTM (1950-2000 date range) and SRTM/IFSAR (2000-2008 date range). The difference rasters were evaluated to exclude difference values that were below a specified vertical change threshold, which was applied spatially by National Land Cover Dataset (NLCD) 1992 and 2006 land cover type, respectively. This spatial application of the vertical change threshold values improved the overall ability to detect vertical change because threshold values in bare earth areas were distinguished from threshold values in heavily vegetated areas. Lidar high-resolution (1.5 m) DEMs were acquired for Perry County, Kentucky from U.S. Department of Agriculture, Natural Resources Conservation Service Geospatial Data Gateway at https://gdg.sc.egov.usda.gov/GDGOrder.aspx#. ESRI Mosaic Datasets were generated from lidar point-cloud data and available topographic DEMs for the specified study area. These data were analyzed to estimate volumetric changes on the land surface at three different periods with lidar acquisitions collected for Perry County, KY on 3/29/12 to 4/6/12. A recent difference raster dataset time span (2008-2012 date range) was analyzed by differencing the Perry County lidar-derived DEM and an IFSAR-derived dataset. The IFSAR-derived data were resampled to the resolution of the lidar DEM (approximately 1-m resolution) and compared with the lidar-derived DEM. Land cover based threshold values were applied spatially to detect vertical change using the lidar/IFSAR difference dataset. Perry County lidar metadata reported that the acquisition required lidar to be collected with an average of 0.68 m point spacing or better and vertical accuracy of 15 cm root mean square error (RMSE) or better. References: Gesch, Dean B., 2006, An inventory and assessment of significant topographic changes in the United States Brookings, S. Dak., South Dakota State University, Ph.D. dissertation, 234 p, at https://topotools.cr.usgs.gov/pdfs/DGesch_dissertation_Nov2006.pdf.
Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
License information was derived automatically
Labour Force Survey (LFS) estimates including measures of uncertainty of the number of households by household size, for regions of England and also Scotland and Wales.
Big Data Security Market Size 2025-2029
The big data security market size is forecast to increase by USD 23.9 billion, at a CAGR of 15.7% between 2024 and 2029.
The market is driven by stringent regulations mandating data protection and an increasing focus on automation in big data security. With the growing volume and complexity of data, organizations are investing significantly in advanced security solutions to mitigate risks and ensure compliance. However, implementing these solutions comes with high financial requirements, posing a challenge for smaller businesses and budget-constrained organizations. Regulations, such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), have intensified the need for robust data security measures. These regulations demand that organizations protect sensitive data from unauthorized access, use, or disclosure.
As a result, companies are investing in big data security solutions that offer advanced encryption, access control, and threat detection capabilities. Another trend in the market is the automation of big data security processes. With the increasing volume and velocity of data, manual security processes are no longer sufficient. Automation helps organizations to respond quickly to threats and maintain continuous security monitoring. However, the high cost of implementing and maintaining these automated solutions can be a significant challenge for many organizations. Intruders, ransomware attacks, unauthorized users, and other threats pose a constant risk to valuable information, intellectual property (IP), and transactional data.
What will be the Size of the Big Data Security Market during the forecast period?
Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by the increasing volume and complexity of data being generated and collected across various sectors. Data governance is a critical aspect of this market, ensuring the secure handling and protection of valuable information. Blue teaming, a collaborative approach to cybersecurity, plays a crucial role in identifying and mitigating threats in real-time. Risk assessment and incident response are ongoing processes that help organizations prepare for and respond to data breaches. Security monitoring, powered by advanced technologies like AI in cybersecurity, plays a vital role in detecting and responding to threats. Data masking and anonymization are essential techniques for protecting sensitive data while maintaining its usability.
Network security, cloud security, and database security are key areas of focus, with ongoing threats requiring continuous vigilance. Threat intelligence and vulnerability management help organizations stay informed about potential risks and prioritize their response efforts. Disaster recovery and business continuity planning are also essential components of a robust security strategy. Cybersecurity insurance, security auditing, access control, penetration testing, and vulnerability scanning are additional services that help organizations fortify their defenses. Zero trust security and application security are emerging areas of focus, reflecting the evolving threat landscape. The market dynamics in this space are continuously unfolding, with new challenges and solutions emerging regularly.
How is this Big Data Security Industry segmented?
The big data security industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.
Deployment
On-premises
Cloud-based
End-user
Large enterprises
SMEs
Solution
Software
Services
Geography
North America
US
Canada
Europe
France
Germany
Italy
Spain
UK
APAC
China
India
Japan
Rest of World (ROW)
By Deployment Insights
The On-premises segment is estimated to witness significant growth during the forecast period. The market: Evolution and Trends in Enterprise Computing Big Data Security encompasses a range of technologies and practices designed to protect an organization's valuable data. Traditional on-premises servers form the backbone of many enterprise data infrastructures, with businesses owning and managing their hardware and software. These infrastructures include servers and storage units, located at secure sites, requiring specialized IT support for maintenance. Data security in this context is a top priority. Companies must establish user access policies, install firewalls and antivirus software, and apply security patches promptly. Network security is crucial, with vulnerability management and threat
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset tabulates the median household income in England. It can be utilized to understand the trend in median household income and to analyze the income distribution in England by household type, size, and across various income brackets.
The dataset will have the following datasets when applicable
Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).
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/.
Explore our comprehensive data analysis and visual representations for a deeper understanding of England median household income. 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 presents median household incomes for various household sizes in England, AR, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.
Key observations
https://i.neilsberg.com/ch/england-ar-median-household-income-by-household-size.jpeg" alt="England, AR median household income, by household size (in 2022 inflation-adjusted dollars)">
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.
Household Sizes:
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 England median household income. You can refer the same here