Dataset population: All usual residents aged 16 and over in employment the week before the census

Location of where people live when working

The location in which an individual lives when they are working.

Place of work

The location in which an individual works.

Geographies of origin areas:

• UK Local Authorities (merged) 2011

Geographies of destination areas:

• UK Local Authorities (merged) 2011
• 2011 UK SWS additional workplace codes

For the area in which people live while they are working, if that address is a work-related second address that is outside of the UK then this is signified by code OD0000005.

*The following codes are used for area of workplace that is not an LAD geographic code:

OD0000001 = Mainly work at or from home

OD0000002 = Offshore installation

OD0000003 = No fixed place

OD0000004 = Outside UK*

Dataset

This dataset was created by Dwarika Teli ♾

Contents

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.

This dataset is about books. It has 6 rows and is filtered where the book is Where people live. It features 7 columns including author, publication date, language, and book publisher.

People supported to live independently through social services PACKAGES OF CARE - (Snapshot) *This indicator has been discontinued.

Most of the United States (U.S.) population live together in a few densely populated areas. While this is a well known fact, visual explanations of this characteristic can be quite striking. These four maps illustrate in different ways where we live, and how we actually inhabit so little of our country's space.Map 1 shows the coastal shoreline counties of the U.S., which are the counties that are directly adjacent to an open ocean, a major estuary, or the Great Lakes. According to 2014 Census data, 39.1 percent of the U.S. population lived in those counties, often within miles of the coast.Map 2 highlights the largest and smallest counties in the U.S. Roughly fifty percent of the U.S. population lives in the country's 144 largest counties, while the roughly other 50 percent lives in 2,998 counties.Map 3 compares America's two largest counties (Los Angeles and Downtown Chicago) with the 14 smallest states.Map 4 compares the population of these two counties with 1,437 of the country's smallest counties. Nearly five percent of America's population lives in the counties covering downtown Los Angeles and downtown Chicago, which is the same proportion as those that live in the country's 1,437 smallest counties.Source: Ana Swanson, Washington Post Wonkblog. September 3, 2015

This dataset provides annual numbers for each state in the United States for 2013-2018. Includes the following data: total population, median income, and number of people living at or below the poverty level.

Helpful information on using U.S. Census data is found at https://censusreporter.org/

High Resolution Live Attacks - Biometric Attack dataset

The anti spoofing dataset includes live-recorded Anti-Spoofing videos from around the world, captured via high-quality webcams with Full HD resolution and above. The videos were gathered by capturing faces of genuine individuals presenting spoofs, using facial presentations. Our dataset proposes a novel approach that learns and detects spoofing techniques, extracting features from the genuine facial images to prevent the capturing of such information by fake users.

The dataset is created on the basis of Phone and Webcam Video Dataset

The dataset contains images and videos of real humans with various views, and colors, making it a comprehensive resource for researchers working on anti-spoofing technologies.

👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset of 95,000+ human images & videos - Full dataset

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The dataset provides data to combine and apply different techniques, approaches, and models to address the challenging task of distinguishing between genuine and spoofed inputs, providing effective anti-spoofing solutions in active authentication systems. These solutions are crucial as newer devices, such as phones, have become vulnerable to spoofing attacks due to the availability of technologies that can create replays, reflections, and depths, making them susceptible to spoofing and generalization.

Our dataset also explores the use of neural architectures, such as deep neural networks, to facilitate the identification of distinguishing patterns and textures in different regions of the face, increasing the accuracy and generalizability of the anti-spoofing models.

Webcam Resolution

The collection of different video resolutions from Full HD (1080p) up to 4K (2160p) is provided, including several intermediate resolutions like QHD (1440p)

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Metadata

Each attack instance is accompanied by the following details:

• Unique attack identifier
• Identifier of the user recording the attack
• User's age
• User's gender
• User's country of origin
• Attack resolution

Additionally, the model of the webcam is also specified.

Metadata is represented in the file_info.csv.

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keywords: liveness detection systems, liveness detection dataset, biometric dataset, biometric data dataset, biometric system attacks, anti-spoofing dataset, face liveness detection, deep learning dataset, face spoofing database, face anti-spoofing, ibeta dataset, human video dataset, video dataset, high quality video dataset, hd video dataset, phone attack dataset, face anti spoofing, large-scale face anti spoofing, rich annotations anti spoofing dataset

People supported to live independently through social services PREVENTION - (Snapshot) *This indicator has been discontinued.

This indicator measures the number of adults aged 18-64/65+ per 1,000 population that are assisted directly through social services assessed/care planned, funded support to live independently, plus those supported through organisations that receive social services grant funded services. The indicator will be age standardised and, if possible, adjusted for likely needs for social care services using needs-weighted population data produced from Relative Needs Formula (RNF) allocation calculations. Issues remain on potential double counting between assessed services and grant funded services and within grant funded services that need to be resolved.

The dataset appears to provide information on the percentage of the total population living in urban agglomerations of more than 1 million people for various countries, spanning multiple years. The columns are: Entity: The name of the country. Code: The country code (likely ISO 3166-1 alpha-3). Year: The year of the data record. Population in urban agglomerations of more than 1 million (% of total population): The percentage of the total population living in urban areas with more than 1 million inhabitants.

This map compares the number of people living above the poverty line to the number of people living below. Why do this?There are people living below the poverty line everywhere. Nearly every area of the country has a balance of people living above the poverty line and people living below it. There is not an "ideal" balance, so this map makes good use of the national ratio of 6 persons living above the poverty line for every 1 person living below it. Please consider that there is constant movement of people above and below the poverty threshold, as they gain better employment or lose a job; as they encounter a new family situation, natural disaster, health issue, major accident or other crisis. There are areas that suffer chronic poverty year after year. This map does not indicate how long people in the area have been below the poverty line. "The poverty rate is one of several socioeconomic indicators used by policy makers to evaluate economic conditions. It measures the percentage of people whose income fell below the poverty threshold. Federal and state governments use such estimates to allocate funds to local communities. Local communities use these estimates to identify the number of individuals or families eligible for various programs." Source: U.S. Census BureauIn the U.S. overall, there are 6 people living above the poverty line for every 1 household living below. Green areas on the map have a higher than normal number of people living above compared to below poverty. Orange areas on the map have a higher than normal number of people living below the poverty line compared to those above in that same area.The map shows the ratio for counties and census tracts, using these layers, created directly from the U.S. Census Bureau's American Community Survey (ACS)For comparison, an older layer using 2013 ACS data is also provided.The layers are updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Poverty status is based on income in past 12 months of survey. Current Vintage: 2014-2018ACS Table(s): B17020Data downloaded from: Census Bureau's API for American Community Survey National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -555555...) have been set to null. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small. NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.

People supported to live independently through social services PREVENTION - (Snapshot)

As of 2024, approximately *** million people lived alone in the United Kingdom, an increase of around ****** when compared with the previous year when around **** million people were living by themselves.

Older people aged 65 or over helped to live at home per 1,000 population aged 65 or over Source: CSCI Performance Assessment Framework (PAF) Publisher: Commission for Social Care Inspection (CSCI) Geographies: County/Unitary Authority, Government Office Region (GOR), National Geographic coverage: England Time coverage: 2006/07 Type of data: Administrative data

People supported to live independently through social services PREVENTION - (Snapshot)

*This indicator has been discontinued.

This data set is pulled from the Ohio Life Births data set. It contains geographic and demographic data about live births in the state of Ohio.

Graph and download economic data for Estimate of People of All Ages in Poverty in Live Oak County, TX (PEAATX48297A647NCEN) from 1989 to 2023 about Live Oak County, TX; child; poverty; TX; persons; and USA.

Note: This dataset is no longer being maintained and will not be updated going forward.

The weekly and cumulative number of residents with confirmed COVID-19 and with COVID-19 associated deaths is obtained from data self-reported by individual assisted living facilities to the Long Term Care Mutual Aid Plan web-based reporting system (www.mutualaidplan.org/ct). Both confirmed and suspect deaths are included.

Confirmed deaths include those among persons who tested positive for COVID-19. Suspected deaths include those among persons with signs and symptoms suggestive of COVID-19 but who did not have a laboratory positive COVID-19 test. Due to differing data collection and processing methods between LTC-MAP and the death data sources used previously, cumulative death data for residents was re-baselined on July 14, 2020. The resident death data before and after July 14, 2020 should not be added due to the differing definitions of COVID-19 associated deaths used and the possibility of duplication of deaths among prior and current data.

The cumulative number of deaths among assisted living residents is based upon data reported by the Office of the Chief Medical Examiner. For public health surveillance, COVID-19-associated deaths include persons who tested positive for COVID-19 around the time of death (laboratory-confirmed) and persons whose death certificate lists COVID-19 disease as a cause of death or a significant condition contributing to death (probable). As of 7/15/20 deaths reported by the Office of the Chief Medical Examiner are no longer being updated on a weekly basis.

Graph and download economic data for Estimate of People Age 0-17 in Poverty in Live Oak County, TX (PEU18TX48297A647NCEN) from 1989 to 2023 about Live Oak County, TX; under 18 years; child; poverty; TX; persons; and USA.