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).

The census is undertaken by the Office for National Statistics every 10 years and gives us a picture of all the people and households in England and Wales. The most recent census took place in March of 2021.The census asks every household questions about the people who live there and the type of home they live in. In doing so, it helps to build a detailed snapshot of society. Information from the census helps the government and local authorities to plan and fund local services, such as education, doctors' surgeries and roads.Key census statistics for Leicester are published on the open data platform to make information accessible to local services, voluntary and community groups, and residents. There is also a dashboard published showcasing various datasets from the census allowing users to view data for Leicester and compare this with national statistics.Further information about the census and full datasets can be found on the ONS website - https://www.ons.gov.uk/census/aboutcensus/censusproductsEthnicityThis dataset provides Census 2021 estimates that classify usual residents in England and Wales by ethnic group. The estimates are as at Census Day, 21 March 2021.Definition: 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.This dataset includes data relating to Leicester City and England overall.

The Area Level Index of Age Diversity (ALIAD) is based on the Simpson's Index of Diversity. It is commonly used in ecological studies to quantify the biodiversity of a habitat as it takes into account both the richness, i.e. the number of species present, and the evenness, i.e. the abundance of each species, within an environment. As species richness and evenness increase, so diversity increases. The index represents the probability that two randomly selected individuals will belong to different groups. It ranges from 0 and 100, with higher values representing greater diversity. ALIAD was computed for each Lower Super Output Area (LSOA) in England and Wales (E&W), each Data Zone (DZ) in Scotland and each Super Output Area (SOA) in Northern Ireland from 2002 to 2019. It is based on the mid-year population estimates (MYPE) for each area for each year. This is information is freely available in accordance with version 3.0 of the Open Government Licence. However, the different national statistical agencies compute MYPE for different age groups. In England and Wales estimates are provided for single-year age groups, i.e. the number of people aged 0, 1, 2, 3, etc. In Scotland estimates are provided for quinary age groups, i.e. the number of people aged 0-4, 5-9, 10-14, etc. In Northern Ireland (NI) estimates are provided for four larger age groups, i.e. 0-15, 16-39, 40-64 and 65+. It was decided to match the age groups to the NI classification as i) this would provide the greatest geographical coverage, ii) the estimates ought to be more robust and iii) in discussions with policy and practice stakeholders these age groups were seen as more meaningful than single-year or quinary age groups. An exact match was possible between the E&W and NI age groups. However, because of the use of quinary age groups it is not possible to get an exact match for all age groups in Scotland. Hence, the age groups used on Scotland are 0-14, 15-39, 40-64 and 65+. The final dataset contains the computed ALIAD values for each of the 34,753 LSOAs, the 6,976 DZs and the 890 SOAs from 2002-2019. ALIAD has a range of 0-100. On this scale 0 would represent total age concentration, i.e. every member of the area is in the same age group, and 100 would represent complete age diversity within the area.There is evidence that Britain is becoming more and more generationally divided. A major part of this is that the places where we live have become increasingly 'age segregated'. This means younger people tend to live in places where there are more younger people and older people tend to live in places where there are more older people. Deep generational divisions can have implications for social cohesion and effective societal functioning. Policy makers are concerned that this could have negative health, economic, social and political costs. Indeed, a recent report by the Resolution Foundation estimated that age-segregation could cost the UK economy £6 billion per year. However, there is currently no research in Britain that has been able to directly test whether living in areas with a greater mix of ages has an impact on people. By linking information on the number of people in different age groups at the local level with information from a long running survey, our project will be the first to do this. We will create a new measure, called the 'area level index of age diversity', for all the residential areas in Great Britain (these are called Lower Super Output Areas in England and Wales and Data Zones in Scotland). Unlike existing measures which tend to focus just on younger versus older adults, this new measure will use information from people of all ages to get a better idea of the mix of age groups in an area. The first thing we intend to do with this information is to produce a series of maps of Britain to show which local areas are more or less age diverse. This information will be very useful for local government, councils, city planners and the like. Once we have done this, we will then link our new measure of age diversity to information on around 50,000 people living in Britain who have been part of a long running study (called the UK Household Longitudinal Survey). This will enable us to see whether living in areas that have people from a wide (or narrow) range of age groups impacts on people's health (e.g. whether the person has an illness or chronic condition), well-being (e.g. loneliness), civic participation (e.g. whether someone volunteers or not), and neighbourhood quality (e.g. whether people trust their neighbours). Our findings will provide a much needed evidence base on the extent of local area level age diversity in Britain and what effect (if any) this has on people's lives. ALIAD was computed for each Lower Super Output Area (LSOA) in England and Wales (E&W), each Data Zone (DZ) in Scotland and each Super Output Area (SOA) in Northern Ireland from 2002 to 2019. It is based on the mid-year population estimates (MYPE) for each area for each year.

The latest population figures produced by the Office for National Statistics (ONS) on 28 June 2018 show that an estimated 534,800 people live in Bradford District – an increase of 2,300 people (0.4%) since the previous year.

Bradford District is the fifth largest metropolitan district (in terms of population) in England, after Birmingham, Leeds, Sheffield and Manchester although the District’s population growth is lower than other major cities.

The increase in the District’s population is largely due to “natural change”- there have been around 3,300 more births than deaths, although this has been balanced by a larger number of people leaving Bradford to live in other parts of the UK than coming to live here and a lower number of international migrants. In 2016/17 the net internal migration was -2,700 and the net international migration was 1,700.

A large proportion of Bradford’s population is dominated by the younger age groups. More than one-quarter (29%) of the District’s population is aged less than 20 and nearly seven in ten people are aged less than 50. Bradford has the highest percentage of the under 16 population in England after the London Borough of Barking and Dagenham, Slough Borough Council and Luton Borough Council.

The population of Bradford is ethnically diverse. The largest proportion of the district’s population (63.9%) identifies themselves as White British. The district has the largest proportion of people of Pakistani ethnic origin (20.3%) in England.

The largest religious group in Bradford is Christian (45.9% of the population). Nearly one quarter of the population (24.7%) are Muslim. Just over one fifth of the district’s population (20.7%) stated that they had no religion.

There are 216,813 households in the Bradford district. Most households own their own home (29.3% outright and 35.7% with a mortgage). The percentage of privately rented households is 18.1%. 29.6% of households were single person households.

Information from the Annual Population Survey in December 2017 found that Bradford has 228,100 people aged 16-64 in employment. At 68% this is significantly lower than the national rate (74.9%). 91,100 (around 1 in 3 people) aged 16-64, are not in work. The claimant count rate is 2.9% which is higher than the regional and national averages.

Skill levels are improving with 26.5% of 16 to 74 year olds educated to degree level. 18% of the district’s employed residents work in retail/wholesale. The percentage of people working in manufacturing has continued to decrease from 13.4% in 2009 to 11.9% in 2016. This is still higher than the average for Great Britain (8.1%).

SafeGraph Places provides baseline location information and addresses for every record in the SafeGraph product suite via the Places schema and polygon information when applicable via the Geometry schema. The current scope of a place is defined as any location humans can visit with the exception of single-family homes. This definition encompasses a diverse set of places ranging from restaurants, grocery stores, and malls; to parks, hospitals, museums, offices, and industrial parks. Premium sets of Places include apartment buildings, Parking Lots, and Point POIs (such as ATMs or transit stations).

SafeGraph Places is a point of interest (POI) data offering with varying coverage depending on the country. Note that address conventions and formatting vary across countries. SafeGraph has coalesced these fields into the Places schema.

This project aimed to quantitatively understand citizens' attitudes to Emotional AI via national surveys (as described in point 6 "Project Description", see above). We developed a demographically representative survey to gauge citizen attitudes to emotion capture technologies in cities in the UK. The survey introduces the overall topic of emotion profiling with the phrase: ‘We would now like to ask your opinion on use of technologies that try to measure and understand emotions (e.g., through computer analysis of social media posts, facial expression, voice, heart rate, gesture, and other data about the body). Closed-ended questions allowed then to explore 10 different use cases (38 questions in total): security, policing, communications, political campaigning, health, transport, education, toys and robots. For each case, positive and negative themes were tested, by grounding each question in an applied use case. In total, nine themes were explored, (although not across all the use cases to minimise survey fatigue).CONTEXT Emotional AI (EAI) technologies sense, learn and interact with citizens' emotions, moods, attention and intentions. Using weak and narrow rather than strong AI, machines read and react to emotion via text, images, voice, computer vision and biometric sensing. Concurrently, life in cities is increasingly technologically mediated. Data-driven sensors, actuators, robots and pervasive networking are changing how citizens experience cities, but not always for the better. Citizen needs and perspectives are often ancillary in emerging smart city deployments, resulting in mistrust in new civic infrastructure and its management (e.g. Alphabet's Sidewalk Labs). We need to avoid these issues repeating as EAI is rolled out in cities. Reading the body is an increasingly prevalent concern, as recent pushback against facial detection and recognition technologies demonstrates. EAI is an extension of this, and as it becomes normalised across the next decade we are concerned about how these systems are governed, social impacts on citizens, and how EAI can be designed in a more ethical manner. In both Japan and UK, we are at a critical juncture where these social, technological and governance structures can be appropriately prepared before mass adoption of EAI, to enable citizens, in all their diversity, to live ethically and well with EAI in cities-as-platforms. Building on our ESRC/AHRC seminars in Tokyo (2019) that considered cross-cultural ethics and EAI, our research will enable a multi-stakeholder (commerce, security, media) and citizen-led interdisciplinary response to EAI for Japan and UK. While these are two of the most advanced nations in regard to AI, the social contexts and histories from which these technologies emerge differ, providing rich scope for reflection and mutual learning. AIMS/OBJECTIVES 1. To assess what it means to live ethically and well with EAI in cities in cross-cultural (UK-Japan) commercial, security and media contexts. 2. To map and engage with the ecology of influential actors developing and working with EAI in UK-Japan. 3. To understand commercial activities, intentions and ethical implications regarding EAI in cities, via interviews with industry, case studies, and analysis of patents. 4. To ascertain how EAI might impact security/policing stakeholders, and organisations in the new media ecology, via interviews with these stakeholders and case studies in UK-Japan. 5. To examine governance approaches for collection and use of intimate data about emotions in public spaces to understand how these guide EAI technological developments, and to build a repository of best practice on EAI in cities. 6. To understand diverse citizens' attitudes to EAI via quantitative national surveys and qualitative workshops to co-design citizen-led, creative visions of what it means to live ethically and well with EAI in cities in UK-Japan. 8. To feed our insights to stakeholders shaping usage of EAI in cities in UK-Japan. 9. To advance surveillance studies, new media studies, information technology law, science & technology studies, security & policing studies, computer ethics and affective computing via: 24 international conference papers; a conference on EAI; 12 international, refereed journal papers; a Special Issue on EAI. APPLICATIONS/BENEFITS We will: - Raise awareness of UK-Japanese stakeholders (technology industry, policymakers, NGOs, security services, urban planners, media outlets, citizens) on how to live ethically and well with EAI in cities, via co-designed, citizen-led, qualitative visions fed into Stakeholder Policy Workshops; a Final Report with clear criteria on ethical usage of EAI in cites; 24 talks with stakeholders; multiple news stories. - Set up a think tank to provide impartial ethical advice on EAI and cross-cultural issues to diverse stakeholders during and after the project. - Advance collaboration between UK-Japan academics, disciplines and stakeholders in EAI. This survey presents closed-ended questions exploring ten use cases focused on applications of emotional AI in security, policing, communications, political campaigning, health, transport, education, toys and robots. These questions were developed for a demographically representative national online omnibus survey implemented by company ICM Unlimited. The survey was conducted online with a sample of 2,068 UK adults aged 18+, between 29 June – 1 July 2022.

This dataverse contains interviews and photographs of fieldwork around historical city-water relationship in Coventry (UK) and Rotterdam (NL) in the second half of the 20th century. Most of the photographs contain images of urban waterways in both cities between 2019 and 2020. Interviews, Oral history.

SafeGraph Places provides baseline Point of Interest (POI) information for every record in the SafeGraph product suite via the Places schema and polygon information when applicable via the Geometry schema. The current scope of a place is defined as any location humans can visit with the exception of single-family homes. This definition encompasses a diverse set of point of interests ranging from restaurants, grocery stores, and malls; to parks, hospitals, museums, offices, and industrial parks. Premium sets of Places include apartment buildings, Parking Lots, and Point POIs (such as ATMs or transit stations).

SafeGraph Places is a point of interest (POI) data offering with varying coverage depending on the country. Note that address conventions and formatting vary across countries. SafeGraph has coalesced these fields into the Places schema.

This dataset provides Census 2021 estimates that classify usual residents aged 16 years and over in England and Wales by NS-SEC and by age. The estimates are as at Census Day, 21 March 2021.

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

Estimates for single year of age between ages 90 and 100+ are less reliable than other ages. Estimation and adjustment at these ages was based on the age range 90+ rather than five-year age bands. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, you can choose to filter areas by:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

National Statistics Socio-economic Classification (NS-SeC)

The National Statistics Socio-economic Classification (NS-SEC) indicates a person's socio-economic position based on their occupation and other job characteristics.

It is an Office for National Statistics standard classification. NS-SEC categories are assigned based on a person's occupation, whether employed, self-employed, or supervising other employees.

Full-time students are recorded in the "full-time students" category regardless of whether they are economically active.

Age

A person’s age on Census Day, 21 March 2021 in England and Wales. Infants aged under 1 year are classified as 0 years of age.

Potential Applications of the Dataset:

1. Geospatial Information: Precise geographical coordinates for each Walgreens store, enabling accurate mapping and spatial analysis. State-wise and city-wise breakdown of store locations for a comprehensive overview.

2. Store Details: Store addresses, including street name, city, state, and zip code, facilitating easy identification and location-based analysis. Contact information, such as phone numbers, providing a direct link to store management.

3. Operational Attributes: Store opening and closing hours, aiding businesses in strategic planning and market analysis. Services and amenities are available at each location, offering insights into the diverse offerings of Walgreens stores.

4. Historical Data: Historical data on store openings and closures, providing a timeline perspective on Walgreens' expansion and market presence.

5. Demographic Insights: Demographic information of the areas surrounding each store, empowering users to understand the local customer base.

6. Comprehensive and Up-to-Date: Regularly updated to ensure the dataset reflects the latest information on Walgreens store locations and attributes. Detailed data quality checks and verification processes for accuracy and reliability.

The dataset is structured in a flexible format, allowing users to tailor their queries and analyses based on specific criteria and preferences.

The Real Estate Adaptation and Innovation within an integrated Retailing system (REPAIR) project, conducted at the University of Glasgow and University of Sheffield, investigated the changes experienced across the retail cores of five UK cities Edinburgh, Glasgow, Hull, Liverpool and Nottingham between 2000 and 2021. The project examined different aspects of the property market and built environment across four separate work streams. The secondary data stored was compiled as part of Work Package A, and allowed for a comparison of property usage and ownership across five case study centres to reveal both similarities and differences over a period of almost two decades (2000-2017). The datasets were created from microlevel data that provide an almost complete picture of building usage and ownership in each case study centre but was constructing by linking data from different public and private sources in a manner not previously attempted. The license agreements prevent sharing of the micro-level data so the stored data is aggregated at different levels of aggregation.The retail sector is crucial to the economic health and vitality of towns and cities and is a core component of the national economy, but is experiencing an ongoing period of change and the challenges faced by centres are being met in different ways, with different outcomes. Consumers are behaving, shopping and using urban centres in new and diverse ways and many retailing centres have experienced falling footfall, retailer closures and a rise in empty retail units. In an attempt to reverse the cycle of decline, centres need to be multi-functional places and policy-makers are encouraging more mixed use development. Large-scale mixed-use re-development of obsolete stock, novel temporary land uses, events and public realm works are being used to try to make urban centres more attractive and increase their competitive edge. Yet, not everyone is experiencing the benefits of these changes. Mistrust, tension and conflict can arise from land use changes and become barriers to further renewal and change, limiting the effectiveness of these "town centre first" policies. A recent ESRC-funded study undertaken by researchers at Manchester Metropolitan University blamed these tensions and lack of co-operation as significant contributors to the continued declined of retailing in many centres (Parker, 2015). This project investigates one of the largest stakeholder groups within the sector. The objectives and behaviour of land and property owners, developers and investors are significant to the use and form of retailing centres. The project explores how ownership and the behaviour of this stakeholder group impact on the sector, by exploring issues around changing ownership and use patterns; innovations in design form; the ability of the industry to respond to change; and the ways the group engages and interacts with other stakeholders in urban centres. Thus, it aims to examine how their expectations, perceptions, practices and co-operation help or limit experimentation with new uses, building types and designs. The mixed method study, using primary and secondary data, explores issues around: whether retailers and landlords in city centres are becoming more or less diverse; whether new design formats, flexible uses and large scale redevelopments can help struggling centres; the extent to which established practices and procedures in the real estate market encourage or even hinder new uses; and whether stakeholders can work together in better ways for the future health of town and city centres. These issues are examined using five case study cities over the period 1997-2017: Glasgow, Edinburgh, Liverpool, Sheffield and Nottingham. The data is constructed by linking data from administrative and commercial datasets, and used to calculate measures to capture the diversity of property use and ownership.

SafeGraph Places provides baseline location information for every record in the SafeGraph product suite via the Places schema and polygon information when applicable via the Geometry schema. The current scope of a place is defined as any location humans can visit with the exception of single-family homes. This definition encompasses a diverse set of places ranging from restaurants, grocery stores, and malls; to parks, hospitals, museums, offices, and industrial parks. Premium sets of Places include apartment buildings, Parking Lots, and Point POIs (such as ATMs or transit stations).

SafeGraph Places is a point of interest (POI) data offering with varying coverage and properties depending on the country. Note that address conventions and formatting vary across countries. SafeGraph has coalesced these fields into the Places schema.

The Real Estate Adaptation and Innovation within an integrated Retailing system (REPAIR) project, conducted at the University of Glasgow and University of Sheffield, investigated the changes experienced across the retail cores of five UK cities Edinburgh, Glasgow, Hull, Liverpool and Nottingham between 2000 and 2021. The project examined different aspects of the property market and built environment across four separate work streams. The primary data stored here relates to Work Package B and was collected via semi-structured interviews with city centre actors, including: property professionals; retailers; architects; planners; and other local authority officials. The interviews investigate the urban form and land use innovations emerging in response to the structural changes experienced in recent years in city centre retail markets. The findings explore the issues related to redundant and vacant properties and adaptive reuse, focusing on: retail unit and shopping centre design innovation; public realm regeneration; the experience economy; and city centre events. Some of the interviews - those conducted in 2021 after the pandemic started - also capture the effects of the pandemic on retailing centres.The retail sector is crucial to the economic health and vitality of towns and cities and is a core component of the national economy, but is experiencing an ongoing period of change and the challenges faced by centres are being met in different ways, with different outcomes. Consumers are behaving, shopping and using urban centres in new and diverse ways and many retailing centres have experienced falling footfall, retailer closures and a rise in empty retail units. In an attempt to reverse the cycle of decline, centres need to be multi-functional places and policy-makers are encouraging more mixed use development. Large-scale mixed-use re-development of obsolete stock, novel temporary land uses, events and public realm works are being used to try to make urban centres more attractive and increase their competitive edge. Yet, not everyone is experiencing the benefits of these changes. Mistrust, tension and conflict can arise from land use changes and become barriers to further renewal and change, limiting the effectiveness of these "town centre first" policies. A recent ESRC-funded study undertaken by researchers at Manchester Metropolitan University blamed these tensions and lack of co-operation as significant contributors to the continued declined of retailing in many centres (Parker, 2015). This project seeks to explore one of the largest stakeholder groups within the sector. The objectives and behaviour of land and property owners, developers and investors are significant to the use and form of retailing centres. The project explores how ownership and the behaviour of this stakeholder group impact on the sector, by exploring issues around changing ownership and use patterns; innovations in design form; the ability of the industry to respond to change; and the ways the group engages and interacts with other stakeholders in urban centres. Thus, it aims to examine how their expectations, perceptions, practices and co-operation help or limit experimentation with new uses, building types and designs. The research will explore issues around: whether retailers and landlords in city centres are becoming more or less diverse; whether new design formats, flexible uses and large scale redevelopments can help struggling centres; the extent to which established practices and procedures in the real estate market encourage or even hinder new uses; and whether stakeholders can work together in better ways for the future health of town and city centres. These issues will be examined using five case study cities over the period 1997-2017: Glasgow, Edinburgh, Liverpool, Sheffield and Nottingham. The project will bring together different data that has not been available previously, to map, measure and identify any links between changes in land and building use, vacancy and ownership over the last 20 years. It will analyse and identify new developments and novel land and building uses and designs and, by talking to developers, designers, planners and occupiers, the researchers will identify the factors shaping these changes and how they impact on cities and shoppers. The project will examine established real estate market practices, such as lease lengths, rent review terms, repair obligations and use clauses to see how adaptable the industry is to change when shoppers and retailers want new and unusual property uses and forms. Finally, the researchers will talk to different centre users, managers and owners to explore how relationships might work well or badly and identify good practice for the creation of new developments and adaptions to the existing building stock to help the retail sector in cities. This part of the REPAIR project used semi-structured interviews to draw on the experiences and opinions of experienced city centre stakeholders working in five major UK retailing centres: Edinburgh, Glasgow, Hull, Liverpool and Nottingham. The participants were selected using a purposive sampling procedure that targeted property professionals, retailers, architects, planners and other local authority officials.