Rank and count of the top names for baby girls, changes in rank since the previous year and breakdown by country, region, mother's age and month of birth.

Rank and count of the top names for baby boys, changes in rank since the previous year and breakdown by country, region, mother's age and month of birth.

Distribution of first and last name frequencies of academic authors by country.

Spreadsheet 1 contains 50 countries, with names based on affiliations in Scopus journal articles 2001-2021.

Spreadsheet 2 contains 200 countries, with names based on affiliations in Scopus journal articles 2001-2021, using a marginally updated last name extraction algorithm that is almost the same except for Dutch/Flemish names.

From the paper: Can national researcher mobility be tracked by first or last name uniqueness?

For example the distribution for the UK shows a single peak for international names, with no national names, Belgium has a national peak and an international peak, and China has mainly a national peak. The 50 countries are:

No Code Country 1 SB Serbia 2 IE Ireland 3 HU Hungary 4 CL Chile 5 CO Columbia 6 NG Nigeria 7 HK Hong Kong 8 AR Argentina 9 SG Singapore 10 NZ New Zealand 11 PK Pakistan 12 TH Thailand 13 UA Ukraine 14 SA Saudi Arabia 15 RO Israel 16 ID Indonesia 17 IL Israel 18 MY Malaysia 19 DK Denmark 20 CZ Czech Republic 21 ZA South Africa 22 AT Austria 23 FI Finland 24 PT Portugal 25 GR Greece 26 NO Norway 27 EG Egypt 28 MX Mexico 29 BE Belgium 30 CH Switzerland 31 SW Sweden 32 PL Poland 33 TW Taiwan 34 NL Netherlands 35 TK Turkey 36 IR Iran 37 RU Russia 38 AU Australia 39 BR Brazil 40 KR South Korea 41 ES Spain 42 CA Canada 43 IT France 44 FR France 45 IN India 46 DE Germany 47 US USA 48 UK UK 49 JP Japan 50 CN China

This dataset comprises a corpus of names, in both the first and middle position, for approximately 22 million individuals born in England and Wales between 1838 and 2014. This data is obtained from birth records made available by a set of volunteer-run genealogical resources - collectively, the 'UK local BMD project' (http://www.ukbmd.org.uk/local) - and has been re-purposed here to demonstrate the applicability of network analysis methods to an onomastic dataset. The ownership and licensing of the intellectual property constituting the original birth records is detailed at https://www.ukbmd.org.uk/TermsAndConditions. Under section 29A of the UK Copyright, Designs and Patents Act 1988, a copyright exception permits copies to be made of lawfully accessible material in order to conduct text and data mining for non-commercial research. The data included in this dataset represents the outcome of such a text-mining analysis. No birth records are included in this dataset, and nor is it possible for records to be reconstructed from the data presented herein. The data comprises an archive of tables, presenting this corpus in various forms: as a rank order of names (in both the first and middle position) by number of registered births per year, and by the total number of births across all years sampled. An overview of the data is also provided, with summary statistics such as the number of usable records registered per year, most popular names per year, and measures of forename diversity and the surname-to-forename usage ratio (an indicator of which forenames are more likely to be transferred uses of surnames). These tables are extensive but not exhaustive, and do not exclude the possibility that errors are present in the corpus. Data are also presented both as '.expression' files (an input format readable by the network analysis tool Graphia Professional) and as '.layout' files, a text file format output by Graphia Professional that describes the characteristics of the network so that it may be replicated. Characteristics of the original birth records that allow the identification of individuals - for instance, full name or location of birth - have been removed.

This dataset is an analysis of the Characteristics of usual residents by whether they have previously served in the UK armed forces, with adjusted estimates for the non-veteran population, based on Census 2021.

People who have previously served in the UK armed forces includes those who have served for at least one day in HM’s Armed Forces, either regular or reserves, or Merchant Mariners who have seen duty on legally defined military operations. It does not include those who have left and since re-entered the regular or reserve UK armed forces, those who have only served in.

The veteran population are older than the general population and also differ in relation to sex and where they live, and these factors interact with other personal characteristics. For example, age can be strongly related to other personal characteristics such as legal partnership status, health and religion. Because of this, veterans may also differ to the usual or non-veteran population when considering related factors such as health and legal partnership status. It is important to be aware of these differences but also to understand when these differences are not attributable to experience of having previously served in the UK armed forces.

Country of birth

The country in which a person was born. For people not born in one of in the four parts of the UK, there was an option to select "elsewhere". People who selected "elsewhere" were asked to write in the current name for their country of birth.

Ethnic group and high-level ethnic group

The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity or physical appearance. Respondents could choose one out of 19 tick-box response categories, including write-in response options. High-level ethnic group refers to the first stage of the two-stage ethnic group question. High-level groups refer to the first stage where the respondent identifies through one of the following options: * "Asian, Asian British, Asian Welsh" * "Black, Black British, Black Welsh, Caribbean or African" * "Mixed or Multiple" * "White" * "Other ethnic group"

General health

A person's assessment of the general state of their health from very good to very bad. This assessment is not based on a person's health over any specified period of time.

Legal partnership status

Classifies a person according to their legal marital or registered civil partnership status on Census Day 21 March 2021.

Gender identity

Gender identity refers to a person’s sense of their own gender, whether male, female or another category such as non-binary. This may or may not be the same as their sex registered at birth.

Religion

The religion people connect or identify with (their religious affiliation), whether or not they practice or have belief in it. This question was voluntary, and the variable includes people who answered the question, including “No religion”, alongside those who chose not to answer this question. This variable classifies responses into the eight tick-box response options. Write-in responses are classified by their "parent" religious affiliation, including “No religion”, where applicable.

Sexual orientation

Sexual orientation is an umbrella term covering sexual identity, attraction, and behaviour. For an individual respondent, these may not be the same. For example, someone in an opposite-sex relationship may also experience same-sex attraction, and vice versa. This means the statistics should be interpreted purely as showing how people responded to the question, rather than being about whom they are attracted to or their actual relationships. We have not provided glossary entries for individual sexual orientation categories. This is because individual respondents may have differing perspectives on the exact meaning.

Usual resident

A usual resident is anyone who on Census Day, 21 March 2021, was in the UK and had stayed or intended to stay in the UK for a period of 12 months or more, or had a permanent UK address and was outside the UK and intended to be outside the UK for less than 12 months.

UK armed forces veteran

People who have previously served in the UK armed forces. This includes those who have served for at least one day in HM’s Armed Forces, either regular or reserves, or Merchant Mariners who have seen duty on legally defined military operations. It does not include those who have left and since re-entered the regular or reserve UK armed forces, those who have only served in foreign armed forces, or those who have served in the UK armed forces and are currently living outside of England and Wales.

Historic lists of top 100 names for baby boys and girls for 1904 to 1994 at 10-yearly intervals.

This dataset provides a list of surnames that are reliably Irish and that can be used for identifying textual references to Irish individuals in the London area and surrounding countryside within striking distance of the capital. This classification of the Irish necessarily includes the Irish-born and their descendants. The dataset has been validated for use on records up to the middle of the nineteenth century, and should only be used in cases in which a few mis-classifications of individuals would not undermine the results of the work, such as large-scale analyses. These data were created through an analysis of the 1841 Census of England and Wales, and validated against the Middlesex Criminal Registers (National Archives HO 26) and the Vagrant Lives Dataset (Crymble, Adam et al. (2014). Vagrant Lives: 14,789 Vagrants Processed by Middlesex County, 1777-1786. Zenodo. 10.5281/zenodo.13103). The sample was derived from the records of the Hundred of Ossulstone, which included much of rural and urban Middlesex, excluding the City of London and Westminster. The analysis was based upon a study of 278,949 adult males. Full details of the methodology for how this dataset was created can be found in the following article, and anyone intending to use this dataset for scholarly research is strongly encouraged to read it so that they understand the strengths and limits of this resource:

Adam Crymble, 'A Comparative Approach to Identifying the Irish in Long Eighteenth Century London', _Historical Methods: A Journal of Quantitative and Interdisciplinary History_, vol. 48, no. 3 (2015): 141-152.

The data here provided includes all 283 names listed in Appendix I of the above paper, but also an additional 209 spelling variations of those root surnames, for a total of 492 names.

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 Great Britain Historical Database has been assembled as part of the ongoing Great Britain Historical GIS Project. The project aims to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain at sub-county scales. Further information about the project is available on A Vision of Britain webpages, where users can browse the database's documentation system online.

These data were originally collected by the Censuses of Population for England and Wales, and for Scotland. They were computerised by the Great Britain Historical GIS Project and its collaborators. They form part of the Great Britain Historical Database, which contains a wide range of geographically-located statistics, selected to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain, generally at sub-county scales.

The census gathered data on levels of educational attainment only from 1951. In 1951 and 1961, attainment was measured simply by the age at which a person's education was completed, rather than by the level of qualifications achieved. These data cover, broadly, the adult population, including many people who had completed their education decades before the relevant census, so the data are indicative of the general level of education of the workforce at the census date, but are a problematic guide to the performance of the education system at that date. The census reports also include cross-tabulations of age of education completion with current age, but not with the level of geographical detail of the transcribed tables.

The 1951 data for England and Wales were computerised by Danny Dorling (now of Oxford University), as part of research funded by the Joseph Rowntree Foundation.

Latest edition information:

For the 2nd edition (June 2021), data for Scotland for 1951 and data for England & Wales and Scotland for 1961 have been added to the study.

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This Special Licence access dataset contains names and addresses from the Integrated Census Microdata (I-CeM) dataset of the censuses of Great Britain for the period 1851 to 1911. These data are made available under Special Licence (SL) access conditions due to commercial sensitivity.

The anonymised main I-CeM database that complements these names and addresses is available under SN 7481. It comprises the Censuses of Great Britain for the period 1851-1911; data are available for England and Wales for 1851-1861 and 1881-1911 (1871 is not currently available for England and Wales) and for Scotland for 1851-1901 (1911 is not currently available for Scotland). The database contains over 180 million individual census records and was digitised and harmonised from the original census enumeration books. It details characteristics for all individuals resident in Great Britain at each of the included Censuses. The original digital data has been coded and standardised; the I-CeM database has consistent geography over time and standardised coding schemes for many census variables.

This dataset of names and addresses for individual census records is organised per country (England and Wales; Scotland) and per census year. Within each data file each census record contains first and last name, street address and an individual identification code (RecID) that allows linking with the corresponding anonymised I-CeM record. The data cannot be used for true linking of individual census records across census years for commercial genealogy purposes nor for any other commercial purposes. The SL arrangements are required to ensure that commercial sensitivity is protected. For information on making an application, see the Access section.

The data were updated in February 2020, with some files redeposited with longer field length limits. Users should note that some name and address fields are truncated due to the limits set by the LDS project that transcribed the original data. No more than 10,000 records out of some 210 million across the study should be affected. Examples include:

• England and Wales:
  • 1851 - truncated at the 24th character (maximum I-CeM field length 95 characters)
  • 1881 - truncated at the 16th character (maximum I-CeM field length 50 characters).
• Scotland: for 1851‐71, truncations affect less than 0.01% of all addresses and for 1851 around 1% at most
  • 1851 - truncated at the 70th character
  • 1861 - truncated at the 76th character
  • 1871 - truncated at the 82th character
  • 1881 - truncated at the 50th character.

Further information about I-CeM can be found on the I-CeM Integrated Microdata Project and I-CeM Guide webpages.

Context

E-commerce has become a new channel to support businesses development. Through e-commerce, businesses can get access and establish a wider market presence by providing cheaper and more efficient distribution channels for their products or services. E-commerce has also changed the way people shop and consume products and services. Many people are turning to their computers or smart devices to order goods, which can easily be delivered to their homes.

Content

This is a sales transaction data set of UK-based e-commerce (online retail) for one year. This London-based shop has been selling gifts and homewares for adults and children through the website since 2007. Their customers come from all over the world and usually make direct purchases for themselves. There are also small businesses that buy in bulk and sell to other customers through retail outlet channels.

The data set contains 500K rows and 8 columns. The following is the description of each column. 1. TransactionNo (categorical): a six-digit unique number that defines each transaction. The letter “C” in the code indicates a cancellation. 2. Date (numeric): the date when each transaction was generated. 3. ProductNo (categorical): a five or six-digit unique character used to identify a specific product. 4. Product (categorical): product/item name. 5. Price (numeric): the price of each product per unit in pound sterling (£). 6. Quantity (numeric): the quantity of each product per transaction. Negative values related to cancelled transactions. 7. CustomerNo (categorical): a five-digit unique number that defines each customer. 8. Country (categorical): name of the country where the customer resides.

There is a small percentage of order cancellation in the data set. Most of these cancellations were due to out-of-stock conditions on some products. Under this situation, customers tend to cancel an order as they want all products delivered all at once.

Inspiration

Information is a main asset of businesses nowadays. The success of a business in a competitive environment depends on its ability to acquire, store, and utilize information. Data is one of the main sources of information. Therefore, data analysis is an important activity for acquiring new and useful information. Analyze this dataset and try to answer the following questions. 1. How was the sales trend over the months? 2. What are the most frequently purchased products? 3. How many products does the customer purchase in each transaction? 4. What are the most profitable segment customers? 5. Based on your findings, what strategy could you recommend to the business to gain more profit?

Photo by CardMapr on Unsplash

This dataset is about books. It has 1 row and is filtered where the book is The condition and portion of the people of England ; A lamentation over the ruins of this oppressed nation ; Canst thou prove in all scripture ; Several queries to be answered by Thomas Ledgard ; To all of the faithless generation ; To all of the worlds professors and people ; The baptism of Christ we own. It features 7 columns including author, publication date, language, and book publisher.

This database, and the accompanying website called ‘SurgeWatch’ (http://surgewatch.stg.rlp.io), provides a systematic UK-wide record of high sea level and coastal flood events over the last 100 years (1915-2014). Derived using records from the National Tide Gauge Network, a dataset of exceedence probabilities from the Environment Agency and meteorological fields from the 20th Century Reanalysis, the database captures information of 96 storm events that generated the highest sea levels around the UK since 1915. For each event, the database contains information about: (1) the storm that generated that event; (2) the sea levels recorded around the UK during the event; and (3) the occurrence and severity of coastal flooding as consequence of the event. The data are presented to be easily assessable and understandable to a wide range of interested parties. The database contains 100 files; four CSV files and 96 PDF files. Two CSV files contain the meteorological and sea level data for each of the 96 events. A third file contains the list of the top 20 largest skew surges at each of the 40 study tide gauge site. In the file containing the sea level and skew surge data, the tide gauge sites are numbered 1 to 40. A fourth accompanying CSV file lists, for reference, the site name and location (longitude and latitude). A description of the parameters in each of the four CSV files is given in the table below. There are also 96 separate PDF files containing the event commentaries. For each event these contain a concise narrative of the meteorological and sea level conditions experienced during the event, and a succinct description of the evidence available in support of coastal flooding, with a brief account of the recorded consequences to people and property. In addition, these contain graphical representation of the storm track and mean sea level pressure and wind fields at the time of maximum high water, the return period and skew surge magnitudes at sites around the UK, and a table of the date and time, offset return period, water level, predicted tide and skew surge for each site where the 1 in 5 year threshold was reached or exceeded for each event. A detailed description of how the database was created is given in Haigh et al. (2015). Coastal flooding caused by extreme sea levels can be devastating, with long-lasting and diverse consequences. The UK has a long history of severe coastal flooding. The recent 2013-14 winter in particular, produced a sequence of some of the worst coastal flooding the UK has experienced in the last 100 years. At present 2.5 million properties and £150 billion of assets are potentially exposed to coastal flooding. Yet despite these concerns, there is no formal, national framework in the UK to record flood severity and consequences and thus benefit an understanding of coastal flooding mechanisms and consequences. Without a systematic record of flood events, assessment of coastal flooding around the UK coast is limited. The database was created at the School of Ocean and Earth Science, National Oceanography Centre, University of Southampton with help from the Faculty of Engineering and the Environment, University of Southampton, the National Oceanography Centre and the British Oceanographic Data Centre. Collation of the database and the development of the website was funded through a Natural Environment Research Council (NERC) impact acceleration grant. The database contributes to the objectives of UK Engineering and Physical Sciences Research Council (EPSRC) consortium project FLOOD Memory (EP/K013513/1).

This dataset provides Census 2021 estimates that classify households with usual residents in England and Wales by various household characteristics, including variations in tenure by household size, household family composition, multi-generational households, and household level information on the age, ethnic group, religion, employment status and occupation of household members. The estimates are as at Census Day, 21 March 2021.

These datasets are part of Household characteristics by tenure, England and Wales: Census 2021, a release of results from the 2021 Census for England and Wales. Figures may differ slightly in future releases because of the impact of removing rounding and applying further statistical processes.

Total counts for some household groups may not match between published tables. This is to protect the confidentiality of households' data. Household counts have been rounded to the nearest 5 and any counts below 10 were suppressed; this is signified by a 'c' in the data tables.

This dataset uses middle layer super output area (MSOA) and lower layer super output area (LSOA) geography boundaries as of 2021 and local authority district geography boundaries as of 2022.

In this dataset, the number of households in an area is broken down by different variables and categories. If you were to sum the counts of households by each variable and category, it may not sum to the total of households in that area. This is because of rounding, suppression and that some tables only include data for certain household groups.

In this dataset, variables may have different categories for different geography levels. When variables are broken down by more categories, they may not sum to the total of the higher level categories due to rounding and suppression.

Social rent is not separated into “housing association, housing co-operative, charitable trust, registered social landlord” and “council or local authority districts” because of respondent error in identifying the type of landlord. This is particularly clear in results for areas which have no local authority districts housing stock, but there are households responding as having a “council or local authority districts” landlord type. Estimates are likely to be accurate when the social rent category is combined.

The Census Quality and Methodology Information report contains important information on:

• the uses and users of the census data
• the strengths and limitations of the census data
• the quality characteristics of the census data
• the methods used to produce the census data

Quality notes can be found here

Housing quality information for Census 2021 can be found here

Household

A household is defined as one person living alone, or a group of people (not necessarily related) living at the same address who share cooking facilities and a living room, sitting room or dining area. This includes all sheltered accommodation units in an establishment (irrespective of whether there are other communal facilities) and all people living in caravans on any type of site that is their usual residence; this will include anyone who has no other usual residence elsewhere in the UK. A household must contain at least one person whose place of usual residence is at the address. A group of short-term residents living together is not classified as a household, and neither is a group of people at an address where only visitors are staying.

Usual resident

For Census 2021, a usual resident of the UK is anyone who, on Census Day, was in the UK and had stayed or intended to stay in the UK for a period of 12 months or more, or had a permanent UK address and was outside the UK and intended to be outside the UK for less than 12 months.

Household reference person (HRP)

A person who serves as a reference point, mainly based on economic activity and age, to characterize a whole household. The person is not necessarily the member of the household in whose name the accommodation is owned or rented.

Tenure

Whether a household owns or rents the accommodation that it occupies. Owner-occupied accommodation can be: owned outright, which is where the household owns all of the accommodation; owned with a mortgage or loan; or part owned on a shared ownership scheme. Rented accommodation can be private rented, for example, rented through a private landlord or letting agent; social rented through a local council or housing association; or lived in rent free, which is where the household does not own the accommodation and does not pay rent to live there, for example living in a relative or friend’s property or live-in carers or nannies. This information is not available for household spaces with no usual residents.

_Household size _

The number of usual residents in the household.

Household family composition

Households according to the relationships between members. Single-family households are classified by the number of dependent children and family type (married, civil partnership or cohabiting couple family, or lone parent family). Other households are classified by the number of people, the number of dependent children and whether the household consists only of students or only of people aged 66 years and over.

Multi-generational households

Households where people from across more than two generations of the same family live together. This includes households with grandparents and grandchildren whether or not the intervening generation also live in the household.

_Household combination of resident age _

Classifies households by the ages of household members on 21 March 2021. Households could be made up of residents aged 15 years and under; residents aged 16 to 64 years; residents aged 65 years and over; or a combination of these.

Ethnic group

The ethnic group that the person completing the census feels they belong to. This could be based on their culture, family background, identity or physical appearance. Respondents could choose one out of 19 tick-box response categories, including write-in response options. For more information, see ONS's Ethnic group, England and Wales: Census 2021 bulletin

Household combination of resident ethnic group

Classifies households by the ethnic groups household members identified with.

Religion

The religion people connect or identify with (their religious affiliation), whether or not they practice or have belief in it. This question was voluntary and includes people who identified with one of 8 tick-box response options, including 'No religion', alongside those who chose not to answer this question. For more information, see ONS's Religion, England and Wales: Census 2021 bulletin

Household combination of resident religion

Classifies households by the religious affiliation of household members who chose to answer the religion question. The classifications may include residents who did not answer the religion question.

Household combination of resident employment status

Classifies households by the employment status of household members aged 16 years and over between 15 and 21 March 2021. Households could be made up of employed residents (employee or self-employed); unemployed residents (looking for work and could start within two weeks, or waiting to start a job that had been offered and accepted); economically inactive residents (unemployed and had not looked for work between 22 February to 21 March 2021, or could not start work within two weeks); or a combination of these.

Occupation

"Classifies what people aged 16 years and over do as their main job. Their job title or details of activities they do in their job and any supervisory or management responsibilities form this classification. This information is used to code responses to an occupation using the Standard Occupational Classification (SOC) 2020. It classifies people who were in employment between 15 March and 21 March 2021, by the SOC code that represents their current occupation. The lowest level of detail available is the four-digit SOC code which includes all codes in three, two and one digit SOC code levels. Occupation classifications include :

• manager, director or senior official occupations (such as Elected Representatives and Senior Police Officers)
• professional occupations (such as Doctors and Teachers)
• associate professional and technical occupations (such as Police Officers and Counsellors)
• administrative or secretarial occupations (such as Office Managers and Receptionists)
• skilled trade occupations (such as Electricians and Chefs)
• caring, leisure or other service occupations (such as Teaching Assistants and Home Carers)
• sales and customer service occupations (such as Cashiers and Shopkeepers)
• process, plant and machine operatives (such as Bus Drivers and Scaffolders)
• elementary occupations (such as Postal Workers and Waiters)"

This is a publication on maternity activity in English NHS hospitals. This report examines data relating to delivery and birth episodes in 2022-23, and the booking appointments for these deliveries. This annual publication covers the financial year ending March 2023. Data is included from both the Hospital Episodes Statistics (HES) data warehouse and the Maternity Services Data Set (MSDS). HES contains records of all admissions, appointments and attendances for patients admitted to NHS hospitals in England. The HES data used in this publication are called 'delivery episodes'. The MSDS collects records of each stage of the maternity service care pathway in NHS-funded maternity services, and includes information not recorded in HES. The MSDS is a maturing, national-level dataset. In April 2019 the MSDS transitioned to a new version of the dataset. This version, MSDS v2.0, is an update that introduced a new structure and content - including clinical terminology, in order to meet current clinical practice and incorporate new requirements. It is designed to meet requirements that resulted from the National Maternity Review, which led to the publication of the Better Births report in February 2016. This is the fourth publication of data from MSDS v2.0 and data from 2019-20 onwards is not directly comparable to data from previous years. This publication shows the number of HES delivery episodes during the period, with a number of breakdowns including by method of onset of labour, delivery method and place of delivery. It also shows the number of MSDS deliveries recorded during the period, with breakdowns including the baby's first feed type, birthweight, place of birth, and breastfeeding activity; and the mothers' ethnicity and age at booking. There is also data available in a separate file on breastfeeding at 6 to 8 weeks. The count of Total Babies includes both live and still births, and previous changes to how Total Babies and Total Deliveries were calculated means that comparisons between 2019-20 MSDS data and later years should be made with care. Information on how all measures are constructed can be found in the HES Metadata and MSDS Metadata files provided below. In this publication we have also included an interactive Power BI dashboard to enable users to explore key NHS Maternity Statistics measures. The purpose of this publication is to inform and support strategic and policy-led processes for the benefit of patient care. This report will also be of interest to researchers, journalists and members of the public interested in NHS hospital activity in England. Any feedback on this publication or dashboard can be provided to enquiries@nhsdigital.nhs.uk, under the subject “NHS Maternity Statistics”.

This data has been taken from LG Inform at http://lginform.local.gov.uk/ data reference ID 31. It shows the percentage of vulnerable people achieving independent living in Plymouth from financial year 2006/2007 to 2010/2011 Percentage of vulnerable people achieving independent living. This is the number of service users (i.e. people who are receiving a Supporting People Service) who have moved on from supported accommodation in a planned way, as a percentage of total service users who have left the service. This was previously reported as NI 141. Source name: Communities and Local Government Collection name: Supporting People Local System (SPLS) Polarity: High is good Polarity is how sentiment is measured "Sentiment is usually considered to have "poles" positive and negative these are often translated into "good" and "bad" sentiment analysis is considered useful to tell us what is good and bad in our information stream

The total number of people registered to vote in Scottish Parliamentary and local government elections was 4,243,800, a decrease of 1,400 (0.03%) compared with December 2021; 955,300 electors (22.5% of the total electorate) had registered for postal voting in the Scottish Parliamentary and local government elections. This represented a very small increase of 800 (0.1%) compared with December 2021; of the 4,243,800 number of people registered to vote at Scottish Parliamentary and local government elections, 76,600 (1.8% of the total electorate) were aged 16 -17; just over 181,500 qualifying foreign nationals resident in Scotland were registered to vote in Scottish Parliamentary and local government elections (4.3% of the total electorate); the number of people registered to vote in UK Parliamentary elections was 4,012,700, a decrease of 16,000 (0.4%) compared with December 2021.

This Location Data & Foot traffic dataset available for all countries include enriched raw mobility data and visitation at POIs to answer questions such as:

-How often do people visit a location? (daily, monthly, absolute, and averages). -What type of places do they visit ? (parks, schools, hospitals, etc) -Which social characteristics do people have in a certain POI? - Breakdown by type: residents, workers, visitors. -What's their mobility like enduring night hours & day hours?
-What's the frequency of the visits partition by day of the week and hour of the day?

Extra insights -Visitors´ relative income Level. -Visitors´ preferences as derived by their visits to shopping, parks, sports facilities, churches, among others.

Overview & Key Concepts Each record corresponds to a ping from a mobile device, at a particular moment in time and at a particular latitude and longitude. We procure this data from reliable technology partners, which obtain it through partnerships with location-aware apps. All the process is compliant with applicable privacy laws.

We clean and process these massive datasets with a number of complex, computer-intensive calculations to make them easier to use in different data science and machine learning applications, especially those related to understanding customer behavior.

Featured attributes of the data Device speed: based on the distance between each observation and the previous one, we estimate the speed at which the device is moving. This is particularly useful to differentiate between vehicles, pedestrians, and stationery observations.

Night base of the device: we calculate the approximated location of where the device spends the night, which is usually their home neighborhood.

Day base of the device: we calculate the most common daylight location during weekdays, which is usually their work location.

Income level: we use the night neighborhood of the device, and intersect it with available socioeconomic data, to infer the device’s income level. Depending on the country, and the availability of good census data, this figure ranges from a relative wealth index to a currency-calculated income.

POI visited: we intersect each observation with a number of POI databases, to estimate check-ins to different locations. POI databases can vary significantly, in scope and depth, between countries.

Category of visited POI: for each observation that can be attributable to a POI, we also include a standardized location category (park, hospital, among others). Coverage: Worldwide.

Delivery schemas We can deliver the data in three different formats:

Full dataset: one record per mobile ping. These datasets are very large, and should only be consumed by experienced teams with large computing budgets.

Visitation stream: one record per attributable visit. This dataset is considerably smaller than the full one but retains most of the more valuable elements in the dataset. This helps understand who visited a specific POI, characterize and understand the consumer's behavior.

Audience profiles: one record per mobile device in a given period of time (usually monthly). All the visitation stream is aggregated by category. This is the most condensed version of the dataset and is very useful to quickly understand the types of consumers in a particular area and to create cohorts of users.

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).